A mobile emergency power supply access point optimization method considering power-off power distribution network recovery sequence

Abstract

The application discloses a kind of mobile emergency power supply access point optimization method considering power-off distribution network recovery sequence, first, the line fault probability of distribution network under extreme natural disaster is evaluated, then mobile emergency power supply access point optimization model and source-network-load-storage collaborative scheduling model considering recovery sequence are respectively constructed, then the linearization of the model constructed is carried out and is solved, and the optimized mobile emergency power supply access point optimization scheme considering power-off distribution network recovery sequence is obtained.The application considers the optimization configuration of mobile emergency power supply access point in sequence recovery process, realizes the collaborative optimization of black start node and recovery path in distribution network, more greatly utilizes local power generation resource in distribution network, and reduces the power-off time of important load.

Description

Technical Field

[0001] This invention belongs to the field of power grid technology, and specifically relates to an optimization method for mobile emergency power supply access points that takes into account the power outage distribution network restoration sequence. Background Technology

[0002] In recent years, due to the frequent occurrence of extreme natural disasters, numerous large-scale power outages have occurred both domestically and internationally, causing enormous economic losses and severe social impacts. Traditional distribution networks are powered by the main grid; when the main grid fails, power outages occur in the distribution network. To address this, some scholars have proposed constructing resilient distribution networks. When the main grid is unable to supply power, microgrids can be built using local distributed power sources and unaffected areas within the distribution network to provide continuous power to critical loads, thereby reducing losses from power outages.

[0003] Many studies on microgrid-based resilience enhancement methods focus on post-disaster microgrid islanding and energy dispatch within the microgrid. Some scholars have proposed that after a power outage, the distribution network should continue operating with a pre-defined microgrid structure, and have studied energy optimization dispatch strategies between microgrids to maximize power supply to critical loads. Others have proposed a power restoration strategy considering microgrid islanding, optimizing the distribution network's partitioning after a power outage and utilizing local distributed generation to supply power to critical loads. While pre-defined microgrid partitioning can improve the resilience of the distribution network after a disaster, existing studies assume that the distribution network can smoothly switch to islanded off-grid operation mode during a fault. In actual operation, due to the uncertainties of distributed generation and loads, successful islanding of microgrids is difficult. Therefore, many scholars have studied sequential restoration methods that use distributed generation to gradually restore power to critical load nodes, targeting the results of islanding. However, existing sequential restoration studies have not utilized mobile emergency power optimization to improve restoration efficiency, resulting in low power outage restoration efficiency for the distribution network. Summary of the Invention

[0004] Considering that mobile emergency power supplies can be quickly dispatched to the vicinity of important load nodes via transportation networks after a power outage, they are a key resource for the rapid restoration of important loads in a resilient distribution network. This invention proposes an optimized configuration method for mobile emergency power supplies that considers the restoration sequence of the distribution network: mobile emergency power supplies are deployed in advance before a disaster occurs and rescheduled after a power outage. Compared with the traditional distribution network restoration method that only considers fixed distributed power sources, the proposed method can optimize the starting point of the restoration path in the microgrid, thereby reducing the outage time of important loads.

[0005] The specific technical solution for achieving the objective of this invention is as follows:

[0006] An optimization method for mobile emergency power supply access points considering the power outage distribution network restoration sequence includes the following steps:

[0007] Step 1: Assess the probability of power distribution network line failures under extreme natural disasters;

[0008] Step 2: Construct an optimization model for mobile emergency power supply access points;

[0009] Step 3: Construct a source-grid-load-storage collaborative scheduling model that considers the recovery order;

[0010] Step 4: Linearize the model and solve it to obtain the optimized scheme of mobile emergency power supply access point considering the power outage distribution network restoration sequence.

[0011] Compared with the prior art, the significant advantages of this invention are:

[0012] The technical solution of this invention takes into account the optimized configuration of mobile emergency power access points during the sequential recovery process, realizes the coordinated optimization of black start nodes and recovery paths in the distribution network, makes greater use of local power generation resources in the distribution network, and reduces the power outage time of important loads.

[0013] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0014] Figure 1 This is a flowchart of the steps of the present invention.

[0015] Figure 2 This is a schematic diagram of the power structure and transportation network topology of the IEEE 13-node power distribution system in an embodiment of the present invention.

[0016] Figure 3 This is a schematic diagram of the movement path of the line fault and the mobile emergency power supply in various scenarios in the embodiments of the present invention.

[0017] Figure 4 This is a schematic diagram of the average load recovery amount under different recovery strategies in the embodiments of the present invention. Detailed Implementation

[0018] Combination Figure 1 An optimization method for mobile emergency power supply access points considering the power outage distribution network restoration sequence includes the following steps:

[0019] Step 1: Assess the probability of power distribution network line failures under extreme natural disasters, specifically as follows:

[0020] If an overhead line or any utility pole fails, the line will be out of service. By mapping the maximum predicted wind speed to the equipment vulnerability curve, the failure probability of the overhead line and utility pole can be obtained, and thus the failure probability of the distribution network line can be obtained.

[0021] ρ b (ω m )=ρ b,l (ω m )+ρ b,p (ω m )-ρ b,l (ω m )ρ b,p (ω m )

[0022]

[0023] Where, ω m ρ represents wind speed. b ρ represents the probability of a line fault. b,l ρ represents the probability of faults in overhead lines. b,p ρ represents the probability of a utility pole failing. p_ind N represents the probability of failure for an individual utility pole. b,p This indicates the number of utility poles.

[0024] Step 2: Considering the possible fault scenarios under extreme natural disasters, a stochastic optimization model is constructed for the mobile emergency power supply access point optimization problem that considers the power distribution network restoration sequence. Specifically, the mobile emergency power supply access point optimization model is as follows:

[0025]

[0026] Where S represents the set of failure scenarios, ρ s Let I represent the probability of scenario s occurring, and w represent the set of loads. i P represents the weight of load i. i L This represents the load demand at node i. This represents the recovery status of the load on node i at time t in microgrid m under scenario s. This represents the load recovery amount at node i at time t in scenario s.

[0027] Before extreme natural disasters occur, mobile emergency power supplies are deployed in advance to prepare for impending power outages. After a power outage, the mobile emergency power supplies are redeployed to their designated locations.

[0028] Furthermore, the above optimization model includes the following constraints:

[0029] (1) A mobile emergency power supply can only be configured on one node, and the number of mobile emergency power supplies that can be configured on a node is limited by the node's capacity:

[0030]

[0031]

[0032] in, This indicates whether the mobile emergency power supply k was configured on node i before the power outage occurred. (Cap) i This indicates the limit on the number of mobile emergency power supplies that can be accommodated on node i;

[0033] (2) After a power outage occurs, a mobile emergency power supply can only be configured on one node, and only one mobile emergency power supply can be configured on one node:

[0034]

[0035]

[0036] in, This indicates whether the mobile emergency power supply k is configured on node i under power outage scenario s;

[0037] (3) It is also necessary to couple the two-stage operation, that is, only mobile emergency power supplies configured in the pre-disaster prevention stage can participate in the power restoration of the distribution network after a power outage:

[0038]

[0039] Step 3: Construct a source-grid-load-storage collaborative scheduling model that considers the recovery order, specifically as follows:

[0040] Step 3-1: When the distribution network is restored sequentially, the power sources with black start capability include mobile emergency power sources, feeders, and fixed black start power sources. For ease of modeling, the nodes where feeders and fixed black start power sources are located can be regarded as mobile emergency power sources with fixed positions.

[0041] Construct a power distribution network restoration path optimization model that considers the participation of mobile emergency power sources:

[0042] In a microgrid, the recovery path must originate from a node connected to a black-start power source, and each microgrid has only one black-start power source.

[0043]

[0044]

[0045]

[0046] The power supply path must meet the following two conditions: 1) There is a connected line; 2) The line does not fail during subsequent power outages.

[0047]

[0048]

[0049] The recovery path in the distribution network must meet the following constraints

[0050]

[0051]

[0052]

[0053]

[0054] in, Let y be a 0-1 variable, representing whether there is a recovery path in microgrid m starting from node i under scenario s, and L represent the line connectivity matrix in the distribution network. ij.s This represents the fault status of line (i,j) in the distribution network under fault scenario s. This represents the recovery path state from node i to node j in microgrid m under scenario s. This indicates that in scenario s, there exists a recovery path in microgrid m that starts from node i and reaches node j; conversely, there is no recovery path. This indicates that there is no recovery path, M represents the set of generated microgrids, and n c Indicates the number of nodes in the distribution network;

[0055] Step 3-2: Construct a virtual power flow model to represent whether nodes continue to be powered by the main network after a disaster. Virtual power flow f ij This does not represent the power flowing along line (i,j), but rather a 0-1 variable used to describe the system topology. The virtual power flow model must satisfy connectivity constraints and cannot flow along faulty lines.

[0056]

[0057]

[0058] Among them, f ij.s Let θ(i) represent the virtual power flow on line (i,j) in scenario s, and let θ(i) represent the set of child nodes of node i.

[0059] Step 3-3: Obtain the load outage duration by calculating the node power restoration time. For node j, whose power is restored by microgrid m, its power restoration time t j Equal to the time when the mobile emergency power supply k arrives at connection point i in the microgrid Add the time required for sequential recovery in microgrid m. The recovery time of a node is determined by the time it takes for the mobile emergency power supply to travel along the transportation network before and after a power outage. The recovery time of a node can be calculated using the following formula:

[0060] First, the two-stage operation is coupled to generate the movement path matrix of the mobile emergency power source k:

[0061]

[0062] Calculate the time required for emergency power supply k to reach node j:

[0063]

[0064] If node j is connected to a black-start power supply, then the time when its power supply is restored is:

[0065]

[0066] If there is no power supply path through node j, t j This will be set as the power outage duration for the distribution network:

[0067]

[0068] According to f ij The value of f can be divided into two cases: when there is virtual power flow on line (i,j), f ij =1, nodes i and j are both in the non-outage area, there is no switching action time, then t j =t i Conversely, f ij =0, nodes i and j are both in the power outage area, and the line switch needs to be closed to restore power sequentially.

[0069]

[0070] in, This represents the movement path matrix of the mobile emergency power supply k. This indicates whether the mobile emergency power supply k was configured on node i before the power outage occurred. This indicates whether the mobile emergency power supply k is configured on node j under power outage scenario s. This indicates the time required for emergency power supply k to reach node j. t represents the time required for a mobile emergency power supply to travel from node i to node j. j.s This represents the recovery time of node j in scenario s. ψ represents the time required for the emergency power supply k to reach node j in scenario s, where ψ is a large constant. T represents the time required to restore line (i,j) in scenario s. R.max Indicates the duration of power outage in the distribution network;

[0071] Steps 3-4: Introduce 0-1 variables and Let represent the relationship between the power supply status of nodes and lines and the power supply path under fault scenario s, respectively, with the following constraints:

[0072]

[0073]

[0074]

[0075]

[0076] in, This represents the power supply status of node i in microgrid m at time t in scenario s. This represents the power supply status of line (h,i) in microgrid m at time t under scenario s.

[0077] To indicate whether the distributed power sources, wind turbines, and loads connected to a certain node have resumed power supply, a 0-1 decision variable u is further introduced, satisfying:

[0078]

[0079] in, This represents the power supply recovery status of the load on node i at time t in scenario s. This represents the power supply recovery status of the distributed power source on node g at time t in scenario s. This indicates the power supply recovery status of the wind turbine at node w at time t in scenario s;

[0080] Steps 3-5: Model the power output and load reduction of the power supply equipment in the system during the sequential restoration of the distribution network, which needs to meet the following constraints:

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093] In the formula, P i B.min P represents the minimum active power output of the black-starting power supply at node i. i B.max This represents the maximum active power output of the black-starting power supply at node i. This represents the minimum reactive power output of the black-start power supply at node i. This represents the maximum active power output of the black-starting power supply at node i. This represents the active power output of the black-start power supply at node i in scenario s at time t. This represents the reactive power output of the black-start power supply at node i in scenario s at time t. This represents the allowable power reduction of the black-start power supply at a time step node i. This represents the allowed power increase of the black-start power supply at a time step node i. This represents the minimum active power output of the non-black-starting power source at node g. This represents the maximum active power output of the non-black-starting power source at node g. This represents the minimum active power output of the non-black-starting power source at node g. This represents the maximum active power output of the non-black-starting power source at node g. Let represent the active power output of the non-black-start power source at node g in microgrid m at time t under scenario s. Let represent the reactive power output of the non-black-start power source at node g in microgrid m at time t under scenario s. This indicates the allowed power reduction for non-black start on a time step node g. γ represents the allowed power increase for non-black start on a time-step node g. g This represents the power factor of the non-black-starting power source at node g. This represents the active power dispatch output of the wind turbine at node w in microgrid m at time t under scenario s. This represents the reactive power output of the wind turbine at node w in microgrid m at time t under scenario s. This represents the rated output of the wind turbine at node w. P represents the capacity of the wind turbine at node w. i L This represents the power demand of the load at node i. This represents the minimum allowable load reduction on node i. This represents the maximum allowable load reduction on node i. This represents the active power reduction of the load on node i in microgrid m at time t under scenario s. σ represents the reactive power reduction of the load at node i in microgrid m at time t under scenario s. i This represents the power factor of the load at node i.

[0094] Step 4: Linearize the model and solve it to obtain the optimized scheme for mobile emergency power supply access points that considers the power outage distribution network restoration sequence, specifically:

[0095] Step 4-1: Linearize the nonlinear constraint terms in the model constructed in Steps 2 and 3;

[0096] (1) The optimization model for mobile emergency power supply access points contains bilinear terms. It needs to be linearized by introducing continuous variables. Replace the bilinear term in the objective function and introduce the following equivalent transformation:

[0097]

[0098] (2) Regarding the constraints on the relationship between the power supply status of nodes and lines and the power supply path under fault scenario s, the variables are... The constraint is obtained by multiplying two discrete variables, resulting in nonlinear variables in the constraint conditions. Therefore, it needs to be linearized. The line power supply state constraint is expressed as:

[0099]

[0100] The wind power capacity constraint is a quadratic constraint, which can be approximated as a polygonal region using the polygonal approximation method. When linearizing the quadratic constraint using the inner approximation method, a set of linear inequality constraints can be used to replace the quadratic constraint. Taking a regular dodecagon as an example, the reduced feasible region can be represented as the following set of inequality constraints:

[0101]

[0102] Step 4-2: Solve the linearized model to obtain an optimized mobile emergency power supply access point scheme that considers the power outage distribution network restoration sequence.

[0103] The linearized model is a mixed-integer stochastic optimization model that can be solved directly.

[0104] A mobile emergency power supply access point optimization system that considers the power outage distribution network restoration sequence includes the following modules:

[0105] Distribution network line fault probability module: used to assess the fault probability of distribution network lines under extreme natural disasters;

[0106] Mobile emergency power supply access point optimization model construction module: used to construct an optimization model for mobile emergency power supply access points;

[0107] Source-grid-load-storage collaborative scheduling model: used to construct a source-grid-load-storage collaborative scheduling model that takes into account the recovery order;

[0108] Solver module: Used to linearize the constructed model and solve it to obtain the optimal power distribution network sequence recovery scheme.

[0109] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, performs the following steps:

[0110] Step 1: Assess the probability of power distribution network line failures under extreme natural disasters;

[0111] Step 2: Construct an optimization model for mobile emergency power supply access points;

[0112] Step 3: Construct a source-grid-load-storage collaborative scheduling model that considers the recovery order;

[0113] Step 4: Linearize the model and solve it to obtain the optimized scheme of mobile emergency power supply access point considering the power outage distribution network restoration sequence.

[0114] A computer-storable medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0115] Step 1: Assess the probability of power distribution network line failures under extreme natural disasters;

[0116] Step 2: Construct an optimization model for mobile emergency power supply access points;

[0117] Step 3: Construct a source-grid-load-storage collaborative scheduling model that considers the recovery order;

[0118] Step 4: Linearize the model and solve it to obtain the optimized scheme of mobile emergency power supply access point considering the power outage distribution network restoration sequence.

[0119] The present invention will be further described below with reference to the embodiments.

[0120] Example

[0121] The effectiveness of the proposed method was verified using an improved IEEE 13-bus distribution system.

[0122] The power structure of the power distribution system and the topology of the transportation network are as follows: Figure 2 As shown, the system is equipped with two mobile emergency power supplies with black-start capability, one distributed power supply without black-start capability, and two wind turbines. The power equipment parameters in the system are shown in Table 1. In Table 1, "M" indicates that the power supply is mobile, "F" indicates that the power supply is stationary, and the numbers in parentheses indicate the connection nodes. The generator status reflects its black-start capability: "1" indicates that the power supply has black-start capability; "0 / 1" indicates that it does not have black-start capability but can supply power when the starting power requirement is met; "0" indicates that the distributed power supply does not participate in the sequential recovery process. The total load in the system is 2MW, and the load demand and weight on each node are randomly generated.

[0123] Table 1 Power Equipment Parameters in the IEEE 13-Node Power Distribution System

[0124] Tab.1 Parameters of distributed generators in IEEE 13-bus system

[0125]

[0126] When a natural disaster occurs, multiple lines in the power distribution network fail.

[0127] In this embodiment, it is assumed that the repair time for faulty equipment in the distribution network is 1 hour, and the interval between adjacent time steps for sequential recovery is 5 minutes. The movement time of the MEG on the traffic network is shown in Table 2, and the time steps required for switching operations on the line are shown in Table 3.

[0128] In order to find the shortest path from the starting point to the destination in the transportation network, this embodiment first calculates the shortest travel time between any two points using Dijkstra's algorithm.

[0129] Table 2. Time required for portable emergency power supplies to move across transportation networks.

[0130] Tab.2 The time step needed for MEG traveling on traffic network

[0131]

[0132]

[0133] Table 3. Time steps required for line switch operation in IEEE 13-node distribution systems

[0134] Tab.3 The time step needed for line switch operation in IEEE 13-bussystem

[0135]

[0136] Before a typhoon disaster arrives, possible sporadic event scenarios and their probabilities are generated based on the line vulnerability curve, and the MEG pre-disaster deployment model is solved based on these scenarios.

[0137] This embodiment illustrates the effectiveness of the proposed model through four sporadic event scenarios, denoted as CU1, CU2, CU3 and CU4, with the probability of each scenario being 0.1, 0.2, 0.4 and 0.3 respectively. Figure 3 The pre-disaster deployment location of MEG, the line fault situation in various scenarios, and the corresponding MEG movement path were displayed.

[0138] Before the power outage, two mobile emergency power supplies were deployed at nodes 692 and 684, respectively, and the pre-disaster MEG deployment scheme was the same in all scenarios. As the power outage actually occurred, specific lines failed under different fault scenarios. In each fault scenario, the mobile emergency power supply moved from the pre-deployment point to the corresponding real-time deployment point through the transportation network, and gradually restored the non-black start units and important loads that were waiting to be started, thereby generating two parallel recovery microgrids.

[0139] Taking CU2 as an example, in this fault scenario, lines 650-632 and 632-633 experience open circuit faults. After the power outage, node 650 continues to be powered by the main grid, with a power outage time of 0. MEG2 moves from node 692 to node 633 to restore the loads on nodes 633 and 634, with a movement time of 20 minutes on the network. MEG1 remains connected to node 684, as potential fault events have been considered beforehand, thus effectively reducing its movement time on the network. Due to the limited generating capacity of MEG1, the loads on nodes 632, 645, and 646 cannot be restored, and complete power restoration requires subsequent emergency repairs by the construction team. CU3 and CU4 also demonstrate the importance of considering non-faulty areas during modeling. Although the power generation capacity of the equipment is sufficient, connecting to the substation via non-faulty lines ensures that the loads do not experience power outages.

[0140] Three sets of comparative examples verify the effectiveness of the proposed mobile emergency power supply configuration strategy in reducing system outage time. In Example 1, mobile emergency power supplies are pre-deployed at specific nodes before the occurrence of extreme disasters, and their locations are not adjusted after a power outage. In Example 2, mobile emergency power supplies are randomly deployed before a disaster, and the post-disaster deployment points and sequential recovery schemes of mobile emergency power supplies are optimized using a single-stage model after the actual power outage. In Example 3, the uncertainty of power outages is considered when optimizing the pre-disaster deployment points of mobile emergency power supplies, but the locations of mobile emergency power supplies are not adjusted after the actual power outage.

[0141] The average load recovery at each time step obtained from the above three examples and the method proposed in this paper is as follows: Figure 4 As shown, the following conclusions can be drawn:

[0142] 1) The average load recovery amount is the smallest in Example 1, indicating that optimizing the mobile emergency power supply access point can reduce the power outage time of important loads during the sequential restoration process.

[0143] 2) Compared to Example 2, the sequential recovery method proposed in this invention can achieve power restoration more quickly. This is because the potential power outage scenarios are considered when deploying mobile emergency power supplies in the pre-disaster phase of prevention and control, which can reduce the travel time of mobile emergency power supplies on the transportation network.

[0144] Compared to Example 3, the method proposed in this invention achieves a smaller load recovery in the first four time steps. This is because mobile emergency power supplies cannot provide power support to the distribution network while operating on the road. However, the total load recovery achieved by the method proposed in this invention is higher, indicating that rescheduling mobile emergency power supplies during the emergency control phase can effectively improve the utilization efficiency of power equipment in the distribution network, thereby restoring more out-of-power loads.

[0145] The above embodiments illustrate and describe the basic principles and main features of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed.

Claims

1. A method for optimizing mobile emergency power supply access points considering the power outage distribution network restoration sequence, characterized in that, Includes the following steps: Step 1: Assess the probability of power distribution network line failures under extreme natural disasters; Step 2: Construct an optimization model for mobile emergency power supply access points: ； Where S represents the set of failure scenarios, Let represent the probability of scenario s occurring, and I represent the set of loads. This represents the weight of load i. This represents the load demand at node i. This represents the recovery status of the load on node i at time t in microgrid m under scenario s. This represents the load recovery amount at node i at time t in scenario s; The optimization model for the mobile emergency power supply access point includes the following constraints: (1) A mobile emergency power supply can only be configured on one node, and the number of mobile emergency power supplies that can be configured on a node is limited by the capacity of that node: ； ； in, This indicates whether the mobile emergency power supply k was configured on node i before the power outage occurred. (Cap) i This indicates the limit on the number of mobile emergency power supplies that can be accommodated on node i; (2) After a power outage occurs, a mobile emergency power supply can only be configured on one node, and only one mobile emergency power supply can be configured on one node: ； ； in, This indicates whether the mobile emergency power supply k is configured on node i under power outage scenario s; (3) Only mobile emergency power supplies configured during the pre-disaster prevention phase can participate in the power restoration of the distribution network after a power outage: ； Step 3: Construct a source-grid-load-storage collaborative scheduling model that considers the recovery order; Step 4: Linearize the model and solve it to obtain the optimized scheme of mobile emergency power supply access point considering the power outage distribution network restoration sequence.

2. The method for optimizing mobile emergency power supply access points considering the power outage distribution network restoration sequence according to claim 1, characterized in that, The assessment of the probability of power distribution network line failures under extreme natural disasters in step 1 specifically involves: If an overhead line or any utility pole fails, the line will be out of service. By mapping the maximum predicted wind speed to the equipment vulnerability curve, the failure probability of the overhead line and utility pole can be obtained, and thus the failure probability of the distribution network line can be obtained. ； ； in, Indicates wind speed. Indicates the probability of a line fault. This indicates the probability of an overhead line failure. This indicates the probability of a utility pole malfunctioning. This represents the probability of failure for an individual utility pole. This indicates the number of utility poles.

3. The method for optimizing mobile emergency power supply access points considering the power outage distribution network restoration sequence according to claim 1, characterized in that, The construction of the source-grid-load-storage collaborative scheduling model considering the recovery order in step 3 is specifically as follows: Step 3-1: Construct a power distribution network restoration path optimization model that considers the participation of mobile emergency power sources: ； ； ； ； ； ； ； ； ； in, Let y be a 0-1 variable, representing whether there is a recovery path in microgrid m starting from node i under scenario s, and L represent the line connectivity matrix in the distribution network. ij.s This represents the fault status of line (i,j) in the distribution network under fault scenario s. This represents the recovery path state from node i to node j in microgrid m under scenario s. =1 indicates that in scenario s, there exists a recovery path in microgrid m that starts from node i and reaches node j; otherwise, =1 indicates a recovery path that starts from node i and reaches node j. =0 indicates that no recovery path exists, M represents the set of generated microgrids, and n c Indicates the number of nodes in the distribution network; Step 3-2: Construct a virtual power flow model to represent whether nodes will continue to be powered by the main network after a disaster. ； ； in, Let θ(i) represent the virtual power flow on line (i,j) in scenario s, and let θ(i) represent the set of child nodes of node i. Step 3-3: Obtain the power outage duration of the load based on the node power supply restoration time: ； ； ； ； ； in, This represents the movement path matrix of the mobile emergency power supply k. This indicates whether the mobile emergency power supply k was configured on node i before the power outage occurred. This indicates whether the mobile emergency power supply k is configured on node j under power outage scenario s. This indicates the time required for emergency power supply k to reach node j. This represents the time required for the mobile emergency power supply to travel from node i to node j. This represents the recovery time of node j in scenario s. This represents the time required for the emergency power supply k to reach node j in scenario s. This represents a very large constant. This represents the time required to restore line (i,j) in scenario s. Indicates the duration of power outages in the distribution network; Steps 3-4: Introduce 0-1 variables and Let represent the relationship between the power supply status of nodes and lines and the power supply path under fault scenario s, respectively, with the following constraints: ； ； ； ； in, This represents the power supply status of node i in microgrid m at time t in scenario s. This represents the power supply status of line (h,i) in microgrid m at time t under scenario s; To indicate whether the distributed power sources, wind turbines, and loads connected to a certain node have resumed power supply, a 0-1 decision variable u is further introduced, satisfying: ； in, This represents the power supply recovery status of the load on node i at time t in scenario s. This represents the power supply recovery status of the distributed power source on node g at time t in scenario s. This indicates the power supply recovery status of the wind turbine at node w at time t in scenario s; Steps 3-5: Model the power output and load reduction of the power supply equipment in the system during the sequential restoration of the distribution network, which needs to meet the following constraints: ； ； ； ； ； ； ； ； ； ； ； ； In the formula, This represents the minimum active power output of the black-starting power supply at node i. This represents the maximum active power output of the black-starting power supply at node i. This represents the minimum reactive power output of the black-start power supply at node i. This represents the maximum active power output of the black-starting power supply at node i. This represents the active power output of the black-start power supply at node i in scenario s at time t. This represents the reactive power output of the black-start power supply at node i in scenario s at time t. This represents the allowable power reduction of the black-start power supply at a time step node i. This represents the allowed power increase of the black-start power supply at a time step node i. This represents the minimum active power output of the non-black-starting power source at node g. This represents the maximum active power output of the non-black-starting power source at node g. This represents the minimum active power output of the non-black-starting power source at node g. This represents the maximum active power output of the non-black-starting power source at node g. Let represent the active power output of the non-black-start power source at node g in microgrid m at time t under scenario s. Let represent the reactive power output of the non-black-start power source at node g in microgrid m at time t under scenario s. This indicates the allowed power reduction for non-black start on a time step node g. This indicates the allowed power increase for a non-black start on a time step node g. This represents the power factor of the non-black-starting power source at node g. This represents the active power dispatch output of the wind turbine at node w in microgrid m at time t under scenario s. This represents the reactive power output of the wind turbine at node w in microgrid m at time t under scenario s. This represents the rated output of the wind turbine at node w. This represents the capacity of the wind turbine at node w. This represents the power demand of the load at node i. This represents the minimum allowable load reduction on node i. This represents the maximum allowable load reduction on node i. This represents the active power reduction of the load on node i in microgrid m at time t under scenario s. This represents the reactive power reduction of the load on node i in microgrid m at time t under scenario s. This represents the power factor of the load at node i.

4. The method for optimizing mobile emergency power supply access points considering the power outage distribution network restoration sequence according to claim 3, characterized in that, The linearization and solution of the constructed model in step 4 specifically involves: Step 4-1: Linearize the nonlinear constraint terms in the model constructed in Steps 2 and 3; Step 4-2: Solve the linearized model to obtain an optimized mobile emergency power access point scheme that takes into account the power outage distribution network restoration sequence.

5. The method for optimizing mobile emergency power supply access points considering the power outage distribution network restoration sequence according to claim 4, characterized in that, The linearization process for the nonlinear constraint terms in step 4-1 is specifically as follows: (1) The optimization model for mobile emergency power supply access points contains bilinear terms. It needs to be linearized by introducing continuous variables. Replace the bilinear term in the objective function and introduce the following equivalent transformation: ； (2) Regarding the constraints on the relationship between the power supply status of nodes and lines and the power supply path under fault scenario s, the variables are... The constraint is obtained by multiplying two discrete variables, resulting in nonlinear variables in the constraint conditions. Therefore, it needs to be linearized. The line power supply state constraint is expressed as: 。 6. A mobile emergency power supply access point optimization system considering the power outage distribution network restoration sequence, characterized in that, Includes the following modules: Distribution network line fault probability module: used to assess the fault probability of distribution network lines under extreme natural disasters; Mobile emergency power access point optimization model construction module: Used to construct the mobile emergency power access point optimization model. ； Where S represents the set of failure scenarios, Let represent the probability of scenario s occurring, and I represent the set of loads. This represents the weight of load i. This represents the load demand at node i. This represents the recovery status of the load on node i at time t in microgrid m under scenario s. This represents the load recovery amount at node i at time t in scenario s; The optimization model for the mobile emergency power supply access point includes the following constraints: (1) A mobile emergency power supply can only be configured on one node, and the number of mobile emergency power supplies that can be configured on a node is limited by the capacity of that node: ； ； in, This indicates whether the mobile emergency power supply k was configured on node i before the power outage occurred. (Cap) i This indicates the limit on the number of mobile emergency power supplies that can be accommodated on node i; (2) After a power outage occurs, a mobile emergency power supply can only be configured on one node, and only one mobile emergency power supply can be configured on one node: ； ； in, This indicates whether the mobile emergency power supply k is configured on node i under power outage scenario s; (3) Only mobile emergency power supplies configured during the pre-disaster prevention phase can participate in the power restoration of the distribution network after a power outage: ； Source-grid-load-storage collaborative scheduling model: used to construct a source-grid-load-storage collaborative scheduling model that takes into account the recovery order; Solver module: Used to linearize the constructed model and solve it to obtain the optimal power distribution network sequence recovery scheme.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-5.

8. A computer-storable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-5.

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