Method for improving elasticity of power system under extreme climate condition

Abstract

The invention relates to a method for improving the elasticity of a power system under an extreme climate condition, and the method comprises the steps: building a cable line and joint fault rate model for the extreme climate aggravated cable line and joint fault risk, integrating cable parameters and operation states, accurately evaluating the fault probability, and providing data support for post-disaster recovery. By designing a temporary warehouse site selection model in the pre-disaster stage, combining with the line fault probability, the road network structure, the node load capacity and the importance degree, the position of the mobile energy storage warehouse is determined by taking the minimum power loss expectation as the target, the adjacent key load of the mobile energy storage warehouse is ensured, the resource allocation cost is reduced, and the response speed is improved. Mobile energy storage resources are called to participate in load recovery after a disaster, load recovery benefits and scheduling cost are balanced, mobile energy storage is taken as emergency equipment, a second-order cone constrained power system is combined to reconstruct an optimal power flow model, and rapid recovery of the load after the disaster is realized by reasonably scheduling the mobile energy storage resources and balancing the recovery benefits and the scheduling cost; and the elasticity of the power system under extreme climate conditions is enhanced.

Description

Technical Field

[0001] This invention relates to the field of power system operation and resilience enhancement, and in particular to a method for enhancing the resilience of power systems under extreme climatic conditions. Background Technology

[0002] In recent years, with global warming and increasingly frequent extreme weather conditions, the safe and stable operation of power systems has posed a significant challenge. As a crucial link in urban power supply, the power system bears the important responsibility of providing electricity to various users. However, under extreme weather conditions such as high temperatures, cable lines in the power system are highly susceptible to damage, leading to various faults and subsequent load loss, severely impacting the normal operation of cities and the quality of life for residents. On the one hand, extreme weather conditions cause cable insulation to expand due to heat, resulting in excessive pressure at cable joints and the formation of a "bamboo joint" phenomenon, exacerbating the risk of cable joint failures. On the other hand, extreme weather also accelerates the aging of cable insulation, threatening the reliable operation of cable-type power systems. After a disaster, traditional load restoration methods mainly rely on fixed grid structures and limited emergency resources. These methods typically suffer from slow response times, requiring considerable time to locate, isolate, and repair faults, making it difficult to restore power to lost loads quickly. Furthermore, traditional dispatching methods are often inflexible in resource allocation, unable to accurately dispatch according to the importance and urgency of loads, resulting in critical loads not being prioritized for restoration and failing to meet the needs of rapid power system recovery under extreme weather conditions.

[0003] How to effectively deal with cable line faults and achieve rapid restoration of power system load under extreme weather conditions has become an important issue that urgently needs to be addressed. Summary of the Invention

[0004] To address the problems and shortcomings of the existing technology, this invention provides a method for resilient enhancement of power systems under extreme weather conditions, and the technical solution adopted is as follows:

[0005] A method for enhancing the resilience of power systems under extreme weather conditions includes the following steps:

[0006] Pre-disaster phase:

[0007] Establish a cable core temperature model to obtain the cable core operating temperature;

[0008] Based on the operating temperature of the cable core, establish a failure rate model for cable lines and cable joints;

[0009] Based on the failure rate model of cable lines and cable joints, the overall failure rate of cable lines is derived;

[0010] A temporary warehouse location selection model is established based on line fault probability, road network structure, node load and importance. The objective function is to minimize the expected power loss to determine the location of the mobile energy storage warehouse.

[0011] Post-disaster phase:

[0012] A mobile energy storage dispatch model is established with the objective function of minimizing load loss while ensuring economical dispatch, and mobile energy storage is called upon to participate in load recovery.

[0013] To achieve rapid load recovery, a power system reconfiguration optimal power flow model based on second-order cone constraints is used to enable rapid load recovery and normal operation after disasters, thereby enhancing the resilience of the power system under extreme weather conditions.

[0014] Furthermore, a cable core temperature model is established, with the following expression:

[0015] T c =T amb +[R1+n(1+λ1)R2+n(1+λ1+λ2)(R3+R4)]+I 2 R0 (1)

[0016] In the formula: T c T represents the operating temperature of the cable core. amb R1 represents the external ambient temperature of the cable, n represents the number of cables, R1, R2, R3, and R4 represent the thermal resistance per unit length of the cable insulation layer, inner sheath, outer sheath, and external ambient temperature (m·K / W), respectively, λ1 and λ2 represent the ratio of metal shielding and armor layer loss to cable core loss, I represents the cable core current, and R0 represents the cable core resistance.

[0017] Furthermore, the cable line failure rate model is expressed as follows:

[0018]

[0019] In the formula: p frc Let ε be the cable line failure rate, ε be the cable shape parameter, μ be the cable characteristic life parameter, and t be the cable life parameter. i For the service life of cable lines, C f T represents the aging rate of cable insulation at operating temperature relative to rated temperature. N The rated operating temperature is σ, and the regression coefficient is σ.

[0020] The failure rate model for the cable joint is expressed as follows:

[0021]

[0022] In the formula: p frj ρ represents the failure rate of cable joints. jThe tension δ borne by the cable joint j μ represents the standard deviation of the tensile strength of insulating materials. j This represents the average tensile strength of the insulating material.

[0023] Furthermore, the overall failure rate of the cable line is expressed as follows:

[0024]

[0025] In the formula: p fr,i Let k1 be the failure rate of cable line i, k2 be the number of cable segments in cable line i, and p be the number of cable joints in cable line i. frc,m,i and p frj,n,i These are the failure rates of the m-th cable segment and the n-th cable joint of line i, respectively.

[0026] The road network structure is expressed as follows:

[0027] G=(N,S) (5)

[0028] In the formula: G is an undirected graph describing the topology of the road network; N is the set of road network nodes; S is the set of connecting lines between road network nodes.

[0029] Furthermore, the temporary warehouse location model is expressed as follows:

[0030]

[0031] Where: N L This represents the total number of lines in the power grid that have experienced faults. Dη represents the total number of nodes affected by a fault on line l; li ω represents the unit power loss at node i caused by a fault on line l; i The load weight under grid node i; t li The travel time for mobile energy storage from a temporary warehouse to the faulty line l.

[0032] Furthermore, the mobile energy storage scheduling model is expressed as follows:

[0033]

[0034] In the formula: C represents the load shedding amount generated by node i during time period t; load Benefits generated from post-disaster load recovery; C MESS The cost of mobile energy storage dispatch; α is the target weighting coefficient; c represents the load shedding amount generated by node i in time period t, assuming mobile resources do not participate in scheduling; load The unit load shedding cost; β iThe cost weights are determined based on the load importance at node i, where the first-level load β i =1, secondary load β i =0.78, Level 3 load β i =0.56; c cap Investment cost per unit capacity for mobile energy storage; E s X represents the configured capacity of the s-th mobile energy storage vehicle; s Let c be the total distance traveled by the s-th mobile energy storage vehicle; road Let B be the unit distance cost of mobile energy storage; T be the set of power system nodes; M be the set of time periods within the post-disaster recovery period; and M be the set of mobile energy storage systems participating in dispatch after the disaster.

[0035] Furthermore, the mobile energy storage scheduling model includes the following constraints:

[0036] At the spatial level, the requirement is that the same mobile energy storage device can only be present in one location within the power system at any given moment, expressed as:

[0037]

[0038] In the formula: L i,s,t For 0-1 variables, if L i,s,t =1 indicates that the s-th mobile energy storage vehicle is located near node i of the power system during time period t; otherwise, it indicates that the mobile energy storage vehicle is not near node i.

[0039] Furthermore, at the time level, it is required that the travel time be considered before each mobile energy storage vehicle arrives at a power system node, expressed as:

[0040]

[0041] In the formula: T i tra N represents the time required for mobile energy storage to travel from a temporary warehouse to node i in the power system. MESS The total number of mobile energy storage units participating in post-disaster dispatch;

[0042] To ensure the economy of dispatching and rapid recovery capability, mobile energy storage reschedules are not considered; the expression is:

[0043]

[0044] In the formula: Let s be the output of the s-th mobile energy storage vehicle at node i during time period t; This represents the upper limit of the output of the s-th mobile energy storage vehicle; Total number of mobile energy storage units deployed for temporary warehouses.

[0045] Furthermore, the power system reconfiguration optimal power flow model incorporating second-order cone constraints includes the following constraints:

[0046] The power constraint is expressed as:

[0047]

[0048] In the formula: p i,t and q i,t Let represent the actual injected active power and reactive power of node i during time period t, respectively; and Let represent the active power output and reactive power output of the generator at node i during time period t, respectively. and Let represent the active power demand and reactive power demand of node i during time period t, respectively; δ(i) is the set of nodes connected to node i by a branch. and Let represent the active power and reactive power flowing from node i to node j during time period t, respectively. and Let r represent the active power and reactive power flowing from node j to node i during time period t; ij and x ij Let i and j represent the resistance and reactance of the branch (i,j), respectively. f is the square of the magnitude of the transmission current of branch (i,j) during time period t; f and b are 0-1 variables, which define the positive and negative directions of the power flow of the branch. f=1 and b=0 means that the positive direction of the power flow is selected from node i to node j.

[0049] Voltage constraint, the expression is:

[0050]

[0051] In the formula: and V i V represents the upper and lower limits of the voltage magnitude at node i, respectively; i,t Let z be the voltage magnitude of node i during time period t; ij,t The variable is 0-1, reflecting the operating status of branch (i,j). A value of 1 indicates that the line is operating normally; K is a very large positive real number.

[0052] Current constraint, the expression is:

[0053]

[0054] In the formula: and I i j Let (i,j) represent the upper and lower limits of the magnitude of the current transmitted by the branch (i,j), respectively; E is the set of branches in the power system.

[0055] Radial constraint, expressed as:

[0056]

[0057] In the formula: and Both are 0-1 variables, used to represent the actual power flow direction of branch (i,j) in time period t. This indicates that the branch power flow is in the positive direction. This indicates that the branch power flow is in the opposite direction; E b The set of branches that are directly connected to the balance node of the power system;

[0058] The second-order cone constraint is expressed as follows:

[0059]

[0060] A computer device includes a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the computer program to implement the steps of a method for enhancing the resilience of a power system under any extreme weather conditions.

[0061] A computer storage medium storing a computer program, which, when executed by a processor, implements the steps of a method for resilient enhancement of a power system under any extreme climatic conditions.

[0062] Compared with existing technologies, its advantages are as follows:

[0063] This invention relates to a method for enhancing the resilience of power systems under extreme weather conditions. This method addresses the increased risk of cable line and joint failures due to extreme weather. It establishes a failure rate model for cables and joints, comprehensively considering cable parameters and operating conditions to accurately assess the probability of failure, providing data support for post-disaster recovery. By designing a temporary warehouse location model before a disaster, considering line failure probability, network structure, node load, and importance, the method determines the location of mobile energy storage warehouses with the goal of minimizing expected power loss, ensuring their proximity to critical loads, reducing resource allocation costs, improving response speed, and guaranteeing priority restoration of critical loads. After a disaster, mobile energy storage resources are mobilized to participate in load restoration, balancing load restoration benefits and dispatch costs. Mobile energy storage is treated as emergency equipment. Combined with a second-order cone constraint-based optimal power flow model for power system reconfiguration, the method achieves rapid load restoration and normal operation after a disaster by rationally dispatching mobile energy storage resources, thereby enhancing the resilience of the power system under extreme weather conditions. Attached Figure Description

[0064] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0065] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0066] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0067] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0068] Example 1

[0069] This invention provides an embodiment of a method for enhancing the resilience of a power system under extreme weather conditions, such as... Figure 1 As shown, Figure 1 This is a flowchart of the method of the present invention.

[0070] Pre-disaster phase:

[0071] Modeling is performed on cable core temperature, underground cable lines, and cable joint failure rates;

[0072] The cable core temperature is expressed as:

[0073] T c =T amb +[R1+n(1+λ1)R2+n(1+λ1+λ2)(R3+R4)]+I 2 R0 (26)

[0074] In the formula: T c T represents the operating temperature of the cable core. amb R1 represents the external ambient temperature of the cable, n represents the number of cables, R2, R3, and R4 represent the thermal resistance (m·K / W) of the cable insulation layer, inner sheath, outer sheath, and external environment per unit length, respectively, λ1 and λ2 represent the ratio of metal shielding and armor layer loss to cable core loss, I represents the cable core current, and R0 represents the cable core resistance.

[0075] The cable line failure rate is expressed as:

[0076]

[0077] In the formula: p frc Let ε be the cable line failure rate, ε be the cable shape parameter, μ be the cable characteristic life parameter, and t be the cable characteristic life parameter. iFor the service life of cable lines, C f T represents the aging rate of cable insulation at operating temperature relative to rated temperature. N σ is the rated operating temperature, and σ is the regression coefficient.

[0078] The formula for cable joint failure rate is as follows:

[0079]

[0080] In the formula: p frj ρ represents the failure rate of cable joints. j The tension δ borne by the cable joint j μ represents the standard deviation of the tensile strength of insulating materials. j This represents the average tensile strength of the insulating material.

[0081] When a cable line is operating normally, the cable line and cable joints can be considered as a series model. Therefore, the overall failure rate of the cable line under extreme weather conditions can be derived, expressed as:

[0082]

[0083] In the formula: p fr,i Let k1 be the failure rate of cable line i, k2 be the number of cable segments in cable line i, and p be the number of cable joints in cable line i. frc,m,i and p frj,n,i These represent the failure rates of the m-th cable segment and the n-th cable joint in line i, respectively.

[0084] The topology of a road network consists of traffic nodes and a set of streets, expressed as:

[0085] G=(N,S) (30)

[0086] In the formula: G is an undirected graph describing the topology of the road network; N is the set of road network nodes; S is the set of connecting lines between road network nodes.

[0087] Based on the fault probability and node load parameters of each power line in the power system, network nodes close to critical loads are selected as temporary warehouse deployment points to ensure that mobile energy storage can quickly reach the power outage nodes, saving travel costs while providing power support. The warehouse location model aims to minimize the average power outage, and its function expression is as follows:

[0088]

[0089] Where: N L This represents the total number of lines in the power grid that have experienced faults. Δη represents the total number of nodes affected by a fault on line l; li ω represents the unit power loss at node i caused by a fault on line l;i The load weight under grid node i; t li The travel time for mobile energy storage from a temporary warehouse to the faulty line l.

[0090] Example 2

[0091] This invention provides another embodiment, which is a method for enhancing the resilience of power systems under extreme climatic conditions, such as... Figure 1 As shown, Figure 1 This is a flowchart of the method of the present invention.

[0092] Post-disaster phase:

[0093] In cases where extreme weather conditions cause cable faults and load loss, mobile energy storage resources are mobilized to participate in the load restoration process. Weights are assigned to load restoration and dispatch costs, which can be adjusted according to actual conditions. A mobile energy storage dispatch model is established with the objective function of minimizing load loss while ensuring economical dispatch. The objective function considers both load restoration benefits and mobile energy storage dispatch costs. Mobile energy storage is treated as emergency dispatch equipment and is a non-independent operating entity; therefore, the grid connection price is not considered. Only the investment cost of mobile energy storage capacity and the relocation cost are calculated. The expression is as follows:

[0094]

[0095] In the formula: C represents the load shedding amount generated by node i during time period t; load Benefits generated from post-disaster load recovery; C MESS The cost of mobile energy storage dispatch; α is the target weighting coefficient; c represents the load shedding amount generated by node i in time period t, assuming mobile resources do not participate in scheduling; load The unit load shedding cost; β i The cost weights are determined based on the load importance at node i, where the first-level load β i =1, secondary load β i =0.78, Level 3 load β i =0.56; c cap Investment cost per unit capacity for mobile energy storage; E s X represents the configured capacity of the s-th mobile energy storage vehicle; s Let c be the total distance traveled by the s-th mobile energy storage vehicle; road Let B be the unit distance cost of mobile energy storage; T be the set of power system nodes; M be the set of time periods within the post-disaster recovery period; and M be the set of mobile energy storage systems participating in dispatch after the disaster.

[0096] The mobile energy storage dispatch model includes the following constraints:

[0097] At the spatial level, the requirement is that the same mobile energy storage vehicle can only be present in one location within the power system at any given moment, with the following constraints:

[0098]

[0099] In the formula: L i,s,t For 0-1 variables, if L i,s,t =1 indicates that the s-th mobile energy storage vehicle is located near node i of the power system during time period t; otherwise, it indicates that the mobile energy storage vehicle is not near node i.

[0100] Furthermore, at the time level, it is required that the travel time be considered before each mobile energy storage vehicle arrives at a power system node, with the following constraints:

[0101]

[0102] In the formula: T i tra N represents the time required for mobile energy storage to travel from a temporary warehouse to node i in the power system. MESS This refers to the total number of mobile energy storage devices participating in post-disaster dispatch.

[0103] To ensure the economy of dispatching and rapid recovery capability, mobile energy storage redistribution is not considered, and the relevant constraints are as follows:

[0104]

[0105] In the formula: Let s be the output of the s-th mobile energy storage vehicle at node i during time period t; This represents the upper limit of the output of the s-th mobile energy storage vehicle; Total number of mobile energy storage units deployed for temporary warehouses.

[0106] To achieve rapid load recovery, an optimal power flow model for power system reconfiguration considering second-order cone constraints is adopted, including the following constraints:

[0107] The power constraint is expressed as:

[0108]

[0109] In the formula: p i,t and q i,t Let represent the actual injected active power and reactive power of node i during time period t, respectively; and Let represent the active power output and reactive power output of the generator at node i during time period t, respectively. and Let represent the active power demand and reactive power demand of node i during time period t, respectively; δ(i) is the set of nodes connected to node i by a branch. and Let represent the active power and reactive power flowing from node i to node j during time period t, respectively. and Let r represent the active power and reactive power flowing from node j to node i during time period t; ij and x ij Let i and j represent the resistance and reactance of the branch (i,j), respectively. f is the square of the magnitude of the current transmitted through branch (i,j) during time period t; f and b are 0-1 variables, which define the positive and negative directions of the power flow in the branch. f=1 and b=0 means that the positive direction of the power flow is selected from node i to node j.

[0110] Voltage constraint, the expression is:

[0111]

[0112] In the formula: and V i V represents the upper and lower limits of the voltage magnitude at node i, respectively; i,t Let z be the voltage magnitude of node i during time period t; ij,t The variable is 0-1, reflecting the operating status of branch (i,j). A value of 1 indicates that the line is operating normally; K is a very large positive real number.

[0113] Current constraint, the expression is:

[0114]

[0115] In the formula: and I i j Let (i,j) represent the upper and lower limits of the magnitude of the current transmitted by the branch (i,j), respectively; E is the set of branches in the power system.

[0116] Radial constraint, expressed as:

[0117]

[0118] In the formula: and Both are 0-1 variables, used to represent the actual power flow direction of branch (i,j) in time period t. This indicates that the branch power flow is in the positive direction. This indicates that the branch power flow is in the opposite direction; E b This refers to the set of branches that are directly connected to the power system's balance node.

[0119] The second-order cone constraint is expressed as follows:

[0120]

[0121] The post-disaster multi-period scheduling model is a mixed integer programming problem, which can be solved directly by calling commercial solvers such as CPLEX and Gurobi.

[0122] After a disaster, by rationally scheduling mobile energy storage resources, the recovery benefits and scheduling costs can be balanced, enabling rapid recovery and normal operation of the post-disaster load and enhancing the resilience of the power system under extreme weather conditions.

[0123] Example 3

[0124] Based on the same inventive concept, embodiments of the present invention also provide a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, it implements the steps of any of the power system resilience enhancement methods under extreme weather conditions described in embodiments 1-2.

[0125] Example 4

[0126] Based on the same inventive concept, this embodiment of the invention also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the extreme climate conditions power system resilience enhancement methods described in embodiments 1-2.

[0127] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0128] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0129] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0130] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for resilient enhancement of power systems under extreme climatic conditions, characterized in that, include: Pre-disaster phase: Establish a cable core temperature model to obtain the cable core operating temperature; Based on the operating temperature of the cable core, establish a failure rate model for cable lines and cable joints; Based on the failure rate model of cable lines and cable joints, the overall failure rate of cable lines is derived; A temporary warehouse location selection model is established based on line fault probability, road network structure, node load and importance. The objective function is to minimize the expected power loss to determine the location of the mobile energy storage warehouse. Post-disaster phase: A mobile energy storage dispatch model is established with the objective function of minimizing load loss while ensuring economical dispatch, and mobile energy storage is called upon to participate in load recovery. To achieve rapid load recovery, a power system reconfiguration optimal power flow model based on second-order cone constraints is used to enable rapid load recovery and normal operation after disasters, thereby enhancing the resilience of the power system under extreme weather conditions.

2. The method for enhancing the resilience of a power system under extreme climatic conditions according to claim 1, characterized in that, The established cable core temperature model is expressed as follows: T c =T amb +[R1+n(1+λ1)R2+n(1+λ1+λ2)(R3+R4)]+I 2 R0 (1) In the formula: T c T represents the operating temperature of the cable core. amb R1 represents the external ambient temperature of the cable, n represents the number of cables, R1, R2, R3, and R4 represent the thermal resistance per unit length of the cable insulation layer, inner sheath, outer sheath, and external ambient temperature (m·K / W), respectively, λ1 and λ2 represent the ratio of metal shielding and armor layer loss to cable core loss, I represents the cable core current, and R0 represents the cable core resistance.

3. The method for enhancing the resilience of a power system under extreme climatic conditions according to claim 1, characterized in that, The cable line failure rate model is expressed as follows: In the formula: p frc Let ε be the cable line failure rate, ε be the cable shape parameter, μ be the cable characteristic life parameter, and t be the cable life parameter. i For the service life of cable lines, C f T represents the aging rate of cable insulation at operating temperature relative to rated temperature. N The rated operating temperature is σ, and the regression coefficient is σ. The failure rate model for the cable joint is expressed as follows: In the formula: p frj ρ represents the failure rate of cable joints. j The tension δ borne by the cable joint j μ represents the standard deviation of the tensile strength of insulating materials. j This represents the average tensile strength of the insulating material.

4. The method for enhancing the resilience of a power system under extreme climatic conditions according to claim 1, characterized in that, The overall failure rate of the cable line is expressed as follows: In the formula: p fr,i Let k1 be the failure rate of cable line i, k2 be the number of cable segments in cable line i, and p be the number of cable joints in cable line i. frc,m,i and p frj,n,i These are the failure rates of the m-th cable segment and the n-th cable joint of line i, respectively. The road network structure is expressed as follows: G=(N,S) (5) In the formula: G is an undirected graph describing the topology of the road network; N is the set of road network nodes; S is the set of connecting lines between road network nodes.

5. The method for enhancing the resilience of a power system under extreme climatic conditions according to claim 1, characterized in that, The temporary warehouse location selection model is expressed as follows: Where: N L This represents the total number of lines in the power grid that have experienced faults. Dη represents the total number of nodes affected by a fault on line l; li The unit power loss generated by the fault of the l-th line to node i; ω i The load weight under grid node i; t li The travel time for mobile energy storage from a temporary warehouse to the faulty line l.

6. The method for enhancing the resilience of a power system under extreme climatic conditions according to claim 1, characterized in that, The mobile energy storage scheduling model is expressed as follows: In the formula: C represents the load shedding amount generated by node i during time period t; load Benefits generated from post-disaster load recovery; C MESS The cost of mobile energy storage dispatch; α is the target weighting coefficient; c represents the load shedding amount generated by node i in time period t, assuming mobile resources do not participate in scheduling; load The unit load shedding cost; β i The cost weights are determined based on the load importance at node i, where the first-level load β i =1, secondary load β i =0.78, Level 3 load β i =0.56; c cap Investment cost per unit capacity for mobile energy storage; E s X represents the configured capacity of the s-th mobile energy storage vehicle; s Let c be the total distance traveled by the s-th mobile energy storage vehicle; road B represents the unit distance mobility cost of mobile energy storage; T represents the set of power system nodes; and T represents the set of time periods within the post-disaster recovery period. M represents the set of mobile energy storage systems that participate in post-disaster dispatch.

7. The method for enhancing the resilience of a power system under extreme climatic conditions according to claim 6, characterized in that, The mobile energy storage scheduling model includes the following constraints: At the spatial level, the requirement is that the same mobile energy storage device can only be present in one location within the power system at any given moment, expressed as: In the formula: L i,s,t For 0-1 variables, if L i,s,t =1 indicates that the s-th mobile energy storage vehicle is located near node i of the power system during time period t; otherwise, it indicates that the mobile energy storage vehicle is not near node i. Furthermore, at the time level, it is required that the travel time be considered before each mobile energy storage vehicle arrives at a power system node, expressed as: In the formula: T i tra The time required for mobile energy storage to travel from a temporary warehouse to node i in the power system; N MESS The total number of mobile energy storage units participating in post-disaster dispatch; To ensure the economy of dispatching and rapid recovery capability, mobile energy storage reschedules are not considered; the expression is: In the formula: Let s be the output of the s-th mobile energy storage vehicle at node i during time period t; This represents the upper limit of the output of the s-th mobile energy storage vehicle; Total number of mobile energy storage units deployed for temporary warehouses.

8. The method for enhancing the resilience of a power system under extreme climatic conditions according to claim 1, characterized in that, The power system reconfiguration optimal power flow model incorporating second-order cone constraints includes the following constraints: The power constraint is expressed as: In the formula: p i,t and q i,t Let represent the actual injected active power and reactive power of node i during time period t, respectively; and Let represent the active power output and reactive power output of the generator at node i during time period t, respectively. and Let represent the active power demand and reactive power demand of node i during time period t, respectively; δ(i) is the set of nodes connected to node i by a branch. and Let represent the active power and reactive power flowing from node i to node j during time period t, respectively. and Let r represent the active power and reactive power flowing from node j to node i during time period t; ij and x ij Let i and j represent the resistance and reactance of the branch (i,j), respectively. f is the square of the magnitude of the transmission current of branch (i,j) during time period t; f and b are 0-1 variables, which define the positive and negative directions of the power flow of the branch. f=1 and b=0 means that the positive direction of the power flow is selected from node i to node j. Voltage constraint, the expression is: In the formula: and V i V represents the upper and lower limits of the voltage magnitude at node i, respectively; i,t Let z be the voltage magnitude of node i during time period t; ij,t The variable is 0-1, reflecting the operating status of branch (i,j). A value of 1 indicates that the line is operating normally; K is a very large positive real number. Current constraint, the expression is: In the formula: and I i j Let (i,j) represent the upper and lower limits of the magnitude of the current transmitted by the branch (i,j), respectively; E is the set of branches in the power system. Radial constraint, expressed as: In the formula: and Both are 0-1 variables, used to represent the actual power flow direction of branch (i,j) in time period t. This indicates that the branch power flow is in the positive direction. This indicates that the branch power flow is in the opposite direction; E b The set of branches that are directly connected to the balance node of the power system; The second-order cone constraint is expressed as follows:

9. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for enhancing the resilience of a power system under extreme climatic conditions as described in any one of claims 1-8.

10. A computer storage medium, characterized in that, The computer storage medium contains a computer program, which, when executed by a processor, implements the steps of a method for enhancing the resilience of a power system under extreme climatic conditions as described in any one of claims 1-8.

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