Planning method and system for power distribution network toughness improvement pre-disaster difference

Abstract

The invention discloses a pre-disaster difference planning method and system for power distribution network toughness improvement, and belongs to the technical field of planning of power distribution network and traffic network toughness improved.The method comprises the steps that lines and roads are reinforced at different grades, the position of a standby energy storage power source is planned, and a multi-grade difference planning model is constructed; in combination with historical data, constructing a fuzzy uncertainty set of faults of partial components under the conditions of different reinforcement levels and disaster intensities; carrying out constraint modeling by adopting a chance constraint model to form fault probability condition data; after-disaster severe scenes are generated, repair team repair and generator tripping and load shedding scheduling are carried out, the total cost function is evaluated, and whether the minimum total cost target is achieved or not is judged. According to the method, difference planning is carried out based on the multi-level fault probability uncertainty and the deep coupling characteristic of the power grid and the traffic network, the pre-disaster investment cost is reduced while the network toughness is improved, the relation between toughness improvement and economy is effectively balanced, and the method has good applicability.

Description

Technical Field

[0001] The present invention belongs to the technical field of planning for improving the resilience of distribution networks and transportation networks, and specifically relates to a planning method and system for pre-disaster differentiation of improving the resilience of distribution networks. Background Art

[0002] In recent years, due to the continued deterioration of the ecological environment and climate change, extreme disasters have become more widespread, frequent, intense, and concurrent. Natural disasters cause significant economic losses, posing a threat to social production, transportation, and people's lives. Therefore, we need to strengthen our ability to withstand disasters and build resilient cities that can quickly recover after disasters.

[0003] As the core of urban infrastructure, the resilience of power and transportation networks directly impacts a city's ability to withstand natural disasters and recover quickly after disasters. Therefore, pre-disaster planning strategies for coupled power and transportation networks are crucial and have significant implications for the resilience of emerging cities. Existing research on measures to enhance pre-disaster resilience typically focuses on pre-disaster reinforcement of power grids, assuming that reinforced lines are completely reliable. This approach fails to consider the impact of uncertainty in failure probabilities brought about by different reinforcement measures. Furthermore, current research focuses solely on strengthening power grids, ignoring the impact of damage and reinforcement to transportation networks on the post-disaster recovery of distribution networks. Therefore, in summary, current disaster resilience planning for distribution networks fails to consider the differences in the impact of different lines on disaster conditions and the uncertainty in failure probabilities associated with different disasters and reinforcement levels. Furthermore, the impact between the power system and transportation networks is neglected, which in turn impacts the distribution network's disaster resilience, post-disaster recovery capabilities, and overall economic benefits. Summary of the Invention

[0004] The present invention provides a planning method and system for improving the pre-disaster diversity of distribution network resilience. The purpose is to solve the problem that the current disaster resistance planning of distribution networks does not take into account the differences in the disaster conditions of different lines and the uncertainty of failure probabilities of different disasters and reinforcement levels, and ignores the impact between the power system and the transportation network, which in turn affects the distribution network's disaster resistance, post-disaster recovery capabilities and economic benefits of all links.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: The present invention provides a planning method for improving the resilience of distribution networks before disasters, comprising the following steps: S1. Based on the basic data of the urban distribution network and transportation network, power lines and urban roads are reinforced in a hierarchical manner. At the same time, the preset installation locations of backup energy storage power sources are planned to form a multi-level differentiated planning model; S2. Combining a multi-level differential planning model with historical data, we construct a fuzzy uncertainty set for the failure of some components in the urban distribution network and transportation network under different reinforcement levels and disaster intensities. S3. Based on the fuzzy uncertainty set, a chance constraint model is used to constrain the situation where the failure probability of the urban distribution network and transportation network exceeds the expected disaster level at each reinforcement level, and form failure probability data; S4. Generate post-disaster severe scenarios based on uncertainty fuzzy sets and failure probability data, conduct repair and machine shedding and load shedding scheduling based on the post-disaster severe scenarios, evaluate the total cost function, and determine whether the minimum total cost target is achieved: if not, return to S2 and proceed again; if so, output the pre-disaster differentiated planning scheme and cost data for each stage.

[0006] In some implementations, in S1, the multi-level difference planning model includes: (1); (2); (3); (4); in, and The reinforcement levels of power lines and urban roads are k The reinforcement cost under and are 0-1 variables indicating whether reinforcement is required, When it is not reinforced, is the unit energy storage configuration cost, A 0-1 variable indicating whether the node is configured with energy storage; They are reinforcement level, power lines, traffic roads and grid node sets, N B Configure an upper limit for energy storage.

[0007] In some implementations, in S2, a fuzzy set model of failure probability uncertainty is constructed to describe the fuzzy uncertainty set of failures of some components in the urban distribution network and transportation network, as follows: Based on several sets of historical data, we can obtain different levels of fault probability data sampling values: ; Define the empirical distribution of a sample of known random variables for ; where is the random variable n Sample collection N middle The Dirac measure of Use the Wasserstein distance between the true distribution and the empirical distribution Describing fuzzy sets: (5); in is the support set of the random variable, for and The joint probability density between is any norm; Fuzzy Sets Based on Wasserstein Distance The definition is as follows: (6); in, Support set The set of probability distributions, taking a multifaceted set , C and is the coefficient vector, is the radius of the Wasserstein sphere.

[0008] In some implementations, in S3, the opportunity constraint model specifically includes: (7); (8); in, is a probability operator; and Disaster levels The probability of failure.

[0009] In some implementations, in S4, the following formula is specifically used for scheduling repairs by maintenance teams and cutting off machines and loads based on severe post-disaster scenarios: (9); in, are the cost coefficients of load shedding, machine shedding and road capacity loss, respectively. and are load shedding power and generator shedding power respectively, is the road loss capacity.

[0010] Furthermore, in S4, the constraints of formula (9) include: maintenance sequence constraints, disaster failure and maintenance logic constraints, and power grid operation and dispatch constraints.

[0011] Furthermore, in S4, the disaster failure and maintenance logic constraints include: (10); (11); (12); in, and are the fault state variables of the power grid and transportation network; and are the current operating status variables of the power grid and transportation network; and Repair state variables for power grid and transportation network.

[0012] Furthermore, in S4, the grid operation and dispatch constraints are: (13); (14); (15); (16); (17); (18); (19); (20); Where: and 0-1 constraint for energy storage charging and discharging; and is the energy storage charging and discharging power; The current amount of energy stored; The efficiency of energy storage charging and discharging; for i Point load power; for i Point generator power; Transmit power to the line; is the line admittance; for i Point phase angle; used uniformly in the above formula. . is the upper limit of each component power, is the lower limit of component power.

[0013] Furthermore, in S4, the maintenance sequence constraints include: (twenty one); (twenty two); (twenty three); (twenty four); (25); (26); (27); (28); in, is the initial capacity of the transportation network; To complete the task After that, the repair sequence variable of the next task i is i ,but is 1; is the initial location of the repair team; 、 、 、 Respectively for tasks i The start time, end time, repair time and time required for the task Then go to the next task i Movement time; For the task Then go to the next task i The moving distance; V is the moving speed of the maintenance team; 、 、 、 They are the maintenance starting location set, end location set, task location set and maintenance team set.

[0014] The present invention also provides a planning system for pre-disaster differentiation of distribution network resilience improvement, the system comprising a model building module, a fuzzy uncertainty set module, a failure probability module, and a differentiation planning module, wherein: Model building module: This module is used to perform hierarchical reinforcement of power lines and urban roads based on the basic data of the urban distribution network and transportation network, and simultaneously plan the preset installation locations of backup energy storage power sources to form a multi-level differentiated planning model; Fuzzy uncertainty set module: This module combines multi-level differential planning models and historical data to construct fuzzy uncertainty sets for failures of some components in urban distribution networks and transportation networks under different reinforcement levels and disaster intensities. Failure probability module: Based on fuzzy uncertainty sets and using a chance constraint model, it constrains modeling for situations where the failure probability of urban distribution networks and transportation networks at various reinforcement levels exceeds the expected disaster level, generating failure probability data. Differential planning module: Generates post-disaster severe scenarios based on uncertainty fuzzy sets and failure probability data, conducts repair team and machine shedding and load shedding scheduling based on the post-disaster severe scenarios, evaluates the total cost function, and determines whether the minimum total cost target is achieved: if not, returns to the fuzzy uncertainty set module and starts again; if so, outputs the pre-disaster differentiated planning scheme and cost data for each stage.

[0015] Compared with the existing technology, the planning method and system for improving the pre-disaster differentiation of distribution network resilience in the present invention have the following beneficial effects: The present invention provides a planning method for pre-disaster differentiation for improving the resilience of distribution networks. Based on the basic data of urban distribution networks and transportation networks, differentiated planning is carried out to enhance the system's disaster resistance and rapid recovery capabilities. Historical operation data is used to construct a fuzzy uncertainty set of failures of various components in the distribution network and transportation network under different reinforcement levels and disaster intensities to characterize their probabilistic characteristics and provide basic support for subsequent scheduling modeling. An opportunity constraint model is used to constrain modeling for situations where the probability of failure of the distribution and transportation systems exceeds the expected disaster level under each reinforcement level, thereby ensuring the robustness and reliability of the system operation under a given confidence level. Based on the above fuzzy uncertainty set, a severe post-disaster scenario is generated and emergency repair resource scheduling and system operation optimization are carried out. By evaluating the total cost function, it is determined whether the minimum total cost target is achieved. The present invention establishes a differential planning model for improving the resilience of distribution networks. The model takes into account the differences in disaster situations in different regions and performs high-level reinforcement on key lines in disaster-prone areas. The model coordinates pre-disaster planning and post-disaster recovery operation, thereby improving the disaster resistance and recovery capabilities of the distribution network and achieving higher recovery efficiency and economic benefits in all links before the disaster. The present invention considers the uncertainty of failure probabilities at different disaster levels and reinforcement levels, and constructs a two-stage resilience improvement model for pre-disaster differential planning and post-disaster network repair based on distributed robust chance constraints; while reducing disaster losses, it also takes into account economic efficiency, and the reinforcement of the transportation network contributes to the rapid recovery of the distribution network after the disaster, reducing the cumulative disaster loss cost. The uncertainty description method for different disaster situations and different reinforcement levels based on chance constraints and Wasserstein distance proposed in the present invention can effectively consider the uncertainty of failure probabilities at different disaster levels and the failure scenarios of lines and roads after reinforcement, making the pre-disaster planning scheme more robust and having better practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings in the specification are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0017] Figure 1This is a flow chart of a planning method for improving the resilience of distribution networks before disasters according to the present invention; Figure 2 This is a schematic diagram of pre-disaster prevention and post-disaster recovery of distribution network-transportation network coupling in an embodiment of the present invention, in which a planning method for pre-disaster differentiation for improving distribution network resilience is provided. DETAILED DESCRIPTION

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0020] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not require further definition or explanation in subsequent drawings.

[0021] In the description of the embodiments of the present invention, it should be noted that if the terms "upper," "lower," "horizontal," "inner," etc. appear, the orientation or positional relationship indicated is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the inventive product is typically placed when in use. These terms are merely for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first," "second," etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0023] In the description of the embodiments of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0024] like Figure 1 As shown, the present invention provides a planning method for improving the resilience of distribution networks before disasters, comprising the following steps: S1. Based on the basic data of the urban distribution network and transportation network, power lines and urban roads are reinforced in a hierarchical manner. At the same time, the preset installation locations of backup energy storage power sources are planned to form a multi-level differentiated planning model; S2. Combining a multi-level differential planning model with historical data, we construct a fuzzy uncertainty set for the failure of some components in the urban distribution network and transportation network under different reinforcement levels and disaster intensities. S3. Based on the fuzzy uncertainty set, a chance constraint model is used to constrain the situation where the failure probability of the urban distribution network and transportation network exceeds the expected disaster level at each reinforcement level, and form failure probability data; S4. Generate post-disaster severe scenarios based on uncertainty fuzzy sets and failure probability data, conduct repair and machine shedding and load shedding scheduling based on the post-disaster severe scenarios, evaluate the total cost function, and determine whether the minimum total cost target is achieved: if not, return to S2 and proceed again; if so, output the pre-disaster differentiated planning scheme and cost data for each stage.

[0025] The present invention takes into account the influence of uncertainty in the probability of failure of power lines / roads under different reinforcement conditions and the level of disasters; establishes a differential planning model, an uncertainty fuzzy set model and a post-disaster recovery scheduling model; performs differential planning on power lines / roads before a disaster to improve the disaster resistance of the distribution network; establishes a post-disaster recovery scheduling model based on severe scenarios after the disaster to reduce the cumulative disaster losses after the disaster. The objective function and the constraints of pre-disaster planning and post-disaster scheduling are determined, and the distribution network resilience improvement planning scheme and post-disaster scheduling recovery result data that consider the uncertainty of multi-level failure probabilities are obtained by solving them. The present invention considers the uncertainty of multi-level failure probabilities and the deep coupling characteristics of the power grid and the transportation network for differential planning, which greatly improves the network resilience while reducing the pre-disaster investment cost, effectively balancing the relationship between resilience improvement and economy, and has good applicability.

[0026] The present invention also provides a planning system for pre-disaster differentiation of distribution network resilience improvement, the system comprising a model building module, a fuzzy uncertainty set module, a failure probability module, and a differentiation planning module, wherein: Model building module: This module is used to perform hierarchical reinforcement of power lines and urban roads based on the basic data of the urban distribution network and transportation network, and simultaneously plan the preset installation locations of backup energy storage power sources to form a multi-level differentiated planning model; Fuzzy uncertainty set module: This module combines multi-level differential planning models and historical data to construct fuzzy uncertainty sets for failures of some components in urban distribution networks and transportation networks under different reinforcement levels and disaster intensities. Failure probability module: Based on fuzzy uncertainty sets and using a chance constraint model, it constrains modeling for situations where the failure probability of urban distribution networks and transportation networks at various reinforcement levels exceeds the expected disaster level, generating failure probability data. Differential planning module: Generates post-disaster severe scenarios based on uncertainty fuzzy sets and failure probability data, conducts repair team and machine shedding and load shedding scheduling based on the post-disaster severe scenarios, evaluates the total cost function, and determines whether the minimum total cost target is achieved: if not, returns to the fuzzy uncertainty set module and starts again; if so, outputs the pre-disaster differentiated planning scheme and cost data for each stage.

[0027] The following is a detailed description of a planning method and system for improving the resilience of distribution networks before disasters according to the present invention through specific embodiments.

[0028] Step 1: Based on the basic data of the urban distribution network and transportation network, differentiated planning is carried out for the urban distribution network and transportation network. This mainly includes different levels of line / road reinforcement and planning of backup energy storage power supply locations: Differential planning model: (1); (2); (3); (4); Where, and The reinforcement levels of urban distribution network and transportation network are k reinforcement costs under and are 0-1 variables indicating whether reinforcement is required, When it is not reinforced; The unit storage configuration cost; A 0-1 variable indicating whether the node is configured with energy storage; They are reinforcement level, power lines, traffic roads and grid node sets. N B Configure an upper limit for energy storage.

[0029] Step 2: Based on historical data, establish the uncertainty fuzzy set of failure probabilities of distribution network and transportation network under different reinforcement levels and disaster levels; based on N The historical data is used to obtain the sampling values ​​of failure probability data for different disasters and reinforcement levels: ; Define the empirical distribution of a sample of known random variables for , where is the random variable n Sample collection N middle The Dirac measure of . Using the Wasserstein distance between the true distribution and the empirical distribution Describing fuzzy sets: (5); Where: is the support set of the random variable; for and The joint probability density between ; is an arbitrary norm. Therefore, the fuzzy set based on Wasserstein distance The definition is as follows: (6); Where: Support set A set of probability distributions; usually a polyhedral set , C and is the coefficient vector; is the radius of the Wasserstein sphere.

[0030] Step 3: Use chance constraints to describe the situation where the uncertainty of failure probability of distribution network and transportation network with different reinforcement levels exceeds the expected disaster level; The opportunity constraint is expressed as follows: (7); (8); Where: is a probability operator; and Disaster levels The chance constraint is that under a given disaster level, the failure probability of lines or roads with different reinforcement levels must not be lower than a given confidence level. The above chance constraints can be further unified as: ; ; Where: 、 are vectors composed of the failure probabilities of the power grid and transportation network respectively; and is the corresponding coefficient vector; and is the corresponding constant vector.

[0031] The present invention provides a planning method for improving the pre-disaster differentiation of distribution network resilience. In order to improve the solution efficiency while ensuring the solution accuracy, some of the above constraints are transformed. The end conditions specifically include: Because the constraints on the power grid and transportation networks are similar, the following example uses the power grid as an example for ease of presentation. Using conditional value at risk (CVaR), the above equation can be converted to a conditional value at risk constraint, as shown below: ; Where: As auxiliary variables, the above conditional risk value constraints are transformed into linear constraints: ; ; ; ; Where: 、 、 It is the dual variable introduced in the transformation process; is an infinite norm.

[0032] The above process transforms Wasserstein's uncertainty fuzzy set and chance constraint part into a linear problem, which is convenient for solving subsequent problems.

[0033] Step 4: Generate a post-disaster severe scenario based on the uncertainty fuzzy set, and schedule repairs and load shedding by the maintenance team according to the severe scenario. Determine whether the total cost of all links is the minimum. If so, output the pre-disaster differential planning scheme and the cost data of each link; otherwise, return to step 2; The severe scenario generation, repair by maintenance team, and machine shedding and load shedding scheduling model are as follows: ; Where: are the cost coefficients of load shedding, machine shedding and road capacity loss respectively; and They are load shedding power and generator shedding power respectively; is the road loss capacity.

[0034] The repair and logical constraints are: (10); (11); (12); Where: and are the fault state variables of the power grid and transportation network; and are the current operating status variables of the power grid and transportation network; and are the repair state variables of the power grid and transportation network; the above state variables are all 0-1 variables.

[0035] The grid operation and dispatch constraints are: (13); (14); (15); (16); (17); (18); (19); (20); Where: and 0-1 constraint for energy storage charging and discharging; and is the energy storage charging and discharging power; The current amount of energy stored; The efficiency of energy storage charging and discharging; for i Point load power; for i Point generator power; Transmit power to the line; is the line admittance; for i Point phase angle; used uniformly in the above formula. . is the upper limit of each component power, is the lower limit of component power.

[0036] The maintenance constraints of the maintenance team are: (twenty one); (twenty two); (twenty three); (twenty four); (25); (26); (27); (28); Where: is the initial capacity of the transportation network; To complete the task After that, the repair sequence variable of the next task i is i ,but is 1; is the initial location of the repair team; 、 、 、 Respectively for tasks i The start time, end time, repair time and time required for the task Then go to the next task i Movement time; For the task Then go to the next task i The moving distance; V is the moving speed of the maintenance team; 、 、 、 They are the maintenance starting location set, end location set, task location set and maintenance team set.

[0037] In some embodiments, the objective function of the present invention may be solved using a C&CG algorithm.

[0038] In summary, the present invention provides a planning method and system for pre-disaster differentiation of distribution network resilience improvement, and proposes a pre-disaster differentiation planning strategy for distribution network resilience improvement that takes into account the uncertainty of failure probabilities of different disasters and reinforcement levels. Considering the resource redundancy problem caused by full-level reinforcement in pre-disaster planning, a multi-level differential planning model for power lines and roads is established, and the Wasserstein distance and chance constraints are used to describe the uncertainty of power line / road failure probabilities under different reinforcement conditions and the impact of disaster levels on uncertainty; a post-disaster recovery scheduling model is established based on the pre-disaster planning scheme and disaster damage situation, and combined with the pre-disaster planning model to form a two-stage distributed robust chance-constrained resilience / resilience improvement optimization strategy, which reduces pre-disaster investment costs while improving network resilience, and effectively balances the relationship between resilience improvement and economy in disaster prevention planning.

[0039] Finally, it should be noted that the above is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in this industry can smoothly implement the present invention as shown in the specification and described above. Any equivalent changes, modifications and evolutions made by using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the scope of protection of the technical solution of the present invention.

Claims

1. A planning method for improving the resilience of distribution networks before disasters, characterized in that: The steps include: S1. Based on the basic data of the urban distribution network and transportation network, power lines and urban roads are reinforced in a hierarchical manner. At the same time, the preset installation locations of backup energy storage power sources are planned to form a multi-level differentiated planning model; S2. Combining a multi-level differential planning model with historical data, we construct a fuzzy uncertainty set for the failure of some components in the urban distribution network and transportation network under different reinforcement levels and disaster intensities. S3. Based on the fuzzy uncertainty set, a chance constraint model is used to constrain the situation where the failure probability of the urban distribution network and transportation network exceeds the expected disaster level at each reinforcement level, and form failure probability data; S4. Generate post-disaster severe scenarios based on uncertainty fuzzy sets and failure probability data, conduct repair and machine shedding and load shedding scheduling based on the post-disaster severe scenarios, evaluate the total cost function, and determine whether the minimum total cost target is achieved: if not, return to S2 and proceed again; if so, output the pre-disaster differentiated planning scheme and cost data for each stage.

2. The planning method for improving the resilience of distribution network before disasters according to claim 1 is characterized in that: In S1, the multi-level difference planning model includes: （1）； （2）； （3）； （4）； in, and The reinforcement levels of power lines and urban roads are k The reinforcement cost under and are 0-1 variables indicating whether reinforcement is required, When it is not reinforced, is the unit energy storage configuration cost, A 0-1 variable indicating whether the node is configured with energy storage; They are reinforcement level, power lines, traffic roads and grid node sets, N B Configure an upper limit for energy storage.

3. The planning method for improving the pre-disaster differentiation of distribution network resilience according to claim 1 is characterized in that: In S2, a fuzzy set model of failure probability uncertainty is constructed to describe the fuzzy uncertainty set of failures of some components in the urban distribution network and transportation network, as follows: Based on several sets of historical data, we can obtain different levels of fault probability data sampling values: ； Define the empirical distribution of a sample of known random variables for ; where is the random variable n Sample collection N middle The Dirac measure of Use the Wasserstein distance between the true distribution and the empirical distribution Describing fuzzy sets: （5）； in is the support set of the random variable, for and The joint probability density between is any norm; Fuzzy Sets Based on Wasserstein Distance The definition is as follows: （6）； in, Support set The set of probability distributions, taking a multifaceted set , C and is the coefficient vector, is the radius of the Wasserstein sphere.

4. The planning method for improving the resilience of distribution network before disasters according to claim 1 is characterized in that: In S3, the opportunity constraint model specifically includes: （7）； （8）； in, is a probability operator; and Disaster levels The probability of failure.

5. The planning method for improving the pre-disaster differentiation of distribution network resilience according to claim 1 is characterized in that: In S4, the following formula is specifically used for scheduling repairs by maintenance teams and cutting off machines and loads based on the severe post-disaster scenario: （9）； in, are the cost coefficients of load shedding, machine shedding and road capacity loss, respectively. and are load shedding power and generator shedding power respectively, is the road loss capacity.

6. The planning method for improving the pre-disaster diversity of distribution network resilience according to claim 5 is characterized in that: In S4, the constraints of formula (9) include: maintenance sequence constraints, disaster failure and maintenance logic constraints, and power grid operation and dispatch constraints.

7. The planning method for improving the pre-disaster diversity of distribution network resilience according to claim 6 is characterized in that: In S4, the disaster failure and maintenance logic constraints include: （10）； （11）； （12）； in, and are the fault state variables of the power grid and transportation network; and are the current operating status variables of the power grid and transportation network; and Repair state variables for power grid and transportation network.

8. The planning method for improving the pre-disaster diversity of distribution network resilience according to claim 6 is characterized in that: In S4, the grid operation and dispatch constraints are: （13）； （14）； （15）； （16）； （17）； （18）； （19）； （20）； Where: and 0-1 constraint for energy storage charging and discharging; and is the energy storage charging and discharging power; The current amount of energy stored; The efficiency of energy storage charging and discharging; for i Point load power; for i Point generator power; Transmit power to the line; is the line admittance; for i point phase angle; It is used uniformly in the above formula. . is the upper limit of each component power, is the lower limit of component power.

9. The planning method for improving the pre-disaster diversity of distribution network resilience according to claim 6 is characterized in that: In S4, the maintenance sequence constraints include: （21）； （22）； （23）； （24）； （25）； （26）； （27）； （28）； in, is the initial capacity of the transportation network; To complete the task After that, the repair sequence variable of the next task i is i ,but is 1; is the initial location of the repair team; 、 、 、 Respectively for tasks i The start time, end time, repair time and time required for the task Then go to the next task i Movement time; For the task Then go to the next task i The moving distance; V is the moving speed of the maintenance team; 、 、 、 They are the maintenance starting location set, end location set, task location set and maintenance team set.

10. The system according to any one of claims 1 to 9, wherein the method for planning the pre-disaster differentiation of distribution network resilience improvement is based on, The system includes a model building module, a fuzzy uncertainty set module, a failure probability module, and a difference planning module, wherein: Model building module: This module is used to perform hierarchical reinforcement of power lines and urban roads based on the basic data of the urban distribution network and transportation network, and simultaneously plan the preset installation locations of backup energy storage power sources to form a multi-level differentiated planning model; Fuzzy uncertainty set module: This module combines multi-level differential planning models and historical data to construct fuzzy uncertainty sets for failures of some components in urban distribution networks and transportation networks under different reinforcement levels and disaster intensities. Failure probability module: Based on fuzzy uncertainty sets and using a chance constraint model, it constrains modeling for situations where the failure probability of urban distribution networks and transportation networks at various reinforcement levels exceeds the expected disaster level, generating failure probability data. Differential planning module: Generates post-disaster severe scenarios based on uncertainty fuzzy sets and failure probability data, conducts repair team and machine shedding and load shedding scheduling based on the post-disaster severe scenarios, evaluates the total cost function, and determines whether the minimum total cost target is achieved: if not, returns to the fuzzy uncertainty set module and starts again; if so, outputs the pre-disaster differentiated planning scheme and cost data for each stage.

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