Urban electric power system toughness improving method and device based on vehicle network interaction

Abstract

The invention provides an urban power system toughness improvement method and device based on vehicle network interaction, and the method constructs a two-stage stochastic optimization model with the minimum total cost of the system as a target, and covers the long-term planning and operation scheduling of the urban power system in a disaster scene. The method can improve the toughness of an urban power system in an extreme weather long-term scene, achieves the collaborative optimization of multi-dimensional costs such as power generation capacity expansion, line reinforcement, operation maintenance and load reduction, effectively reduces the comprehensive cost, and enhances the dynamic response capability of the system. A fusion channel of vehicle-network interaction and urban power grid toughness planning is opened, and a technical path capable of being popularized is provided for safe, flexible and economical operation of a power system under future climate uncertainty.

Description

Technical Field

[0001] This invention belongs to the field of power system technology, specifically relating to a method and device for improving the resilience of urban power systems based on vehicle-grid interaction. Background Technology

[0002] As a core infrastructure of modern energy systems, urban power grids are widely used in industrial production, residential life, and public services. With the intensification of global climate change and the frequent occurrence of extreme weather events, power grid operation safety is facing a systemic threat. Related technologies utilize the coordinated operation of weather forecasting, equipment hardening, and emergency dispatch to construct a resilience enhancement system covering the entire process from weather warning to fault recovery. Specifically, this system includes key components such as weather data acquisition, power grid vulnerability analysis, resource reserve planning, and dynamic response control. A two-stage stochastic optimization model serves as the core tool, using scenario tree modeling to handle weather uncertainties and achieve coordinated decision-making between long-term planning and short-term response. Based on this, vehicle-to-grid (V2G) technology, as a new type of flexible resource, provides minute-level power support to the power grid through the spatiotemporal dynamic adjustment of electric vehicle energy storage systems. However, existing technologies have not yet formed a systematic, multi-dimensional cost-coordinated optimization framework.

[0003] However, existing power grid resilience optimization methods directly employ a single-dimensional infrastructure cost accounting model, failing to integrate implicit cost elements such as operation and maintenance, and load shedding. This can lead to imbalances in planning schemes or suboptimal resource allocation, thereby affecting the economic efficiency and sustainability of power grid investment. Specifically, traditional optimization models typically employ deterministic scenario analysis, but they have limitations in handling the spatiotemporal evolution of long-term extreme weather, with the disconnect between fault prediction models and actual operational data being particularly prominent. Existing vehicle-grid interaction applications mostly focus on single-point charging and discharging control, lacking a coupling mechanism with power grid topology, meteorological parameters, and multiple types of cost functions, resulting in a flexible resource utilization rate of less than 40%. Therefore, how to construct a four-dimensional cost function encompassing power generation capacity expansion, line reinforcement, operation and maintenance, and load shedding, and how to achieve dynamic resource allocation under meteorological uncertainty through a two-stage stochastic optimization framework, has become a key technical bottleneck in improving urban power grid resilience. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this invention is to propose a method for improving the resilience of urban power systems based on vehicle-to-grid interaction.

[0006] The second objective of this invention is to propose a device for enhancing the resilience of urban power systems based on vehicle-to-grid interaction.

[0007] To achieve the above objectives, a first aspect of the present invention proposes a method for enhancing the resilience of urban power systems based on vehicle-grid interaction, comprising: S1. Based on the spatiotemporal evolution characteristics of extreme weather factors, a line fault prediction model considering the power grid topology is established. S2. Construct a four-dimensional cost function that includes the cost of power generation expansion, the cost of line reinforcement, the cost of operation, and the cost of load reduction. With the goal of minimizing the sum of the cost functions, establish a two-stage stochastic optimization model. S3 utilizes vehicle-to-grid (V2G) interaction technology as a flexible resource, and solves the optimal charging and discharging scheduling strategy based on the two-stage stochastic optimization model to achieve dynamic balance between power supply and demand. S4. Adjust grid operating parameters and electric vehicle charging and discharging control strategies based on optimization results to improve the fault recovery capability of urban power systems under extreme weather scenarios. S5 constructs an extreme weather scenario tree based on Monte Carlo simulation, and verifies the reliability of scenario generation through multi-source heterogeneous data fusion processing technology.

[0008] In one embodiment of the present invention, S1 includes: S11 quantifies the fault probability of lines in different regions through correlation analysis of power grid topology and meteorological parameters; S12 is a power grid simulation platform based on digital twins, which simulates the spatiotemporal propagation characteristics of line faults under extreme weather conditions.

[0009] In one embodiment of the present invention, S2 includes: S21, adopts a dynamic weight allocation algorithm to adjust the weight coefficient of each cost item according to the power grid operation status and weather forecast accuracy; S22 is a two-stage stochastic optimization model, in which the first stage is used for long-term infrastructure planning and the second stage is used for short-term dynamic response after extreme weather events.

[0010] In one embodiment of the present invention, S3 includes: S31, based on the electric vehicle charging and discharging behavior prediction model, predicts the available energy storage capacity of the vehicle under extreme weather conditions; S32, combined with the real-time load forecasting and adjustment module, dynamically corrects the execution priority of the charging and discharging scheduling strategy.

[0011] In one embodiment of the present invention, S4 includes: S41, through a parallel computing architecture of a mixed integer programming solver, generates multi-level emergency response plans in real time.

[0012] To achieve the above objectives, a second aspect of the present invention provides a device for enhancing the resilience of urban power systems based on vehicle-to-grid interaction, comprising: The line fault prediction module is used to establish a line fault prediction model that takes into account the power grid topology based on the spatiotemporal evolution characteristics of extreme weather factors. The multidimensional cost optimization modeling module is used to construct a four-dimensional cost function that includes power generation capacity expansion costs, line reinforcement costs, operating costs, and load reduction costs, and to establish a two-stage stochastic optimization model with the goal of minimizing the sum of the cost functions. The elastic resource scheduling module is used to utilize vehicle-to-grid interaction technology as an elastic resource, and solve the optimal charging and discharging scheduling strategy based on the two-stage stochastic optimization model to achieve dynamic balance between power supply and demand. The power grid parameter and strategy adjustment module is used to adjust the power grid operating parameters and electric vehicle charging and discharging control strategies based on optimization results, thereby improving the fault recovery capability of the urban power system under extreme weather scenarios. The scenario building module is used to construct extreme weather scenario trees based on Monte Carlo simulations and to verify the reliability of scenario generation through multi-source heterogeneous data fusion processing technology.

[0013] This invention presents a method for enhancing the resilience of urban power systems based on vehicle-to-grid (V2G) interaction. This method constructs a two-stage stochastic optimization model with the objective of minimizing total system cost, covering long-term planning and operation scheduling of urban power systems under disaster scenarios. First, for extreme blizzard events, a line failure probability model based on meteorological factors such as wind speed and precipitation intensity is constructed, filling the gap in existing planning methods for transmission line reliability modeling under extreme scenarios. Second, the first stage of the planning model focuses on investment decisions for generator capacity expansion and transmission line reinforcement, while the second stage introduces operation scheduling under multiple scenarios, balancing economic efficiency with load reduction costs, significantly enhancing the system's adaptability to extreme risks. Furthermore, at the operational level, three electric vehicle operation modes (plug-and-play, one-way smart charging (V1G), and vehicle-to-grid (V2G)) are incorporated for the first time, and the system characterizes their differentiated contributions to grid flexibility and resilience. Results show that the V2G mode can actively supply energy to the grid during disasters, significantly alleviating peak loads and power shortages, becoming a key resource for improving system resilience. This technical solution opens up a channel for the integration of vehicle-to-grid interaction and urban power grid resilience planning, providing a scalable technical path for the safe, flexible, and economical operation of power systems in the face of future climate uncertainty.

[0014] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0015] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a method for improving the resilience of urban power systems based on vehicle-to-grid interaction according to an embodiment of the present invention; Figure 2 This is a structural diagram of a vehicle-to-grid (V2G) system resilience enhancement device according to an embodiment of the present invention. Detailed Implementation

[0016] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0018] The following description, with reference to the accompanying drawings, illustrates a method and apparatus for enhancing the resilience of urban power systems based on vehicle-to-grid interaction, according to an embodiment of the present invention.

[0019] Figure 1 This is a flowchart of an automatic recovery control method for communication interruption of a wind turbine lidar according to an embodiment of the present invention, as shown below. Figure 1 As shown, it includes: S1. Based on the spatiotemporal evolution characteristics of extreme weather factors, a line fault prediction model considering the power grid topology is established. S2. Construct a four-dimensional cost function that includes the cost of power generation expansion, the cost of line reinforcement, the cost of operation, and the cost of load reduction. With the goal of minimizing the sum of the cost functions, establish a two-stage stochastic optimization model. S3 utilizes vehicle-to-grid (V2G) interaction technology as a flexible resource, and solves the optimal charging and discharging scheduling strategy based on the two-stage stochastic optimization model to achieve dynamic balance between power supply and demand. S4. Adjust grid operating parameters and electric vehicle charging and discharging control strategies based on optimization results to improve the fault recovery capability of urban power systems under extreme weather scenarios. S5 constructs an extreme weather scenario tree based on Monte Carlo simulation, and verifies the reliability of scenario generation through multi-source heterogeneous data fusion processing technology.

[0020] The following is a detailed description of a method and apparatus for improving the resilience of urban power systems based on vehicle-to-grid interaction, according to an embodiment of the present invention.

[0021] This invention proposes a method for improving the resilience of urban power systems based on vehicle-grid interaction, comprising the following steps: S10, Scenario-driven line fault modeling method.

[0022] By analyzing the correlation between power grid topology and meteorological parameters, the probability of line faults in different regions is quantified; based on a digital twin power grid simulation platform, the spatiotemporal propagation characteristics of line faults under extreme weather conditions are simulated.

[0023] Blizzards pose a serious threat to power systems, primarily manifested in icing of transmission lines and the accompanying high wind loads. The accumulation of ice significantly increases the mechanical stress on lines, especially under strong wind conditions, potentially triggering line vibrations or overloads, thus significantly increasing the probability of line failure. This section details modeling methods for estimating line failure probability under blizzard conditions.

[0024] S101, Icing process.

[0025] Modeling of ice accumulation from icing rate The calculation begins. This rate can be established from a range of meteorological factors, including: hourly precipitation rate. icing coefficient and experience index Their relationship can be expressed as: (1) duration of icing Inside, the line Equivalent ice load on It depends on several factors, including ice thickness per unit time. The density of ice and water ( , ), and wind speed The physics-based estimation model can be expressed as: (2) in, The conversion factor is used to convert dynamic wind pressure into an equivalent radial load. This represents the average span width of the line. Equation (2) comprehensively describes the influence of icing load under the combined action of precipitation and wind.

[0026] S102, Blizzard Movement and Exposure Modeling.

[0027] This model considers the evolution of blizzards in both spatial and temporal dimensions. The total duration of the blizzard passing through the power system region is also considered. Its average moving speed With total path length The decision is made, and its calculation expression is: (3) Each time step during this period Coordinates of the Blizzard Center All of these will be updated. This allows us to calculate the distance from Blizzard's headquarters to each transmission line. radial distance This allows for the quantification of the exposure of each route to blizzard intensity.

[0028] S103, Distance-based baseline failure probability.

[0029] To incorporate this into a stochastic optimization model, this paper employs a distance-based method to determine the impact of a specific blizzard scenario. Below, the line Baseline failure probability This probability is the distance from the Blizzard Center to the route. With strong influence radius The ratio is a function of the ratio, and an attenuation coefficient is introduced. .

[0030] (4) Equation (4) assigns the following failure probabilities to lines in different regions: lines within the strong influence radius are assigned a higher failure probability; lines in the external influence area have a failure probability that decreases exponentially; and lines far from the blizzard center have a failure probability of 0. For normal operation scenarios... ,have .

[0031] S104, Methods for representing failure scenarios.

[0032] In extreme blizzard scenarios, heavy precipitation and high wind speeds significantly increase the failure probability of multiple transmission lines, leading to a sharp decline in their availability. This situation can be analogized to a failure scenario, namely the extreme case of multiple critical components failing simultaneously. Although the model does not explicitly enumerate all possible failure combinations, by simulating these high-probability failure states in blizzard scenarios, the optimization framework can effectively capture the system's operational behavior under adverse conditions. This implicit modeling approach aims to ensure that the system's resilience and safety are fully considered in overall planning decisions. Specific failure enumeration will be discussed in subsequent studies; this paper uses a scenario sampling method to approximate the system's risk envelope.

[0033] S20. A Resilience Planning Method for Urban Power Systems in Extreme Event Scenarios. Using long-term extreme weather events as the time scale, and minimizing the sum of generation capacity expansion costs, line reinforcement costs, operating costs, and load reduction costs as the objective function, a two-stage stochastic optimization planning model is constructed.

[0034] A dynamic weight allocation algorithm is adopted to adjust the weight coefficients of each cost item according to the power grid operation status and weather forecast accuracy; a two-stage stochastic optimization model is used, in which the first stage is used for long-term infrastructure planning and the second stage is used for short-term dynamic response after extreme weather events.

[0035] The objective of the two-stage stochastic optimization model constructed in this paper is to minimize the expected annualized total cost of the system. This cost includes the investment cost of the first stage and the expected value of the operating cost of the second stage under all scenarios. Its objective function is defined as follows: (5) in, Representing a scene The probability of occurrence, This is the typical day length (e.g., 24 hours). Power generation cost: per generator. Unit cost coefficient The cost of load reduction is determined jointly by the effort put in by each node. In time Scene Reduced load and its unit loss of load cost A joint decision.

[0036] The total investment cost is determined by the sum of the generator expansion cost and the transmission line reinforcement cost.

[0037] (6) in, Indicates that the generator Capacity expansion The corresponding unit cost; and when the binary decision variables hour, Indicates the line The cost required for reinforcement.

[0038] S201. The first phase of decision-making includes decisions on strengthening transmission lines and expanding generator capacity. Specific investment constraints are as follows: (7a) (7b) (7c) (7d) Constraint (7a) Ensure that total investment expenditure does not exceed the budget. Constraint (7b) specifies the expansion capacity for each generator. It is a non-negative number and does not exceed the upper limit. Constraint (7c) requires that at least a portion of the line (proportion) must be present. ) was reinforced, among which Represents the total number of lines; constraint (7d) specifies the decision variables for line reinforcement. It is a binary variable.

[0039] S202, The second-stage operational constraints must be in every scenario. With each time step The following constraints are defined to indicate the operational feasibility conditions of the system after the investment decisions in the first phase are made. Specifically, these constraints include the following: (1) Power grid operation constraints.

[0040] (8) (8a) (8b) (8c) (8d) (8e) Node power balance constraint (8) ensures that each bus at every time Each scenario The power input (including generation and EV injection) must be balanced with the load, line power flow, and load shedding. The generator output must not exceed the expanded capacity limit. It cannot be less than zero (8a). This constraint may assume that the original capacity has been included, or assume it to be zero. Load reduction cannot exceed actual load demand (8b). Line power flow is calculated using line admittance and voltage phase angle difference (8c). Line power flow must not exceed its thermal limit; if the line is damaged ( If the flow rate is zero (8d), the system needs to specify a bus as the phase angle reference, which is usually set to 0 (8e).

[0041] (2) Line status determination.

[0042] The line operating status used in formula (8e) (A value of 1 indicates that the line is operational, and a value of 0 indicates that it is faulty.) This is determined by the effective failure probability of the line. Compared with the preset availability threshold Decision. Effective failure probability of the line. Depends on: Baseline failure probability This comes from formula (4); reinforcement decision ; and reinforcement effect coefficient.

[0043] (9) Then calculate the line In the scene Availability And determine its operating status. : (10) (11) Therefore, if the decision to strengthen Enabling to increase the effective failure probability a is reduced enough that the line's availability is reduced. Meets or exceeds the threshold If the line is in normal operation, then the line is considered to be operating normally. Otherwise, the line is considered to be in a fault state, i.e. .

[0044] (3) Electric vehicle aggregator constraint: Based on the electric vehicle charging and discharging behavior prediction model, predict the available energy storage capacity of the vehicle under extreme weather conditions; S32, Combined with the real-time load prediction and adjustment module, dynamically correct the execution priority of the charging and discharging scheduling strategy.

[0045] The following constraints apply to each electric vehicle in the aggregator. : (12a) (12b) (12c) (12d) (12e) (12f) (12g) (12h) These constraints detail the charging behavior during the constant current / constant voltage (CC / CV) phase and are expressed through thresholds. Connect the components; and simultaneously adjust the connection based on the connection status. Enforce power limiting according to formula (12g) (wherein) The evolution of SOC is controlled; and equation (12h) ensures that SOC remains within the range of 0–100%, while simultaneously meeting the requirements of reaching the initial SOC and leaving the target SOC. It should be noted that the upper limit of discharge power equation (12f) will change with the current SOC relative to the threshold. It varies in height.

[0046] (13) in, It is a collection of electric vehicles connected to bus b. This net power exchange may be affected by the bus. Up to charging station capacity Limitations: (14) This indicates the maximum total charging power of the electric vehicles on bus b. This represents the maximum total power discharged by the electric vehicle on bus b.

[0047] (15) (16) Ultimately, the specific operational constraints depend on the operating mode of the electric vehicle aggregator.

[0048] Plug & Play Mode: Discharge is not allowed, i.e.: (17) Charging power Typically when electric vehicles are connected (i.e. This means starting to attempt to reach the target value while adhering to the battery's state of charge (SOC) limits, and may also be affected by implicit grid limitations or explicit power curtailment strategies.

[0049] In V1G mode (i.e., unidirectional smart charging mode), discharging is prohibited, and its mathematical expression is: (18) Charging power It is controllable, and its constraints are:

[0050] In V2G mode: Charging power and discharge power All are controllable within the custom limits.

[0051] For analytical purposes, the entire electric vehicle fleet is defined as ( The total power and average SOC (state of charge) are as follows: (19) (20) (4) Modeling of uncertain parameters of electric vehicles.

[0052] Driver behavior introduces a key element of randomness, which significantly impacts the flexibility of electric vehicle aggregators. For each vehicle... Consider three key random variables: Initial power The cutoff interval is .

[0053] Arrival time The cutoff interval is .

[0054] Departure time The cutoff interval is And satisfy .

[0055] Here This represents a Gaussian distribution truncated to a specified interval. Triplet The actual value will determine: (i) the binary connection state , and (ii) the boundary conditions in constraint (8h).

[0056] S30, based on the operation coordination strategy of electric vehicle interaction, generates multi-level emergency response plans in real time through the parallel computing architecture of mixed integer programming solver.

[0057] An operational coordination strategy based on electric vehicle interaction. Specifically, it includes the following steps: S301. Establish an electric vehicle (EV) aggregator model, accurately considering the initial state of charge (SOC), charging and discharging power limits, charging and discharging efficiency, and the random arrival and departure times of users for each individual electric vehicle.

[0058] S302. The initial SOC of electric vehicles is modeled using a truncated normal distribution, with the SOC value ranging from 30% to 90%. The arrival and departure times of electric vehicles are also modeled using a truncated normal distribution to reflect the randomness in actual use.

[0059] S303. Based on system load conditions under extreme blizzard scenarios, evaluate the performance of three operating modes: Plug and Play (PnP), One-Way Smart Charging (V1G), and Vehicle-to-Grid (V2G). Through comparative analysis, it is determined that the V2G mode, compared with other modes, can proactively inject power into the grid when there is a large-scale load reduction, effectively reducing load loss during critical periods and improving the grid's resilience in extreme events.

[0060] S304. Taking into account the overall SOC level of the electric vehicle cluster and the battery protection requirements, design an optimized operation strategy under extreme conditions to maximize the use of energy storage resources in electric vehicle batteries to support the power grid, alleviate system operation pressure, and thus improve the stability and resilience of the power grid.

[0061] S305. Through economic analysis of the planning scheme, clarify the synergistic effect between the V2G model and the investment in power infrastructure reinforcement, so as to reduce system operating costs and load loss costs, significantly improve system resilience and achieve optimal comprehensive economic benefits.

[0062] To achieve the above embodiments, such as Figure 2 As shown, this embodiment also provides a vehicle-grid interaction-based urban power system resilience enhancement device 10, which includes a line fault prediction module 100, a multi-dimensional cost optimization modeling module 200, a flexible resource scheduling module 300, a power grid parameter and strategy adjustment module 400, and a scenario construction module 500.

[0063] The line fault prediction module 100 is used to establish a line fault prediction model that takes into account the power grid topology based on the spatiotemporal evolution characteristics of extreme weather factors. The multidimensional cost optimization modeling module 200 is used to construct a four-dimensional cost function that includes power generation expansion costs, line reinforcement costs, operating costs, and load reduction costs, and to establish a two-stage stochastic optimization model with the goal of minimizing the sum of the cost functions. The elastic resource scheduling module 300 is used to utilize vehicle-to-grid interaction technology as an elastic resource, and solve the optimal charging and discharging scheduling strategy based on the two-stage stochastic optimization model to achieve dynamic balance between power supply and demand. The power grid parameter and strategy adjustment module 400 is used to adjust the power grid operating parameters and electric vehicle charging and discharging control strategies according to the optimization results, thereby improving the fault recovery capability of the urban power system under extreme weather scenarios. The Scene Building Module 500 is used to build extreme weather scene trees based on Monte Carlo simulation and to verify the reliability of scene generation through multi-source heterogeneous data fusion processing technology.

[0064] Furthermore, the aforementioned line fault prediction module 100 is also used for: By analyzing the correlation between power grid topology and meteorological parameters, the probability of line failure in different regions is quantified. A power grid simulation platform based on digital twins is used to simulate the spatiotemporal propagation characteristics of line faults under extreme weather conditions.

[0065] Furthermore, the aforementioned multidimensional cost optimization modeling module 200 is also used for: A dynamic weight allocation algorithm is adopted to adjust the weight coefficients of each cost item according to the power grid operation status and weather forecast accuracy; A two-stage stochastic optimization model is used, with the first stage for long-term infrastructure planning and the second stage for short-term dynamic response after extreme weather events.

[0066] Furthermore, the aforementioned elastic resource scheduling module 300 is also used for: Based on the electric vehicle charging and discharging behavior prediction model, the available energy storage capacity of the vehicle under extreme weather conditions is predicted. By combining the real-time load forecasting and adjustment module, the execution priority of the charging and discharging scheduling strategy is dynamically corrected.

[0067] Furthermore, the aforementioned power grid parameter and strategy adjustment module 400 is also used for: A parallel computing architecture using a mixed-integer programming solver is used to generate multi-level emergency response plans in real time.

[0068] This invention discloses a vehicle-to-grid (V2G) interaction-based urban power system resilience enhancement device, which can improve the resilience of urban power systems under long-term extreme weather scenarios, achieve synergistic optimization of multi-dimensional costs such as power generation capacity expansion, line reinforcement, operation and maintenance, and load reduction, effectively reduce overall costs and enhance the system's dynamic response capability.

[0069] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0070] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for improving the resilience of urban power systems based on vehicle-grid interaction, characterized in that, include: S1. Based on the spatiotemporal evolution characteristics of extreme weather factors, a line fault prediction model considering the power grid topology is established. S2. Construct a four-dimensional cost function that includes the cost of power generation expansion, the cost of line reinforcement, the cost of operation, and the cost of load reduction. With the goal of minimizing the sum of the cost functions, establish a two-stage stochastic optimization model. S3 utilizes vehicle-to-grid (V2G) interaction technology as a flexible resource, and solves the optimal charging and discharging scheduling strategy based on the two-stage stochastic optimization model to achieve dynamic balance between power supply and demand. S4. Adjust grid operating parameters and electric vehicle charging and discharging control strategies based on optimization results to improve the fault recovery capability of urban power systems under extreme weather scenarios. S5 constructs an extreme weather scenario tree based on Monte Carlo simulation, and verifies the reliability of scenario generation through multi-source heterogeneous data fusion processing technology.

2. The method as described in claim 1, characterized in that, S1 includes: S11 quantifies the fault probability of lines in different regions through correlation analysis of power grid topology and meteorological parameters; S12 is a power grid simulation platform based on digital twins, which simulates the spatiotemporal propagation characteristics of line faults under extreme weather conditions.

3. The method as described in claim 1, characterized in that, The S2 includes: S21, adopts a dynamic weight allocation algorithm to adjust the weight coefficient of each cost item according to the power grid operation status and weather forecast accuracy; S22 is a two-stage stochastic optimization model, in which the first stage is used for long-term infrastructure planning and the second stage is used for short-term dynamic response after extreme weather events.

4. The method as described in claim 1, characterized in that, The S3 further includes: S31, based on the electric vehicle charging and discharging behavior prediction model, predicts the available energy storage capacity of the vehicle under extreme weather conditions; S32, combined with the real-time load forecasting and adjustment module, dynamically corrects the execution priority of the charging and discharging scheduling strategy.

5. The method as described in claim 1, characterized in that, The adjustment of power grid operating parameters and electric vehicle charging and discharging control strategies based on optimization results also includes: S41, through a parallel computing architecture of a mixed integer programming solver, generates multi-level emergency response plans in real time.

6. A device for enhancing the resilience of urban power systems based on vehicle-grid interaction, characterized in that, include: The line fault prediction module is used to establish a line fault prediction model that takes into account the power grid topology based on the spatiotemporal evolution characteristics of extreme weather factors. The multidimensional cost optimization modeling module is used to construct a four-dimensional cost function that includes power generation capacity expansion costs, line reinforcement costs, operating costs, and load reduction costs, and to establish a two-stage stochastic optimization model with the goal of minimizing the sum of the cost functions. The elastic resource scheduling module is used to utilize vehicle-to-grid interaction technology as an elastic resource, and solve the optimal charging and discharging scheduling strategy based on the two-stage stochastic optimization model to achieve dynamic balance between power supply and demand. The power grid parameter and strategy adjustment module is used to adjust the power grid operating parameters and electric vehicle charging and discharging control strategies based on optimization results, thereby improving the fault recovery capability of the urban power system under extreme weather scenarios. The scenario building module is used to construct extreme weather scenario trees based on Monte Carlo simulations and to verify the reliability of scenario generation through multi-source heterogeneous data fusion processing technology.

7. The apparatus as claimed in claim 6, characterized in that, The line fault prediction module is also used for: By analyzing the correlation between power grid topology and meteorological parameters, the probability of line failure in different regions is quantified. A power grid simulation platform based on digital twins is used to simulate the spatiotemporal propagation characteristics of line faults under extreme weather conditions.

8. The apparatus as claimed in claim 6, characterized in that, The multidimensional cost optimization modeling module is also used for: A dynamic weight allocation algorithm is adopted to adjust the weight coefficients of each cost item according to the power grid operation status and weather forecast accuracy; A two-stage stochastic optimization model is used, with the first stage for long-term infrastructure planning and the second stage for short-term dynamic response after extreme weather events.

9. The apparatus as claimed in claim 6, characterized in that, The elastic resource scheduling module is also used for: Based on the electric vehicle charging and discharging behavior prediction model, the available energy storage capacity of the vehicle under extreme weather conditions is predicted. By combining the real-time load forecasting and adjustment module, the execution priority of the charging and discharging scheduling strategy is dynamically corrected.

10. The apparatus as claimed in claim 6, characterized in that, The power grid parameter and strategy adjustment module is also used for: A parallel computing architecture using a mixed-integer programming solver is used to generate multi-level emergency response plans in real time.

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