Power-traffic network multi-source dynamic scheduling method and device based on typhoon time-space characteristics and medium

Abstract

The invention discloses an electric power-traffic network multi-source dynamic scheduling method and device based on typhoon time-space characteristics and a medium, and relates to the technical field of electric power scheduling. The method aims at solving the problem that under typhoon disasters, a traditional superposition method has large randomness when a series model of a wire tower is calculated. The method comprises the following steps: inputting power grid parameters into an element fault rate model to obtain a strong wind-induced lead tower fault rate curve; inputting typhoon disaster information into the typhoon spatio-temporal evolution model for wind field reconstruction, and combining a strong wind-induced lead tower fault rate curve with a reconstructed wind field to obtain a power distribution network disaster loss thermal distribution diagram; accumulating the fault rates of all the power grid branches in the disaster loss thermodynamic distribution diagram of the power distribution network in all the time periods to obtain rolling update fault rates of the power distribution network branches; and inputting the rolling update fault rate of the power distribution network branch into the two-stage robust toughness improvement model, and obtaining load reduction power, an MESS arrangement result and a power grid reconstruction result for power-traffic network dynamic scheduling.

Description

Technical Field

[0001] This application belongs to the technical field of power dispatching. Background Art

[0002] Improving distribution network resilience during typhoon disasters faces multiple challenges. Traditional models for connecting conductors and towers in series require statistical calculations to determine the branch failure rate after the series connection, based on the fixed damage probability of the conductors and towers. While the traditional superposition method is computationally efficient, the locations and times of each fault point are generated in an unordered manner by the model, failing to reflect the concentrated impact of the typhoon area. Furthermore, the model fails to consider the impact of known disaster intensity and movement paths on the power grid, resulting in a high degree of randomness. Summary of the Invention

[0003] This application aims to solve the problem that under typhoon disasters, the traditional superposition method cannot reflect the concentrated disaster situation in the typhoon area when calculating the series model of the conductor tower, and does not consider the impact of the known disaster intensity and movement path on the power grid, which has a large randomness. Now, a multi-source dynamic scheduling method, equipment and medium for the power-transportation network based on the spatiotemporal characteristics of typhoons are provided. This application deconstructs the relationship between the spatiotemporal characteristics of typhoons and power grid failures, realizes the prediction of disaster loss heat map and rolling update of failure probability, and iteratively calculates PV uncertainty and deployment status with a two-stage robust resilience improvement model, and finally obtains a grid resilience improvement plan that balances investment and effect, and improves the comprehensive resilience of the power grid in three dimensions: system performance drop, total load loss and emergency recovery level.

[0004] The first aspect of the present application provides a multi-source dynamic dispatching method for a power-transportation network based on the spatiotemporal characteristics of typhoons, comprising: Inputting grid parameters into a component failure rate model to obtain a strong wind-induced conductor tower failure rate curve, wherein the grid parameters include the load, geographical location, and equipment parameters of each node in the distribution network; Inputting typhoon disaster information into a typhoon spatiotemporal evolution model to reconstruct the wind field, combining the strong wind-induced conductor tower failure rate curve with the reconstructed wind field to obtain a thermal distribution map of the distribution network disaster loss, wherein the typhoon disaster information includes: typhoon path data and typhoon intensity data; Accumulating the failure rate of each power grid branch in all time periods in the distribution network disaster loss thermal distribution map to obtain a rolling update failure rate of the distribution network branch; The rolling update failure rate of the distribution network branch is input into a two-stage robust resilience improvement model to obtain load reduction power, MESS layout results and grid reconstruction results for dynamic scheduling of the power-transportation network.

[0005] In a possible design, the equipment parameters of the above power grid include: horizontal wind load per unit length of conductor, which is expressed as: , in, is the horizontal wind load per unit length of the conductor, is the wind load partial factor, is the wind pressure height variation coefficient, is the conductor wind load shape coefficient, is the wire diameter, Design wind speed for the conductor, is the angle between wind speed and conductor.

[0006] In a possible design, the equipment parameters of the above power grid include the tower bending moment, which is expressed as: , in, is the tower bending moment, is the standard air density, Design wind speed for the tower, is the tower wind load shape coefficient, is the tower's wind-exposed area, is the height of the wind pressure resultant point.

[0007] In one possible design, the above-mentioned typhoon spatiotemporal evolution model is the Batts model.

[0008] In a possible design, the objective function expression of the above two-stage robust resilience improvement model is: , in, Configure the cost for MESS, Cost of grid reconstruction, To reduce the cost of load, and are the decision variable sets of the first and second stages in the two-stage robust resilience improvement model, is the photovoltaic output box uncertainty set.

[0009] In one possible design, the above MESS configuration costs The expression is: , The grid reconstruction cost The expression is: , The load reduction cost The expression is: , Where, is the node set of the power grid, For the node The cost of configuring a unit MESS, For the MESS and nodes The connection status, and are the sets of section switches and tie switches in the power grid, and The branches in the power grid Single operation cost of middle section switch and tie switch, Branch in the power grid The connection status, For nodes Unit load reduction cost, For nodes The load active power reduction amount.

[0010] In one possible design, the above and are the decision variable sets of the first and second stages in the two-stage robust resilience improvement model, The expressions for the photovoltaic output box uncertainty set are as follows: , , , Where, For nodes The load reactive reduction amount, and Node The load active and reactive power, and The branches in the power grid The active and reactive power, For nodes Voltage squared, Branch in the power grid The square of the current, is the uncertainty, For nodes The predicted photovoltaic output, For nodes The actual photovoltaic output.

[0011] In a possible design, the constraints of the above two-stage robust resilience improvement model include: MESS configuration constraints, distribution network radiation constraints, switch and risk line constraints, load reduction constraints, unit output constraints and distribution network flow constraints.

[0012] The second aspect of the present application provides a multi-source dynamic dispatching device for an electric power-transportation network based on the spatiotemporal characteristics of a typhoon. The multi-source dynamic dispatching device for an electric power-transportation network based on the spatiotemporal characteristics of a typhoon includes a processor and a memory. The memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the multi-source dynamic dispatching method for an electric power-transportation network based on the spatiotemporal characteristics of a typhoon as described above.

[0013] A third aspect of the present application provides a computer storage medium, wherein the computer storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned power-transportation network multi-source dynamic scheduling method based on the spatiotemporal characteristics of typhoons.

[0014] Beneficial effects of this application: This application proposes a multi-source dynamic dispatch method, equipment, and medium for the power and transportation grid based on the spatiotemporal characteristics of typhoons. By deconstructing the correlation between spatiotemporal characteristics and grid failures during typhoon landfall, this application accurately reflects the concentrated impact of the grid and develops a plan to enhance grid resilience, effectively improving system performance, total load loss, and emergency recovery capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of the multi-source dynamic dispatching method for the power-transportation network based on the spatiotemporal characteristics of typhoons described in this application; Figure 2 This is a flow chart of a method for multi-source dynamic scheduling and coordinated resilience improvement of the power-transportation network based on the spatiotemporal characteristics of typhoons as described in a specific embodiment; Figure 3 is the probability of transmission tower failure caused by strong wind; Figure 4 Typhoon travel path and improved IEEE-33 node system; Figure 5 Thermal distribution of IEEE-33 node distribution network disaster losses; Figure 6 Rolling update failure rate for IEEE-33 node distribution network branches; Figure 7 It is a two-stage robust resilience improvement model; Figure 8 This is the resilience improvement curve of the distribution network under typhoon disasters. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0017] During typhoon disasters, the uncertainty of photovoltaic (PV) output and the discrepancies in the number and location of emergency resource deployment remain constant issues. The former directly impacts grid power generation costs, while the latter affects the initial investment costs and resilience improvements. Quantifying the degree of uncertainty and determining the number and location of resource deployment have become unavoidable challenges in improving grid resilience. However, given the complex structure of power grids, resource deployment involves a cost-effective balance between cost and effectiveness. Developing a unified resource deployment approach from a grid topology perspective requires a comprehensive analysis of grid operation and disaster conditions.

[0018] In view of this, the embodiment of the present application provides a multi-source dynamic dispatching method for power-transportation network based on the spatiotemporal characteristics of typhoons, in order to solve the above problems. Figure 2 , the scheme of the implementation method of this application is described in detail.

[0019] Specific embodiment 1: The multi-source dynamic dispatching method of the power-transportation network based on the spatiotemporal characteristics of typhoons described in this embodiment includes: Inputting grid parameters into a component failure rate model to obtain a strong wind-induced conductor tower failure rate curve, wherein the grid parameters include the load, geographical location, and equipment parameters of each node in the distribution network; Inputting typhoon disaster information into a typhoon spatiotemporal evolution model to reconstruct the wind field, combining the strong wind-induced conductor tower failure rate curve with the reconstructed wind field to obtain a thermal distribution map of the distribution network disaster loss, wherein the typhoon disaster information includes: typhoon path data and typhoon intensity data; Accumulating the failure rate of each power grid branch in all time periods in the distribution network disaster loss thermal distribution map to obtain a rolling update failure rate of the distribution network branch; The rolling update failure rate of the distribution network branch is input into a two-stage robust resilience improvement model to obtain load reduction power, MESS layout results and grid reconstruction results for dynamic scheduling of the power-transportation network.

[0020] In one embodiment, the equipment parameters of the power grid include: horizontal wind load per unit length of the conductor, which is expressed as: , in, is the horizontal wind load per unit length of the conductor, is the wind load partial factor, is the wind pressure height variation coefficient, is the conductor wind load shape coefficient, is the wire diameter, Design wind speed for the conductor, is the angle between wind speed and conductor.

[0021] In one embodiment, the equipment parameters of the power grid include tower bending moment, which is expressed as: , in, is the tower bending moment, is the standard air density, Design wind speed for the tower, is the tower wind load shape coefficient, is the tower's wind-exposed area, is the height of the wind pressure resultant point.

[0022] In one embodiment, the typhoon spatiotemporal evolution model is a Batts model.

[0023] In one embodiment, the objective function expression of the two-stage robust resilience improvement model is: , in, Configure the cost for MESS, Cost of grid reconstruction, To reduce the cost of load, and are the decision variable sets of the first and second stages in the two-stage robust resilience improvement model, is the photovoltaic output box uncertainty set.

[0024] In one embodiment, the MESS configuration cost The expression is: , The grid reconstruction cost The expression is: , The load reduction cost The expression is: , Where, is the node set of the power grid, For the node The cost of configuring a unit MESS, For the MESS and nodes The connection status, and are the sets of section switches and tie switches in the power grid, and The branches in the power grid Single operation cost of middle section switch and tie switch, Branch in the power grid The connection status, For nodes Unit load reduction cost, For nodes The load active power reduction amount.

[0025] In one embodiment, the and are the decision variable sets of the first and second stages in the two-stage robust resilience improvement model, The expressions for the photovoltaic output box uncertainty set are as follows: , , , Where, For nodes The load reactive reduction amount, and Node The load active and reactive power, and The branches in the power grid The active and reactive power, For nodes Voltage squared, Branch in the power grid The square of the current, is the uncertainty, For nodes The predicted photovoltaic output, For nodes The actual photovoltaic output.

[0026] In one embodiment, the constraints of the two-stage robust resilience improvement model include: MESS configuration constraints, distribution network radiation constraints, switch and risk line constraints, load reduction constraints, unit output constraints and distribution network flow constraints.

[0027] To further introduce the embodiments of this application, Figure 2A method for multi-source dynamic dispatch of power and transportation networks based on the spatiotemporal characteristics of typhoons is provided, comprising steps (1) to (3). The numbering of each step does not necessarily limit the order in which they are executed. Each step is described in detail below: (1) Determine grid parameters, including the load, geographical location, and equipment parameters of each node in the distribution network. Input the grid parameters into a strong wind-induced component failure rate model to obtain a strong wind-induced conductor tower failure rate curve.

[0028] By loading the typhoon disaster information provided by the meteorological department (including typhoon path data and typhoon intensity data) into the typhoon spatiotemporal evolution model to reconstruct the wind field, the thermal distribution of distribution network disaster losses and the rolling update failure rate of distribution network branches can be obtained, accurately reflecting the concentrated disaster situation of the power grid under typhoon weather.

[0029] The above operations are implemented as follows: Component failures are caused by the static and dynamic loads on towers and conductors caused by typhoons. When wind speeds exceed the design threshold of conductor towers, their mechanical structures are damaged, reducing the reliability of the distribution network. According to national standards and transmission line design specifications, the key parameters of conductor towers are as follows: Average distance between 10kV distribution network towers , typical steel core aluminum stranded wire diameter , design bending moment of concrete pole The relationship between the wind load on the conductor and the tower bending moment and its design wind speed is shown in equations (1) and (2): , Where: is the horizontal wind load per unit length of the conductor; is the wind load partial coefficient, take =0.61; is the wind pressure height variation coefficient, take =1.0; is the conductor wind load shape coefficient, when When <17mm, take =1.2, when Take when ≥17mm =1.1; is the conductor diameter. Taking the typical steel core aluminum stranded wire as an example, =25mm; Design wind speed for conductors; is the angle between wind speed and conductor.

[0030] , is the standard air density, take =1.225kg / m³; The design wind speed of the tower refers to the maximum wind speed that the tower can withstand based on its structural design. is the tower wind load coefficient. The tower wind load is a steel pipe tower or a cylindrical pole. =1.0; is the tower's wind-exposed area, i.e. the pole's projected area; is the height of the wind pressure resultant point, =10m.

[0031] From the above formulas (1) and (2), the design wind speed of the conductor is ≈30.24m / s, tower design wind speed =34.64m / s.

[0032] The IEEE-33 node distribution network contains multiple branches, each of which has a certain number of conductors and towers. Assume that the traffic network structure is the same as the power grid topology, the road length between adjacent nodes is 2km, and the road congestion at different times after the disaster is known. The strong wind-induced component failure rate model sorts out the relationship between the typhoon instantaneous wind speed and the component failure rate, and obtains Figure 3 The moderate-to-strong wind-induced failure rate curve for conductors and towers shows the localized failure rate of individual conductors or towers under the influence of a typhoon. Considering the impact of wind loads on conductors and towers, the failure probability increases significantly when the mechanical strength threshold is exceeded, with a truncated exponential distribution of the probability and the external wind speed.

[0033] The typhoon spatiotemporal evolution model reconstructs the wind field based on the Batts model. After the typhoon disaster information is loaded into the Batts model, the Batts typhoon model converts the typhoon path and the wind speed at each point in the wind field into mathematical expressions. Figure 3 The failure rate curve of conductor towers caused by moderate to strong winds and the geographical location of the typhoon during its movement are used to obtain the thermal distribution map of the damage, such as Figure 5 As shown in , it represents the overall failure rate of a single branch in different time periods. Figure 5 The data of all time periods of each branch are accumulated one by one to obtain Figure 6 Medium rolling update failure rate.

[0034] The failure rate of rolling update of distribution network branches is as follows: Figure 6As shown, a color scale is used to represent the cumulative failure rate of lines, with red, yellow-green, and blue corresponding to high, medium, and low risk levels, respectively. Typical high-risk branch 34, due to its long length and two passages through the typhoon's maximum wind speed zone, had a cumulative failure rate of 90.83%. To reduce the risk of cascading failures, this line must remain disconnected during the typhoon's passage. The 13 medium-risk branches are primarily located in the high-wind speed zone along the typhoon's path, and their failure rates vary dynamically with distance from the typhoon's center. The eight low-risk lines are concentrated in the typhoon's eye and surrounding wind speed attenuation zones, and have relatively low failure rates. Therefore, the color-scale distribution of the cumulative failure rate verifies the strong spatial coupling between the typhoon's trajectory and the line failure rate. Furthermore, the failure data for each branch can guide the differentiated scheduling and deployment of subsequent MESS (mobile energy storage system), providing a quantitative basis for developing resilience enhancement strategies.

[0035] (2) If Figure 7 As shown in the figure, a two-stage robust resilience improvement model is established before a typhoon disaster to address the uncertainty and deployment status of photovoltaic (PV). Through a two-stage coordinated optimization of prevention and response, the system load loss in the worst-case scenario is minimized. The rolling update failure rate is incorporated into the two-stage robust resilience improvement model before a disaster. In the first stage, under a fixed PV output, the optimal MESS and grid reconstruction solution is determined by minimizing the MESS configuration cost and grid reconstruction cost as the objective function. In the second stage, under a fixed grid structure and MESS connection status, the minimum load reduction cost is determined under the worst-case PV output conditions. The two stages are iterated to minimize the overall loss. After multiple iterations meet the convergence accuracy, the load reduction power, MESS deployment results, and grid reconstruction results are output.

[0036] The objective function of the two-stage robust resilience improvement model is: , , Where: and are the decision variable sets of the first and second stages in the two-stage robust resilience improvement model, is the photovoltaic output box uncertainty set, Value by and Joint decision-making; Configure costs for MESS; Reconfigure the grid for costs; Cost reduction for load; is a set of nodes; For the node Cost of configuring a unit of MESS; is a Boolean variable, indicating the MESS and nodes The connection status, when the value is 1, it means MESS connected to the node , when the value is 0, it means no connection; and are the sets of section switches and tie switches respectively; and are the single operation costs of the section switch and tie switch respectively; For branch The connection status, when the value is 1, it is connected, and when the value is 0, it is not connected; Reduce costs per unit load; It is the load active power reduction.

[0037] In the two-stage robust resilience improvement model, the decision variables and uncertainty variables are as follows: , , , Where: is the load reactive reduction amount; and are load active and reactive power respectively; and are branch active and reactive power respectively; and are the square of node voltage and branch current respectively; is the uncertainty, indicating the severity of photovoltaic output; Forecast output for photovoltaics; Actual output for photovoltaics.

[0038] The two-stage robust resilience enhancement model's constraints include: MESS configuration constraints, distribution network radiation constraints, switch and risk line constraints, load curtailment constraints, unit output constraints, and distribution network power flow constraints. The model solves the following: load curtailment power, diesel generator and EV power (electric vehicle charging and discharging power), PV output power, MESS layout results, and grid reconfiguration results.

[0039] (III) Analyze the experimental results to determine the optimal number and layout of MESSs, and derive the optimal uncertainty of PV output from the total cost and the number of disconnected branches. Compare the traditional unconfigured MESS dispatch strategy with the dispatch strategy of this application to obtain a distribution network resilience improvement curve under typhoon disasters. Then, using a dynamic indicator set to evaluate resilience, it is concluded that the proposed multi-source coordinated resilience improvement strategy for the power-transportation network significantly improves the robustness, rapidity, and recovery of the power grid under typhoon disasters.

[0040] To verify the practicality of this implementation, under the same grid and typhoon parameters, we first verified the impact of the number and location of MESSes on system operation. Secondly, we determined the optimal uncertainty of PV output. Finally, we compared the performance recovery curve of the traditional grid with that of the resilient grid to quantify the improvement in system resilience.

[0041] This implementation method sets up four pre-disaster MESS pre-deployment schemes for analysis to verify the effectiveness and rationality of the model.

[0042] Scheme 1: Deterministic power system resource scheduling model without MESS.

[0043] Scheme 2: Deterministic power system resource pre-deployment model considering MESS pre-layout.

[0044] Scheme 3: Power system resource dispatch model with PV output uncertainty without MESS.

[0045] Scheme 4: Power system resource collaborative pre-deployment model considering MESS pre-layout and PV output uncertainty.

[0046] To verify the upper limit of MESS quantity The impact on system operation is as follows: =2 is the base, different The solution results are shown in Table 1.

[0047] Table 1 Different Impact on system operation when ≤3, as The total cost gradually decreases with the increase of the value, but due to the limitation of MESS dispatching capacity, the system load reduction still exceeds 2140kWh, and the operation economy is poor. When ≥4, the load reduction of the proposed model and the total operating cost have a marginal effect, the active power shortage filling rate exceeds 97%, and the operating cost is reduced by 60.1% compared with the benchmark. However, excessive configuration will increase the grid operation and maintenance cost, which is not conducive to the long-term stable operation of the distribution network. In summary, the system economy and There is a significant correlation, and the optimal deployment locations of MESS are nodes 25 and 7, and the suboptimal locations are nodes 4 and 30.

[0048] Considering the uncertainty of PV output The impact of scenario 4 =0.35 as the benchmark, considering the robust analysis under extreme weather scenarios, the uncertainty The selection of should be between 0.2 and 0.5, and the results are shown in Table 2.

[0049] along with With the increase of load reduction, the load reduction cost increases linearly (increase of 9.1%). Every 0.05 increase corresponds to an increase in cost of approximately 60 yuan. =0.35 to 0.40, the number of actively disconnected branches can be dynamically adjusted to a minimum of 1, reflecting the effectiveness of grid reconstruction and achieving a balance between system economy and robustness.

[0050] Table 2 Different Impact on system operation A comparative experiment was conducted between the traditional un-deployed MESS dispatching strategy and the dispatching strategy of this application to detect the resilience improvement effect of various aspects of the power grid, such as Figure 8 shown.

[0051] The resilience improvement effect was quantitatively evaluated by analyzing curve characteristics using the "ΦΛEΠ" dynamic indicator set. Compared to traditional power grids, the proposed "prevention-response-recovery" multi-source collaborative resilience improvement strategy reduced system performance drops by 10.97%, reduced overall load losses by 14.30%, and increased emergency recovery capabilities by 5.22%, effectively enhancing the resilience of the distribution network.

[0052] Specific embodiment 2: The multi-source dynamic dispatching device of the power-transportation network based on the time-space characteristics of typhoons described in this embodiment includes a processor and a memory, and the memory stores at least one instruction. The at least one instruction is loaded and executed by the processor to implement the multi-source dynamic dispatching method of the power-transportation network based on the time-space characteristics of typhoons as described in specific embodiment 1.

[0053] Specific embodiment three: A computer storage medium described in this embodiment stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the multi-source dynamic scheduling method of the power-transportation network based on the spatiotemporal characteristics of typhoons as described in specific embodiment one.

[0054] Although the present application is described herein with reference to specific embodiments, it should be understood that these embodiments are merely illustrative of the principles and applications of the present application. It should therefore be understood that many modifications may be made to the illustrative embodiments, and that other arrangements may be devised, without departing from the spirit and scope of the present application as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in ways other than those described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in other described embodiments.

Claims

1. A multi-source dynamic dispatching method for power-transportation networks based on the spatiotemporal characteristics of typhoons, characterized by: include: Inputting grid parameters into a component failure rate model to obtain a strong wind-induced conductor tower failure rate curve, wherein the grid parameters include the load, geographical location, and equipment parameters of each node in the distribution network; Inputting typhoon disaster information into a typhoon spatiotemporal evolution model to reconstruct the wind field, combining the strong wind-induced conductor tower failure rate curve with the reconstructed wind field to obtain a thermal distribution map of the distribution network disaster loss, wherein the typhoon disaster information includes: typhoon path data and typhoon intensity data; Accumulating the failure rate of each power grid branch in all time periods in the distribution network disaster loss thermal distribution map to obtain a rolling update failure rate of the distribution network branch; The rolling update failure rate of the distribution network branch is input into a two-stage robust resilience improvement model to obtain load reduction power, MESS layout results and grid reconstruction results for dynamic scheduling of the power-transportation network.

2. The method for multi-source dynamic dispatching of power-transportation network based on typhoon spatiotemporal characteristics according to claim 1 is characterized in that: The equipment parameters of the power grid include: horizontal wind load per unit length of conductor, which is expressed as: ， in, is the horizontal wind load per unit length of the conductor, is the wind load partial factor, is the wind pressure height variation coefficient, is the conductor wind load shape coefficient, is the wire diameter, Design wind speed for the conductor, is the angle between wind speed and conductor.

3. The method for multi-source dynamic dispatching of power-transportation network based on typhoon spatiotemporal characteristics according to claim 1 or 2, characterized in that: The equipment parameters of the power grid include the tower bending moment, which is expressed as follows: ， in, is the tower bending moment, is the standard air density, Design wind speed for the tower, is the tower wind load shape coefficient, is the tower's wind-exposed area, is the height of the wind pressure resultant point.

4. The method for multi-source dynamic dispatching of power-transportation network based on typhoon spatiotemporal characteristics according to claim 1, characterized in that: The typhoon spatiotemporal evolution model is the Batts model.

5. The method for multi-source dynamic dispatching of power-transportation network based on typhoon spatiotemporal characteristics according to claim 1, characterized in that: The objective function expression of the two-stage robust resilience improvement model is: ， in, Configure the cost for MESS, Cost of grid reconstruction, To reduce the cost of load, and are the decision variable sets of the first and second stages in the two-stage robust resilience improvement model, is the photovoltaic output box uncertainty set.

6. The method for multi-source dynamic dispatching of power-transportation network based on typhoon spatiotemporal characteristics according to claim 5, characterized in that: The MESS configuration cost The expression is: ， The grid reconstruction cost The expression is: ， The load reduction cost The expression is: ， Where, is the node set of the power grid, For the node The cost of configuring a unit MESS, For the MESS and nodes The connection status, and are the sets of section switches and tie switches in the power grid, and The branches in the power grid Single operation cost of middle section switch and tie switch, Branch in the power grid The connection status, For nodes Unit load reduction cost, For nodes The load active power reduction amount.

7. The method for multi-source dynamic dispatching of power-transportation network based on typhoon spatiotemporal characteristics according to claim 6, characterized in that: described and are the decision variable sets of the first and second stages in the two-stage robust resilience improvement model, The expressions for the photovoltaic output box uncertainty set are as follows: ， ， ， Where, For nodes The load reactive reduction amount, and Node The load active and reactive power, and The branches in the power grid The active and reactive power, For nodes Voltage squared, Branch in the power grid The square of the current, is the uncertainty, For nodes The predicted photovoltaic output, For nodes The actual photovoltaic output.

8. The method for multi-source dynamic dispatching of power-transportation network based on typhoon spatiotemporal characteristics according to claim 5, characterized in that: The constraints of the two-stage robust resilience improvement model include: MESS configuration constraints, distribution network radiation constraints, switch and risk line constraints, load reduction constraints, unit output constraints and distribution network flow constraints.

9. The multi-source dynamic dispatching equipment of the power-transportation network based on the spatiotemporal characteristics of typhoons is characterized by: The multi-source dynamic dispatching device for the power-transportation network based on the spatiotemporal characteristics of typhoons includes a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the multi-source dynamic dispatching method for the power-transportation network based on the spatiotemporal characteristics of typhoons as described in one of claims 1 to 8.

10. A computer storage medium, characterized in that The computer storage medium stores at least one instruction, which is loaded and executed by the processor to implement the multi-source dynamic scheduling method for the power-transportation network based on the spatiotemporal characteristics of typhoons as described in any one of claims 1 to 8.

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