Power-traffic coupling system prevention and control method for toughness improvement

Abstract

The invention discloses a power-traffic coupling system prevention and control method for toughness improvement. The method comprises the following steps: 1) obtaining network parameters and operation parameters of a power system and a traffic system; 2) acquiring scene data, including active load and reactive load of a power system, travel demands of different starting and ending points in a traffic system, and proportions of electric vehicles; 3) based on the acquired network parameters, the operating parameters and the scene data of the two systems, constructing an operating constraint under a conventional working condition and an operating constraint under a fault working condition; and 4) based on the constructed constraint, taking a price signal and distributed power supply output as regulation and control objects, and taking minimization of the operation cost of the two systems under a conventional working condition as a target function, establishing a power-traffic coupling system prevention and control model oriented to toughness improvement, and solving to obtain a prevention and control strategy for ensuring the operation safety of the power-traffic coupling system after the fault.

Description

Technical Field

[0001] This invention belongs to the field of power-transportation integration and vehicle-to-grid interaction, and specifically relates to a preventive control method for power-transportation coupled systems aimed at improving resilience. Background Technology

[0002] With the rapid popularization of electric vehicles and the large-scale deployment of charging infrastructure, the coupling relationship between the power system and the transportation system is becoming increasingly close and complex. On the one hand, congestion and regulatory policies in the transportation network affect the driving trajectory of electric vehicles, thereby changing the load distribution of charging stations in the power grid. On the other hand, queuing status at charging stations and electricity prices also influence the charging choices of electric vehicle users, affecting the dynamic distribution of traffic flow. This deep interdependence can easily trigger cascading failures across systems when faced with disturbances such as sudden equipment failures and extreme weather, posing a serious threat to the reliability and resilience of these two critical infrastructures. Therefore, there is an urgent need for a preventive regulation method that can synergistically optimize the power and transportation systems to improve their overall resilience under fault scenarios.

[0003] Current research on the coordinated operation of power-transportation coupled systems still has significant limitations. First, most existing studies only consider fault scenarios within a single system (such as only traffic link disruption or only charging station outage), lacking analysis of the bidirectional collaborative impact of simultaneous physical component failures in both networks, resulting in insufficient robustness of the formulated control strategies. Second, when modeling driver behavior, there is a common assumption that all users can be aware of the fault and replan their routes before departure, which is overly idealistic. In reality, when a large number of drivers encounter faults, their limited route choices will have a significant impact on the post-fault system state, and this crucial behavior is not effectively characterized in existing models. Summary of the Invention

[0004] The purpose of this invention is to propose a preventative control method for power-transportation coupled systems aimed at improving resilience. Its core lies in constructing a safety constraint optimization model that comprehensively considers both-side component failure scenarios, such as traffic link closures and charging station outages. This invention innovatively describes the decision-making behavior of drivers on the road after a failure, based on real-time road network and charging station status, making the model more realistic and significantly improving the effectiveness and practicality of the control strategy. Based on this model, this invention can collaboratively optimize and formulate a set of preventative operational strategies before a failure occurs, including adjusting congestion pricing on key traffic links and scheduling the output of distributed power sources. This strategy, by pre-guiding and optimizing the distribution of power and traffic flow, ensures that both systems can maintain safe and stable operation after any single critical failure, thereby significantly enhancing the overall resilience and reliability of the power-transportation coupled system.

[0005] Technical Solution: To solve the above-mentioned technical problems and achieve the above-mentioned objectives, this invention proposes a preventive control method for power-transportation coupled systems aimed at improving resilience. This method includes the following steps:

[0006] 1) Obtain network parameters and operating parameters of the power system and transportation system. The network parameters include the network topology of the power system, line resistance, line reactance, network topology of the transportation system, free passage time of road segments, road segment capacity, charging station capacity and maximum waiting time. The operating parameters include generator cost parameters, unit travel time cost of users, set of faulty components in the power system, and set of faulty components in the transportation system.

[0007] 2) Acquire scenario data, including the active and reactive loads of the power system, travel demand at different origin and destination points in the transportation system, and the proportion of electric vehicles;

[0008] 3) Based on the acquired network parameters, operating parameters, and scenario data of the two systems, construct operating constraints under normal operating conditions and operating constraints under fault conditions;

[0009] 4) Based on the constraints established in step 3), with price signals and distributed power output as the control objects, and minimizing the operating costs of the two systems under normal operating conditions as the objective function, a preventive control model for the power-transportation coupled system oriented towards resilience improvement is established, and the preventive control strategy to ensure the safe operation of the power-transportation coupled system after a failure is obtained by solving the model.

[0010] Furthermore, in step 3), the operating constraints under normal working conditions are constructed as follows:

[0011] (1)

[0012] (2)

[0013] (3)

[0014] (4)

[0015] (5)

[0016] (6)

[0017] (7)

[0018] (8)

[0019] (9)

[0020] (10)

[0021] (11)

[0022] (12)

[0023] (13)

[0024] (14)

[0025] (15)

[0026] (16)

[0027] (17)

[0028] (18)

[0029] In equations (1)-(18), the superscript 0 indicates the normal operating condition, and the subscript l indicates the road segment number; This indicates the traffic flow on road segment l under normal operating conditions; Represents the set of origin and destination points for transportation demand; Indicates the path number; This represents the set of paths for fuel-powered vehicles between their start and end points; This represents the number of fuel-powered vehicles that choose path k under normal operating conditions. These are the indication parameters for the path and road segment of a fuel-powered vehicle. If road segment l is located on path k, then... Select 1 if the value is 1, otherwise select 0. This represents the set of electric vehicle paths between the start and end points. This represents the number of electric vehicles that choose path k under normal operating conditions. The parameters indicate the path and road segment of the electric vehicle. If road segment l is located on path k, then... Select 1 if the value is 1, otherwise select 0. This indicates the path of the electric vehicle and the indication parameters of the charging station. If charging station 'a' is located on path 'k', then... Select 1 if the value is 1, otherwise select 0. Indicates a set of traffic segments; This indicates the number of electric vehicles heading to charging station a under normal operating conditions. Indicates a collection of charging stations; This represents the demand for fuel-powered vehicle travel between the starting point r and the destination s under normal operating conditions. This represents the electric vehicle travel demand between the starting point r and the destination s under normal operating conditions. This indicates the travel time on road segment l under normal operating conditions; This indicates the free travel time for road segment l; This indicates the traffic capacity of road segment a; This indicates the charging time at charging station a under normal operating conditions; Indicates the amount of electricity charged per vehicle; This indicates the charging power of the charging station; This indicates the queuing time at charging station a when it is fully loaded; This indicates the maximum service capacity of charging station a; This represents the toll cost that a fuel-powered vehicle must pay when choosing route k under normal operating conditions; Indicates cost per unit of time; This indicates the road toll levied on vehicles passing through section l under normal operating conditions; This represents the toll cost that an electric vehicle needs to pay when choosing route k under normal operating conditions; Indicates the benchmark electricity price; This indicates the charging service fee under normal operating conditions; and These represent the minimum cost of travel for fuel vehicles and the minimum cost of travel for electric vehicles between rs under normal operating conditions, respectively; the subscripts i, j, and h all represent grid nodes; ij and jh represent the transmission lines connecting the corresponding nodes; and These represent the active power and reactive power flowing through line ij under normal operating conditions, respectively. and These represent the active power and reactive power generated by the power source at node i under normal operating conditions, respectively. and These represent the active load and reactive load at node i under normal operating conditions, respectively. This represents the set of all child nodes connected to node j; and These are the active power and reactive power flowing through line jh under normal operating conditions, respectively. This represents the voltage amplitude at node i under normal operating conditions. The voltage amplitude at node j under normal operating conditions; and These are the resistance and reactance of line ij, respectively; Indicates the voltage amplitude at the checkpoint node; The apparent power limit of line ij; and These are the upper and lower limits of the voltage amplitude at node i, respectively; , These are the upper and lower limits of the active power output of distributed generation sources, respectively. , These are the upper and lower limits of the reactive power output of distributed power sources, respectively. This represents the normal active load at node j; This represents the set of charging stations powered by power system node j;

[0030] Equation (1) represents the traffic system segment flow conservation constraint; Equation (2) represents the charging station flow conservation constraint; Equation (3) represents the fuel vehicle travel demand conservation constraint; Equation (4) represents the electric vehicle travel demand conservation constraint; Equation (5) represents the nonlinear relationship between segment flow and segment travel time; Equation (6) represents the nonlinear relationship between queuing vehicles at charging stations and queuing time at charging stations; Equation (7) defines the toll cost of fuel vehicles; Equation (8) defines the toll cost of electric vehicles, including charging cost and time cost; Equation (9) represents the Wardr of fuel vehicles. Equation (10) represents the Wardrop user balance criterion for electric vehicles; Equation (11) represents the active power balance constraint of the power system; Equation (12) represents the reactive power balance constraint of the power system; Equation (13) represents Ohm's law of the power system; Equation (14) represents the capacity constraint of the power line power of the power system; Equation (15) represents the upper and lower limit constraints of the node voltage; Equation (16) represents the upper and lower limit constraints of the active power output of distributed generation; Equation (17) represents the upper and lower limit constraints of the reactive power output of distributed generation; Equation (18) defines the active load of the node.

[0031] Furthermore, in step 3), the operational constraints under fault conditions are constructed as follows:

[0032] (19)

[0033] (20)

[0034] (twenty one)

[0035] (twenty two)

[0036] (twenty three)

[0037] (twenty four)

[0038] (25)

[0039] (26)

[0040] (27)

[0041] (28)

[0042] (29)

[0043] In equations (19)-(29), the superscript w represents the fault scenario. This represents the redistributed traffic flow on road segment l under scenario w; This represents the redistributed fuel vehicle traffic on path k in scenario w; This represents the redistributed electric vehicle traffic flow on path k in scenario w; This represents the number of electric vehicles heading to charging station a in scenario w; This indicates the redistribution demand for fuel vehicles between rs under scenario w; This indicates the electric vehicle redistribution demand between rs and rs under scenario w; This indicates the travel time on road segment l under scenario w; This represents the charging queue time at charging station a in scenario w. This represents the redistribution of travel costs for fuel-powered vehicles under scenario w. This represents the redistribution of electric vehicle travel costs under scenario w; Let represent the minimum redistribution cost of fuel-powered vehicles and the minimum redistribution cost of electric vehicles between rs under scenario w; This represents the active power output of unit i in scenario w; This represents the upper limit of the ramp constraint for unit i;

[0044] Equation (19) represents the traffic flow conservation constraint of the redistributed traffic flow in the traffic system; Equation (20) represents the traffic flow conservation constraint of the redistributed traffic flow in the charging station; Equation (21) represents the redistribution of the travel demand conservation constraint for fuel vehicles; Equation (22) represents the redistribution of the travel demand conservation constraint for electric vehicles; Equation (23) represents the nonlinear relationship between traffic flow and travel time of a road segment under fault scenarios, which is determined by the traffic flow of the road segment before the fault and the traffic flow redistributed to the road segment after the fault; Equation (24) represents the relationship between the number of vehicles queuing at the charging station and the queuing time at the charging station under fault scenarios. The nonlinear relationship is determined by the traffic flow of the charging station before the fault and the traffic flow redistributed to the charging station after the fault; Equation (25) defines the passage cost of redistributing fuel vehicles; Equation (26) defines the passage cost of redistributing electric vehicles; Equation (27) represents the Wardrop user balance criterion for redistributing fuel vehicles; Equation (28) represents the Wardrop user balance criterion for redistributing electric vehicles; Equation (29) represents the ramping constraint of distributed power sources in the power system, that is, the change in active power output under normal operating conditions and under any fault operating conditions shall not exceed the ramping limit allowed by the unit.

[0045] Furthermore, in step 4), the constructed preventive control model for the power-transportation coupled system aimed at improving resilience is as follows:

[0046] (30)

[0047] st (1)—(29)

[0048] in, , , These are the three generation parameters of the distributed power source located at node i; This represents the set of nodes in the distribution network.

[0049] Equation (30) represents the objective function of the coordinated scheduling of the power-transportation coupled system under normal operating conditions, including the travel costs of fuel vehicles on the transportation side, the travel and charging costs of electric vehicles, and the operating costs of the power system.

[0050] Furthermore, the present invention proposes a computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the aforementioned preventive control method for a power-transportation coupled system aimed at improving resilience.

[0051] Furthermore, the present invention proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned preventive control method for a power-transportation coupled system aimed at improving resilience.

[0052] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0053] This invention innovatively describes the route reselection decision-making behavior of drivers on the road after a failure, based on real-time road network and charging station status. This makes the model more realistic and significantly improves the effectiveness and practicality of the control strategy. Based on this model, this invention can collaboratively optimize and formulate a set of preventative operational strategies before a failure occurs, including adjusting congestion pricing on key traffic links and scheduling the output of distributed power sources. By pre-guiding and optimizing the distribution of power and traffic flow, this strategy ensures that both systems can maintain safe and stable operation after any single critical failure, thereby significantly enhancing the overall resilience and reliability of the power-traffic coupling system. Attached Figure Description

[0054] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0055] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0056] like Figure 1 As shown, this invention proposes a preventive control method for power-transportation coupled systems aimed at improving resilience. This method includes the following steps:

[0057] 1) Obtain network parameters and operating parameters of the power system and transportation system. The network parameters include the network topology of the power system, line resistance, line reactance, network topology of the transportation system, free passage time of road segments, road segment capacity, charging station capacity and maximum waiting time. The operating parameters include generator cost parameters, unit travel time cost of users, set of faulty components in the power system, and set of faulty components in the transportation system.

[0058] 2) Acquire scenario data, including the active and reactive loads of the power system, travel demand at different origin and destination points in the transportation system, and the proportion of electric vehicles;

[0059] 3) Based on the acquired network parameters, operating parameters, and scenario data of the two systems, construct operating constraints under normal operating conditions and operating constraints under fault conditions;

[0060] 4) Based on the constraints established in step 3), with price signals and distributed power output as the control objects, and minimizing the operating costs of the two systems under normal operating conditions as the objective function, a preventive control model for the power-transportation coupled system oriented towards resilience improvement is established, and the preventive control strategy to ensure the safe operation of the power-transportation coupled system after a failure is obtained by solving the model.

[0061] Furthermore, in step 3), the operating constraints under normal working conditions are constructed as follows:

[0062] (1)

[0063] (2)

[0064] (3)

[0065] (4)

[0066] (5)

[0067] (6)

[0068] (7)

[0069] (8)

[0070] (9)

[0071] (10)

[0072] (11)

[0073] (12)

[0074] (13)

[0075] (14)

[0076] (15)

[0077] (16)

[0078] (17)

[0079] (18)

[0080] In equations (1)-(18), the superscript 0 indicates the normal operating condition, and the subscript l indicates the road segment number; This indicates the traffic flow on road segment l under normal operating conditions; Represents the set of origin and destination points for transportation demand; Indicates the path number; This represents the set of paths for fuel-powered vehicles between their start and end points; This represents the number of fuel-powered vehicles that choose path k under normal operating conditions. These are the indication parameters for the path and road segment of a fuel-powered vehicle. If road segment l is located on path k, then... Select 1 if the value is 1, otherwise select 0. This represents the set of electric vehicle paths between the start and end points. This represents the number of electric vehicles that choose path k under normal operating conditions. The parameters indicate the path and road segment of the electric vehicle. If road segment l is located on path k, then... Select 1 if the value is 1, otherwise select 0. This indicates the path of the electric vehicle and the indication parameters of the charging station. If charging station 'a' is located on path 'k', then... Select 1 if the value is 1, otherwise select 0. Indicates a set of traffic segments; This indicates the number of electric vehicles heading to charging station a under normal operating conditions. Indicates a collection of charging stations; This represents the demand for fuel-powered vehicle travel between the starting point r and the destination s under normal operating conditions. This represents the electric vehicle travel demand between the starting point r and the destination s under normal operating conditions. This indicates the travel time on road segment l under normal operating conditions; This indicates the free travel time for road segment l; This indicates the traffic capacity of road segment a; This indicates the charging time at charging station a under normal operating conditions; Indicates the amount of electricity charged per vehicle; This indicates the charging power of the charging station; This indicates the queuing time at charging station a when it is fully loaded; This indicates the maximum service capacity of charging station a; This represents the toll cost that a fuel-powered vehicle must pay when choosing route k under normal operating conditions; Indicates cost per unit of time; This indicates the road toll levied on vehicles passing through section l under normal operating conditions; This represents the toll cost that an electric vehicle needs to pay when choosing route k under normal operating conditions; Indicates the benchmark electricity price; This indicates the charging service fee under normal operating conditions; and These represent the minimum cost of travel for fuel vehicles and the minimum cost of travel for electric vehicles between rs under normal operating conditions, respectively; the subscripts i, j, and h all represent grid nodes; ij and jh represent the transmission lines connecting the corresponding nodes; and These represent the active power and reactive power flowing through line ij under normal operating conditions, respectively. and These represent the active power and reactive power generated by the power source at node i under normal operating conditions, respectively. and These represent the active load and reactive load at node i under normal operating conditions, respectively. This represents the set of all child nodes connected to node j; and These are the active power and reactive power flowing through line jh under normal operating conditions, respectively. This represents the voltage amplitude at node i under normal operating conditions. The voltage amplitude at node j under normal operating conditions; and These are the resistance and reactance of line ij, respectively; Indicates the voltage amplitude at the checkpoint node; The apparent power limit of line ij; and These are the upper and lower limits of the voltage amplitude at node i, respectively; , These are the upper and lower limits of the active power output of distributed generation sources, respectively. , These are the upper and lower limits of the reactive power output of distributed power sources, respectively. This represents the normal active load at node j; This represents the set of charging stations powered by power system node j;

[0081] Equation (1) represents the traffic system segment flow conservation constraint; Equation (2) represents the charging station flow conservation constraint; Equation (3) represents the fuel vehicle travel demand conservation constraint; Equation (4) represents the electric vehicle travel demand conservation constraint; Equation (5) represents the nonlinear relationship between segment flow and segment travel time; Equation (6) represents the nonlinear relationship between queuing vehicles at charging stations and queuing time at charging stations; Equation (7) defines the toll cost of fuel vehicles; Equation (8) defines the toll cost of electric vehicles, including charging cost and time cost; Equation (9) represents the Wardr of fuel vehicles. Equation (10) represents the Wardrop user balance criterion for electric vehicles; Equation (11) represents the active power balance constraint of the power system; Equation (12) represents the reactive power balance constraint of the power system; Equation (13) represents Ohm's law of the power system; Equation (14) represents the capacity constraint of the power line power of the power system; Equation (15) represents the upper and lower limit constraints of the node voltage; Equation (16) represents the upper and lower limit constraints of the active power output of distributed generation; Equation (17) represents the upper and lower limit constraints of the reactive power output of distributed generation; Equation (18) defines the active load of the node.

[0082] Furthermore, in step 3), the operational constraints under fault conditions are constructed as follows:

[0083] (19)

[0084] (20)

[0085] (twenty one)

[0086] (twenty two)

[0087] (twenty three)

[0088] (twenty four)

[0089] (25)

[0090] (26)

[0091] (27)

[0092] (28)

[0093] (29)

[0094] In equations (19)-(29), the superscript w represents the fault scenario. This represents the redistributed traffic flow on road segment l under scenario w; This represents the redistributed fuel vehicle traffic on path k in scenario w; This represents the redistributed electric vehicle traffic flow on path k in scenario w; This represents the number of electric vehicles heading to charging station a in scenario w; This indicates the redistribution demand for fuel vehicles between rs under scenario w; This indicates the electric vehicle redistribution demand between rs and rs under scenario w; This indicates the travel time on road segment l under scenario w; This represents the charging queue time at charging station a in scenario w. This represents the redistribution of travel costs for fuel-powered vehicles under scenario w. This represents the redistribution of electric vehicle travel costs under scenario w; Let represent the minimum redistribution cost of fuel-powered vehicles and the minimum redistribution cost of electric vehicles between rs under scenario w; This represents the active power output of unit i in scenario w; This represents the upper limit of the ramp constraint for unit i;

[0095] Equation (19) represents the traffic flow conservation constraint of the redistributed traffic flow in the traffic system; Equation (20) represents the traffic flow conservation constraint of the redistributed traffic flow in the charging station; Equation (21) represents the redistribution of the travel demand conservation constraint for fuel vehicles; Equation (22) represents the redistribution of the travel demand conservation constraint for electric vehicles; Equation (23) represents the nonlinear relationship between traffic flow and travel time of a road segment under fault scenarios, which is determined by the traffic flow of the road segment before the fault and the traffic flow redistributed to the road segment after the fault; Equation (24) represents the relationship between the number of vehicles queuing at the charging station and the queuing time at the charging station under fault scenarios. The nonlinear relationship is determined by the traffic flow of the charging station before the fault and the traffic flow redistributed to the charging station after the fault; Equation (25) defines the passage cost of redistributing fuel vehicles; Equation (26) defines the passage cost of redistributing electric vehicles; Equation (27) represents the Wardrop user balance criterion for redistributing fuel vehicles; Equation (28) represents the Wardrop user balance criterion for redistributing electric vehicles; Equation (29) represents the ramping constraint of distributed power sources in the power system, that is, the change in active power output under normal operating conditions and under any fault operating conditions shall not exceed the ramping limit allowed by the unit.

[0096] Furthermore, in step 4), the constructed preventive control model for the power-transportation coupled system aimed at improving resilience is as follows:

[0097] (30)

[0098] st (1)—(29)

[0099] in, , , These are the three generation parameters of the distributed power source located at node i; This represents the set of nodes in the distribution network.

[0100] Equation (30) represents the objective function of the coordinated scheduling of the power-transportation coupled system under normal operating conditions, including the travel costs of fuel vehicles on the transportation side, the travel and charging costs of electric vehicles, and the operating costs of the power system.

[0101] Furthermore, the present invention proposes a computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the aforementioned preventive control method for a power-transportation coupled system aimed at improving resilience.

[0102] Furthermore, the present invention proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned preventive control method for a power-transportation coupled system aimed at improving resilience.

[0103] The above description merely illustrates the embodiments of the present invention, and while the description is relatively specific and detailed, it should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the appended claims.

Claims

1. A preventive control method for a power-transportation coupled system aimed at improving resilience, characterized in that, Includes the following steps: 1) Obtain network parameters and operating parameters of the power system and transportation system. The network parameters include the network topology of the power system, line resistance, line reactance, network topology of the transportation system, free passage time of road segments, road segment capacity, charging station capacity and maximum waiting time. The operating parameters include generator cost parameters, unit travel time cost of users, set of faulty components in the power system, and set of faulty components in the transportation system. 2) Acquire scenario data, including the active and reactive loads of the power system, travel demand at different origin and destination points in the transportation system, and the proportion of electric vehicles; 3) Based on the acquired network parameters, operating parameters, and scenario data of the two systems, construct operating constraints under normal operating conditions and operating constraints under fault conditions; 4) Based on the constraints established in step 3), with price signals and distributed power output as the control objects, and minimizing the operating costs of the two systems under normal operating conditions as the objective function, a preventive control model for the power-transportation coupled system oriented towards resilience improvement is established, and the preventive control strategy to ensure the safe operation of the power-transportation coupled system after a failure is obtained by solving the model.

2. The method for preventive control of a power-transportation coupled system aimed at improving resilience according to claim 1, characterized in that, In step 3), the operating constraints under normal working conditions are constructed as follows: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) In equations (1)-(18), the superscript 0 indicates the normal operating condition, and the subscript l indicates the road segment number; This indicates the traffic flow on road segment l under normal operating conditions; Represents the set of origin and destination points for transportation demand; Indicates the path number; This represents the set of paths for fuel-powered vehicles between their start and end points; This represents the number of fuel-powered vehicles that choose path k under normal operating conditions. These are the indication parameters for the path and road segment of a fuel-powered vehicle. If road segment l is located on path k, then... Select 1 if the value is 1, otherwise select 0. This represents the set of electric vehicle paths between the start and end points. This represents the number of electric vehicles that choose path k under normal operating conditions. The parameters indicate the path and road segment of the electric vehicle. If road segment l is located on path k, then... Select 1 if the value is 1, otherwise select 0. This indicates the path of the electric vehicle and the indication parameters of the charging station. If charging station 'a' is located on path 'k', then... Select 1 if the value is 1, otherwise select 0. Indicates a set of traffic segments; This indicates the number of electric vehicles heading to charging station a under normal operating conditions. Indicates a collection of charging stations; This represents the demand for fuel-powered vehicle travel between the starting point r and the destination s under normal operating conditions. This represents the electric vehicle travel demand between the starting point r and the destination s under normal operating conditions. This indicates the travel time on road segment l under normal operating conditions; This indicates the free travel time for road segment l; This indicates the traffic capacity of road segment a; This indicates the charging time at charging station a under normal operating conditions; Indicates the amount of electricity charged per vehicle; This indicates the charging power of the charging station; This indicates the queuing time at charging station a when it is fully loaded; This indicates the maximum service capacity of charging station a; This represents the toll cost that a fuel-powered vehicle must pay when choosing route k under normal operating conditions; Indicates cost per unit of time; This indicates the road toll levied on vehicles passing through section l under normal operating conditions; This represents the toll cost that an electric vehicle needs to pay when choosing route k under normal operating conditions; Indicates the benchmark electricity price; This indicates the charging service fee under normal operating conditions; and These represent the minimum cost of travel for fuel vehicles and the minimum cost of travel for electric vehicles between rs under normal operating conditions, respectively; the subscripts i, j, and h all represent grid nodes; ij and jh represent the transmission lines connecting the corresponding nodes; and These represent the active power and reactive power flowing through line ij under normal operating conditions, respectively. and These represent the active power and reactive power generated by the power source at node i under normal operating conditions, respectively. and These represent the active load and reactive load at node i under normal operating conditions, respectively. This represents the set of all child nodes connected to node j; and These are the active power and reactive power flowing through line jh under normal operating conditions, respectively. This represents the voltage amplitude at node i under normal operating conditions. The voltage amplitude at node j under normal operating conditions; and These are the resistance and reactance of line ij, respectively; Indicates the voltage amplitude at the checkpoint node; The apparent power limit of line ij; and These are the upper and lower limits of the voltage amplitude at node i, respectively; , These are the upper and lower limits of the active power output of distributed generation sources, respectively. , These are the upper and lower limits of the reactive power output of distributed power sources, respectively. This represents the normal active load at node j; This represents the set of charging stations powered by power system node j; Equation (1) represents the traffic flow conservation constraint of road segments; Equation (2) represents the charging station flow conservation constraint; Equation (3) represents the fuel vehicle travel demand conservation constraint; Equation (4) represents the electric vehicle travel demand conservation constraint; Equation (5) represents the nonlinear relationship between road segment flow and road segment travel time; Equation (6) represents the nonlinear relationship between the number of vehicles queuing at charging stations and the queuing time at charging stations; Equation (7) defines the toll cost of fuel vehicles; Equation (8) defines the toll cost of electric vehicles, including charging cost and time cost; Equation (9) represents the Ward cost of fuel vehicles. Wardrop user balance criterion; Equation (10) represents the Wardrop user balance criterion for electric vehicles; Equation (11) represents the active power balance constraint of the power system; Equation (12) represents the reactive power balance constraint of the power system; Equation (13) represents Ohm's law of the power system; Equation (14) represents the capacity constraint of the power line power of the power system; Equation (15) represents the upper and lower limit constraints of the node voltage; Equation (16) represents the upper and lower limit constraints of the active power output of distributed generation; Equation (17) represents the upper and lower limit constraints of the reactive power output of distributed generation; Equation (18) defines the active load of the node.

3. The preventive control method for a power-transportation coupled system aimed at improving resilience according to claim 2, characterized in that, In step 3), the operational constraints under fault conditions are constructed as follows: (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) In equations (19)-(29), the superscript w represents the fault scenario. This represents the redistributed traffic flow on road segment l under scenario w; This represents the redistributed fuel vehicle traffic on path k in scenario w; This represents the redistributed electric vehicle traffic flow on path k in scenario w; This represents the number of electric vehicles heading to charging station a in scenario w; This indicates the redistribution demand for fuel vehicles between rs under scenario w; This indicates the electric vehicle redistribution demand between rs and rs under scenario w; This indicates the travel time on road segment l under scenario w; This represents the charging queue time at charging station a in scenario w. This represents the redistribution of travel costs for fuel-powered vehicles under scenario w. This represents the redistribution of electric vehicle travel costs under scenario w; Let represent the minimum redistribution cost of fuel-powered vehicles and the minimum redistribution cost of electric vehicles between rs under scenario w; This represents the active power output of unit i in scenario w; This represents the upper limit of the ramp constraint for unit i; Equation (19) represents the traffic flow conservation constraint of the redistributed traffic flow in the traffic system; Equation (20) represents the traffic flow conservation constraint of the redistributed traffic flow in the charging station; Equation (21) represents the redistribution of the travel demand conservation constraint for fuel vehicles; Equation (22) represents the redistribution of the travel demand conservation constraint for electric vehicles; Equation (23) represents the nonlinear relationship between traffic flow and travel time of a road segment under fault scenarios, which is determined by the traffic flow of the road segment before the fault and the traffic flow redistributed to the road segment after the fault; Equation (24) represents the relationship between the number of vehicles queuing at the charging station and the queuing time at the charging station under fault scenarios. The nonlinear relationship is determined by the traffic flow of the charging station before the fault and the traffic flow redistributed to the charging station after the fault; Equation (25) defines the passage cost of redistributing fuel vehicles; Equation (26) defines the passage cost of redistributing electric vehicles; Equation (27) represents the Wardrop user balance criterion for redistributing fuel vehicles; Equation (28) represents the Wardrop user balance criterion for redistributing electric vehicles; Equation (29) represents the ramping constraint of distributed power sources in the power system, that is, the change in active power output under normal operating conditions and under any fault operating conditions shall not exceed the ramping limit allowed by the unit.

4. A preventive control method for a power-transportation coupled system aimed at improving resilience, as described in claim 3, is characterized in that... In step 4), the constructed preventive control model for the power-transportation coupled system aimed at improving resilience is as follows: (30) st (1)—(29) in, , , These are the three generation parameters of the distributed power source located at node i; Represents the set of nodes in a distribution network; Equation (30) represents the objective function of the coordinated scheduling of the power-transportation coupled system under normal operating conditions, including the travel costs of fuel vehicles on the transportation side, the travel and charging costs of electric vehicles, and the operating costs of the power system.

5. A computer device, characterized in that, The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the preventive control method for a power-transportation coupled system for resilience enhancement as described in claims 1-4.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the preventive control method for a power-transportation coupled system for resilience enhancement as described in claims 1-4.

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