Time-event driven power-traffic coupled system co-simulation method

Abstract

The application discloses a time-event driven power-traffic coupling system joint simulation method, relates to the technical field of power and traffic system joint simulation, and comprises the following steps: S1, constructing a power-traffic bidirectional coupling system; S2, building a three-module joint simulation framework; S3, setting a time-event double driving information interaction mechanism, wherein the time driving mechanism is used for realizing the periodic state synchronization of the power system and the traffic system, and the event driving mechanism is used for realizing the cross-system information updating caused by the sudden state change under the disaster scene; and S4, based on the execution of the time-event double driving information interaction mechanism, executing the joint simulation and outputting the simulation result. The time-event driven power-traffic coupling system joint simulation method provided by the application solves the problems that the bidirectional action process between the power system and the traffic system under the rainstorm disaster is not fully described, and the power and traffic time scales are difficult to coordinate in the prior art.

Description

Technical Field

[0001] This invention relates to the field of joint simulation technology for power and transportation systems, and in particular to a time-event-driven joint simulation method for power-transportation coupled systems. Background Technology

[0002] With accelerated urbanization and frequent extreme rainstorms, the coupling relationship between power systems and transportation systems is becoming increasingly close. During rainstorm disasters, power outages cause traffic lights to malfunction, leading to widespread traffic congestion; congestion, in turn, hinders the movement of mobile emergency resources, which in turn restricts the post-disaster recovery of the power system. This two-way impact results in a nonlinear cascading amplification characteristic in the power-transportation coupled system. Accurately characterizing the two-way interaction process between the power and transportation systems under rainstorm disasters has become a critical issue urgently needing to be addressed in the field of urban infrastructure disaster prevention and mitigation. Existing modeling methods are still insufficient in characterizing this two-way interaction process and cannot meet the needs of coupling analysis in rainstorm scenarios. On the one hand, transportation systems are often simplified to static inputs or fixed capacity parameters, making it difficult to reflect the dynamic changes in traffic conditions under disaster conditions; on the other hand, the descriptions of key coupling components such as traffic lights and mobile emergency resources are relatively coarse, making it difficult to support the analysis and expression of the two-way interaction mechanism of the power-transportation system.

[0003] Meanwhile, power systems and transportation systems differ significantly in their dynamic characteristics and time scales. Power system state updates after a fault typically occur on a minute or even hourly scale, while traffic flow evolution, traffic light switching, and vehicle movement are continuous dynamic processes requiring smaller simulation steps for accurate characterization. Simultaneously, rainstorm disasters exhibit significant spatiotemporal evolution characteristics; rainfall intensity, water depth, power equipment failures, and road capacity all continuously change over time and space. These factors increase the complexity of dynamic simulation of coupled systems and pose challenges to co-simulation in terms of time scale coordination, state interaction, and computational efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a time-event-driven joint simulation method for power-transportation coupled systems, which solves the problems of insufficient characterization of the two-way interaction process between power systems and transportation systems under rainstorm disasters and the difficulty in coordinating the time scales of power and transportation in existing technologies.

[0005] To achieve the above objectives, this invention provides a time-event-driven co-simulation method for power-transportation coupled systems, comprising the following steps: S1. Construct a two-way coupled power-transportation system, including a power system model, a transportation system model, and coupling element models; S2. Build a three-module joint simulation framework, including a power system simulation module, a transportation system simulation module, and a coupling coordination module; S3. Based on the three-module joint simulation framework, a time-event dual-drive information interaction mechanism is set up, including two parts: a time-driven mechanism and an event-driven mechanism. The time-driven mechanism is used to realize the periodic state synchronization between the power system and the transportation system, and the event-driven mechanism is used for cross-system information updates caused by sudden state changes in disaster scenarios. S4. Based on the execution time-event dual-driven information interaction mechanism, perform joint simulation and output simulation results.

[0006] Therefore, the present invention employs the above-mentioned time-event-driven co-simulation method for power-transportation coupled systems, which has the following beneficial effects: (1) Achieve accurate characterization of the bidirectional coupling effect between power and transportation: Taking traffic lights and mobile emergency resources as key coupling elements, a dynamic mapping relationship between power supply status and traffic capacity was established, forming a bidirectional action chain of "power failure → traffic light failure → traffic congestion → delay of emergency resources → obstruction of power restoration", realizing a unified description and dynamic simulation of the influence transmission, feedback constraint and recovery process between the power system and the transportation system; (2) Effectively coordinate the differences in multiple time scales between the power system and the transportation system: adopt differentiated simulation step size settings, and through the information interaction mechanism that combines time-driven and event-driven approaches, realize the collaborative simulation of the two heterogeneous systems under the same simulation framework, and avoid computational redundancy or information distortion caused by uniform step size; (3) Balancing computational efficiency and simulation accuracy: An adaptive macro-micro hybrid mechanism is introduced, using the macro-cellular transmission model as the global framework, automatically switching to the micro-model in congested areas and using a smaller step size for fine characterization; Experiments show that the simulation time of the method of this invention is only 4.27 minutes, which is significantly lower than the average time of SUMO 36.80 min, and the average error of load recovery time is only 1.82%, achieving a good balance between efficiency and accuracy; (4) Real-time response to cross-system sudden disturbances: Taking three key events as triggering conditions, such as initial disaster faults, power system faults and changes in the status of mobile emergency resources, cross-system information interaction is initiated in real time, accurately capturing the nonlinear dynamic feedback process of coupled systems under disaster disturbances, providing effective technical support for disaster damage assessment and emergency decision-making in rainstorm disaster scenarios.

[0007] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0008] Figure 1 This is an overall flowchart of the time-event-driven co-simulation method for power-transportation coupled systems according to the present invention; Figure 2 This is a structural diagram of the co-simulation framework of Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the time-event dual-driven information interaction mechanism of Embodiment 1 of the present invention; Figure 4 This is a flowchart of the co-simulation process in Embodiment 1 of the present invention; Figure 5 This is a topological structure diagram of Embodiment 2 of the present invention; Figure 6 This is the total load variation curve of the power system in Embodiment 2 of the present invention; Figure 7 This is the average traffic speed change curve during the fault process in Embodiment 2 of the present invention; Figure 8 This is a schematic diagram of the load recovery time at the fault point in Embodiment 2 of the present invention; Figure 9 This is a schematic diagram of traffic capacity during a fault process in Embodiment 2 of the present invention; wherein, (a) is the average traffic congestion rate during the simulation period, and (b) is the average congestion rate of the intersection before and after the fault. Figure 10 The following is a comparison chart of the method of the present invention and the SUMO simulation results in Embodiment 2 of the present invention; wherein, (a) is a comparison chart of average speed, (b) is a schematic diagram of speed consistency, (c) is a comparison chart of traffic flow in congested road sections, and (d) is a comparison chart of travel time of mobile emergency resources. Detailed Implementation

[0009] The following detailed description of embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0010] Example 1 Please see Figure 1 A time-event-driven co-simulation method for power-transportation coupled systems includes the following steps: S1. Construct a two-way coupled power-transportation system, including a power system model, a transportation system model, and a coupling element model.

[0011] S11. Construct the power system model into a multi-level AC power flow model; S111. Through a multi-level AC power flow model, the power grid is described as a bus and branch topology result. A multi-voltage level power grid topology is built, the transformer mapping relationship between different voltage levels is established, and the calculation and core constraint equations are defined, including the bus total power equation of the multi-level urban power system, the node power balance relationship equation of the radial distribution system, the line power constraint equation, and the voltage calculation and constraint equation. The total bus power equation for a multi-level urban power system, considering the influence of transformer branches at different voltage levels, is expressed as follows: ; The power balance equation for the nodes in a radial power distribution system is as follows: ; ; The voltage calculation and constraint equations are as follows: ; ; ; The line power constraint equation is: ; ; ; In the formula, , , and All are busbar nodes; , All are voltage level indexes; and Voltage levels The set of busbars and the set of branches; Voltage level The set of busbars; Indicates that by node Pointing to node The side road; Voltage level Next node Complex power injection, and Indicates at voltage level Next, node and nodes The voltage vector; For voltage level Next, node Voltage phasors; Nodes within the same voltage level and Branch admittance between For connection voltage level and The equivalent admittance of the transformer branch; and branch road Active power and reactive power; and branch road Active power and reactive power; and For nodes Active and reactive loads; and For the unit to the node Injected active and reactive power; and For nodes and nodes The square of the voltage amplitude; Represents a node Voltage amplitude; and branch road Resistance and reactance; and These are the lower and upper limits of the node voltage amplitude; branch road Apparent power Set its transmission limit; and They are nodes The lower and upper limits of the active power output of the generating unit; and They are nodes The lower and upper limits of the reactive power output of the generator unit; S112, Define node power supply state variables By calculating AC power flow, it is determined whether the node voltage after a fault meets the operating constraints, and an output interface from the power system to the transportation system is established; if , then it represents a node Power supply is normal at the current moment; if , then it represents a node Power outage; when node When the equipment itself malfunctions, Change from 1 to 0; when node When the upstream power supply fails and there is no backup power supply, It may also become 0 due to voltage exceeding the limit caused by power flow transfer.

[0012] S12. Improve the Cellular Transmission Model (CTM) to adapt it to disaster scenarios, and use the improved CTM as a traffic system model. S121. Heavy rain-induced flooding directly impacts road traffic, leading to a decrease in vehicle speed. A speed reduction factor is introduced to characterize the impact of heavy rain on traffic capacity, and the fixed free-flow speed is modified to reflect the actual speed affected by disasters and real-time road conditions. The expression is: ; In the formula, Represents a traffic cell index; Represents cell At any moment The actual speed; Represents cell At any moment density, ; Indicates blockage density; Indicates the depth of water accumulation. Indicates time Based on water depth A determined speed reduction factor, ; Indicates the free-flow velocity; S122, Design an adaptive macro-micro hybridization mechanism: when the cell The density exceeds the blockage density At this point, the cell switches from a macroscopic model to a local microscopic model, and the intelligent driver model (IDM) is used to characterize the vehicle. The car-following behavior is observed, and the simulation step size is switched to a locally refined microstep size. Microscopic model output cell outflow and replace the outflow of the macro model. The expression is: ; ; ; In the formula, Indicates vehicle index; Indicates vehicle The actual acceleration; Indicates the maximum acceleration; Indicates the acceleration index; Indicates vehicle The actual distance from the vehicle in front; Indicates the desired spacing; Indicates the minimum static distance between vehicles; Indicates the expected time interval; Indicates comfortable deceleration; and Representing vehicles and the speed of the vehicle in front; This represents the number of vehicles that cross the downstream boundary of a cell within a macroscopic step size; Represents cell At any moment Micro-outflow; Indicates the simulation step size of the traffic system; S123. Establish signalized intersection control logic that considers the impact of power supply, distinguish different vehicle flows at each intersection entrance cell, including left turn, straight ahead, right turn, and U-turn, represent the flow distribution for each turn using a turning ratio coefficient, define the power supply status factor and turning permission factor, and calculate the effective transfer flow controlled by the traffic lights, expressed as: ; ; ; ; In the formula, , and They represent the upstream cells respectively. Downstream cells The ratio of right turns, straight ahead, and left turns / U-turns should be equal to 1. Indicates effective transfer of traffic; The transfer permission factor is determined jointly by the power supply status and phase permission. and They represent cells respectively Transmission capability and cells Receiving capability; Indicates the index of the signalized intersection. and They represent the intersections. Upstream and downstream cell sets; , ; Indicates upstream cell At any moment Traffic flow that can be diverted; Indicates that it is composed of upstream cells Downstream cells Steering distribution ratio coefficient; If the signal lights are powered normally , According to the preset signal cycle, if the power supply is interrupted... , The emergency rules are dynamically adjusted, thus affecting road capacity; the expression for the transfer permit factor is: ; In the formula, Indicates phase Permitted driving directions Indicates upstream cell Corresponding driving direction; Indicates that the vehicle originates from the cell. Transfer to cell Steering type, Indicates phase The set of allowed steering types; S13. Construct a model of coupled components, including a traffic light model and a mobile emergency resource model; S131. If a traffic light experiences a power outage, the current phase is frozen, and the transmission flow of the controlled upstream cell at the intersection decreases proportionally, as expressed by: ; ; In the formula, Indicating a power outage at a traffic light, the cell... exist The actual effective transmission volume / passage capacity at time t; Represents cell Sending capacity; This represents the reduction factor, reflecting the decrease in effective traffic capacity caused by vehicle congestion; Meanwhile, the intersection adopts the "right turn priority + main road priority" no-signal traffic rule. Vehicles passing through the controlled intersection also maintain passage according to the no-signal traffic rule, simulating the self-organizing behavior of vehicles in actual situations. S132. Construct a mobile emergency resource model: Model resources such as mobile power generation vehicles, emergency repair vehicles, and drainage vehicles as task vehicles constrained by traffic flow status, establish a mapping relationship between resource types and fault types, and construct an operation duration matrix. The expression for calculating the relative position of mobile emergency resources during a signal cycle is: ; The expression for calculating the waiting time of mobile emergency resources at traffic lights is: ; The expression for calculating the travel time of mobile emergency resources along the path is: ; The expression for calculating the actual arrival time of mobile emergency resources at the fault point is: ; The expression for the constraint service matching relationship is: ; ; The expression for calculating the job duration required to handle different types of faults for different types of resources is: ; In the formula, Indicates time Mobile emergency resources Relative position within a signal cycle; Indicates being in a cell The moment; Represents cell The traffic light cycle; G c Indicates the duration of the green light; Indicates the duration of the red light. ; Representing resources Waiting time caused by traffic lights; Indicates the current cell phase; Representing resources exist Departure time and arrival location Total travel time; Representing resources Arrival Location The path; Indicates the next cell on the path; Indicates cell length; Representing resources Actual arrival location The moment; Indicates the start and departure times; Indicates service matching relationship; Represents the set of fault points; Indicates the fault point index; Indicates the time of the next failure point for the same resource service; Representing resources In position The duration of the assignment; Represents the task duration matrix. The elements are , The type is resources Handling fault types Required processing time; Representing resources Type; Indicates position The type of fault.

[0013] S2. Establish the three-module joint simulation framework. Please refer to [link / reference]. Figure 2 It includes a power system simulation module, a transportation system simulation module, and a coupling coordination module.

[0014] S21. Construct a power system simulation module to simulate the dynamic response process of the power system under rainstorm disasters. The inputs are power grid topology parameters, load data and fault scenario sequences, and the outputs are the voltage amplitude of each node, the power flow distribution of branches and the power supply status of nodes.

[0015] S22. Construct a traffic system simulation module to simulate the dynamic evolution of traffic flow under rainstorm disasters. The inputs are road network topology parameters, traffic demand data, traffic light timing schemes and time-varying water depth sequences. The outputs are traffic flow density, average speed and congestion rate of each road segment.

[0016] S23. Construct a coupling coordination module to connect the power system and the transportation system.

[0017] S3, please refer to Figure 3 A time- and event-driven information interaction mechanism is set up, consisting of two parts: a time-driven mechanism and an event-driven mechanism. The time-driven mechanism is used to achieve periodic state synchronization between the power system and the transportation system, while the event-driven mechanism is used for cross-system information updates caused by sudden state changes in disaster scenarios, including initial fault events, power system fault events, and mobile emergency resource state change events.

[0018] S31. Let the total simulation time be... The power system simulation step size is The simulation step size for the traffic system is ,and , Represents the coefficient. It is a positive integer.

[0019] S32. Establish a time-driven mechanism, specifying the simulation step size for each power system. At the end, a periodic state interaction is triggered; S321. The power system simulation module performs power flow calculations, updates the power supply state variables of each node, detects newly faulty nodes, and forms a fault set. ; S322, The coupling coordination module uses the mapping matrix between traffic lights and power supply nodes. In addition to the power supply status variables of each node, a signal light state vector is formed. ; S323, Set up faults and traffic light state vector Passed to the traffic system simulation module; S324, The traffic system simulation module, based on the received fault set... and traffic light state vector Update the power supply status variables in the intersection control logic; S325, the traffic system simulation module feeds back current traffic status information, including the congestion rate of each road segment, to the coupling coordination module. and average speed ; S326, the coupling and coordination module transmits traffic status information to the power system simulation module to update the travel time estimates of mobile emergency resources.

[0020] S33. Establish an event-driven mechanism: Define three types of cross-system interaction events to trigger information interaction in real time; The three types of cross-system interaction events are: initial disaster fault, power system fault, and change in the status of mobile emergency resources; Initial disaster fault: When the simulation reaches the moment of disaster occurrence, the initial disaster fault is triggered; the power system simulation module updates the fault set, including the power supply state variables of the affected nodes. Set to 0; the traffic system simulation module adjusts the traffic capacity according to the degree of road damage; Power system fault: After the power system simulation module updates its state, if any node is detected as faulty... If the value changes from 1 to 0, a new fault is detected, which may cause the traffic lights to lose power. The power system simulation module sends the fault node information to the traffic system simulation module, and the traffic system simulation module returns the speed of each road segment at the current time. Mobile emergency resource status change: The power system simulation module makes resource scheduling decisions based on fault information and current traffic conditions, and transmits the resource type, starting point, and target fault point to the traffic system simulation module; the traffic system simulation module incorporates the resource vehicle into the traffic flow simulation and calculates the travel time; after the resource vehicle arrives at the target fault point and completes maintenance or emergency power supply operations, the traffic system simulation module transmits an operation completion signal to the power system simulation module; the power system simulation module updates the power supply status variable of the corresponding node from 0 to 1, and synchronously updates the node's power supply status, traffic light status, and traffic flow status accordingly, completing the power-traffic coupled system status update.

[0021] S4. Joint simulation driven by both time and event; please refer to [link / reference]. Figure 4 Output the simulation results.

[0022] S41. Joint Simulation Initialization: Obtain the topology results and parameter information of the multi-voltage level power grid, including bus nodes, branch connection relationships, UPS configuration, and initial operating mode, and establish the power supply status set of each node; import the road traffic network structure and its attribute information, including road segment length, number of lanes, intersection topology relationships, signal timing scheme, road segment capacity, free flow speed, and OD traffic demand matrix, and initialize the traffic density, speed, and flow status of each cell; set a unified simulation starting point and give the power system simulation step size. Traffic system simulation step size and local micro-refinement step size , Through the above initialization, the two heterogeneous subsystems can enter a state where they can advance collaboratively within a unified time frame.

[0023] S42. Determine whether there are new node failures or new road sections blocked based on the disaster scenario: If a certain power equipment is at time... When a fault occurs, the status of the corresponding node or branch changes, and the local power supply capacity decreases. This is judged as a failure of the corresponding node or branch, and the fault node information of the power system is output. If the water accumulation in a certain road section exceeds the allowable threshold, its traffic capacity is dynamically reduced. In severe cases, it can directly turn into an impassable state, which is judged as a traffic obstruction of the road section, and the road location information of the reduced traffic capacity is output. Since the rainstorm process has obvious time-varying and spatial heterogeneity, the disaster input is not applied all at once, but is continuously updated with the simulation clock, so as to more realistically reflect the process of extreme disasters gradually evolving from local impact to system-level impact. The rainstorm disaster scenario is not a static, single disturbance, but a temporal scenario composed of rainfall processes, water accumulation evolution, changes in equipment failure probability, and reduced road capacity. At any given moment, the input disaster scenario is represented as: ; In the formula, Indicates time The disaster scenario state vector; Represents the set of water depths; Represents the set of node states; Represents the set of road capacity; S43. Perform power flow calculations and run CTM simulation; The power system simulation module is based on step size Perform periodic updates, execute power flow calculations, and determine the power grid operating status, node power supply capacity, and load shedding distribution; Based on the current cell density, upstream transmission capacity, and downstream reception capacity, the traffic system simulation module calculates vehicle flow by improving the cell transmission model and updates cell density, speed, and queuing status. For normally passable road sections, vehicles run at the free-flow speed after disaster correction. For road sections affected by water accumulation, vehicle speed is dynamically reduced according to the water depth. For completely impassable road sections, cell outflow is limited to zero, causing upstream vehicle accumulation and congestion propagation.

[0024] S44. Information interaction is based on a time-event dual-driven information interaction mechanism.

[0025] S45. After triggering the mobile emergency resource dispatch request, the traffic system simulation module calculates the travel time of the mobile emergency resources and generates the estimated arrival time.

[0026] S46. After the mobile emergency resources arrive, the operation is completed according to the operation duration matrix. After the operation is completed, the traffic system simulation module sends an operation completion signal to the power system simulation module, and the power system simulation module updates the status of the faulty equipment and the power supply status of the nodes.

[0027] S47. Determine whether the co-simulation has reached the total simulation time. If the target is not reached, execute S42-S46 repeatedly; if the target is reached, end the simulation and output the node voltage and power supply status, load loss, road speed, density, flow rate, traffic capacity, traffic light status, arrival and completion time of mobile emergency resources at each time point.

[0028] Example 2: Experimental Verification Experimental setup Topology such as Figure 5 As shown, the power system comprises 112 bus nodes, covering voltage levels from 220kV to 10kV, including 10 220kV nodes, 10 110 / 35kV nodes, and 92 10kV distribution nodes, with a total load of 764.50MW. The traffic system consists of 3 areas, containing 72 nodes and 237 road sections, including 45 traffic signal coupling nodes. UPS systems are provided for nodes B11, B23, O3, and G11. The traffic signals use a 4-phase configuration: north-south straight (right turn), north-south left turn (U-turn), east-west straight (right turn), and east-west left turn (U-turn). Unless otherwise specified, CTM parameter settings are shown in Table 1. The traffic signal timing schemes differ for each area; details are shown in Table 2. The mobile emergency resource originates at node B2. Initial fault scenario settings are shown in Table 3.

[0029] Table 1: CTM Parameter Table

[0030] Table 2: Traffic Light Timing Scheme

[0031] Table 3: Fault Scenario Settings

[0032] Experimental Verification 1: Analysis of the Impact of Power Failures on Dynamic Traffic Flow To quantify the impact of power outages on the dynamic response of the transportation system, a systematic analysis of the operational characteristics of the transportation network at both macro and micro levels was conducted by comparing traffic flow changes under normal operation and power outage scenarios. In the outage scenario, a power system load loss event occurred, with a total load loss of 43.10 MW. The load time-series changes are as follows: Figure 6 As shown. Macro-level analysis of the transportation system primarily uses average road network speed and average traffic congestion rate as indicators to reflect the overall capacity and congestion status of the transportation network, such as... Figure 7 As shown.

[0033] Under normal operating conditions, traffic flow remains orderly, vehicle throughput is high, average speed is stable at normal driving levels, and the network congestion rate remains at a low level of 0.46, indicating that the traffic system can effectively support peak-hour traffic demand under undisturbed conditions. However, after a power outage, traffic capacity significantly decreased due to the failure of traffic lights at key intersections. Macroscopic analysis shows that the average speed of the traffic network decreased by a maximum of 30.64% during the peak of the outage, while the traffic congestion rate increased significantly, reaching a maximum of 0.72, an increase of 56.52% compared to normal conditions. The traffic distribution during this period shows increased vehicle merging conflicts, a significant decline in traffic order, and the gradual formation and spread of queues at nodes to upstream and downstream areas, leading to a decrease in the throughput of upstream roads and severely impacting the overall traffic network capacity.

[0034] Further micro-analysis was conducted to compare traffic congestion at different faulty intersections. Figure 9 This indicates significant differences in the congestion effects at intersections. For example, intersection G10 experienced a rapid increase and sustained high congestion rate after the fault, suggesting that this node faces heavy traffic pressure and, typically acting as a traffic hub, its limited capacity significantly impacts traffic flow across the entire network. In contrast, other intersections such as B3 and G21 showed relatively small changes in congestion rates even with signal power outages. This is likely related to lower vehicle traffic volume at these intersections or the existence of alternative routes, indicating a clear spatial heterogeneity in the impact of different intersections on the traffic network under fault conditions. The micro-level analysis reveals the dominant role of key nodes in the dynamic evolution of traffic flow under fault conditions and can provide a scientific basis for the scheduling and priority recovery strategies of mobile emergency resources.

[0035] The above analysis shows that the impact of power system failures on the transportation system exhibits distinct spatiotemporal characteristics and node dependence. At the macro level, the average speed and congestion rate of the entire network show significant fluctuations, indicating the overall suppressive effect of fault disturbances on traffic capacity. At the micro level, congestion responses at different intersections vary considerably, with traffic restrictions at key nodes having the most significant impact on traffic flow.

[0036] Experimental Verification 2: Analysis of the Impact of Traffic Capacity on Power Restoration Efficiency To quantify the key impact of traffic system status on power recovery after a disaster, a comparative analysis was conducted on two scenarios: one considering the dynamic impact of traffic, and the time required to restore power supply to the fault point in the power system was used as a metric.

[0037] (1) Scenario 1: Using the coupled model in this paper, the dynamic scheduling process of mobile emergency resources is simulated based on the traffic congestion caused by power outage.

[0038] (2) Scenario 2: Assuming that the traffic network is not affected by the power outage of traffic lights, vehicles are set up using a static allocation model, and mobile emergency resources can reach any fault point without obstacles in the theoretical shortest time.

[0039] Figure 8 This indicates that the recovery time for mobile emergency resources in Scenario 1 is longer than that in Scenario 2. Referring to Table 4, at the 159th minute after the deployment of mobile emergency resources, Scenario 2 had recovered 90% of its load, while Scenario 1 had only recovered approximately 72%. To achieve full load recovery, the total recovery time for Scenario 1 was 10.17% longer than that for Scenario 2, with a maximum delay of 31.43%.

[0040] Table 4: Load restored at the same time

[0041] Experimental Verification 3: Accuracy and Computational Efficiency Analysis To verify the accuracy of the method of this invention, a comparative verification was conducted with the mature traffic simulation software SUMO. The comparative experiment used the same road network, OD demand, and traffic parameters, with the simulation duration set to 24 hours as described above. The scenario was simplified to simulate a traffic light failure event at 5 hours. Average speed, traffic flow on typical congested road sections, and mobile emergency resource dispatch time were selected as comparative indicators. Figure 10It can be seen that the average speed curve obtained by the method of this invention is basically consistent with that obtained by SUMO, both showing a process of rapid speed decrease under disaster impact, low-speed operation during the fault duration, and gradual speed recovery during the recovery phase. In the comparison of traffic flow in highly congested road sections, the traffic flow levels obtained by the two methods before and after the fault are quite similar, and the direction of change is consistent, which can reflect the impact of the decline in local traffic capacity after traffic light failure. The comparison results of mobile emergency resource scheduling time show little difference, verifying the ability of the method of this invention to characterize the vehicle driving process under congestion, and can provide decision support for mobile emergency resource scheduling under disaster. In summary, the method of this invention can accurately describe the two-way impact process of the power-traffic coupled system under disaster scenarios. In terms of simulation time, the average simulation time required by the method of this invention is only 4.27 minutes, which is about 8.6 times better than SUMO's 36.80 minutes. Therefore, the method of this invention can significantly shorten the calculation time while maintaining good accuracy, and has good practicality.

[0042] To analyze the efficiency of the co-simulation, six different simulation step sizes were set, and the simulation time and average relative error were calculated using the smallest step size as a reference. The results are shown in Table 5. When the step size increased from 10 seconds to 90 seconds, the computation time decreased from 4.67 minutes to 1.20 minutes. However, after the step size reached a certain value, the simulation time basically stabilized. As the step size increased, the simulation accuracy showed a downward trend, and the average relative error increased to 18.65%, indicating that increasing the step size may underestimate the impact of sudden changes in traffic flow and the propagation of queuing phenomena, thus affecting the calculation results of the travel time of mobile emergency resources and the power load recovery time. In summary, in small-scale examples, the impact of step size changes on the overall computational efficiency is relatively limited; however, the simulation accuracy decreases with increasing step size, weakening the model's ability to characterize sudden changes in traffic flow, queuing propagation, and cross-system feedback processes.

[0043] Table 5: Comparison of computational efficiency and average relative error under different time lengths

[0044] Therefore, this invention adopts the aforementioned time-event-driven joint simulation method for power-traffic coupled systems, using traffic lights and mobile emergency resources as key coupling elements. It establishes a dynamic mapping relationship between power supply status and traffic capacity, achieving accurate characterization of the bidirectional coupling impact of power and traffic. Differentiated simulation step sizes are used, and a time-driven and event-driven information interaction mechanism is employed to enable collaborative simulation of two heterogeneous systems within the same simulation framework, avoiding computational redundancy or information distortion caused by uniform step sizes. Balancing computational efficiency and simulation accuracy, an adaptive macro-micro hybrid mechanism is introduced, using a macro-cellular transmission model as the global framework and automatically switching to a micro-model with smaller step sizes for fine characterization in congested areas. Triggering three key events—initial disaster faults, power system faults, and changes in the status of mobile emergency resources—instantaneous cross-system information interaction is initiated, accurately capturing the nonlinear dynamic feedback process of the coupled system under disaster disturbances, providing effective technical support for disaster damage assessment and emergency decision-making in rainstorm disaster scenarios.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A time-event-driven co-simulation method for power-transportation coupled systems, characterized in that, Includes the following steps: S1. Construct a two-way coupled power-transportation system, including a power system model, a transportation system model, and coupling element models; S2. Build a three-module joint simulation framework, including a power system simulation module, a transportation system simulation module, and a coupling coordination module; S3. Based on the three-module joint simulation framework, a time-event dual-drive information interaction mechanism is set up, including two parts: a time-driven mechanism and an event-driven mechanism. The time-driven mechanism is used to realize the periodic state synchronization between the power system and the transportation system, and the event-driven mechanism is used for cross-system information updates caused by sudden state changes in disaster scenarios. S4. Based on the execution time-event dual-driven information interaction mechanism, perform joint simulation and output simulation results.

2. The time-event-driven co-simulation method for power-transportation coupled systems according to claim 1, characterized in that, The specific process of S1 is as follows: S11. Construct the power system model into a multi-level AC power flow model; S12. Improve the Cellular Transmission Model (CTM) to adapt it to disaster scenarios, and use the improved CTM as a traffic system model. S13. Construct a model of coupled components, including a traffic light model and a mobile emergency resource model.

3. The time-event-driven co-simulation method for power-transportation coupled systems according to claim 2, characterized in that, The specific process of S11 is as follows: S111. Through a multi-level AC power flow model, the power grid is described as a bus and branch topology result. A multi-voltage level power grid topology is built, the transformer mapping relationship between different voltage levels is established, and the calculation and core constraint equations are defined, including the bus total power equation of the multi-level urban power system, the node power balance relationship equation of the radial distribution system, the line power constraint equation, and the voltage calculation and constraint equation. The total bus power equation for a multi-level urban power system, considering the influence of transformer branches at different voltage levels, is expressed as follows: ； The power balance equation for the nodes in a radial power distribution system is as follows: ； ； The voltage calculation and constraint equations are as follows: ； ； ； The line power constraint equation is: ； ； ； In the formula, , , and All are busbar nodes; , All are voltage level indexes; and Voltage levels The set of busbars and the set of branches; Voltage level The set of busbars; Indicates that by node Pointing to node The side road; Voltage level Next node Complex power injection, and Indicates at voltage level Next, node and nodes The voltage vector; For voltage level Next, node Voltage phasors; Nodes within the same voltage level and Branch admittance between For connection voltage level and The equivalent admittance of the transformer branch; and branch road Active power and reactive power; and branch road Active power and reactive power; and For nodes Active and reactive loads; and For the unit to the node Injected active and reactive power; and For nodes and nodes The square of the voltage amplitude; Represents a node Voltage amplitude; and branch road Resistance and reactance; and These are the lower and upper limits of the node voltage amplitude; branch road Apparent power Set its transmission limit; and They are nodes The lower and upper limits of the active power output of the generating unit; and They are nodes The lower and upper limits of the reactive power output of the generator unit; S112, Define node power supply state variables By calculating AC power flow, it is determined whether the node voltage after a fault meets the operating constraints, and an output interface from the power system to the transportation system is established; if , then it represents a node Power supply is normal at the current moment; like , then it represents a node Power outage; when node When the equipment itself malfunctions, Change from 1 to 0; when node When the upstream power supply fails and there is no backup power supply, It becomes 0 because the voltage exceeds the limit.

4. The time-event-driven co-simulation method for power-transportation coupled systems according to claim 3, characterized in that, The specific process of S12 is as follows: S121. Introduce a speed reduction factor to characterize the impact of rainstorms on traffic capacity, and improve the fixed free-flow speed to the actual speed affected by disasters and real-time road conditions. The expression is: ； In the formula, Represents a traffic cell index; Represents cell At any moment The actual speed; Represents cell At any moment density, ; Indicates blockage density; Indicates the depth of water accumulation. Indicates time Based on water depth A determined speed reduction factor, ; Indicates the free-flow velocity; S122, Design an adaptive macro-micro hybridization mechanism: when the cell The density exceeds the blockage density At this point, the cell switches from a macroscopic model to a local microscopic model, and the intelligent driver model (IDM) is used to characterize the vehicle. The car-following behavior is observed, and the simulation step size is switched to a locally refined microstep size. Microscopic model output cell outflow and replace the outflow of the macro model. The expression is: ； ； ； In the formula, Indicates vehicle index; Indicates vehicle The actual acceleration; Indicates the maximum acceleration; Indicates the acceleration index; Indicates vehicle The actual distance from the vehicle in front; Indicates the desired spacing; Indicates the minimum static distance between vehicles; Indicates the expected time interval; Indicates comfortable deceleration; and Representing vehicles and the speed of the vehicle in front; This represents the number of vehicles that cross the downstream boundary of a cell within a macroscopic step size; Represents cell At any moment Micro-outflow; Indicates the simulation step size of the traffic system; S123. Establish signalized intersection control logic that considers the impact of power supply, distinguish different vehicle flows at each intersection entrance cell, including left turn, straight ahead, right turn, and U-turn, represent the flow distribution for each turn using a turning ratio coefficient, define the power supply status factor and turning permission factor, and calculate the effective transfer flow controlled by the traffic lights, expressed as: ； ； ； ； In the formula, , and They represent the upstream cells respectively. Downstream cells The ratio of right turns, straight driving, and left turns / U-turns should be equal to 1. Indicates effective transfer of traffic; The transfer permission factor is determined jointly by the power supply status and phase permission. and They represent cells respectively Transmission capability and cells Receiving capability; Indicates the signalized intersection index. and They represent the intersections. Upstream and downstream cell sets; , ; Indicates upstream cell At any moment Traffic flow that can be diverted; Indicates that it is composed of upstream cells Downstream cells Steering distribution ratio coefficient; If the signal lights are powered normally , According to the preset signal cycle, if the power supply is interrupted... , The emergency rules are dynamically adjusted, thus affecting road capacity; the expression for the transfer permit factor is: ； In the formula, Indicates phase Permitted driving directions Indicates upstream cell Corresponding driving direction; Indicates that the vehicle originates from the cell. Transfer to cell Steering type, Indicates phase The set of allowed steering types.

5. The time-event-driven co-simulation method for power-transportation coupled systems according to claim 4, characterized in that, The specific process of S13 is as follows: S131. If a traffic light experiences a power outage, the current phase is frozen, and the transmission flow of the controlled upstream cell at the intersection decreases proportionally, as expressed by: ； ； In the formula, Indicating a power outage at a traffic light, the cell... exist The actual effective transmission volume / passage capacity at any given moment; Represents cell Sending capacity; This represents the reduction factor, reflecting the decrease in effective traffic capacity caused by vehicle congestion; S132. Construct a mobile emergency resource model: Model resources such as mobile power generation vehicles, emergency repair vehicles, and drainage vehicles as task vehicles constrained by traffic flow status, establish a mapping relationship between resource types and fault types, and construct an operation duration matrix. The expression for calculating the relative position of mobile emergency resources during a signal cycle is: ； The expression for calculating the waiting time of mobile emergency resources at traffic lights is: ； The expression for calculating the travel time of mobile emergency resources along the path is: ； The expression for calculating the actual arrival time of mobile emergency resources at the fault point is: ； The expression for the constraint service matching relationship is: ； ； The expression for calculating the job duration required to handle different types of faults for different types of resources is: ； In the formula, Indicates time Mobile emergency resources Relative position within a signal cycle; Indicates being in a cell The moment; Represents cell The traffic light cycle; G c Indicates the duration of the green light; Indicates the duration of the red light. ; Representing resources Waiting time caused by traffic lights; Indicates the current cell phase; Representing resources exist Departure time and arrival location Total travel time; Representing resources Arrival Location The path; Indicates the next cell on the path; Indicates cell length; Representing resources Actual arrival location The moment; Indicates the start and departure times; Indicates service matching relationship; Represents the set of fault points; Indicates the fault point index; Indicates the time of the next failure point for the same resource service; Representing resources In position The duration of the assignment; Represents the task duration matrix. The elements are , The type is resources Handling fault types Required processing time; Representing resources Type; Indicates position The type of fault.

6. The time-event-driven co-simulation method for power-transportation coupled systems according to claim 5, characterized in that, The specific process in S2 is as follows: S21. Construct a power system simulation module to simulate the dynamic response process of the power system under rainstorm disaster. The input is the power grid topology parameters, load data and fault scenario sequence, and the output is the voltage amplitude of each node, the power flow distribution of the branch and the power supply status of the node. S22. Construct a traffic system simulation module to simulate the dynamic evolution of traffic flow under rainstorm disasters. The inputs are road network topology parameters, traffic demand data, traffic light timing schemes and time-varying water depth sequences. The outputs are traffic flow density, average speed and congestion rate of each road segment. S23. Construct a coupling coordination module to connect the power system and the transportation system.

7. The time-event-driven co-simulation method for power-transportation coupled systems according to claim 6, characterized in that, The specific process in S3 is as follows: S31. Let the total simulation time be... The power system simulation step size is The simulation step size for the traffic system is ,and , Represents the coefficient. It is a positive integer; S32. Establish a time-driven mechanism, specifying the simulation step size for each power system. At the end, a periodic state interaction is triggered; S33. Establish an event-driven mechanism: Define three types of cross-system interaction events to trigger information interaction in real time.

8. The time-event-driven co-simulation method for power-transportation coupled systems according to claim 7, characterized in that, The specific details of the time-driven mechanism in S32 are as follows: S321. The power system simulation module performs power flow calculations, updates the power supply state variables of each node, detects newly faulty nodes, and forms a fault set. ; S322, The coupling coordination module uses the mapping matrix between traffic lights and power supply nodes. In addition to the power supply status variables of each node, a signal light state vector is formed. ; S323, Set up faults and traffic light state vector Passed to the traffic system simulation module; S324, The traffic system simulation module, based on the received fault set... and traffic light state vector Update the power supply status variables in the intersection control logic; S325, the traffic system simulation module feeds back current traffic status information, including the congestion rate of each road segment, to the coupling coordination module. and average speed ; S326, the coupling and coordination module transmits traffic status information to the power system simulation module to update the travel time estimates of mobile emergency resources.

9. The time-event-driven co-simulation method for power-transportation coupled systems according to claim 8, characterized in that, The three types of cross-system interaction events in S33 are: initial disaster fault, power system fault, and change in the status of mobile emergency resources. The three types of cross-system interaction events are: initial disaster fault, power system fault, and change in the status of mobile emergency resources; Initial disaster failure: When the simulation reaches the moment the disaster occurs, the initial disaster failure is triggered. The power system simulation module updates the fault set by including the power supply state variables of the affected nodes. Set to 0; the traffic system simulation module adjusts the traffic capacity according to the degree of road damage; Power system fault: After the power system simulation module updates its status, if a node fault is detected... If the value changes from 1 to 0, a new fault is determined to have occurred. The power system simulation module sends fault node information to the traffic system simulation module, and the traffic system simulation module returns the current speed of each road segment. Mobile emergency resource status change: The power system simulation module completes resource scheduling decisions based on fault information and current traffic conditions, and transmits the resource type, starting point, and target fault point to the traffic system simulation module; the traffic system simulation module incorporates the vehicle with this resource into the traffic flow simulation and calculates the travel time. After the resource vehicle arrives at the target fault point and completes the repair or emergency power supply operation, the traffic system simulation module transmits the operation completion signal to the power system simulation module; the power system simulation module updates the power supply status variable of the corresponding node from 0 to 1, and accordingly updates the node power supply status, traffic light status and traffic flow status, completing the power-traffic coupling system status update.

10. The time-event-driven co-simulation method for power-transportation coupled systems according to claim 9, characterized in that, The specific process of S4 is as follows: S41. Joint Simulation Initialization: Obtain the topology results and parameter information of the power grid at multiple voltage levels, and establish the power supply status set of each node; import the road traffic network structure and attribute information, and initialize the traffic density, speed, and flow status of each cell; set a unified simulation starting point and specify the power system simulation step size. Traffic system simulation step size and local micro-refinement step size , ; S42. Determine whether there are new node failures or new road sections blocked based on the disaster scenario: If a certain power equipment is at time... When a fault occurs, the status of the corresponding node or branch changes, the local power supply capacity decreases, and the corresponding node or branch is judged to have failed, and the power system fault node information is output. If the water accumulation on a certain road segment exceeds the allowable threshold, the traffic capacity is dynamically reduced, and the road segment is judged as obstructed. The road location information of the reduced traffic capacity is output. At any given time, the input disaster scenario is represented as: ； In the formula, Indicates time The disaster scenario state vector; Represents the set of water depths; Represents the set of node states; Represents the set of road capacity; S43. Perform power flow calculations and run CTM simulation; The power system simulation module is based on step size Perform periodic updates, execute power flow calculations, and determine the power grid operating status, node power supply capacity, and load shedding distribution; Based on the current cell density, upstream transmission capacity, and downstream reception capacity, the traffic system simulation module calculates vehicle flow by improving the cell transmission model and updates cell density, speed, and queuing status. For normally passable road sections, vehicles run at the free-flow speed after disaster correction. For road sections affected by water accumulation, vehicle speed is dynamically reduced according to the water depth. For completely impassable road sections, cell outflow is limited to zero, leading to upstream vehicle accumulation and congestion propagation; S44. Information interaction is based on a time-event dual-driven information interaction mechanism; S45. After triggering the mobile emergency resource dispatch request, the traffic system simulation module calculates the travel time of the mobile emergency resources and generates the estimated arrival time. S46. After the mobile emergency resources arrive, the operation is completed according to the operation duration matrix. After the operation is completed, the traffic system simulation module sends an operation completion signal to the power system simulation module, and the power system simulation module updates the status of the faulty equipment and the power supply status of the node. S47. Determine whether the co-simulation has reached the total simulation time. ; If not reached, then execute S42-S46 in a loop; If the target is reached, the simulation ends, and the node voltage and power supply status, load loss, road segment speed, density, flow rate, traffic capacity, traffic light status, arrival time of mobile emergency resources, and operation completion time are output at each time point.

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