Electric transportation coupling and collaborative scheduling method and terminal considering traffic network topology

Abstract

The present invention relates to the technical field of power grid control, and discloses an electric-traffic coupled collaborative scheduling method and a terminal considering the traffic network topology. The method constructs a traffic system scheduling model based on the current road topology information and road traffic flow of the traffic system, and constructs a power system scheduling model based on the power generation information of the power system. The electric-traffic coupled system scheduling model constructed by the traffic system scheduling model and the power system scheduling model is used to solve the operating cost, so as to obtain the target road topology information and power generation data under the optimal operating cost, thereby generating a collaborative scheduling scheme. Compared with the isolated scheduling process of the power grid and the traffic network, the present invention makes full use of the flexibility of the traffic network topology, realizes the redistribution of traffic flow by adjusting the road topology information of the traffic system for topology reconstruction, thereby guiding the charging behavior of electric vehicles, and reducing the power generation cost of the power grid and the travel cost of the traffic network at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid control, and particularly to an electric-traffic coupled collaborative scheduling method and terminal considering the topology of the traffic network. Background Art

[0002] The global fossil energy crisis has drawn increasing attention. The widespread use of electric vehicles is still accelerating, and the rapid growth of the electric vehicle ownership rate has made the connection between the power grid and the urban traffic network closer. Therefore, the collaborative scheduling of the power grid and the traffic network to achieve the optimal operation of the coupled system is crucial.

[0003] Currently, the power dispatching of the power grid and the traffic flow dispatching of the traffic network are usually processed separately. In the face of the scenario where a large number of electric vehicles are connected to the power grid for charging, this separated dispatching mode is likely to lead to overloading of power lines and traffic congestion on roads. On the one hand, as an important load of the power system, electric vehicles may uncontrollably choose the same charging stations when charging their batteries during peak hours, resulting in over-limit power feeding of power lines and having an adverse impact on the safe and economic operation of the power system. On the other hand, a large number of electric vehicles may queue near the same charging station at the same time, which not only exacerbates the burden on the power system but also easily causes traffic jams near the charging station. Therefore, electric vehicles play a crucial role in coupling the two systems. By reasonably guiding the charging behavior of electric vehicles, it is expected to significantly reduce the congestion and costs of both the power grid and the traffic network.

[0004] Although there have been relevant studies dedicated to solving the problem of electric-traffic coupled collaborative scheduling and exploring the flexibility of electric vehicles and charging stations in the traffic network, these studies usually ignore the flexibility of the traffic network topology. In existing studies, the traffic network topology is often regarded as a fixed factor. In fact, through strategies such as roadblock setting, personnel guidance, and signal light control, the topology of the traffic network can be reconstructed. Therefore, in the problem of electric-traffic coupled collaborative scheduling, the use of traffic network topology reconstruction to improve the performance of the overall system has not been fully considered. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an electric-traffic coupled collaborative scheduling method and terminal considering the topology of the traffic network, which realizes the redistribution of traffic flow through the topology reconstruction of the traffic network, thereby guiding the charging behavior of electric vehicles and reducing the power generation cost of the power grid and the travel cost of the traffic network.

[0006] To solve the above technical problem, a technical solution adopted by the present invention is:

[0007] An electric-traffic coupled collaborative scheduling method considering the topology of the traffic network, comprising:

[0008] Obtain the current road topology information and road traffic flow of the traffic system in the electric transportation coupling system, and construct a traffic system scheduling model according to the current road topology information and the road traffic flow;

[0009] Obtain the power generation information of each generator set in the power system of the electric transportation coupling system, and construct a power system scheduling model according to the power generation information;

[0010] Construct an electric transportation coupling system scheduling model with the minimum operating cost as the optimization goal according to the traffic system scheduling model and the power system scheduling model;

[0011] Calculate the traffic system scheduling model and the power system scheduling model respectively based on the optimal operating cost obtained by solving the electric transportation coupling system scheduling model, obtain the reconstructed target road topology information and power generation data, and generate a coordinated scheduling plan according to the target road topology information and the power generation data.

[0012] To solve the above technical problems, another technical solution adopted by the present invention is:

[0013] An electric transportation coupling coordinated scheduling terminal considering the traffic network topology includes a memory, a processor, and a computer program stored on the memory and running on the processor. When the processor executes the computer program, each step in the above-mentioned electric transportation coupling coordinated scheduling method considering the traffic network topology is implemented.

[0014] The beneficial effects of the present invention are as follows: A traffic system scheduling model is constructed based on the current road topology information and road traffic flow of the traffic system, and a power system scheduling model is constructed based on the power generation information of the power system. The electric transportation coupling system scheduling model constructed by the traffic system scheduling model and the power system scheduling model is used to solve the operating cost to obtain the target road topology information and power generation data under the optimal operating cost, so as to generate a coordinated scheduling plan. Compared with the isolated scheduling of the power grid and the traffic network, the present invention makes full use of the flexibility of the traffic network topology structure, realizes the redistribution of traffic flow by adjusting the road topology information of the traffic system for topology reconstruction, thereby guiding the charging behavior of electric vehicles, and at the same time reducing the power generation cost of the power grid and the travel cost of the traffic network. Description of the Drawings

[0015] Figure 1 It is a step flow chart of an electric transportation coupling coordinated scheduling method considering the traffic network topology provided by an embodiment of the present invention;

[0016] Figure 2 It is a structural schematic diagram of an electric transportation coupling coordinated scheduling terminal considering the traffic network topology provided by an embodiment of the present invention;

[0017] Label Description:

[0018] 100. An electric-traffic coupled collaborative scheduling terminal considering the topology of the transportation network; 101. A memory; 102. A processor. Specific implementation manner

[0019] To describe the technical content, achieved objectives and effects of the present invention in detail, the following is described in conjunction with the implementation manners and accompanied by the drawings.

[0020] An embodiment of the present invention provides an electric-traffic coupled collaborative scheduling method considering the topology of the transportation network, including:

[0021] Obtain the current road topology information and road traffic flow of the transportation system in the electric-traffic coupled system, and construct a transportation system scheduling model according to the current road topology information and the road traffic flow;

[0022] Obtain the power generation information of each generator set in the power system of the electric-traffic coupled system, and construct a power system scheduling model according to the power generation information;

[0023] Construct an electric-traffic coupled system scheduling model with the minimum operation cost as the optimization objective according to the transportation system scheduling model and the power system scheduling model;

[0024] Based on the optimal operation cost obtained by solving the electric-traffic coupled system scheduling model, calculate the transportation system scheduling model and the power system scheduling model respectively to obtain the reconstructed target road topology information and power generation data, and generate a collaborative scheduling plan according to the target road topology information and the power generation data.

[0025] As can be seen from the above description, the beneficial effects of the present invention are as follows: A transportation system scheduling model is constructed based on the current road topology information and road traffic flow of the transportation system, and at the same time, a power system scheduling model is constructed based on the power generation information of the power system. The electric-traffic coupled system scheduling model constructed by the transportation system scheduling model and the power system scheduling model is used to solve the operation cost, so as to obtain the target road topology information and power generation data under the optimal operation cost, and then generate a collaborative scheduling plan. Compared with the isolated scheduling of the power grid and the transportation network, the present invention makes full use of the flexibility of the topology structure of the transportation network, realizes the redistribution of traffic flow by adjusting the road topology information of the transportation system for topology reconstruction, thereby guiding the charging behavior of electric vehicles, and at the same time reducing the power generation cost of the power grid and the travel cost of the transportation network.

[0026] Further, constructing a transportation system scheduling model according to the current road topology information and the road traffic flow includes:

[0027] Construct a lane topology reconstruction model according to the current road topology information;

[0028] Construct a traffic system scheduling model based on the road traffic flow and the lane topology reconstruction model.

[0029] As can be seen from the above description, through strategies such as roadblock setting, personnel guidance, and signal light control, the topological structure of the road can be adjusted, thereby realizing the expansion and reduction of the road traffic flow. Combining the road traffic flow demand of the current traffic system and the lane topology reconstruction model to construct a traffic system scheduling model effectively utilizes the flexibility of the road topological structure.

[0030] Furthermore, the current road topological information includes the original road capacity, the current road direction, and the current road number;

[0031] Constructing a lane topology reconstruction model based on the current road topological information includes:

[0032] Construct a road reverse model that adjusts the original road capacity to the target road capacity based on the current road direction and the current road number;

[0033] Determine the road direction constraint conditions and road number constraint conditions for the traffic system to adjust from the original road capacity to the target road capacity.

[0034] As can be seen from the above description, constructing a road reverse model increases the road capacity in the driving direction with more traffic flow, enabling vehicles to pass more quickly, reducing the driving time cost of vehicles, thereby guiding electric vehicles to charging stations that are farther away but have a surplus power load for charging. While avoiding road congestion, the burden on the power system is reduced.

[0035] Furthermore, constructing a road reverse model that adjusts the original road capacity to the target road capacity based on the current road direction and the current road number includes:

[0036] ;

[0037] Among them, represents the target road capacity after the adjustment of road a , represents the original road capacity before the adjustment of road a , represents the target road capacity after the adjustment of the road in the opposite direction to a , represents the original road capacity before the adjustment of the road in the opposite direction to a , and respectively represent the number of roads a undergoing direction reversal in two opposite directions, and A represents the set of roads;

[0038] The road direction constraint conditions and road quantity constraint conditions for determining that the traffic system is adjusted from the original road capacity to the target road capacity include:

[0039] ;

[0040] ;

[0041] Among them, and are binary variables representing different directions of road a , represents the total number of roads.

[0042] As can be seen from the above description, by flexibly adjusting the driving direction of the road, the road capacity is increased, enabling vehicles to pass more quickly. In addition, during the process of adjusting the road direction, the road direction constraints and road quantity constraints are satisfied to avoid affecting normal traffic and ensure the normal operation of the traffic system.

[0043] Furthermore, constructing a traffic system scheduling model according to the road traffic flow and the lane topology reconstruction model includes:

[0044] Determining the driving time cost of the road according to the road traffic flow, and setting the objective function of the traffic system scheduling model according to the road traffic flow and the driving time cost:

[0045] ;

[0046] ;

[0047] Among them, represents the traffic flow of road a , represents the driving time cost of road a when the traffic flow is , A represents the set of roads, represents the economic value of time, represents the driving time of free flow, represents the adjusted target road capacity of road a .

[0048] As can be seen from the above description, the traffic system scheduling model aims to minimize the total time cost of all vehicle driving, which can ensure that electric vehicles drive through the current section in the shortest time, reduce unnecessary power consumption, and thus guide electric vehicles to charging stations with a longer distance but a surplus of power load to reduce the load of the charging stations.

[0049] Further, constructing a traffic system scheduling model based on the road traffic flow and the lane topology reconstruction model further includes:

[0050] Determine the constraint conditions of the road traffic flow based on path allocation in the traffic system.

[0051] As can be seen from the above description, setting the constraint conditions of the road traffic flow based on path allocation can guide vehicles to choose paths more reasonably, thereby alleviating traffic congestion. At the same time, it can effectively optimize the road topology structure and improve the throughput capacity of traffic flow.

[0052] Further, the power generation information includes the power generation power and power generation cost of each generator set;

[0053] Constructing a power system scheduling model based on the power generation information includes:

[0054] Construct a power system scheduling model with the minimum power generation cost as the objective function based on the power generation power of each generator set;

[0055] Determine the constraint conditions of the upper and lower power limits in the power system, the constraint conditions of the energy storage power, and the constraint conditions of the active power balance of the nodes including electric vehicle charging stations.

[0056] As can be seen from the above description, setting the upper and lower power limit constraint conditions can ensure the safe operation of the power grid. Setting the energy storage power constraint conditions can ensure the charge and discharge balance of the energy storage nodes. As the coupling node of the power system and the traffic system, setting the active power balance constraint conditions of the nodes including electric vehicle charging stations can fully consider the coupling interaction between the power system and the traffic system, that is, consider the impact of the charging demand of electric vehicles on the power system, and ensure the stable operation of the power system. Through the three constraint conditions, the reliability of the power system operation can be effectively guaranteed.

[0057] Further, constructing an electric-traffic coupling system scheduling model based on the traffic system scheduling model and the power system scheduling model includes:

[0058] Set the coupling objective function of the electric-traffic coupling system scheduling model based on the objective function of the traffic system scheduling model, the objective function of the power system scheduling model, and the lane topology reconstruction cost:

[0059] ;

[0060] represents the coupling objective function, represents the objective function of the traffic system scheduling model ， represents the objective function of the power system scheduling model, represents the lane topology reconstruction cost;

[0061] Among them, represents the i th generator set's power generation cost when the power generation power is . represents the i th generator set's t time period's power generation power. S represents the set of generator sets, represents the traffic flow of road a, represents road a 's travel time cost when the traffic flow is . A represents the set of roads, and are binary variables, representing different directions of road a . represents the unit cost of changing the lane direction.

[0062] As can be seen from the above description, the electric-traffic coupling system scheduling model aims to minimize the sum of the vehicle travel time cost, the power grid power generation cost, and the road topology reconstruction cost. It can consider the vehicle travel time cost and the traffic topology reconstruction cost while minimizing the power generation cost, achieving the collaborative optimization of the electric and traffic systems.

[0063] Furthermore, based on the optimal operating cost obtained by solving the electric-traffic coupling system scheduling model, the traffic system scheduling model and the power system scheduling model are calculated respectively, and the reconstructed target road topology information and power generation data obtained include:

[0064] Based on the optimal operating cost, the traffic system scheduling model and the power system scheduling model are calculated respectively to obtain the target road traffic flow and power generation data;

[0065] Substitute the target road traffic flow into the lane topology reconstruction model for solution calculation to obtain the target road topology information.

[0066] As can be seen from the above description, the power generation data corresponding to the power system and the target road traffic flow corresponding to the traffic system are determined when determining the optimal operating cost. Thus, based on the target road traffic flow corresponding to the traffic system, the final lane topology structure is determined, and the traffic network topology is reconstructed based on the final lane topology structure to increase the road capacity to the required target road traffic flow, improving the comprehensive benefits of the electric-traffic coupling system.

[0067] Another embodiment of the present invention provides an electric-traffic coupled collaborative scheduling terminal considering the traffic network topology, including a memory, a processor, and a computer program stored on the memory and running on the processor. When the processor executes the computer program, each step in the above-mentioned electric-traffic coupled collaborative scheduling method considering the traffic network topology is implemented.

[0068] As can be seen from the above description, the beneficial effects of the present invention are as follows: Based on the current road topology information and road traffic flow of the traffic system, a traffic system scheduling model is constructed, and at the same time, a power system scheduling model is constructed based on the power generation information of the power system. The electric-traffic coupled system scheduling model constructed by the traffic system scheduling model and the power system scheduling model is used to solve the operating cost, so as to obtain the target road topology information and power generation data under the optimal operating cost, and then generate a collaborative scheduling plan. Compared with the isolated scheduling of the power grid and the traffic network, the present invention makes full use of the flexibility of the traffic network topology structure, realizes the redistribution of traffic flow by adjusting the road topology information of the traffic system for topology reconstruction, thereby guiding the charging behavior of electric vehicles, and at the same time reducing the power generation cost of the power grid and the travel cost of the traffic network.

[0069] The above-mentioned electric-traffic coupled collaborative scheduling method and terminal of the present invention can be applied to the operation and control scenarios of power grids with multiple energy forms, which will be described below through specific embodiments:

[0070] Please refer to Figure 1 , Embodiment 1 of the present invention is:

[0071] An electric-traffic coupled collaborative scheduling method considering the traffic network topology includes:

[0072] S1. Obtain the current road topology information and road traffic flow of the traffic system in the electric-traffic coupled system, and construct a traffic system scheduling model according to the current road topology information and the road traffic flow.

[0073] Specifically, constructing a traffic system scheduling model according to the current road topology information and the road traffic flow in step S1 includes:

[0074] S11. Construct a lane topology reconstruction model according to the current road topology information.

[0075] Among them, the current road topology information includes the original road capacity, the current road direction, and the current road number. Step S11 includes:

[0076] S111. Construct a road reverse model for adjusting the original road capacity to the target road capacity based on the current road direction and the current road number.

[0077] Specifically, step S111 includes:

[0078] ;

[0079] Among them, represents the target road capacity after adjustment, a and represents the original road capacity before adjustment, a where represents the road in the opposite direction of and a represents the target road capacity after adjustment, where represents the road in the opposite direction of a and represents the original road capacity before adjustment, and a respectively represent the number of roads with direction reversal in two opposite directions, and A represents the set of roads.

[0080] Among them, in step S111 and The determination method is specifically as follows:

[0081] ;

[0082] Among them, and are binary variables representing different directions of the road a , represents the number of lanes with variable directions. If is 1, the directions of all lanes in the road are changed. If is 0.6, the directions of (0.6 * total number of lanes) lanes in the road are changed. In this embodiment, is 0.5.

[0083] S112. Determine the road direction constraint conditions and road quantity constraint conditions for adjusting the traffic system from the original road capacity to the target road capacity.

[0084] Specifically, step S112 includes:

[0085] ;

[0086] ;

[0087] Among them, and are binary variables representing different directions of the road a , represents the total number of roads.

[0088] S12. Construct a traffic system scheduling model based on the road traffic flow and the lane topology reconstruction model.

[0089] Specifically, step S12 includes:

[0090] S121. Determine the travel time cost of the road according to the road traffic flow, and set the objective function of the traffic system scheduling model according to the road traffic flow and the travel time cost:

[0091] ;

[0092] ;

[0093] Among them, represents the traffic flow of road a , represents the travel time cost of road a when the traffic flow is , A represents the set of roads, represents the economic value of time, represents the travel time of free flow, represents the adjusted target road capacity of road a .

[0094] S122. Determine the constraint conditions of the road traffic flow in the traffic system based on path allocation.

[0095] Specifically, step S122 includes:

[0096] Constraint condition 1: The sum of the traffic flows of all paths between any one O (Origin, starting point)-D (Destination, ending point) pair is equal to the total travel demand of the O-D pair, specifically expressed as:

[0097] ;

[0098] Among them, represents the traffic flow on path i in O-D pair r , among which, , represents the travel demand of electric vehicles in O-D pair i , I represents the set of O-D pairs, represents the path set of O-D pair i .

[0099] Constraint condition 2: The road traffic flow is equal to the total traffic flow passing through this section on all paths between O-D pairs, specifically expressed as:

[0100] A;

[0101] Among them, represents a road section a whether it belongs to a path i , if = 1, the road section a belongs to the path i , if = 0, the road section a belongs to other paths.

[0102] S2. Obtain the power generation information of each generator set in the power system of the electric transportation coupling system, and construct a power system dispatch model according to the power generation information.

[0103] Among them, the power generation information includes the power generation power and power generation cost of each generator set.

[0104] Specifically, constructing a power system dispatch model according to the power generation information in step S2 includes:

[0105] S21. Construct a power system dispatch model with the minimum power generation cost as the objective function according to the power generation power of each generator set.

[0106] Among them, step S21 is specifically: , ) + ), represents the i th generator set at t time period, and respectively represent the cost first term coefficient and cost second term coefficient of the i th generator set.

[0107] S22. Determine the constraint conditions of the power upper and lower limits in the power system, the constraint conditions of the electric energy storage power, and the constraint conditions of the active power balance of the nodes including the electric vehicle charging stations.

[0108] Specifically, the constraint conditions of the power upper and lower limits are:

[0109] ;

[0110] ;

[0111] Among them, represents the power of line ij , represents the power upper limit of line ij , H represents the line set, represents the i th generator set att The generated power during a time period represents the lower limit of the output power of the generating unit at node i and represents the upper limit of the output power of the generating unit at node i .

[0112] Specifically, the constraint conditions for the electric energy storage power are as follows:

[0113] ;

[0114] , ;

[0115] where represents the charging power of the electric energy storage, represents the discharging power of the electric energy storage, represents the charging power at node i, S char represents the set of nodes connected to the charging station in the power grid.

[0116] The constraint conditions for the active power balance of the nodes containing electric vehicle charging stations are specifically as follows: First, determine the active power balance constraints of the power system, then determine the power balance constraints of the charging station, and finally substitute the power balance constraints of the charging station into the active power balance constraints of the power system to obtain the constraint conditions for the active power balance of the nodes containing electric vehicle charging stations. Specifically, the active power balance constraints of the power system are as follows:

[0117] ;

[0118] ;

[0119] where S represents the set of generating units, H represents the set of lines, represents the i th generating unit t during the time period, i represents the total load power at node represents the power of line ij , represents the charging power at node i , and respectively represent the phase angles at node i and node j during the t time period, and = 0, represents the susceptance of line ij .

[0120] Specifically, the power balance constraint of the charging station is as follows:

[0121] , ;

[0122] , , ;

[0123] Among them, represents the total load power of node i , represents the charging power of node i , represents the charging power of each electric vehicle, represents the original load power of node i , S char represents the set of nodes connected to the charging station in the power grid, A char represents the set of roads with charging stations in the transportation network.

[0124] In summary, the constraint condition for the active power balance of the node containing the electric vehicle charging station is:

[0125] , .

[0126] S3. Construct an electric-traffic coupled system scheduling model with the minimum operating cost as the optimization objective according to the traffic system scheduling model and the power system scheduling model.

[0127] Specifically, step S3 includes:

[0128] S31. Set the coupled objective function of the electric-traffic coupled system scheduling model based on the objective function of the traffic system scheduling model, the objective function of the power system scheduling model, and the lane topology reconstruction cost:

[0129] ;

[0130] represents the coupled objective function, represents the objective function of the traffic system scheduling model ， represents the objective function of the power system scheduling model, represents the lane topology reconstruction cost;

[0131] Among them, represents the i th generator set at the power generation cost when the power generation power is , represents thei The power generation power of a generator set during t the period, S represents the set of generator sets, represents the road a traffic flow, represents the road a when the traffic flow is the driving time cost at this time, A represents the set of roads, and are binary variables, representing the road a in different directions, represents the cost unit price for changing the lane direction.

[0132] S4. Calculate the traffic system scheduling model and the power system scheduling model respectively based on the optimal operating cost obtained by solving the electric traffic coupling system scheduling model, obtain the reconstructed target road topology information and power generation data, and generate a coordinated scheduling plan according to the target road topology information and the power generation data.

[0133] Specifically, step S4 includes:

[0134] S41. Calculate the target road traffic flow and power generation data respectively based on the optimal operating cost for the traffic system scheduling model and the power system scheduling model;

[0135] S42. Substitute the target road traffic flow into the lane topology reconstruction model for solution calculation to obtain the target road topology information.

[0136] Please refer to Figure 2 For the second embodiment of the present invention:

[0137] An electric traffic coupling coordinated scheduling terminal 100 considering the traffic network topology includes a memory 101, a processor 102, and a computer program stored on the memory 101 and running on the processor 102. When the processor executes the computer program, it realizes each step in the electric traffic coupling coordinated scheduling method considering the traffic network topology in Embodiment 1.

[0138] In summary, the present invention provides an electric-traffic coupled collaborative scheduling method and terminal considering the traffic network topology. By obtaining the current road topology information and road traffic flow of the traffic system to construct a traffic system scheduling model, the flexibility of the traffic network topology structure is fully utilized, and the road topology information can be adjusted according to the actual situation to realize the redistribution of traffic flow, which helps to guide the more efficient driving of vehicles such as electric vehicles and reduce unnecessary power consumption and road congestion. At the same time, the power generation information of each generator set in the power system is obtained, and based on this, a power system scheduling model is constructed, fully considering constraints such as power upper and lower limits, electric energy storage power, and active power balance of nodes including electric vehicle charging stations to ensure the safe and stable operation of the power system, and also fully considering the impact of electric vehicle charging demand on the power system. In addition, the present invention combines the traffic system scheduling model and the power system scheduling model to construct an electric-traffic coupled system scheduling model, comprehensively considering the interaction effects between the traffic system and the power system, realizing the collaborative optimization between the two systems, which can not only improve the utilization efficiency of roads and power facilities, reduce the operation cost, but also enhance the convenience of traffic travel and the stability of the power system.

[0139] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in the related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A method for coordinated dispatching of electric and traffic coupling taking into account traffic network topology, characterized in that: include: Acquire current road topology information and road traffic flow of a traffic system in the electric-traffic coupling system, and construct a traffic system scheduling model according to the current road topology information and the road traffic flow; Acquire power generation information of each generator set in the power system of the power-transport coupling system, and construct a power system dispatching model according to the power generation information; According to the traffic system dispatching model and the power system dispatching model, a dispatching model of the electric-traffic coupling system is constructed with minimization of operating cost as the optimization goal; Based on the optimal operating cost obtained by solving the electric-traffic coupling system scheduling model, the traffic system scheduling model and the electric power system scheduling model are calculated respectively to obtain the reconstructed target road topology information and power generation data, and a coordinated scheduling plan is generated according to the target road topology information and the power generation data; Constructing a traffic system scheduling model according to the current road topology information and the road traffic flow includes: Constructing a lane topology reconstruction model according to the current road topology information; Constructing a traffic system scheduling model according to the road traffic volume and the lane topology reconstruction model; Constructing a traffic system scheduling model according to the road traffic flow and the lane topology reconstruction model includes: Determine the travel time cost of the road according to the road traffic flow, and set the objective function of the traffic system scheduling model according to the road traffic flow and the travel time cost: ； ； in, Indicates the road a of traffic volume, Indicates the road a In traffic volume The travel time cost is , T represents the travel time, A represents the road set, Represents the economic value of time, Indicates the road a The travel time when the free flow is 0, Indicates the road a Adjusted target road capacity; Constructing the electric-traffic coupling system dispatch model according to the traffic system dispatch model and the electric power system dispatch model includes: The coupling objective function of the electric-traffic coupling system scheduling model is set based on the objective function of the traffic system scheduling model, the objective function of the electric power system scheduling model and the lane topology reconstruction cost: ； represents the coupling objective function, Represents the objective function of the traffic system scheduling model ， represents the objective function of the power system dispatch model, represents the lane topology reconstruction cost; in, Indicates i The generating capacity of the generator set is The cost of electricity generation at Indicates i Generator sets in t The power generation of the time period, S represents the set of generator sets, Indicates the road a of traffic volume, Indicates the road a In traffic volume The travel time cost is , A represents the road set, and is a binary variable, indicating the road a different directions, Indicates the unit cost of changing lane direction.

2. The method according to claim 1, characterized in that The current road topology information includes original road capacity, current road direction and current road quantity; Constructing a lane topology reconstruction model according to the current road topology information includes: Constructing a road reverse model in which the original road capacity is adjusted to the target road capacity based on the current road direction and the current road quantity; The road direction constraint condition and the road quantity constraint condition for adjusting the traffic system from the original road capacity to the target road capacity are determined.

3. The method according to claim 2, characterized in that Constructing a road reverse model in which the original road capacity is adjusted to the target road capacity based on the current road direction and the current road quantity includes: ； in, Indicates the road a Adjusted target road capacity, Indicates the road a The original road capacity before adjustment, Representation and Road in opposite direction a Adjusted target road capacity, Representation and Road in opposite direction a The original road capacity before adjustment, and Represents two roads in opposite directions a The number of roads that have their directions reversed, A represents the road set; Determining the road direction constraint condition and the road quantity constraint condition for adjusting the traffic system from the original road capacity to the target road capacity includes: ； ； in, and is a binary variable, indicating the road a different directions, Indicates the total number of roads.

4. The method according to claim 1, characterized in that Constructing a traffic system scheduling model according to the road traffic volume and the lane topology reconstruction model also includes: Determine the constraint conditions of the road traffic flow in the transportation system based on the path allocation.

5. The method according to claim 1, characterized in that The power generation information includes the power generation power and power generation cost of each generator set; Constructing a power system dispatch model according to the power generation information includes: According to the power generation power of each generator set, a power system dispatching model with minimization of power generation cost as an objective function is constructed; Determine the power upper and lower limit constraints, the energy storage power constraints, and the active power balance constraints of the nodes including the electric vehicle charging station in the power system.

6. The method according to claim 1, characterized in that The optimal operating cost obtained by solving the electric-traffic coupling system scheduling model is used to calculate the traffic system scheduling model and the electric power system scheduling model respectively, and the reconstructed target road topology information and power generation data are obtained, including: Based on the optimal operating cost, the traffic system dispatching model and the power system dispatching model are respectively calculated to obtain target road traffic flow and power generation data; Substitute the target road traffic flow into the lane topology reconstruction model to solve and calculate to obtain the target road topology information.

7. An electric-traffic coupling coordinated dispatching terminal taking into account the topology of a traffic network, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, each step of the method for coordinated scheduling of electric and traffic coupling taking into account the topology of the traffic network as claimed in any one of claims 1 to 6 is implemented.

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