Regional electricity-hydrogen automobile coupling traffic flow optimization scheduling method and system

Abstract

The invention discloses a regional electricity-hydrogen automobile coupling traffic flow optimization scheduling method and system, and relates to the technical field of power system optimization scheduling. The method comprises the following steps: receiving road network topology distribution and comprehensive energy supplement facility distribution in a delimited region; according to the road network topology distribution and the comprehensive energy complementing facility distribution, travel OD pairs of the electric vehicle and hydrogen power vehicle users are obtained; according to the automobile characteristics of the electric automobile and the hydrogen-powered automobile, the lowest-cost travel path of the electric automobile and the hydrogen-powered automobile is planned; and according to the lowest-cost travel paths of the electric vehicle and the hydrogen-powered vehicle, performing traffic flow division on the travel OD pairs of the vehicle users of the electric vehicle and the hydrogen-powered vehicle by using a user equalization algorithm to obtain optimal traffic flow distribution.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization and scheduling, and specifically to a method and system for optimizing and scheduling regional electric-hydrogen vehicle coupled traffic flow. Background Art

[0002] With the proposal of my country's dual carbon goals, the penetration rate of new energy in the power system is getting higher and higher. In this context, the country has proposed the goal of building a new power system, with ensuring energy and power security as the basic premise, meeting the power demand of economic and social development as the primary goal, maximizing the consumption of new energy as the main task, and a strong smart grid as the hub platform. It is supported by the interaction of source, grid, load and storage and multi-energy complementarity, and has the basic characteristics of clean and low-carbon, safe and controllable, flexible and efficient, intelligent and friendly, and open and interactive power systems. In the new power system, the demand for new comprehensive energy replenishment facilities has become more urgent. In this context, it is necessary to design and calculate the traffic flow scheduling optimization strategy for the coexistence of multiple vehicles in the transportation network. Summary of the invention

[0003] In order to solve the deficiencies mentioned in the above background technology, the purpose of the present invention is to provide a regional electric hydrogen vehicle coupled traffic flow optimization scheduling method and system, which optimizes the scheduling calculation of regional traffic flow by designing, scheduling and model-driven calculation of traffic flow.

[0004] In the first aspect, the purpose of the present invention can be achieved by the following technical solution: a method for optimizing the scheduling of regional electric hydrogen vehicle coupled traffic flow, the method comprising the following steps:

[0005] Receive the road network topology distribution and comprehensive energy replenishment facility distribution in the designated area, and obtain the travel OD pairs of electric vehicle and hydrogen-powered vehicle users according to the road network topology distribution and comprehensive energy replenishment facility distribution;

[0006] Plan the lowest-cost travel routes for electric and hydrogen vehicles based on their vehicle characteristics;

[0007] According to the lowest cost travel paths of electric vehicles and hydrogen-powered vehicles, the user equilibrium algorithm is used to divide the traffic flow of the travel OD pairs of electric vehicle and hydrogen-powered vehicle users to obtain the optimal traffic flow distribution.

[0008] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the road network topology includes: road nodes, road connection methods, road lengths, free flow of roads, and charging time of stations.

[0009] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the energy replenishment means of the comprehensive energy replenishment facility include: charging and hydrogen charging, and the acceptable charging capacity of the comprehensive energy replenishment facility is given by the operation status of the power grid.

[0010] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the vehicle characteristics of the electric vehicle and the hydrogen-powered vehicle include:

[0011] Electric vehicle features:

[0012] Electric vehicles need to occupy charging piles in the station to charge, and the charging method is DC fast charging;

[0013] The power consumption of electric vehicles on the road is proportional to the mileage;

[0014] Electric vehicle users have a matching estimate between the remaining power and the mileage to the destination;

[0015] Hydrogen powered car features:

[0016] Hydrogen-powered vehicles need to occupy hydrogen charging piles in the station to recharge with liquid hydrogen in hoses;

[0017] The amount of hydrogen consumed by a hydrogen-powered car on the road is proportional to the mileage;

[0018] Hydrogen car users have a matching estimate between remaining energy and destination mileage;

[0019] Considering safety, hydrogen-powered vehicles have strict upper and lower limits on fuel.

[0020] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the total travel loss of the electric vehicle:

[0021] For each OD pair, w∈W, the PGA expression of the EV is as follows:

[0022] For each OD pair, w∈W, the PGA expression of the EV is as follows:

[0023]

[0024] The objective function of the equation is to minimize the total travel loss of a given OD pair, where x a-e and x k-e This is the traffic flow result obtained by UE-TAP. It is a 01 variable indicating whether the path passes through segment a;

[0025] The constraints include:

[0026]

[0027] Constraint (2) is to determine the starting point and end point of the path, where Δ is the node connection matrix in the UTN system, I w is an N-dimensional vector. Constraints (3) and (4) represent the charging options of electric vehicles and their energy constraints on each road section. Indicates the remaining energy of the electric vehicle at the v node and the u node, and Respectively represent the remaining energy of the electric vehicle at the v node and the u node d a represents the length of the road section, β represents the energy loss per unit section, and ρ is an auxiliary variable. represents the charging status of the electric vehicle at node u, and M is a constant. Constraint (5) restricts the charging behavior at each charging node, constraints (6) and (7) indicate the energy constraints of the EV, and constraint (8) represents the electric vehicle user's expectation of the remaining power, γ e Represents the expected coefficient of remaining power of electric vehicles.

[0028] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: the overall energy consumption expression of the hydrogen-powered vehicle is:

[0029]

[0030] The objective function of the equation is to minimize the total travel loss of a given OD pair, where x a-h and x k-h This is the traffic flow result obtained by UE-TAP. It is a 01 variable indicating whether the path passes through segment a;

[0031] The constraints are:

[0032] Δy w =I w (10)

[0033]

[0034] in:

[0035]

[0036] In the formula, According to the efficiency of water electrolysis, the overall constraints within the station are:

[0037]

[0038] In combination with the first aspect, in certain implementations of the first aspect, the method also includes: the lowest cost travel path based on electric vehicles and hydrogen-powered vehicles follows the principle of lowest user travel cost, and the user travel cost is mainly composed of two parts: user road travel time and user recharging time.

[0039] In combination with the first aspect, in some implementations of the first aspect, the method further includes: using the user equilibrium algorithm to divide the traffic flow of the travel OD pairs of electric vehicle users and hydrogen-powered vehicle users to obtain the process of optimal traffic flow distribution:

[0040]

[0041] In the formula, f p,w represents the traffic flow of OD pair w on path p. and represent the basic fee and service fee of the station respectively. Equations (20) to (22) represent the path flow f p,w The relationship between road section a and charging station k, δ p,w,a and δ p,w,k Respectively represent the traffic flow f p,w Whether passing through section a or charging at charging station k, equations (23) and (24) are the congestion conditions of section a and charging station k respectively, where c a and c k represent the road capacity and charging station capacity respectively. Constraints (25) to (29) ensure that the traffic flow on each road is positive and the sum is equal to the demand of the corresponding OD pair. Constraint (30) represents the load size of charging station k and the traffic flow f p,w The relationship between.

[0042] In the second aspect, in order to achieve the above-mentioned purpose, the present invention discloses a regional electric hydrogen vehicle coupled traffic flow optimization scheduling system, comprising:

[0043] A data processing module is used to receive the road network topology distribution and the comprehensive energy replenishment facility distribution in the designated area, and obtain the travel OD pairs of electric vehicle and hydrogen-powered vehicle users according to the road network topology distribution and the comprehensive energy replenishment facility distribution;

[0044] A path planning module, used to plan the lowest-cost travel path for electric and hydrogen vehicles based on their vehicle characteristics;

[0045] The traffic flow distribution module is used to divide the traffic flow of the travel OD pairs of electric vehicle and hydrogen-powered vehicle users according to the lowest cost travel paths of electric vehicles and hydrogen-powered vehicles using the user equilibrium algorithm to obtain the optimal traffic flow distribution.

[0046] In conjunction with the second aspect, in certain implementations of the second aspect, the system further includes: the road network topology in the data processing module includes: road nodes, road connection methods, road lengths, free flows of roads, and charging time of stations;

[0047] Or the energy replenishment means of the comprehensive energy replenishment facility in the data processing module include: charging and hydrogen charging, and the acceptable charging capacity of the comprehensive energy replenishment facility is given by the operation status of the power grid;

[0048] Preferably, the vehicle characteristics of electric vehicles and hydrogen-powered vehicles in the path planning module include:

[0049] Electric vehicle features:

[0050] Electric vehicles need to occupy charging piles in the station to charge, and the charging method is DC fast charging;

[0051] The power consumption of electric vehicles on the road is proportional to the mileage;

[0052] Electric vehicle users have a matching estimate between the remaining power and the mileage to the destination;

[0053] Hydrogen powered car features:

[0054] Hydrogen-powered vehicles need to occupy hydrogen charging piles in the station to recharge with liquid hydrogen in hoses;

[0055] The amount of hydrogen consumed by a hydrogen-powered car on the road is proportional to the mileage;

[0056] Hydrogen car users have a matching estimate between remaining energy and destination mileage;

[0057] Considering safety, hydrogen-powered vehicles have strict upper and lower limits on fuel;

[0058] Total travel loss of electric vehicles in the path planning module:

[0059] For each OD pair, w∈W, the PGA expression of the EV is as follows:

[0060]

[0061] The objective function of the equation is to minimize the total travel loss of a given OD pair, where x a-e and x k-e This is the traffic flow result obtained by UE-TAP. It is a 01 variable indicating whether the path passes through segment a;

[0062] The constraints include:

[0063] Δy w =I w(2)

[0064]

[0065] Constraint (2) is to determine the starting point and end point of the path, where Δ is the node connection matrix in the UTN system, I w is an N-dimensional vector. Constraints (3) and (4) represent the charging options of electric vehicles and their energy constraints on each road section. Indicates the remaining energy of the electric vehicle at the v node and the u node, and Respectively represent the remaining energy of the electric vehicle at the v node and the u node d a represents the length of the road section, β represents the energy loss per unit section, and ρ is an auxiliary variable. represents the charging status of the electric vehicle at node u, and M is a constant. Constraint (5) restricts the charging behavior at each charging node, constraints (6) and (7) indicate the energy constraints of the EV, and constraint (8) represents the electric vehicle user's expectation of the remaining power, γ e Represents the expected coefficient of remaining power of electric vehicles.

[0066] Total travel loss of hydrogen-powered vehicles in the path planning module:

[0067]

[0068] The objective function of the equation is to minimize the total travel loss of a given OD pair, where x a-h and x k-h This is the traffic flow result obtained by UE-TAP. It is a 01 variable indicating whether the path passes through segment a;

[0069] The constraints are:

[0070] Δy w =I w (10)

[0071]

[0072] Preferably, in formula (11) Indicates the remaining energy of the electric vehicle at the v node and the u node, and Respectively represent the remaining energy of the electric vehicle at the v node and the u node d a represents the length of the road section, β represents the energy loss per unit section, and ρ is an auxiliary variable. represents the charging status of the electric vehicle at node u, and M is a constant. Constraints (16) represent the electric vehicle user’s expectation of remaining energy, γ hRepresents the expected coefficient of remaining energy of electric vehicles.

[0073] Preferably, the vehicle flow distribution module uses a user equilibrium algorithm to divide the travel OD pairs of electric vehicle and hydrogen-powered vehicle users into vehicle flows, and obtains the process of optimal vehicle flow distribution:

[0074]

[0075] In the formula, f p,w represents the traffic flow of OD pair w on path p. and represent the basic fee and service fee of the station respectively. Equations (20) to (22) represent the path flow f p,w The relationship between road section a and charging station k, δ p,w,a and δ p,w,k Respectively represent the traffic flow f p,w Whether passing through section a or charging at charging station k, equations (23) and (24) are the congestion conditions of section a and charging station k respectively, where c a and c k represent the road capacity and charging station capacity respectively. Constraints (25) to (29) ensure that the traffic flow on each road is positive and the sum is equal to the demand of the corresponding OD pair. Constraint (30) represents the load size of charging station k and the traffic flow f p,w The relationship between.

[0076] Beneficial effects of the present invention:

[0077] The present invention models vehicle travel planning for multiple energy replenishment needs, proposes different vehicle travel strategies, and optimizes vehicle flow scheduling for road topologies containing multiple energy replenishment needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0079] Figure 1 It is a schematic flow chart of the method of the present invention;

[0080] Figure 2 It is a schematic diagram of the overall scheduling strategy flow of the present invention;

[0081] Figure 3 2 is a simulation result diagram of this embodiment;

[0082] Figure 4 It is a schematic diagram of the system structure of the present invention.

[0083] Figure 5 This is a road traffic flow diagram calculated by the present invention taking 20 nodes as an example. DETAILED DESCRIPTION

[0084] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] Embodiment 1:

[0086] The following is an introduction to the relevant terms involved in the embodiments of the present application:

[0087] Electric vehicle: Electric vehicle (BEV) refers to a vehicle that uses an on-board power source as its motive force, uses an electric motor to drive its wheels, and complies with all requirements of road traffic and safety regulations.

[0088] Hydrogen-powered vehicles: There are two types of hydrogen-powered vehicles. Hydrogen internal combustion vehicles (HICEVs) are vehicles that are powered by an internal combustion engine that burns hydrogen (usually obtained by decomposing methane or electrolyzing water) and oxygen in the air. Hydrogen fuel cell vehicles (FCEVs) use hydrogen or hydrogen-containing substances and oxygen in the air to generate electricity through a fuel cell, which then drives the motor, which in turn drives the vehicle. The generator of this type of vehicle converts the chemical energy of hydrogen into mechanical energy, either by burning hydrogen in an internal combustion engine or by reacting oxygen and hydrogen in a fuel cell to run the motor.

[0089] like Figure 1 As shown, a method for optimizing the scheduling of regional electric-hydrogen vehicle coupled traffic flow comprises the following steps:

[0090] S101: receiving the road network topology distribution and the comprehensive energy replenishment facility distribution in the designated area, and obtaining the travel OD pairs of electric vehicle and hydrogen-powered vehicle users according to the road network topology distribution and the comprehensive energy replenishment facility distribution;

[0091] The road network topology includes: road nodes, road connection methods, road length, free flow of roads, and charging time of stations;

[0092] The energy replenishment methods of the comprehensive energy replenishment facility include: charging and hydrogen charging. The acceptable charging capacity of the comprehensive energy replenishment facility is given by the operation of the power grid;

[0093] S102: Plan the lowest cost travel routes for electric vehicles and hydrogen vehicles based on their characteristics;

[0094] Vehicle features for electric and hydrogen vehicles include:

[0095] Electric vehicle features:

[0096] Electric vehicles need to occupy charging piles in the station to charge, and the charging method is DC fast charging;

[0097] The power consumption of electric vehicles on the road is proportional to the mileage;

[0098] Electric vehicle users have a matching estimate between the remaining power and the mileage to the destination;

[0099] Hydrogen powered car features:

[0100] Hydrogen-powered vehicles need to occupy hydrogen charging piles in the station to recharge with liquid hydrogen in hoses;

[0101] The amount of hydrogen consumed by a hydrogen-powered car on the road is proportional to the mileage;

[0102] Hydrogen car users have a matching estimate between remaining energy and destination mileage;

[0103] Considering safety, hydrogen-powered vehicles have strict upper and lower limits on fuel.

[0104] Total travel loss of electric vehicles:

[0105] For each OD pair, w∈W, the PGA expression of the EV is as follows:

[0106]

[0107] The objective function of the equation is to minimize the total travel loss of a given OD pair, where x a-e and x k-e This is the traffic flow result obtained by UE-TAP. It is a 01 variable indicating whether the path passes through segment a;

[0108] The constraints include:

[0109] Δy w =I w (2)

[0110]

[0111] Constraint (2) is to determine the starting point and end point of the path, where Δ is the node connection matrix in the UTN system, I w is an N-dimensional vector. Constraints (3) and (4) represent the charging options of electric vehicles and their energy constraints on each road section. Indicates the remaining energy of the electric vehicle at the v node and the u node, and Respectively represent the remaining energy of the electric vehicle at the v node and the u node d a represents the length of the road section, β represents the energy loss per unit section, and ρ is an auxiliary variable. represents the charging status of the electric vehicle at node u, and M is a constant. Constraint (5) restricts the charging behavior at each charging node, constraints (6) and (7) indicate the energy constraints of the EV, and constraint (8) represents the electric vehicle user's expectation of the remaining power, γ e Represents the expected coefficient of remaining power of electric vehicles.

[0112] The overall energy consumption of hydrogen-powered vehicles is expressed as follows:

[0113]

[0114] The objective function of the equation is to minimize the total travel loss of a given OD pair, where x a-h and x k-h This is the traffic flow result obtained by UE-TAP. It is a 01 variable indicating whether the path passes through segment a;

[0115] The constraints are:

[0116] Δy w =I w (10)

[0117]

[0118] Preferably, in formula (11) Indicates the remaining energy of the electric vehicle at the v node and the u node, and Respectively represent the remaining energy of the electric vehicle at the v node and the u node d a represents the length of the road section, β represents the energy loss per unit section, and ρ is an auxiliary variable. represents the charging status of the electric vehicle at node u, and M is a constant. Constraints (16) represent the electric vehicle user’s expectation of remaining energy, γ h Represents the expected coefficient of remaining energy of electric vehicles.

[0119] in:

[0120]

[0121] In the formula, According to the efficiency of water electrolysis, the overall constraints within the station are:

[0122]

[0123] S103: Based on the lowest cost travel paths of electric vehicles and hydrogen-powered vehicles, a user equilibrium algorithm is used to divide the traffic flow of the travel OD pairs of electric vehicle and hydrogen-powered vehicle users to obtain the optimal traffic flow distribution.

[0124] The lowest cost travel path for electric and hydrogen-powered vehicles follows the principle of lowest user travel cost. The user travel cost mainly consists of two parts: the user's road travel time and the user's recharging time.

[0125] The process of using the user equilibrium algorithm to divide the traffic flow of electric vehicle and hydrogen-powered vehicle users' travel OD pairs to obtain the optimal traffic flow distribution:

[0126]

[0127]

[0128] In the formula, f p,w represents the traffic flow of OD pair w on path p. and represent the basic fee and service fee of the station respectively. Equations (20) to (22) represent the path flow f p,w The relationship between road section a and charging station k, δ p,w,a and δ p,w,k Respectively represent the traffic flow f p,w Whether passing through section a or charging at charging station k, equations (23) and (24) are the congestion conditions of section a and charging station k respectively, where c a and c k represent the road capacity and charging station capacity respectively. Constraints (25) to (29) ensure that the traffic flow on each road is positive and the sum is equal to the demand of the corresponding OD pair. Constraint (30) represents the load size of charging station k and the traffic flow f p,w The relationship between.

[0129] Among them, the process of repeated iterative solution.

[0130]

[0131] Embodiment 2: In the second aspect, in order to achieve the above-mentioned purpose, the present invention discloses a regional electric hydrogen vehicle coupled traffic flow optimization scheduling system, comprising:

[0132] The data processing module 11 is used to receive the road network topology distribution and the comprehensive energy replenishment facility distribution in the designated area, and obtain the travel OD pairs of electric vehicle and hydrogen-powered vehicle users according to the road network topology distribution and the comprehensive energy replenishment facility distribution;

[0133] A path planning module 12, for planning the lowest cost travel path for the electric vehicle and the hydrogen-powered vehicle according to the vehicle characteristics of the electric vehicle and the hydrogen-powered vehicle;

[0134] The traffic flow distribution module 13 is used to divide the traffic flow of the travel OD pairs of electric vehicle and hydrogen-powered vehicle users according to the lowest cost travel paths of electric vehicles and hydrogen-powered vehicles using a user equilibrium algorithm to obtain the optimal traffic flow distribution.

[0135] In conjunction with the second aspect, in certain implementations of the second aspect, the system further includes: the road network topology in the data processing module 11 includes: road nodes, road connection methods, road lengths, free flows of roads, and charging time of stations;

[0136] Or the energy replenishment means of the comprehensive energy replenishment facility in the data processing module 11 include: charging and hydrogen charging, and the acceptable charging capacity of the comprehensive energy replenishment facility is given by the operation status of the power grid;

[0137] Preferably, the vehicle characteristics of electric vehicles and hydrogen-powered vehicles in the path planning module 12 include:

[0138] Electric vehicle features:

[0139] Electric vehicles need to occupy charging piles in the station to charge, and the charging method is DC fast charging;

[0140] The power consumption of electric vehicles on the road is proportional to the mileage;

[0141] Electric vehicle users have a matching estimate between the remaining power and the mileage to the destination;

[0142] Hydrogen powered car features:

[0143] Hydrogen-powered vehicles need to occupy hydrogen charging piles in the station to recharge with liquid hydrogen in hoses;

[0144] The amount of hydrogen consumed by a hydrogen-powered car on the road is proportional to the mileage;

[0145] Hydrogen car users have a matching estimate between remaining energy and destination mileage;

[0146] Considering safety, hydrogen-powered vehicles have strict upper and lower limits on fuel;

[0147] Total travel loss of electric vehicles in the path planning module:

[0148]

[0149] The objective function of the equation is to minimize the total travel loss of a given OD pair, where x a-e and xk-e This is the traffic flow result obtained by UE-TAP. It is a 01 variable indicating whether the path passes through segment a;

[0150] The constraints include:

[0151] Δy w =I w (2)

[0152]

[0153]

[0154] Constraint (2) is to determine the starting point and end point of the path, where Δ is the node connection matrix in the UTN system, I w is an N-dimensional vector. Constraints (3) and (4) represent the charging options of electric vehicles and their energy constraints on each road section. Indicates the remaining energy of the electric vehicle at the v node and the u node, and Respectively represent the remaining energy of the electric vehicle at the v node and the u node d a represents the length of the road section, β represents the energy loss per unit section, and ρ is an auxiliary variable. represents the charging status of the electric vehicle at node u, and M is a constant. Constraint (5) restricts the charging behavior at each charging node, constraints (6) and (7) indicate the energy constraints of the EV, and constraint (8) represents the electric vehicle user's expectation of the remaining power, γ e Represents the expected coefficient of remaining power of electric vehicles.

[0155] The overall energy consumption of hydrogen-powered vehicles is expressed as follows:

[0156]

[0157] The objective function of the equation is to minimize the total travel loss of a given OD pair, where x a-h and x k-h This is the traffic flow result obtained by UE-TAP. It is a 01 variable indicating whether the path passes through segment a;

[0158] The constraints are:

[0159] Δy w =I w (10)

[0160]

[0161]

[0162] Preferably, in formula (11) Indicates the remaining energy of the electric vehicle at the v node and the u node, and Respectively represent the remaining energy of the electric vehicle at the v node and the u node d a represents the length of the road section, β represents the energy loss per unit section, and ρ is an auxiliary variable. represents the charging status of the electric vehicle at node u, and M is a constant. Constraints (16) represent the electric vehicle user’s expectation of remaining energy, γ h Represents the expected coefficient of remaining energy of electric vehicles.

[0163] in:

[0164]

[0165] In the formula, According to the efficiency of water electrolysis, the overall constraints within the station are:

[0166]

[0167] Preferably, the lowest cost travel path for electric vehicles and hydrogen-powered vehicles in the traffic flow distribution module 13 follows the principle of lowest user travel cost, and the user travel cost is mainly composed of two parts: user road travel time and user recharging time;

[0168] Preferably, the vehicle flow distribution module 13 uses a user equilibrium algorithm to divide the travel OD pairs of electric vehicle and hydrogen-powered vehicle users into vehicle flows, and obtains the process of optimal vehicle flow distribution:

[0169]

[0170]

[0171] In the formula, f p,w represents the traffic flow of OD pair w on path p. and represent the basic fee and service fee of the station respectively. Equations (20) to (22) represent the path flow f p,w The relationship between road section a and charging station k, δ p,w,a and δ p,w,k Respectively represent the traffic flow f p,w Whether passing through section a or charging at charging station k, equations (23) and (24) are the congestion conditions of section a and charging station k respectively, where c a and c krepresent the road capacity and charging station capacity respectively. Constraints (25) to (29) ensure that the traffic flow on each road is positive and the sum is equal to the demand of the corresponding OD pair. Constraint (30) represents the load size of charging station k and the traffic flow f p,w The relationship between.

[0172] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0173] It needs to be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and the computer program is executed by a processor to execute the above method. The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0174] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0175] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure may have various changes and improvements, and these changes and improvements fall within the scope of the present disclosure to be protected.

Claims

1. A method for optimizing the scheduling of regional electric-hydrogen vehicle coupled traffic flow, characterized in that: The method comprises the following steps: Receive the road network topology distribution and comprehensive energy replenishment facility distribution in the designated area, and obtain the travel OD pairs of electric vehicle and hydrogen-powered vehicle users according to the road network topology distribution and comprehensive energy replenishment facility distribution; Plan the lowest-cost travel routes for electric and hydrogen vehicles based on their vehicle characteristics; According to the lowest cost travel paths of electric vehicles and hydrogen-powered vehicles, the user equilibrium algorithm is used to divide the traffic flow of the travel OD pairs of electric vehicle and hydrogen-powered vehicle users to obtain the optimal traffic flow distribution.

2. A method for optimizing the regional electric-hydrogen vehicle coupled traffic flow according to claim 1, characterized in that: The road network topology includes: road nodes, road connection mode, road length, road free flow, and charging time of stations.

3. A method for optimizing the regional electric-hydrogen vehicle coupled traffic flow according to claim 1, characterized in that: The energy replenishment means of the comprehensive energy replenishment facility include: charging and hydrogen charging. The acceptable charging capacity of the comprehensive energy replenishment facility is given by the operation status of the power grid.

4. A method for optimizing the dispatching of regional electric-hydrogen vehicle coupled traffic flow according to claim 1, characterized in that: The vehicle features of the electric and hydrogen vehicles include: Electric vehicle features: Electric vehicles need to occupy charging piles in the station to charge, and the charging method is DC fast charging; The power consumption of electric vehicles on the road is proportional to the mileage; Electric vehicle users have a matching estimate between the remaining power and the mileage to the destination; Hydrogen powered car features: Hydrogen-powered vehicles need to occupy hydrogen charging piles in the station to recharge with liquid hydrogen in hoses; The amount of hydrogen consumed by a hydrogen-powered car on the road is proportional to the mileage; Hydrogen car users have a matching estimate between remaining energy and destination mileage; Considering safety, hydrogen-powered vehicles have strict upper and lower limits on fuel.

5. The method for optimizing the regional electric-hydrogen vehicle coupled traffic flow according to claim 1 is characterized in that: The total travel loss of electric vehicles: For each OD pair, w∈W, the PGA expression of the EV is as follows: The objective function of the equation is to minimize the total travel loss of a given OD pair, where x a-e and x k-e This is the traffic flow result obtained by UE-TAP. It is a 01 variable indicating whether the path passes through segment a; The constraints include: Constraint (2) is to determine the starting point and end point of the path, where Δ is the node connection matrix in the UTN system, I w is an N-dimensional vector. Constraints (3) and (4) represent the charging options of electric vehicles and their energy constraints on each road section. Indicates the remaining energy of the electric vehicle at the v node and the u node, and Respectively represent the remaining energy of the electric vehicle at the v node and the u node d a represents the length of the road section, β represents the energy loss per unit section, ρ is an auxiliary variable, represents the charging status of the electric vehicle at node u, M is a constant, constraint (5) restricts the charging behavior at each charging node, constraints (6) and (7) indicate the energy constraints of the EV, and constraint (8) represents the electric vehicle user's expectation of the remaining power, γ e Represents the expected coefficient of remaining power of electric vehicles.

6. A method for optimizing the dispatching of regional electric-hydrogen vehicle coupled traffic flow according to claim 1, characterized in that: The overall energy consumption expression of the hydrogen-powered vehicle is: The objective function of the equation is to minimize the total travel loss of a given OD pair, where x a-h and x k-h This is the traffic flow result obtained by UE-TAP. It is a 01 variable indicating whether the path passes through segment a; The constraints are: Δy w =I w (10) In formula (11) Indicates the remaining energy of the electric vehicle at the v node and the u node, and Respectively represent the remaining energy of the electric vehicle at the v node and the u node d a represents the length of the road section, β represents the energy loss per unit section, ρ is an auxiliary variable, represents the charging status of the electric vehicle at node u, M is a constant, and constraints (16) represent the electric vehicle user’s expectation of the remaining energy, γ h Represents the expected coefficient of remaining energy of electric vehicles; in: In the formula, According to the efficiency of water electrolysis, the overall constraints within the station are:

7. A method for optimizing the dispatching of regional electric-hydrogen vehicle coupled traffic flow according to claim 1, characterized in that: The lowest-cost travel route based on electric and hydrogen-powered vehicles follows the principle of lowest user travel cost. The user travel cost mainly consists of two parts: user road travel time and user recharging time.

8. A method for optimizing the regional electric-hydrogen vehicle coupled traffic flow according to claim 1, characterized in that: The process of using the user equilibrium algorithm to divide the traffic flow of the travel OD pairs of electric vehicle and hydrogen-powered vehicle users to obtain the optimal traffic flow distribution is as follows: In the formula, f p,w represents the traffic flow of OD pair w on path p, and represent the basic fee and service fee of the station respectively. Equations (20) to (22) represent the path flow f p,w The relationship between road section a and charging station k, δ p,w,a and δ p,w,k Respectively represent the traffic flow f p,w Whether passing through section a or charging at charging station k, equations (23) and (24) are the congestion conditions of section a and charging station k respectively, where c a and c k represent the road capacity and charging station capacity respectively. Constraints (25) to (31) ensure that the traffic flow on each road is positive and the sum is equal to the demand of the corresponding OD pair. Constraint (32) represents the load size of charging station k and the traffic flow f p,w The relationship between.

9. A regional electric-hydrogen vehicle coupled traffic flow optimization scheduling system, characterized in that: include: A data processing module is used to receive the road network topology distribution and the comprehensive energy replenishment facility distribution in the designated area, and obtain the travel OD pairs of electric vehicle and hydrogen-powered vehicle users according to the road network topology distribution and the comprehensive energy replenishment facility distribution; A path planning module, used to plan the lowest-cost travel path for electric and hydrogen vehicles based on their vehicle characteristics; The traffic flow distribution module is used to divide the traffic flow of the travel OD pairs of electric vehicle and hydrogen-powered vehicle users according to the lowest cost travel paths of electric vehicles and hydrogen-powered vehicles using the user equilibrium algorithm to obtain the optimal traffic flow distribution.

10. A regional electric-hydrogen vehicle coupled traffic flow optimization scheduling system according to claim 9, characterized in that: The road network topology in the data processing module includes: road nodes, road connection mode, road length, free flow of roads, and charging time of stations; Or the energy replenishment means of the comprehensive energy replenishment facility in the data processing module include: charging and hydrogen charging, and the acceptable charging capacity of the comprehensive energy replenishment facility is given by the operation status of the power grid; Preferably, the vehicle characteristics of electric vehicles and hydrogen-powered vehicles in the path planning module include: Electric vehicle features: Electric vehicles need to occupy charging piles in the station to charge, and the charging method is DC fast charging; The power consumption of electric vehicles on the road is proportional to the mileage; Electric vehicle users have a matching estimate between the remaining power and the mileage to the destination; Hydrogen powered car features: Hydrogen-powered vehicles need to occupy hydrogen charging piles in the station to recharge with liquid hydrogen in hoses; The amount of hydrogen consumed by a hydrogen-powered car on the road is proportional to the mileage; Hydrogen car users have a matching estimate between remaining energy and destination mileage; Considering safety, hydrogen-powered vehicles have strict upper and lower limits on fuel; Total travel loss of electric vehicles in the data processing module: For each OD pair, w∈W, the PGA expression of the EV is as follows: The objective function of the equation is to minimize the total travel loss of a given OD pair, where x a-e and x k-e This is the traffic flow result obtained by UE-TAP. It is a 01 variable indicating whether the path passes through segment a; The constraints include: Δy w =I w (2) Constraint (2) is to determine the starting point and end point of the path, where Δ is the node connection matrix in the UTN system, I w is an N-dimensional vector. Constraints (3) and (4) represent the charging options of electric vehicles and their energy constraints on each road section. Indicates the remaining energy of the electric vehicle at the v node and the u node, and Respectively represent the remaining energy of the electric vehicle at the v node and the u node d a represents the length of the road section, β represents the energy loss per unit section, ρ is an auxiliary variable, represents the charging status of the electric vehicle at node u, M is a constant, constraint (5) restricts the charging behavior at each charging node, constraints (6) and (7) indicate the energy constraints of the EV, and constraint (8) represents the electric vehicle user's expectation of the remaining power, γ e Represents the expected coefficient of remaining power of electric vehicles; The overall energy consumption expression of hydrogen-powered vehicles in the data processing module is: The objective function of the equation is to minimize the total travel loss of a given OD pair, where x a-h and x k-h This is the traffic flow result obtained by UE-TAP. It is a 01 variable indicating whether the path passes through segment a; The constraints are: Δy w =I w (10) In formula (11) Indicates the remaining energy of the electric vehicle at the v node and the u node, and Respectively represent the remaining energy of the electric vehicle at the v node and the u node d a represents the length of the road section, β represents the energy loss per unit section, ρ is an auxiliary variable, represents the charging status of the electric vehicle at node u, M is a constant, and constraints (16) represent the electric vehicle user’s expectation of the remaining energy, γ h Represents the expected coefficient of remaining energy of electric vehicles; in: In the formula, According to the efficiency of water electrolysis, the overall constraints within the station are: Preferably, the lowest cost travel path for electric and hydrogen-powered vehicles in the traffic flow distribution module follows the principle of lowest user travel cost, and the user travel cost is mainly composed of two parts: user road travel time and user recharging time; Preferably, the vehicle flow distribution module uses a user equilibrium algorithm to divide the travel OD pairs of electric vehicle and hydrogen-powered vehicle users into vehicle flows, and obtains the process of optimal vehicle flow distribution: In the formula, f p,w represents the traffic flow of OD pair w on path p, and represent the basic fee and service fee of the station respectively. Equations (20) to (22) represent the path flow f p,w The relationship between road section a and charging station k, δ p,w,a and δ p,w,k Respectively represent the traffic flow f p,w Whether passing through section a or charging at charging station k, equations (23) and (24) are the congestion conditions of section a and charging station k respectively, where c a and c k represent the road capacity and charging station capacity respectively. Constraints (25) to (31) ensure that the traffic flow on each road is positive and the sum is equal to the demand of the corresponding OD pair. Constraint (32) represents the load size of charging station k and the traffic flow f p,w The relationship between.

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