Traffic flow-charging flow regulation and control method considering low carbon-economic target

Abstract

The invention discloses a traffic flow-charging flow regulation and control method considering a low-carbon-economic target. Low-carbon transformation of an electric power system and a traffic system is supported by fully excavating space flexibility of an electric vehicle and a fuel vehicle. Acquiring network parameters and operation parameters of the power system and the traffic system; secondly, acquiring scene data, including an active load and a reactive load of a power system and a travel demand in a traffic system; and finally, considering operation constraints of an electric power system and a traffic system, taking a price signal and distributed power supply output as regulation and control objects, taking the sum of the operation cost and the carbon emission cost of the two systems as a target function, establishing a traffic flow-charging flow regulation and control model considering a low carbon-economic target, and solving to obtain an optimal regulation and control scheme. The obtained regulation and control scheme can comprehensively consider low-carbon targets and economic targets in operation of a power system and a traffic system, effectively avoid social benefit loss under a single target, and promote low-carbon-economic operation of the two systems.

Description

Technical Field

[0001] The present invention belongs to the field of electric vehicle charging navigation, and in particular relates to a traffic flow-charging flow control method taking low-carbon-economic goals into consideration. Background Art

[0002] In the context of the development of energy Internet, the power system is an important part of the energy system, and the transportation system is an important load of the energy system. With the increasing penetration of electric vehicles year by year, the coupling relationship between the power system and the transportation system is becoming increasingly close, and public charging facilities have become the intermediate hub for the coupling of the two systems. The frequent charging behavior of large-scale electric vehicles through charging facilities has created new interactions between the power system and the transportation system, and the operation of the two systems has gradually changed from being independent to being highly coupled. In this case, the road structure, congestion status and traffic control policies of the transportation network will affect the driving mode of electric vehicles, and the choice of charging location and time will significantly affect the spatiotemporal distribution of power load, and thus affect the operating status of the power system; in turn, the location, capacity, electricity price and charging queue time of charging facilities will also affect the travel plans and driving behaviors of electric vehicles, and further affect the flow distribution of the transportation system. Therefore, under the influence of electric vehicles, there is an interactive impact between the power system and the transportation system, and the operation of the two is bound to become more complicated.

[0003] The joint regulation of traffic flow and charging flow is usually based on the cooperation between the transportation department and the power department. The driving routes and charging options of fuel vehicles and electric vehicles are regulated by road tolls and charging station service fees. At the same time, by dispatching the output of distributed power sources, the economic optimization of power flow and the minimum overall travel time of traffic flow are achieved under the premise of ensuring the safety of the power grid. It can be seen that the current research mainly considers the economic efficiency of the operation of the two networks. However, the carbon emissions of electricity and transportation account for 40% and 9% respectively. Although the electrification of transportation can reduce the carbon emissions on the transportation side to a certain extent, if the charging source of electric vehicles is a traditional coal-fired power plant, the carbon emissions of the power sector will increase instead of decrease. The current dispatching method with economy as the main goal is difficult to meet the needs of carbon emission reduction in my country. To this end, it is necessary to add emission reduction targets on the basis of economy. On the one hand, by improving the operating efficiency of the transportation side to reduce traffic congestion, thereby reducing the carbon emissions directly generated by fuel vehicles; on the other hand, matching the charging load of electric vehicles with clean energy to avoid the increase of indirect carbon emissions caused by electric vehicles, and finally achieving coordinated low-carbon dispatch of the two systems. Summary of the invention

[0004] Purpose of the invention: The technical problem to be solved by the present invention is to provide a traffic flow-charging flow control method taking into account low-carbon-economic goals in view of the shortcomings of the prior art.

[0005] Technical solution, in order to achieve the above invention object, the present invention proposes a traffic flow-charging flow control method considering low-carbon-economic goals, the method comprising the following steps:

[0006] 1) Obtaining network parameters and operating parameters of the power system and the transportation system, wherein the network parameters include the network topology, line resistance / reactance of the power system, the network topology, free travel time and capacity of the road section, charging station capacity and maximum waiting time of the transportation system, and the operating parameters include the power generation cost parameters of the unit, the unit price of electricity purchased by the upper power grid, the carbon emission parameters of the unit, the unit travel time cost of the travel user, and the carbon tax;

[0007] 2) Obtain scenario data, including active load and reactive load of the power system, and travel demand at different starting and ending points in the transportation system;

[0008] 3) Based on the obtained network parameters, operating parameters and scenario data of the two systems, with the power system operation constraints and traffic system operation constraints as constraints, price signals and distributed power output as control objects, and minimizing the sum of the operating costs and carbon emission costs of the two systems as the objective function, a traffic flow-charging flow control model considering low-carbon-economic goals is established, and the optimal control plan is obtained by solving it.

[0009] Furthermore, in step 3), the objective function F = EC TN +EC PDN +tax(EM TN +EM PDN ),in:

[0010]

[0011] Among them, F, EC TN ,EC PDN , EM TN , EM PDN They represent the total operating cost of the two systems, the operating cost of the transportation system, the operating cost of the power system, the carbon emissions of the transportation system, and the carbon emissions of the power system, respectively. The specific calculation methods are shown in equations (1)-(4). The subscript rs represents the starting and ending points; the subscript a represents the road section; the subscript l represents the charging station; the subscripts i, j, and h represent the grid nodes; Represents a set of traffic sections; Represents the charging section set; E N represents the set of nodes in the distribution network; π(0) represents the set of child nodes connected to the root node; tax represents the carbon tax; ω represents the unit time cost; represents the fuel vehicle toll imposed on road section a; and They represent the traffic volume and travel time on section a respectively; and Respectively represent the number of electric vehicles and charging time in charging station l; γ l and E e Respectively represent the charging price of charging station l and the charging capacity of each electric vehicle; a i , b i , c i are the three power generation parameters of the distributed generation at node i; represents the active output value of the distributed generation at node i; ρ and P 0j They represent the unit price of electricity purchased from the main grid and the amount of electricity purchased from the main grid respectively; represents the length of section a in the traffic system; e Co2 Indicates the amount of carbon emissions generated per unit of electricity generated.

[0012] Furthermore, in step 3), the power system operation constraints are established as follows:

[0013]

[0014] Formula (5) represents the active power balance constraint of the power system; Formula (6) represents the reactive power balance constraint of the power system; Formula (7) represents Ohm's law of the power system; Formula (8) represents the capacity constraint of the power line of the power system; Formula (9) represents the upper and lower limit constraints of the node voltage; Formula (10) represents the upper and lower limit constraints of the active output of the distributed power source; Formula (11) represents the upper and lower limit constraints of the reactive output of the distributed power source; Formula (12) defines the active load of the node, including the conventional power load of the node and the charging load of the electric vehicle connected to the node;

[0015] Where, subscript l represents the charging station; subscripts i, j, h represent grid nodes; π(j) represents the set of all child nodes connected to node j; P ij and Q ij are respectively the active power and reactive power flowing through line ij; P jh With Q jh are respectively the active power and reactive power flowing through line jh; r ij With x ij are the resistance and reactance of line ij respectively; V i is the voltage amplitude of node i; V j is the voltage amplitude of node j; V0 represents the voltage amplitude of the gateway node; and V i are the upper and lower limits of the voltage amplitude of node i respectively; is the apparent power upper limit of line ij; and are the active power and reactive power generated by the power supply at node i respectively; and are the active load and reactive load at node i respectively; p i They are respectively the upper and lower limits of the active output of distributed power sources; q i are the upper and lower limits of the reactive power output of distributed generation, respectively; C(j) represents the set of charging stations powered by power system node j; represents the conventional active load at node j.

[0016] Furthermore, in step 3), the traffic system operation constraints are set as follows:

[0017]

[0018]

[0019] Formula (13) represents the conservation constraint of the traffic system section flow; Formula (14) represents the conservation constraint of the charging station flow; Formula (15) represents the conservation constraint of the travel demand of fuel vehicles; Formula (16) represents the conservation constraint of the travel demand of electric vehicles; Formula (17) represents the nonlinear relationship between the section flow and the section travel time; Formula (18) represents the nonlinear relationship between the number of vehicles queuing at the charging station and the length of the queuing time at the charging station; Formula (19) defines the passage cost of fuel vehicles, including time cost and paid section tolls; Formula (20) defines the passage cost of electric vehicles, including charging cost, time cost and paid road tolls; Formula (21) represents the Wardrop user equilibrium criterion for fuel vehicles; Formula (22) represents the Wardrop user equilibrium criterion for electric vehicles;

[0020] In the formula, and They represent the path sets of fuel vehicles and electric vehicles between rs respectively; represents the free travel time of section a; represents the traffic capacity of road section a; It represents the travel cost that a fuel vehicle needs to pay when choosing path k; represents the upper limit of the service capacity of charging station l; represents the queue time of charging station l when it is fully loaded; p ser Indicates the charging power of the charging station; It represents the travel cost that the electric vehicle needs to pay when choosing path k; Indicates the path and section indicator parameters of the fuel vehicle. If section a is on path k, then Take 1, otherwise take 0; Represents the indicative parameters of the path and road section of the electric vehicle. If road section a is located on path k, then Take 1, otherwise take 0; Represents the path and charging station indicator parameters of the electric vehicle. If charging station l is located on path k, then Take 1, otherwise take 0; and They represent the fuel vehicle flow and electric vehicle flow that select path k respectively; τ g and τ e Respectively represent the proportion of fuel vehicles and electric vehicles in the total travel demand; q rs represents the travel demand between the starting point r and the destination s; and They represent the minimum fuel vehicle travel cost and the minimum electric vehicle travel cost between rs respectively.

[0021] In addition, the present invention proposes a computer device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements any of the steps of a traffic flow-charging flow control method taking into account a low-carbon-economic goal.

[0022] In addition, the present invention proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the traffic flow-charging flow control methods considering low-carbon-economic goals are implemented.

[0023] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0024] The present invention takes into account the economic cost and carbon emission cost in the power grid and transportation network at the same time, and achieves the best overall benefit by regulating the traffic flow and charging flow. The results of the example test show that the method proposed in the present invention can significantly reduce the carbon emissions in the power-transportation network compared with the existing method, and maximize the social benefits of the power grid-transportation network operation.

[0025] The present invention takes into account the direct carbon emissions of fuel vehicles in the transportation network and the indirect carbon emissions caused by charging electric vehicles in the power grid. The regulation targets take into account both carbon reduction targets and economic targets. The regulation results can promote the coordinated low-carbon operation of the power and transportation networks under the energy transformation goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flow chart of a traffic flow-charging flow control strategy considering low-carbon-economic goals according to the present invention;

[0027] Figure 2 This is the coupled power grid-transportation network diagram. DETAILED DESCRIPTION

[0028] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0029] like Figure 1 As shown, the present invention proposes a traffic flow-charging flow control method considering low-carbon-economic goals, and the method includes the following steps:

[0030] 1) Obtaining network parameters and operating parameters of the power system and the transportation system, wherein the network parameters include the network topology, line resistance / reactance of the power system, the network topology, free travel time and capacity of the road section, charging station capacity and maximum waiting time of the transportation system, and the operating parameters include the power generation cost parameters of the unit, the unit price of electricity purchased by the upper power grid, the carbon emission parameters of the unit, the unit travel time cost of the travel user, and the carbon tax;

[0031] 2) Obtain scenario data, including active load and reactive load of the power system, and travel demand at different starting and ending points in the transportation system;

[0032] 3) Based on the obtained network parameters, operating parameters and scenario data of the two systems, with the power system operation constraints and traffic system operation constraints as constraints, price signals and distributed power output as control objects, and minimizing the sum of the operating costs and carbon emission costs of the two systems as the objective function, a traffic flow-charging flow control model considering low-carbon-economic goals is established, and the optimal control plan is obtained by solving it.

[0033] Furthermore, in step 3), the objective function F = EC TN +EC PDN +tax(EM TN +EM PDN ),in:

[0034]

[0035] Among them, F, EC TN ,EC PDN , EM TN , EM PDN They represent the total operating cost of the two systems, the operating cost of the transportation system, the operating cost of the power system, the carbon emissions of the transportation system, and the carbon emissions of the power system, respectively. The specific calculation methods are shown in equations (1)-(4). The subscript rs represents the starting and ending points; the subscript a represents the road section; the subscript l represents the charging station; the subscripts i, j, and h represent the grid nodes; Represents a set of traffic sections; Represents the charging section set; E Nrepresents the set of nodes in the distribution network; π(0) represents the set of child nodes connected to the root node; tax represents the carbon tax; ω represents the unit time cost; represents the fuel vehicle toll imposed on road section a; and They represent the traffic volume and travel time on section a respectively; and Respectively represent the number of electric vehicles and charging time in charging station l; γ l and E e Respectively represent the charging price of charging station l and the charging capacity of each electric vehicle; a i , b i , c i are the three power generation parameters of the distributed generation at node i; represents the active output value of the distributed generation at node i; ρ and P 0j They represent the unit price of electricity purchased from the main grid and the amount of electricity purchased from the main grid respectively; represents the length of section a in the traffic system; e CO2 Indicates the amount of carbon emissions generated per unit of electricity generated.

[0036] Furthermore, in step 3), the power system operation constraints are established as follows:

[0037]

[0038] V j =V i -(r ij P ij +x ij Q ij ) / V0 (7)

[0039]

[0040] Formula (5) represents the active power balance constraint of the power system; Formula (6) represents the reactive power balance constraint of the power system; Formula (7) represents Ohm's law of the power system; Formula (8) represents the capacity constraint of the power line of the power system; Formula (9) represents the upper and lower limit constraints of the node voltage; Formula (10) represents the upper and lower limit constraints of the active output of the distributed power source; Formula (11) represents the upper and lower limit constraints of the reactive output of the distributed power source; Formula (12) defines the active load of the node, including the conventional power load of the node and the charging load of the electric vehicle connected to the node;

[0041] Where, subscript l represents the charging station; subscripts i, j, h represent grid nodes; π(j) represents the set of all child nodes connected to node j; P ij and Q ij are respectively the active power and reactive power flowing through line ij; Pjh With Q jh are respectively the active power and reactive power flowing through line jh; r ij With x ij are the resistance and reactance of line ij respectively; V i is the voltage amplitude of node i; V j is the voltage amplitude of node j; V0 represents the voltage amplitude of the gateway node; and V i are the upper and lower limits of the voltage amplitude of node i respectively; is the apparent power upper limit of line ij; and are the active power and reactive power generated by the power supply at node i respectively; and are the active load and reactive load at node i respectively; p i They are respectively the upper and lower limits of the active output of distributed power sources; q i are the upper and lower limits of the reactive power output of distributed generation, respectively; C(j) represents the set of charging stations powered by power system node j; represents the conventional active load at node j.

[0042] Furthermore, in step 3), the traffic system operation constraints are set as follows:

[0043]

[0044]

[0045] Formula (13) represents the conservation constraint of the traffic system section flow; Formula (14) represents the conservation constraint of the charging station flow; Formula (15) represents the conservation constraint of the travel demand of fuel vehicles; Formula (16) represents the conservation constraint of the travel demand of electric vehicles; Formula (17) represents the nonlinear relationship between the section flow and the section travel time; Formula (18) represents the nonlinear relationship between the number of vehicles queuing at the charging station and the length of the queuing time at the charging station; Formula (19) defines the passage cost of fuel vehicles, including time cost and paid section tolls; Formula (20) defines the passage cost of electric vehicles, including charging cost, time cost and paid road tolls; Formula (21) represents the Wardrop user equilibrium criterion for fuel vehicles; Formula (22) represents the Wardrop user equilibrium criterion for electric vehicles;

[0046] In the formula, and They represent the path sets of fuel vehicles and electric vehicles between rs respectively; represents the free travel time of section a; represents the traffic capacity of road section a; It represents the travel cost that a fuel vehicle needs to pay when choosing path k; represents the upper limit of the service capacity of charging station l; represents the queue time of charging station l when it is fully loaded; p ser Indicates the charging power of the charging station; It represents the travel cost that the electric vehicle needs to pay when choosing path k; Indicates the path and section indicator parameters of the fuel vehicle. If section a is on path k, then Take 1, otherwise take 0; Represents the indicative parameters of the path and road section of the electric vehicle. If road section a is located on path k, then Take 1, otherwise take 0; Represents the path and charging station indicator parameters of the electric vehicle. If charging station l is located on path k, then Take 1, otherwise take 0; and They represent the fuel vehicle flow and electric vehicle flow that select path k respectively; τ g and τ e Respectively represent the proportion of fuel vehicles and electric vehicles in the total travel demand; q rs represents the travel demand between the starting point r and the destination s; and They represent the minimum fuel vehicle travel cost and the minimum electric vehicle travel cost between rs respectively.

[0047] In addition, the present invention proposes a computer device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements any of the steps of a traffic flow-charging flow control method taking into account a low-carbon-economic goal.

[0048] In addition, the present invention proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the traffic flow-charging flow control methods considering low-carbon-economic goals are implemented.

[0049] The following example illustrates the superiority of the traffic flow-charging flow control strategy of the present invention. The example tested by the present invention is composed of a 3-node traffic network coupled with a 5-node power grid. The topology of the two networks is as follows: Figure 2 The economic control scheme and the low-carbon-economic coordinated control scheme proposed by the present invention are respectively used to control the traffic flow-charging flow, and the superiority of the method proposed by the present invention is demonstrated by comparing the results of the two.

[0050] Table 1 Comparison of results under different control schemes

[0051]

[0052] The dispatch results are shown in Table 1. Considering only economic efficiency for coordinated regulation can minimize the economic cost of the power-transportation network, but the carbon emission cost is the highest in the three scenarios ($392.69); considering only carbon emissions can minimize the carbon emission costs of the two networks, but ignoring the economic cost, resulting in the highest driving cost per user and the overall cost of the two networks in the three scenarios ($478.45). In contrast, the regulation method proposed in the present invention takes into account both economic costs and carbon emission costs, effectively weighs the conflict between the two types of objectives, and achieves the minimum overall social cost ($715.62), which is 3.12% and 14.32% lower than the other two regulation methods, respectively.

[0053] The above only expresses the implementation methods of the invention, which are described in a more specific and detailed manner, but cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the invention, which all belong to the protection scope of the invention. Therefore, the protection scope of the invention patent shall be based on the attached claims.

Claims

1. A traffic flow-charging flow control method considering low-carbon-economic goals, characterized in that: The method comprises the following steps: 1) Obtaining network parameters and operating parameters of the power system and the transportation system, wherein the network parameters include the network topology, line resistance / reactance of the power system, the network topology, free travel time and capacity of the road section, charging station capacity and maximum waiting time of the transportation system, and the operating parameters include the power generation cost parameters of the unit, the unit price of electricity purchased by the upper power grid, the carbon emission parameters of the unit, the unit travel time cost of the travel user, and the carbon tax; 2) Obtain scenario data, including active load and reactive load of the power system, and travel demand at different starting and ending points in the transportation system; 3) Based on the obtained network parameters, operating parameters and scenario data of the two systems, with the power system operation constraints and traffic system operation constraints as constraints, price signals and distributed power output as control objects, and minimizing the sum of the operating costs and carbon emission costs of the two systems as the objective function, a traffic flow-charging flow control model considering low-carbon-economic goals is established, and the optimal control plan is obtained by solving it.

2. A traffic flow-charging flow control method considering low-carbon-economic goals according to claim 1, characterized in that: In step 3), the objective function F = EC TN +EC PDN +tax(EM TN +EM PDN ),in: Among them, F, EC TN ,EC PDN , EM TN , EM PDN They represent the total operating cost of the two systems, the operating cost of the transportation system, the operating cost of the power system, the carbon emissions of the transportation system, and the carbon emissions of the power system, respectively. The specific calculation methods are shown in equations (1)-(4). The subscript rs represents the starting and ending points; the subscript a represents the road section; the subscript l represents the charging station; the subscripts i, j, and h represent the grid nodes; Represents a set of traffic sections; Represents the charging section set; E N represents the set of nodes in the distribution network; π(0) represents the set of child nodes connected to the root node; tax represents the carbon tax; ω represents the unit time cost; represents the fuel vehicle toll imposed on road section a; and They represent the traffic volume and travel time on section a respectively; and Respectively represent the number of electric vehicles and charging time in charging station l; γ l and E e Respectively represent the charging price of charging station l and the charging capacity of each electric vehicle; a i , b i , c i are the three power generation parameters of the distributed generation at node i; represents the active output value of the distributed generation at node i; ρ and P 0j They represent the unit price of electricity purchased from the main grid and the amount of electricity purchased from the main grid respectively; represents the length of section a in the traffic system; e CO2 Indicates the amount of carbon emissions generated per unit of electricity generated.

3. A traffic flow-charging flow control method considering low-carbon-economic goals according to claim 2, characterized in that: In step 3), the power system operation constraints are established as follows: Formula (5) represents the active power balance constraint of the power system; Formula (6) represents the reactive power balance constraint of the power system; Formula (7) represents Ohm's law of the power system; Formula (8) represents the capacity constraint of the power line of the power system; Formula (9) represents the upper and lower limit constraints of the node voltage; Formula (10) represents the upper and lower limit constraints of the active output of the distributed power source; Formula (11) represents the upper and lower limit constraints of the reactive output of the distributed power source; Formula (12) defines the active load of the node, including the conventional power load of the node and the charging load of the electric vehicle connected to the node; In the formula, the subscript l represents the charging station; Subscripts i, j, and h represent grid nodes, and π(j) represents the set of all child nodes connected to node j; P ij and Q ij are respectively the active power and reactive power flowing through line ij; P jh With Q jh are respectively the active power and reactive power flowing through line jh; r ij With x ij are the resistance and reactance of line ij respectively; V i is the voltage amplitude of node i; V j is the voltage amplitude of node j; V0 represents the voltage amplitude of the gateway node; and V i are the upper and lower limits of the voltage amplitude of node i respectively; is the apparent power upper limit of line ij; and are the active power and reactive power generated by the power supply at node i respectively; and are the active load and reactive load at node i respectively; pi They are respectively the upper and lower limits of the active output of distributed power sources; q i are the upper and lower limits of the reactive power output of distributed generation, respectively; C(j) represents the set of charging stations powered by power system node j; represents the conventional active load at node j.

4. A traffic flow-charging flow control method considering low-carbon-economic goals according to claim 3, characterized in that: In step 3), the traffic system operation constraints are set as follows: Formula (13) represents the conservation constraint of the traffic system section flow; Formula (14) represents the conservation constraint of the charging station flow; Formula (15) represents the conservation constraint of the travel demand of fuel vehicles; Formula (16) represents the conservation constraint of the travel demand of electric vehicles; Formula (17) represents the nonlinear relationship between the section flow and the section travel time; Formula (18) represents the nonlinear relationship between the number of vehicles queuing at the charging station and the length of the queuing time at the charging station; Formula (19) defines the passage cost of fuel vehicles, including time cost and paid section tolls; Formula (20) defines the passage cost of electric vehicles, including charging cost, time cost and paid road tolls; Formula (21) represents the Wardrop user equilibrium criterion for fuel vehicles; Formula (22) represents the Wardrop user equilibrium criterion for electric vehicles; In the formula, and They represent the path sets of fuel vehicles and electric vehicles between rs respectively; represents the free travel time of section a; represents the traffic capacity of road section a; It represents the travel cost that a fuel vehicle needs to pay when choosing path k; represents the upper limit of the service capacity of charging station l; Indicates the queue time at charging station l when it is fully loaded; p ser Indicates the charging power of the charging station; It represents the travel cost that the electric vehicle needs to pay when choosing path k; Indicates the path and section indicator parameters of the fuel vehicle. If section a is on path k, then Take 1, otherwise take 0; Represents the indicative parameters of the path and road section of the electric vehicle. If road section a is located on path k, then Take 1, otherwise take 0; Represents the path and charging station indicator parameters of the electric vehicle. If charging station l is located on path k, then Take 1, otherwise take 0; and They represent the fuel vehicle flow and electric vehicle flow that select path k respectively; τ g and τ e Respectively represent the proportion of fuel vehicles and electric vehicles in the total travel demand; q rs represents the travel demand between the starting point r and the destination s; and They represent the minimum fuel vehicle travel cost and the minimum electric vehicle travel cost between rs respectively.

5. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of a traffic flow-charging flow control method considering low-carbon-economic goals described in claims 1-4 are implemented.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a traffic flow-charging flow control method considering low-carbon-economic goals described in claims 1-4 are implemented.

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