An electric vehicle charging scheduling method and system considering an electric carbon market

Abstract

The application discloses an electric vehicle charging scheduling method and system considering an electricity-carbon market. The method first constructs an electricity-carbon market coordination model, wherein the electricity-carbon market coordination model comprises a low-carbon operation model of a power transmission network, a coordinated operation model of a power distribution network, a carbon emission flow tracking model of the power transmission network, and a carbon emission flow tracking model of the power distribution network. Then, based on a user equilibrium theory, a traffic flow model of the electric vehicle is constructed with the minimum total travel time, charging time and charging cost of the electric vehicle as the target. Further, based on the electricity-carbon market coordination model and the traffic flow model, a charging station planning and charging integrated management model of the electric vehicle is constructed. Finally, the electricity-carbon market coordination model, the traffic flow model and the charging station planning and charging integrated management model are solved to obtain an electric vehicle charging scheduling strategy. The application can optimize the charging strategy of the electric vehicle, reduce carbon emissions and relieve the load pressure of the power grid, and improve the renewable energy consumption capacity.

Description

Technical Field

[0001] This invention relates to the field of power system and transportation system collaborative optimization technology, and in particular to an electric vehicle charging scheduling method and system that takes into account the carbon market. Background Technology

[0002] Global energy transition has become a critical issue in addressing climate change and ensuring a sustainable future. The integration of decarbonization efforts in the power sector with transportation electrification has spurred the emergence of eco-transport systems. Eco-transport systems are a holistic approach that combines power grid operation with electricity and carbon markets to optimize energy use and reduce emissions. By considering the dynamic interactions between these systems, the eco-transport framework supports the development of sustainable transportation solutions that align with broader energy transition goals.

[0003] However, current electric vehicle charging scheduling methods mainly focus on grid load balance and user charging costs, but do not consider the coordination of the electricity carbon market, lack the integration of market mechanisms such as carbon prices and carbon quotas, lack accurate quantification of the carbon emissions on the generation side implied during the charging process, and lack coordinated modeling of traffic flow and power flow, resulting in unreasonable layout of charging stations, concentrated charging time, exacerbating the peak-valley difference of the power grid, and increasing dependence on fossil energy. Summary of the Invention

[0004] The purpose of this invention is to provide an electric vehicle charging scheduling method and system that takes into account the carbon market, optimizes the charging strategy of electric vehicles, reduces carbon emissions and alleviates grid load pressure, and improves the capacity for renewable energy absorption.

[0005] To achieve the above objectives, the present invention provides an electric vehicle charging scheduling method considering the electric carbon market, comprising:

[0006] Construct a collaborative model for the electricity carbon market; wherein, the collaborative model for the electricity carbon market includes a low-carbon operation model for the transmission network, a collaborative operation model for the distribution network, a carbon emission flow tracking model for the transmission network, and a carbon emission flow tracking model for the distribution network;

[0007] Based on user equilibrium theory, a traffic flow model for electric vehicles is constructed with the goal of minimizing the total driving time, charging time, and charging cost of electric vehicles.

[0008] Based on the aforementioned electric carbon market collaboration model and traffic flow model, a charging station planning and charging integration management model for electric vehicles is constructed.

[0009] The electric vehicle charging scheduling strategy is obtained by solving the electric carbon market collaboration model, the traffic flow model, and the charging station planning and charging integration management model.

[0010] Optionally, solving the electric carbon market collaborative model, the traffic flow model, and the charging station planning and charging integration management model to obtain the electric vehicle charging scheduling strategy includes:

[0011] The low-carbon operation model of the transmission network is solved to obtain the power flow and nodal electricity price of the transmission network;

[0012] Based on the power flow of the transmission network, the carbon emission flow tracking model of the transmission network is solved to obtain the carbon flow and nodal carbon potential of the transmission network.

[0013] Based on the nodal electricity price of the transmission network, the cooperative operation model of the distribution network is solved to obtain the power flow, nodal electricity price and power demand of the distribution network.

[0014] Based on the power flow of the distribution network and the carbon flow and nodal carbon potential of the transmission network, the carbon emission flow tracking model of the distribution network is solved to obtain the carbon flow and nodal carbon potential of the distribution network.

[0015] Solving the traffic flow model yields an estimated basic electric vehicle charging demand;

[0016] Based on the nodal electricity price of the distribution network, the carbon flow of the distribution network, the nodal carbon potential of the distribution network, and the estimated basic electric vehicle charging demand, the charging station planning and charging integration management model is solved to obtain the current electric vehicle charging demand and charging price.

[0017] Determine whether the convergence condition is met;

[0018] If yes, then output the final electric vehicle charging demand and charging price; if no, then based on the current electric vehicle charging demand and charging price, re-solve the power distribution network collaborative operation model and the traffic flow model until the convergence condition is met, and output the final electric vehicle charging demand and charging price.

[0019] Optionally, the objective function of the low-carbon operation model of the power transmission network is:

[0020]

[0021] Among them, Min.F TN Let Ω represent the objective function of the low-carbon operation model of the power transmission network, where i represents the i-th node in the power transmission network, t represents time, and Ω represents the set. G Ω represents the set of thermal power units in the power transmission network. BESS a represents the collection of battery energy storage in the power transmission network. i b i c i This represents the fuel cost coefficient of thermal power units in transmission network node i. This represents the output of the thermal power unit at node i in the power transmission network at time t. This represents the battery degradation cost at node i of the power grid. This represents the battery charging power of the electric vehicle at time t at node i in the power grid. This represents the carbon price of node i in the power transmission network at time t. This represents the carbon allowance purchased by the generating unit at node i in the transmission network at time t. This represents the carbon allowance sold by the generating unit at node i in the transmission network at time t;

[0022] The constraints of the low-carbon operation model of the power transmission network include active power balance constraints, reactive power balance constraints, AC power flow constraints, capacity constraints of thermal power units, ramping constraints, node voltage constraints, power flow constraints, energy balance constraints of battery energy storage systems, energy storage capacity constraints, charging and discharging power constraints of electric vehicles, and carbon emission constraints of thermal power units.

[0023] Optionally, the constraints of the low-carbon operation model of the power transmission network include:

[0024]

[0025] Where j represents other power grid nodes connected to transmission network node i. This represents the set of other power grid nodes connected to transmission grid node i. This represents the output of renewable energy units at node i in the power transmission network at time t. This represents the active power demand of the lower-level distribution network at time t for transmission network node i. This represents the charging power of the electric vehicle battery storage at transmission network node i at time t. This represents the discharge power of the electric vehicle battery energy storage at transmission network node i at time t. This represents the active power flow at time t on the line between node i and node j in the transmission network. This represents the reactive power of the thermal power unit at node i in the transmission network at time t. This represents the reactive power demand of the lower-level distribution network at time t for node i in the transmission network. This represents the reactive power flow at time t on the line between node i and node j in the transmission network. This represents the active power flow on the line between node i and node j in the transmission network. U represents the reactive power flow on the line between node i and node j in the transmission network. i U represents the voltage at node i in the transmission network. j Let θ represent the voltage at node j in the transmission network. ij G represents the phase angle on the line between node i and node j in the transmission network. ij B represents the conductance on the line between node i and node j in the transmission network. ijP represents the susceptance on the line between node i and node j in the transmission network. i G,max This represents the maximum active power of the thermal power unit in node i of the transmission network. This represents the maximum reactive power of the thermal power unit in node i of the transmission network. This represents the ramp-up rate of the generator unit at node i in the power transmission network. This represents the downhill ramp rate of the generator unit at node i in the power transmission network. and U represents the minimum and maximum node voltages of node i in the transmission network, respectively. i,t This represents the voltage at node i in the power transmission network at time t. and Let represent the maximum active power flow and maximum reactive power flow at time t on the line between node i and node j in the transmission network. This represents the energy storage state of the battery energy storage at transmission network node i at time t. This represents the energy storage state of the battery energy storage at transmission network node i at time t+1. and These represent the charging efficiency and discharging efficiency of battery energy storage in the power transmission network, respectively. and Let represent the minimum and maximum energy stored in the battery at node i of the transmission network, respectively. and Cap represents the maximum charging power and maximum discharging power of the battery storage at node i of the transmission network, respectively. i This represents the carbon allowance allocated to unit i in the power transmission network. This represents the emission factor of node i in the power transmission network.

[0026] Optionally, the objective function of the distribution network collaborative operation model is:

[0027]

[0028] Among them, Min.F DN Let m represent the objective function of the distribution network collaborative operation model, m represent the distribution network node, n represent other distribution network nodes connected to distribution network node m, and Ω represent the other nodes connected to distribution network node m. MG Ω represents the set of nodes where the micro-generator unit is located. BESS Ω represents the set of nodes where energy storage is located, and Ω represents the set of nodes in the distribution network. Ω represents the collection of feeders connecting the distribution network and the substation. FD For the collection of other feeders, P represents the nodal price transmitted from the upstream transmission network at time t. t ST This represents the transmission power of the substation at time t. Let a represent the power of a small thermal power unit at node m in the distribution network at time t. m b m c m This represents the fuel cost coefficient of thermal power units at node m in the distribution network. This represents the carbon price of node m in the distribution network at time t. This represents the carbon allowance purchased by the generating unit at time t at node m in the distribution network. This represents the carbon allowance sold by the generating unit at time t at node m in the distribution network. This represents the battery degradation cost at distribution network node m. This represents the battery charging power of the electric vehicle at node m in the distribution network at time t. I represents the average nodal price of electricity in the distribution network. mn,t Z represents the square of the current at time t on the line between node m and node n in the distribution network. ST This indicates the impedance of the substation. This represents the impedance of the feeder on the line between node m and node n in the distribution network.

[0029] The constraints of the distribution network collaborative operation model include active power balance constraints, reactive power balance constraints, power flow constraints, node voltage constraints, power flow constraints, energy balance constraints of battery energy storage systems, energy storage state constraints of battery energy storage systems, charging and discharging power constraints of electric vehicles, and indirect emission constraints of end users.

[0030] Optionally, the constraints of the distribution network collaborative operation model include:

[0031]

[0032]

[0033] U min ≤U m,t ≤U max ;

[0034]

[0035] in, Let m represent the set of nodes adjacent to node m in the distribution network. This represents the active power flow at time t on the line between node m and node n in the distribution network. This represents the transmission power of the substation at time t for node m in the distribution network. This represents the output of renewable energy units at node m in the distribution network at time t. This represents the power of a small thermal power unit at node m in the distribution network at time t. This represents the discharge power of the electric vehicle battery energy storage at distribution network node m at time t. This represents the load of distribution network node m at time t, excluding electric vehicles. This represents the charging power of the electric vehicle battery energy storage at distribution network node m at time t. This represents the electric vehicle load at distribution network node m at time t. This represents the load reduction at node m in the distribution network at time t. This represents the reactive power flow at time t on the line between node m and node n in the distribution network. This represents the reactive power transmitted by the substation at node m in the distribution network at time t. U represents the reactive power output of a small generator at node m in the distribution network at time t. m,t U represents the voltage at node m in the distribution network at time t. n,t Let r represent the voltage at node n in the distribution network at time t. mn x represents the resistance of the line between node m and node n in the distribution network. mn U0 represents the reactance on the line between node m and node n in the distribution network, and I represents the reference voltage of the distribution network node. mn,t U represents the square of the current at time t on the line between node m and node n in the distribution network. min U represents the minimum voltage at a distribution network node. max Indicates the maximum voltage at a distribution network node. This represents the maximum active power flow at time t on the line between grid node m and node n. This represents the maximum reactive power flow at time t on the line between node m and node n in the distribution network. This represents the energy storage state of the battery energy storage at distribution network node m at time t+1. and These represent the charging efficiency and discharging efficiency of battery energy storage in the distribution network, respectively. This represents the minimum energy required for battery storage at distribution network node m. This represents the energy stored in the battery at node m in the distribution network at time t. This represents the maximum energy stored in the battery at node m in the distribution network. This represents the maximum charging power of the electric vehicle battery energy storage at distribution network node m at time t. This represents the maximum discharge power of the electric vehicle battery energy storage at node m in the distribution network at time t. This represents the emission factor of node m in the distribution network. Cap represents the total load or electric vehicle load of distribution network node m at time t. m This represents the carbon allowance allocated to unit m at node m of the distribution network. This represents the carbon allowance purchased by the generating unit at time t at node m in the distribution network. This represents the carbon allowance sold by the generating unit at time t at node m in the distribution network.

[0036] Optionally, the objective function of the carbon emission flow tracking model for the power transmission network is:

[0037]

[0038] in, This represents the carbon potential of node i in the power transmission network at time t. This represents the output of the thermal power unit at node i in the power transmission network at time t. This represents the discharge power of the electric vehicle battery energy storage at transmission network node i at time t. This represents the carbon potential of energy storage at node i in the transmission network at time t. This represents the active power flow at time t on the line between node i and node j in the transmission network. This represents the carbon potential of the line branch between node i and node j in the transmission network. Γ represents the output of renewable energy units at node i in the power grid at time t. ij This represents the power injection node on the line between node i and node j in the transmission network;

[0039] The objective function of the carbon emission flow tracking model for the power distribution network is:

[0040]

[0041] in, This represents the carbon potential of distribution network node m at time t, which is inherited from the upstream transmission system. This represents the transmission power of the substation at time t for node m in the distribution network. This represents the carbon intensity of distribution network node m at time t at the substation node. This represents the power of a small thermal power unit at node m in the distribution network at time t. This represents the carbon emission intensity of a small generator at node m in the distribution network. This represents the discharge power of the electric vehicle battery energy storage at distribution network node m at time t. This represents the carbon potential of energy storage at node m in the distribution network at time t. This represents the active power flow at time t on the line between node m and node n in the distribution network. This represents the carbon potential of the line branch between node m and node n in the distribution network. This represents the output of new energy generating units at node m in the distribution network at time t.

[0042] Optionally, the objective function of the traffic flow model is:

[0043]

[0044] in, Let A represent the time cost, and let C represent the set of traffic segments. a,t Let t represent the traffic flow on road a at time t. a,t Ω represents the time taken to travel on road a at time t. cs Let x represent the set of charging stations for electric vehicles. h This indicates the car charging at the h-th charging station. This represents the time at time t for charging at the h-th charging station. Let x represent the charging price for electric vehicles at the h-th charging station. h,t E represents the car charging at the h-th charging station at time t. Cd Indicates charging needs;

[0045] The constraints of the traffic flow model can be expressed as:

[0046]

[0047] Where od represents a traffic segment starting at point o and ending at point d, (o,d)∈O represents the set of traffic segments, and a represents the a-th traffic segment, a∈A p This represents the index of the road segment, where p represents the p-th path. Represents the set of all paths. Let represent the traffic flow on the p-th path in the traffic segment from o to d at time t. The traffic flow of fuel vehicles on the p-th path of the traffic segment from o to d at time t. This represents the traffic demand for electric vehicles on the traffic segment from point O to point D at time t. c represents the traffic demand for gasoline-powered vehicles on the road segment from o to d at time t. a,t This represents the traffic flow on road segment a at time t. It is a 0-1 parameter; it is 1 if the p-th path passes through the a-th path, and 0 otherwise. h,t This indicates the car that is charging at the h-th charging station at time t. t represents the virtual charging link for the h-th charging station. a,t This represents the time taken to traverse road segment a at time t. The time taken to traverse road segment a during idle periods is represented by ξ and τ, where ξ and τ both represent road resistance parameters in the road impedance function. a This represents the traffic flow on road segment a. This represents the road capacity of the a-th road segment. This represents the charging time at the h-th charging station at time t. Let represent the charging time of the h-th charging station during idle time, and Que(·) be the queuing function in queuing theory. S represents the maximum traffic attraction of the h-th charging station. ini E represents the average initial state of charge of the battery. Con,EV L represents the energy consumption of an electric vehicle per kilometer. a E represents the length of the a-th road segment. max Indicates the maximum capacity of the electric vehicle. Θ represents the energy required to reach the first electric vehicle charging station on the p-th path. NCP Θ represents the set of non-charging paths. CP This represents the set of charging paths.

[0048] Optionally, the objective function of the charging station planning and charging integration management model is:

[0049]

[0050] Among them, Ω cs This refers to a collection of charging stations for electric vehicles. This represents the initial charging price at the h-th charging station at time t. This represents the carbon potential of node m in the distribution network at time t. This represents the carbon price of node m in the distribution network at time t. This represents the electricity price at node m in the distribution network at time t. Let represent the electric vehicle charging demand at the h-th charging station at time t;

[0051] The constraints of the charging station planning and charging integration management model are as follows:

[0052]

[0053] Where ε represents the price elasticity constraint. This represents the electric vehicle charging demand at the h-th charging station at time t. This represents the basic electric vehicle charging demand at the h-th charging station based on the estimate at time t. This represents the initial charging price at the h-th charging station at time t. This represents the base charging price at the h-th charging station at time t.

[0054] To achieve the above objectives, the present invention also provides an electric vehicle charging scheduling system considering the carbon market, comprising:

[0055] The electricity carbon market collaborative model construction module is used to construct an electricity carbon market collaborative model; wherein, the electricity carbon market collaborative model includes a transmission network low-carbon operation model, a distribution network collaborative operation model, a transmission network carbon emission flow tracking model, and a distribution network carbon emission flow tracking model;

[0056] The traffic flow model building module is used to construct a traffic flow model for electric vehicles based on user equilibrium theory, with the goal of minimizing the total driving time, charging time, and charging cost of electric vehicles.

[0057] The charging station planning and charging integration management model construction module is used to construct an electric vehicle charging station planning and charging integration management model based on the electric carbon market collaboration model and the traffic flow model.

[0058] The model solving module is used to solve the electric carbon market collaborative model, the traffic flow model, and the charging station planning and charging integration management model to obtain the electric vehicle charging scheduling strategy.

[0059] Compared to existing technologies, this invention provides an electric vehicle charging scheduling method and system that considers the electricity carbon market. Firstly, by constructing an electricity carbon market collaborative model, it comprehensively considers factors such as low-carbon operation of the transmission network, coordinated operation of the distribution network, and carbon emission flow tracking, optimizing the allocation of electricity resources in the electricity carbon market and helping to guide the power system towards low-carbon operation. By tracking carbon emission flows, the carbon emissions during electricity production and transmission can be clearly identified, prompting grid operators to take measures to reduce carbon emissions. Simultaneously, based on user equilibrium theory, a traffic flow model for electric vehicles is constructed, combining traffic flow with charging demand, taking into account the travel behavior and charging demand characteristics of electric vehicle users. This allows for the rational planning of the location and scale of charging stations based on traffic flow, user travel time, charging time, and other factors, and enables integrated charging management. Finally, by solving the electricity carbon market collaborative model, the traffic flow model, and the charging station planning and integrated charging management model, a comprehensive electric vehicle charging scheduling strategy considering multiple factors can be obtained. This strategy can provide electric vehicles with precise scheduling plans for charging time, location, and charging power based on real-time information such as grid operation status, carbon emissions, traffic flow, and user demand. This enables intelligent and efficient management of the charging process, improves the convenience and reliability of electric vehicle charging, further reduces carbon emissions, alleviates grid load pressure, and enhances the capacity for renewable energy absorption. Attached Figure Description

[0060] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a schematic flowchart of an electric vehicle charging scheduling method considering the electric carbon market provided by an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of a process for solving the charging management model according to an embodiment of the present invention;

[0063] Figure 3 This is a schematic diagram of the power structure of an IEEE 30-bus system provided in an embodiment of the present invention;

[0064] Figure 4 This is a schematic diagram of the coupling between a power distribution network and a transportation network provided in an embodiment of the present invention;

[0065] Figure 5 This is a graph showing the carbon potential change trend of the nodes where the charging stations are located under different cases provided in the embodiments of the present invention;

[0066] Figure 6 This is a graph showing the electricity price change trend of the distribution network node where the electric vehicle is located under different cases provided in the embodiments of the present invention;

[0067] Figure 7 This is a graph showing the price change trend of electric vehicle charging under different cases provided in the embodiments of the present invention;

[0068] Figure 8 This is a graph showing the changing trends of electric vehicle charging demand under different scenarios provided in the embodiments of the present invention;

[0069] Figure 9 This is a structural block diagram of an electric vehicle charging scheduling system that takes into account the carbon market, provided by an embodiment of the present invention. Detailed Implementation

[0070] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] See Figure 1 , Figure 1 This is a flowchart illustrating an electric vehicle charging scheduling method considering the electric carbon market, provided by an embodiment of the present invention. The electric vehicle charging scheduling method considering the electric carbon market includes steps S1 to S4:

[0072] Step S1: Construct a collaborative model for the electricity carbon market; wherein the collaborative model for the electricity carbon market includes a low-carbon operation model for the transmission network, a collaborative operation model for the distribution network, a carbon emission flow tracking model for the transmission network, and a carbon emission flow tracking model for the distribution network.

[0073] In one optional embodiment, the objective function of the low-carbon operation model of the power grid includes the fuel cost of thermal power units, the battery degradation cost, and the carbon quota trading cost;

[0074] Specifically, the objective function of the low-carbon operation model of the power transmission network is:

[0075]

[0076] Among them, Min.F TN Let Ω represent the objective function of the low-carbon operation model of the power transmission network, where i represents the i-th node in the power transmission network, t represents time, and Ω represents the set. G Ω represents the set of thermal power units in the power transmission network. BESS a represents the collection of battery energy storage in the power transmission network. i b i c i This represents the fuel cost coefficient of thermal power units in transmission network node i. This represents the output of the thermal power unit at node i in the power transmission network at time t. This represents the battery degradation cost at node i of the power grid. This represents the battery charging power of the electric vehicle at time t at node i in the power grid. This represents the carbon price of node i in the power transmission network at time t. This represents the carbon allowance purchased by the generating unit at node i in the transmission network at time t. This represents the carbon allowance sold by the generating unit at node i in the transmission network at time t;

[0077] The constraints of the low-carbon operation model of the power transmission network include active power balance constraints, reactive power balance constraints, AC power flow constraints, capacity constraints of thermal power units, ramping constraints, node voltage constraints, power flow constraints, energy balance constraints of battery energy storage systems, energy storage capacity constraints, charging and discharging power constraints of electric vehicles, and carbon emission constraints of thermal power units.

[0078] Specifically, the constraints of the low-carbon operation model of the power transmission network include:

[0079]

[0080]

[0081] Where j represents other power grid nodes connected to transmission network node i. This represents the set of other power grid nodes connected to transmission grid node i. This represents the output of renewable energy units at node i in the power transmission network at time t. This represents the active power demand of the lower-level distribution network at time t for transmission network node i. This represents the charging power of the electric vehicle battery storage at transmission network node i at time t. This represents the discharge power of the electric vehicle battery energy storage at transmission network node i at time t. This represents the active power flow at time t on the line between node i and node j in the transmission network. This represents the reactive power of the thermal power unit at node i in the transmission network at time t. This represents the reactive power demand of the lower-level distribution network at time t for node i in the transmission network. This represents the reactive power flow at time t on the line between node i and node j in the transmission network. This represents the active power flow on the line between node i and node j in the transmission network. U represents the reactive power flow on the line between node i and node j in the transmission network. i U represents the voltage at node i in the transmission network. j Let θ represent the voltage at node j in the transmission network. ij G represents the phase angle on the line between node i and node j in the transmission network. ij B represents the conductance on the line between node i and node j in the transmission network. ij P represents the susceptance on the line between node i and node j in the transmission network. i G,max This represents the maximum active power of the thermal power unit in node i of the transmission network. This represents the maximum reactive power of the thermal power unit in node i of the transmission network. This represents the ramp-up rate of the generator unit at node i in the power transmission network. This represents the downhill ramp rate of the generator unit at node i in the power transmission network. and U represents the minimum and maximum node voltages of node i in the transmission network, respectively. i,t This represents the voltage at node i in the power transmission network at time t. and Let represent the maximum active power flow and maximum reactive power flow at time t on the line between node i and node j in the transmission network. This represents the energy storage state of the battery energy storage at transmission network node i at time t. This represents the energy storage state of the battery energy storage at transmission network node i at time t+1. and These represent the charging efficiency and discharging efficiency of battery energy storage in the power transmission network, respectively. and Let represent the minimum and maximum energy stored in the battery at node i of the transmission network, respectively. and Cap represents the maximum charging power and maximum discharging power of the battery storage at node i of the transmission network, respectively. i This represents the carbon allowance allocated to unit i in the power transmission network. This represents the emission factor of node i in the power transmission network.

[0082] It should be noted that Equation (2) is the active power balance constraint, Equation (3) is the reactive power balance constraint, Equations (4) and (5) are the AC power flow constraints, Equation (6) is the maximum capacity constraint of thermal power units, Equation (7) is the ramp constraint, i.e., ramp limit, which refers to the constraint on the rate of change of active power of thermal power units (thermal power units) per unit time, and specifies the maximum rate of increase or decrease of active power of thermal power units in adjacent time intervals, Equation (8) is the node voltage constraint, Equation (9) is the power flow constraint, Equation (10) is the energy balance constraint of battery energy storage system (BESS), Equation (11) is the energy storage capacity constraint, Equation (12) is the charging and discharging power constraint of electric vehicles, and Equation (13) indicates that the actual carbon emissions of thermal power units should be within the allocated quota plus the purchased quota minus the sold quota.

[0083] It is worth noting that the objective function of the low-carbon operation model for the power transmission network integrates economic costs and carbon market signals, and the constraints cover physical laws, equipment safety, grid stability, and policy compliance, forming a comprehensive dispatch optimization framework. By incorporating environmental costs into power generation decisions through carbon quota trading costs and emission constraints, resources are guided towards low-carbon units, while ensuring that dispatch schemes meet the physical limitations of grid operation (such as power flow, voltage, and equipment capacity).

[0084] In one optional embodiment, the objective function of the distribution network collaborative operation model includes the cost of energy purchase by the main transmission system, the fuel cost of micro thermal power units, the revenue from the purchase and sale of quotas by end users, the cost of battery degradation, and the cost of power loss.

[0085] Specifically, the objective function of the power distribution network collaborative operation model is:

[0086]

[0087] Among them, Min.F DN Let m represent the objective function of the distribution network collaborative operation model, and let Ω represent the distribution network node. MG Ω represents the set of nodes where the micro-generator unit is located. BESS Ω represents the set of nodes where energy storage is located, and Ω represents the set of nodes in the distribution network. Ω represents the collection of feeders connecting the distribution network and the substation. FD For the collection of other feeders, P represents the nodal price transmitted from the upstream transmission network at time t. t ST This represents the transmission power of the substation at time t. Let a represent the power of a small thermal power unit at node m in the distribution network at time t.m b m c m This represents the fuel cost coefficient of thermal power units at node m in the distribution network. This represents the carbon price of node m in the distribution network at time t. This represents the carbon allowance purchased by the generating unit at time t at node m in the distribution network. This represents the carbon allowance sold by the generating unit at time t at node m in the distribution network. This represents the battery degradation cost at distribution network node m. This represents the battery charging power of the electric vehicle at node m in the distribution network at time t. I represents the average nodal price of electricity in the distribution network. mn,t Z represents the square of the current at time t on the line between node m and node n in the distribution network. ST This indicates the impedance of the substation. This represents the impedance of the feeder on the line between node m and node n in the distribution network.

[0088] The constraints of the distribution network collaborative operation model include active power balance constraints, reactive power balance constraints, power flow constraints, node voltage constraints, power flow constraints, energy balance constraints of battery energy storage systems, energy storage state constraints of battery energy storage systems, charging and discharging power constraints of electric vehicles, and indirect emission constraints of end users.

[0089] Specifically, the constraints of the power distribution network collaborative operation model are as follows:

[0090]

[0091]

[0092] U min ≤U m,t ≤U max (20)

[0093]

[0094] in, Let m represent the set of nodes adjacent to node m in the distribution network. This represents the active power flow at time t on the line between node m and node n in the distribution network. This represents the transmission power of the substation at time t for node m in the distribution network. This represents the output of renewable energy units at node m in the distribution network at time t. This represents the power of a small thermal power unit at node m in the distribution network at time t. This represents the discharge power of the electric vehicle battery energy storage at distribution network node m at time t. This represents the load of distribution network node m at time t, excluding electric vehicles. This represents the charging power of the electric vehicle battery energy storage at distribution network node m at time t. This represents the electric vehicle load at distribution network node m at time t. This represents the load reduction at node m in the distribution network at time t. This represents the reactive power flow at time t on the line between node m and node n in the distribution network. This represents the reactive power transmitted by the substation at node m in the distribution network at time t. U represents the reactive power output of a small generator at node m in the distribution network at time t. m,t U represents the voltage at node m in the distribution network at time t. n,t Let r represent the voltage at node n in the distribution network at time t. mn x represents the resistance of the line between node m and node n in the distribution network. mn U0 represents the reactance on the line between node m and node n in the distribution network, and I represents the reference voltage of the distribution network node. mn,t U represents the square of the current at time t on the line between node m and node n in the distribution network. min U represents the minimum voltage at a distribution network node. max Indicates the maximum voltage at a distribution network node. This represents the maximum active power flow at time t on the line between grid node m and node n. This represents the maximum reactive power flow at time t on the line between node m and node n in the distribution network. This represents the energy storage state of the battery energy storage at distribution network node m at time t+1. and These represent the charging efficiency and discharging efficiency of battery energy storage in the distribution network, respectively. This represents the minimum energy required for battery storage at distribution network node m. This represents the energy stored in the battery at node m in the distribution network at time t. This represents the maximum energy stored in the battery at node m in the distribution network. This represents the maximum charging power of the electric vehicle battery energy storage at distribution network node m at time t. This represents the maximum discharge power of the electric vehicle battery energy storage at node m in the distribution network at time t. This represents the emission factor of node m in the distribution network. Cap represents the total load or electric vehicle load of distribution network node m at time t. m This represents the carbon allowance allocated to unit m at node m of the distribution network. This represents the carbon allowance purchased by the generating unit at time t at node m in the distribution network. This represents the carbon allowance sold by the generating unit at time t at node m in the distribution network. This represents the set of nodes connected to node n in the distribution network.

[0095] It should be noted that equations (15) and (16) are the active power balance constraint and reactive power balance constraint in the distribution network, respectively. Equations (17)-(19) are DistFlow equations, which are power flow constraints. Equation (20) is the node voltage constraint. Equations (21) and (22) are power flow constraints. Equation (23) is the energy balance constraint of the battery energy storage system (BESS). Equation (24) is the energy storage state constraint of the BESS. Equation (25) is the charging and discharging power constraint of electric vehicles. Equation (26) indicates that the indirect carbon emissions of end users should be within the allocated quota plus the purchased quota minus the sold quota.

[0096] It is worth noting that, in order to comprehensively address carbon emissions from both the supply and consumption sides, this embodiment of the invention levies a carbon tax on the emission factors of thermal power units on the power generation side, constraining their carbon emission quotas through formula (13); on the consumption side, it levies a carbon tax on end-users and electric vehicle charging behavior based on carbon flow tracking results, constraining indirect carbon emissions from users through formula (26), thereby achieving coordinated control of carbon emissions from both the supply and consumption sides. This embodiment of the invention refers to this mechanism as a "dual carbon tax mechanism," which incentivizes all stakeholders to adopt clean technologies and promotes a fairer sharing of carbon emission costs by implementing taxes at each stage of the supply chain, while also encouraging producers to reduce their environmental impact.

[0097] Furthermore, in order to accurately track the carbon footprint from the power generation end to the demand end, this embodiment of the invention constructs a carbon emission flow tracking model. This model can provide a structured framework for accurately allocating carbon emissions and reflecting the real environmental impact of consumer choices.

[0098] First, nodal carbon potential reflects the amount of carbon emissions carried per unit power at a specific node during power transmission. Its magnitude depends on the carbon potential and emission intensity of each transmission line injecting power into that node, as well as the proportion of power injected by these lines in the total injected power. If a node has multiple transmission lines injecting power, and some of these lines connect to generators with high carbon emission intensity, then the node's carbon potential will be relatively high, meaning that the node "carries" a significant amount of carbon emissions during power transmission.

[0099] Therefore, the nodal carbon potential in the transmission network can be calculated using the following formula:

[0100]

[0101] in, This represents the carbon potential of node i in the power transmission network at time t. This represents the output of the thermal power unit at node i in the power transmission network at time t. Indicates the emission intensity of node i in the transmission network. This represents the set of transmission lines that inject power into node i of the transmission network. This represents the active power flow at time t on the line between node i and node j in the transmission network. This represents the line carbon potential on the line between node i and node j in the transmission network. This represents the output of renewable energy units at node i in the power transmission network at time t. This represents the active power flow at time t on the line between node i and node j in the transmission network.

[0102] Furthermore, if we consider battery energy storage systems within the network, the carbon intensity of energy storage in the transmission network can be expressed as:

[0103]

[0104] in, This represents the carbon potential of energy storage at node i in the transmission network at time t. This represents the carbon potential of energy storage at node i in the transmission network at time t+1. This represents the energy storage state of the battery energy storage at transmission network node i at time t. This represents the carbon potential of node i in the power transmission network at time t. Let Δt represent the charging power of the electric vehicle battery storage at time t of the power grid node i, where Δt represents the time interval.

[0105] Therefore, in one alternative embodiment, the objective function of the carbon emission flow tracking model for the power transmission network is:

[0106]

[0107] in, This represents the carbon potential of node i in the power transmission network at time t. This represents the output of the thermal power unit at node i in the power transmission network at time t. This represents the discharge power of the electric vehicle battery energy storage at transmission network node i at time t. This represents the carbon potential of energy storage at node i in the transmission network at time t. This represents the active power flow at time t on the line between node i and node j in the transmission network. This represents the carbon potential of the line branch between node i and node j in the transmission network. Γ represents the output of renewable energy units at node i in the power grid at time t. ij This represents the power injection node on the line between power grid node i and node j.

[0108] It should be noted that in formula (29), the numerator is based on the numerator of formula (27) and includes an additional step. This is because the carbon emissions from energy storage discharge have been taken into account. It is the discharge power of the electric vehicle battery energy storage at transmission network node i at time t. This represents the carbon potential of the energy storage at the corresponding moment, incorporating the carbon emissions from energy storage discharge into the total carbon emission calculation. The denominator is expanded based on the denominator of formula (27). This is because the energy storage discharge power has also become part of the power balance of the distribution network nodes and the calculation of carbon potential. It needs to be included in the total power calculation of the denominator to make the carbon potential calculation more comprehensive and accurate, taking into account the power and carbon emission factors of thermal power units, new energy units, line transmission and energy storage discharge.

[0109] Understandably, the objective function of the carbon emission flow tracing model for the transmission network described above tracks the carbon flow from generation nodes to demand nodes at the transmission level. Similarly, the objective function for tracking the carbon flow to end users at the distribution level, i.e., the objective function of the carbon emission flow tracing model for the distribution network, can be expressed as:

[0110]

[0111] in, This represents the carbon potential of distribution network node m at time t, which is inherited from the upstream transmission system. This represents the transmission power of the substation at time t for node m in the distribution network. This represents the carbon intensity of distribution network node m at time t at the substation node. This represents the power of a small thermal power unit at node m in the distribution network at time t. This represents the carbon emission intensity of a small generator at node m in the distribution network. This represents the discharge power of the electric vehicle battery energy storage at distribution network node m at time t. This represents the carbon potential of energy storage at node m in the distribution network at time t. This represents the active power flow at time t on the line between node m and node n in the distribution network. This represents the carbon potential of the line branch between node m and node n in the distribution network. This represents the output of new energy generating units at node m in the distribution network at time t.

[0112] It is worth noting that applying carbon emission flow tracking models is crucial for implementing a dual carbon tax mechanism. By quantifying carbon emissions at each stage of the energy process, this model ensures that producers and consumers pay taxes based on their actual carbon contributions, promoting transparency and enhancing industry-wide accountability. Furthermore, the model's ability to track carbon emissions through electricity flow allows for more detailed carbon tax collection at the consumption level. Consumers will pay a tax on the carbon emissions contained in the electricity they consume, not just on emissions generated at production points. This ensures that the carbon tax reflects emissions throughout the entire lifecycle of energy consumption, thus allocating environmental costs more accurately and equitably.

[0113] Step S2: Based on user equilibrium theory, construct a traffic flow model for electric vehicles with the goal of minimizing the total driving time, charging time, and charging cost of electric vehicles;

[0114] It should be noted that the widespread use of electric vehicles not only changes traffic flow patterns but also requires more refined collaboration between the power and transportation systems in planning and scheduling. Therefore, to study the synergistic effects between the transportation sector and the electricity and carbon markets, this invention develops an electric vehicle-integrated traffic flow model as a fundamental analytical tool to investigate how the increasing prevalence of electric vehicles impacts traffic dynamics, electricity demand, and carbon emissions in urban and regional networks.

[0115] In this embodiment of the invention, the objective function of the traffic flow model aims to minimize the total travel time of vehicles, the charging time of electric vehicles, and the charging cost of electric vehicles.

[0116] Therefore, in one alternative embodiment, the objective function of the traffic flow model is:

[0117]

[0118] in, Let A represent the time cost, and let C represent the set of traffic segments. a,t Let t represent the traffic flow on road a at time t. a,t Ω represents the time taken to travel on road a at time t. cs Let x represent the set of charging stations for electric vehicles. h This indicates the car charging at the h-th charging station. This represents the time at time t for charging at the h-th charging station. Let x represent the charging price for electric vehicles at the h-th charging station. h,t E represents the car charging at the h-th charging station at time t. Cd Indicates charging needs;

[0119] Specifically, the constraints of the traffic flow model can be expressed as:

[0120]

[0121] Where od represents a traffic segment starting at point o and ending at point d, (o,d)∈O represents the set of traffic segments, and a represents the a-th traffic segment, a∈A p This represents the index of the road segment, where p represents the p-th path. Represents the set of all paths. Let represent the traffic flow on the p-th path in the traffic segment from o to d at time t. The traffic flow of fuel vehicles on the p-th path of the traffic segment from o to d at time t. This represents the traffic demand for electric vehicles on the traffic segment from point O to point D at time t. c represents the traffic demand for gasoline-powered vehicles on the road segment from o to d at time t. a,t This represents the traffic flow on road segment a at time t. It is a 0-1 parameter; it is 1 if the p-th path passes through the a-th path, and 0 otherwise. h,t This indicates the car that is charging at the h-th charging station at time t. t represents the virtual charging link for the h-th charging station. a,t This represents the time taken to traverse road segment a at time t. The time taken to traverse road segment a during idle periods is represented by ξ and τ, where ξ and τ both represent road resistance parameters in the road impedance function. a This represents the traffic flow on road segment a. This represents the road capacity of the a-th road segment. This represents the charging time at the h-th charging station at time t. Let represent the charging time of the h-th charging station during idle time. Que(·) is the queuing function in queuing theory, which is a mathematical theory and method for studying the random aggregation and dispersal phenomena of systems and the working process of random service systems. S represents the maximum traffic attraction of the h-th charging station. ini E represents the average initial state of charge (SOC) of the battery. Con,EV L represents the energy consumption of an electric vehicle per kilometer. a E represents the length of the a-th road segment. max Indicates the maximum capacity of the electric vehicle. Θ represents the energy required to reach the first electric vehicle charging station on the p-th path. NCP Θ represents the set of non-charging paths. CP This represents the set of charging paths.

[0122] It should be noted that Equation (33) represents traffic flow, so it is a positive value; Equation (34) means that the total traffic flow from the starting point to the destination should be equal to the traffic demand; Equation (35) means that the traffic volume on a road is equal to the sum of the traffic flows on all paths passing through that road; Equation (36) means that the number of electric vehicles in the charging station is equal to the total traffic flow through the relevant charging links; Equation (37) is a commonly used road impedance function, namely the Public Roads Authority (BPR) function; Equation (38) calculates the charging time based on the queuing model; Equation (39) limits the capacity of the charging station; Equation (40) means that the path chosen by the electric vehicle should ensure that the vehicle has enough energy to reach the destination in the case of a non-charging path (where there is no charging station); Equation (41) means that the path chosen by the electric vehicle should ensure that it has enough energy to reach the first charging station.

[0123] It should be noted that the traffic flow model integrated with electric vehicles is based on the user equilibrium (UE) model and extended to reflect user travel behavior and decisions while considering the balance between traffic flow and energy demand.

[0124] Specifically, the User Equilibrium (UE) model is based on assumptions about user travel behavior. Its core principle is that each user attempts to minimize their own travel costs. When an equilibrium state is reached, no user can reduce their costs by unilaterally changing their travel routes. Traffic flow models follow this theory, incorporating user travel decision-making behavior. However, in addition to traditional travel costs (such as travel time), they integrate cost factors specific to electric vehicles, such as charging time and cost, to analyze the behavior of electric vehicles in the traffic network and the distribution of traffic flow.

[0125] The UE (User Experience) model aims to achieve a balanced optimization of user travel costs within a transportation system. Traffic flow models inherit this goal and extend it. Their objective function, while minimizing vehicle travel time, incorporates the charging time and cost of electric vehicles at charging stations, striving to find a balance between traffic flow and energy demand to minimize the overall system cost. This objective reflects both the inheritance and development of the traditional UE model, considering both traffic congestion mitigation and the energy consumption and cost of electric vehicles.

[0126] Furthermore, some constraints of the traffic flow model are based on the fundamental constraints of the UE model and adjusted to suit the characteristics of electric vehicles. Regarding traffic flow demand balance (Equations 33-35), the principle of flow conservation from origin to destination in the UE model is followed. The road impedance function (Equation 37) uses the BPR function, reflecting the impact of traffic flow on road travel time, a common method in the UE model. Simultaneously, a range constraint (Equations 40-41) is added for electric vehicles to ensure sufficient battery power when choosing a route, whether it's a non-charging route or a route to the first charging station. This is a special constraint set considering the energy characteristics of electric vehicles, which differ from traditional gasoline vehicles.

[0127] It is worth noting that the traffic flow model determines the charging paths and the temporal and spatial distribution of charging demand for electric vehicles. Through the constraints of this model, such as driving range constraints and charging station capacity constraints, the charging demand of electric vehicles in different regions and at different times can be obtained, providing crucial demand data for charging station planning and integrated charging management models.

[0128] Step S3: Based on the electric carbon market collaboration model and the traffic flow model, construct a charging station planning and charging integration management model for electric vehicles;

[0129] Specifically, based on the traffic flow model, the traffic capture volume of the h-th charging station can be expressed as:

[0130]

[0131] in, This represents the traffic capture volume of the newly built h-th charging station. express, It is a 01 variable; if the p-th path passes through the h-th charging station, it is 1; otherwise, it is 0.

[0132] Furthermore, the charging demand of the charging station can be estimated using formula (42):

[0133]

[0134] in, Based on the estimated basic electric vehicle charging demand at the h-th charging station at time t, D CH κ represents the total charging demand of the electric vehicle system, f represents the percentage of charging stations chosen, and f represents the percentage of charging stations selected. t trip This represents the proportion of electric vehicle trips at time t. Ω represents the traffic capture volume of the newly built h-th charging station. cs This refers to a collection of charging stations for electric vehicles. The variable is 0 or 1. It is 1 if the h-th charging station is built, and 0 otherwise. Υ represents a maximum number.

[0135] It should be noted that Equation (43) indicates that the estimated electric vehicle charging demand is proportional to its traffic capture volume. If no charging stations are built, it means that there is no charging demand.

[0136] Therefore, in one optional embodiment, the objective function of the charging station planning and charging integration management model can be expressed as:

[0137]

[0138] Among them, Ω cs This refers to a collection of charging stations for electric vehicles. This represents the initial charging price at the h-th charging station at time t. This represents the carbon potential of node m in the distribution network at time t. This represents the carbon price of node m in the distribution network at time t. This represents the electricity price at node m in the distribution network at time t. Let represent the electric vehicle charging demand at the h-th charging station at time t;

[0139] Specifically, the constraints of the charging station planning and charging integration management model are as follows:

[0140]

[0141] Where ε represents the price elasticity constraint. This represents the electric vehicle charging demand at the h-th charging station at time t. This represents the basic electric vehicle charging demand at the h-th charging station based on the estimate at time t. This represents the initial charging price at the h-th charging station at time t. This represents the carbon price of node m in the distribution network at time t. This represents the base charging price for the m-th charging station at time t.

[0142] It should be noted that in the low-carbon operation model and carbon emission flow tracking model of the power transmission network, carbon quota trading costs and carbon emission limits affect power generation costs, thereby changing electricity prices. The distribution network collaborative operation model and its carbon emission flow tracking model introduce a dual carbon tax mechanism, resulting in differences in electricity prices and carbon costs across different time periods and regions. These price signals are important economic factors in charging station planning and integrated charging management models. When planning charging stations, it is necessary to consider electricity prices in different regions and select locations with lower costs for construction. In integrated charging management, a reasonable charging pricing strategy should be formulated based on carbon costs and electricity prices to guide users to charge during low-carbon, low-price periods, thereby reducing carbon emissions, alleviating grid load pressure, and improving the capacity for renewable energy absorption.

[0143] Therefore, by combining the economic factors (electricity price, carbon cost) provided by the electric carbon market collaborative model and the demand and layout factors provided by the traffic flow model, a more scientific electric vehicle charging station planning and charging integration management model can be constructed.

[0144] Step S4: Solve the electric carbon market collaborative model, the traffic flow model, and the charging station planning and charging integration management model to obtain the electric vehicle charging scheduling strategy.

[0145] In one optional embodiment, solving the electric carbon market collaboration model, the traffic flow model, and the charging station planning and charging integration management model to obtain the electric vehicle charging scheduling strategy includes:

[0146] The low-carbon operation model of the transmission network is solved to obtain the power flow and nodal electricity price of the transmission network;

[0147] Based on the power flow of the transmission network, the carbon emission flow tracking model of the transmission network is solved to obtain the carbon flow and nodal carbon potential of the transmission network.

[0148] Based on the nodal electricity price of the transmission network, the cooperative operation model of the distribution network is solved to obtain the power flow, nodal electricity price and power demand of the distribution network.

[0149] Based on the power flow of the distribution network and the carbon flow and nodal carbon potential of the transmission network, the carbon emission flow tracking model of the distribution network is solved to obtain the carbon flow and nodal carbon potential of the distribution network.

[0150] Solving the traffic flow model yields an estimated basic electric vehicle charging demand;

[0151] Based on the nodal electricity price of the distribution network, the carbon flow of the distribution network, the nodal carbon potential of the distribution network, and the estimated basic electric vehicle charging demand, the charging station planning and charging integration management model is solved to obtain the current electric vehicle charging demand and charging price.

[0152] Determine whether the convergence condition is met;

[0153] If yes, then output the final electric vehicle charging demand and charging price; if no, then based on the current electric vehicle charging demand and charging price, re-solve the power distribution network collaborative operation model and the traffic flow model until the convergence condition is met, and output the final electric vehicle charging demand and charging price.

[0154] Specifically, such as Figure 2 As shown, Figure 2 This is a schematic diagram illustrating a process for solving the charging management model according to an embodiment of the present invention. Figure 2 As shown, the solution process includes the following steps:

[0155] Step 1, Initialization: The process begins, entering the collaborative solution of transmission network, distribution network, transportation network and carbon footprint tracking;

[0156] Step 2, Solving the low-carbon operation model of the transmission network: First, based on formulas (1)-(13), the low-carbon operation model of the transmission network is solved to realize the coupled operation of the electricity market and the carbon market, and the power flow and the electricity price of the transmission network nodes are obtained.

[0157] Step 3: Solving the carbon emission flow tracking model of the power transmission network: The power flow of the power transmission network is used as the input of the carbon flow model of the power transmission network, and then the carbon footprint tracking is solved based on formulas (27)-(30) to obtain the carbon flow and node carbon potential of the power transmission network;

[0158] Step 4: Solving the distribution network collaborative operation model: Based on the nodal electricity price of the transmission network and formulas (14)-(26), analyze the distribution network operation and demand-side carbon emission responsibility, solve the distribution network collaborative operation model, and obtain the distribution network power flow, distribution network nodal electricity price, and distribution network power demand.

[0159] Step 5: Solving the carbon emission flow tracking model of the distribution network: Combining the power flow of the distribution network and the carbon flow and nodal carbon potential of the transmission network, the carbon footprint is tracked from the transmission network to the distribution network through formula (31) to obtain the carbon flow and nodal carbon potential of the distribution network.

[0160] Step 6: Solve the traffic flow model: Based on formulas (32)-(41), solve the traffic flow model considering electric vehicles and estimate the charging demand of electric vehicles.

[0161] Step 7: Solving the charging station planning and charging integration management model: The distribution network node electricity price, distribution network carbon flow and node carbon potential, and electric vehicle charging demand are used as inputs to the charging management model. The model is solved based on formulas (42)-(45) to carry out charging station planning and charging demand analysis, and to obtain electric vehicle charging demand and charging price.

[0162] Step 8, Convergence Check: Check if the current calculation results have converged. If converged, the process ends, and the final electric vehicle charging demand and charging price are output. If not converged, the charging price and charging demand are fed back to the power distribution network and transportation network to adjust the power demand of the power distribution network and the charging demand of electric vehicles. Repeat the above steps until the decision variables shown in the figure reach the convergence accuracy, for example, the L2 norm change is less than 10 to the power of -4.

[0163] In summary, the electric vehicle charging scheduling method considering the electricity carbon market provided by this invention first establishes a joint model of the transmission and distribution systems for low-carbon operation. It considers the synergistic effects between the carbon and electricity markets, where thermal power units at the transmission level must fulfill their carbon responsibilities according to quota mechanisms, while end-users at the distribution level must adhere to demand-side carbon responsibilities. Based on electricity flow, carbon footprints can be tracked from generation to end-user. Node prices in the distribution network, model carbon intensity, and electric vehicle demand derived from traffic network modeling will further influence the planning and integrated charging management of electric vehicle fast charging stations. Price-incentivized charging management strategies and electric vehicle charging demand will, in turn, affect electric vehicle traffic flow and charging flow in the transportation network. Furthermore, electric vehicle demand also affects the total load in the distribution network, which in turn affects the operation of the transmission network, ultimately forming a cycle. This method allows for the study of an integrated framework for electric vehicles participating in an eco-transportation system within an electricity-carbon market.

[0164] To further verify the superiority of the electric vehicle charging scheduling method considering the carbon market provided by this invention, the following will detail how the method is verified in a modified IEEE 30-node system through three case studies.

[0165] It should be noted that the simulation was performed on a PC equipped with an Intel Core™ i9-10980HK CPU @ 5.10GHz, 32.00GB RAM and an RTX GeForce 2080.

[0166] The specific case scenarios are set up as follows:

[0167] Case 1: Operation of power grids and planning and integrated charging management of charging stations without carbon emission trading mechanisms.

[0168] Case 2: Consider carbon emissions trading only in the operation of the power grid on the generation side.

[0169] Case 3: Considering the double carbon tax, users and electric vehicles need to account for carbon costs through carbon tracking (i.e., using the method provided in the embodiments of this invention).

[0170] See Figure 3 , Figure 3 This is a schematic diagram of the power structure of an IEEE 30-bus system provided in an embodiment of the present invention. See also... Figure 4 , Figure 4 This is a schematic diagram of the coupling between a power distribution network and a transportation network provided in an embodiment of the present invention.

[0171] First, such as Figure 3As shown, this IEEE 30-node system comprises an IEEE 33-node distribution network. Different symbols are used to distinguish components. Solid black wavy lines represent thermal power units, traditional power generation units; dashed black wavy lines represent renewable energy units, representing the renewable energy generation portion. Downward arrows indicate loads, i.e., the electricity demand side. The dashed box on the left marks the lower-level distribution network, connected to the main network on the right, indicating the hierarchical relationship between this system and the lower-level distribution network. Nodes are numbered (e.g., 1 to 30), and the nodes are connected by lines, demonstrating the transmission and distribution paths of electricity between nodes. This primarily presents the distribution of thermal power units, renewable energy units, and loads within the network, aiding in understanding power flow, power-load balance, and grid structure characteristics within the system.

[0172] like Figure 4 As shown, the coupling points between the power distribution network and the transportation network are candidate locations for electric vehicle charging station planning. White circles represent transportation network nodes, black circles represent power distribution network nodes, and black arrows represent roads, illustrating the connections and directions of traffic between transportation network nodes. Dashed lines represent coupling relationships, indicating the interaction between the transportation system and the power distribution network system. Figure 4 The lower part is the power distribution network system, which contains 33 power distribution nodes (numbered 1-33). There is a transformer on the left. The nodes are connected by lines to form a power distribution network. Some nodes (such as 23-25) form independent branches. Figure 4 The upper part represents the transportation network system. Transportation network nodes (such as 1, 2, 3, 7, 8, etc.) are interconnected by roads. The direction of the arrows indicates the traffic flow, and some nodes allow bidirectional traffic (such as nodes 1 and 2, nodes 3 and 4, etc.). Red lines connect transportation network nodes to power distribution network nodes. For example, some transportation network nodes are connected to some power distribution network nodes, visually demonstrating the coupling between the transportation and power distribution systems. This illustrates the mutual influence when the two systems operate in tandem, such as the transmission relationship of electric vehicle charging load between the transportation and power distribution systems.

[0173] See Figure 5 , Figure 5 This is a graph showing the carbon potential change trend of the nodes where charging stations are located under different cases provided in the embodiments of the present invention. Figure 5This study primarily demonstrates the daily carbon intensity variations of a planned electric vehicle charging station node under different operational scenarios. Throughout the day, the carbon intensity trends in the three cases are similar: peaking in the early morning and evening, and significantly decreasing around noon, indicating that more renewable energy sources, such as solar power, may be fed into the grid during these periods. Case 3 exhibits the lowest carbon intensity throughout the day, suggesting that its strategy significantly reduces indirect carbon emissions related to electric vehicle charging. This demonstrates that the dual carbon tax and carbon tracking mechanisms in this embodiment effectively reduce indirect carbon emissions related to electric vehicle charging, promote more sustainable energy use, and exhibit significant advantages in carbon management.

[0174] See Figure 6 , Figure 6 This is a graph showing the trend of electricity price changes at the distribution network node where the electric vehicle is located under different cases provided in the embodiments of the present invention. Figure 6 This primarily illustrates the marginal price (DLMP) of a node in a planned electric vehicle charging station operation over 24 hours under different operating scenarios. All three cases exhibit a similar pattern: the distribution network node price rises from the early morning, peaks in the late afternoon—coinciding with peak electricity demand—and then gradually declines at night. This pattern reflects typical daily electricity usage trends, with increased demand during the day and evening, leading to higher generation costs. It can be concluded that the carbon policy in the different cases has a relatively small impact on the DLMP of the distribution network node, and therefore is unlikely to significantly affect the energy purchase cost of the charging station. Case 3, however, lowers charging prices during peak hours and slightly increases them at night. This pricing strategy, related to carbon intensity and grid demand management, can shift electric vehicle charging demand to off-peak hours, effectively regulating grid load and demonstrating the superiority of this invention in balancing grid pressure.

[0175] See Figure 7 , Figure 7 This is a graph showing the price change trend of electric vehicle charging under different cases provided in the embodiments of the present invention. Figure 7This paper primarily showcases the price changes of electric vehicle (EV) charging in three cases within a planned charging station network. Cases 1 and 2 exhibit very similar price patterns, indicating that their demand-side carbon management or operational strategies are similar, resulting in a relatively small impact on charging costs. In contrast, Case 3 demonstrates a significantly different price trajectory, with lower prices during daytime peak hours and slightly higher prices at night. This difference may stem from a more aggressive carbon management strategy in Case 3, such as involving a robust carbon pricing mechanism to reduce peak-hour demand or encourage energy use during off-peak hours. The lower charging prices during daytime peak hours and slightly higher prices at night are indirectly related to carbon intensity and grid demand management. Typically, when demand is high, the grid is under the greatest pressure, often requiring the activation of less efficient, fossil fuel-based power plants, which increases carbon emissions. By strategically reducing charging prices during these peak hours, Case 3 aims to shift EV charging demand to off-peak hours. This shift not only helps to manage grid load more effectively but also aligns with periods when clean, renewable energy is more likely to be available, thereby reducing carbon intensity associated with EV charging. This demonstrates the superiority of this invention in guiding charging behavior and optimizing energy utilization.

[0176] See Figure 8 , Figure 8 This is a graph showing the changing trends of electric vehicle charging demand under different scenarios provided in the embodiments of the present invention. Figure 8 The presentation primarily showcases the electric vehicle charging demand at planned charging stations. Different scenarios reveal distinct demand patterns, peaking during typical commuting hours and declining during midday and late-night periods. Cases 1 and 2 exhibit similar demand curves, indicating that their similar operational strategies failed to significantly alter charging behavior during peak hours. In contrast, Case 3 shows a significant decrease in demand during peak hours, suggesting that effective demand management strategies encourage charging during off-peak hours. This also demonstrates that Case 3 employs advanced charging management models or technologies, such as grid load- or carbon intensity-based charging management, which effectively regulates consumer behavior, alleviates grid pressure, and aligns with periods of lower carbon emissions. This difference in demand highlights the potential of targeted strategies to influence electric vehicle charging habits in support of grid stability and environmental goals.

[0177] Referring to Table 1, which presents the quantitative results of electric vehicle charging station operation indicators for different cases provided in the embodiments of the present invention. As shown in Table 1, Case 1 has the highest revenue at $4712.46 and the highest profit at $3923.88, indicating that it adopts a more traditional charging station management approach aimed at maximizing financial returns. However, the revenue and profit of Case 2 and Case 3 both decreased, with Case 3 having the lowest revenue and profit at $4618.13 and $3828.67, respectively. This trend suggests that these strategies prioritize environmental impact over maximizing revenue generation, such as price adjustments during peak demand periods to reduce grid overload. Indirect emissions decreased significantly across all cases, from 7.12 tons in Case 1 to 5.19 tons in Case 3, highlighting the effective environmental management strategy in Case 3, which reduced the carbon footprint per unit of electricity supply. Similarly, the total charging demand met decreased slightly from Case 1 to Case 3, which may reflect strategic demand management or improved energy efficiency. It is worth noting that the charging sustainability index, measured by emissions per megawatt-hour (tons / MWh), improved across the cases—from 0.59 in Case 1 to 0.45 in Case 3—indicating that Case 3 not only supports lower emissions but also promotes more sustainable energy use.

[0178] Table 1. Quantitative results of different cases regarding electric vehicle charging station operation indicators.

[0179] Case 1 Case 2 Case 3 Earnings ($) 4712.46 4624.96 4618.13 Profit ($) 3923.88 3853.43 3828.67 Indirect emissions (tons) 7.12 6.51 5.19 Meeting the charging requirements (kWh) 11992.59 11427.63 11383.14 Environmental sustainability of charging (ton / MWh) 0.59 0.57 0.45

[0180] In summary, through comparative analysis of Cases 1, 2, and 3, the superiority of the embodiments of the present invention in terms of carbon management, grid load regulation, and energy sustainability can be fully demonstrated from multiple dimensions such as carbon emissions, electricity prices, charging prices, charging demand, and charging station operation indicators.

[0181] See Figure 9 , Figure 9 This is a structural block diagram of an electric vehicle charging scheduling system 200 considering the carbon market, provided by an embodiment of the present invention. The electric vehicle charging scheduling system 200 considering the carbon market includes:

[0182] The electricity carbon market collaborative model construction module 21 is used to construct an electricity carbon market collaborative model; wherein, the electricity carbon market collaborative model includes a transmission network low-carbon operation model, a distribution network collaborative operation model, a transmission network carbon emission flow tracking model, and a distribution network carbon emission flow tracking model;

[0183] Traffic flow model construction module 22 is used to construct a traffic flow model for electric vehicles based on user equilibrium theory, with the goal of minimizing the total driving time, charging time and charging cost of electric vehicles.

[0184] The charging station planning and charging integration management model construction module 23 is used to construct an electric vehicle charging station planning and charging integration management model based on the electric carbon market collaboration model and the traffic flow model.

[0185] The model solving module 24 is used to solve the electric carbon market collaborative model, the traffic flow model, and the charging station planning and charging integration management model to obtain the electric vehicle charging scheduling strategy.

[0186] In one optional embodiment, the model solving module 24 is specifically used for:

[0187] The low-carbon operation model of the transmission network is solved to obtain the power flow and nodal electricity price of the transmission network;

[0188] Based on the power flow of the transmission network, the carbon emission flow tracking model of the transmission network is solved to obtain the carbon flow and nodal carbon potential of the transmission network.

[0189] Based on the nodal electricity price of the transmission network, the cooperative operation model of the distribution network is solved to obtain the power flow, nodal electricity price and power demand of the distribution network.

[0190] Based on the power flow of the distribution network and the carbon flow and nodal carbon potential of the transmission network, the carbon emission flow tracking model of the distribution network is solved to obtain the carbon flow and nodal carbon potential of the distribution network.

[0191] Solving the traffic flow model yields an estimated basic electric vehicle charging demand;

[0192] Based on the nodal electricity price of the distribution network, the carbon flow of the distribution network, the nodal carbon potential of the distribution network, and the estimated basic electric vehicle charging demand, the charging station planning and charging integration management model is solved to obtain the current electric vehicle charging demand and charging price.

[0193] Determine whether the convergence condition is met;

[0194] If yes, then output the final electric vehicle charging demand and charging price; if no, then based on the current electric vehicle charging demand and charging price, re-solve the power distribution network collaborative operation model and the traffic flow model until the convergence condition is met, and output the final electric vehicle charging demand and charging price.

[0195] It should be noted that the electric vehicle charging scheduling system considering the carbon market provided in this embodiment of the invention is used to execute all the process steps of the electric vehicle charging scheduling method considering the carbon market in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0196] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for scheduling electric vehicle charging considering the carbon market, characterized in that, include: Construct a collaborative model for the electricity carbon market; wherein, the collaborative model for the electricity carbon market includes a low-carbon operation model for the transmission network, a collaborative operation model for the distribution network, a carbon emission flow tracking model for the transmission network, and a carbon emission flow tracking model for the distribution network; Based on user equilibrium theory, a traffic flow model for electric vehicles is constructed with the goal of minimizing the total driving time, charging time, and charging cost of electric vehicles. Based on the aforementioned electric carbon market collaboration model and traffic flow model, a charging station planning and charging integration management model for electric vehicles is constructed. The electric vehicle charging scheduling strategy is obtained by solving the electric carbon market collaboration model, the traffic flow model, and the charging station planning and charging integration management model. The process of solving the electric carbon market collaboration model, the traffic flow model, and the charging station planning and charging integration management model to obtain the electric vehicle charging scheduling strategy includes: The low-carbon operation model of the transmission network is solved to obtain the power flow and nodal electricity price of the transmission network; Based on the power flow of the transmission network, the carbon emission flow tracking model of the transmission network is solved to obtain the carbon flow and nodal carbon potential of the transmission network. Based on the nodal electricity price of the transmission network, the cooperative operation model of the distribution network is solved to obtain the power flow, nodal electricity price and power demand of the distribution network. Based on the power flow of the distribution network and the carbon flow and nodal carbon potential of the transmission network, the carbon emission flow tracking model of the distribution network is solved to obtain the carbon flow and nodal carbon potential of the distribution network. Solving the traffic flow model yields an estimated basic electric vehicle charging demand; Based on the nodal electricity price of the distribution network, the carbon flow of the distribution network, the nodal carbon potential of the distribution network, and the estimated basic electric vehicle charging demand, the charging station planning and charging integration management model is solved to obtain the current electric vehicle charging demand and charging price. Determine whether the convergence condition is met; If yes, then output the final electric vehicle charging demand and charging price; if no, then based on the current electric vehicle charging demand and charging price, re-solve the power distribution network collaborative operation model and the traffic flow model until the convergence condition is met, and output the final electric vehicle charging demand and charging price.

2. The electric vehicle charging scheduling method considering the carbon market as described in claim 1, characterized in that, The objective function of the low-carbon operation model of the power transmission network is: ； in, This represents the objective function of the low-carbon operation model of the power transmission network. This represents the i-th power grid node in the transmission network. Indicates time, Represents a set, This represents the set of thermal power units in the power transmission network. This represents the collection of battery energy storage devices in the power transmission network. , , This represents the fuel cost coefficient of thermal power units in transmission network node i. This represents the output of the thermal power unit at node i in the power transmission network at time t. This represents the battery degradation cost at node i of the power grid. This represents the battery charging power of the electric vehicle at time t, which is the value of the power grid node i. This represents the carbon price of transmission network node i at time t. This represents the carbon allowance purchased by the generating unit at time t of the transmission network node i. This represents the carbon allowance sold by the generating unit at time t of the transmission network node i; The constraints of the low-carbon operation model of the power transmission network include active power balance constraints, reactive power balance constraints, AC power flow constraints, capacity constraints of thermal power units, ramping constraints, node voltage constraints, power flow constraints, energy balance constraints of battery energy storage systems, energy storage capacity constraints, charging and discharging power constraints of electric vehicles, and carbon emission constraints of thermal power units.

3. The electric vehicle charging scheduling method considering the carbon market as described in claim 2, characterized in that, The constraints of the low-carbon operation model of the power transmission network include: ； ； ； ； ， ； ， ； ； ， ； ； ； ， ； ； Where j represents other power grid nodes connected to transmission network node i. This represents the set of other power grid nodes connected to transmission grid node i. This represents the output of renewable energy units at node i in the power transmission network at time t. This represents the active power demand of the lower-level distribution network at time t for transmission network node i. This represents the charging power of the electric vehicle battery storage at transmission network node i at time t. This represents the discharge power of the electric vehicle battery energy storage at transmission network node i at time t. Let represent the active power flow at time t on the line between node i and node j in the transmission network. This represents the reactive power of the thermal power unit at node i in the transmission network at time t. This represents the reactive power demand of the lower-level distribution network at time t for node i in the transmission network. This represents the reactive power flow at time t on the line between node i and node j in the transmission network. This represents the active power flow on the line between node i and node j in the transmission network. This represents the reactive power flow on the line between node i and node j in the transmission network. This represents the voltage at node i in the transmission network. This represents the voltage at node j in the transmission network. This represents the phase angle on the line between node i and node j in the transmission network. This represents the electrical conductance on the line between node i and node j in the transmission network. This represents the susceptance on the line between node i and node j in the transmission network. This represents the maximum active power of the thermal power units in node i of the transmission network. This represents the maximum reactive power of the thermal power unit in node i of the transmission network. This represents the ramp-up rate of the generator unit at node i in the power transmission network. This represents the downhill ramp rate of the unit at node i of the transmission network. and Let i represent the minimum and maximum node voltages of node i in the transmission network, respectively. This represents the voltage at node i in the power transmission network at time t. and Let represent the maximum active power flow and maximum reactive power flow at time t on the line between node i and node j in the transmission network. This represents the energy storage state of the battery energy storage at transmission network node i at time t. This represents the energy storage state of the battery energy storage at transmission network node i at time t+1. and These represent the charging efficiency and discharging efficiency of battery energy storage in the power transmission network, respectively. and Let these represent the minimum and maximum energy stored in the battery at node i of the transmission network, respectively. and These represent the maximum charging power and maximum discharging power of the battery storage at node i of the transmission network, respectively. This represents the carbon allowance allocated to unit i in the power transmission network. This represents the emission factor of node i in the power transmission network.

4. The electric vehicle charging scheduling method considering the carbon market as described in claim 1, characterized in that, The objective function of the power distribution network collaborative operation model is: ； in, Let m represent the objective function of the distribution network collaborative operation model, where m represents a node in the distribution network, and n represents other nodes connected to node m. This represents the set of nodes where the micro-generator unit is located. This represents the set of nodes where energy storage is located. Represents the set of nodes in the distribution network. This represents the collection of feeders connecting the distribution network and the substation. For the collection of other feeders, This represents the nodal electricity price transmitted from the upstream transmission network at time t. This represents the transmission power of the substation at time t. This represents the power of a small thermal power unit at node m in the distribution network at time t. , , This represents the fuel cost coefficient of thermal power units at node m in the distribution network. This represents the carbon price of distribution network node m at time t. This represents the carbon allowance purchased by the generating unit at time t for node m in the distribution network. This represents the carbon allowance sold by the generating unit at time t at node m in the distribution network. This represents the battery degradation cost at distribution network node m. This represents the battery charging power of the electric vehicle at node m in the distribution network at time t. This represents the average nodal price of electricity in the distribution network. This represents the square of the current at time t on the line between node m and node n in the distribution network. This indicates the impedance of the substation. This represents the impedance of the feeder on the line between node m and node n in the distribution network. The constraints of the distribution network collaborative operation model include active power balance constraints, reactive power balance constraints, power flow constraints, node voltage constraints, power flow constraints, energy balance constraints of battery energy storage systems, energy storage state constraints of battery energy storage systems, charging and discharging power constraints of electric vehicles, and indirect emission constraints of end users.

5. The electric vehicle charging scheduling method considering the carbon market as described in claim 4, characterized in that, The constraints of the distribution network collaborative operation model include: ； ； ； ； ； ； ； ； ； ； ， ； ； in, Let m represent the set of nodes adjacent to node m in the distribution network. This represents the active power flow at time t on the line between node m and node n in the distribution network. This represents the transmission power of the substation at time t for node m in the distribution network. This represents the output of renewable energy units at node m in the distribution network at time t. This represents the power of a small thermal power unit at node m in the distribution network at time t. This represents the discharge power of the electric vehicle battery energy storage at distribution network node m at time t. This represents the load of distribution network node m at time t, excluding electric vehicles. This represents the charging power of the electric vehicle battery energy storage at distribution network node m at time t. This represents the electric vehicle load at distribution network node m at time t. This represents the load reduction at node m in the distribution network at time t. This represents the reactive power flow at time t on the line between node m and node n in the distribution network. This represents the reactive power transmitted by the substation at node m in the distribution network at time t. This represents the reactive power output of a small generator at node m in the distribution network at time t. This represents the voltage at node m in the distribution network at time t. This represents the voltage at node n in the distribution network at time t. This represents the resistance of the line between node m and node n in the distribution network. This represents the reactance on the line between node m and node n in the distribution network. Indicates the reference voltage of the distribution network node. This represents the square of the current at time t on the line between node m and node n in the distribution network. This represents the minimum voltage at a distribution network node. Indicates the maximum voltage at a distribution network node. Let represent the maximum active power flow at time t on the line between grid node m and node n. This represents the maximum reactive power flow at time t on the line between node m and node n in the distribution network. This represents the energy storage state of the battery energy storage at distribution network node m at time t +1. and These represent the charging efficiency and discharging efficiency of battery energy storage in the distribution network, respectively. This represents the minimum energy required for battery storage at distribution network node m. This represents the energy stored in the battery at node m in the distribution network at time t. This represents the maximum energy stored in the battery at node m in the distribution network. This represents the maximum charging power of the electric vehicle battery energy storage at distribution network node m at time t. This represents the maximum discharge power of the electric vehicle battery energy storage at distribution network node m at time t. This represents the emission factor of node m in the distribution network. This represents the total load or electric vehicle load of distribution network node m at time t. This represents the carbon allowance allocated to unit m at node m of the distribution network. This represents the carbon allowance purchased by the generating unit at time t for node m in the distribution network. This represents the carbon allowance sold by the generating unit at time t at node m in the distribution network.

6. The electric vehicle charging scheduling method considering the carbon market as described in claim 1, characterized in that, The objective function of the carbon emission flow tracking model for the power transmission network is: ； ， ； in, This represents the carbon potential of node i in the power transmission network at time t. This represents the output of thermal power units at transmission network node i at time t. This represents the discharge power of the electric vehicle battery energy storage at transmission network node i at time t. This represents the carbon potential of energy storage at node i in the transmission network at time t. Let represent the active power flow at time t on the line between node i and node j in the transmission network. This represents the carbon potential of the line branch between node i and node j in the transmission network. This represents the output of renewable energy units at node i in the power transmission network at time t. This represents the power injection node on the line between node i and node j in the transmission network; The objective function of the carbon emission flow tracking model for the power distribution network is: ； in, This represents the carbon potential of distribution network node m at time t, which is inherited from the upstream transmission system. This represents the transmission power of the substation at time t for node m in the distribution network. This represents the carbon intensity of distribution network node m at time t at the substation node. This represents the power of a small thermal power unit at node m in the distribution network at time t. This represents the carbon emission intensity of a small generator at node m in the distribution network. This represents the discharge power of the electric vehicle battery energy storage at distribution network node m at time t. This represents the carbon potential of energy storage at node m in the distribution network at time t. This represents the active power flow at time t on the line between node m and node n in the distribution network. This represents the carbon potential of the line branch between node m and node n in the distribution network. This represents the output of new energy generating units at node m in the distribution network at time t.

7. The electric vehicle charging scheduling method considering the carbon market as described in claim 1, characterized in that, The objective function of the traffic flow model is: ； in, Indicates time cost, Represents a set of traffic segments. Represents the time t. Traffic flow on this road, Indicates time t at the time of Travel time on this road This refers to a collection of charging stations for electric vehicles. This indicates the car charging at the h-th charging station. This represents the time at time t for charging at the h-th charging station. This represents the charging price for electric vehicles at the h-th charging station. This indicates the car that is charging at the h-th charging station at time t. Indicates charging needs; The constraints of the traffic flow model can be expressed as: ； , ； ； ； ； ； ； ， ； ， ； in, This represents a traffic segment starting at point o and ending at point d. This represents a set of traffic segments, where 'a' represents the a-th segment. Indicates the index of the road segment. This represents the p-th path. Represents the set of all paths. Let represent the traffic flow on the p-th path in the traffic segment from o to d at time t. The traffic flow of fuel vehicles on the p-th path of the traffic segment from o to d at time t. This represents the traffic demand for electric vehicles on the traffic segment from point O to point D at time t. This represents the traffic demand for gasoline-powered vehicles on the traffic segment from point O to point D at time t. This represents the traffic flow on road segment a at time t. It is a 0 / 1 parameter; it is 1 if the p-th path passes through the a-th path, and 0 otherwise. This indicates the car that is charging at the h-th charging station at time t. This represents the virtual charging link for the h-th charging station. This represents the time taken to traverse road segment a at time t. This represents the time taken to travel through road segment a during idle time. , Both represent the road resistance parameter in the road impedance function. This represents the traffic flow on road segment a. This represents the road capacity of the a-th road segment. This represents the charging time at the h-th charging station at time t. This represents the charging time of the h-th charging station during idle periods. Let be the queuing function in queuing theory. This represents the maximum traffic attraction of the h-th charging station. This represents the average initial state of charge of the battery. This indicates the energy consumption of an electric vehicle per kilometer. This represents the length of the a-th road segment. Indicates the maximum capacity of the electric vehicle. This represents the energy required to reach the first electric vehicle charging station on the p-th path. Represents the set of non-charging paths. This represents the set of charging paths.

8. The electric vehicle charging scheduling method considering the carbon market as described in claim 1, characterized in that, The objective function of the charging station planning and integrated charging management model is: ； in, This refers to a collection of charging stations for electric vehicles. This represents the initial charging price at the h-th charging station at time t. This represents the carbon potential of node m in the distribution network at time t. This represents the carbon price of node m in the distribution network at time t. This represents the electricity price at node m in the distribution network at time t. Let represent the electric vehicle charging demand at the h-th charging station at time t; The constraints of the charging station planning and charging integration management model are as follows: ； in, This indicates a price elasticity constraint. This represents the electric vehicle charging demand at the h-th charging station at time t. This represents the basic electric vehicle charging demand at the h-th charging station based on the estimate at time t. This represents the initial charging price at the h-th charging station at time t. This represents the base charging price at the h-th charging station at time t.

9. An electric vehicle charging scheduling system considering the carbon market, characterized in that, include: The electricity carbon market collaborative model construction module is used to construct an electricity carbon market collaborative model; wherein, the electricity carbon market collaborative model includes a transmission network low-carbon operation model, a distribution network collaborative operation model, a transmission network carbon emission flow tracking model, and a distribution network carbon emission flow tracking model; The traffic flow model building module is used to construct a traffic flow model for electric vehicles based on user equilibrium theory, with the goal of minimizing the total driving time, charging time, and charging cost of electric vehicles. The charging station planning and charging integration management model construction module is used to construct an electric vehicle charging station planning and charging integration management model based on the electric carbon market collaboration model and the traffic flow model. The model solving module is used to solve the electric carbon market collaborative model, the traffic flow model, and the charging station planning and charging integration management model to obtain the electric vehicle charging scheduling strategy. The model solving module is specifically used for: The low-carbon operation model of the transmission network is solved to obtain the power flow and nodal electricity price of the transmission network; Based on the power flow of the transmission network, the carbon emission flow tracking model of the transmission network is solved to obtain the carbon flow and nodal carbon potential of the transmission network. Based on the nodal electricity price of the transmission network, the cooperative operation model of the distribution network is solved to obtain the power flow, nodal electricity price and power demand of the distribution network. Based on the power flow of the distribution network and the carbon flow and nodal carbon potential of the transmission network, the carbon emission flow tracking model of the distribution network is solved to obtain the carbon flow and nodal carbon potential of the distribution network. Solving the traffic flow model yields an estimated basic electric vehicle charging demand; Based on the nodal electricity price of the distribution network, the carbon flow of the distribution network, the nodal carbon potential of the distribution network, and the estimated basic electric vehicle charging demand, the charging station planning and charging integration management model is solved to obtain the current electric vehicle charging demand and charging price. Determine whether the convergence condition is met; If yes, then output the final electric vehicle charging demand and charging price; if no, then based on the current electric vehicle charging demand and charging price, re-solve the power distribution network collaborative operation model and the traffic flow model until the convergence condition is met, and output the final electric vehicle charging demand and charging price.

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