Electric vehicle charging scheduling method and system considering electricity-carbon market

Abstract

The invention discloses an electric vehicle charging scheduling method and system considering a power-carbon market, and the method comprises the steps: firstly constructing a power-carbon market cooperation model which comprises a power transmission network low-carbon operation model, a power distribution network cooperation operation model, a power transmission network carbon emission flow tracking model, and a power distribution network carbon emission flow tracking model; then, on the basis of a user equilibrium theory, a traffic flow model of the electric vehicle is constructed with the purpose of minimizing the total driving time, the charging time and the charging cost of the electric vehicle, and then a charging station planning and charging integrated management model of the electric vehicle is constructed on the basis of the electricity-carbon market cooperation model and the traffic flow model; and finally, solving the power-carbon market cooperation model, the traffic flow model and the charging station planning and charging integrated management model to obtain an electric vehicle charging scheduling strategy. By adopting the method, the charging strategy of the electric vehicle can be optimized, the carbon emission is reduced, the load pressure of a power grid is relieved, and the renewable energy consumption capability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated optimization of power systems and transportation systems, and in particular to a method and system for scheduling charging of electric vehicles taking into account the electricity-carbon market. Background Art

[0002] The global push for energy transition has become a critical issue for addressing climate change and ensuring a sustainable future. The convergence of power sector decarbonization efforts and transport electrification has led to the emergence of an ecological transport system. This holistic approach integrates power network operations with electricity and carbon markets to optimize energy use and reduce emissions. By considering the dynamic interactions between these systems, the ecological transport framework supports the development of sustainable transport solutions that align with broader energy transition goals.

[0003] However, the current electric vehicle charging scheduling method mainly focuses on grid load balancing and user charging costs, but does not consider the coordination of the electricity and carbon markets. It lacks the integration of market mechanisms such as carbon prices and carbon quotas, and lacks accurate quantification of the carbon emissions on the power generation side implicit in the charging process. At the same time, the lack of coordinated modeling of traffic flow and power flow has led to an unreasonable layout of charging stations and concentrated charging periods, exacerbating the peak-to-valley difference of the power grid and increasing dependence on fossil energy. Summary of the Invention

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

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

[0006] Constructing a coordinated model for the electricity-carbon market; wherein the coordinated model includes a low-carbon operation model for the transmission network, a coordinated 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 the 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 electricity-carbon market collaborative model and the traffic flow model, a charging station planning and charging integrated management model for electric vehicles is constructed;

[0009] The electricity-carbon market collaborative 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.

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

[0011] Solving the low-carbon operation model of the transmission network to obtain the power flow and node electricity prices of the transmission network;

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

[0013] Solving the distribution network collaborative operation model based on the node electricity prices of the transmission network to obtain the power flow, node electricity prices and power demand of the distribution network;

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

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

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

[0017] Determine whether the convergence conditions are met;

[0018] If so, the final electric vehicle charging demand and charging price are output; if not, based on the current electric vehicle charging demand and charging price, the distribution network collaborative operation model and the traffic flow model are re-solved until the convergence conditions are met, and the final electric vehicle charging demand and charging price are output.

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

[0020]

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

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

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

[0024]

[0025] Where j represents other grid nodes connected to the transmission grid node i, represents the set of other grid nodes connected to the transmission grid node i, represents the output of the new energy unit at the transmission grid node i at time t, represents the active power demand of the lower-level distribution network at the transmission network node i at time t, represents the charging power of the electric vehicle battery energy storage at the transmission grid node i at time t, represents the discharge power of the electric vehicle battery energy storage at the transmission grid node i at time t, represents the active power flow at time t on the line between node i and node j in the transmission network, represents the reactive power of the thermal power unit at the transmission grid node i at time t, represents the reactive power demand of the lower-level distribution network at the transmission network node i at time t, represents the reactive power flow at time t on the line between node i and node j in the transmission network, represents the active power flow on the line between node i and node j in the transmission network, It represents the reactive power flow on the line between node i and node j in the transmission network, U i represents the voltage of the transmission network node i, U j represents the voltage of transmission network node j, θ ij represents the phase angle on the line between node i and node j in the transmission network, G ij represents the conductance of the line between node i and node j in the transmission network, B ijP represents the susceptance on the line between node i and node j in the transmission network. i G,max represents the maximum active power of the thermal power unit in the transmission network node i, represents the maximum reactive power of the thermal power unit in the transmission network node i, represents the upward ramp rate of the unit at the transmission grid node i, represents the downward ramp rate of the unit at transmission grid node i, and They represent the minimum and maximum node voltages of transmission network node i, U i,t represents the voltage of the transmission network node i at time t, and They represent the maximum active power flow and the maximum reactive power flow at time t on the line between node i and node j in the transmission network, represents the energy storage state of the battery energy storage at the transmission grid node i at time t, represents the energy storage state of the battery energy storage at the transmission grid node i at time t+1, and They represent the charging efficiency and discharging efficiency of battery energy storage in the transmission grid, and They represent the minimum and maximum energy of the battery storage at the transmission grid node i, and They represent the maximum charging power and maximum discharging power of the battery energy storage at the transmission grid node i, Cap i represents the carbon quota allocated to the unit i at the transmission grid node; represents the emission factor of transmission grid node i.

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

[0027]

[0028] Among them, Min.F DN represents the objective function of the distribution network coordinated operation model, m represents the grid node of the distribution network, n represents other grid nodes connected to the distribution network node m, Ω MG represents the node set where the micro-unit is located, Ω BESS represents the node set where the energy storage is located, Ω represents the node set of the distribution network, represents the set of feeders connecting the distribution network and the substation, Ω FD is the set of other feeders, represents the node electricity price transmitted from the upper transmission network at time t, P t ST represents the transmission power of the substation at time t, represents the power of the small thermal power unit at the distribution network node m at time t, a m 、b m 、c m represents the fuel cost coefficient of the thermal power unit at the distribution network node m, represents the carbon price of distribution network node m at time t, represents the carbon quota purchased by the unit at distribution network node m at time t, represents the carbon quota sold by the unit at distribution network node m at time t, represents the battery degradation cost of distribution network node m, represents the battery charging power of the electric vehicle at distribution network node m at time t, represents the average node electricity price of the distribution network, I mn,t It represents the square of the current at time t on the line between node m and node n in the distribution network, Z ST represents the impedance of the substation, 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 the active power balance constraint, reactive power balance constraint, power flow constraint, node voltage constraint, power flow constraint, energy balance constraint of the battery energy storage system, energy storage state constraint of the battery energy storage system, charging and discharging power constraint 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, represents the set of nodes adjacent to the distribution network node m, represents the active power flow at time t on the line between node m and node n in the distribution network, represents the transmission power of the substation at the distribution network node m at time t, represents the output of the new energy unit at the distribution network node m at time t, represents the power of the small thermal power unit at the distribution network node m at time t, represents the discharge power of the electric vehicle battery energy storage at distribution network node m at time t, represents the load of distribution network node m at time t excluding electric vehicles, represents the charging power of the electric vehicle battery energy storage at the distribution network node m at time t, represents the electric vehicle load of distribution network node m at time t, represents the load reduction of distribution network node m at time t, represents the reactive power flow at time t on the line between node m and node n in the distribution network, represents the reactive power transmitted by the substation at the distribution network node m at time t, represents the reactive power output of the small generator at the distribution network node m at time t, U m,t It represents the voltage of the distribution network node m at time t, U n,t represents the voltage of distribution network node n at time t, r mn represents the resistance of the line between node m and node n in the distribution network, x mn represents the reactance on the line between the distribution network node m and node n, U0 represents the reference voltage of the distribution network node, I mn,t It represents the square of the current at time t on the line between node m and node n in the distribution network, U min Indicates the minimum voltage of the distribution network node, U max Indicates the maximum voltage of the distribution network node, It represents the maximum active power flow at time t on the line between grid node m and node n, It represents the maximum reactive power flow on the line between node m and node n in the distribution network at time t, represents the energy storage state of the battery energy storage at the distribution network node m at time t+1, and They represent the charging efficiency and discharging efficiency of battery energy storage in the distribution network, represents the minimum energy stored in the battery at node m in the distribution network, represents the energy stored in the battery at distribution network node m at time t, represents the maximum energy stored in the battery at node m in the distribution network, represents the maximum charging power of the electric vehicle battery energy storage at the distribution network node m at time t, represents the maximum discharge power of the electric vehicle battery energy storage at distribution network node m at time t, represents the emission factor of distribution network node m, Cap represents the total load or electric vehicle load of distribution network node m at time t, m represents the carbon quota allocated to the unit at node m in the distribution network, represents the carbon quota purchased by the unit at distribution network node m at time t, It represents the carbon quota sold by the unit at distribution network node m at time t.

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

[0037]

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

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

[0040]

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

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

[0043]

[0044] in, represents the time cost, A represents the set of traffic sections, c a,t represents the traffic flow on road a at time t, t a,t represents the time spent traveling on road a at time t, Ω cs represents the set of charging stations for electric vehicles, x h represents the car charging at the hth charging station, represents the charging time at the hth charging station at time t, represents the charging price of electric vehicles at the h-th charging station, x h,t represents the car charging at the hth charging station at time t, E Cd Indicates charging demand;

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

[0046]

[0047] Among them, od represents the traffic section with o as the starting point and d as the end point, (o,d)∈O represents the set of traffic sections, a represents the ath section, a∈A p Represents the index of the road segment, p represents the p-th path, Represents the set of all paths, represents the traffic flow on the pth path on the traffic section from o to d at time t, The traffic flow of fuel vehicles on the pth path on the traffic section from o to d at time t, represents the traffic demand of electric vehicles on the traffic section from o to d at time t, represents the traffic demand of fuel vehicles on the traffic section from o to d at time t, c a,t represents the traffic flow of the a-th road section at time t, It is a 01 parameter, which is 1 if the pth path is through the ath path, otherwise it is 0, x h,t represents the car charging at the hth charging station at time t, represents the charging virtual link of the h-th charging station, t a,t represents the time taken to pass through section a at time t, represents the time it takes to pass through section a when idle, ξ and τ represent the road resistance parameters in the road impedance function, and c a represents the traffic flow on road section a, represents the road capacity of the a-th road section, represents the charging time of the hth charging station at time t, represents the charging time of the hth charging station when it is idle, Que(·) is the queuing function in queuing theory, represents the maximum traffic attraction of the h-th charging station, S ini represents the average initial battery state of charge, E Con,EV Indicates the energy consumption of electric vehicles per kilometer, L a represents the length of the a-th road segment, E max represents the maximum capacity of the electric vehicle, represents the energy required to reach the first electric vehicle charging station on the pth path, Θ NCP represents the set of non-charging paths, Θ CP Represents a collection of charging paths.

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

[0049]

[0050] Among them, Ω cs represents the set of charging stations for electric vehicles, represents the initial charging price of the hth charging station at time t, represents the carbon potential of the distribution network node m at time t, represents the carbon price of distribution network node m at time t, represents the electricity price of distribution network node m at time t, represents the electric vehicle charging demand at the h-th charging station at time t;

[0051] The constraints of the charging station planning and charging integrated management model are:

[0052]

[0053] Among them, ε represents the price elasticity constraint, represents the electric vehicle charging demand at the hth charging station at time t, represents the basic electric vehicle charging demand at the hth charging station at time t based on the estimation, represents the initial charging price of the hth charging station at time t, represents the basic charging price of 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 electric carbon market, comprising:

[0055] An electricity-carbon market collaborative model construction module, configured 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] Traffic flow model construction module, which 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] A charging station planning and charging integrated management model construction module, used to construct a charging station planning and charging integrated management model for electric vehicles based on the electricity-carbon market collaborative model and the traffic flow model;

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

[0059] Compared to existing technologies, the present invention provides a method and system for scheduling electric vehicle charging that takes the electricity-carbon market into account. First, by constructing a coordinated model for the electricity-carbon market, it comprehensively considers factors such as the low-carbon operation of the transmission network, the coordinated operation of the distribution network, and the tracking of carbon emission flows. This optimizes the allocation of power resources in the electricity-carbon market, 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 clarified, prompting grid operators to take measures to reduce carbon emissions. Furthermore, 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 needs of electric vehicle users. This allows for the rational planning of the location and scale of charging stations, as well as integrated charging management, based on factors such as traffic flow and user travel and charging times. Finally, by solving the coordinated model for the electricity-carbon market, the traffic flow model, and the charging station planning and integrated charging management model, a charging scheduling strategy for electric vehicles that comprehensively considers multiple factors can be derived. This strategy can provide electric vehicles with precise scheduling plans such as charging time, location, and charging power based on real-time information such as the grid's operating status, carbon emissions, traffic flow, and user needs, thereby achieving intelligent and efficient management of the charging process, improving the convenience and reliability of electric vehicle charging, further reducing carbon emissions, alleviating grid load pressure, and improving the ability to absorb renewable energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings used in the implementation methods. Obviously, the drawings described below are only some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 This is a flow chart of a method for scheduling electric vehicle charging that takes into account the electric carbon market, provided by an embodiment of the present invention;

[0062] Figure 2 is a schematic diagram of a flow chart for solving the charging management model provided by an embodiment of the present invention;

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

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

[0065] Figure 5 This is a carbon potential change trend diagram of the node where the charging station is located under different cases provided by the embodiment 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 by the embodiment of the present invention;

[0067] Figure 7 This is a graph showing the changing trends of electric vehicle charging prices under different cases provided by an embodiment of the present invention;

[0068] Figure 8 This is a graph showing the changing trend of electric vehicle charging demand under different cases provided by an embodiment of the present invention;

[0069] Figure 9 This is a structural block diagram of an electric vehicle charging scheduling system considering the electric carbon market provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0071] See also Figure 1 , Figure 1 1 is a flow chart of a method for scheduling charging of electric vehicles taking into account the electricity-carbon market provided by an embodiment of the present invention. The method for scheduling charging of electric vehicles taking into account the electricity-carbon market comprises steps S1 to S4:

[0072] Step S1: Constructing 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;

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

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

[0075]

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

[0077] The constraints of the low-carbon operation model of the transmission network include the active power balance constraint, reactive power balance constraint, AC power flow constraint, capacity constraint of thermal power units, ramp constraint, node voltage constraint, power flow constraint, energy balance constraint of battery energy storage system, energy storage capacity constraint, charge and discharge power constraint of electric vehicles, and carbon emission constraint of thermal power units.

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

[0079]

[0080]

[0081] Where j represents other grid nodes connected to the transmission grid node i, represents the set of other grid nodes connected to the transmission grid node i, represents the output of the new energy unit at the transmission grid node i at time t, represents the active power demand of the lower-level distribution network at the transmission network node i at time t, represents the charging power of the electric vehicle battery energy storage at the transmission grid node i at time t, represents the discharge power of the electric vehicle battery energy storage at the transmission grid node i at time t, represents the active power flow at time t on the line between node i and node j in the transmission network, represents the reactive power of the thermal power unit at the transmission grid node i at time t, represents the reactive power demand of the lower-level distribution network at the transmission network node i at time t, represents the reactive power flow at time t on the line between node i and node j in the transmission network, represents the active power flow on the line between node i and node j in the transmission network, It represents the reactive power flow on the line between node i and node j in the transmission network, U i represents the voltage of the transmission network node i, U j represents the voltage of transmission network node j, θ ij represents the phase angle on the line between node i and node j in the transmission network, G ij represents the conductance of the line between node i and node j in the transmission network, B ij P represents the susceptance on the line between node i and node j in the transmission network. i G,max represents the maximum active power of the thermal power unit in the transmission network node i, represents the maximum reactive power of the thermal power unit in the transmission network node i, represents the upward ramp rate of the unit at the transmission grid node i, represents the downward ramp rate of the unit at transmission grid node i, and They represent the minimum and maximum node voltages of transmission network node i, U i,t represents the voltage of the transmission network node i at time t, and They represent the maximum active power flow and the maximum reactive power flow at time t on the line between node i and node j in the transmission network, represents the energy storage state of the battery energy storage at the transmission grid node i at time t, represents the energy storage state of the battery energy storage at the transmission grid node i at time t+1, and They represent the charging efficiency and discharging efficiency of battery energy storage in the transmission grid, and They represent the minimum and maximum energy of the battery storage at the transmission grid node i, and They represent the maximum charging power and maximum discharging power of the battery energy storage at the transmission grid node i, Cap i represents the carbon quota allocated to the unit i at the transmission grid node; represents the emission factor of transmission grid node i.

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

[0083] It's worth noting that the objective function of the transmission grid's low-carbon operation model integrates economic costs and carbon market signals, while constraints encompass physical laws, equipment safety, grid stability, and policy compliance, forming a comprehensive dispatch optimization framework. By factoring in carbon quota trading costs and emissions constraints, environmental costs are incorporated into power generation decisions, directing resources toward low-carbon units while ensuring that dispatch plans meet the physical constraints of grid operation (such as power flow, voltage, and equipment capacity).

[0084] In an optional embodiment, the objective function of the distribution network collaborative operation model includes the cost of energy purchased by the parent transmission system, the fuel cost of the micro thermal generator set, the quota purchase and sale income of the end user, the battery degradation cost, and the power loss cost;

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

[0086]

[0087] Among them, Min.F DN represents the objective function of the distribution network coordinated operation model, m represents the grid node of the distribution network, Ω MG represents the node set where the micro-unit is located, Ω BESS represents the node set where the energy storage is located, Ω represents the node set of the distribution network, represents the set of feeders connecting the distribution network and the substation, Ω FD is the set of other feeders, represents the node electricity price transmitted from the upper transmission network at time t, P t ST represents the transmission power of the substation at time t, represents the power of the small thermal power unit at the distribution network node m at time t, am 、b m 、c m represents the fuel cost coefficient of the thermal power unit at the distribution network node m, represents the carbon price of distribution network node m at time t, represents the carbon quota purchased by the unit at distribution network node m at time t, represents the carbon quota sold by the unit at distribution network node m at time t, represents the battery degradation cost of distribution network node m, represents the battery charging power of the electric vehicle at distribution network node m at time t, represents the average node electricity price of the distribution network, I mn,t It represents the square of the current at time t on the line between node m and node n in the distribution network, Z ST represents the impedance of the substation, 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 the battery energy storage system, energy storage state constraints of the battery energy storage system, charging and discharging power constraints of electric vehicles, and indirect emission constraints of end users.

[0089] Specifically, the constraints of the distribution network coordinated operation model are:

[0090]

[0091]

[0092] U min ≤U m,t ≤U max ; (20)

[0093]

[0094] in, represents the set of nodes adjacent to the distribution network node m, represents the active power flow at time t on the line between node m and node n in the distribution network, represents the transmission power of the substation at the distribution network node m at time t, represents the output of the new energy unit at the distribution network node m at time t, represents the power of the small thermal power unit at the distribution network node m at time t, represents the discharge power of the electric vehicle battery energy storage at distribution network node m at time t, represents the load of distribution network node m at time t excluding electric vehicles, represents the charging power of the electric vehicle battery energy storage at the distribution network node m at time t, represents the electric vehicle load of distribution network node m at time t, represents the load reduction of distribution network node m at time t, represents the reactive power flow at time t on the line between node m and node n in the distribution network, represents the reactive power transmitted by the substation at the distribution network node m at time t, represents the reactive power output of the small generator at the distribution network node m at time t, U m,t It represents the voltage of the distribution network node m at time t, U n,t represents the voltage of distribution network node n at time t, r mn represents the resistance of the line between node m and node n in the distribution network, x mn represents the reactance on the line between the distribution network node m and node n, U0 represents the reference voltage of the distribution network node, I mn,t It represents the square of the current at time t on the line between node m and node n in the distribution network, U min Indicates the minimum voltage of the distribution network node, U max Indicates the maximum voltage of the distribution network node, It represents the maximum active power flow at time t on the line between grid node m and node n, It represents the maximum reactive power flow on the line between node m and node n in the distribution network at time t, represents the energy storage state of the battery energy storage at the distribution network node m at time t+1, and They represent the charging efficiency and discharging efficiency of battery energy storage in the distribution network, represents the minimum energy stored in the battery at node m in the distribution network, represents the energy stored in the battery at distribution network node m at time t, represents the maximum energy stored in the battery at node m in the distribution network, represents the maximum charging power of the electric vehicle battery energy storage at the distribution network node m at time t, represents the maximum discharge power of the electric vehicle battery energy storage at distribution network node m at time t, represents the emission factor of distribution network node m, Cap represents the total load or electric vehicle load of distribution network node m at time t, m represents the carbon quota allocated to the unit at node m in the distribution network, represents the carbon quota purchased by the unit at distribution network node m at time t, represents the carbon quota sold by the unit at distribution network node m at time t, 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 constraints and reactive power balance constraints 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 range of 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 levels, the present embodiment imposes 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 consumer side, a carbon tax is imposed on end users and electric vehicle charging behaviors based on carbon flow tracking results, constraining users' indirect carbon emissions through formula (26), thereby achieving coordinated control of carbon emissions on the supply and consumption sides. The present embodiment refers to this mechanism as a "dual carbon tax mechanism." By implementing taxation at all links in the supply chain, it incentivizes all stakeholders to adopt clean technologies, promotes fairer sharing of carbon emission costs, and encourages producers to reduce their environmental impact.

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

[0098] First, the node carbon potential reflects the amount of carbon emissions per unit of power at a specific node during power transmission. Its value depends on the carbon potential and emission intensity of the transmission lines injecting power into the node, as well as the contribution of these lines' injected power to the total injected power. If a node has multiple transmission lines injecting power, and some of these lines connect to power generators with high carbon emission intensities, the node carbon potential will be relatively high, indicating that the node "carries" more carbon emissions during power transmission.

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

[0100]

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

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

[0103]

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

[0105] Therefore, in an optional embodiment, the objective function of the carbon emission flow tracking model of the transmission grid is:

[0106]

[0107] in, represents the carbon potential of transmission grid node i at time t, represents the output of thermal power unit at transmission grid node i at time t, represents the discharge power of the electric vehicle battery energy storage at the transmission grid node i at time t, represents the energy storage carbon potential of transmission grid node i at time t, represents the active power flow at time t on the line between node i and node j in the transmission network, represents the carbon potential of the line branch between node i and node j in the transmission network, represents the output of the new energy unit at the transmission grid node i at time t, Γ ij represents the power injection node on the line between 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 is increased by This is because the carbon emissions from energy storage discharge are taken into account. is the discharge power of the electric vehicle battery energy storage at transmission grid node i at time t, is the energy storage carbon potential at the corresponding moment, and the carbon emissions of energy storage discharge are included in the total carbon emissions calculation. The denominator is based on the denominator of formula (27) with the addition of This is because the energy storage discharge power also becomes a part that affects the power balance and carbon potential calculation of distribution network nodes. It needs to be included in the total power calculation in the denominator to make the carbon potential calculation more comprehensive and accurate, and comprehensively consider the power and carbon emission factors of thermal power units, new energy units, line transmission, and energy storage discharge.

[0109] It can be understood that the objective function of the carbon emission flow tracking model of the transmission network tracks the carbon flow from the power generation node to the demand node at the transmission level. Similarly, the objective function of the carbon flow tracking model at the distribution level to the end user can be expressed as:

[0110]

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

[0112] It's worth noting that the application of a carbon emissions flow tracking model is crucial for implementing a dual carbon tax mechanism. By quantifying carbon emissions at every stage of the energy process, this model ensures that producers and consumers pay taxes based on their actual carbon contributions, promoting transparency and strengthening accountability across the industry. Furthermore, the model's ability to track carbon emissions through electricity flows allows for a more granular approach to carbon taxation at the consumer level. Consumers pay a tax on the carbon emissions embodied in the electricity they consume, not just on emissions generated at the point of production. This ensures that the carbon tax reflects emissions throughout the entire life cycle of energy consumption, allocating environmental costs more accurately and equitably.

[0113] Step S2: Based on the user equilibrium theory, a traffic flow model of electric vehicles is constructed 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 sophisticated collaboration between the power and transportation systems in planning and scheduling. Therefore, to study the synergy between the transportation sector and the electricity and carbon markets, this embodiment of the present invention develops an electric vehicle-integrated traffic flow model as a basic analytical tool to study how the increasing popularity of electric vehicles affects traffic dynamics, power demand, and carbon emissions in urban and regional networks.

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

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

[0117]

[0118] in, represents the time cost, A represents the set of traffic sections, c a,t represents the traffic flow on road a at time t, t a,t represents the time spent traveling on road a at time t, Ω cs represents the set of charging stations for electric vehicles, x h represents the car charging at the hth charging station, represents the charging time at the hth charging station at time t, represents the charging price of electric vehicles at the h-th charging station, x h,t represents the car charging at the hth charging station at time t, E Cd Indicates charging demand;

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

[0120]

[0121] Among them, od represents the traffic section with o as the starting point and d as the end point, (o,d)∈O represents the set of traffic sections, a represents the ath section, a∈A p Represents the index of the road segment, p represents the p-th path, Represents the set of all paths, represents the traffic flow on the pth path on the traffic section from o to d at time t, The traffic flow of fuel vehicles on the pth path on the traffic section from o to d at time t, represents the traffic demand of electric vehicles on the traffic section from o to d at time t, represents the traffic demand of fuel vehicles on the traffic section from o to d at time t, c a,t represents the traffic flow of the a-th road section at time t, It is a 01 parameter, which is 1 if the pth path is through the ath path, otherwise it is 0, x h,t represents the car charging at the hth charging station at time t, represents the charging virtual link of the h-th charging station, t a,t represents the time taken to pass through section a at time t, represents the time it takes to pass through section a when idle, ξ and τ represent the road resistance parameters in the road impedance function, and c a represents the traffic flow on road section a, represents the road capacity of the a-th road section, represents the charging time of the hth charging station at time t, represents the charging time of the hth charging station when idle, Que(·) is the queuing function in queuing theory, which is a mathematical theory and method for studying the random gathering and dispersion phenomena of systems and the working process of random service systems. represents the maximum traffic attraction of the h-th charging station, S ini Indicates the average initial battery state of charge SOC, E Con,EV Indicates the energy consumption of electric vehicles per kilometer, L a represents the length of the a-th road segment, E max represents the maximum capacity of the electric vehicle, represents the energy required to reach the first electric vehicle charging station on the pth path, Θ NCP represents the set of non-charging paths, Θ CP Represents a collection of charging paths.

[0122] It should be noted that formula (33) represents traffic flow, so it is a positive value. Formula (34) indicates that the total traffic flow from the starting point to the end point should be equal to the traffic demand. Formula (35) indicates that the traffic volume on a road is equal to the sum of the traffic flows on all paths passing through the road. Formula (36) indicates that the number of electric vehicles in the charging station is equal to the total traffic flow through the relevant charging links. Formula (37) is a commonly used road impedance function, namely the Bureau of Public Roads (BPR) function. Formula (38) calculates the charging time based on the queuing model. Formula (39) limits the capacity of the charging station. Formula (40) indicates that the path selected by the electric vehicle should ensure that the vehicle has enough energy to reach the destination in the case of a non-charging path (no charging station on the path). Formula (41) indicates that the path selected by the electric vehicle should ensure that there is 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 as the basic framework and is expanded in combination with the characteristics of electric vehicles. It is used to reflect user travel behavior and decision-making while considering the balance between traffic flow and energy demand.

[0124] Specifically, the User Equilibrium (UE) model is based on the assumption that each user attempts to minimize their travel costs. When equilibrium is reached, no user can unilaterally change their travel routes to reduce their own costs. Traffic flow models follow this theory, taking into account user travel decision-making. However, they incorporate cost factors unique to electric vehicles, such as charging time and cost, in addition to traditional travel costs (such as driving time). This allows for analysis of the behavior of electric vehicles in transportation networks and the distribution of traffic flows.

[0125] The UE model aims to achieve balanced optimization of user travel costs within the transportation system. The traffic flow model inherits this goal and expands it. Its objective function not only minimizes vehicle travel time but also incorporates the charging time and cost of electric vehicles at charging stations. It strives to find a balance between traffic flow and energy demand to minimize the overall cost of the entire system. This goal reflects the inheritance and development of the traditional UE model, taking into account both the alleviation of traffic congestion and the energy consumption and cost of electric vehicles.

[0126] In addition, some constraints in the traffic flow model are based on the basic constraints of the UE model and have been adjusted to take into account the characteristics of electric vehicles. Regarding traffic demand balancing (Equations 33-35), the UE model adheres to the principle of flow conservation from the starting point to the end point. The road impedance function (Equation 37) uses the BPR function, which reflects the impact of traffic flow on road travel time, a common method in the UE model. Furthermore, a range constraint (Equations 40-41) has been added for electric vehicles to ensure that the vehicle has sufficient charge when selecting a route, whether it is a non-charging route or the route to the first charging station. This is a special constraint designed to take into account the energy characteristics of electric vehicles, which differ from those of traditional fuel vehicles.

[0127] It's worth noting that the traffic flow model determines the temporal and spatial distribution of EV charging routes and demand. By applying constraints such as range and charging station capacity, the model can determine EV charging demand in different regions and at different times, providing critical demand data for charging station planning and integrated charging management models.

[0128] Step S3: constructing a charging station planning and charging integrated management model for electric vehicles based on the electricity-carbon market collaborative model and the traffic flow model;

[0129] Specifically, based on the traffic flow model, the traffic capture amount of the hth charging station can be expressed as:

[0130]

[0131] in, represents the traffic capture volume of the newly built h-th charging station, express, is a 01 variable, which is 1 if the pth path passes through the hth charging station, 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 hth charging station at time t, D CH represents the total charging demand of the electric vehicle system, κ represents the ratio of charging stations selected for charging, and f t trip represents the proportion of electric vehicles traveling at time t, represents the traffic capture volume of the newly built h-th charging station, Ω cs represents the set of charging stations for electric vehicles, is a 01 variable, which is 1 if the hth charging station is built, otherwise it is 0, and Υ represents a very large number;

[0135] It should be noted that formula (43) indicates that the estimated EV charging demand is proportional to its traffic capture. If no charging station is built, it means there is no charging demand.

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

[0137]

[0138] Among them, Ω cs represents the set of charging stations for electric vehicles, represents the initial charging price of the hth charging station at time t, represents the carbon potential of the distribution network node m at time t, represents the carbon price of distribution network node m at time t, represents the electricity price of distribution network node m at time t, represents 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 integrated management model are:

[0140]

[0141] Among them, ε represents the price elasticity constraint, represents the electric vehicle charging demand at the hth charging station at time t, represents the basic electric vehicle charging demand at the hth charging station at time t based on the estimation, represents the initial charging price of the hth charging station at time t, represents the carbon price of distribution network node m at time t, represents the basic charging price of the mth charging station at time t.

[0142] It's important to note that in the transmission network's low-carbon operation model and its carbon emission flow tracking model, carbon quota trading costs and carbon emission limits affect power generation costs, thereby changing electricity prices. The introduction of a dual carbon tax mechanism in the distribution network's coordinated operation model and its carbon emission flow tracking model results 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's important to consider electricity prices in different regions and select locations with lower costs. In integrated charging management, reasonable charging pricing strategies are 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 renewable energy absorption capacity.

[0143] Therefore, by combining the economic factors (electricity prices, carbon costs) provided by the electricity-carbon market synergy model and the demand and layout factors provided by the traffic flow model, a more scientific electric vehicle charging station planning and charging integrated management model can be constructed.

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

[0145] In an optional embodiment, solving the electricity-carbon market collaborative model, the traffic flow model, and the charging station planning and charging integrated management model to obtain an electric vehicle charging scheduling strategy includes:

[0146] Solving the low-carbon operation model of the transmission network to obtain the power flow and node electricity prices of the transmission network;

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

[0148] Solving the distribution network collaborative operation model based on the node electricity prices of the transmission network to obtain the power flow, node electricity prices and power demand of the distribution network;

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

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

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

[0152] Determine whether the convergence conditions are met;

[0153] If so, the final electric vehicle charging demand and charging price are output; if not, based on the current electric vehicle charging demand and charging price, the distribution network collaborative operation model and the traffic flow model are re-solved until the convergence conditions are met, and the final electric vehicle charging demand and charging price are output.

[0154] Specifically, if Figure 2 As shown, Figure 2 A schematic diagram of a flow chart for solving the charging management model provided by 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 the transmission network, distribution network, transportation network and carbon footprint tracking;

[0156] Step 2: Solve the low-carbon operation model of the transmission network: First, solve the low-carbon operation model of the transmission network based on formulas (1)-(13) to achieve the coupled operation of the power market and the carbon market, and obtain the transmission network flow and transmission network node electricity prices;

[0157] Step 3: Solve the transmission network carbon emission flow tracking model: Take the transmission network flow as the input of the transmission network carbon flow model, and then perform carbon footprint tracking based on formulas (27)-(30) to obtain the carbon flow and node carbon potential of the transmission network;

[0158] Step 4: Solve the distribution network coordinated operation model: Based on the node 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 coordinated operation model, and obtain the distribution network flow, distribution network node electricity price, and distribution network power demand.

[0159] Step 5: Solve the distribution network carbon emission flow tracking model: Combine the distribution network flow and the carbon flow and node carbon potential of the transmission network, and use formula (31) to trace the carbon footprint from the transmission network to the distribution network to obtain the distribution network carbon flow and node carbon potential;

[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: Charging station planning and charging integrated management model solution: The node electricity price of the distribution network, the carbon flow and carbon potential of the distribution network, and the charging demand of electric vehicles are used as the input of the charging management model. The model is solved based on formulas (42)-(45) to perform charging station planning and charging demand analysis, and obtain the charging demand and charging price of electric vehicles.

[0162] Step 8, Convergence Verification: Check whether the current calculation results have converged. If so, the process ends and the final EV charging demand and price are output. If not, the charging price and demand are fed back to the distribution network and transportation network to adjust the distribution network power demand and EV charging demand. Repeat the above steps until the decision variables shown in the figure reach convergence accuracy, for example, the change in the second norm is less than ten to the power of negative fourth.

[0163] In summary, the present invention provides an electric vehicle charging scheduling method that considers the electricity-carbon market. First, a joint model of the transmission and distribution systems for low-carbon operation is developed. The synergy between the carbon market and the electricity market is considered. Thermal power units at the transmission level must fulfill their carbon responsibilities according to a quota mechanism, while end users at the distribution level must adhere to demand-side carbon responsibilities. Based on power flow, the carbon footprint can be tracked from the power generation end to the end user. Node electricity prices, model carbon intensity, and electric vehicle demand derived from transportation network modeling in the distribution network 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 influence electric vehicle traffic and charging flows within the transportation network. Furthermore, electric vehicle demand will affect the total load in the distribution network, which in turn affects the operation of the transmission network, ultimately forming a cycle. This approach allows for the study of a framework for the integration of electric vehicles into an ecological transportation system that participates in the electricity-carbon market.

[0164] In order to further verify the superiority of the electric vehicle charging scheduling method considering the electricity-carbon market provided by the implementation of the present invention, the following will describe in detail how to verify the method in the modified IEEE 30-node system through three cases.

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

[0166] The specific case scenario is set up as follows:

[0167] Case 1: Power network operation and charging station planning and integrated charging management without a carbon emissions trading mechanism.

[0168] Case 2: Carbon emissions trading is only considered in the operation of the power network on the power generation side.

[0169] Case 3: Considering double carbon taxation, users and electric vehicles need to consider carbon costs through carbon tracking (i.e., using the method provided in the embodiment of the present invention).

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

[0171] First, if Figure 3As shown in the figure, this IEEE 30-node system includes an IEEE 33-node distribution network. Different symbols are used to distinguish components in the diagram. The solid black wavy symbol represents a thermal power unit, a traditional power generation unit. The dashed black wavy symbol represents a renewable energy unit, reflecting the renewable energy generation component. The downward arrow represents the load, i.e., the power demand side. The dashed box on the left is labeled the lower-level distribution network, which is connected to the main network on the right, indicating the hierarchical relationship between this system and the lower-level distribution network. The nodes in the diagram are numbered (e.g., 1 to 30) and connected by lines, showing the power transmission and distribution paths between nodes. This primarily illustrates the distribution of thermal power units, renewable energy units, and loads within the network, helping to understand the power flow, source-load balance, and grid structure characteristics within the system.

[0172] like Figure 4 As shown in the figure, the coupling points between the distribution network and the transportation network are candidate locations for electric vehicle charging station planning. White circles represent transportation network nodes, black circles represent distribution network nodes, and black arrows represent roads, reflecting the connection relationship and traffic direction between transportation network nodes. Dashed lines represent coupling relationships, indicating the interaction between the transportation system and the distribution network system. Figure 4 The lower part is the distribution network system, which includes 33 distribution network nodes (numbered 1-33). There is a transformer on the left. The nodes are connected by lines to form a distribution network, and some nodes (such as 23-25) form independent branches. Figure 4 The upper half of the diagram represents the transportation network system. Transportation network nodes (such as 1, 2, 3, 7, and 8) are interconnected by roads. Arrows indicate traffic flow, and some nodes have two-way traffic (such as nodes 1 and 2, and nodes 3 and 4). Red lines connect transportation network nodes to distribution network nodes, for example, connecting transportation network nodes to some distribution network nodes. This visually demonstrates the coupling between the transportation and distribution network systems and illustrates their mutual impact when operating in tandem, such as the transfer of electric vehicle charging load between the transportation and distribution systems.

[0173] See also Figure 5 , Figure 5 This is a carbon potential change trend diagram of the nodes where charging stations are located under different cases provided by the embodiments of the present invention. Figure 5This study primarily demonstrates the daily carbon intensity changes at a specific node where an electric vehicle charging station is planned, under different operational scenarios. Throughout the day, the three cases exhibit similar carbon intensity trends, peaking in the early morning and evening hours and declining significantly during the midday period, suggesting that more renewable energy, such as solar energy, may be fed into the grid during these periods. Case 3 exhibits the lowest carbon intensity throughout the day, indicating that its strategy significantly reduces indirect carbon emissions associated with electric vehicle charging. This demonstrates that the dual carbon tax and carbon tracking mechanism in this embodiment of the present invention can effectively reduce indirect carbon emissions associated with electric vehicle charging, promote more sustainable energy use, and demonstrate significant advantages in carbon management.

[0174] See also Figure 6 , Figure 6 This is a graph showing the changing trend of electricity prices at distribution network nodes where electric vehicles are located under different cases provided by an embodiment of the present invention. Figure 6 The data mainly shows the node marginal electricity price (DLMP) for a node where an electric vehicle charging station is planned to be set up over a 24-hour period under different operating scenarios. All three cases show a similar pattern, namely that the distribution network node electricity price starts to rise in the early morning, reaches a peak in the late afternoon - which coincides with the peak electricity demand period - and then gradually declines at night. This pattern reflects the typical daily electricity usage trend, with increased demand during the day and evening hours, which in turn leads to higher power generation costs. It can be concluded that the carbon policy in the different cases has a relatively small impact on the distribution network node electricity price (DLMP), and is therefore unlikely to significantly affect the energy purchase cost of the charging station. Case 3, on the other hand, reduces the charging price during peak hours and slightly increases the price at night. This pricing strategy is related to carbon intensity and grid demand management. It can shift the demand for electric vehicle charging to non-peak hours, effectively regulating the grid load, and demonstrates the superiority of the embodiments of the present invention in balancing grid pressure.

[0175] See also Figure 7 , Figure 7 This is a trend chart of electric vehicle charging prices under different cases provided by an embodiment of the present invention. Figure 7This report primarily illustrates the evolution of electric vehicle charging prices for three planned charging stations. Cases 1 and 2 exhibit very similar pricing patterns, suggesting similar demand-side carbon management or operational strategies, resulting in a minimal impact on charging costs. In contrast, Case 3 exhibits a significantly different price trajectory, with lower prices during daytime peak hours and slightly higher prices at night. This difference may stem from Case 3's more aggressive carbon management strategy, such as a strong carbon pricing mechanism to reduce peak demand or encourage off-peak energy use. The lower daytime peak charging prices and slightly higher nighttime prices are indirectly related to carbon intensity and grid demand management. Typically, grid pressure is greatest when demand is high, 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 manage grid load more efficiently but also aligns with times when clean, renewable energy is more likely to be available, thereby reducing the carbon intensity associated with EV charging. This demonstrates the superiority of embodiments of the present invention in guiding charging behavior and optimizing energy utilization.

[0176] See also Figure 8 , Figure 8 3 is a graph showing the changing trend of electric vehicle charging demand under different cases provided by an embodiment of the present invention. Figure 8 The main focus is on the demand for electric vehicle charging at the planned charging stations. The different scenarios show distinct demand patterns, with peaks during typical commuting hours and declines during midday and late night hours. Cases 1 and 2 show similar demand curves, indicating that their operating strategies are similar and fail to significantly change charging behavior during peak hours. In contrast, Case 3 shows a significant reduction in demand during peak hours, which is due to an effective demand management strategy that encourages charging during off-peak hours. This may also indicate that Case 3 uses advanced charging management models or technologies, such as charging management based on grid load or carbon intensity, which can effectively regulate consumer behavior, alleviate grid pressure, and align with periods of lower carbon emissions. This difference in demand highlights the potential of targeted strategies to influence electric vehicle charging habits to support grid stability and environmental goals.

[0177] See Table 1, which shows the quantitative results of different case studies provided by the present invention regarding operational indicators for electric vehicle charging stations. As shown in Table 1, Case 1 achieved the highest revenue, $4,712.46, and the highest profit, $3,923.88, indicating a more traditional charging station management approach aimed at maximizing financial returns. However, Cases 2 and 3 both saw a decrease in revenue and profit, with Case 3 achieving the lowest revenue and profit, $4,618.13 and $3,828.67, respectively. This trend suggests that these strategies prioritize environmental impact over maximizing revenue generation, such as adjusting prices during peak demand periods to reduce overload on the grid. Indirect emissions decreased significantly across the cases, from 7.12 tons in Case 1 to 5.19 tons in Case 3, highlighting the effective environmental management strategies employed in Case 3, which reduced the carbon footprint per unit of electricity supplied. Similarly, the total charging demand met decreased slightly from Case 1 to Case 3, potentially reflecting strategic demand management or improved energy efficiency. Notably, the charging sustainability metric, measured in tons of 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 in terms of electric vehicle charging station operation indicators

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

[0180] In summary, through the comparative analysis of Cases 1, 2, and 3, the superiority of the embodiments of the present invention in 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 operating indicators.

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

[0182] An 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] A traffic flow model building module 22 is used to build 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] A charging station planning and charging integrated management model construction module 23 is used to construct a charging station planning and charging integrated management model for electric vehicles based on the electricity-carbon market collaborative model and the traffic flow model;

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

[0186] In an optional embodiment, the model solving module 24 is specifically configured to:

[0187] Solving the low-carbon operation model of the transmission network to obtain the power flow and node electricity prices of the transmission network;

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

[0189] Solving the distribution network collaborative operation model based on the node electricity prices of the transmission network to obtain the power flow, node electricity prices and power demand of the distribution network;

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

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

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

[0193] Determine whether the convergence conditions are met;

[0194] If so, the final electric vehicle charging demand and charging price are output; if not, based on the current electric vehicle charging demand and charging price, the distribution network collaborative operation model and the traffic flow model are re-solved until the convergence conditions are met, and the final electric vehicle charging demand and charging price are output.

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

[0196] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. 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 electric carbon market, characterized in that: include: Constructing a coordinated model for the electricity-carbon market; wherein the coordinated model includes a low-carbon operation model for the transmission network, a coordinated 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 the 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 electricity-carbon market collaborative model and the traffic flow model, a charging station planning and charging integrated management model for electric vehicles is constructed; The electricity-carbon market collaborative 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.

2. The electric vehicle charging scheduling method considering the electric carbon market as claimed in claim 1, characterized in that: Solving the electricity-carbon market collaborative model, the traffic flow model, and the charging station planning and charging integrated management model to obtain an electric vehicle charging scheduling strategy includes: Solving the low-carbon operation model of the transmission network to obtain the power flow and node electricity prices of the transmission network; Based on the power flow of the transmission network, a carbon emission flow tracking model of the transmission network is solved to obtain the carbon flow and node carbon potential of the transmission network; Solving the distribution network collaborative operation model based on the node electricity prices of the transmission network to obtain the power flow, node electricity prices and power demand of the distribution network; Based on the power flow of the distribution network and the carbon flow and node carbon potential of the transmission network, a carbon emission flow tracking model of the distribution network is solved to obtain the carbon flow and node carbon potential of the distribution network; Solving the traffic flow model to obtain an estimated basic electric vehicle charging demand; Based on the node electricity price, carbon flow, carbon potential of the distribution network and the estimated basic electric vehicle charging demand, the charging station planning and charging integrated management model is solved to obtain the current electric vehicle charging demand and charging price; Determine whether the convergence conditions are met; If so, the final electric vehicle charging demand and charging price are output; if not, based on the current electric vehicle charging demand and charging price, the distribution network collaborative operation model and the traffic flow model are re-solved until the convergence conditions are met, and the final electric vehicle charging demand and charging price are output.

3. The electric vehicle charging scheduling method considering the electric carbon market as claimed in claim 1, characterized in that: The objective function of the low-carbon operation model of the transmission network is: Among them, Min.F TN represents the objective function of the low-carbon operation model of the transmission network, i represents the i-th grid node in the transmission network, t represents time, Ω represents the set, and Ω G represents the set of thermal power units in the transmission network, Ω BESS represents the battery energy storage set of the transmission grid, a i 、b i 、c i represents the fuel cost coefficient of the thermal power unit in the transmission network node i, represents the output of thermal power unit at transmission grid node i at time t, represents the battery degradation cost of transmission grid node i, represents the battery charging power of the electric vehicle at the transmission grid node i at time t, represents the carbon price of transmission grid node i at time t, represents the carbon quota purchased by the unit at transmission grid node i at time t, represents the carbon quota sold by the unit at transmission grid node i at time t; The constraints of the low-carbon operation model of the transmission network include the active power balance constraint, reactive power balance constraint, AC power flow constraint, capacity constraint, ramp constraint, node voltage constraint, power flow constraint, energy balance constraint of the battery energy storage system, energy storage capacity constraint, charging and discharging power constraint of electric vehicles, and carbon emission constraint of thermal power units.

4. The electric vehicle charging scheduling method considering the electric carbon market as claimed in claim 3, characterized in that: The constraints of the low-carbon operation model of the transmission grid include: Where j represents other grid nodes connected to the transmission grid node i, represents the set of other grid nodes connected to the transmission grid node i, represents the output of the new energy unit at the transmission grid node i at time t, represents the active power demand of the lower-level distribution network at the transmission network node i at time t, represents the charging power of the electric vehicle battery energy storage at the transmission grid node i at time t, represents the discharge power of the electric vehicle battery energy storage at the transmission grid node i at time t, represents the active power flow at time t on the line between node i and node j in the transmission network, represents the reactive power of the thermal power unit at the transmission grid node i at time t, represents the reactive power demand of the lower-level distribution network at the transmission network node i at time t, represents the reactive power flow at time t on the line between node i and node j in the transmission network, represents the active power flow on the line between node i and node j in the transmission network, It represents the reactive power flow on the line between node i and node j in the transmission network, U i represents the voltage of the transmission network node i, U j represents the voltage of transmission network node j, θ ij represents the phase angle on the line between node i and node j in the transmission network, G ij represents the conductance of the line between node i and node j in the transmission network, B ij P represents the susceptance on the line between node i and node j in the transmission network. i G,max represents the maximum active power of the thermal power unit in the transmission network node i, represents the maximum reactive power of the thermal power unit in the transmission network node i, represents the upward ramp rate of the unit at the transmission grid node i, represents the downward ramp rate of the unit at transmission grid node i, and They represent the minimum and maximum node voltages of transmission network node i, U i,t represents the voltage of the transmission network node i at time t, and They represent the maximum active power flow and the maximum reactive power flow at time t on the line between node i and node j in the transmission network, represents the energy storage state of the battery energy storage at the transmission grid node i at time t, represents the energy storage state of the battery energy storage at the transmission grid node i at time t+1, and They represent the charging efficiency and discharging efficiency of battery energy storage in the transmission grid, and They represent the minimum and maximum energy of the battery storage at the transmission grid node i, and They represent the maximum charging power and maximum discharging power of the battery energy storage at the transmission grid node i, Cap i represents the carbon quota allocated to the unit i at the transmission grid node; represents the emission factor of transmission grid node i.

5. The electric vehicle charging scheduling method considering the electric carbon market as claimed in claim 1, characterized in that: The objective function of the distribution network coordinated operation model is: Among them, Min.F DN represents the objective function of the distribution network coordinated operation model, m represents the grid node of the distribution network, n represents other grid nodes connected to the distribution network node m, Ω MG represents the node set where the micro-unit is located, Ω BESS represents the node set where the energy storage is located, Ω represents the node set of the distribution network, represents the set of feeders connecting the distribution network and the substation, Ω FD is the set of other feeders, represents the node electricity price transmitted from the upper transmission network at time t, P t ST represents the transmission power of the substation at time t, represents the power of the small thermal power unit at the distribution network node m at time t, a m 、b m 、c m represents the fuel cost coefficient of the thermal power unit at the distribution network node m, represents the carbon price of distribution network node m at time t, represents the carbon quota purchased by the unit at distribution network node m at time t, represents the carbon quota sold by the unit at distribution network node m at time t, represents the battery degradation cost of distribution network node m, represents the battery charging power of the electric vehicle at distribution network node m at time t, represents the average node electricity price of the distribution network, I mn,t It represents the square of the current at time t on the line between node m and node n in the distribution network, Z ST represents the impedance of the substation, 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 the active power balance constraint, reactive power balance constraint, power flow constraint, node voltage constraint, power flow constraint, energy balance constraint of the battery energy storage system, energy storage state constraint of the battery energy storage system, charging and discharging power constraint of electric vehicles, and indirect emission constraints of end users.

6. The electric vehicle charging scheduling method considering the electric carbon market as claimed in claim 5, characterized in that: The constraints of the distribution network collaborative operation model include: IN min ≤U m,t ≤U max ; in, represents the set of nodes adjacent to the distribution network node m, represents the active power flow at time t on the line between node m and node n in the distribution network, represents the transmission power of the substation at the distribution network node m at time t, represents the output of the new energy unit at the distribution network node m at time t, represents the power of the small thermal power unit at the distribution network node m at time t, represents the discharge power of the electric vehicle battery energy storage at distribution network node m at time t, represents the load of distribution network node m at time t excluding electric vehicles, represents the charging power of the electric vehicle battery energy storage at the distribution network node m at time t, represents the electric vehicle load of distribution network node m at time t, represents the load reduction of distribution network node m at time t, represents the reactive power flow at time t on the line between node m and node n in the distribution network, represents the reactive power transmitted by the substation at the distribution network node m at time t, represents the reactive power output of the small generator at the distribution network node m at time t, U m,t It represents the voltage of the distribution network node m at time t, U n,t represents the voltage of distribution network node n at time t, r mn represents the resistance of the line between node m and node n in the distribution network, x mn represents the reactance on the line between the distribution network node m and node n, U0 represents the reference voltage of the distribution network node, I mn,t It represents the square of the current at time t on the line between node m and node n in the distribution network, U min Indicates the minimum voltage of the distribution network node, U max Indicates the maximum voltage of the distribution network node, It represents the maximum active power flow at time t on the line between grid node m and node n, It represents the maximum reactive power flow on the line between node m and node n in the distribution network at time t, represents the energy storage state of the battery energy storage at the distribution network node m at time t+1, and They represent the charging efficiency and discharging efficiency of battery energy storage in the distribution network, represents the minimum energy stored in the battery at node m in the distribution network, represents the energy stored in the battery at distribution network node m at time t, represents the maximum energy stored in the battery at node m in the distribution network, represents the maximum charging power of the electric vehicle battery energy storage at the distribution network node m at time t, represents the maximum discharge power of the electric vehicle battery energy storage at distribution network node m at time t, represents the emission factor of distribution network node m, Cap represents the total load or electric vehicle load of distribution network node m at time t, m represents the carbon quota allocated to the unit at node m in the distribution network, represents the carbon quota purchased by the unit at distribution network node m at time t, It represents the carbon quota sold by the unit at distribution network node m at time t.

7. The electric vehicle charging scheduling method considering the electric carbon market as claimed in claim 1, characterized in that: The objective function of the carbon emission flow tracking model of the transmission network is: in, represents the carbon potential of transmission grid node i at time t, represents the output of thermal power unit at transmission grid node i at time t, represents the discharge power of the electric vehicle battery energy storage at the transmission grid node i at time t, represents the energy storage carbon potential of transmission grid node i at time t, represents the active power flow at time t on the line between node i and node j in the transmission network, represents the carbon potential of the line branch between node i and node j in the transmission network, represents the output of the new energy unit at the transmission grid node i at time t, Γ ij represents the power injection node on the line between transmission grid node i and node j; The objective function of the carbon emission flow tracking model of the distribution network is: in, represents the carbon potential of the distribution network node m at time t, which is inherited from the upper transmission system. represents the transmission power of the substation at the distribution network node m at time t, represents the carbon intensity of the substation node at distribution network node m at time t, represents the power of the small thermal power unit at the distribution network node m at time t, represents the carbon emission intensity of small generators at distribution network node m, represents the discharge power of the electric vehicle battery energy storage at distribution network node m at time t, represents the energy storage carbon potential of distribution network node m at time t, represents the active power flow at time t on the line between node m and node n in the distribution network, represents the carbon potential of the line branch between the distribution network node m and node n, Represents the output of the new energy unit at distribution network node m at time t.

8. The electric vehicle charging scheduling method considering the electric carbon market as claimed in claim 1, characterized in that: The objective function of the traffic flow model is: in, represents the time cost, A represents the set of traffic sections, c a,t represents the traffic flow on road a at time t, t a,t represents the time spent traveling on road a at time t, Ω cs represents the set of charging stations for electric vehicles, x h represents the car charging at the hth charging station, represents the charging time at the hth charging station at time t, represents the charging price of electric vehicles at the h-th charging station, x h,t represents the car charging at the hth charging station at time t, E Cd Indicates charging demand; The constraints of the traffic flow model can be expressed as: Among them, od represents the traffic section with o as the starting point and d as the end point, (o,d)∈O represents the set of traffic sections, a represents the ath section, a∈A p Represents the index of the road segment, p represents the p-th path, Represents the set of all paths, represents the traffic flow on the pth path on the traffic section from o to d at time t, The traffic flow of fuel vehicles on the pth path on the traffic section from o to d at time t, represents the traffic demand of electric vehicles on the traffic section from o to d at time t, represents the traffic demand of fuel vehicles on the traffic section from o to d at time t, c a,t represents the traffic flow of the a-th road section at time t, It is a 01 parameter, which is 1 if the pth path is through the ath path, otherwise it is 0, x h,t represents the car charging at the hth charging station at time t, represents the charging virtual link of the h-th charging station, t a,t represents the time taken to pass through section a at time t, represents the time it takes to pass through section a when idle, ξ and τ represent the road resistance parameters in the road impedance function, and c a represents the traffic flow on road section a, represents the road capacity of the a-th road section, represents the charging time of the hth charging station at time t, represents the charging time of the hth charging station when it is idle, Que(·) is the queuing function in queuing theory, represents the maximum traffic attraction of the h-th charging station, S ini represents the average initial battery state of charge, E Con,EV Indicates the energy consumption of electric vehicles per kilometer, L a represents the length of the a-th road segment, E max represents the maximum capacity of the electric vehicle, represents the energy required to reach the first electric vehicle charging station on the pth path, Θ NCP represents the set of non-charging paths, Θ CP Represents a collection of charging paths.

9. The electric vehicle charging scheduling method considering the electric carbon market as claimed in claim 1, characterized in that: The objective function of the charging station planning and charging integrated management model is: Among them, Ω cs represents the set of charging stations for electric vehicles, represents the initial charging price of the hth charging station at time t, represents the carbon potential of the distribution network node m at time t, represents the carbon price of distribution network node m at time t, represents the electricity price of distribution network node m at time t, represents the electric vehicle charging demand at the h-th charging station at time t; The constraints of the charging station planning and charging integrated management model are: Among them, ε represents the price elasticity constraint, represents the electric vehicle charging demand at the hth charging station at time t, represents the basic electric vehicle charging demand at the hth charging station at time t based on the estimation, represents the initial charging price of the hth charging station at time t, represents the basic charging price of the h-th charging station at time t.

10. An electric vehicle charging scheduling system considering the electric carbon market, characterized in that: include: An electricity-carbon market collaborative model construction module, configured 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; Traffic flow model construction module, which 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; A charging station planning and charging integrated management model construction module, used to construct a charging station planning and charging integrated management model for electric vehicles based on the electricity-carbon market collaborative model and the traffic flow model; The model solving module is used to solve the electricity-carbon market collaborative model, the traffic flow model and the charging station planning and charging integrated management model to obtain an electric vehicle charging scheduling strategy.

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