Electric vehicle low-carbon cooperative regulation and control method based on node carbon energy level mapping

Abstract

The invention relates to the field of electric power system and electric traffic cooperative operation optimization, and provides an electric vehicle low-carbon cooperative regulation and control method based on node carbon energy level mapping. The method comprises the steps of performing random sampling by adopting a Monte Carlo method, generating charging and discharging demand data conforming to statistical characteristics, constructing a charging and discharging demand model of an electric vehicle cluster, and calculating a real-time carbon energy level of each node of a power grid; and based on the charging and discharging demand model of the electric vehicle cluster and the real-time carbon energy level of each node of the power grid, further constructing an optimal scheduling strategy, and by taking the total cost minimization as a target and combining the time-of-use electricity price and a carbon quota transaction mechanism, establishing an optimization model and solving to obtain a charging and discharging strategy. According to the method, the electric vehicle can be dynamically stimulated to be charged at low price in the clean power generation rich area, active feedback is achieved in the high-carbon node load peak period, and the comprehensive benefit improvement of users and regional carbon reduction are achieved at the same time.

Description

Technical Field

[0001] This invention belongs to the field of power system and electric transportation cooperative operation optimization, and specifically relates to a low-carbon cooperative control method for electric vehicles based on nodal carbon level mapping. Background Technology

[0002] Electric vehicles (EVs), as a crucial component of distributed flexible loads, are profoundly transforming power grid operation due to their widespread integration. While existing technologies incentivize load migration through time-of-use pricing, they generally overlook the spatial heterogeneity of carbon resource flows across power grid nodes and their impact on local carbon emission characteristics. Furthermore, mainstream scheduling methods largely rely on static response models, failing to integrate the evolutionary trends of EV behavior and the integration of clean energy into nodes, thus hindering refined, real-time low-carbon regulation. Particularly in the context of asynchronous load disturbances across multiple nodes, there is a lack of dynamic strategy mechanisms capable of real-time assessment and driving of node carbon level evolution. Therefore, there is an urgent need to develop a novel, adaptive, and learning-oriented distributed EV low-carbon scheduling method tailored to local carbon ecological characteristics. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a low-carbon synergistic regulation of electric vehicles based on nodal carbon level mapping, thus solving the problems in existing technologies.

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] A method for low-carbon coordinated regulation of electric vehicles based on nodal carbon level mapping includes the following steps:

[0006] The Monte Carlo method was used for random sampling to generate charging and discharging demand data that conformed to statistical characteristics. A charging and discharging demand model for electric vehicle clusters was constructed, and the real-time carbon energy level of each node in the power grid was calculated.

[0007] Based on the charging and discharging demand model of electric vehicle clusters and the real-time carbon energy level of each node in the power grid, an optimized scheduling strategy is further constructed. With the goal of minimizing total cost, an optimization model is established and the charging and discharging strategy is obtained by combining time-of-use pricing and carbon quota trading mechanisms.

[0008] Furthermore, the battery state of charge of the electric vehicle at each time step is dynamically updated based on its charging and discharging power, battery rated capacity and charging and discharging efficiency, and meets the following constraints: the battery state of charge should be between the minimum safe SOC and the maximum permissible SOC; all charging and discharging power participating in the scheduling must meet the node voltage stability and V2X safety requirements.

[0009] Furthermore, the node carbon level C i,t Calculated using the following formula:

[0010]

[0011] Among them, P RE,i,t P represents the renewable energy output of node i at time t. L,i,t β represents the load power at node i. i γ i η represents the nodal structure coefficient and the carbon conversion coefficient, respectively. fossil It is a carbon emission factor of fossil fuels.

[0012] Furthermore, the objective function for optimizing the model is:

[0013]

[0014] Among them, C char,t For charging costs, C carbon,t For carbon emission costs, C load,t The load balancing cost is T, where T is the time period and C is the load balancing cost. total The total optimized cost.

[0015] Furthermore, the charging cost C char,t The formula for calculation is:

[0016]

[0017] Among them, C char,t N represents the charging cost at time t; EV P represents the total number of electric vehicles participating in V2G scheduling; char,i,t π represents the charging power of the i-th electric vehicle at time t; t The electricity price at time t; Δt represents the time step;

[0018] The carbon emission cost C carbon,t The formula for calculation is:

[0019]

[0020] Among them, C i,t q represents the carbon energy level of grid node i at time t; t This represents the carbon trading price at time t;

[0021] The load balancing cost C load,t The formula for calculation is:

[0022]

[0023] Where α represents the load balancing penalty factor; P dis,i,t Let represent the discharge power of the i-th EV at time t.

[0024] Furthermore, the constraints of the optimization model include:

[0025] The charging and discharging power of each electric vehicle must meet the following boundary limits:

[0026]

[0027] in, This indicates the available charging and discharging capacity during a given period, which is determined by the vehicle's status and the user's policy.

[0028] Power balance in the power grid should satisfy the constraints within the region:

[0029]

[0030] Where Ω represents the set of nodes in the j-th region.

[0031] A low-carbon coordinated control system for electric vehicles based on nodal carbon level mapping includes:

[0032] Model building module: The Monte Carlo method is used to perform random sampling to generate charging and discharging demand data that conforms to statistical characteristics, build a charging and discharging demand model for electric vehicle clusters, and calculate the real-time carbon energy level of each node in the power grid.

[0033] Optimization Module: Based on the charging and discharging demand model of electric vehicle clusters and the real-time carbon energy level of each node in the power grid, an optimized scheduling strategy is further constructed. With the goal of minimizing total cost, the optimization model is established and the charging and discharging strategy is obtained by combining time-of-use pricing and carbon quota trading mechanisms.

[0034] A computer storage medium storing a readable program that, when the program is run, can execute the above-described method for low-carbon coordinated control of electric vehicles based on nodal carbon level mapping.

[0035] An electronic device includes: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0036] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described method for low-carbon coordinated regulation of electric vehicles based on node carbon level mapping.

[0037] A computer program product includes computer instructions that instruct a computing device to perform operations corresponding to the above-described method for low-carbon coordinated regulation of electric vehicles based on nodal carbon level mapping.

[0038] The beneficial effects of this invention are:

[0039] 1. This invention combines time-of-use pricing and carbon quota trading to optimize the charging and discharging strategies of electric vehicles, enabling users to charge during periods of low electricity prices, thereby effectively reducing charging costs. Furthermore, it employs the Monte Carlo method to predict the charging and discharging demand of electric vehicles, improving scheduling accuracy and avoiding unnecessary peak-hour charging fees.

[0040] 2. This invention accurately identifies charging periods and locations with lower carbon emissions by calculating the carbon energy level of each node in the power grid in real time. This guides electric vehicles to charge during low-carbon periods or at nodes with abundant low-carbon power sources, thereby reducing overall carbon emissions. Furthermore, a carbon trading mechanism is used to quantify carbon emission costs, incentivizing electric vehicle users to actively choose low-carbon charging solutions, achieving a dual optimization of economic and environmental benefits.

[0041] 3. This invention achieves peak-hour discharge and off-peak-hour charging through coordinated charging and discharging of electric vehicle groups, optimizing the grid load curve and reducing power system instability. Furthermore, it employs V2G (Vehicle-to-Grid) technology, allowing electric vehicles to supply power back to the grid during periods of high grid load, improving grid operating efficiency and providing additional benefits to users.

[0042] 4. This invention uses a convex optimization algorithm to quickly solve the charging and discharging scheduling problem, ensuring real-time performance and making it suitable for dynamic power grid environments.

[0043] 5. This invention reduces charging costs through intelligent scheduling, allows users to profit in the carbon trading market, and obtains discharge subsidies through V2G technology, thereby improving overall economic benefits. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart of the scheduling method of the present invention;

[0046] Figure 2 This is a schematic diagram of the convex optimization algorithm solution of the present invention;

[0047] Figure 3 This is the carbon energy level diagram of each node at time 0 in Embodiment 2 of the present invention. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Example 1

[0050] like Figure 1 As shown, a low-carbon synergistic regulation method for electric vehicles based on nodal carbon level mapping includes the following steps:

[0051] S1. The Monte Carlo Sampling method is used to perform random sampling to generate charging and discharging demand data that conforms to statistical characteristics, construct a charging and discharging demand model for electric vehicle clusters, and calculate the real-time carbon energy level of each node in the power grid.

[0052] 1) Monte Carlo sampling modeling

[0053] In order to reasonably simulate the grid connection time, grid disconnection time, and target charge (SOC demand) of electric vehicle (EV) users, this embodiment uses the Monte Carlo method for random sampling to generate charging and discharging demand data that conforms to statistical characteristics.

[0054] The Monte Carlo method is used to simulate the charging and discharging needs of EV users, considering the following random variables: grid connection time, grid disconnection time, target capacity, and initial battery state of charge (SOC). To more realistically reflect user behavior, these variables are assumed to follow a certain probability distribution and are generated through Monte Carlo sampling.

[0055] Assume that the EV user network access time follows a normal distribution, with the mean being the typical charging time during the day or night:

[0056]

[0057] 0≤T in,i ≤24 (2)

[0058] Where, μ in σ represents the average typical network access time; in T represents the standard deviation of network access time; in,i This represents the network access time of the i-th electric vehicle; if the sampled value exceeds the range, a truncated normal distribution can be used.

[0059] Assuming the time of leaving the network is related to the time of joining the network, and is generally later than the time of joining the network, and follows a normal distribution:

[0060]

[0061] T in,i ≤T out,i ≤24 (4)

[0062] Where, μ stay σ represents the average parking time. stay T represents the standard deviation of parking duration. out,i Let T represent the off-grid time of the i-th electric vehicle; if T out,i If the value is greater than 24, it is considered a cross-day charge and can be charged using T. out,i =T out,i -24 is used for cyclical adjustment.

[0063] EV users typically target battery levels between 80% and 100%, which can be modeled using a Beta distribution:

[0064] SOC target,i ~Beta(α,β)(5)

[0065] Among them, SOC target,i Let represent the target state of charge of the i-th electric vehicle; α and β are two shape parameters of the Beta distribution used to model the target state of charge of the electric vehicle.

[0066] Assume that the user's initial SOC follows a uniform distribution:

[0067] SOC init,i ~U(0.2,0.8)(6)

[0068] Where U(a,b) represents a uniform distribution.

[0069] The charging requirements for electric vehicles are as follows:

[0070] E char,i =(SOC) target,i -SOC init,i )·E cap,i (7)

[0071] Among them, E char,i Indicates the energy required for charging; SOC target,i Represents the target SOC; SOC init,i Indicates the initial SOC; E cap,i This represents the battery capacity of the i-th EV.

[0072] Calculate the required charging power per hour based on the user's available charging time:

[0073]

[0074] Among them, Pchar,i,t Let η represent the charging power of the i-th EV at time t, and let η represent the charging efficiency.

[0075] If the power grid needs to reduce peak loads, some EVs may participate in V2G discharge:

[0076] E dis,i =γ·E cap,i (9)

[0077] Where γ represents the maximum percentage of discharge allowed by the user.

[0078] 2) Charging and discharging demand model for electric vehicle clusters;

[0079] Considering the charging and discharging needs of a cluster of electric vehicles, the energy change of the electric vehicle's battery satisfies:

[0080]

[0081] Among them, P char,i,t P represents the charging power of the i-th EV at time t; dis,i,t E represents the discharge power of the i-th EV at time t; cap,i This represents the battery capacity (kWh) of the i-th EV; SOC i,t η represents the battery charging state of the i-th EV at time t; η represents the charging efficiency.

[0082] The charge / discharge constraints are expressed as follows:

[0083]

[0084] in, The available charging and discharging capacity during the time period is determined by the vehicle status and user policy; Ω represents the set of nodes in the j-th region.

[0085] 3) Calculation of carbon energy level at power grid nodes

[0086] Carbon emissions from the power grid are influenced by power generation methods and load distribution, and can be calculated using a carbon emission flow model. The carbon emission flow model includes calculations of carbon emission flow rate, carbon emission flow density, and nodal carbon levels. The expression for carbon level is as follows:

[0087]

[0088] Among them, P RE,i,t P represents the renewable energy output of node i at time t. L,i,t β represents the load power at node i. i γ i η represents the nodal structure coefficient and the carbon conversion coefficient, respectively. fossilIt is a carbon emission factor of fossil fuels.

[0089] S2, based on the charging and discharging demand model of electric vehicle clusters and the real-time carbon energy level of each node in the power grid, further constructs an optimized scheduling strategy. With the goal of minimizing total cost, it combines time-of-use pricing and carbon quota trading mechanisms to establish an optimization model and solve for the charging and discharging strategy.

[0090] The optimization objective of the optimization model is to minimize charging costs, carbon emission costs, and load balancing costs, while satisfying electric vehicle charging and discharging constraints and grid power balance constraints.

[0091] 1) Optimize the objective function of the model;

[0092]

[0093] Among them, C char,t For charging costs, C carbon,t For carbon emission costs, C load,t The load balancing cost is T, where T is the time period and C is the load balancing cost. total The total optimized cost.

[0094] Charging cost C char,t This is the economic cost of charging an electric vehicle (EV) based on time-of-use pricing (TOU); the calculation formula is as follows:

[0095]

[0096] Wherein, the charging cost at time t; N EV P represents the total number of electric vehicles participating in V2G scheduling; char,i,t π represents the charging power of the i-th electric vehicle at time t; t The electricity price at time t; Δt represents the time step.

[0097] Carbon emission cost C carbon,t Carbon emissions from electric vehicles (EVs) during charging, caused by the power supply from the grid, and the corresponding economic costs, are the primary factors influencing these costs. These costs are mainly determined by charging power, node carbon potential, and carbon trading price.

[0098]

[0099] Where, λ i,t q represents the carbon energy level of grid node i at time t; t This represents the carbon trading price at time t.

[0100] Load balancing cost Cload,t This refers to the impact of electric vehicles (EVs) on grid load balancing during charging and discharging. Its main objective is peak shaving and valley filling, reducing grid load fluctuations and preventing overcharging leading to grid overload or underloading causing power waste. Load balancing costs are typically modeled using a quadratic function to minimize charging and discharging power fluctuations and ensure the grid load curve is as smooth as possible.

[0101]

[0102] Where α represents the load balancing penalty factor, used to adjust the impact of load fluctuations on the optimization objective; N EV P represents the total number of electric vehicles participating in V2G scheduling; char,i,t P represents the charging power of the i-th EV at time t; dis,i,t Let represent the discharge power of the i-th EV at time t.

[0103] 2) Optimize the constraints of the model;

[0104] The charging and discharging constraints for electric vehicles are:

[0105]

[0106] in, This indicates the available charging and discharging capacity during a given period, which is determined by the vehicle's status and the user's policy.

[0107] Power balance constraints of the power grid:

[0108]

[0109] Where Ω represents the set of nodes in the j-th region.

[0110] 3) Solving the optimization model

[0111] like Figure 2 As shown, the specific steps for solving the optimization model are as follows:

[0112] Step 1, Define the optimization problem;

[0113] With the objective of minimizing total costs (charging costs, carbon emission costs, and load balancing costs), a mathematical expression for the optimization problem is constructed. Constraints are determined, including battery SOC limits, charge / discharge power limits, and grid power balance constraints.

[0114] Step 2: Check the feasibility of the objective function;

[0115] Analyze the mathematical properties of the objective function to determine if it is convex, ensuring solvability. If it is non-convex, use appropriate convex optimization transformations or heuristic methods to solve it.

[0116] Step 3: Construct an optimization solution algorithm;

[0117] Gradient descent, Lagrange multiplier method, or convex optimization algorithm can be used to solve the problem. For large-scale optimization problems, second-order optimization (such as interior-point method) or heuristic algorithms (such as genetic algorithm, particle swarm optimization) can be used.

[0118] Step 4: Iteratively calculate the gradient of the objective function;

[0119] Calculate the gradient at each time step and update the charging / discharging strategy. In each iteration, check if the current solution satisfies the constraints; if not, adjust the calculation direction.

[0120] Step 5, check the constraints;

[0121] Ensure that the charging and discharging power, SOC constraints, and grid power balance of all vehicles meet the requirements. If not, adjust the optimization direction and recalculate the optimization variables.

[0122] Step 6: Calculate the error and determine convergence;

[0123] Calculate the error between the current iterative solution and the previous iterative solution. If the error is lower than a set threshold, convergence is considered achieved, and the iteration terminates. If convergence has not occurred, iterative calculation continues until the accuracy requirement is met or the maximum number of iterations is reached.

[0124] Step 7: Output the optimal solution;

[0125] The optimal charging and discharging scheduling strategy is generated, and scheduling instructions are sent to the electric vehicle group. The optimization results are recorded, the grid load status is updated, and the optimization calculation for the next time period begins.

[0126] Based on a similar inventive concept, this embodiment of the invention also provides a computer storage medium storing a readable program that, when the program is run, can execute the above-described method for low-carbon coordinated regulation of electric vehicles based on nodal carbon level mapping.

[0127] Based on a similar inventive concept, this invention provides an electronic device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0128] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described method for low-carbon coordinated regulation of electric vehicles based on node carbon level mapping.

[0129] Based on a similar inventive concept, this invention also provides a computer program product, including computer instructions, which instruct a computing device to perform the operation corresponding to the above-described method for low-carbon coordinated regulation of electric vehicles based on nodal carbon level mapping.

[0130] Example 2

[0131] In this embodiment, an experimental verification of the low-carbon coordinated control method for electric vehicles based on node carbon level mapping proposed in Example 1 is conducted to illustrate the superiority of the scheduling method.

[0132] A simulation environment was established based on the IEEE 14-node distributed network test system. The Monte Carlo method was used to generate raw data for a total of 500 electric vehicles. The entry time and daily mileage of the electric vehicles followed a normal distribution. The Monte Carlo sampling settings had a mean and standard deviation of u1 = 15.9 and o1 = 4.0, respectively. The Monte Carlo sampling settings for daily mileage had a mean and standard deviation of u2 = 3.4 and o2 = 0.5, respectively. The maximum battery capacity of the electric vehicles was 15 kWh, and the charging / discharging power was 3 kW. Nodes 5 and 9 were used as charging / discharging nodes. Considering the continuity of the scheduling strategy, it was necessary to complete the charging tasks of all vehicles before 12:00 pm each day, while simultaneously updating the daily basic load forecast data. Therefore, the initial simulation time in this embodiment was 0:00, and it ended at 0:00 the following day.

[0133] To analyze the nodal carbon potential Figure 3 The figure shows the carbon emission intensity of each node at 0, which is the node carbon potential. Node carbon potential reflects the carbon emission level of each node in the power system. Node 1 has a carbon potential of 875 kg / MWh, which is consistent with the carbon emission intensity of this node; the carbon potentials of nodes 2 and 6 are higher than the carbon emission factors of the generators at these nodes because the units of node 1 inject electricity into nodes 2 and 6 through transmission lines; node 8 is a clean energy unit with no other units injecting carbon, so its carbon potential is 0.

[0134] Both charging costs and carbon emission costs are closely related to the total load curve. Cost analyses were conducted before and after the scheduling. The original charging cost was 9657.2 yuan, and the carbon emission cost was 163.3 yuan. The average daily total cost per vehicle was 19.6 yuan. After scheduling, the cost was 8717.5 yuan, and the carbon emission cost was 116.3 yuan, with an average daily total cost per vehicle of 17.7 yuan. After scheduling, charging costs decreased by 9.7%, carbon emission costs decreased by 28.8%, and the average daily total cost per vehicle decreased by 9.6%. Figure 3It also indicates that the charging and emission behavior of most electric vehicles is relatively normal, as they are emitting and charging during peak hours. Only a small number of electric vehicles have repeatedly spent time charging and emitting across the network, thus this scheduling strategy reduces the cost of battery degradation to some extent.

[0135] Example 3

[0136] A low-carbon coordinated control system for electric vehicles based on nodal carbon level mapping includes:

[0137] Model building module: The Monte Carlo method is used to perform random sampling to generate charging and discharging demand data that conforms to statistical characteristics, build a charging and discharging demand model for electric vehicle clusters, and calculate the real-time carbon energy level of each node in the power grid.

[0138] Optimization Module: Based on the charging and discharging demand model of electric vehicle clusters and the real-time carbon energy level of each node in the power grid, an optimized scheduling strategy is further constructed. With the goal of minimizing total cost, the optimization model is established and the charging and discharging strategy is obtained by combining time-of-use pricing and carbon quota trading mechanisms.

[0139] The methods of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses the code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for performing the methods shown herein.

[0140] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for low-carbon coordinated regulation of electric vehicles based on nodal carbon level mapping, characterized in that, Includes the following steps: The Monte Carlo method was used for random sampling to generate charging and discharging demand data that conformed to statistical characteristics. A charging and discharging demand model for electric vehicle clusters was constructed, and the real-time carbon energy level of each node in the power grid was calculated. Based on the charging and discharging demand model of electric vehicle clusters and the real-time carbon energy level of each node in the power grid, an optimized scheduling strategy is further constructed. With the goal of minimizing total cost, an optimization model is established and the charging and discharging strategy is obtained by combining time-of-use pricing and carbon quota trading mechanisms.

2. The method for low-carbon coordinated regulation of electric vehicles based on nodal carbon level mapping according to claim 1, characterized in that, The battery state of charge of an electric vehicle at each time step is dynamically updated based on its charging and discharging power, battery rated capacity, and charging and discharging efficiency, and meets the following constraints: the battery state of charge should be between the minimum safe SOC and the maximum permissible SOC; all charging and discharging power participating in the scheduling must meet the node voltage stability and V2X safety requirements.

3. The method for low-carbon coordinated regulation of electric vehicles based on nodal carbon level mapping according to claim 1, characterized in that, The carbon energy level C of the node i,t Calculated using the following formula: Among them, P RE,i,t P represents the renewable energy output of node i at time t. L,i,t β represents the load power at node i. i γ i η represents the nodal structure coefficient and the carbon conversion coefficient, respectively. fossil It is a carbon emission factor of fossil fuels.

4. The method for low-carbon coordinated regulation of electric vehicles based on nodal carbon level mapping according to claim 1, characterized in that, The objective function for optimizing the model is: Among them, C char,t For charging costs, C carbon,t For carbon emission costs, C load,t The load balancing cost is T, where T is the time period and C is the load balancing cost. total The total optimized cost.

5. The method for low-carbon coordinated regulation of electric vehicles based on nodal carbon level mapping according to claim 4, characterized in that, The charging cost C char,t The formula for calculation is: Where C char,t N represents the charging cost at time t; EV P represents the total number of electric vehicles participating in V2G scheduling; char,i,t π represents the charging power of the i-th electric vehicle at time t; t The electricity price at time t; Δt represents the time step; The carbon emission cost C carbon,t The formula for calculation is: Among them, C i,t q represents the carbon energy level of grid node i at time t; t This represents the carbon trading price at time t; The load balancing cost C load,t The formula for calculation is: Where α represents the load balancing penalty factor; P dis,i,t Let represent the discharge power of the i-th EV at time t.

6. The method for low-carbon coordinated regulation of electric vehicles based on nodal carbon level mapping according to claim 4, characterized in that, The constraints of the optimization model include: The charging and discharging power of each electric vehicle must meet the following boundary limits: in, This indicates the available charging and discharging capacity during a given period, which is determined by the vehicle's status and the user's policy. Power balance in the power grid should satisfy the constraints within the region: Where Ω represents the set of nodes in the j-th region.

7. A low-carbon coordinated control system for electric vehicles based on nodal carbon level mapping, characterized in that, include: Model building module: The Monte Carlo method is used to perform random sampling to generate charging and discharging demand data that conforms to statistical characteristics, build a charging and discharging demand model for electric vehicle clusters, and calculate the real-time carbon energy level of each node in the power grid. Optimization Module: Based on the charging and discharging demand model of electric vehicle clusters and the real-time carbon energy level of each node in the power grid, an optimized scheduling strategy is further constructed. With the goal of minimizing total cost, the optimization model is established and the charging and discharging strategy is obtained by combining time-of-use pricing and carbon quota trading mechanisms.

8. A computer storage medium storing a readable program that, when the program is run, can execute the low-carbon coordinated control method for electric vehicles based on nodal carbon level mapping as described in any one of claims 1-7.

9. An electronic device comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the low-carbon coordinated control method for electric vehicles based on node carbon level mapping as described in any one of claims 1-7.

10. A computer program product comprising computer instructions that instruct a computing device to perform an operation corresponding to the method for low-carbon coordinated regulation of electric vehicles based on nodal carbon level mapping as described in any one of claims 1-7.

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