Carbon footprint tracking method and system based on electric vehicle path planning

Abstract

The invention provides a carbon footprint tracking method and system based on electric vehicle path planning, and belongs to the technical field of path planning and carbon footprint tracking, and the method comprises the steps: obtaining the road network planning reference data of a target region, and inputting the data into a preset road network planning model, so as to enable the road network planning model to generate a corresponding road network planning result; according to the road network planning result and the real-time operation data of the power system of the target area, performing power flow planning on the power system through a preset power flow planning model, and determining the active power flow distribution of the power system and the actual active power of each generator; and according to the carbon emission intensity of each generator, the actual active power of each generator and the active power flow distribution of the power system, calculating the node carbon potential of each power grid node. The problem that in the prior art, carbon emission accounting is disjointed with the dynamic behavior of an electric vehicle user can be solved.

Description

Technical Field

[0001] This application relates to the fields of route planning and carbon footprint tracking technology, and in particular to a carbon footprint tracking method and system based on electric vehicle route planning. Background Technology

[0002] With the rapid proliferation of electric vehicles (EVs) in urban transportation systems, EV charging loads have become one of the fastest-growing new load types in the power system. The highly stochastic spatiotemporal distribution of EV charging behavior presents new challenges to power system dispatching and carbon emission management. Meanwhile, virtual power plants (VPPs), as important platforms for integrating distributed energy resources and flexible loads, are increasingly being used to improve grid operational flexibility and promote the consumption of renewable energy.

[0003] In existing technologies, methods for calculating carbon emissions from power systems mainly include static calculation methods based on marginal carbon emission factors on the generation side, and methods that incorporate carbon emissions as an objective function into power system optimal scheduling models. These methods typically assume that the spatiotemporal distribution of electric vehicle charging load is known or controllable, neglecting the autonomous decision-making behavior of electric vehicle users during actual travel and charging. Because the charging station selection and route planning of electric vehicle users are non-cooperative and dynamic, existing methods struggle to accurately reflect the carbon emission contributions of each node in actual operation, leading to discrepancies between carbon emission calculation results and reality. For modeling electric vehicle route selection behavior, existing research often employs shortest path algorithms (such as Dijkstra's algorithm) or path allocation methods based on traffic flow theory. These methods can simulate the travel paths of electric vehicle users to some extent, but often fail to fully consider actual factors such as charging station queuing and traffic congestion, resulting in differences between simulation results and reality. Furthermore, existing route simulation methods often prioritize traffic efficiency as the sole objective, lacking a comprehensive consideration of the impact on power system carbon emissions. Regarding carbon footprint tracking, existing technologies mainly focus on carbon flow tracking methods based on power flow analysis. These methods analyze the power flow distribution of the power system and calculate the carbon emission factor of each node to allocate carbon emissions during the power flow process. However, traditional carbon flow tracing methods are mostly based on static load distribution and fail to take into account the dynamic path selection behavior of electric vehicle users, making it difficult to accurately model the carbon potential at charging station access points. Summary of the Invention

[0004] This application provides a carbon footprint tracking method and system based on electric vehicle route planning, which can solve the problem of the disconnect between carbon emission accounting and the dynamic behavior of electric vehicle users in the prior art, and improve the accuracy and rationality of carbon footprint tracking.

[0005] In a first aspect, embodiments of this application provide a carbon footprint tracking method based on electric vehicle route planning, including: Obtain road network planning reference data for the target area, including road network parameters, charging prices at each charging station, and distribution data of electric vehicles waiting to be charged; The road network planning reference data is input into a preset road network planning model so that the road network planning model can perform path optimization for each electric vehicle based on the road network planning reference data and a preset path optimization algorithm, thereby generating a corresponding road network planning result. The road network planning result includes the starting node, target node and driving path of each electric vehicle. The road network planning model is constructed based on a non-cooperative game model. The additional load of each power grid node in the target area is determined based on the road network planning results; Based on the real-time operating data of the power system of each additional load and the target area, power flow planning is performed on the power system through a preset power flow planning model to determine the active power flow distribution of the power system and the actual active power generated by each generator. Based on the carbon emission intensity of each generator, the actual active power generated by each generator, and the active power flow distribution of the power system, the nodal carbon potential of each grid node is calculated, and the carbon footprint tracking of the power system is completed based on the nodal carbon potential.

[0006] This application provides a carbon footprint tracking method based on electric vehicle route planning. By combining a non-cooperative game-based road network planning model with power system flow analysis, it fully considers the impact of electric vehicle charging behavior on power system carbon emissions, solving the problem of disconnect between carbon emission accounting and the dynamic behavior of electric vehicle users in existing technologies. Specifically, this application generates road network planning results through route optimization based on road network parameters, charging prices, and electric vehicle distribution data, achieving accurate prediction of electric vehicle user behavior. Then, based on the road network planning results, it determines the additional load of grid nodes, accurately quantifying the impact of electric vehicle user behavior on the power system. Finally, based on the additional load and real-time operating data of the power system, it performs power flow planning and node carbon potential calculation, realizing joint modeling and analysis of electric vehicle user behavior, power load distribution, and spatial distribution of carbon emissions. This improves the spatiotemporal accuracy and rationality of carbon footprint tracking, laying the foundation for refined carbon management and policy formulation.

[0007] In one possible implementation, the road network planning model performs path optimization for each of the electric vehicles based on the road network planning reference data and a preset path optimization algorithm, thereby generating corresponding road network planning results, including: Based on the road network planning reference data and the path optimization algorithm, the path optimization is performed on each electric vehicle in a sequential loop until the loop stopping condition is met. Based on the current path optimization results of each electric vehicle, the road network planning result is generated. In each path optimization process, the current path optimization result of the current electric vehicle is updated based on the current path optimization results of other electric vehicles, the road network planning reference data, the preset road network planning objective function, and the road network planning constraints. If the current path optimization result of each electric vehicle is the same as the previous path optimization result, then the sequential loop stops.

[0008] This embodiment further refines the path optimization process by updating the path of each electric vehicle in a sequential, iterative manner until all paths stabilize. This mechanism realistically simulates the non-cooperative game behavior of drivers in reality, where each user adjusts their route based on current road conditions to optimize personal costs, and each user's decision-making affects the path planning of other users. Through multiple iterative iterations, even if starting from a certain node in the first round might lead to highly distorted results, each EV will naturally choose a new optimal path based on its own situation at the beginning of the next round, thus resolving unreasonable results and converging the final result to a Nash equilibrium state. This ensures the stability and reliability of the path planning results, avoids biases caused by static assumptions, and makes carbon footprint tracking more reflective of real-world interactions, improving simulation quality and decision support capabilities, thereby enhancing the accuracy and rationality of carbon footprint tracking.

[0009] Furthermore, updating the current path optimization result of the current electric vehicle based on the current path optimization results of other electric vehicles, the road network planning reference data, the preset road network planning objective function, and the road network planning constraints includes: Several candidate routes for the current electric vehicle are generated based on the road network planning reference data; Based on the current path optimization results of the other electric vehicles and the road network planning reference data, the current road traffic flow and the current throughput of each charging station are calculated. Based on the current road traffic flow and the current throughput, the traffic time and waiting time for each candidate path are calculated. Based on the traffic time and waiting time of each candidate path, road network planning reference data, road network planning objective function, and road network planning constraints, the optimal path is determined from each candidate path, and then the optimal path is replaced with the current path optimization result of the current electric vehicle.

[0010] This application provides further specific steps for path updating, including generating candidate paths, calculating current road traffic flow and charging station throughput, and evaluating traffic time and waiting time. In this application embodiment, path selection is not only based on the shortest distance but also considers congestion and queuing factors in real-world driving scenarios, thereby generating more reasonable and practical path planning. Furthermore, during the path optimization process for each current electric vehicle, road traffic flow data and charging station throughput data are not fixed but dynamically determined based on the path optimization results of other electric vehicles. This allows the model to automatically optimize the path optimization results of each electric vehicle through iterative iteration, ultimately obtaining more accurate and reasonable road network planning results. This provides more accurate load data for subsequent carbon potential calculations, enhancing the overall effectiveness of carbon footprint tracking.

[0011] In one possible implementation, the construction of the road network planning model based on a non-cooperative game model includes: To minimize the traffic time cost, waiting time cost, and charging economic cost of electric vehicles, an individual optimization objective function is constructed. With the goal of minimizing the total cost of each electric vehicle, an overall optimization objective function is constructed. Construct the conversion relationship between road traffic volume and travel time; Establish the conversion relationship between charging station throughput and waiting time; Construct road capacity constraints and charging station charging power constraints; By combining the objective functions, transformation relationships, and constraints, the road network planning model is constructed.

[0012] This application provides a method for constructing a road network planning model. It defines individual and overall optimization objective functions, establishes the conversion relationship between traffic flow and traffic time, and between throughput and waiting time, and incorporates constraints on road capacity and charging power. This modeling method respects the autonomous decision-making rights of electric vehicle users while preventing inefficiencies caused by excessive competition through system-level constraints. It balances individual interests with overall system benefits, ensuring the scientific and economical nature of route planning and improving the accuracy and rationality of carbon footprint tracking.

[0013] In one possible implementation, the step of performing power flow planning on the power system using a preset power flow planning model based on the real-time operating data of each of the additional loads and the power system in the target area, to determine the active power flow distribution of the power system and the actual active power generated by each generator, includes: The active load of each node in the power system is updated according to the additional loads to obtain the updated operating data of the power system. The updated operating data includes the power grid topology, the resistance, reactance, current upper and lower limits, power flow upper limit of each branch, the updated active load, reactive load, voltage upper and lower limits of each power grid node, and the maximum active output and reactive power compensator capacity of each generator. The updated operating data is input into the power flow planning model, so that the power flow planning model can solve the problem using the second-order cone programming method based on the preset power flow planning objective function and power flow planning constraints, and then output the active power flow distribution of the power system and the actual active power generated by each generator.

[0014] This application provides a specific implementation method for power flow planning. By updating the power system operation data through the additional load of each node and solving it using the second-order cone programming method, the accuracy and efficiency of power flow calculation are ensured, a reliable data foundation is provided for the derivation of node carbon potential, and the real-time performance and applicability of carbon footprint tracking are improved.

[0015] Furthermore, the calculation of the nodal carbon potential of each grid node based on the carbon emission intensity of each generator, the actual active power generated by each generator, and the active power flow distribution of the power system includes: Based on the carbon emission intensity of each generator, the actual active power generated by each generator, and the active power flow distribution of the power system, the nodal carbon potential definition equation for each grid node is determined. The nodal carbon potential definition equation is the ratio of the carbon flow input to the energy output of the current node as the nodal carbon potential of the current node. The carbon flow input includes the carbon flow input of the actual parent node of the current node and the carbon flow input of the current node's own power supply. The energy output includes the energy consumed by the load of the current node and the energy flowing to the actual child nodes of the current node. The nodal carbon potential definition equations for each of the power grid nodes are converted into matrix form and solved jointly through matrix operations to obtain the nodal carbon potential of each of the power grid nodes.

[0016] This application proposes a method for calculating nodal carbon potential. Based on the principles of carbon flow conservation and energy conservation, equations are defined and transformed into matrix form for joint solution. This approach not only simplifies the calculation process and reduces error accumulation but also ensures the consistency and clarity of the physical meaning of carbon potential values, making carbon footprint tracking results more interpretable and valuable for application.

[0017] In one possible implementation, the carbon footprint tracking method further includes using a variable step-size self-approximation algorithm to iteratively update the current charging price of each charging station and the current node carbon potential of each power grid node if the charging price of each charging station is affected by the node carbon potential, so as to obtain the final node carbon potential of each power grid node. In each iteration update process, the reference carbon potential of each power grid node is determined by road network planning, power flow planning and node carbon potential calculation based on the current charging price of each charging station, the road network parameters and the distribution data. Then, the current node carbon potential of each power grid node is updated based on the reference carbon potential and historical carbon potential of each power grid node. Finally, the current charging price of each charging station is updated based on the current node carbon potential, and the current node carbon potential is used as the historical carbon potential for the next iteration update. If the difference between each of the reference carbon potentials and each of the historical carbon potentials in any iteration update process is less than a preset tolerance, then the iteration update is stopped, and each of the reference carbon potentials is determined as the final node carbon potential of each of the power grid nodes.

[0018] This application further considers the application scenario where charging prices are affected by node carbon potential. When charging prices are affected by node carbon potential, each time the node carbon potential is obtained based on path planning, it will inversely affect the charging price. The charging price, in turn, will further affect the user's path planning, leading to a highly coupled mutual influence between charging prices and node carbon potential, forming a more complex bidirectional feedback scenario. To address this application scenario, this application employs a variable-step-size self-approximation algorithm to continuously adjust the price and carbon potential until convergence. This effectively solves the bidirectional feedback problem between carbon potential and user behavior, ensuring the stability and self-consistency of the system during dynamic changes. This improves the adaptability and robustness of carbon footprint tracking, enabling it to obtain reliable results even under complex interactive conditions, thus enhancing the accuracy and rationality of carbon footprint tracking.

[0019] Secondly, embodiments of this application provide a carbon footprint tracking system based on electric vehicle route planning, including an acquisition module, a road network planning module, an additional load determination module, a power flow planning module, and a carbon flow tracking module; The acquisition module is used to acquire road network planning reference data for the target area. The road network planning reference data includes road network parameters, charging prices of each charging station, and distribution data of each electric vehicle waiting to be charged. The road network planning module is used to input the road network planning reference data into a preset road network planning model, so that the road network planning model can perform path optimization for each electric vehicle according to the road network planning reference data and a preset path optimization algorithm, thereby generating a corresponding road network planning result. The road network planning result includes the starting node, target node and driving path of each electric vehicle. The road network planning model is constructed based on a non-cooperative game model. The additional load determination module is used to determine the additional load of each power grid node in the target area based on the road network planning results; The power flow planning module performs power flow planning on the power system based on the real-time operating data of each additional load and the power system in the target area, using a preset power flow planning model, to determine the active power flow distribution of the power system and the actual active power generated by each generator. The carbon flow tracking module is used to calculate the nodal carbon potential of each grid node based on the carbon emission intensity of each generator, the actual active power generated by each generator, and the active power flow distribution of the power system, and to complete the carbon footprint tracking of the power system based on the nodal carbon potential.

[0020] Furthermore, the road network planning model performs path optimization for each of the electric vehicles based on the road network planning reference data and a preset path optimization algorithm, thereby generating corresponding road network planning results, including: Based on the road network planning reference data and the path optimization algorithm, the path optimization is performed on each electric vehicle in a sequential loop until the loop stopping condition is met. Based on the current path optimization results of each electric vehicle, the road network planning result is generated. In each path optimization process, the current path optimization result of the current electric vehicle is updated based on the current path optimization results of other electric vehicles, the road network planning reference data, the preset road network planning objective function, and the road network planning constraints. If the current path optimization result of each electric vehicle is the same as the previous path optimization result, then the sequential loop stops.

[0021] Furthermore, the carbon footprint tracking system also includes an iterative update module. The iterative update module is used to iteratively update the current charging price of each charging station and the current node carbon potential of each power grid node using a variable step size self-approximation algorithm if the charging price of each charging station is affected by the node carbon potential, so as to obtain the final node carbon potential of each power grid node. In each iteration update process, the reference carbon potential of each power grid node is determined by road network planning, power flow planning and node carbon potential calculation based on the current charging price of each charging station, the road network parameters and the distribution data. Then, the current node carbon potential of each power grid node is updated based on the reference carbon potential and historical carbon potential of each power grid node. Finally, the current charging price of each charging station is updated based on the current node carbon potential, and the current node carbon potential is used as the historical carbon potential for the next iteration update. If the difference between each of the reference carbon potentials and each of the historical carbon potentials in any iteration update process is less than a preset tolerance, then the iteration update is stopped, and each of the reference carbon potentials is determined as the final node carbon potential of each of the power grid nodes. Attached Figure Description

[0022] Figure 1 A flowchart illustrating a carbon footprint tracking method based on electric vehicle route planning, provided for an embodiment of this application; Figure 2 A schematic diagram of an outer-inner two-layer iterative framework for implementing a carbon footprint tracking method is provided in an embodiment of this application. Figure 3 This is a schematic diagram of the structure of a carbon footprint tracking system based on electric vehicle route planning provided in an embodiment of this application. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0024] It should be noted that the step numbers in this document are only for the convenience of explaining the specific embodiments and are not intended to limit the order in which the steps are performed. In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0025] Example 1: like Figure 1 As shown, Embodiment 1 provides a carbon footprint tracking method based on electric vehicle route planning, including steps S1-S5: Step S1: Obtain road network planning reference data for the target area. The road network planning reference data includes road network parameters, charging prices of each charging station, and distribution data of each electric vehicle waiting to be charged. Step S2: Input the road network planning reference data into the preset road network planning model so that the road network planning model can perform path optimization for each electric vehicle according to the road network planning reference data and the preset path optimization algorithm, thereby generating the corresponding road network planning result. The road network planning result includes the starting node, target node and driving path of each electric vehicle. The road network planning model is constructed based on a non-cooperative game model. Step S3: Determine the additional load of each power grid node in the target area based on the road network planning results; Step S4: Based on the real-time operating data of the power system of each additional load and the target area, perform power flow planning on the power system through a preset power flow planning model to determine the active power flow distribution of the power system and the actual active power generated by each generator. Step S5: Calculate the nodal carbon potential of each power grid node based on the carbon emission intensity of each generator, the actual active power generated by each generator, and the active power flow distribution of the power system, and complete the carbon footprint tracking of the power system based on the nodal carbon potential.

[0026] This application provides a carbon footprint tracking method based on electric vehicle route planning. By combining a non-cooperative game-based road network planning model with power system flow analysis, it fully considers the impact of electric vehicle charging behavior on power system carbon emissions, solving the problem of disconnect between carbon emission accounting and the dynamic behavior of electric vehicle users in existing technologies. Specifically, this application generates road network planning results through route optimization based on road network parameters, charging prices, and electric vehicle distribution data, achieving accurate prediction of electric vehicle user behavior. Then, based on the road network planning results, it determines the additional load of grid nodes, accurately quantifying the impact of electric vehicle user behavior on the power system. Finally, based on the additional load and real-time operating data of the power system, it performs power flow planning and node carbon potential calculation, realizing joint modeling and analysis of electric vehicle user behavior, power load distribution, and spatial distribution of carbon emissions. This improves the spatiotemporal accuracy and rationality of carbon footprint tracking, laying the foundation for refined carbon management and policy formulation.

[0027] In one possible implementation, in step S2, the road network planning model performs path optimization for each of the electric vehicles based on the road network planning reference data and a preset path optimization algorithm, thereby generating corresponding road network planning results, including: Based on the road network planning reference data and the path optimization algorithm, the path optimization is performed on each electric vehicle in a sequential loop until the loop stopping condition is met. Based on the current path optimization results of each electric vehicle, the road network planning result is generated. In each path optimization process, the current path optimization result of the current electric vehicle is updated based on the current path optimization results of other electric vehicles, the road network planning reference data, the preset road network planning objective function, and the road network planning constraints. If the current path optimization result of each electric vehicle is the same as the previous path optimization result, then the sequential loop stops.

[0028] This embodiment further refines the path optimization process by updating the path of each electric vehicle in a sequential, iterative manner until all paths stabilize. This mechanism realistically simulates the non-cooperative game behavior of drivers in reality, where each user adjusts their route based on current road conditions to optimize personal costs, and each user's decision-making affects the path planning of other users. Through multiple iterative iterations, even if starting from a certain node in the first round might lead to highly distorted results, each EV will naturally choose a new optimal path based on its own situation at the beginning of the next round, thus resolving unreasonable results and converging the final result to a Nash equilibrium state. This ensures the stability and reliability of the path planning results, avoids biases caused by static assumptions, and makes carbon footprint tracking more reflective of real-world interactions, improving simulation quality and decision support capabilities, thereby enhancing the accuracy and rationality of carbon footprint tracking.

[0029] Furthermore, updating the current path optimization result of the current electric vehicle based on the current path optimization results of other electric vehicles, the road network planning reference data, the preset road network planning objective function, and the road network planning constraints includes: Several candidate routes for the current electric vehicle are generated based on the road network planning reference data; Based on the current path optimization results of the other electric vehicles and the road network planning reference data, the current road traffic flow and the current throughput of each charging station are calculated. Based on the current road traffic flow and the current throughput, the traffic time and waiting time for each candidate path are calculated. Based on the traffic time and waiting time of each candidate path, road network planning reference data, road network planning objective function, and road network planning constraints, the optimal path is determined from each candidate path, and then the optimal path is replaced with the current path optimization result of the current electric vehicle.

[0030] This application provides further specific steps for path updating, including generating candidate paths, calculating current road traffic flow and charging station throughput, and evaluating traffic time and waiting time. In this application embodiment, path selection is not only based on the shortest distance but also considers congestion and queuing factors in real-world driving scenarios, thereby generating more reasonable and practical path planning. Furthermore, during the path optimization process for each current electric vehicle, road traffic flow data and charging station throughput data are not fixed but dynamically determined based on the path optimization results of other electric vehicles. This allows the model to automatically optimize the path optimization results of each electric vehicle through iterative iteration, ultimately obtaining more accurate and reasonable road network planning results. This provides more accurate load data for subsequent carbon potential calculations, enhancing the overall effectiveness of carbon footprint tracking.

[0031] In one possible implementation, the construction of the road network planning model based on a non-cooperative game model includes: To minimize the traffic time cost, waiting time cost, and charging economic cost of electric vehicles, an individual optimization objective function is constructed. With the goal of minimizing the total cost of each electric vehicle, an overall optimization objective function is constructed. Construct the conversion relationship between road traffic volume and travel time; Establish the conversion relationship between charging station throughput and waiting time; Construct road capacity constraints and charging station charging power constraints; By combining the objective functions, transformation relationships, and constraints, the road network planning model is constructed.

[0032] This application provides a method for constructing a road network planning model. It defines individual and overall optimization objective functions, establishes the conversion relationship between traffic flow and traffic time, and between throughput and waiting time, and incorporates constraints on road capacity and charging power. This modeling method respects the autonomous decision-making rights of electric vehicle users while preventing inefficiencies caused by excessive competition through system-level constraints. It balances individual interests with overall system benefits, ensuring the scientific and economical nature of route planning and improving the accuracy and rationality of carbon footprint tracking.

[0033] In a preferred embodiment, the specific construction process of the road network planning model is as follows: The individual optimization objective for any EV owner in the road network planning model is: In the formula: In the first Starting from the first road network node The cost function of EV No. To convert time costs into economic costs using a conversion factor, To go to the first Waiting time at each charging station For the section of road The time spent on For a road network node Departing for the charging station A feasible pathway For the first The charging price at each charging station This is a collection of all charging stations.

[0034] The optimization goal of the overall road network planning is: In the formula: The total equivalent cost for all EV owners The first in the road network The total number of vehicles at each node. This represents the total number of nodes in the road network.

[0035] Next, we define the concepts of road traffic flow and charging station throughput: In the formula: For path Traffic flow To determine the first Starting from the first road network node Did the EV pass through the route? Boolean variable, For the first The throughput of each charging station To determine the first Starting from the first road network node Is the EV at a charging station? A Boolean variable for charging.

[0036] Traffic flow on roads and the throughput of charging stations directly affect the travel and waiting times of EV owners. In the formula: For road infrastructure transit time, This is the traffic interference coefficient. For road capacity, This is the congestion amplification factor. The basic queuing time for charging, This represents the saturation constraint coefficient. In the model... The parameters take into account the impact of some irrational behaviors (such as lose-lose congestion and random queuing services) on the actual behavioral process, thus supplementing the behavioral economics significance of the model.

[0037] For road network planning problems, there are also road capacity constraints, as well as equality constraints between the charging power of charging stations and the load characteristics of the upstream power grid. In the formula: In the first Starting from the first road network node The charging power of the EV.

[0038] It should be noted that the charging power here refers to the ratio of total energy consumption to the length of the period under consideration, not the rated power of the charging port. Therefore, it is adjustable; for example, EV owners' willingness to charge more may decrease when costs are high. In the formula: Based on basic charging power, To achieve full charging power, To reduce the risk of high prices, This represents the expected cost.

[0039] A key challenge in solving road network planning models is that the branch weights in the road network topology graph are not fixed when EV drivers search for the optimal path. Therefore, this application improves the Dijkstra algorithm by first reading the optimal path of the EV from the previous iteration before path optimization. Then, using the formulas for calculating road traffic flow and charging station throughput, the total network traffic flow without the EV is calculated. Next, assuming the EV traverses all branches, the branch traffic flow is updated, and the updated corresponding cost is used as the branch weight in the optimization graph.

[0040] In a preferred embodiment, the road network planning process is a sequential, iterative process, with the following specific steps: 1. Obtain road network planning reference data for the target area. This data includes road network parameters, charging prices at each charging station, and distribution data of electric vehicles waiting to be charged. Road network parameters include node topology, coupling relationships with power system nodes, charging station locations, basic transit time for each route, traffic flow interference coefficient, capacity, congestion amplification coefficient, basic queuing time, capacity, and saturation constraint coefficient for each charging station. Distribution data for electric vehicles waiting to be charged includes the number of vehicles distributed across each node, time-economic conversion coefficient for each vehicle owner, basic charging power, full charging power, high-price resistance coefficient, and expected cost.

[0041] 2. Construct the optimal pathfinding function for EV owners under a certain road network environment, with the optimal principle being the lowest cost. Specifically, the implementation of the optimal pathfinding function consists of two steps. First, based on the improved Dijkstra algorithm provided in this application, calculate the shortest path for the EV to all charging stations (here, the shortest path refers to the path with the minimum sum of branch weights in the road network graph, where the weights have taken into account dynamic changes in the road network). Then, based on the pathfinding results, convert the time cost of each path into economic cost, add the charging electricity price of the charging station as the total cost, and finally find the lowest total cost and return the destination charging station and the shortest path for the corresponding path.

[0042] 3. Starting with the first EV at the first node of the road network, each EV is instructed to perform path optimization in sequence, and the optimization results (including charging station selection, path selection, and charging power) are recorded. Before the next EV performs road network planning, the road network, charging stations, etc. need to be updated based on the optimization results of the previous EV.

[0043] It should be noted that this optimization algorithm can start directly from the node and EV with the number 1, or any node and EV in the road network can be chosen as the first choice; this does not affect the overall implementation of the algorithm. The reason is that multiple iterations are required. Even if starting from a certain node in the first round might lead to highly skewed results, each EV will naturally choose a new optimal path based on its own situation in the next round, thus resolving the unreasonable results. Due to the termination condition of the iteration rounds, when the iteration terminates, each EV cannot independently change its own decision to obtain a lower cost, i.e., a Nash equilibrium is reached. This is necessarily a reasonable result, because if it were unreasonable or there were optimization opportunities, the number of nearby vehicles would definitely change the optimal path strategy.

[0044] 4. After traversing all electric vehicles in step 3, start the next round of road network planning from the first EV at the first node of the road network. If the new planning result is completely consistent with the original plan, output the new planning result as the road network planning result; otherwise, repeat step 4.

[0045] Then, in step S3, the sum of the planned charging loads of EV vehicles received by each charging station is taken as the additional load of the power system at the corresponding node, based on the road network planning results.

[0046] In one possible implementation, in step S4, the process of performing power flow planning on the power system using a preset power flow planning model based on the real-time operating data of each additional load and the power system in the target area, to determine the active power flow distribution of the power system and the actual active power generated by each generator, includes: The active load of each node in the power system is updated according to the additional loads to obtain the updated operating data of the power system. The updated operating data includes the power grid topology, the resistance, reactance, current upper and lower limits, power flow upper limit of each branch, the updated active load, reactive load, voltage upper and lower limits of each power grid node, and the maximum active output and reactive power compensator capacity of each generator. The updated operating data is input into the power flow planning model, so that the power flow planning model can solve the problem using the second-order cone programming method based on the preset power flow planning objective function and power flow planning constraints, and then output the active power flow distribution of the power system and the actual active power generated by each generator.

[0047] This application provides a specific implementation method for power flow planning. By updating the power system operation data through the additional load of each node and solving it using the second-order cone programming method, the accuracy and efficiency of power flow calculation are ensured, a reliable data foundation is provided for the derivation of node carbon potential, and the real-time performance and applicability of carbon footprint tracking are improved.

[0048] In a preferred embodiment, the objective function for power flow planning is: In the formula: Let be the objective function for optimal power flow, i.e., the sum of network losses. This is a power grid topology diagram. For flow through branch road The square of the current magnitude, branch road The resistance is such that the power flow planning objective function is constructed based on Ohm's law.

[0049] The constraints that power flow planning needs to satisfy include node power balance constraints: In the formula: and They are respectively the branches through which the flow passes The active and reactive current flows are defined here, with the positive direction of the current flow defined as the node. Flow to Node , For nodes The set of child nodes and They are nodes The active and reactive loads, For nodes The parent node, and They are nodes The active and reactive power flow injected by the power source. branch road The reactance.

[0050] Voltage drop equation: In the formula: For nodes The square of the voltage magnitude. Applying a second-order cone relaxation to the power flow equation, we have: Power plant grid connection voltage balance constraints: In the formula: It is the set of nodes containing distributed power sources in the power grid topology matrix.

[0051] Upper and lower limits of power grid operating parameters: In the formula: and These are the lower and upper limits of the node voltage, respectively. This is the upper limit of the branch current. This is the upper limit of the branch flow.

[0052] Output limits and upper / lower constraints for distributed power sources: In the formula: and They are nodes The lower and upper limits of the active power output of the power supply. and They are nodes The lower and upper limits of the reactive power output of the power supply.

[0053] Positive trend constraint: Furthermore, the load on charging stations must be taken into account within the scope of grid topology nodes: In the formula: For charging stations Node numbers in the power grid topology diagram.

[0054] Based on the above mathematical model, the specific operation process of step S4 is as follows: 4.1: Obtain real-time operating data of the power system, including the power grid topology, resistance, reactance, current upper and lower limits, power flow upper limit, active load, reactive load, voltage upper and lower limits of each node, generator deployment (including maximum active output and reactive power compensator capacity), and additional load of each charging station's corresponding node.

[0055] 4.2: Add the additional load brought by the EV to the active load of the original power system.

[0056] 4.3: Solve the optimal power flow problem by calling the second-order cone programming method.

[0057] 4.4: Output the active power flow distribution and actual active power generated by the generator for the optimal solution.

[0058] Furthermore, in step S5, calculating the nodal carbon potential of each grid node based on the carbon emission intensity of each generator, the actual active power generated by each generator, and the active power flow distribution of the power system includes: Based on the carbon emission intensity of each generator, the actual active power generated by each generator, and the active power flow distribution of the power system, the nodal carbon potential definition equation for each grid node is determined. The nodal carbon potential definition equation is the ratio of the carbon flow input to the energy output of the current node as the nodal carbon potential of the current node. The carbon flow input includes the carbon flow input of the actual parent node of the current node and the carbon flow input of the current node's own power supply. The energy output includes the energy consumed by the load of the current node and the energy flowing to the actual child nodes of the current node. The nodal carbon potential definition equations for each of the power grid nodes are converted into matrix form and solved jointly through matrix operations to obtain the nodal carbon potential of each of the power grid nodes.

[0059] This application proposes a method for calculating nodal carbon potential. Based on the principles of carbon flow conservation and energy conservation, equations are defined and transformed into matrix form for joint solution. This approach not only simplifies the calculation process and reduces error accumulation but also ensures the consistency and clarity of the physical meaning of carbon potential values, making carbon footprint tracking results more interpretable and valuable for application.

[0060] In a preferred embodiment, the specific calculation process for the nodal carbon potential of each power grid node in step S5 is as follows: For a single node, the definition of its nodal carbon potential is: In the formula: For nodes The actual parent node is defined as the node from which the positive active power flow must originate. The reason for identifying the actual parent node is that the current flow upon which the carbon flow relies must be the active power flow before losses occur; otherwise, some of the actual carbon emissions will disappear with network losses, resulting in a discrepancy between the final calculated carbon emissions and the actual emissions. For the first The carbon emission factor of a generator node, i.e., the carbon emissions per unit of active power. For nodes The actual child nodes, For nodes The node carbon potential.

[0061] For each node, a nodal carbon potential definition equation can be written. The number of equations is the same as the number of unknowns in the nodal carbon potential to be solved, therefore it is linearly solvable. The definition of the nodal carbon potential can be written in matrix form as follows: In the formula: For the parent current matrix, The nodal carbon potential variable matrix, For the carbon emission matrix of power plants, For sub-current matrix, Let be the load matrix. The parent power flow matrix is ​​defined as follows: In the formula: This represents the total number of nodes in the power grid topology. Therefore: in The nodal carbon potential variable matrix is ​​defined as follows: The definition of a power plant carbon emission matrix is: The sub-current flow matrix is ​​defined as follows: The load matrix is ​​defined as follows: Then, through simple matrix operations, the solution equations for the nodal carbon potential can be obtained as follows: Considering the possibility of isolated nodes in the power system, if It contains zero yuan, so set it as a small amount. Then the calculation is performed; in this embodiment, the value is taken as 10. -6By solving the above equations, the nodal carbon potential can be derived after obtaining the power flow calculation results, thus enabling carbon flow tracking.

[0062] It is important to note that in steps S1-S5 of this embodiment, the pricing rule for the charging station is independent of the nodal carbon potential (e.g., a fixed charging price is preset). Therefore, the three steps of road network planning, power flow planning, and carbon footprint tracking are fixed processes, indicating that there is a definite nodal carbon potential corresponding to this pricing rule, which is the desired outcome. However, if the pricing is related to the nodal carbon potential or other relevant parameters (e.g., a higher charging price is set for a higher nodal carbon potential), then it is necessary to update the current carbon potential and charging station price according to an iterative algorithm, and solve for the true solution of the nodal carbon potential through multiple iterations.

[0063] In one possible implementation, the carbon footprint tracking method further includes using a variable step-size self-approximation algorithm to iteratively update the current charging price of each charging station and the current node carbon potential of each power grid node if the charging price of each charging station is affected by the node carbon potential, so as to obtain the final node carbon potential of each power grid node. In each iteration update process, the reference carbon potential of each power grid node is determined by road network planning, power flow planning and node carbon potential calculation based on the current charging price of each charging station, the road network parameters and the distribution data. Then, the current node carbon potential of each power grid node is updated based on the reference carbon potential and historical carbon potential of each power grid node. Finally, the current charging price of each charging station is updated based on the current node carbon potential, and the current node carbon potential is used as the historical carbon potential for the next iteration update. If the difference between each of the reference carbon potentials and each of the historical carbon potentials in any iteration update process is less than a preset tolerance, then the iteration update is stopped, and each of the reference carbon potentials is determined as the final node carbon potential of each of the power grid nodes.

[0064] This application further considers the application scenario where charging prices are affected by node carbon potential. When charging prices are affected by node carbon potential, each time the node carbon potential is obtained based on path planning, it will inversely affect the charging price. The charging price, in turn, will further affect the user's path planning, leading to a highly coupled mutual influence between charging prices and node carbon potential, forming a more complex bidirectional feedback scenario. To address this application scenario, this application employs a variable-step-size self-approximation algorithm to continuously adjust the price and carbon potential until convergence. This effectively solves the bidirectional feedback problem between carbon potential and user behavior, ensuring the stability and self-consistency of the system during dynamic changes. This improves the adaptability and robustness of carbon footprint tracking, enabling it to obtain reliable results even under complex interactive conditions, thus enhancing the accuracy and rationality of carbon footprint tracking.

[0065] In a preferred embodiment, based on the above application scenario, this application provides a two-layer iterative structure employing an outer-inner layer, specifically as follows: Figure 2 As shown. Figure 2 A two-layer decoupled architecture of outer layer (iterative coordination) and inner layer (physical modeling) was constructed. The variable step size self-approximation algorithm effectively solves the cyclic dependency problem of carbon potential-electric system load distribution. At the same time, this design keeps the system solution process with low computational complexity.

[0066] The outer layer serves as an iterative coordination layer for electricity consumption, aiming to solve the cyclical dependency problem between a given pricing method and carbon potential. Its function is to send each pricing set as input to the inner layer, which then performs calculations and outputs the reference node carbon potential, which is received by the outer layer. The outer layer updates the pricing based on the reference carbon potential until the reference carbon potential matches the planned pricing, at which point it outputs the current carbon potential result.

[0067] The inner layer is the physical model layer, containing three core modules: road network planning, optimal power flow, and carbon flow tracing. Its function is to output the nodal carbon potential of the power grid after processing the given charging station electricity price from the outer layer through these three modules. Specifically: The role of the road network planning module is to simulate the charging selection behavior of EV users based on a non-cooperative game model, input the electricity price, output the actual load of each charging station, and further obtain the power grid load distribution.

[0068] The function of the optimal power flow module is to receive the power grid topology map and load information, and with the goal of minimizing network losses, use second-order cone programming to solve the power flow distribution of the power grid and output the results.

[0069] The role of the carbon flow tracing module is to calculate the carbon potential of each node in the power grid based on the optimal power flow calculation results and the carbon emission factor of the generator.

[0070] The outer model updates the current carbon potential of each grid node based on its reference carbon potential and historical carbon potential. It acquires historical carbon potential data (or, if none exists, an initial carbon potential can be specified), reference carbon potential data (if this is the first iteration, either historical or initial carbon potential can be used), and a specified pricing mechanism (a mechanism for determining the charging price of EV charging stations based on carbon potential, grid data, or other potentially usable data). The output is the charging price of each EV charging station connected to the power system. The specific process is as follows: The current carbon potential can be obtained using historical carbon potential and reference carbon potential. In this invention, the iterative algorithm employs a variable step-size self-approximation algorithm, namely… In the formula: For the first After the next iteration, the node Historical carbon potential For Substituting the reference carbon potential obtained after solving the inner-layer series problem. For the variable step size scaling factor, we have In the formula: For the sake of reliability, a coefficient of 0.5 is typically used in this invention. Because when starting from the... Iteration to the 1st generation In this cycle, the "current carbon potential" obtained in this round becomes the historical carbon potential for the next round, so in Figure 2 The relationship between the two is a two-way arrow.

[0071] In summary, the embodiments of this application have at least the following three beneficial effects: (1) Solving the problem of the disconnect between carbon emission accounting and the dynamic behavior of electric vehicle users in the existing technology: This invention establishes a non-cooperative pathfinding simulation model for electric vehicle users, which can accurately reflect the dynamic decision-making behavior of users in the transportation and power systems, so that the carbon footprint tracking results are closer to the actual operation.

[0072] (2) Achieve accurate modeling of carbon potential of charging station access points: This invention deeply integrates traffic network model and power flow model. By simulating the path selection and charging behavior of electric vehicle users and combining carbon flow tracking theory, it achieves accurate quantification of carbon emission contribution (i.e., node carbon potential) of each charging station access point in different time periods and traffic conditions.

[0073] (3) Provide accurate data support for subsequent research: The carbon potential data of charging station nodes obtained by this invention can serve as a refined carbon emission indicator, providing solid and reliable basic data for virtual power plants or power grid dispatch centers to formulate differentiated pricing, optimize dispatch strategies and evaluate emission reduction effects.

[0074] Example 2: like Figure 3 As shown, Embodiment 2 provides a carbon footprint tracking system based on electric vehicle route planning, including an acquisition module 10, a road network planning module 20, an additional load determination module 30, a power flow planning module 40, and a carbon flow tracking module 50. The acquisition module 10 is used to acquire road network planning reference data for the target area. The road network planning reference data includes road network parameters, charging prices of each charging station, and distribution data of each electric vehicle waiting to be charged. The road network planning module 20 is used to input the road network planning reference data into a preset road network planning model, so that the road network planning model can perform path optimization for each electric vehicle according to the road network planning reference data and a preset path optimization algorithm, thereby generating a corresponding road network planning result. The road network planning result includes the starting node, target node and driving path of each electric vehicle. The road network planning model is constructed based on a non-cooperative game model. The additional load determination module 30 is used to determine the additional load of each power grid node in the target area based on the road network planning results; The power flow planning module 40 is used to perform power flow planning on the power system based on the real-time operating data of each additional load and the power system in the target area, through a preset power flow planning model, to determine the active power flow distribution of the power system and the actual active power generated by each generator. The carbon flow tracking module 50 is used to calculate the nodal carbon potential of each grid node based on the carbon emission intensity of each generator, the actual active power generated by each generator and the active power flow distribution of the power system, and to complete the carbon footprint tracking of the power system based on the nodal carbon potential.

[0075] In one possible implementation, the road network planning model performs path optimization for each of the electric vehicles based on the road network planning reference data and a preset path optimization algorithm, thereby generating corresponding road network planning results, including: Based on the road network planning reference data and the path optimization algorithm, the path optimization is performed on each electric vehicle in a sequential loop until the loop stopping condition is met. Based on the current path optimization results of each electric vehicle, the road network planning result is generated. In each path optimization process, the current path optimization result of the current electric vehicle is updated based on the current path optimization results of other electric vehicles, the road network planning reference data, the preset road network planning objective function, and the road network planning constraints. If the current path optimization result of each electric vehicle is the same as the previous path optimization result, then the sequential loop stops.

[0076] Furthermore, updating the current path optimization result of the current electric vehicle based on the current path optimization results of other electric vehicles, the road network planning reference data, the preset road network planning objective function, and the road network planning constraints includes: Several candidate routes for the current electric vehicle are generated based on the road network planning reference data; Based on the current path optimization results of the other electric vehicles and the road network planning reference data, the current road traffic flow and the current throughput of each charging station are calculated. Based on the current road traffic flow and the current throughput, the traffic time and waiting time for each candidate path are calculated. Based on the traffic time and waiting time of each candidate path, road network planning reference data, road network planning objective function, and road network planning constraints, the optimal path is determined from each candidate path, and then the optimal path is replaced with the current path optimization result of the current electric vehicle.

[0077] In one possible implementation, the construction of the road network planning model based on a non-cooperative game model includes: To minimize the traffic time cost, waiting time cost, and charging economic cost of electric vehicles, an individual optimization objective function is constructed. With the goal of minimizing the total cost of each electric vehicle, an overall optimization objective function is constructed. Construct the conversion relationship between road traffic volume and travel time; Establish the conversion relationship between charging station throughput and waiting time; Construct road capacity constraints and charging station charging power constraints; By combining the objective functions, transformation relationships, and constraints, the road network planning model is constructed.

[0078] In one possible implementation, the step of performing power flow planning on the power system using a preset power flow planning model based on the real-time operating data of each of the additional loads and the power system in the target area, to determine the active power flow distribution of the power system and the actual active power generated by each generator, includes: The active load of each node in the power system is updated according to the additional loads to obtain the updated operating data of the power system. The updated operating data includes the power grid topology, the resistance, reactance, current upper and lower limits, power flow upper limit of each branch, the updated active load, reactive load, voltage upper and lower limits of each power grid node, and the maximum active output and reactive power compensator capacity of each generator. The updated operating data is input into the power flow planning model, so that the power flow planning model can solve the problem using the second-order cone programming method based on the preset power flow planning objective function and power flow planning constraints, and then output the active power flow distribution of the power system and the actual active power generated by each generator.

[0079] Furthermore, the calculation of the nodal carbon potential of each grid node based on the carbon emission intensity of each generator, the actual active power generated by each generator, and the active power flow distribution of the power system includes: Based on the carbon emission intensity of each generator, the actual active power generated by each generator, and the active power flow distribution of the power system, the nodal carbon potential definition equation for each grid node is determined. The nodal carbon potential definition equation is the ratio of the carbon flow input to the energy output of the current node as the nodal carbon potential of the current node. The carbon flow input includes the carbon flow input of the actual parent node of the current node and the carbon flow input of the current node's own power supply. The energy output includes the energy consumed by the load of the current node and the energy flowing to the actual child nodes of the current node. The nodal carbon potential definition equations for each of the power grid nodes are converted into matrix form and solved jointly through matrix operations to obtain the nodal carbon potential of each of the power grid nodes.

[0080] In one possible implementation, the carbon footprint tracking system further includes an iterative update module, which is used to iteratively update the current charging price of each charging station and the current node carbon potential of each power grid node using a variable step-size self-approximation algorithm if the charging price of each charging station is affected by the node carbon potential, so as to obtain the final node carbon potential of each power grid node. In each iteration update process, the reference carbon potential of each power grid node is determined by road network planning, power flow planning and node carbon potential calculation based on the current charging price of each charging station, the road network parameters and the distribution data. Then, the current node carbon potential of each power grid node is updated based on the reference carbon potential and historical carbon potential of each power grid node. Finally, the current charging price of each charging station is updated based on the current node carbon potential, and the current node carbon potential is used as the historical carbon potential for the next iteration update. If the difference between each of the reference carbon potentials and each of the historical carbon potentials in any iteration update process is less than a preset tolerance, then the iteration update is stopped, and each of the reference carbon potentials is determined as the final node carbon potential of each of the power grid nodes.

[0081] This application provides a carbon footprint tracking system based on electric vehicle route planning. By combining a non-cooperative game-based road network planning model with power system flow analysis, it fully considers the impact of electric vehicle charging behavior on power system carbon emissions, solving the problem of disconnect between carbon emission accounting and the dynamic behavior of electric vehicle users in existing technologies. Specifically, this application generates road network planning results through route optimization based on road network parameters, charging prices, and electric vehicle distribution data, achieving accurate prediction of electric vehicle user behavior. Then, based on the road network planning results, it determines the additional load of grid nodes, accurately quantifying the impact of electric vehicle user behavior on the power system. Finally, based on the additional load and real-time operating data of the power system, it performs power flow planning and node carbon potential calculation, realizing joint modeling and analysis of electric vehicle user behavior, power load distribution, and spatial distribution of carbon emissions. This improves the spatiotemporal accuracy and rationality of carbon footprint tracking, laying the foundation for refined carbon management and policy formulation.

[0082] For a more detailed explanation of the working principle and procedures of this embodiment, please refer to the relevant description in Embodiment 1.

[0083] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application for those skilled in the art.

Claims

1. A carbon footprint tracking method based on electric vehicle route planning, characterized in that, include: Obtain road network planning reference data for the target area, including road network parameters, charging prices at each charging station, and distribution data of electric vehicles waiting to be charged; The road network planning reference data is input into a preset road network planning model so that the road network planning model can perform path optimization for each electric vehicle based on the road network planning reference data and a preset path optimization algorithm, thereby generating a corresponding road network planning result. The road network planning result includes the starting node, target node and driving path of each electric vehicle. The road network planning model is constructed based on a non-cooperative game model. The additional load of each power grid node in the target area is determined based on the road network planning results; Based on the real-time operating data of the power system of each additional load and the target area, power flow planning is performed on the power system through a preset power flow planning model to determine the active power flow distribution of the power system and the actual active power generated by each generator. Based on the carbon emission intensity of each generator, the actual active power generated by each generator, and the active power flow distribution of the power system, the nodal carbon potential of each grid node is calculated, and the carbon footprint tracking of the power system is completed based on the nodal carbon potential.

2. The carbon footprint tracking method based on electric vehicle route planning as described in claim 1, characterized in that, The road network planning model performs path optimization for each of the electric vehicles based on the road network planning reference data and a preset path optimization algorithm, thereby generating corresponding road network planning results, including: Based on the road network planning reference data and the path optimization algorithm, the path optimization is performed on each electric vehicle in a sequential loop until the loop stopping condition is met. Based on the current path optimization results of each electric vehicle, the road network planning result is generated. In each path optimization process, the current path optimization result of the current electric vehicle is updated based on the current path optimization results of other electric vehicles, the road network planning reference data, the preset road network planning objective function, and the road network planning constraints. If the current path optimization result of each electric vehicle is the same as the previous path optimization result, then the sequential loop stops.

3. The carbon footprint tracking method based on electric vehicle route planning as described in claim 2, characterized in that, The step of updating the current path optimization result of the current electric vehicle based on the current path optimization results of other electric vehicles, the road network planning reference data, the preset road network planning objective function, and the road network planning constraints includes: Several candidate routes for the current electric vehicle are generated based on the road network planning reference data; Based on the current path optimization results of the other electric vehicles and the road network planning reference data, the current road traffic flow and the current throughput of each charging station are calculated. Based on the current road traffic flow and the current throughput, the traffic time and waiting time for each candidate path are calculated. Based on the traffic time and waiting time of each candidate path, road network planning reference data, road network planning objective function, and road network planning constraints, the optimal path is determined from each candidate path, and then the optimal path is replaced with the current path optimization result of the current electric vehicle.

4. The carbon footprint tracking method based on electric vehicle route planning as described in claim 1, characterized in that, The road network planning model is constructed based on a non-cooperative game theory model, including: To minimize the traffic time cost, waiting time cost, and charging economic cost of electric vehicles, an individual optimization objective function is constructed. With the goal of minimizing the total cost of each electric vehicle, an overall optimization objective function is constructed. Construct the conversion relationship between road traffic volume and travel time; Establish the conversion relationship between charging station throughput and waiting time; Construct road capacity constraints and charging station charging power constraints; By combining the objective functions, transformation relationships, and constraints, the road network planning model is constructed.

5. The carbon footprint tracking method based on electric vehicle route planning as described in claim 1, characterized in that, The step of performing power flow planning on the power system based on the real-time operating data of each additional load and the power system in the target area, using a preset power flow planning model, to determine the active power flow distribution of the power system and the actual active power generated by each generator, includes: The active load of each node in the power system is updated according to the additional loads to obtain the updated operating data of the power system. The updated operating data includes the power grid topology, the resistance, reactance, current upper and lower limits, power flow upper limit of each branch, the updated active load, reactive load, voltage upper and lower limits of each power grid node, and the maximum active output and reactive power compensator capacity of each generator. The updated operating data is input into the power flow planning model, so that the power flow planning model can solve the problem using the second-order cone programming method based on the preset power flow planning objective function and power flow planning constraints, and then output the active power flow distribution of the power system and the actual active power generated by each generator.

6. The carbon footprint tracking method based on electric vehicle route planning as described in claim 1, characterized in that, The calculation of the nodal carbon potential of each grid node based on the carbon emission intensity of each generator, the actual active power generated by each generator, and the active power flow distribution of the power system includes: Based on the carbon emission intensity of each generator, the actual active power generated by each generator, and the active power flow distribution of the power system, the nodal carbon potential definition equation for each grid node is determined. The nodal carbon potential definition equation is the ratio of the carbon flow input to the energy output of the current node as the nodal carbon potential of the current node. The carbon flow input includes the carbon flow input of the actual parent node of the current node and the carbon flow input of the current node's own power supply. The energy output includes the energy consumed by the load of the current node and the energy flowing to the actual child nodes of the current node. The nodal carbon potential definition equations for each of the power grid nodes are converted into matrix form and solved jointly through matrix operations to obtain the nodal carbon potential of each of the power grid nodes.

7. A carbon footprint tracking method based on electric vehicle route planning as described in any one of claims 1-6, characterized in that, The carbon footprint tracking method further includes, if the charging price of each charging station is affected by the node carbon potential, using a variable step size self-approximation algorithm to iteratively update the current charging price of each charging station and the current node carbon potential of each power grid node, so as to obtain the final node carbon potential of each power grid node. In each iteration update process, the reference carbon potential of each power grid node is determined by road network planning, power flow planning and node carbon potential calculation based on the current charging price of each charging station, the road network parameters and the distribution data. Then, the current node carbon potential of each power grid node is updated based on the reference carbon potential and historical carbon potential of each power grid node. Finally, the current charging price of each charging station is updated based on the current node carbon potential, and the current node carbon potential is used as the historical carbon potential for the next iteration update. If the difference between each of the reference carbon potentials and each of the historical carbon potentials in any iteration update process is less than a preset tolerance, then the iteration update is stopped, and each of the reference carbon potentials is determined as the final node carbon potential of each of the power grid nodes.

8. A carbon footprint tracking system based on electric vehicle route planning, characterized in that, It includes an acquisition module, a road network planning module, an additional load determination module, a power flow planning module, and a carbon flow tracking module; The acquisition module is used to acquire road network planning reference data for the target area. The road network planning reference data includes road network parameters, charging prices of each charging station, and distribution data of each electric vehicle waiting to be charged. The road network planning module is used to input the road network planning reference data into a preset road network planning model, so that the road network planning model can perform path optimization for each electric vehicle according to the road network planning reference data and a preset path optimization algorithm, thereby generating a corresponding road network planning result. The road network planning result includes the starting node, target node and driving path of each electric vehicle. The road network planning model is constructed based on a non-cooperative game model. The additional load determination module is used to determine the additional load of each power grid node in the target area based on the road network planning results; The power flow planning module performs power flow planning on the power system based on the real-time operating data of each additional load and the power system in the target area, using a preset power flow planning model, to determine the active power flow distribution of the power system and the actual active power generated by each generator. The carbon flow tracking module is used to calculate the nodal carbon potential of each grid node based on the carbon emission intensity of each generator, the actual active power generated by each generator, and the active power flow distribution of the power system, and to complete the carbon footprint tracking of the power system based on the nodal carbon potential.

9. A carbon footprint tracking system based on electric vehicle route planning as described in claim 8, characterized in that, The road network planning model performs path optimization for each of the electric vehicles based on the road network planning reference data and a preset path optimization algorithm, thereby generating corresponding road network planning results, including: Based on the road network planning reference data and the path optimization algorithm, the path optimization is performed on each electric vehicle in a sequential loop until the loop stopping condition is met. Based on the current path optimization results of each electric vehicle, the road network planning result is generated. In each path optimization process, the current path optimization result of the current electric vehicle is updated based on the current path optimization results of other electric vehicles, the road network planning reference data, the preset road network planning objective function, and the road network planning constraints. If the current path optimization result of each electric vehicle is the same as the previous path optimization result, then the sequential loop stops.

10. A carbon footprint tracking system based on electric vehicle route planning as described in claim 8 or 9, characterized in that, The carbon footprint tracking system also includes an iterative update module. If the charging price of each charging station is affected by the node carbon potential, the iterative update module uses a variable step size self-approximation algorithm to iteratively update the current charging price of each charging station and the current node carbon potential of each power grid node to obtain the final node carbon potential of each power grid node. In each iteration update process, the reference carbon potential of each power grid node is determined by road network planning, power flow planning and node carbon potential calculation based on the current charging price of each charging station, the road network parameters and the distribution data. Then, the current node carbon potential of each power grid node is updated based on the reference carbon potential and historical carbon potential of each power grid node. Finally, the current charging price of each charging station is updated based on the current node carbon potential, and the current node carbon potential is used as the historical carbon potential for the next iteration update. If the difference between each of the reference carbon potentials and each of the historical carbon potentials in any iteration update process is less than a preset tolerance, then the iteration update is stopped, and each of the reference carbon potentials is determined as the final node carbon potential of each of the power grid nodes.

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