Electric vehicle carbon emission tracing and simulation scheduling method and system, medium and equipment

Abstract

This invention provides a method, system, medium, and device for carbon emission tracing and simulation scheduling of electric vehicles. The method includes: establishing an optimization model based on a constructed virtual power plant model containing electric vehicle charging load, an electric vehicle lifecycle carbon emission tracing model, and a green certificate and carbon-coupled source-load interaction response model. The optimization model aims to minimize the total cost of the combined overall operating cost of the virtual power plant model and the scheduling compensation cost of the green certificate and carbon-coupled source-load interaction response model. A column constraint generation algorithm and strong duality theory are used to solve the optimization model to obtain the optimal electric vehicle charging scheduling scheme. The optimal electric vehicle charging scheduling scheme is encapsulated into a smart contract and deployed on a blockchain mainnet. The smart contract is used to simulate the scheduling of electric vehicles within the electric vehicle charging station. This achieves reliable carbon footprint tracing, coordinated response of green certificates and carbon trading, and robust scheduling.

Description

Technical Field

[0001] This invention belongs to the field of interdisciplinary technology of power system and energy blockchain, specifically involving a method, system, medium and equipment for carbon emission tracing and simulation scheduling of electric vehicles. Background Technology

[0002] Electric vehicles (EVs) are a crucial vehicle for energy conservation and emission reduction in the transportation sector, experiencing explosive growth in scale. However, the diverse power sources for EV charging, including both high-carbon-emission traditional thermal power and zero-carbon-emission wind and solar power, make it difficult to accurately measure the true carbon reduction benefits throughout their entire lifecycle. Therefore, how to accurately trace the carbon emissions during the EV charging process is a key research focus for achieving carbon reduction through the integration of transportation and energy.

[0003] Meanwhile, demand response mechanisms have become a key means to guide electric vehicle users to participate in low-carbon scheduling. However, existing electric vehicle carbon emission management suffers from several problems, including difficulty in accurately tracing carbon footprints, a lack of effective coordination between green certificates and carbon scheduling mechanisms, and insufficient robustness of optimized scheduling schemes due to the dual uncertainties of wind and solar power output and load. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, in a first aspect, this invention proposes a method for tracing and simulating the scheduling of carbon emissions from electric vehicles, comprising: Based on the collected battery state of charge, battery rated capacity and charging interface protocol parameters of electric vehicles, as well as the physical characteristics of electric vehicle charging stations and internal equipment of electric vehicles, a virtual power plant model containing electric vehicle charging load is constructed. Based on the collected charging amount and charging power curves of electric vehicles, the corresponding grid carbon emission factors for the time period, and the virtual power plant model, a carbon emission source tracing model for the entire life cycle of electric vehicles is constructed. Based on the renewable energy power generation output curves associated with green certificates for electric vehicle charging stations, the spatiotemporal distribution characteristics of carbon emission intensity, and the physical constraints of the power injected into the nodes on the grid side connecting electric vehicles, a green certificate-carbon coupled source-load interaction response model is constructed. Based on the virtual power plant model, the electric vehicle life cycle carbon emission tracing model, and the green certificate and carbon-coupled source-load interaction response model, an optimization model is established with the goal of minimizing the total cost of the virtual power plant model and the scheduling compensation cost of the green certificate and carbon-coupled source-load interaction response model. The optimization model is solved using a column constraint generation algorithm and strong duality theory to obtain the optimal electric vehicle charging scheduling scheme. The optimal electric vehicle charging scheduling scheme is encapsulated into a smart contract and deployed on the blockchain mainnet; the smart contract is used to simulate the scheduling of electric vehicles in the electric vehicle charging station.

[0005] Preferably, the construction of a virtual power plant model containing the electric vehicle charging load, based on the collected battery state of charge, battery rated capacity, and charging interface protocol parameters of the electric vehicle, as well as the acquired physical characteristics of the electric vehicle charging station and the internal equipment of the electric vehicle, includes: Based on the acquired physical characteristics of the electric vehicle's internal equipment, the collected state of charge and rated capacity of the electric vehicle's battery, and the charging interface protocol parameters, the range of electric vehicle battery charge values ​​is determined; according to the electric vehicle battery charge value range and user travel needs, the rigid charging power to meet the user's basic travel needs and the flexible charging power for electric vehicle charging stations to participate in scheduling are determined. Based on the acquired physical characteristics of the electric vehicle charging station, the charge range of the electric vehicle battery, and the user's travel needs, the total charging power of the electric vehicle charging station is determined. An electric vehicle charging load model is constructed based on the total charging power of the electric vehicle charging station, the rigid charging power required to meet users' basic travel needs, and the flexible charging power of the electric vehicle charging station participating in scheduling. Based on the electric vehicle battery charge range, the electric vehicle battery state of charge and total battery capacity, an electric vehicle battery energy storage state and charge / discharge constraint model is constructed. The electric vehicle charging load model and the electric vehicle battery energy storage state and charge / discharge constraint model are used as a virtual power plant model.

[0006] Preferably, the electric vehicle charging load model satisfies the following formula:

[0007] In the formula: for Total charging power of electric vehicle charging stations at any given time; for A rigid charging power that always meets users' basic travel needs; for The flexible charging power of electric vehicle charging stations participating in the scheduling at any time.

[0008] Preferably, the electric vehicle battery energy storage state and charge / discharge constraint model satisfies the following formula:

[0009]

[0010]

[0011]

[0012]

[0013] In the formula: for The battery charge value in the state of charge of the electric vehicle at any given time. for The battery charge value in the state of charge of the electric vehicle at any given time. Improve battery charging efficiency. for Battery charging power at all times. The time step for the scheduling period. This refers to the total battery capacity. for Battery discharge power at all times For battery discharge efficiency, This represents the lower limit of the range of electric vehicle battery charge values. This represents the upper limit of the range of electric vehicle battery charge values. express Check whether the battery is charging at all times; The upper limit of battery charging power, This is the upper limit of battery discharge power. express Whether the battery is in a discharged state at any time.

[0014] Preferably, the step of constructing a full life-cycle carbon emission traceability model for electric vehicles based on the collected charging amount and charging power curves of electric vehicles, the corresponding grid carbon emission factors for the time period, and the virtual power plant model includes: Based on the collected charging amount and charging power curves of electric vehicles, as well as the grid carbon emission factor for the corresponding time period, the total carbon emissions generated during the electric vehicle charging process are determined. Based on the virtual power plant model, the carbon emission coefficient of grid-side power supply, the carbon emission coefficient of standby diesel generator, the output power of grid-side power supply, and the output power of standby diesel generator are determined. A carbon emission traceability model for the entire life cycle of electric vehicles is constructed based on the total carbon emissions generated during the electric vehicle charging process, the carbon emission coefficient of the power supply from the grid, the carbon emission coefficient of the backup diesel generator, the output power of the power supply from the grid, and the output power of the backup diesel generator.

[0015] Preferably, the carbon emission traceability model for the entire life cycle of electric vehicles satisfies the following formula:

[0016] In the formula, The total carbon emissions generated during the charging process of electric vehicles. N The total time period for operation. For a moment, The carbon emission factor of supplying electricity to the grid side, for The output power of the power supply from the grid side at any given time. The carbon emission coefficient of the standby diesel generator. for The output power of the standby diesel generator is always available.

[0017] Preferably, the step of constructing a green certificate-carbon coupled source-load interaction response model based on the renewable energy power generation output curve corresponding to the green certificate associated with the electric vehicle charging station, the spatiotemporal distribution characteristics of carbon emission intensity, and the physical constraints of the power injected by the nodes on the grid side connecting the electric vehicle, includes: The actual number of green certificates issued is determined based on the renewable energy power generation output curves corresponding to the green certificates associated with electric vehicle charging stations. Based on the spatiotemporal distribution characteristics of the carbon emission intensity, the length of the carbon emission interval is determined; Based on the actual number of green certificates issued and the total carbon emissions generated during the electric vehicle charging process, the equivalent carbon emissions after offsetting with green certificates are determined. Based on the length of the carbon emission range, the baseline coefficient of the tiered carbon emission and the penalty factor of the tiered carbon emission, as well as the equivalent carbon emission after offset by green certificates, a tiered green certificate and carbon coupling penalty amount is constructed.

[0018] Preferably, the tiered green certificate and carbon coupling penalty amount satisfy the following formula:

[0019] In the formula, For tiered carbon emissions, As a benchmark coefficient for tiered carbon emissions, This represents the equivalent carbon emissions after offsetting with green certificates. The length of the carbon emission range. It serves as a penalty factor for tiered carbon emissions.

[0020] Preferably, the equivalent carbon emissions after offsetting with green certificates satisfy the following formula:

[0021] In the formula, This represents the equivalent carbon emissions after offsetting with green certificates. The carbon offset factor corresponding to a unit of green certificate. The total carbon emissions generated during the charging process of electric vehicles. This refers to the total carbon offset corresponding to the actual issued green certificates.

[0022] Preferably, the step of using a column constraint generation algorithm and strong duality theory to solve the optimization model to obtain the optimal electric vehicle charging scheduling scheme includes: The optimization model is then converted into a robust optimization model. The robust optimization model is decomposed into a main problem and preliminary subproblems using a column constraint generation algorithm; the preliminary subproblems are then transformed into mixed-integer linear programming forms using strong duality theory, thus forming subproblems. Within a relative gap set by the objective function value related to carbon emission source tracing, the main problem is solved to find the first-stage scheduling scheme for electric vehicle charging, and the lower bound of the objective function value is updated. Based on the first-stage scheduling scheme, with the goal of finding the second-stage scheduling scheme for electric vehicle charging under the worst-case scenario of uncertain wind and solar power output and load, the sub-problems are solved, and the upper bound of the objective function value is updated. Based on the updated upper and lower bounds of the objective function value, the relative gap is recalculated, and the main problem and sub-problems are solved iteratively until the recalculated relative gap is less than a preset threshold. Based on the solution results at this time, the optimal electric vehicle charging scheduling scheme is determined.

[0023] Preferably, the robust optimization model includes: an uncertainty set model of wind and solar power output and load power and a two-stage robust optimization model; The uncertainty set model for wind and solar power output and load power satisfies the following formula:

[0024] In the formula: For the vector set of uncertain variables of wind and solar power output and load power, Let be the vector of uncertain variables related to wind and solar power output and load power. for Uncertain variables in the relationship between wind and solar power output and load power at any given time. for Predicted values ​​of wind and solar power output and load power at any given time; for The upper limit of the fluctuation of wind and solar power output and load power at any given time; for The ratio of the deviation between the power output of wind and solar power and the load power at any given time. N The total time period for operation. For a moment, For the uncertainty adjustment parameters of wind and solar power output and load power; The two-stage robust optimization model satisfies the following formula:

[0025] in, For the first-stage decision variables based on the day-ahead scheduling plan and equipment start-up and shutdown, For the second-stage decision variables based on real-time scheduling and carbon emissions, In order to be in and Given the feasible region, c is the first-stage penalty coefficient vector, d is the second-stage penalty coefficient vector, and T is the transpose of the matrix.

[0026] Preferably, the step of encapsulating the optimal electric vehicle charging scheduling scheme into a smart contract and deploying it on the blockchain mainnet; and using the smart contract to simulate the scheduling of electric vehicles within the electric vehicle charging station, includes: The optimal electric vehicle charging scheduling scheme and transaction settlement rules are encapsulated into a smart contract. Deploy the smart contract on the blockchain mainnet; The smart contract is used to simulate the scheduling and transfer of electric vehicles within the electric vehicle charging station.

[0027] Secondly, this invention application also proposes an electric vehicle carbon emission traceability and simulated trading system, comprising: The virtual power plant model building module is used to build a virtual power plant model containing electric vehicle charging load based on the collected battery state of charge, battery rated capacity and charging interface protocol parameters of electric vehicles, as well as the physical characteristics of electric vehicle charging stations and internal equipment of electric vehicles. The source traceability model construction module is used to construct a source traceability model for the carbon emissions of electric vehicles throughout their entire life cycle based on the collected charging amount and charging power curve of electric vehicles, the corresponding grid carbon emission factors for the time period, and the virtual power plant model. The response model construction module is used to construct a green certificate and carbon-coupled source-load interaction response model based on the renewable energy power generation output curve corresponding to the green certificate associated with the electric vehicle charging station, the spatiotemporal distribution characteristics of carbon emission intensity, and the physical constraints of the power injected by the nodes on the grid side connected to the electric vehicle. The optimization model construction module is used to establish an optimization model based on the virtual power plant model, the electric vehicle life cycle carbon emission traceability model, and the green certificate and carbon coupling source-load interaction response model, with the goal of minimizing the total cost of the superposition of the overall operating cost of the virtual power plant model and the scheduling compensation cost of the green certificate and carbon coupling source-load interaction response model. The electric vehicle charging scheduling module is used to solve the optimization model using a column constraint generation algorithm and strong duality theory to obtain the optimal electric vehicle charging scheduling scheme. The simulation scheduling module is used to encapsulate the optimal electric vehicle charging scheduling scheme into a smart contract and deploy it in the blockchain mainnet; and to use the smart contract to simulate the scheduling of electric vehicles in the electric vehicle charging station.

[0028] Preferably, the virtual power plant model construction module is specifically used for: Based on the acquired physical characteristics of the electric vehicle's internal equipment, the collected state of charge and rated capacity of the electric vehicle's battery, and the charging interface protocol parameters, the range of electric vehicle battery charge values ​​is determined; according to the electric vehicle battery charge value range and user travel needs, the rigid charging power to meet the user's basic travel needs and the flexible charging power for electric vehicle charging stations to participate in scheduling are determined. Based on the acquired physical characteristics of the electric vehicle charging station, the charge range of the electric vehicle battery, and the user's travel needs, the total charging power of the electric vehicle charging station is determined. An electric vehicle charging load model is constructed based on the total charging power of the electric vehicle charging station, the rigid charging power required to meet users' basic travel needs, and the flexible charging power of the electric vehicle charging station participating in scheduling. Based on the electric vehicle battery charge range, the electric vehicle battery state of charge and total battery capacity, an electric vehicle battery energy storage state and charge / discharge constraint model is constructed. The electric vehicle charging load model and the electric vehicle battery energy storage state and charge / discharge constraint model are used as a virtual power plant model.

[0029] Preferably, the electric vehicle charging load model satisfies the following formula:

[0030] In the formula: for Total charging power of electric vehicle charging stations at any given time; for A rigid charging power that always meets users' basic travel needs; for The flexible charging power of electric vehicle charging stations participating in the scheduling at any time.

[0031] Preferably, the electric vehicle battery energy storage state and charge / discharge constraint model satisfies the following formula:

[0032]

[0033]

[0034]

[0035]

[0036] In the formula: for The battery charge value in the state of charge of the electric vehicle at any given time. for The battery charge value in the state of charge of the electric vehicle at any given time. Improve battery charging efficiency. for Battery charging power at all times. The time step for the scheduling period. This refers to the total battery capacity. for Battery discharge power at all times For battery discharge efficiency, This represents the lower limit of the range of electric vehicle battery charge values. This represents the upper limit of the range of electric vehicle battery charge values. express Check whether the battery is charging at all times; The upper limit of battery charging power, This is the upper limit of battery discharge power. express Whether the battery is in a discharged state at any time.

[0037] Preferably, the source tracing model construction module is specifically used for: Based on the collected charging amount and charging power curves of electric vehicles, as well as the grid carbon emission factor for the corresponding time period, the total carbon emissions generated during the electric vehicle charging process are determined. Based on the virtual power plant model, the carbon emission coefficient of grid-side power supply, the carbon emission coefficient of standby diesel generator, the output power of grid-side power supply, and the output power of standby diesel generator are determined. A carbon emission traceability model for the entire life cycle of electric vehicles is constructed based on the total carbon emissions generated during the electric vehicle charging process, the carbon emission coefficient of the power supply from the grid, the carbon emission coefficient of the backup diesel generator, the output power of the power supply from the grid, and the output power of the backup diesel generator.

[0038] Preferably, the carbon emission traceability model for the entire life cycle of electric vehicles satisfies the following formula:

[0039] In the formula, The total carbon emissions generated during the charging process of electric vehicles.N The total time period for operation. For a moment, The carbon emission factor of supplying electricity to the grid side, for The output power of the power supply from the grid side at any given time. The carbon emission coefficient of the standby diesel generator. for The output power of the standby diesel generator is always available.

[0040] Preferably, the response model construction module is specifically used for: The actual number of green certificates issued is determined based on the renewable energy power generation output curves corresponding to the green certificates associated with electric vehicle charging stations. Based on the spatiotemporal distribution characteristics of the carbon emission intensity, the length of the carbon emission interval is determined; Based on the actual number of green certificates issued and the total carbon emissions generated during the electric vehicle charging process, the equivalent carbon emissions after offsetting with green certificates are determined. Based on the length of the carbon emission range, the baseline coefficient of the tiered carbon emission and the penalty factor of the tiered carbon emission, as well as the equivalent carbon emission after offset by green certificates, a tiered green certificate and carbon coupling penalty amount is constructed.

[0041] Preferably, the tiered green certificate and carbon coupling penalty amount satisfy the following formula:

[0042] In the formula, For tiered carbon emissions, As a benchmark coefficient for tiered carbon emissions, This represents the equivalent carbon emissions after offsetting with green certificates. The length of the carbon emission range. It serves as a penalty factor for tiered carbon emissions.

[0043] Preferably, the equivalent carbon emissions after offsetting with green certificates satisfy the following formula:

[0044] In the formula, This represents the equivalent carbon emissions after offsetting with green certificates. The carbon offset factor corresponding to a unit of green certificate. The total carbon emissions generated during the charging process of electric vehicles. This refers to the total carbon offset corresponding to the actual issued green certificates.

[0045] Preferably, the power dispatching scheme module is specifically used for: The optimization model is then converted into a robust optimization model. The robust optimization model is decomposed into a main problem and preliminary subproblems using a column constraint generation algorithm; the preliminary subproblems are then transformed into mixed-integer linear programming forms using strong duality theory, thus forming subproblems. Within a relative gap set by the objective function value related to carbon emission source tracing, the main problem is solved to find the first-stage scheduling scheme for electric vehicle charging, and the lower bound of the objective function value is updated. Based on the first-stage scheduling scheme, with the goal of finding the second-stage scheduling scheme for electric vehicle charging under the worst-case scenario of uncertain wind and solar power output and load, the sub-problems are solved, and the upper bound of the objective function value is updated. Based on the updated upper and lower bounds of the objective function value, the relative gap is recalculated, and the main problem and sub-problems are solved iteratively until the recalculated relative gap is less than a preset threshold. Based on the solution results at this time, the optimal electric vehicle charging scheduling scheme is determined.

[0046] Preferably, the robust optimization model includes: an uncertainty set model of wind and solar power output and load power and a two-stage robust optimization model; The uncertainty set model for wind and solar power output and load power satisfies the following formula:

[0047] In the formula: For the vector set of uncertain variables of wind and solar power output and load power, Let be the vector of uncertain variables related to wind and solar power output and load power. for Uncertain variables in the relationship between wind and solar power output and load power at any given time. for Predicted values ​​of wind and solar power output and load power at any given time; for The upper limit of the fluctuation of wind and solar power output and load power at any given time; for The ratio of the deviation between the power output of wind and solar power and the load power at any given time. N The total time period for operation. For a moment, For the uncertainty adjustment parameters of wind and solar power output and load power; The two-stage robust optimization model satisfies the following formula:

[0048] in, For the first-stage decision variables based on the day-ahead scheduling plan and equipment start-up and shutdown, For the second-stage decision variables based on real-time scheduling and carbon emissions, In order to be in and Given the feasible region, c is the first-stage penalty coefficient vector, d is the second-stage penalty coefficient vector, and T is the transpose of the matrix.

[0049] Preferably, the simulation scheduling module is specifically used for: The optimal electric vehicle charging scheduling scheme and transaction settlement rules are encapsulated into a smart contract. Deploy the smart contract on the blockchain mainnet; The smart contract is used to simulate the scheduling and transfer of electric vehicles within the electric vehicle charging station.

[0050] Thirdly, this application also proposes an electronic device, comprising: at least one processor and a memory; wherein the memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the electric vehicle carbon emission tracing and simulation scheduling method is implemented.

[0051] Fourthly, this application also proposes a readable storage medium having an executable program stored thereon, which, when executed, implements the aforementioned method for tracing and simulating the carbon emission of electric vehicles.

[0052] Compared with the closest prior art, the present invention application has the following beneficial effects: The present invention provides a method and system for carbon emission tracing and simulation scheduling of electric vehicles, comprising: constructing a virtual power plant model containing electric vehicle charging load based on the collected battery state of charge, battery rated capacity and charging interface protocol parameters of electric vehicles, as well as the acquired physical characteristics of electric vehicle charging stations and internal equipment of electric vehicles; and constructing a full life cycle carbon emission tracing model of electric vehicles based on the collected charging amount and charging power curve of electric vehicles and the corresponding grid carbon emission factor for the corresponding time period, and the virtual power plant model. Based on the renewable energy power generation output curves and spatiotemporal distribution characteristics of carbon emission intensity corresponding to the green certificates associated with electric vehicle charging stations, as well as the physical constraints of the power injected by the nodes on the grid side connecting electric vehicles, a green certificate-carbon coupled source-load interaction response model is constructed. Based on the virtual power plant model, the electric vehicle lifecycle carbon emission tracing model, and the green certificate-carbon coupled source-load interaction response model, an optimization model is established with the objective of minimizing the total cost of the combined overall operating cost of the virtual power plant model and the scheduling compensation cost of the green certificate-carbon coupled source-load interaction response model. The optimization model is solved using a column constraint generation algorithm and strong duality theory to obtain the optimal electric vehicle charging scheduling scheme. The optimal electric vehicle charging scheduling scheme is encapsulated as a smart contract and deployed on the blockchain mainnet. The smart contract is used to simulate the scheduling of electric vehicles within the electric vehicle charging station. By constructing a virtual power plant model, a carbon emission traceability model, and a green certificate and carbon-coupled source-load interaction response model, an optimization model is further built. Then, a column constraint generation algorithm and strong duality theory are used to solve the optimization model and encapsulate it into a smart contract for deployment on the blockchain mainnet. This realizes the credible traceability of carbon footprint and the coordinated response of green certificates and carbon trading. Since the virtual power plant model is incorporated into the simulation scheduling of electric vehicles in the electric vehicle charging station, the uncertainties of wind and solar power output and load can be fully considered, ensuring that the final electric vehicle charging solution has high robustness. Attached Figure Description

[0053] Figure 1 The flowchart of a method for tracing and simulating the carbon emissions of electric vehicles provided in this invention application Figure 1 ; Figure 2 The flowchart of a method for tracing and simulating the carbon emissions of electric vehicles provided in this invention application Figure 2 ; Figure 3 The present invention provides an architecture for a carbon emission tracing and simulation scheduling system for electric vehicles. Figure 1 ; Figure 4 The present invention provides an architecture for a carbon emission tracing and simulation scheduling system for electric vehicles. Figure 2 ; Figure 5 This is a schematic diagram of the operation of an electronic device provided in this invention application. Detailed Implementation

[0054] The specific embodiments of this invention will be further described in detail below with reference to the accompanying drawings.

[0055] Example 1: like Figure 1-2As shown, this invention application proposes a method for tracing and simulating the scheduling of carbon emissions from electric vehicles, which may include the following steps: Step 1: Based on the collected battery state of charge, battery rated capacity, and charging interface protocol parameters of the electric vehicle, as well as the physical characteristics of the electric vehicle charging station and the internal equipment of the electric vehicle, construct a virtual power plant model containing electric vehicle charging loads; wherein, each charging load node in the virtual power plant model is bound to the corresponding electric vehicle's battery management system hardware address, and each charging load node can be bound to one electric vehicle. Step 2: Based on the collected charging amount and charging power curves of electric vehicles, the corresponding grid carbon emission factors for the time period, and the virtual power plant model, construct a carbon emission traceability model for the entire life cycle of electric vehicles. Step 3: Based on the renewable energy power generation output curves corresponding to the green certificates associated with electric vehicle charging stations, the spatiotemporal distribution characteristics of carbon emission intensity, and the physical constraints of the power injected by the nodes on the grid side connecting electric vehicles, construct a green certificate and carbon-coupled source-load interaction response model. Step 4: Based on the virtual power plant model, the electric vehicle life cycle carbon emission traceability model, and the green certificate and carbon coupling source-load interaction response model, establish an optimization model with the goal of minimizing the total cost of the virtual power plant model and the scheduling compensation cost of the green certificate and carbon coupling source-load interaction response model. Step 5: Solve the optimization model using the column constraint generation algorithm and strong duality theory to obtain the optimal electric vehicle charging scheduling scheme; Step 6: Encapsulate the optimal electric vehicle charging scheduling scheme into a smart contract and deploy it on the blockchain mainnet; use the smart contract to simulate the scheduling of electric vehicles in the electric vehicle charging station.

[0056] In steps 1-6 above, firstly, based on the collected electric vehicle battery state of charge, rated capacity, charging interface protocol parameters, and the physical characteristics of the charging station and vehicle internal equipment, a virtual power plant model is simulated to simulate the operating characteristics of a load including electric vehicle charging. Secondly, using the electric vehicle's charging capacity and power curves, and the corresponding grid carbon emission factors for the time period, combined with the virtual power plant model, a full life-cycle carbon emission traceability model for electric vehicles is simulated to track the carbon emission footprint during the charging process. Finally, based on the renewable energy generation output curves corresponding to the green certificates associated with the charging load, the spatiotemporal distribution characteristics of carbon emission intensity, and the physical constraints of grid-side node injection power, a green certificate-carbon coupled source-load interaction response model is simulated to simulate the collaborative interaction process between green certificates and carbon emissions. The simulated virtual power plant model, the full life-cycle carbon emission traceability model for electric vehicles, and the green certificate-carbon coupled source-load interaction response model provide a model foundation for the establishment and solution of subsequent optimization models.

[0057] The step 1 above, which involves constructing a virtual power plant model containing electric vehicle charging load based on the collected battery state of charge, battery rated capacity, and charging interface protocol parameters of the electric vehicle, as well as the acquired physical characteristics of the electric vehicle charging station and the internal equipment of the electric vehicle, may include the following steps: Step 1.1: Based on the acquired physical characteristics of the electric vehicle's internal equipment, the collected state of charge and rated capacity of the electric vehicle's battery, and the charging interface protocol parameters, determine the electric vehicle's battery charge value range; based on the electric vehicle's battery charge value range and user travel needs, determine the rigid charging power to meet the user's basic travel needs, and the flexible charging power for the electric vehicle charging station to participate in scheduling. Step 1.2: Based on the acquired physical characteristics of the electric vehicle charging station, the charge range of the electric vehicle battery, and the user's travel needs, determine the total charging power of the electric vehicle charging station; Step 1.3: Based on the total charging power of the electric vehicle charging station, the rigid charging power required to meet users' basic travel needs, and the flexible charging power of the electric vehicle charging station participating in scheduling, construct an electric vehicle charging load model; Step 1.4: Based on the electric vehicle battery charge range, the electric vehicle battery state of charge, and the total battery capacity, construct an electric vehicle battery energy storage state and charge / discharge constraint model; Step 1.5: Use the electric vehicle charging load model and the electric vehicle battery energy storage state and charge / discharge constraint model as a virtual power plant model.

[0058] In one embodiment, during steps 1.1-1.2, taking an electric vehicle charging station connected to 100 electric vehicles of the same model as an example, each vehicle has a rated battery capacity of 60kWh. By collecting data from the battery management system, the current battery charge status of each vehicle is found to be between 30% and 80%. Based on the battery safety operating range determined in the vehicle manual, the lower limit of the battery charge value range is set to 20%, and the upper limit to 90%. According to user travel needs, each vehicle is determined to consume 15kWh of electricity per day for basic travel, corresponding to a rigid charging power of 7kW / vehicle; the remaining dispatchable capacity (battery charge value within the range and with no less than 20% remaining after meeting user travel needs) is used for demand response, and the flexible charging power range is determined to be 0-30kW / vehicle. This electric vehicle charging station is equipped with 10 DC fast charging piles, with a maximum output power of 60kW per pile. Considering the transformer capacity of the electric vehicle charging station is 500kVA and the simultaneous rate is 0.8, the upper limit of the total charging power of the electric vehicle charging station is determined to be 400kW. Based on the total rigid demand of 100 vehicles determined in step 1.1 (7kW × 100 = 700kW), this value exceeds the charging station's power limit of 400kW, therefore it is impossible to simultaneously meet the rigid charging demand of all vehicles, necessitating a time-segmented scheduling strategy. Simultaneously, the maximum elastic aggregated power of 100 vehicles is 100 × 30kW = 3000kW, far exceeding the 400kW limit, indicating that the elastic scheduling capability is limited by the charging station's physical limitations rather than the vehicle's potential. Therefore, the total charging power of the electric vehicle charging station... The value range is determined to be 0 to 400kW, where the lower limit of 0 corresponds to the state of no charging demand or complete cessation of charging, and the upper limit of 400kW is constrained by the transformer capacity and the simultaneity rate.

[0059] The electric vehicle charging load model described in step 1.3 satisfies the following formula:

[0060] In the formula: for Total charging power of electric vehicle charging stations at any given time; for A rigid charging power that always meets users' basic travel needs; for The flexible charging power of electric vehicle charging stations participating in the scheduling at any time.

[0061] Let t=14:00 be the time period to meet the rigid charging power required for users' basic travel needs. =200kW, the flexible charging power of electric vehicle charging stations participating in the dispatching process =150kW (using price incentives to encourage users to provide deferred charging load), then the total charging power of the electric vehicle charging station during this period is... =350kW, which falls within the range of 0-400kW for the total charging power of electric vehicle charging stations, proving the rationality of the above-mentioned range for the total charging power of electric vehicle charging stations.

[0062] The electric vehicle battery energy storage state and charge / discharge constraint model described in step 1.5 satisfy the following formula:

[0063]

[0064]

[0065]

[0066]

[0067] In the formula: for The battery charge value in the electric vehicle's State of Charge (SOC) at any given time. for The battery charge value in the state of charge of the electric vehicle at any given time. Improve battery charging efficiency. for Battery charging power at all times. The time step for the scheduling period. This refers to the total battery capacity. for Battery discharge power at all times For battery discharge efficiency, This represents the lower limit of the range of electric vehicle battery charge values. This indicates the upper limit of the range of electric vehicle battery charge values. express Whether the battery is charging at any given time, for example A binary variable indicating whether the battery is in a charging state; for example, when the battery is charging... A value of 1 indicates that the battery is not charged. The value is 0; The upper limit of battery charging power, This is the upper limit of battery discharge power. express Whether the battery is in a discharged state at any time, for example A binary variable indicating whether the battery is in a discharged state; for example, if the battery is in a discharged state, it will be... A value of 1 indicates that the battery is not discharging. The value is 0.

[0068] Step 1.5 uses the electric vehicle charging load model, along with the battery energy storage state and charge / discharge constraint model, as the core mathematical description of the virtual power plant model. This virtual power plant model can aggregate 100 electric vehicles (for example only) at an electric vehicle charging station into a dispatchable energy storage resource, which will be used in subsequent steps for carbon emission calculation and optimized scheduling, ensuring that the charging and discharging power meets the battery capacity, SOC upper and lower limits, and charge / discharge mutual exclusion conditions.

[0069] Step 2, which involves constructing a life-cycle carbon emission traceability model for electric vehicles based on the collected charging amount and power curves of electric vehicles, the corresponding grid carbon emission factors for the time period, and the virtual power plant model, may include the following steps: Step 2.1: Based on the collected charging amount and charging power curves of electric vehicles, as well as the grid carbon emission factor for the corresponding time period, determine the total carbon emissions generated during the electric vehicle charging process. Step 2.2: Based on the virtual power plant model, determine the carbon emission coefficient of grid-side power supply, the carbon emission coefficient of the standby diesel generator, the output power of grid-side power supply, and the output power of the standby diesel generator; Step 2.3: Based on the total carbon emissions generated during the electric vehicle charging process, the carbon emission coefficient of the power grid supply, the carbon emission coefficient of the backup diesel generator, the output power of the power grid supply, and the output power of the backup diesel generator, construct a carbon emission traceability model for the entire life cycle of electric vehicles.

[0070] In one embodiment, during steps 2.1-2.3, based on the actual charging power curves of electric vehicles collected for each time period and combined with the corresponding regional grid carbon emission factors obtained from the grid dispatch center, the carbon emissions generated during the charging process of electric vehicles in each time period are calculated. These are then accumulated to obtain the total carbon emissions generated during the entire day's charging process. Subsequently, a carbon emission traceability model for the entire lifecycle of electric vehicles is constructed using a virtual power plant model. The output power of the grid-side power supply and the output power of the backup diesel generator can be encrypted using a hash algorithm and recorded on the blockchain sidechain. The carbon emission traceability model for the entire lifecycle of electric vehicles satisfies the following formula:

[0071] In the formula, The total carbon emissions generated during the charging process of electric vehicles. N The total time period for operation. For a moment, The carbon emission factor of supplying electricity to the grid side, for The output power of the power supply from the grid side at any given time. The carbon emission coefficient of the standby diesel generator. for The output power of the standby diesel generator is always available.

[0072] Step 3, which involves constructing a green certificate-carbon coupled source-load interaction response model based on the renewable energy power generation output curves corresponding to the green certificates associated with electric vehicle charging stations, the spatiotemporal distribution characteristics of carbon emission intensity, and the physical constraints of the power injected into the grid-side nodes connecting electric vehicles, may include the following steps: Step 3.1: Determine the actual number of green certificates issued based on the renewable energy power generation output curves corresponding to the green certificates associated with electric vehicle charging stations; Step 3.2: Determine the length of the carbon emission interval based on the spatiotemporal distribution characteristics of the carbon emission intensity; Step 3.3: Based on the actual number of green certificates issued and the total carbon emissions generated during the electric vehicle charging process, determine the equivalent carbon emissions after offsetting with green certificates; Step 3.4: Based on the carbon emission interval length, the baseline coefficient of tiered carbon emissions, the penalty factor of tiered carbon emissions, and the equivalent carbon emissions after green certificate offset, construct the tiered green certificate and carbon coupling penalty amount.

[0073] In one embodiment, during steps 3.1-3.4, the green certificate associated with the electric vehicle charging station can be a green certificate applied for by the electric vehicle charging station company, or issued according to national regulations, such as one green certificate for every 1000 kWh of renewable energy electricity. Each green certificate associated with an electric vehicle charging station corresponds to its own renewable energy (such as wind power and photovoltaic) power generation output curve. The amount of renewable energy electricity consumed by the charging station through purchase or self-production is statistically analyzed to determine the actual number of green certificates issued. Simultaneously, based on the spatiotemporal distribution characteristics of carbon emission intensity (differences in grid carbon emission factors at different times and in different regions), and combined with the interval division rules of the tiered carbon trading mechanism, the length of the carbon emission interval is determined. Then, based on the actual number of green certificates issued, and combined with the total carbon emissions generated during the electric vehicle charging process calculated in step 2, the equivalent carbon emissions after green certificate offsetting (i.e., the total carbon emissions generated during the electric vehicle charging process minus the actual number of green certificates issued) are determined. Finally, based on the length of the carbon emission range, the benchmark coefficient and penalty factor of the tiered carbon emission, and the equivalent carbon emission after offsetting with green certificates, a tiered green certificate and carbon-coupled penalty is constructed to reflect the principle that the higher the carbon emission, the higher the carbon cost it bears.

[0074] In step 3.4, to reflect the principle that high-emission entities bear higher carbon costs, a tiered carbon trading mechanism is adopted to price the equivalent carbon emission rights trading volume in segments. Specifically, a benchmark coefficient and penalty factor are applied to different segments according to the trading volume, and the total carbon trading cost is calculated progressively, thus obtaining the tiered green certificate and carbon-coupled penalty amount, satisfying the following formula:

[0075] In the formula, For tiered carbon emissions, As a benchmark coefficient for tiered carbon emissions, This represents the equivalent carbon emissions after offsetting with green certificates. The length of the carbon emission range. It serves as a penalty factor for tiered carbon emissions.

[0076] The equivalent carbon emissions after offsetting with green certificates, as described above, satisfy the following formula:

[0077] In the formula, This represents the equivalent carbon emissions after offsetting with green certificates. The carbon offset factor corresponding to a unit of green certificate. The total carbon emissions generated during the charging process of electric vehicles. This refers to the total carbon offset corresponding to the actual issued green certificates.

[0078] The overall operating cost of the virtual power plant model in step 4 may include: grid power purchase cost, backup diesel generator fuel cost, and electric vehicle charging demand response compensation cost. The scheduling compensation cost of the green certificate and carbon-coupled source-load interaction response model may include: green certificate trading cost and tiered carbon trading cost.

[0079] Step 5, which employs a column constraint generation algorithm and strong duality theory to solve the optimization model and obtain the optimal electric vehicle charging scheduling scheme, includes the following steps: Step 5.1: Convert the optimization model into a robust optimization model; Step 5.2: Using a column constraint generation algorithm, the robust optimization model is decomposed into a main problem and preliminary subproblems; using strong duality theory, the preliminary subproblems are transformed into mixed integer linear programming forms, forming subproblems; Step 5.3: Within the relative gap set based on the objective function value related to carbon emission source tracing, solve the main problem to find the first-stage scheduling scheme for electric vehicle charging scheduling, and determine the lower bound of the objective function value. The process involves updating the scheme; based on the first-stage scheduling scheme, and with the objective of finding a second-stage scheduling scheme for electric vehicle charging under the worst-case scenario of uncertain wind and solar power output and load, the sub-problems are solved, and the upper bound of the objective function value is determined. Update; Step 5.4: Based on the updated upper and lower bounds of the objective function value, recalculate the relative gap, and iteratively solve the main problem and sub-problems until the recalculated relative gap is less than a preset threshold. hour( Based on the solution results at this time, the optimal electric vehicle charging scheduling scheme is determined.

[0080] In one embodiment, during steps 5.1-5.4, the optimization model is expressed as a two-stage robust optimization model. The first-stage decision variables in the two-stage robust optimization model can be the day-ahead power purchase and sale plan and the equipment start-up / shutdown status. The second-stage decision variables can be real-time scheduling and carbon trading volume. A column constraint generation algorithm is used to decompose the robust optimization model into a main problem and preliminary subproblems. Using strong duality theory, the preliminary subproblems are transformed into mixed-integer linear programming forms, forming subproblems. The Big-M method is used to linearize the bilinear terms in the subproblems. Within a preset relative gap, the main problem is solved to obtain the first-stage scheduling scheme for electric vehicle charging (including the day-ahead power purchase and sale plan and equipment start-up / shutdown), and the lower bound of the objective function value is updated. Based on the first-stage scheduling scheme, with the objective of finding the second-stage scheduling scheme under the worst-case scenario of wind and solar power output and load uncertainty, the subproblems are solved, and the upper bound of the objective function value is updated. The relative gap is recalculated based on the updated upper and lower bounds. The main problem and subproblems are solved iteratively until the relative gap is less than the preset convergence accuracy. The iteration stops when the current solution is output as the optimal electric vehicle charging scheduling scheme.

[0081] The robust optimization model mentioned in step 5.1 includes: an uncertainty set model of wind and solar power output and load power and a two-stage robust optimization model; The uncertainty set model for wind and solar power output and load power satisfies the following formula:

[0082] In the formula: For the vector set of uncertain variables of wind and solar power output and load power, Let be the vector of uncertain variables related to wind and solar power output and load power. for Uncertain variables in the relationship between wind and solar power output and load power at any given time. for Predicted values ​​of wind and solar power output and load power at any given time; for The upper limit of the fluctuation of wind and solar power output and load power at any given time; for The ratio of the deviation between the power output of wind and solar power and the load power at any given time. N The total time period for operation. For a moment, For the uncertainty adjustment parameters of wind and solar power output and load power; The two-stage robust optimization model satisfies the following formula:

[0083] in, For the first-stage decision variables based on the day-ahead scheduling plan and equipment start-up and shutdown, For the second-stage decision variables based on real-time scheduling and carbon emissions, In order to be in and Given the feasible region, c is the first-stage penalty coefficient vector, d is the second-stage penalty coefficient vector, and T is the transpose of the matrix.

[0084] In one embodiment, step 5.4, determining the optimal electric vehicle charging scheduling scheme based on the solution results at this time, includes: Based on the current solution, with a rolling cycle of 15 minutes to 1 hour, the optimal electric vehicle charging scheduling scheme of the previous cycle is used as the initial state for optimization in the next cycle, and this process is repeated to generate a dynamic optimal electric vehicle charging scheduling scheme that adapts to real-time fluctuations. While iteratively solving for the optimal electric vehicle charging scheduling scheme, several feasible scheduling schemes under different uncertainty scenarios are retained as a backup scheduling scheme library. The backup scheduling schemes should satisfy all the constructed constraints (examples include: upper and lower limits of electric vehicle SOC, charging and discharging power limits, power balance constraints, and carbon emission limits). The optimal electric vehicle charging scheduling scheme is substituted into all constraints to check whether the upper and lower limits of electric vehicle SOC, charging and discharging power limits, power balance constraints, and carbon emission limits are satisfied. If there are violations of the constraints, the deviation of each constraint is calculated, and the electric vehicle charging scheduling scheme is corrected using the slack variable method. The corrected electric vehicle charging scheduling scheme is taken as the final optimal electric vehicle charging scheduling scheme.

[0085] Step 6, which involves encapsulating the optimal electric vehicle charging scheduling scheme into a smart contract and deploying it on the blockchain mainnet, and then using the smart contract to simulate the scheduling of electric vehicles within the charging station, may include the following steps: Step 6.1: Encapsulate the optimal electric vehicle charging scheduling scheme and transaction settlement rules into a smart contract; Step 6.2: Deploy the smart contract on the blockchain mainnet; Step 6.3: Use the smart contract to simulate scheduling and transfer of electric vehicles in the electric vehicle charging station.

[0086] In one embodiment, during steps 6.1-6.3, the optimal electric vehicle charging scheduling scheme and transaction settlement rules are written as Solidity smart contract code, which is then compiled and deployed to the blockchain mainnet. The optimal electric vehicle charging scheduling scheme undergoes feasibility verification and error analysis, requiring it to meet battery constraints and power balance constraints. If these constraints are not met, the optimal electric vehicle charging scheduling scheme needs to be updated. By reading real-time carbon emission data from the virtual power plant model, when preset conditions are met (such as actual carbon emissions exceeding carbon allowances or green certificate holdings reaching a trading threshold), the smart contract automatically triggers a transaction, completing the asset transfer of carbon allowances and green certificates.

[0087] Following step 6, the following steps are also included: In step 7, a formal verification tool is used to mathematically prove the key properties of the smart contract. At the same time, various attack scenarios (such as replay attacks, unauthorized calls, integer overflows, etc.) are simulated on the test network to conduct security tests and ensure that the smart contract does not have any known vulnerabilities.

[0088] Step 8: Deploy on-chain monitoring agents and off-chain monitoring services in the blockchain to collect real-time data on the status of blockchain consensus nodes, smart contract event logs, and the actual output and load data of each device in the virtual power plant model. When an anomaly is detected, the preset response strategy is automatically triggered, including alarm notification, suspension of transaction execution, execution rollback, or switching to a backup scheduling scheme.

[0089] In summary, the method of the present invention has the following advantages: 1. Achieve credible carbon footprint traceability: Utilize blockchain technology combined with a virtual power plant model to construct a carbon emission traceability model for the entire life cycle of electric vehicles, ensuring the accuracy and immutability of carbon emission data.

[0090] 2. Promote the synergy between green certificates and carbon trading: Establish a source-load interaction response model that couples green certificates and carbon, combine the green certificate offset mechanism with the tiered carbon trading penalty mechanism, avoid double counting of carbon footprint, and improve the renewable energy absorption capacity and carbon asset trading security.

[0091] 3. Improve operational economy: An optimization model is established with the goal of minimizing the sum of the overall operating cost and dispatch compensation cost of the virtual power plant model. Under the premise of considering the dual uncertainties of wind and solar power output and load, the economically optimal dispatch is achieved.

[0092] 4. The solution method is efficient and reliable: The optimization model is solved by using a column constraint generation algorithm and strong duality theory. The convergence of the solution process and the optimality of the scheduling scheme are guaranteed by iteratively solving the main problem and subproblems.

[0093] 5. Scheduling Automation and Security: The optimal electric vehicle charging scheduling scheme is encapsulated into a smart contract and deployed on the blockchain mainnet to realize the automatic execution of carbon quota and green certificate trading, thereby improving the efficiency and security of the scheduling system.

[0094] Existing carbon trading and management systems typically employ a centralized architecture, which suffers from trust issues such as data tampering and opaque traceability processes. This invention addresses these issues by constructing a virtual power plant model incorporating electric vehicle charging loads, a full lifecycle carbon emission traceability model, and a green certificate-carbon coupled source-load interaction response model. This effectively avoids these trust problems. Since green certificates represent the environmental value of green electricity, while carbon emission allowances represent the environmental costs of fossil fuel consumption, their interaction can easily lead to double counting of carbon footprints. Current research on virtual power plants or electric vehicles participating in grid optimization and dispatch primarily focuses on the time-transfer characteristics of loads or a single carbon trading mechanism. However, the method of this invention establishes an optimization model with the goal of minimizing total cost based on a virtual power plant model, a full life-cycle carbon emission traceability model, and a green certificate-carbon coupled source-load interaction response model. It uses a column constraint generation algorithm and strong duality theory to perform a two-stage robust optimization solution. Finally, the optimal scheduling scheme is encapsulated as a smart contract and deployed on the blockchain mainnet. Under the premise of ensuring credible traceability of carbon footprint and green certificate-carbon coordinated response, it effectively copes with the dual uncertainties of wind and solar power output and electric vehicle load, avoids the time transfer characteristics of load or a single carbon trading mechanism, and realizes a complete closed loop from accurate carbon emission measurement, coordinated response, robust optimization to blockchain automatic scheduling.

[0095] Example 2: like Figure 3 As shown, the present invention also provides an electric vehicle carbon emission traceability and simulated trading system, comprising: The virtual power plant model building module is used to build a virtual power plant model containing electric vehicle charging load based on the collected battery state of charge, battery rated capacity and charging interface protocol parameters of electric vehicles, as well as the physical characteristics of electric vehicle charging stations and internal equipment of electric vehicles. The source traceability model construction module is used to construct a source traceability model for the carbon emissions of electric vehicles throughout their entire life cycle based on the collected charging amount and charging power curve of electric vehicles, the corresponding grid carbon emission factors for the time period, and the virtual power plant model. The response model construction module is used to construct a green certificate and carbon-coupled source-load interaction response model based on the renewable energy power generation output curve corresponding to the green certificate associated with the electric vehicle charging station, the spatiotemporal distribution characteristics of carbon emission intensity, and the physical constraints of the power injected by the nodes on the grid side connected to the electric vehicle. The optimization model construction module is used to establish an optimization model based on the virtual power plant model, the electric vehicle life cycle carbon emission traceability model, and the green certificate and carbon coupling source-load interaction response model, with the goal of minimizing the total cost of the superposition of the overall operating cost of the virtual power plant model and the scheduling compensation cost of the green certificate and carbon coupling source-load interaction response model. The electric vehicle charging scheduling module is used to solve the optimization model using a column constraint generation algorithm and strong duality theory to obtain the optimal electric vehicle charging scheduling scheme. The simulation scheduling module is used to encapsulate the optimal electric vehicle charging scheduling scheme into a smart contract and deploy it in the blockchain mainnet; and to use the smart contract to simulate the scheduling of electric vehicles in the electric vehicle charging station.

[0096] Furthermore, the virtual power plant model construction module is specifically used for: Based on the acquired physical characteristics of the electric vehicle's internal equipment, the collected state of charge and rated capacity of the electric vehicle's battery, and the charging interface protocol parameters, the range of electric vehicle battery charge values ​​is determined; according to the electric vehicle battery charge value range and user travel needs, the rigid charging power to meet the user's basic travel needs and the flexible charging power for electric vehicle charging stations to participate in scheduling are determined. Based on the acquired physical characteristics of the electric vehicle charging station, the charge range of the electric vehicle battery, and the user's travel needs, the total charging power of the electric vehicle charging station is determined. An electric vehicle charging load model is constructed based on the total charging power of the electric vehicle charging station, the rigid charging power required to meet users' basic travel needs, and the flexible charging power of the electric vehicle charging station participating in scheduling. Based on the electric vehicle battery charge range, the electric vehicle battery state of charge and total battery capacity, an electric vehicle battery energy storage state and charge / discharge constraint model is constructed. The electric vehicle charging load model and the electric vehicle battery energy storage state and charge / discharge constraint model are used as a virtual power plant model.

[0097] Furthermore, the electric vehicle charging load model satisfies the following formula:

[0098] In the formula: for Total charging power of electric vehicle charging stations at any given time; for A rigid charging power that always meets users' basic travel needs; for The flexible charging power of electric vehicle charging stations participating in the scheduling at any time.

[0099] Furthermore, the electric vehicle battery energy storage state and charge / discharge constraint model satisfies the following formula:

[0100]

[0101]

[0102]

[0103]

[0104] In the formula: for The battery charge value in the state of charge of the electric vehicle at any given time. for The battery charge value in the state of charge of the electric vehicle at any given time. Improve battery charging efficiency. for Battery charging power at all times. The time step for the scheduling period. This refers to the total battery capacity. for Battery discharge power at all times For battery discharge efficiency, This represents the lower limit of the range of electric vehicle battery charge values. This represents the upper limit of the range of electric vehicle battery charge values. express Check whether the battery is charging at all times; The upper limit of battery charging power, This is the upper limit of battery discharge power. express Whether the battery is in a discharged state at any time.

[0105] Furthermore, the source tracing model construction module is specifically used for: Based on the collected charging amount and charging power curves of electric vehicles, as well as the grid carbon emission factor for the corresponding time period, the total carbon emissions generated during the electric vehicle charging process are determined. Based on the virtual power plant model, the carbon emission coefficient of grid-side power supply, the carbon emission coefficient of standby diesel generator, the output power of grid-side power supply, and the output power of standby diesel generator are determined. A carbon emission traceability model for the entire life cycle of electric vehicles is constructed based on the total carbon emissions generated during the electric vehicle charging process, the carbon emission coefficient of the power supply from the grid, the carbon emission coefficient of the backup diesel generator, the output power of the power supply from the grid, and the output power of the backup diesel generator.

[0106] Furthermore, the electric vehicle lifecycle carbon emission traceability model satisfies the following formula:

[0107] In the formula, The total carbon emissions generated during the charging process of electric vehicles. N The total time period for operation. For a moment, The carbon emission factor of supplying electricity to the grid side, for The output power of the power supply from the grid side at any given time. The carbon emission coefficient of the standby diesel generator. for The output power of the standby diesel generator is always available.

[0108] Furthermore, the response model construction module is specifically used for: The actual number of green certificates issued is determined based on the renewable energy power generation output curves corresponding to the green certificates associated with electric vehicle charging stations. Based on the spatiotemporal distribution characteristics of the carbon emission intensity, the length of the carbon emission interval is determined; Based on the actual number of green certificates issued and the total carbon emissions generated during the electric vehicle charging process, the equivalent carbon emissions after offsetting with green certificates are determined. Based on the length of the carbon emission range, the baseline coefficient of the tiered carbon emission and the penalty factor of the tiered carbon emission, as well as the equivalent carbon emission after offset by green certificates, a tiered green certificate and carbon coupling penalty amount is constructed.

[0109] Furthermore, the tiered green certificate and carbon coupling penalty amount satisfy the following formula:

[0110] In the formula, For tiered carbon emissions, As a benchmark coefficient for tiered carbon emissions, This represents the equivalent carbon emissions after offsetting with green certificates. The length of the carbon emission range. It serves as a penalty factor for tiered carbon emissions.

[0111] Furthermore, the equivalent carbon emissions after offsetting with green certificates satisfy the following formula:

[0112] In the formula, This represents the equivalent carbon emissions after offsetting with green certificates. The carbon offset factor corresponding to a unit of green certificate. The total carbon emissions generated during the charging process of electric vehicles. This refers to the total carbon offset corresponding to the actual issued green certificates.

[0113] Furthermore, the power dispatching scheme module is specifically used for: The optimization model is then converted into a robust optimization model. The robust optimization model is decomposed into a main problem and preliminary subproblems using a column constraint generation algorithm; the preliminary subproblems are then transformed into mixed-integer linear programming forms using strong duality theory, thus forming subproblems. Within a relative gap set by the objective function value related to carbon emission source tracing, the main problem is solved to find the first-stage scheduling scheme for electric vehicle charging, and the lower bound of the objective function value is updated. Based on the first-stage scheduling scheme, with the goal of finding the second-stage scheduling scheme for electric vehicle charging under the worst-case scenario of uncertain wind and solar power output and load, the sub-problems are solved, and the upper bound of the objective function value is updated. Based on the updated upper and lower bounds of the objective function value, the relative gap is recalculated, and the main problem and sub-problems are solved iteratively until the recalculated relative gap is less than a preset threshold. Based on the solution results at this time, the optimal electric vehicle charging scheduling scheme is determined.

[0114] Furthermore, the robust optimization model includes: an uncertainty set model of wind and solar power output and load power, and a two-stage robust optimization model; The uncertainty set model for wind and solar power output and load power satisfies the following formula:

[0115] In the formula: For the vector set of uncertain variables of wind and solar power output and load power, Let be the vector of uncertain variables related to wind and solar power output and load power. for Uncertain variables in the relationship between wind and solar power output and load power at any given time. for Predicted values ​​of wind and solar power output and load power at any given time; for The upper limit of the fluctuation of wind and solar power output and load power at any given time; for The ratio of the deviation between the power output of wind and solar power and the load power at any given time. NThe total time period for operation. For a moment, For the uncertainty adjustment parameters of wind and solar power output and load power; The two-stage robust optimization model satisfies the following formula:

[0116] in, For the first-stage decision variables based on the day-ahead scheduling plan and equipment start-up and shutdown, For the second-stage decision variables based on real-time scheduling and carbon emissions, In order to be in and Given the feasible region, c is the first-stage penalty coefficient vector, d is the second-stage penalty coefficient vector, and T is the transpose of the matrix.

[0117] Furthermore, the simulated scheduling module is specifically used for: The optimal electric vehicle charging scheduling scheme and transaction settlement rules are encapsulated into a smart contract. Deploy the smart contract on the blockchain mainnet; The smart contract is used to simulate the scheduling and transfer of electric vehicles within the electric vehicle charging station.

[0118] Finally, as Figure 4 As shown, the system described in this invention can be divided into three layers from bottom to top in its overall architecture: physical energy layer (bottom layer), information and control layer (middle layer), and blockchain and transaction layer (top layer). (1) Physical Energy Layer (Bottom Layer): Corresponds to the virtual power plant model construction module. This layer covers the upper-level power grid, distributed photovoltaic arrays, centralized energy storage power stations, and electric vehicle charging stations (i.e., Electric Vehicle, EV charging stations). To achieve refined control, this system subdivides the load within the electric vehicle charging station into rigid charging loads (immediate demand) and elastic charging loads (i.e., demand response from electric vehicles to the grid, V2G, Vehicle-to-Grid). This layer is responsible for the bidirectional and unidirectional flow of electrical energy between the bottom-level devices and reports real-time operating status / carbon emission source data to the upper layer in real time. The virtual power plant model construction module constructs a virtual power plant model containing electric vehicle charging loads based on the collected electric vehicle battery state of charge, battery rated capacity, and charging interface protocol parameters, as well as the acquired physical characteristics of the electric vehicle charging station and the internal equipment of the electric vehicle, providing physical entity modeling support for the upper layer; (2) Information and Control Layer (Middle Layer): Corresponds to the source tracing model construction module, response model construction module, optimization model construction module, and power dispatching scheme module. The core of this layer is the virtual power plant dispatching center and the multi-source data acquisition and hash encryption module. This layer collects the operating data of physical equipment downwards: Based on the source tracing model construction module, the electric vehicle full life cycle carbon emission source tracing model is constructed according to the collected electric vehicle charging power, charging power curve, and corresponding grid carbon emission factor of the time period, combined with the virtual power plant model; Based on the response model construction module, the green certificate and carbon coupling source-load interaction response model is constructed according to the renewable energy power generation output curve, the spatiotemporal distribution characteristics of carbon emission intensity, and the physical constraints of the power injected by the grid-side nodes connecting the electric vehicle, according to the green certificate associated with the electric vehicle charging station; Based on the optimization model construction module, an optimization model is established with the goal of minimizing the total cost of the virtual power plant model and the scheduling compensation cost of the green certificate and carbon coupling source-load interaction response model; Based on the power dispatching scheme module, the column constraint generation algorithm and strong duality theory are used to perform calculations based on two-stage robust optimization on the optimization model to obtain the optimal electric vehicle charging dispatching scheme, and the day-ahead and real-time optimal dispatching instructions are issued to the lower layer. Meanwhile, this layer is responsible for hashing the carbon footprint data collected from the lower layer and uploading it to the blockchain as encrypted carbon footprint data, which is then transmitted to the blockchain layer.

[0119] (3) Blockchain and Transaction Layer (Top Layer): Corresponding to the simulation scheduling module. This layer consists of a consensus node network, a distributed immutable ledger, and a smart contract library. This layer receives and anchors encrypted carbon footprint data from the information and control layer, and automatically executes the green certificate-tiered carbon bidirectional interaction logic through deployed smart contracts. The simulation scheduling module encapsulates the optimal electric vehicle charging scheduling scheme into a smart contract and deploys it in the blockchain mainnet, using the smart contract to simulate the scheduling of electric vehicles in the electric vehicle charging station. When the preset triggering conditions are met, the smart contract automatically completes carbon asset liquidation and status update, and feeds back the liquidation results and auxiliary scheduling instructions to the virtual power plant scheduling center, forming a complete physical-information-value closed loop.

[0120] Through the above three-layer architecture design, this invention organically integrates the virtual power plant model building module, traceability model building module, response model building module, optimization model building module, power dispatching scheme module, and simulation dispatching module from bottom to top, realizing the entire process from physical equipment operation, data acquisition and encryption, model building and robust optimization solution, to automatic transaction settlement of blockchain smart contracts. It systematically solves the technical problems of reliable traceability of carbon footprint, green certificate and carbon collaborative response, and robust dispatching under dual uncertainties in the carbon emission management of electric vehicles.

[0121] Example 3: like Figure 5As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0122] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the electric vehicle carbon emission tracing and simulation scheduling method in the above embodiments.

[0123] Example 4: Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the electric vehicle carbon emission tracing and simulation scheduling method described in the above embodiments.

[0124] Those skilled in the art will understand that embodiments of this invention can be provided as methods, systems, or computer program products. Therefore, this invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0125] This invention application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims pending approval.

Claims

1. A method for tracing and simulating the carbon emission sources of electric vehicles, characterized in that, include: Based on the collected battery state of charge, battery rated capacity and charging interface protocol parameters of electric vehicles, as well as the physical characteristics of electric vehicle charging stations and internal equipment of electric vehicles, a virtual power plant model containing electric vehicle charging load is constructed. Based on the collected charging amount and charging power curves of electric vehicles, the corresponding grid carbon emission factors for the time period, and the virtual power plant model, a carbon emission source tracing model for the entire life cycle of electric vehicles is constructed. Based on the renewable energy power generation output curves associated with green certificates for electric vehicle charging stations, the spatiotemporal distribution characteristics of carbon emission intensity, and the physical constraints of the power injected into the nodes on the grid side connecting electric vehicles, a green certificate-carbon coupled source-load interaction response model is constructed. Based on the virtual power plant model, the electric vehicle life cycle carbon emission tracing model, and the green certificate and carbon-coupled source-load interaction response model, an optimization model is established with the goal of minimizing the total cost of the virtual power plant model and the scheduling compensation cost of the green certificate and carbon-coupled source-load interaction response model. The optimization model is solved using a column constraint generation algorithm and strong duality theory to obtain the optimal electric vehicle charging scheduling scheme. The optimal electric vehicle charging scheduling scheme is encapsulated into a smart contract and deployed on the blockchain mainnet; the smart contract is used to simulate the scheduling of electric vehicles in the electric vehicle charging station.

2. The method according to claim 1, characterized in that, The virtual power plant model, which includes the electric vehicle charging load, is constructed based on the collected battery state of charge, battery rated capacity, and charging interface protocol parameters of the electric vehicle, as well as the acquired physical characteristics of the electric vehicle charging station and the internal equipment of the electric vehicle. This includes: Based on the acquired physical characteristics of the electric vehicle's internal equipment, the collected state of charge and rated capacity of the electric vehicle's battery, and the charging interface protocol parameters, the range of electric vehicle battery charge values ​​is determined; according to the electric vehicle battery charge value range and user travel needs, the rigid charging power to meet the user's basic travel needs and the flexible charging power for electric vehicle charging stations to participate in scheduling are determined. Based on the acquired physical characteristics of the electric vehicle charging station, the charge range of the electric vehicle battery, and the user's travel needs, the total charging power of the electric vehicle charging station is determined. An electric vehicle charging load model is constructed based on the total charging power of the electric vehicle charging station, the rigid charging power required to meet users' basic travel needs, and the flexible charging power of the electric vehicle charging station participating in scheduling. Based on the electric vehicle battery charge range, the electric vehicle battery state of charge and total battery capacity, an electric vehicle battery energy storage state and charge / discharge constraint model is constructed. The electric vehicle charging load model and the electric vehicle battery energy storage state and charge / discharge constraint model are used as a virtual power plant model.

3. The method according to claim 2, characterized in that, The electric vehicle charging load model satisfies the following formula: In the formula: for Total charging power of electric vehicle charging stations at any given time; for A rigid charging power that always meets users' basic travel needs; for The flexible charging power of electric vehicle charging stations participating in the scheduling at any time.

4. The method according to claim 2, characterized in that, The electric vehicle battery energy storage state and charge / discharge constraint model satisfies the following formula: In the formula: for The battery charge value in the state of charge of the electric vehicle at any given time. for The battery charge value in the state of charge of the electric vehicle at any given time. Improve battery charging efficiency. for Battery charging power at all times. The time step for the scheduling period. This refers to the total battery capacity. for Battery discharge power at all times For battery discharge efficiency, This represents the lower limit of the range of electric vehicle battery charge values. This represents the upper limit of the range of electric vehicle battery charge values. express Check whether the battery is charging at all times; The upper limit of battery charging power, This is the upper limit of battery discharge power. express Whether the battery is in a discharged state at any time.

5. The method according to claim 1, characterized in that, The process involves constructing a full lifecycle carbon emission traceability model for electric vehicles based on the collected charging amount and power curves of electric vehicles, the corresponding grid carbon emission factors for the time period, and the virtual power plant model. This includes: Based on the collected charging amount and charging power curves of electric vehicles, as well as the grid carbon emission factor for the corresponding time period, the total carbon emissions generated during the electric vehicle charging process are determined. Based on the virtual power plant model, the carbon emission coefficient of grid-side power supply, the carbon emission coefficient of standby diesel generator, the output power of grid-side power supply, and the output power of standby diesel generator are determined. A carbon emission traceability model for the entire life cycle of electric vehicles is constructed based on the total carbon emissions generated during the charging process of the electric vehicle, the carbon emission coefficient of the power supply from the grid, the carbon emission coefficient of the backup diesel generator, the output power of the power supply from the grid, and the output power of the backup diesel generator.

6. The method according to claim 5, characterized in that, The carbon emission traceability model for the entire life cycle of electric vehicles satisfies the following formula: In the formula, The total carbon emissions generated during the charging process of electric vehicles. N The total time period for operation. For a moment, The carbon emission factor of supplying electricity to the grid side, for The output power of the power supply from the grid side at any given time. The carbon emission coefficient of the standby diesel generator. for The output power of the standby diesel generator is always available.

7. The method according to claim 1, characterized in that, The method involves constructing a green certificate-carbon coupled source-load interaction response model based on the renewable energy power generation output curves corresponding to the green certificates associated with electric vehicle charging stations, the spatiotemporal distribution characteristics of carbon emission intensity, and the physical constraints of the power injected into the grid-side nodes connecting electric vehicles. This model includes: The actual number of green certificates issued is determined based on the renewable energy power generation output curves corresponding to the green certificates associated with electric vehicle charging stations. Based on the spatiotemporal distribution characteristics of the carbon emission intensity, the length of the carbon emission interval is determined; Based on the actual number of green certificates issued and the total carbon emissions generated during the electric vehicle charging process, the equivalent carbon emissions after offsetting with green certificates are determined. Based on the length of the carbon emission range, the baseline coefficient of the tiered carbon emission and the penalty factor of the tiered carbon emission, as well as the equivalent carbon emission after offset by green certificates, a tiered green certificate and carbon coupling penalty amount is constructed.

8. The method according to claim 7, characterized in that, The tiered green certificate and carbon coupling penalty amount satisfy the following formula: In the formula, For tiered carbon emissions, As a benchmark coefficient for tiered carbon emissions, This represents the equivalent carbon emissions after offsetting with green certificates. The length of the carbon emission range. It serves as a penalty factor for tiered carbon emissions.

9. The method according to claim 8, characterized in that, The equivalent carbon emissions after offsetting with green certificates satisfy the following formula: In the formula, This represents the equivalent carbon emissions after offsetting with green certificates. The carbon offset factor corresponding to a unit of green certificate. The total carbon emissions generated during the charging process of electric vehicles. This refers to the total carbon offset corresponding to the actual issued green certificates.

10. The method according to claim 1, characterized in that, The optimization model is solved using a column constraint generation algorithm and strong duality theory to obtain the optimal electric vehicle charging scheduling scheme, including: The optimization model is then converted into a robust optimization model. The robust optimization model is decomposed into a main problem and preliminary subproblems using a column constraint generation algorithm; the preliminary subproblems are then transformed into mixed-integer linear programming forms using strong duality theory, thus forming subproblems. Within a relative gap set by the objective function value related to carbon emission source tracing, the main problem is solved to find the first-stage scheduling scheme for electric vehicle charging, and the lower bound of the objective function value is updated. Based on the first-stage scheduling scheme, with the goal of finding the second-stage scheduling scheme for electric vehicle charging under the worst-case scenario of uncertain wind and solar power output and load, the sub-problems are solved, and the upper bound of the objective function value is updated. Based on the updated upper and lower bounds of the objective function value, the relative gap is recalculated, and the main problem and sub-problems are solved iteratively until the recalculated relative gap is less than a preset threshold. Based on the solution results at this time, the optimal electric vehicle charging scheduling scheme is determined.

11. The method according to claim 10, characterized in that, The robust optimization model includes: an uncertainty set model of wind and solar power output and load power, and a two-stage robust optimization model; The uncertainty set model for wind and solar power output and load power satisfies the following formula: In the formula: For the vector set of uncertain variables of wind and solar power output and load power, Let be the vector of uncertain variables related to wind and solar power output and load power. for Uncertain variables in the relationship between wind and solar power output and load power at any given time. for Predicted values ​​of wind and solar power output and load power at any given time; for The upper limit of the fluctuation of wind and solar power output and load power at any given time; for The ratio of the deviation between the power output of wind and solar power and the load power at any given time. N The total time period for operation. For a moment, For the uncertainty adjustment parameters of wind and solar power output and load power; The two-stage robust optimization model satisfies the following formula: in, For the first-stage decision variables based on the day-ahead scheduling plan and equipment start-up and shutdown, For the second-stage decision variables based on real-time scheduling and carbon emissions, In order to be in and Given the feasible region, c is the first-stage penalty coefficient vector, d is the second-stage penalty coefficient vector, and T is the transpose of the matrix.

12. The method according to claim 1, characterized in that, The optimal electric vehicle charging scheduling scheme is encapsulated into a smart contract and deployed on the blockchain mainnet; The smart contract is used to simulate the scheduling of electric vehicles within the electric vehicle charging station, including: The optimal electric vehicle charging scheduling scheme and transaction settlement rules are encapsulated into a smart contract. Deploy the smart contract on the blockchain mainnet; The smart contract is used to simulate the scheduling and transfer of electric vehicles within the electric vehicle charging station.

13. A carbon emission traceability and simulated trading system for electric vehicles, characterized in that, include: The virtual power plant model building module is used to build a virtual power plant model containing electric vehicle charging load based on the collected battery state of charge, battery rated capacity and charging interface protocol parameters of electric vehicles, as well as the physical characteristics of electric vehicle charging stations and internal equipment of electric vehicles. The source traceability model construction module is used to construct a source traceability model for the carbon emissions of electric vehicles throughout their entire life cycle based on the collected charging amount and charging power curve of electric vehicles, the corresponding grid carbon emission factors for the time period, and the virtual power plant model. The response model construction module is used to construct a green certificate and carbon-coupled source-load interaction response model based on the renewable energy power generation output curve corresponding to the green certificate associated with the electric vehicle charging station, the spatiotemporal distribution characteristics of carbon emission intensity, and the physical constraints of the power injected by the nodes on the grid side connected to the electric vehicle. The optimization model construction module is used to establish an optimization model based on the virtual power plant model, the electric vehicle life cycle carbon emission traceability model, and the green certificate and carbon coupling source-load interaction response model, with the goal of minimizing the total cost of the superposition of the overall operating cost of the virtual power plant model and the scheduling compensation cost of the green certificate and carbon coupling source-load interaction response model. The electric vehicle charging scheduling module is used to solve the optimization model using a column constraint generation algorithm and strong duality theory to obtain the optimal electric vehicle charging scheduling scheme. The simulation scheduling module is used to encapsulate the optimal electric vehicle charging scheduling scheme into a smart contract and deploy it in the blockchain mainnet; and to use the smart contract to simulate the scheduling of electric vehicles in the electric vehicle charging station.

14. The system according to claim 13, characterized in that, The virtual power plant model construction module is specifically used for: Based on the acquired physical characteristics of the electric vehicle's internal equipment, the collected state of charge and rated capacity of the electric vehicle's battery, and the charging interface protocol parameters, the range of electric vehicle battery charge values ​​is determined; according to the electric vehicle battery charge value range and user travel needs, the rigid charging power to meet the user's basic travel needs and the flexible charging power for electric vehicle charging stations to participate in scheduling are determined. Based on the acquired physical characteristics of the electric vehicle charging station, the charge range of the electric vehicle battery, and the user's travel needs, the total charging power of the electric vehicle charging station is determined. An electric vehicle charging load model is constructed based on the total charging power of the electric vehicle charging station, the rigid charging power required to meet users' basic travel needs, and the flexible charging power of the electric vehicle charging station participating in scheduling. Based on the electric vehicle battery charge range, the electric vehicle battery state of charge and total battery capacity, an electric vehicle battery energy storage state and charge / discharge constraint model is constructed. The electric vehicle charging load model and the electric vehicle battery energy storage state and charge / discharge constraint model are used as a virtual power plant model.

15. The system according to claim 14, characterized in that, The electric vehicle charging load model satisfies the following formula: In the formula: for Total charging power of electric vehicle charging stations at any given time; for A rigid charging power that always meets users' basic travel needs; for The flexible charging power of electric vehicle charging stations participating in the scheduling at any time.

16. The system according to claim 14, characterized in that, The electric vehicle battery energy storage state and charge / discharge constraint model satisfies the following formula: In the formula: for The battery charge value in the state of charge of the electric vehicle at any given time. for The battery charge value in the state of charge of the electric vehicle at any given time. Improve battery charging efficiency. for Battery charging power at all times. The time step for the scheduling period. This refers to the total battery capacity. for Battery discharge power at all times For battery discharge efficiency, This represents the lower limit of the range of electric vehicle battery charge values. This represents the upper limit of the range of electric vehicle battery charge values. express Check whether the battery is charging at all times; The upper limit of battery charging power, This is the upper limit of battery discharge power. express Whether the battery is in a discharged state at any time.

17. The system according to claim 13, characterized in that, The source tracing model construction module is specifically used for: Based on the collected charging amount and charging power curves of electric vehicles, as well as the grid carbon emission factor for the corresponding time period, the total carbon emissions generated during the electric vehicle charging process are determined. Based on the virtual power plant model, the carbon emission coefficient of grid-side power supply, the carbon emission coefficient of standby diesel generator, the output power of grid-side power supply, and the output power of standby diesel generator are determined. A carbon emission traceability model for the entire life cycle of electric vehicles is constructed based on the total carbon emissions generated during the charging process of the electric vehicle, the carbon emission coefficient of the power supply from the grid, the carbon emission coefficient of the backup diesel generator, the output power of the power supply from the grid, and the output power of the backup diesel generator.

18. The system according to claim 17, characterized in that, The carbon emission traceability model for the entire life cycle of electric vehicles satisfies the following formula: In the formula, The total carbon emissions generated during the charging process of electric vehicles. N The total time period for operation. For a moment, The carbon emission factor of supplying electricity to the grid side, for The output power of the power supply from the grid side at any given time. The carbon emission coefficient of the standby diesel generator. for The output power of the standby diesel generator is always available.

19. The system according to claim 13, characterized in that, The response model construction module is specifically used for: The actual number of green certificates issued is determined based on the renewable energy power generation output curves corresponding to the green certificates associated with electric vehicle charging stations. Based on the spatiotemporal distribution characteristics of the carbon emission intensity, the length of the carbon emission interval is determined; Based on the actual number of green certificates issued and the total carbon emissions generated during the electric vehicle charging process, the equivalent carbon emissions after offsetting with green certificates are determined. Based on the length of the carbon emission range, the baseline coefficient of the tiered carbon emission and the penalty factor of the tiered carbon emission, as well as the equivalent carbon emission after offset by green certificates, a tiered green certificate and carbon coupling penalty amount is constructed.

20. The system according to claim 19, characterized in that, The tiered green certificate and carbon coupling penalty amount satisfy the following formula: In the formula, For tiered carbon emissions, As a benchmark coefficient for tiered carbon emissions, This represents the equivalent carbon emissions after offsetting with green certificates. The length of the carbon emission range. It serves as a penalty factor for tiered carbon emissions.

21. The system according to claim 20, characterized in that, The equivalent carbon emissions after offsetting with green certificates satisfy the following formula: In the formula, This represents the equivalent carbon emissions after offsetting with green certificates. The carbon offset factor corresponding to a unit of green certificate. The total carbon emissions generated during the charging process of electric vehicles. This refers to the total carbon offset corresponding to the actual issued green certificates.

22. The system according to claim 13, characterized in that, The power dispatching scheme module is specifically used for: The optimization model is then converted into a robust optimization model. The robust optimization model is decomposed into a main problem and preliminary subproblems using a column constraint generation algorithm; the preliminary subproblems are then transformed into mixed-integer linear programming forms using strong duality theory, thus forming subproblems. Within a relative gap set based on the objective function value related to carbon emission source tracing, the main problem is solved to find the first-stage scheduling scheme for electric vehicle charging scheduling, and the lower bound of the objective function value is updated. Based on the first-stage scheduling scheme, with the goal of finding a second-stage scheduling scheme for electric vehicle charging scheduling under the worst-case scenario of uncertain wind and solar power output and load, the sub-problem is solved and the upper bound of the objective function value is updated. Based on the updated upper and lower bounds of the objective function value, the relative gap is recalculated, and the main problem and sub-problems are solved iteratively until the recalculated relative gap is less than a preset threshold. Based on the solution results at this time, the optimal electric vehicle charging scheduling scheme is determined.

23. The system according to claim 22, characterized in that, The robust optimization model includes: an uncertainty set model of wind and solar power output and load power, and a two-stage robust optimization model; The uncertainty set model for wind and solar power output and load power satisfies the following formula: In the formula: For the vector set of uncertain variables of wind and solar power output and load power, Let be the vector of uncertain variables related to wind and solar power output and load power. for Uncertain variables in the relationship between wind and solar power output and load power at any given time. for Predicted values ​​of wind and solar power output and load power at any given time; for The upper limit of the fluctuation of wind and solar power output and load power at any given time; for The ratio of the deviation between the power output of wind and solar power and the load power at any given time. N The total time period for operation. For a moment, For the uncertainty adjustment parameters of wind and solar power output and load power; The two-stage robust optimization model satisfies the following formula: in, For the first-stage decision variables based on the day-ahead scheduling plan and equipment start-up and shutdown, For the second-stage decision variables based on real-time scheduling and carbon emissions, In order to be in and Given the feasible region, c is the first-stage penalty coefficient vector, d is the second-stage penalty coefficient vector, and T is the transpose of the matrix.

24. The system according to claim 13, characterized in that, The simulated scheduling module is specifically used for: The optimal electric vehicle charging scheduling scheme and transaction settlement rules are encapsulated into a smart contract. Deploy the smart contract on the blockchain mainnet; The smart contract is used to simulate the scheduling and transfer of electric vehicles within the electric vehicle charging station.

25. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, an electric vehicle carbon emission tracing and simulation scheduling method as described in any one of claims 1 to 12 is implemented.

26. A readable storage medium, characterized in that, It contains an execution program, which, when executed, implements a method for tracing and simulating the carbon emission sources of electric vehicles as described in any one of claims 1 to 12.

Images