A low-carbon charging method and system for electric vehicles based on dynamic carbon emission factors

Abstract

The present invention provides a low-carbon charging method and system for electric vehicles based on dynamic carbon emission factors, which relates to the technical field of low-carbon charging scheme optimization, and aims to effectively reduce the carbon emission factors caused by charging, including obtaining the current battery status and charging demand information of electric vehicles and verifying the information; obtaining the distributed power supply prediction data in the power grid and service area; establishing a double-layer optimization model for electric vehicle charging behavior based on the distributed power supply prediction data in the power grid and service area, and the double-layer optimization model for electric vehicle charging behavior includes an upper model and a lower model; iteratively solving the double-layer optimization model for electric vehicle charging behavior to obtain the optimal low-carbon charging strategy for electric vehicles. The present invention has the advantages of realizing green energy use and carbon reduction in high-speed service areas.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon charging scheme optimization, and in particular to a low-carbon charging method and system for electric vehicles based on a dynamic carbon emission factor. Background Art

[0002] At present, carbon emissions from the transportation industry account for about 10% of carbon emissions from terminal energy consumption. The low-carbon transformation of the transportation industry is crucial to achieving the dual carbon goals.

[0003] Electric vehicles do not directly generate carbon emissions during use and are considered an important means to reduce carbon emissions in the transportation industry. As the penetration rate of electric vehicles continues to rise, the source of carbon emissions in the transportation sector has gradually shifted from direct emissions generated by fuel combustion to indirect emissions generated by electricity consumption. However, coal-fired power generation accounts for nearly 60% of total power generation in China's power system. The widespread use of non-clean electricity has led to the carbon footprint of electric vehicles during charging that cannot be ignored. As an important node in the transportation network, the highway service area has the characteristics of large traffic flow and high charging demand. Its power supply sources include power supply from the grid, distributed power sources and energy storage devices. Different power supply combinations will cause the carbon emission factor to fluctuate significantly during the charging process, and a low carbon emission factor cannot be guaranteed at all stages. For example, during the period when the emission factor caused by power supply from the grid is low and the output of distributed power sources is large, the carbon emission factor caused by charging is low, while during the period when the emission factor caused by power supply from the grid is high and the output of distributed power sources is low, the carbon emission factor caused by charging will increase.

[0004] Therefore, it is necessary to optimize the charging plan to effectively reduce the carbon emission factors caused by charging, and then achieve green energy use and carbon reduction in highway service areas. Summary of the invention

[0005] The object of the present invention is to provide a low-carbon charging method and system for electric vehicles based on a dynamic carbon emission factor, which can effectively reduce the carbon emission factor caused by charging.

[0006] The present invention is achieved through the following technical solutions: A low-carbon charging method for electric vehicles based on a dynamic carbon emission factor comprises the following steps: Obtaining the current battery status and charging requirement information of the electric vehicle and performing information verification, wherein the current battery status and charging requirement information includes the total battery capacity, the minimum charging power, the maximum charging power of the electric vehicle, the current state of charge of the battery, the target state of charge of the battery, and the expected charging time; Obtain forecast data of distributed power sources in the power grid and service area; A double-layer optimization model for electric vehicle charging behavior is established based on the current battery status and charging demand information of the electric vehicle and the prediction data of the distributed power supply in the power grid and the service area, wherein the double-layer optimization model for electric vehicle charging behavior includes an upper model and a lower model, wherein the output of the upper model is the input of the lower model and the output of the lower model is the feedback input of the upper model, the upper model is a prediction model for electric vehicle charging carbon emission factors, and the lower model is an optimization model for electric vehicle charging behavior; The electric vehicle charging behavior double-layer optimization model is iteratively solved to obtain the optimal low-carbon charging strategy for the electric vehicle.

[0007] Preferably, the method for obtaining the current battery status and charging requirement information of the electric vehicle is: The current battery status and charging requirement information are obtained through a charging interface or a user interaction interface.

[0008] Preferably, the method for performing information verification is: Verify whether the battery target state of charge is greater than the battery current state of charge, verify whether the battery target state of charge and the battery current state of charge are both within the range of 0 to 100%, and verify whether the expected charging time does not exceed the maximum allowed charging time of the charging station; If the above verification results are all yes, the verification passes, otherwise the verification fails.

[0009] Preferably, the method for obtaining the prediction data of distributed power sources in the power grid and service area is: Set the cycle time and divide the cycle time into time period; Obtain multiple prediction values ​​through the upper grid communication interface and the local energy management system in the service area. The prediction values ​​include time periods within the cycle time. Prediction of carbon emission factor of power grid , No. Distributed in the cycle time period Distributed power output forecast value , Represents the collection of all distributed generation sources and the time period within the cycle time The predicted value of common load except charging piles , , and obtain the grid carbon emission factor prediction curve within the cycle time, the distributed power generation output prediction curve and the ordinary load prediction curve except the charging pile.

[0010] Preferably, for the missing predicted values, the acquisition method is: Obtain the historical time series of the forecast object and perform a sequence stationarity verification. If the verification passes, forecast the data using the autoregressive distributed lag time series forecasting method. If the verification fails, perform a logarithmic difference transformation and then use the autoregressive distributed lag time series forecasting method to forecast the data. The method for verifying the stationarity of the sequence is to verify whether the historical time series has a unit root. If so, the stability verification is passed; otherwise, the stability verification is failed.

[0011] Preferably, the method for predicting by the autoregressive distributed lag time series prediction method is: ; in, is the predicted value to be predicted at time t, is a constant term, is the coefficient of the autoregressive term, m represents the number of the autoregressive term, The time period corresponding to the predicted value to be predicted The historical value of is the coefficient of the distributed lag term, n is the number of the distributed lag term, is the set of independent variables, is the order of the autoregressive term, is the order of the distributed lag term, stands for white noise; The method for determining the order of the autoregressive term and the order of the distributed lag term is to determine the order by using the AIC criterion method or the BIC criterion method, and the order determination formula is as follows: ; ; in, is the likelihood function, is the model order number, is the highest order of model fitting, is the sequence width, represents the order evaluation parameter determined by the AIC criterion method, represents the order evaluation parameter determined by the BIC criterion method; The smaller the order evaluation parameter is, the better the order is. A threshold of the order evaluation parameter is set, and the order corresponding to the order evaluation parameter that does not exceed the threshold of the order evaluation parameter is selected for order determination.

[0012] Preferably, the method for establishing the upper layer model is: A service area transportation and energy fusion network operation simulation model is established, and the objective function of the service area transportation and energy fusion network operation simulation model is: ; in, is the total power supply of the power grid during the forecast period, The price of electricity purchased for supplying electricity to the grid; Establish system load balancing constraints: ; in, For the service area energy storage device in the period The power generation output inside For the service area energy storage device in the period Internal charging output, Represents the collection of all charging piles in the service area, For the period The power supplied by the internal power grid, For charging pile In time period Charging power; To create power output constraints: ; in, and Respectively Distributed power generation in the time period The minimum and maximum output of Establish energy storage operation constraints: ; ; ; ; ; in, and The energy storage device in the service area is in the time period and The state of charge, is the self-discharge rate of the energy storage device in the service area, and are the charging efficiency and discharging efficiency of the energy storage device in the service area, is the total capacity of the energy storage device in the service area, and are the lower and upper limits of the state of charge of the energy storage device in the service area, respectively. and Both are 0 / 1 variables and are used to represent the service area energy storage device in the time period The charge and discharge state, when charging For 0 When 1, discharge For 1 is 0, and They are the upper limit of discharge power and upper limit of charging power of energy storage device in service area respectively; Based on the objective function and various constraints of the service area transportation and energy fusion network operation simulation model, the power flow distribution of the service area in each period is predicted; Based on the power flow distribution in the service area during each period, a prediction model for the carbon emission factor of the hybrid power grid is established: ; in, Charging pile node The carbon emission factor of electricity Charging pile node A collection of injection sources, Charging pile node The injection source The power, Charging pile node The injection source The carbon flow density or carbon emission factor of the unit power generation; The carbon emission factor of each node is recursively calculated starting from the source node to obtain the carbon emission factor value of each node in the energy fusion network.

[0013] Preferably, the method for establishing the lower layer model is: An electric vehicle charging behavior optimization model based on dynamic carbon emission factors is established. The objective function of the electric vehicle charging behavior optimization model based on dynamic carbon emission factors is:

[0014] in, Charging pile node In time period Carbon emission factor; Establish charging power constraints: ; in, and Charging piles In time period The lower and upper limits of the charging power; Establish charging demand constraints: ; ; in, The number representing the electric car, and Respectively in the time period and The state of charge, is the total capacity of the electric vehicle battery, For charging efficiency, Expected charging time The electrical state, is the target state of charge of the battery of the electric vehicle, Time period for electric vehicles of charging power.

[0015] The present invention also provides an electric vehicle low-carbon charging system based on a dynamic carbon emission factor, which is applied to the above-mentioned electric vehicle low-carbon charging method based on a dynamic carbon emission factor, comprising: An information acquisition and verification module is used to obtain the current battery status and charging demand information of the electric vehicle and perform information verification; Prediction module, used to obtain prediction data of distributed power sources in the power grid and service area; A model building module, used to establish a double-layer optimization model of electric vehicle charging behavior based on the prediction data of the distributed power supply in the power grid and the service area, wherein the double-layer optimization model of electric vehicle charging behavior includes an upper model and a lower model, wherein the output of the upper model is the input of the lower model and the output of the lower model is the feedback input of the upper model, the upper model is a prediction model of electric vehicle charging carbon emission factors, and the lower model is an optimization model of electric vehicle charging behavior; The solution module is used to iteratively solve the double-layer optimization model of the electric vehicle charging behavior to obtain the optimal low-carbon charging strategy for the electric vehicle.

[0016] The technical solution of the present invention has at least the following advantages and beneficial effects: The present invention focuses on the high-speed service area scenario of traffic and energy integration, and adopts carbon emission factor prediction technology and optimization algorithm to meet the charging needs of users while minimizing the negative impact of charging behavior on the environment; Compared with the existing electric vehicle charging strategy optimization method, the method of the present invention has better carbon reduction and emission reduction effects and scenario applicability, and can effectively reduce the carbon emissions of electric vehicle charging in the high-speed service area scenario; The feedback iterative solution method of the present invention can dynamically adjust the charging strategy to adapt to the charging demand, grid conditions and distributed power supply conditions in different time periods, ensuring that the low-carbon goal is achieved while meeting the charging demand of electric vehicles; The present invention has reasonable design, wide applicability, high cost performance, is easy to apply to various charging scenarios, and is conducive to promotion and implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1A schematic flow chart of a low-carbon charging method for electric vehicles based on a dynamic carbon emission factor provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0019] Example 1 This embodiment provides a low-carbon charging method for electric vehicles based on a dynamic carbon emission factor. Figure 1 , including the following steps: Obtaining the current battery status and charging requirement information of the electric vehicle and performing information verification, wherein the current battery status and charging requirement information includes the total battery capacity, the minimum charging power, the maximum charging power of the electric vehicle, the current state of charge of the battery, the target state of charge of the battery, and the expected charging time; Obtain forecast data of distributed power sources in the power grid and service area; A double-layer optimization model for electric vehicle charging behavior is established based on the current battery status and charging demand information of the electric vehicle and the prediction data of the distributed power supply in the power grid and the service area, wherein the double-layer optimization model for electric vehicle charging behavior includes an upper model and a lower model, wherein the output of the upper model is the input of the lower model and the output of the lower model is the feedback input of the upper model, the upper model is a prediction model for electric vehicle charging carbon emission factors, and the lower model is an optimization model for electric vehicle charging behavior; The electric vehicle charging behavior double-layer optimization model is iteratively solved to obtain the optimal low-carbon charging strategy for the electric vehicle.

[0020] This embodiment first obtains the charging demand of electric vehicle users and the prediction data of distributed power sources in the power grid and service area to construct a basic data set. On this basis, a two-layer optimization model for electric vehicle charging behavior is established. The upper optimization model includes the service area energy-transportation fusion network operation simulation model and the energy-transportation fusion power grid carbon emission factor prediction model to realize the prediction of charging carbon emission factors. The lower optimization model is an electric vehicle charging behavior optimization model based on dynamic carbon emission factors to optimize the charging strategy. Finally, the two-layer optimization model is iteratively solved to obtain the optimal low-carbon charging strategy for electric vehicles in high-speed service areas, thereby achieving carbon reduction and emission reduction.

[0021] In this embodiment, the method for obtaining the current battery status and charging requirement information of the electric vehicle is: The current battery status and charging requirement information are obtained through a charging interface or a user interaction interface.

[0022] On this basis, the method for information verification is preferably: Verify whether the battery target state of charge is greater than the battery current state of charge, verify whether the battery target state of charge and the battery current state of charge are both within the range of 0 to 100%, and verify whether the expected charging time does not exceed the maximum allowed charging time of the charging station; If the above verification results are all yes, the verification is passed, otherwise the verification fails. If it fails, a reminder message will be returned until the verification passes.

[0023] As a preferred solution of this embodiment, the method for obtaining the prediction data of distributed power sources in the power grid and service area is: Set the cycle time and divide the cycle time into Generally speaking, the cycle time is preferably set to 24 hours, and the time period can be preferably divided into 5-minute periods; Obtain multiple prediction values ​​through the upper grid communication interface and the local energy management system in the service area. The prediction values ​​include time periods within the cycle time. Prediction of carbon emission factor of power grid , No. Distributed in the cycle time period Distributed power output forecast value , Represents the collection of all distributed generation sources and the time period within the cycle time The predicted value of common load except charging piles , , and obtain the grid carbon emission factor prediction curve within the cycle time, the distributed power generation output prediction curve and the ordinary load prediction curve except the charging pile.

[0024] Furthermore, for the missing forecast values, the system predicts the above data through the stored historical data set and the built-in autoregressive distributed lag time series prediction method. The stationarity of the series is the premise for time series prediction, so the data needs to be tested for stationarity first. The specific acquisition method is: Obtain the historical time series of the forecast object and perform a sequence stationarity verification. If the verification passes, forecast the data using the autoregressive distributed lag time series forecasting method. If the verification fails, perform a logarithmic difference transformation and then use the autoregressive distributed lag time series forecasting method to forecast the data. The method for verifying the stationarity of the sequence is to verify whether the historical time series has a unit root. If so, the stability verification is passed; otherwise, the stability verification is failed.

[0025] The unit root test is one of the most commonly used stationarity test methods. Its core idea is to test whether there is a unit root in the time series data. The general formula of the unit root test is as follows: ; in, For time The original time series, is a constant term, is the time trend term, For time time series, is the hysteresis coefficient, is the error term, represents the difference, d is the total number of difference terms, is the difference coefficient, and z is the number of the difference term.

[0026] Next, the method for predicting by the autoregressive distributed lag time series prediction method is: ; in, is the predicted value to be predicted at time t, is a constant term, is the coefficient of the autoregressive term, m represents the number of the autoregressive term, The time period corresponding to the predicted value to be predicted The historical value of is the coefficient of the distributed lag term, n is the number of the distributed lag term, is the set of independent variables, is the order of the autoregressive term, is the order of the distributed lag term, stands for white noise; The method for determining the order of the autoregressive term and the order of the distributed lag term is to determine the order by using the AIC criterion method or the BIC criterion method, and the order determination formula is as follows: ; ; in, is the likelihood function, is the model order number, is the highest order of model fitting, is the sequence width, represents the order evaluation parameter determined by the AIC criterion method, represents the order evaluation parameter determined by the BIC criterion method; The smaller the order evaluation parameter is, the better the order is. A threshold of the order evaluation parameter is set, and the order corresponding to the order evaluation parameter that does not exceed the threshold of the order evaluation parameter is selected for order determination.

[0027] As a preferred solution, the method for establishing the upper layer model is: A service area transportation and energy fusion network operation simulation model is established. The objective function of the service area transportation and energy fusion network operation simulation model is to establish the optimal system economy, specifically: ; in, is the total power supply of the power grid during the forecast period, The price of electricity purchased for supplying electricity to the grid; Establish system load balancing constraints: ; in, For the service area energy storage device in the period The power generation output inside For the service area energy storage device in the period Internal charging output, Represents the collection of all charging piles in the service area, For the period The power supplied by the internal power grid, For charging pile In time period Charging power; To create power output constraints: ; in, and Respectively Distributed power generation in the time period The minimum and maximum output of Establish energy storage operation constraints: ; ; ; ; ; in, and The energy storage device in the service area is in the time period and The state of charge, is the self-discharge rate of the energy storage device in the service area, and are the charging efficiency and discharging efficiency of the energy storage device in the service area, is the total capacity of the energy storage device in the service area, and are the lower and upper limits of the state of charge of the energy storage device in the service area, respectively. and Both are 0 / 1 variables and are used to represent the service area energy storage device in the time period The charge and discharge state, when charging For 0 When 1, discharge For 1 is 0, and They are the upper limit of discharge power and upper limit of charging power of energy storage device in service area respectively; Based on the objective function and various constraints of the service area transportation and energy fusion network operation simulation model, the power flow distribution of the service area in each period is predicted; Based on the power flow distribution in the service area during each period, a prediction model for the carbon emission factor of the hybrid power grid is established: ; in, Charging pile node The carbon emission factor of electricity Charging pile node A collection of injection sources, Charging pile node The injection source The power, Charging pile node The injection source The carbon flow density or carbon emission factor of the unit power generation; The carbon emission factor of each node is recursively calculated starting from the source node to obtain the carbon emission factor value of each node in the energy fusion network. That is, it is obtained from the carbon emission factor prediction model of the hybrid power grid in the upper model.

[0028] On the other hand, the method for establishing the lower layer model is: An electric vehicle charging behavior optimization model based on dynamic carbon emission factors is established. The objective function of the electric vehicle charging behavior optimization model based on dynamic carbon emission factors is:

[0029] in, Charging pile node In time period Carbon emission factor; Establish charging power constraints: ; in, and Charging piles In time period The lower and upper limits of the charging power; Establish charging demand constraints: ; ; in, The number representing the electric car, and Respectively indicate the time period and The state of charge, is the total capacity of the electric vehicle battery, For charging efficiency, Expected charging time The electrical state, is the target state of charge of the battery of the electric vehicle, Time period for electric vehicles of charging power.

[0030] This embodiment establishes a two-layer optimization model for electric vehicle charging behavior. In the upper model, first, according to the service area transportation and energy fusion network operation boundary conditions, a service area transportation and energy fusion network operation simulation model is established to calculate the predicted power flow distribution of the service area. Specifically, based on the service area transportation and energy fusion network, the predicted service area power flow distribution in each time period can be obtained. On this basis, a transportation and energy fusion power grid carbon emission factor prediction model based on the operation simulation results is established to calculate the dynamic carbon emission factor of the charging pile in each time period. It should be noted that for a single node, the carbon emission factor of its electricity consumption is equal to the injected carbon emissions divided by the injected power. The obtained carbon emission factor is then used in the lower model to establish the objective function of the electric vehicle charging behavior optimization model based on the dynamic carbon emission factor. The purpose of the objective function is to minimize the carbon emissions from electricity consumption generated by charging.

[0031] This two-layer optimization model design is because after the charging pile optimizes the charging behavior strategy, the charging load curve of each charging pile will change, which in turn affects the system power flow and carbon emission factor. Therefore, the two-layer optimization model in this embodiment is established. The predicted value of the dynamic electricity carbon emission factor of the charging pile calculated by the upper model is used as the input of the lower model, and the charging pile load curve calculated by the lower model is used as the feedback of the upper model. Then, iterative optimization is performed until convergence, and the optimal charging strategy for each charging pile can be obtained. It should be noted that when the user selects the fastest charging mode, the charging strategy of the charging pile is not optimized, and its charging power satisfy , until charged to .

[0032] Example 2 This embodiment provides an electric vehicle low-carbon charging system based on a dynamic carbon emission factor, which is applied to an electric vehicle low-carbon charging method based on a dynamic carbon emission factor in the above embodiment, including: An information acquisition and verification module is used to obtain the current battery status and charging demand information of the electric vehicle and perform information verification; Prediction module, used to obtain prediction data of distributed power sources in the power grid and service area; A model building module, used to establish a double-layer optimization model of electric vehicle charging behavior based on the prediction data of the distributed power supply in the power grid and the service area, wherein the double-layer optimization model of electric vehicle charging behavior includes an upper model and a lower model, wherein the output of the upper model is the input of the lower model and the output of the lower model is the feedback input of the upper model, the upper model is a prediction model of electric vehicle charging carbon emission factors, and the lower model is an optimization model of electric vehicle charging behavior; The solution module is used to iteratively solve the double-layer optimization model of the electric vehicle charging behavior to obtain the optimal low-carbon charging strategy for the electric vehicle.

[0033] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A low-carbon charging method for electric vehicles based on a dynamic carbon emission factor, characterized in that: The following steps are involved: Obtaining the current battery status and charging requirement information of the electric vehicle and performing information verification, wherein the current battery status and charging requirement information includes the total battery capacity, the minimum charging power, the maximum charging power of the electric vehicle, the current state of charge of the battery, the target state of charge of the battery, and the expected charging time; Obtain forecast data of distributed power sources in the power grid and service area; A double-layer optimization model for electric vehicle charging behavior is established based on the current battery status and charging demand information of the electric vehicle and the prediction data of the distributed power supply in the power grid and the service area, wherein the double-layer optimization model for electric vehicle charging behavior includes an upper model and a lower model, wherein the output of the upper model is the input of the lower model and the output of the lower model is the feedback input of the upper model, the upper model is a prediction model for electric vehicle charging carbon emission factors, and the lower model is an optimization model for electric vehicle charging behavior; The electric vehicle charging behavior double-layer optimization model is iteratively solved to obtain the optimal low-carbon charging strategy for the electric vehicle.

2. A low-carbon charging method for electric vehicles based on a dynamic carbon emission factor according to claim 1, characterized in that: The method for obtaining the current battery status and charging demand information of the electric vehicle is: The current battery status and charging requirement information are obtained through a charging interface or a user interaction interface.

3. A low-carbon charging method for electric vehicles based on a dynamic carbon emission factor according to claim 2, characterized in that: The method for information verification is: Verify whether the battery target state of charge is greater than the battery current state of charge, verify whether the battery target state of charge and the battery current state of charge are both within the range of 0 to 100%, and verify whether the expected charging time does not exceed the maximum allowed charging time of the charging station; If the above verification results are all yes, the verification passes, otherwise the verification fails.

4. A low-carbon charging method for electric vehicles based on a dynamic carbon emission factor according to claim 1, characterized in that: The method for obtaining the forecast data of distributed power sources in the power grid and service area is: Set the cycle time and divide the cycle time into time period; Obtain multiple prediction values ​​through the upper grid communication interface and the local energy management system in the service area. The prediction values ​​include time periods within the cycle time. Prediction of carbon emission factor of power grid , No. Distributed in the cycle time period Distributed power output forecast value , Represents the collection of all distributed generation sources and the time period within the cycle time The predicted value of common load except charging piles , , and obtain the grid carbon emission factor prediction curve within the cycle time, the distributed power generation output prediction curve and the ordinary load prediction curve except the charging pile.

5. A low-carbon charging method for electric vehicles based on a dynamic carbon emission factor according to claim 4, characterized in that: For missing predicted values, the method to obtain them is: Obtain the historical time series of the forecast object and perform a sequence stationarity verification. If the verification passes, forecast the data using the autoregressive distributed lag time series forecasting method. If the verification fails, perform a logarithmic difference transformation and then use the autoregressive distributed lag time series forecasting method to forecast the data. The method for verifying the stationarity of the sequence is to verify whether the historical time series has a unit root. If so, the stability verification is passed; otherwise, the stability verification is failed.

6. A low-carbon charging method for electric vehicles based on a dynamic carbon emission factor according to claim 5, characterized in that: The method for predicting by the autoregressive distributed lag time series prediction method is: ； in, is the predicted value to be predicted at time t, is a constant term, is the coefficient of the autoregressive term, m represents the number of the autoregressive term, The time period corresponding to the predicted value to be predicted The historical value of is the coefficient of the distributed lag term, n is the number of the distributed lag term, is the set of independent variables, is the order of the autoregressive term, is the order of the distributed lag term, stands for white noise; The method for determining the order of the autoregressive term and the order of the distributed lag term is to determine the order by using the AIC criterion method or the BIC criterion method, and the order determination formula is as follows: ； ； in, is the likelihood function, is the model order number, is the highest order of model fitting, is the sequence width, represents the order evaluation parameter determined by the AIC criterion method, represents the order evaluation parameter determined by the BIC criterion method; The smaller the order evaluation parameter is, the better the order is. A threshold of the order evaluation parameter is set, and the order corresponding to the order evaluation parameter that does not exceed the threshold of the order evaluation parameter is selected for order determination.

7. A low-carbon charging method for electric vehicles based on a dynamic carbon emission factor according to claim 6, characterized in that: The method for establishing the upper layer model is: A service area transportation and energy fusion network operation simulation model is established, and the objective function of the service area transportation and energy fusion network operation simulation model is: ； in, is the total power supply of the power grid during the forecast period, The price of electricity purchased for power supply to the grid; Establish system load balancing constraints: ； in, For the service area energy storage device in the period The power generation output inside For the service area energy storage device in the period Internal charging output, Represents the collection of all charging piles in the service area, For the period The power supplied by the internal power grid, For charging pile In time period Charging power; To create power output constraints: ； in, and Respectively Distributed power generation in the time period The minimum and maximum output of Establish energy storage operation constraints: ； ； ； ； ； in, and The energy storage device in the service area is in the time period and The state of charge, is the self-discharge rate of the energy storage device in the service area, and are the charging efficiency and discharging efficiency of the energy storage device in the service area, is the total capacity of the energy storage device in the service area, and are the lower and upper limits of the state of charge of the energy storage device in the service area, respectively. and Both are 0 / 1 variables and are used to represent the service area energy storage device in the time period The charge and discharge state, when charging For 0 When 1, discharge For 1 is 0, and They are the upper limit of discharge power and upper limit of charging power of energy storage device in service area respectively; Based on the objective function and various constraints of the service area transportation and energy fusion network operation simulation model, the power flow distribution of the service area in each period is predicted; Based on the power flow distribution in the service area during each period, a prediction model for the carbon emission factor of the hybrid power grid is established: ； in, Charging pile node The carbon emission factor of electricity Charging pile node A collection of injection sources, Charging pile node The injection source The power, Charging pile node The injection source The carbon flow density or carbon emission factor of the unit power generation; The carbon emission factor of each node is recursively calculated starting from the source node to obtain the carbon emission factor value of each node in the energy fusion network.

8. A low-carbon charging method for electric vehicles based on a dynamic carbon emission factor according to claim 7, characterized in that: The method of establishing the lower layer model is: An electric vehicle charging behavior optimization model based on dynamic carbon emission factors is established. The objective function of the electric vehicle charging behavior optimization model based on dynamic carbon emission factors is: in, Charging pile node In time period Carbon emission factor; Establish charging power constraints: ; in, and Charging piles In time period The lower and upper limits of the charging power; Establish charging demand constraints: ; ; in, The number representing the electric car, and Respectively in the time period and The state of charge, is the total capacity of the electric vehicle battery, For charging efficiency, Expected charging time The electrical state, is the target state of charge of the battery of the electric vehicle, Time period for electric vehicles of charging power.

9. A low-carbon charging system for electric vehicles based on dynamic carbon emission factors, applied to a low-carbon charging method for electric vehicles based on dynamic carbon emission factors as claimed in any one of claims 1 to 8, characterized in that: include: An information acquisition and verification module is used to obtain the current battery status and charging demand information of the electric vehicle and perform information verification; Prediction module, used to obtain prediction data of distributed power sources in the power grid and service area; A model building module, used to establish a double-layer optimization model of electric vehicle charging behavior based on the prediction data of the distributed power supply in the power grid and the service area, wherein the double-layer optimization model of electric vehicle charging behavior includes an upper model and a lower model, wherein the output of the upper model is the input of the lower model and the output of the lower model is the feedback input of the upper model, the upper model is a prediction model of electric vehicle charging carbon emission factors, and the lower model is an optimization model of electric vehicle charging behavior; The solution module is used to iteratively solve the double-layer optimization model of the electric vehicle charging behavior to obtain the optimal low-carbon charging strategy for the electric vehicle.

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