Method and device for guiding electric vehicle charging and discharging based on traffic and carbon emissions

Abstract

The present application relates to a method and device for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions. The method comprises: obtaining fuel vehicle data, time constraints, resource constraints, and carbon emission reduction constraints for the road network-grid to be guided within any current time period; obtaining the charge capacity, time data, resource data, and carbon data of the electric vehicle; inputting the time data into a time cost acquisition model to obtain the time cost; inputting the resource data into a resource cost acquisition model to obtain the resource cost; inputting the carbon data and fuel vehicle data into a pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction; and obtaining the corresponding carbon emission reduction benefits based on the carbon emission reduction; inputting the charge capacity, time cost, resource cost, and carbon emission reduction benefits into a target model to obtain target data that satisfies the constraints; and guiding the charging and discharging of the road network-grid to be guided based on the target data. This method can improve the system stability of the road network-grid coupling system.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicle operation control, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions. Background Art

[0002] Against the backdrop of accelerating the development of interactive vehicle-grid integration models and the construction of new power systems, electric vehicles (EVs) serve as both electricity consumption terminals and energy storage units at the end of the power grid, effectively enhancing the flexibility of the power system. On the one hand, while ensuring that the total load demand remains constant, EVs can be interrupted during power consumption periods with variable interruption times, enabling flexible adjustment of charging levels during different time periods. On the other hand, EVs can serve as distributed energy storage during periods of high renewable energy output, providing the power system with a substantial amount of flexibility resources, thereby effectively improving the power system's ability to absorb fluctuating renewable resources.

[0003] At present, electric vehicle charging and discharging strategies are mainly divided into centralized control and distributed control. They regulate the charging behavior of electric vehicle users from the perspective of time-of-use electricity prices, and mainly consider price incentives under the power demand response mechanism to guide users' electricity consumption behavior.

[0004] However, the current electric vehicle charging and discharging strategy used to guide the charging and discharging of electric vehicles has the problem of low system stability of the road network-grid coupling system. Summary of the Invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions, which can improve system stability, in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions, comprising:

[0007] Obtain fuel vehicle data for the network to be guided and the power grid in any current time period, the time constraints, resource constraints, and carbon emission reduction constraints of the network to be guided and the power grid in the current time period, as well as the charging capacity, time data, resource data, and carbon data of electric vehicles;

[0008] Inputting the time data into a pre-built time cost acquisition model to obtain the time cost in the current period, and inputting the resource data into a pre-built resource cost acquisition model to obtain the resource cost in the current period;

[0009] Input carbon data and fuel vehicle data into a pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction in the current period, and obtain the corresponding carbon emission reduction benefits based on the carbon emission reduction;

[0010] Input charging capacity, time cost, resource cost and carbon emission reduction benefits into a pre-built target cost data acquisition model to obtain the target charging capacity, target time cost, target resource cost and target carbon emission reduction benefits that meet the time constraints, resource constraints and carbon emission reduction constraints;

[0011] According to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit, charging and discharging guidance is carried out for electric vehicles in the guided road network-power grid.

[0012] In one embodiment, obtaining the charge level of the electric vehicle includes:

[0013] Obtain charging data, driving power consumption data, and discharge data of electric vehicles in the current period;

[0014] Inputting the charging data, driving power consumption data, and discharge data into a charging power acquisition module in a pre-built charging capacity acquisition model, and obtaining a first charging capacity in the current period through the charging power acquisition module;

[0015] Input the driving power consumption data into the power acquisition module in the charging capacity acquisition model, and obtain the power consumption in the current period through the power acquisition module;

[0016] Inputting the discharge data into a discharge power acquisition module in a charge capacity acquisition model, and obtaining a first discharge capacity in a current period through the discharge power acquisition module;

[0017] The charge capacity in the current time period is obtained according to the first charge capacity, the power consumption and the first discharge capacity.

[0018] In an exemplary embodiment, the time data includes charging time data, discharging time data, and travel time data: the charging time data includes charging duration and charging waiting time, the discharging time data includes discharging duration and discharging waiting time, and the travel time data includes road section delay time and intersection delay time of any road section;

[0019] Input the time data into the pre-built time cost acquisition model to obtain the time cost in the current period, including:

[0020] Input charging duration, charging waiting time, road section delay time, and intersection delay time into the charging time cost acquisition module in the time cost acquisition model to obtain the charging time cost in the current period;

[0021] Input the discharge duration, discharge waiting time, road section delay time and intersection delay time into the discharge time cost acquisition module in the time cost acquisition module to obtain the discharge time cost in the current period;

[0022] Based on the charging time cost and the discharging time cost, the time cost in the current period is obtained.

[0023] In one embodiment, the resource data includes charging resource data and discharging resource data: the charging resource data includes a charging electricity price and a second charging amount, and the discharging resource data includes a discharging electricity price and a second discharging amount;

[0024] Input resource data into the pre-built resource cost acquisition model to obtain the resource cost for the current period, including:

[0025] The charging electricity price, the second charging amount, the discharging electricity price, and the second discharging amount are input into a resource cost acquisition model to obtain the resource cost in the current period.

[0026] In one embodiment, the carbon data includes a charging node carbon potential and a charging node loss carbon flow rate;

[0027] Input carbon data and fuel vehicle data into the pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction for the current period, including:

[0028] The carbon potential of the charging node and the carbon flow rate of the charging node loss are input into the carbon emission acquisition module in the carbon emission reduction acquisition model, and the first carbon emission and the second carbon emission are obtained through the carbon emission acquisition module; the first carbon emission is used to characterize the carbon emission under the scenario of two-way interaction between the electric vehicle and the power grid, and the second carbon emission is used to characterize the carbon emission under the scenario of two-way charging of the electric vehicle;

[0029] Obtaining carbon emissions in a current period according to the first carbon emissions and the second carbon emissions;

[0030] Based on carbon emissions and fuel vehicle data, obtain carbon emission reductions in the current period.

[0031] In an exemplary embodiment, obtaining carbon emission reductions in a current period based on carbon emissions and fuel vehicle data includes:

[0032] Input the fuel vehicle data into the baseline carbon emissions acquisition module in the carbon emission reduction acquisition model, and obtain the baseline carbon emissions in the current period through the baseline carbon emissions acquisition module;

[0033] Subtract carbon emissions from baseline carbon emissions to get carbon emission reductions.

[0034] In a second aspect, the present application also provides a device for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions, comprising:

[0035] A data acquisition module is used to obtain fuel vehicle data for the road network-grid to be guided in any current time period, the time constraints, resource constraints, and carbon emission reduction constraints of the road network-grid to be guided in the current time period, and the charging capacity, time data, resource data, and carbon data of electric vehicles;

[0036] The first constraint construction module is used to input time data into a pre-built time cost acquisition model to obtain the time cost in the current period, and input resource data into a pre-built resource cost acquisition model to obtain the resource cost in the current period;

[0037] The second constraint construction module is used to input carbon data and fuel vehicle data into a pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction in the current period and obtain the corresponding carbon emission reduction benefits based on the carbon emission reduction;

[0038] A target data acquisition module is used to input the charging capacity, time cost, resource cost, and carbon emission reduction benefits into a pre-built target cost data acquisition model to obtain the target charging capacity, target time cost, target resource cost, and target carbon emission reduction benefits that meet the time constraints, resource constraints, and carbon emission reduction constraints;

[0039] The charging and discharging guidance module is used to guide the charging and discharging of electric vehicles in the guided road network-power grid according to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit.

[0040] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0041] Obtain fuel vehicle data for the network to be guided and the power grid in any current time period, the time constraints, resource constraints, and carbon emission reduction constraints of the network to be guided and the power grid in the current time period, as well as the charging capacity, time data, resource data, and carbon data of electric vehicles;

[0042] Inputting the time data into a pre-built time cost acquisition model to obtain the time cost in the current period, and inputting the resource data into a pre-built resource cost acquisition model to obtain the resource cost in the current period;

[0043] Input carbon data and fuel vehicle data into a pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction in the current period, and obtain the corresponding carbon emission reduction benefits based on the carbon emission reduction;

[0044] Input charging capacity, time cost, resource cost and carbon emission reduction benefits into a pre-built target cost data acquisition model to obtain the target charging capacity, target time cost, target resource cost and target carbon emission reduction benefits that meet the time constraints, resource constraints and carbon emission reduction constraints;

[0045] According to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit, charging and discharging guidance is carried out for electric vehicles in the guided road network-power grid.

[0046] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0047] Obtain fuel vehicle data for the network to be guided and the power grid in any current time period, the time constraints, resource constraints, and carbon emission reduction constraints of the network to be guided and the power grid in the current time period, as well as the charging capacity, time data, resource data, and carbon data of electric vehicles;

[0048] Inputting the time data into a pre-built time cost acquisition model to obtain the time cost in the current period, and inputting the resource data into a pre-built resource cost acquisition model to obtain the resource cost in the current period;

[0049] Input carbon data and fuel vehicle data into a pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction in the current period, and obtain the corresponding carbon emission reduction benefits based on the carbon emission reduction;

[0050] Input charging capacity, time cost, resource cost and carbon emission reduction benefits into a pre-built target cost data acquisition model to obtain the target charging capacity, target time cost, target resource cost and target carbon emission reduction benefits that meet the time constraints, resource constraints and carbon emission reduction constraints;

[0051] According to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit, charging and discharging guidance is carried out for electric vehicles in the guided road network-power grid.

[0052] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0053] Obtain fuel vehicle data for the network to be guided and the power grid in any current time period, the time constraints, resource constraints, and carbon emission reduction constraints of the network to be guided and the power grid in the current time period, as well as the charging capacity, time data, resource data, and carbon data of electric vehicles;

[0054] Inputting the time data into a pre-built time cost acquisition model to obtain the time cost in the current period, and inputting the resource data into a pre-built resource cost acquisition model to obtain the resource cost in the current period;

[0055] Input carbon data and fuel vehicle data into a pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction in the current period, and obtain the corresponding carbon emission reduction benefits based on the carbon emission reduction;

[0056] Input charging capacity, time cost, resource cost and carbon emission reduction benefits into the pre-built target cost acquisition model to obtain the target charging capacity, target time cost, target resource cost and target carbon emission reduction benefits that meet the time constraints, resource constraints and carbon emission reduction constraints;

[0057] According to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit, charging and discharging guidance is carried out for electric vehicles in the guided road network-power grid.

[0058] The above-mentioned method, device, computer equipment, computer-readable storage medium and computer program product for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions obtain the fuel vehicle data of the road network-grid to be guided in any current time period, the time constraints, resource constraints and carbon emission reduction constraints of the road network-grid to be guided in the current time period, and obtain the charging amount, time data, resource data and carbon data of the electric vehicle, input the time data into a pre-built time cost acquisition model to obtain the time cost in the current time period, and input the resource data into a pre-built resource cost acquisition model to obtain the resource cost in the current time period, and input the carbon data and fuel vehicle data into a pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction in the current time period, and obtain the corresponding carbon emission reduction benefit based on the carbon emission reduction, input the charging amount, time cost, resource cost and carbon emission reduction benefit into a pre-built target cost data acquisition model to obtain the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit that meet the time constraints, resource constraints and carbon emission reduction constraints, and guide the charging and discharging of electric vehicles in the road network-grid to be guided based on the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit. By comprehensively considering multi-source data in the road network-grid, the charging and discharging of the electric vehicles contained therein are guided, avoiding the limitations of guiding users' electricity consumption behavior by only considering price incentives under the electricity demand response mechanism. At the same time, carbon emission reduction constraints are added to improve the feasibility and scientific nature of charging and discharging guidance, thereby improving the system stability of the road network-grid coupling system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0060] Figure 1 This is a diagram of an application environment for a method for guiding electric vehicle charging and discharging based on traffic and carbon emissions in one embodiment;

[0061] Figure 2 1 is a flow chart of a method for guiding charging and discharging of electric vehicles based on traffic and carbon emissions in one embodiment;

[0062] Figure 3 A schematic flow chart of a method for guiding charging and discharging of electric vehicles based on traffic and carbon emissions in another embodiment;

[0063] Figure 4 A structural block diagram of a device for guiding electric vehicle charging and discharging based on traffic and carbon emissions in one embodiment;

[0064] Figure 5 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0066] The method for guiding electric vehicle charging and discharging based on traffic and carbon emissions provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the road network to be guided and the power grid are coupled systems of the power grid and the road network, which work together to maintain the normal operation of the system. The system includes a distribution network and a transportation network. The distribution network includes a high-voltage network, a medium-voltage network, a low-voltage network, and power flow, and the transportation network includes traffic flow. The distribution network and the transportation network exchange information flows through a distributed traffic information system. The road network to be guided and the power grid communicate with server 102 via the network. The data storage system can store data that server 102 needs to process. The data storage system can be integrated with server 102 or placed in the cloud or other network servers. Server 102 obtains fuel vehicle data of the road network-grid to be guided in any current time period, time constraints, resource constraints and carbon emission reduction constraints of the road network-grid to be guided in the current time period, and obtains the charging amount, time data, resource data and carbon data of the electric vehicle, inputs the time data into a pre-built time cost acquisition model to obtain the time cost in the current time period, and inputs the resource data into a pre-built resource cost acquisition model to obtain the resource cost in the current time period, inputs the carbon data and fuel vehicle data into a pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction in the current time period, and obtains the corresponding carbon emission reduction benefit based on the carbon emission reduction, inputs the charging amount, time cost, resource cost and carbon emission reduction benefit into a pre-built target cost data acquisition model to obtain the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit that meet the time constraints, resource constraints and carbon emission reduction constraints, and finally guides the charging and discharging of the electric vehicles in the road network-grid to be guided based on the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit. The server 102 may be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0067] In an exemplary embodiment, Figure 2 As shown, a method for guiding electric vehicle charging and discharging based on traffic and carbon emissions is provided, and the method is applied to Figure 1 The server 102 in the example is used as an example to illustrate the process, including the following steps S201 to S205.

[0068] Step S201, obtain the fuel vehicle data of the road network-grid to be guided in any current time period, the time constraints, resource constraints and carbon emission reduction constraints of the road network-grid to be guided in the current time period, and obtain the charging amount, time data, resource data and carbon data of the electric vehicle.

[0069] Among them, the road network-power grid can be understood as the coupling system of the transportation network and the power grid, the fuel vehicle data can be understood as various types of data generated during the operation of fuel-consuming vehicles, the charging amount can be understood as the amount of electricity obtained by electric vehicles from the power grid or other electric vehicles during use, the time data can be understood as the time consumed by electric vehicles during charging, discharging and traveling, the resource data can be understood as the resource data consumed by electric vehicles during charging and discharging in the current period, and the carbon data can be understood as the data emitted by the use of electric vehicles.

[0070] Among them, time constraints can be understood as the charging time constraints, discharging time constraints, travel time constraints, etc. corresponding to the road network-power grid to be guided in the current period. Similarly, resource constraints can be understood as the charging resource constraints, discharging resource constraints, etc. corresponding to the road network-power grid to be guided in the current period.

[0071] Time constraints:

[0072] 1. For the two scenarios of electric vehicle interaction with the grid and electric vehicle bidirectional charging and discharging, there is a maximum tolerance time for electric vehicle users: Considering road traffic safety, it is assumed that the interaction between electric vehicles and the grid and the bidirectional charging and discharging of electric vehicles are both carried out within the charging station:

[0073] F(t)≤t n,max

[0074] t n,max The maximum tolerable time for the nth electric vehicle to reach the charging station from its current location.

[0075] 2. For electric vehicle users, the location of charging stations and driving routes should take into account the remaining power of the electric vehicle at that time to avoid the situation where the electric vehicle runs out of power during charging or discharging:

[0076]

[0077] Where ω is the energy consumption coefficient of electric vehicles considering weather, road conditions, and driving habits, and the unit is MWh / km; L n,s is the driving distance of the nth electric vehicle to the sth charging station, in km; and are the current battery state of charge of the nth electric vehicle and the lower limit of the battery state of charge considering the battery life, in %; C battery is the battery capacity in MWh.

[0078] Resource constraints:

[0079] Corresponding resource constraints:

[0080] 1. There are constraints on the transformer capacity of the charging area grid. When electric vehicles are connected to the charging pile node in the grid to charge, the charging station the electric vehicles go to should meet the capacity range of the regional transformer:

[0081] P i，t,basic +P i，t，ev ≤η transformer *W transformer,max *cosφ i

[0082] Among them, P i,t,basic is the basic active load of region i at time t; P i,t,ev is the electric vehicle load connected to the grid for charging and discharging in region i at time t; η transformer is the operating efficiency of the transformer in region i; W transformer,max is the maximum capacity of the transformer in area i, cosφ i is the power factor of transformer operation in area i.

[0083] 2. For the weight coefficients of charging and discharging electricity prices of the nth electric vehicle, the sum of the two should be equal to 1:

[0084]

[0085] 3. For the charging and discharging electricity prices of the sth charging station at time t, based on the historical charging and discharging electricity price data of the charging station and taking full account of market factors, there are upper and lower limits for the electricity prices:

[0086]

[0087] Among them, carbon emission reduction can be understood as the difference between the carbon emitted by using fuel vehicles and the carbon generated by using electric vehicles under the same conditions, and carbon emission reduction constraints can be understood as carbon emission reduction constraints that are in line with environmental conditions during the current period.

[0088] Carbon emission reduction constraints:

[0089] 1. The charging and discharging of electric vehicles must meet the regional load balance constraints and system backup demand constraints:

[0090]

[0091] where u i,t represents the start and stop status of the i-th unit at time t, u i,t =1 means enabled, u i,t =0 means deactivation; the sum of the outputs of all units at time t is balanced with the base load and the electric vehicle load; the maximum output of the i-th unit at time t must be greater than or equal to the sum of the electric vehicle, base load and spare capacity.

[0092] 2. The electric vehicle load in each period must be less than the dispatchable electric vehicle load in that period:

[0093] 0≤P ev,t ≤N*P charge,max

[0094] Among them, N is the number of electric vehicles being charged at time t, P charge,max The maximum charging power for each electric vehicle.

[0095] 3. In order to maintain battery life and prevent deep charging, the charging amount of each electric vehicle in each period should be less than the upper limit of its battery capacity:

[0096] E t ≤α*N*E max

[0097] Where N is the number of electric vehicles being charged at time t, α is the charging protection coefficient, which is 0.9, and E max is the maximum capacity of a single electric vehicle battery, E t is the remaining power of the electric vehicle battery at time t.

[0098] For example, server 102 obtains fuel vehicle data for any current time period of the network-grid to be guided, along with the time constraints, resource constraints, and carbon reduction constraints of the network-grid to be guided within the current time period, as well as the charge capacity, time data, resource data, and carbon data of the electric vehicles within the network. By acquiring multi-source data and corresponding constraints for the network-grid to be guided, a solid data foundation is laid for subsequently correctly guiding the charging and discharging behavior of the electric vehicles within it. Furthermore, pre-acquiring the constraints within the system facilitates considering actual conditions during subsequent data adjustments, avoiding invalid data adjustments.

[0099] Step S202 : inputting the time data into a pre-built time cost acquisition model to obtain the time cost in the current period, and inputting the resource data into a pre-built resource cost acquisition model to obtain the resource cost in the current period.

[0100] Optionally, server 102 inputs the time data into a pre-built time cost acquisition model to obtain the time cost for the current period through the time cost acquisition model, and inputs the resource data into a pre-built resource cost acquisition model to obtain the resource cost for the current period through the resource cost acquisition model. Obtaining the time cost and resource cost through the time cost acquisition model and the resource cost acquisition model, and calculating the cost, provides a prerequisite for subsequently obtaining the corresponding target time cost and target resource cost.

[0101] Step S203: input the carbon data and fuel vehicle data into a pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction in the current period, and obtain the corresponding carbon emission reduction benefits based on the carbon emission reduction.

[0102] Among them, carbon emission reduction benefits can be understood as the benefits corresponding to the current reduction in carbon emissions.

[0103] For example, server 102 inputs carbon data and fuel vehicle data into a pre-built carbon emission reduction acquisition model. The model then determines the carbon emissions reductions from replacing fuel vehicles with electric vehicles during the current period. The model then calculates the corresponding carbon emission reduction benefits based on the reduced carbon emissions and the carbon price in the carbon market during the current trading cycle. By organically integrating the perspectives of traffic flow and power system carbon emissions, and applying theories of traffic flow distribution and power system carbon emission flows, the costs of charging and discharging electric vehicles and the benefits of participating in carbon market transactions are precisely quantified. This model then constructs corresponding carbon emission reduction benefits and constraints, laying a data foundation for subsequent guidance on charging and discharging electric vehicles.

[0104] Step S204, input the charging amount, time cost, resource cost and carbon emission reduction benefit into a pre-built target cost data model to obtain the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit that meet the time constraints, resource constraints and carbon emission reduction constraints.

[0105] Step S205 , guiding charging and discharging of electric vehicles in the guided road network-grid according to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit.

[0106] Among them, the target charging capacity can be understood as the amount of electricity that the road network-power grid to be guided can provide for charging electric vehicles in the current period; the target time cost can be understood as the target charging time, target discharging time and target travel time of the current electric vehicle; the target resource cost can be understood as the target charging resources and target discharging resources of the electric vehicle; the target carbon emission reduction benefit can be understood as the maximum carbon emission reduction benefit that the road network-power grid to be guided can achieve under the constraints in the current period.

[0107] Optionally, server 102 inputs the charging capacity, time cost, resource cost, and carbon emission reduction benefits into a pre-built target model to obtain a target charging capacity, target time cost, target resource cost, and target carbon emission reduction benefits that satisfy the time constraints, resource constraints, and carbon emission reduction constraints. Based on the target charging capacity, target time cost, target resource cost, and target carbon emission reduction benefits, server 102 guides the charging and discharging of electric vehicles in the guided road network-grid, so that the charging capacity in the current period is updated to the target charging capacity, the time cost is updated to the target time cost, the resource cost is updated to the target resource cost, and the carbon emission reduction benefits are updated to the target carbon emission reduction benefits. By using the two indicators of the cost of electric vehicle users' travel and the benefits of electric vehicles participating in the carbon market, and using the unit charging cost / benefit of electric vehicle users as an incentive signal, orderly charging and discharging behavior of electric vehicle users is guided, thereby improving the operational stability of the road network-grid system.

[0108] In the above-mentioned method for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions, the fuel vehicle data of the road network-grid to be guided in any current time period, the time constraints, resource constraints and carbon emission reduction constraints of the road network-grid to be guided in the current time period are obtained, and the charging amount, time data, resource data and carbon data of the electric vehicle are obtained. The time data is input into a pre-built time cost acquisition model to obtain the time cost in the current time period, and the resource data is input into a pre-built resource cost acquisition model to obtain the resource cost in the current time period, and the carbon data and fuel vehicle data are input into a pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction in the current time period, and the corresponding carbon emission reduction benefits are obtained according to the carbon emission reduction. The charging amount, time cost, resource cost and carbon emission reduction benefits are input into a pre-built target cost data acquisition model to obtain the target charging amount, target time cost, target resource cost and target carbon emission reduction benefits that meet the time constraints, resource constraints and carbon emission reduction constraints. According to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefits, the electric vehicles in the road network-grid to be guided are guided for charging and discharging. By comprehensively considering multi-source data in the road network-grid, the charging and discharging of the electric vehicles contained therein are guided, avoiding the limitations of guiding users' electricity consumption behavior by only considering price incentives under the electricity demand response mechanism. At the same time, carbon emission reduction constraints are added to improve the feasibility and scientific nature of charging and discharging guidance, thereby improving the system stability of the road network-grid coupling system.

[0109] In one embodiment, obtaining the charging amount of an electric vehicle includes: obtaining charging data, driving power consumption data, and discharge data of the electric vehicle in a current time period; inputting the charging data, driving power consumption data, and discharge data into a charging power acquisition module in a pre-built charging amount acquisition model, and obtaining a first charging amount in the current time period through the charging power acquisition module; inputting the driving power consumption data into an electric power acquisition module in the charging amount acquisition model, and obtaining the power consumption in the current time period through the electric power acquisition module; inputting the discharge data into a discharge power acquisition module in the charging amount acquisition model, and obtaining a first discharge amount in the current time period through the discharge power acquisition module; and obtaining the charging amount in the current time period based on the first charging amount, power consumption, and first discharge amount.

[0110] Among them, charging data can be understood as the charging power and power loss of an electric vehicle at a charging pile Internet node at a certain moment, and the charging power and power loss output by an electric vehicle when charging another electric vehicle at a certain moment; driving power consumption data can be understood as the output power of an electric vehicle during the driving process at a certain moment; discharge data can be understood as the discharge power and discharge loss of an electric vehicle at a charging pile Internet node at a certain moment, and the output power and discharge loss of an electric vehicle when discharging to another electric vehicle at a certain moment.

[0111] For example, server 102 obtains the charging power and power loss of an electric vehicle at a charging pile access node at a certain moment in the current time period, as well as the charging power and power loss output by the electric vehicle when charging another electric vehicle at a certain moment, and the output power during driving at that moment. Similarly, server 102 also obtains the output power and discharge power loss of the electric vehicle at the charging pile access node at that moment, as well as the output power and discharge power loss when the electric vehicle discharges to another electric vehicle at that moment. This data is input into a pre-built charge capacity acquisition model to obtain the charge capacity for the current time period. This not only captures the power interaction information between the road network and the power grid, but also considers the power interaction between electric vehicles, improving the accuracy of charge capacity acquisition and, in turn, improving the accuracy of electric vehicle charging and discharging guidance.

[0112] The specific process is as follows:

[0113] Optionally, (1) the power model during charging and bidirectional charging (i.e., the first component of the aforementioned charging power acquisition module) is:

[0114] or

[0115] Among them, E i,charge The power obtained by charging the i-th electric vehicle from the initial time 0 to time t of the grid, in kWh; P i,tis the charging power of the i-th electric vehicle at the charging pile network node at time t, in kW. ij,Bicharge The amount of electricity that the i-th electric car charges the j-th electric car from the initial time 0 to the time t, in kWh; P ij,Bicharge,t The charging power output when the i-th electric vehicle charges the j-th electric vehicle, in kW.

[0116] (2) Considering the energy loss during the charging process and the different charging loss levels when the remaining battery power is different, the average loss charging coefficient is not used here. The power loss model during the charging process of the electric vehicle (i.e., the second component of the aforementioned charging power acquisition module) is:

[0117] or

[0118] Among them, E loss,i,charge is the amount of electricity consumed by the i-th electric vehicle from the initial time 0 to the time t, in kWh; P lossc,i,t is the power loss of the i-th electric vehicle when charging at the charging pile from time 0 to time t, in kW; E loss,ij,Bicharge P is the amount of power consumed by the i-th electric vehicle charging the j-th electric vehicle from the initial time 0 to the time t, in kWh; loss,ij,Bicharge,t为 The power loss when the i-th electric car charges the j-th electric car from the initial time 0 to time t, in kW.

[0119] The corresponding first charge capacity is obtained according to the results of (1) and (2).

[0120] (3) The power model of electric vehicles during driving (i.e., the aforementioned power acquisition module) is:

[0121]

[0122] Among them, E i,load is the electric energy consumed by the i-th electric vehicle during its driving process from the initial time 0 to the time t, in kWh; P i,t,load is the output power of the i-th vehicle during its driving process at time t, in kW.

[0123] (4) The power model of the electric vehicle during the discharge process (i.e., the first component of the discharge power acquisition module) is:

[0124]

[0125] Among them, E i,discharge P is the amount of electricity discharged from the i-th electric vehicle to the charging pile grid node from the initial time 0 to time t, in kWh; i,t,disis the discharge power of the i-th electric vehicle at the charging pile network node at time t, in kW; E ji,Bidischarge P is the amount of electricity discharged from the jth electric vehicle to the ith vehicle from the initial time 0 to the time t, in kWh; ji,Bidischarge,t is the output power when the j-th electric vehicle discharges to the i-th electric vehicle, in kW.

[0126] (5) Considering the energy loss during the discharge process and the different levels of discharge loss when the remaining battery capacity is different, the average loss discharge coefficient is not used here. The power loss model during the discharge process of the electric vehicle (i.e., the second component of the discharge power acquisition module) is:

[0127] or

[0128] Among them, E loss,i,discharge P is the amount of power consumed by the i-th electric vehicle during the discharge process from the initial time 0 to the time t to the charging pile network node, in kWh; lossd,i,t E is the discharge loss power of the i-th electric vehicle supplying power to the charging pile network node at time t, in kW; loss,ji,Bidischarge P is the amount of power consumed by the j-th electric vehicle when discharging the i-th electric vehicle from the initial time 0 to the time t, in kWh; loss,ji,Bidischarge,t is the discharge power loss when the jth electric vehicle discharges the ith electric vehicle from the initial time 0 to time t, in kW. Using the above formula, a charging and discharging power model that considers electric vehicle losses can be established for two scenarios: bidirectional charging and discharging of electric vehicles and interaction between electric vehicles and the grid.

[0129] The first discharge capacity is obtained based on the results of (4) and (5). The first charge capacity, power consumption and second discharge capacity are summed to obtain the charge capacity of the current period.

[0130] The above method helps to understand the bidirectional charging and discharging between electric vehicles, and the bidirectional charging and discharging between electric vehicles and the power grid, thereby improving the accuracy of the calculated charging amount.

[0131] In an exemplary embodiment, the time data includes charging time data, discharging time data, and travel time data: the charging time data includes charging duration and charging waiting time, the discharging time data includes discharging duration and discharging waiting time, and the travel time data includes road section delay time and intersection delay time of any road section;

[0132] Inputting time data into a pre-built time cost acquisition model to obtain the time cost in the current period, including: inputting charging duration, charging waiting time, road section delay time and intersection delay time into the charging time cost acquisition module in the time cost acquisition model to obtain the charging time cost in the current period; inputting discharging duration, discharging waiting time, road section delay time and intersection delay time into the discharging time cost acquisition module in the time cost acquisition module to obtain the discharging time cost in the current period; and obtaining the time cost in the current period based on the charging time cost and the discharging time cost.

[0133] For example, the time cost incurred by electric vehicle users due to travel and charging and discharging behavior (i.e., the aforementioned time cost acquisition model) can be described as:

[0134] C time =μ*F(t)

[0135]

[0136] Wherein, μ is the time cost conversion coefficient, unit is yuan / min, F(t) is the time cost of electric vehicle travel and charging and discharging, unit is min; D k,j,t is the delay time of the electric vehicle passing through the road kj at time t, in min; I j,t The electric car passes through road k at time t j The intersection delay time, in min; T charge and T discharge are the duration of the charging and discharging process of the electric vehicle, in min; T wait,c and T wait,dis are the waiting time for the charging and discharging process of electric vehicles, respectively, in min;

[0137] D k,j,t and I j,t The specific calculation method is as follows: k,j,t is the free flow travel time of road section k, j, which can be obtained from the actual distance l of the road section k,j and electric vehicle speed v k,j Calculation shows that c1, c2, c3, and c4 are the road section parameters under different road levels, and k1 is the traffic load coefficient of the road section; I j is the free flow travel time of a vehicle passing through intersection j, T1 is the average free flow time of a single vehicle passing through the stop line of the intersection, p1, p2, p3, and p4 are the intersection parameters under different road levels, and k2 is the traffic load coefficient of the intersection.

[0138]

[0139] in,

[0140]

[0141] T charge and T discharge The duration of the charging and discharging process of the electric vehicle, respectively, in min; SOC f and SOC s The state of charge when the electric vehicle user expects to complete charging and the state of charge when starting charging are respectively expressed in %; C battery is the battery capacity, in MWh; P i,t 、P ij,Bicharge,t are the power of electric vehicles charging at the charging pile node and the power of the i-th electric vehicle charging the j-th electric vehicle, in kW. f’ and SOC s’ The state of charge at the completion of discharge and the state of charge at the start of discharge are determined by negotiation among electric vehicle users, operators, transportation departments, and power grid departments, respectively, in %. C battery is the battery capacity, in MWh; P i,t,dis 、P ji,Bidischarge,t They are respectively the power discharged by the electric vehicle at the charging pile node and the power discharged by the j-th electric vehicle to the i-th electric vehicle, in kW.

[0142] This approach allows for a comprehensive assessment of the time costs associated with charging and discharging electric vehicle users. This assessment helps optimize charging station layout, improve charging efficiency, enhance user experience, and enable better energy management in the interaction between electric vehicles and the grid, thereby reducing data processing costs.

[0143] In one embodiment, the resource data includes charging resource data and discharging resource data: the charging resource data includes a charging electricity price and a second charging amount, and the discharging resource data includes a discharging electricity price and a second discharging amount;

[0144] Inputting resource data into a pre-built resource cost acquisition model to obtain the resource cost in the current period includes: inputting the charging electricity price, the second charging amount, the discharging electricity price and the second discharging amount into the resource cost acquisition model to obtain the resource cost in the current period.

[0145] Optionally, the traffic network operation status and the power grid operation status jointly determine the economic cost model of the electric vehicle charging and discharging process (i.e., the aforementioned resource cost acquisition model), where both the charging process and the discharging process are taken into account, specifically:

[0146]

[0147] in, and are the charging and discharging electricity prices of the sth charging station at time t, taking into account the traffic flow and grid information flow, both in yuan / kWh; and are the weight coefficients for charging and discharging of the nth electric vehicle, respectively, which are determined by the electric vehicle user, operator, transportation department, and power grid department through negotiation, and are dimensionless numbers; and are the charging and discharging amounts of the nth electric vehicle at the sth charging station / electric vehicle at time t, in kWh.

[0148] By dynamically adjusting the charging and discharging electricity prices, the economic burden of electric vehicle users can be reduced. Taking into account the grid's voltage transformation capacity, the charging and discharging behavior of electric vehicles can be reasonably scheduled, thereby improving the system operation stability of the road network-grid.

[0149] In one embodiment, the carbon data includes a charging node carbon potential and a charging node loss carbon flow rate;

[0150] Input the carbon data and fuel vehicle data into a pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction in the current period, including: inputting the charging node carbon potential and the charging node loss carbon flow rate into the carbon emission acquisition module in the carbon emission reduction acquisition model, and obtaining the first carbon emissions and the second carbon emissions through the carbon emission acquisition module; the first carbon emissions are used to characterize the carbon emissions in the scenario of two-way interaction between electric vehicles and power grids, and the second carbon emissions are used to characterize the carbon emissions in the scenario of two-way charging of electric vehicles; based on the first carbon emissions and the second carbon emissions, the carbon emissions in the current period are obtained; based on the carbon emissions and fuel vehicle data, the carbon emission reduction in the current period is obtained.

[0151] For example,

[0152] ① For the scenario of two-way interaction between electric vehicles and the power grid:

[0153] (1) When an electric vehicle is charging at a charging pile node in the power grid, the carbon potential at the charging pile node depends on the node where the charging pile is connected to the power grid, which is determined by the characteristics of the power unit in the power grid and the network topology, and is recorded as (n is the number of the electric vehicle, t is the corresponding time), and further through the electric vehicle power model established above, the carbon flow rate of the electric vehicle entering with the power during this period is obtained (n is the number of the electric car, t is the corresponding time):

[0154]

[0155] in, is the carbon potential of electric vehicles at the charging station node in the grid, in tCO2 / kWh; is the carbon flow rate corresponding to the charging power of electric vehicles at the charging pile node of the power grid, in tCO2 / h; P i,t is the charging power of the i-th electric vehicle at the charging pile network node at time t, in kW.

[0156] (2) At the same time, there is a corresponding charging loss in the process of charging electric vehicles from the power grid. The carbon flow rate corresponding to this part of the loss is:

[0157]

[0158] in, is the carbon potential of electric vehicles at the charging station node in the grid, in tCO2 / kWh; is the carbon flow rate corresponding to the charging loss of electric vehicles at the charging pile node of the power grid, in tCO2 / h; P lossc,i,t is the power loss of the i-th electric vehicle when charging at the charging pile from the initial time 0 to the time t, in kW.

[0159] (3) When an electric vehicle discharges back to the grid, it is equivalent to a power unit, and its corresponding equivalent carbon emission intensity is determined by the previous time period.

[0160] Taking the process of charging, driving, and supplying electricity to the grid as an example (the specific order is not limited and can be reversed, only the general principle is abstracted here), the theoretical calculation of carbon flow for discharging / using electricity to the grid is described. At time 0, the battery capacity of the i-th electric vehicle is 1% SOC. The initial carbon emissions of the battery during this period are classified as the production stage, that is, its node carbon potential is considered to be 0; from time 0 to t1, the charging power P i,t1 When the power is SOC2%, the corresponding node carbon potential is During the period from t1 to t2, the normal driving consumes power to SOC3%. After the driving process is completed, at the moment from t2 to t3, the power P is supplied to the grid. i,t2,dis Discharge to SOC1%. At the same time, after the electric vehicle is charged at the grid charging pile node, the charging capacity of the electric vehicle is E store (t), the carbon flow corresponding to this part of electricity is F store (t). The power loss during the charging and discharging process is not differentiated from the driving power and is expressed uniformly.

[0161] During charging:

[0162]

[0163] in, is the carbon flow rate corresponding to the charging of the i-th electric vehicle at the grid charging pile node, in tCO2 / h; P i，t (t) is the charging power of the i-th electric vehicle at the charging pile network node at time t, in kW.

[0164] During driving and discharging, electric vehicles inject power into the grid as equivalent units, and their corresponding equivalent carbon emission intensity is determined by the previous period.

[0165] According to the power balance and carbon emission conservation relationship:

[0166]

[0167] P i，t (t)=[P i，t，dis (t)+P load′ (t)]

[0168] in, is the carbon flow rate corresponding to the charging of the i-th electric vehicle at the grid charging pile node, in tCO2 / h; R load′ (t) is the carbon flow rate corresponding to the charge and discharge loss and the electricity used during driving, in tCO2 / h; is the carbon flow rate corresponding to the discharge of the i-th electric vehicle at the charging pile node of the power grid, in tCO2 / h; P load′ (t) is the sum of charging and discharging power loss and driving power, and the unit is kW.

[0169] By combining the above four equations, the carbon emission factor of the electric vehicle during discharge is equal to the node carbon potential of the charging pile node during charging, so we have:

[0170]

[0171] Therefore, the carbon flow rate of an electric vehicle during discharge is:

[0172] The carbon flow rate of electric vehicles during driving is:

[0173] The carbon emission rate during electric vehicle charging is:

[0174] Therefore, the carbon emissions of electric vehicles in the above series of processes (i.e. the first carbon emissions) are:

[0175]

[0176] ②For the scenario of bidirectional charging of electric vehicles;

[0177] (1) When the i-th electric car charges the j-th electric car, the carbon potential of the node where the j-th electric car obtains the electricity is determined by a series of charging and discharging behaviors of the i-th electric car. The carbon potential of the i-th electric car is recorded as (n is the number of the electric vehicle, t is the corresponding time), and further, through the aforementioned power parameters, the carbon flow rate of the j-th electric vehicle entering during the period with the power provided by the i-th electric vehicle is obtained. (n is the number of the electric car, t is the corresponding time):

[0178]

[0179] (2) At the same time, there is a corresponding charging loss when the i-th electric vehicle charges the j-th electric vehicle. The carbon flow rate corresponding to this part of the loss is:

[0180]

[0181] (3) When the jth electric vehicle discharges to the i-th electric vehicle, the j-th electric vehicle is equivalent to a power unit, and its corresponding equivalent carbon emission intensity is jointly determined by the previous time period.

[0182] Taking the process of charging electric vehicle i to electric vehicle j, driving and consuming electricity, and then electric vehicle j to electric vehicle i as an example (the specific order is not limited and can be changed, only the general principle is abstracted here), the theoretical calculation of the carbon flow of bidirectional discharge / power consumption is described. The initial carbon emission of the battery during this period is classified as the production stage, that is, its node carbon potential is considered to be 0; from 0 to t1, the charging power P ij,Bicharge,t1 When the power is SOC2%, the corresponding node carbon potential is During the period from t1 to t2, the normal driving consumes the power until the SOC is 3%. After the driving process is completed, at the moment from t2 to t3, the power P of the i-th electric vehicle is supplied. ji,Bidischarge,t2 Discharge to SOC1%. At the same time, assuming that when the i-th electric car charges the j-th electric car, the energy stored by the electric car is E bistore (t), the carbon flow corresponding to this part of energy is F bistore (t).

[0183] During charging:

[0184]

[0185] During the driving and discharging process, since the i-th electric vehicle acts as an equivalent unit to inject power into the j-th electric vehicle, its corresponding equivalent carbon emission intensity is determined by the previous period, at this time:

[0186]

[0187] in, The carbon flow rate corresponding to the charging of the jth electric vehicle by the i-th electric vehicle is tCO2 / h; The carbon flow rate includes the power loss of bidirectional charging and discharging and the electricity used during driving, and the unit is tCO2 / h; The carbon flow rate corresponding to the j-th electric vehicle charging the i-th electric vehicle is tCO2 / h; This is the sum of the bidirectional charging and discharging power loss and driving power, and the unit is kW.

[0188] By combining the above four equations, we can obtain the carbon emission flow density during discharge under the bidirectional charging scenario:

[0189]

[0190] Therefore, the carbon flow rate during discharge in the bidirectional charging scenario is:

[0191] The carbon flow rate during driving under the bidirectional charging scenario is:

[0192] The carbon flow rate during the charging process in the bidirectional charging scenario is:

[0193] Therefore, the carbon emissions of electric vehicles in the above series of processes (i.e. the second carbon emissions) are:

[0194]

[0195] The first carbon emission value is added to the second carbon emission value to obtain the carbon emission value for the current period. The carbon emission reduction for the current period is then calculated based on the carbon emission value and the fuel vehicle data. This method dynamically monitors carbon emissions during the charging process, ensuring the accuracy of carbon emission data and, in turn, improving the accuracy of carbon emission reduction data.

[0196] In one embodiment, based on carbon emissions and fuel vehicle data, the carbon emission reduction in the current period is obtained, including: inputting the fuel vehicle data into the baseline carbon emissions acquisition module in the carbon emission reduction acquisition model, and obtaining the baseline carbon emissions in the current period through the baseline carbon emissions acquisition module; using the baseline carbon emissions to subtract the carbon emissions to obtain the carbon emission reduction.

[0197] Optionally, the baseline carbon emission model based on the conversion of equivalent mileage of fuel vehicles (i.e., the aforementioned baseline carbon emission acquisition module) is specifically as follows:

[0198] E ofc =e ofc *L M =S F *HV F *eF *ρ*L M

[0199]

[0200] Among them E ofc is the carbon emissions under the baseline; e ofc is the carbon emission factor per kilometer of fuel vehicles, which is determined by the fuel consumption rate S F , calorific value HV of fuel F , CO2 emission factor of fuel e F , and fuel density ρ are multiplied together to obtain; L M is the equivalent mileage of fuel vehicles and electric vehicles, α driving and β load are the correction coefficients for the effects of driving habits and vehicle load on the energy consumption of electric vehicles, E charge is the charge capacity of the electric vehicle, ω basic and ω air They are the basic energy consumption of electric vehicles and air conditioning energy consumption per kilometer respectively.

[0201] Therefore, the carbon emission reduction of electric vehicles is: E R =E ofc -E 总

[0202] The carbon emission reduction benefits obtained by electric vehicles participating in the carbon trading market are: R =p market *E R .p market is the carbon market price during the trading cycle. Further considering traffic flow as a key factor influencing EV charging strategies, the cost of EV travel and the benefits of EV participation in the carbon market are used as incentives to guide orderly charging and discharging behavior.

[0203] In an exemplary embodiment, Figure 3 As shown, a specific implementation method of a method for guiding electric vehicle charging and discharging based on traffic and carbon emissions is provided (the following data are all in a certain example and are not limited to this case in which the solution of this application can be implemented):

[0204] S1: Based on the two scenarios of bidirectional charging and discharging of electric vehicles and interaction between electric vehicles and the power grid, a model of electric vehicle charging, driving power consumption and discharge power is established with the grid connection node of the charging pile as the boundary.

[0205] Furthermore, the model of electric vehicle charging and bidirectional charging, charging loss, driving power consumption, discharge, and discharge power loss established in step S1 specifically includes:

[0206] (1) The power model during charging and bidirectional charging is:

[0207] or

[0208] Among them, E i,charge The power obtained by charging the i-th electric vehicle from the initial time 0 to time t of the grid, in kWh; P i,t is the charging power of the i-th electric vehicle at the charging pile network node at time t, in kW. ij,Bicharge The amount of electricity that the i-th electric car charges the j-th electric car from the initial time 0 to the time t, in kWh; P ij,Bicharge,t The charging power output when the i-th electric vehicle charges the j-th electric vehicle, in kW.

[0209] (2) Considering the energy loss during the charging process and the different charging loss levels when the remaining battery power is different, the average loss charging coefficient is not used here. The power loss model during the charging process of electric vehicles is:

[0210] or

[0211] Among them, E loss,i,charge is the amount of electricity consumed by the i-th electric vehicle from the initial time 0 to the time t, in kWh; P lossc,i,t is the power loss of the i-th electric vehicle when charging at the charging pile from time 0 to time t, in kW; E loss,ij,Bicharge P is the amount of power consumed by the i-th electric vehicle charging the j-th electric vehicle from the initial time 0 to the time t, in kWh; loss,ij,Bicharge,t为 The power loss when the i-th electric car charges the j-th electric car from the initial time 0 to time t, in kW.

[0212] (3) The power model of electric vehicles during driving is:

[0213]

[0214] Among them, E i,load is the electric energy consumed by the i-th electric vehicle during its driving process from the initial time 0 to the time t, in kWh; P i,t,load is the output power of the i-th vehicle during its driving process at time t, in kW.

[0215] (4) The power model of electric vehicles during discharge is:

[0216] or

[0217] Among them, E i,discharge P is the amount of electricity discharged from the i-th electric vehicle to the charging pile grid node from the initial time 0 to time t, in kWh; i,t,dis is the discharge power of the i-th electric vehicle at the charging pile network node at time t, in kW; E ji,Bidischarge P is the amount of electricity discharged from the jth electric vehicle to the ith vehicle from the initial time 0 to the time t, in kWh; ji,Bidischarge,t is the output power when the j-th electric vehicle discharges to the i-th electric vehicle, in kW.

[0218] (5) Considering the energy loss during the discharge process and the different levels of discharge loss when the remaining battery capacity is different, the average loss discharge coefficient is not used here. The power loss model of the electric vehicle during the discharge process is:

[0219] or

[0220] Among them, E loss,i,discharge P is the amount of power consumed by the i-th electric vehicle during the discharge process from the initial time 0 to the time t to the charging pile network node, in kWh; lossd,i,t E is the discharge loss power of the i-th electric vehicle supplying power to the charging pile network node at time t, in kW; loss,ji,Bidischarge P is the amount of power consumed by the j-th electric vehicle when discharging the i-th electric vehicle from the initial time 0 to the time t, in kWh; loss,ji,Bidischarge,t is the discharge power loss when the jth electric vehicle discharges the ith electric vehicle from the initial time 0 to time t, in kW. Using the above formula, a charging and discharging power model that considers electric vehicle losses can be established for two scenarios: bidirectional charging and discharging of electric vehicles and interaction between electric vehicles and the grid.

[0221] S2: Based on traffic flow theory, establish an electric vehicle user-charging pile travel cost decision model and determine the corresponding constraints and objective function.

[0222] The electric vehicle user-charging pile travel cost decision model established in step S2 specifically includes:

[0223] (1) The time cost incurred by electric vehicle users due to travel and charging and discharging behavior can be described as:

[0224] C time =μ*F(t)

[0225]

[0226] Wherein, μ is the time cost conversion coefficient, unit is yuan / min, F(t) is the time cost of electric vehicle travel and charging and discharging, unit is min; D k,j,t is the delay time of the electric vehicle passing through the road kj at time t, in min; I j,t The electric car passes through road k at time t j The intersection delay time, in min; T charge and T discharge are the duration of the charging and discharging process of the electric vehicle, in min; T wait,c and T wait,dis are the waiting time for the charging and discharging process of electric vehicles, respectively, in min;

[0227] D k,j,t and I j,t The specific calculation method is as follows: k,j,t is the free flow travel time of road section k, j, which can be obtained from the actual distance l of the road section k,j and electric vehicle speed v k,j Calculation shows that c1, c2, c3, and c4 are the road section parameters under different road levels, and k1 is the traffic load coefficient of the road section; I j is the free flow travel time of a vehicle passing through intersection j, T1 is the average free flow time of a single vehicle passing through the stop line of the intersection, p1, p2, p3, and p4 are the intersection parameters under different road levels, and k2 is the traffic load coefficient of the intersection.

[0228]

[0229] in,

[0230]

[0231] T charge and T discharge The duration of the charging and discharging process of the electric vehicle, respectively, in min; SOC f and SOC s The state of charge when the electric vehicle user expects to complete charging and the state of charge when starting charging are respectively expressed in %; C battery is the battery capacity, in MWh; P i,t 、P ij,Bicharge,t are the power of electric vehicles charging at the charging pile node and the power of the i-th electric vehicle charging the j-th electric vehicle, in kW. f’ and SOC s’The state of charge at the completion of discharge and the state of charge at the start of discharge are determined by negotiation among electric vehicle users, operators, transportation departments, and power grid departments, respectively, in %. C battery is the battery capacity, in MWh; P i,t,dis 、P ji,Bidischarge,t They are respectively the power discharged by the electric vehicle at the charging pile node and the power discharged by the j-th electric vehicle to the i-th electric vehicle, in kW.

[0232] (2) The operating status of the transportation network and the power grid jointly determine the economic cost model of the electric vehicle charging and discharging process. Here, both the charging process and the discharging process are taken into account. Specifically,

[0233]

[0234] in, and are the charging and discharging electricity prices of the sth charging station at time t, taking into account the traffic flow and grid information flow, both in yuan / kWh; and are the weight coefficients for charging and discharging of the nth electric vehicle, respectively, which are determined by the electric vehicle user, operator, transportation department, and power grid department through negotiation, and are dimensionless numbers; and are the charging and discharging amounts of the nth electric vehicle at the sth charging station / electric vehicle at time t, in kWh.

[0235] (3) The above electric vehicle charging and discharging time cost and economic cost model has the following constraints:

[0236] ① For the two scenarios of electric vehicle interaction with the grid and electric vehicle bidirectional charging and discharging, there is a maximum tolerance time for electric vehicle users: Considering the driving safety of road traffic, it is assumed that the interaction between electric vehicles and the grid and the bidirectional charging and discharging of electric vehicles are both carried out within the charging station:

[0237] F(t)≤t n,max

[0238] t n,max The maximum tolerable time for the nth electric vehicle to reach the charging station from its current location.

[0239] ② For electric vehicle users, the location of charging stations and driving routes should take into account the remaining power of the electric vehicle at that time to avoid the situation where the electric vehicle runs out of power during charging or discharging:

[0240]

[0241] Where ω is the energy consumption coefficient of electric vehicles considering weather, road conditions, and driving habits, and the unit is MWh / km; L n,s is the driving distance of the nth electric vehicle to the sth charging station, in km; and are the current battery state of charge of the nth electric vehicle and the lower limit of the battery state of charge considering the battery life, in %; C battery is the battery capacity in MWh.

[0242] ③ There are constraints on the transformer capacity of the charging area grid. When electric vehicles are connected to the charging pile node in the grid to charge, the charging station that the electric vehicles go to should meet the capacity range of the regional transformer:

[0243]

[0244] Among them, P i,t,basic is the basic active load of region i at time t; P i,t,ev is the electric vehicle load connected to the grid for charging and discharging in region i at time t; η transformer is the operating efficiency of the transformer in region i; W transformer,max is the maximum capacity of the transformer in area i, cosφ i is the power factor of transformer operation in area i.

[0245] ④ For the weight coefficients of the charging and discharging electricity prices of the nth electric vehicle, the sum of the two should be equal to 1:

[0246]

[0247] ⑤ For the charging and discharging electricity prices of the sth charging station at time t, based on the historical charging and discharging electricity price data of the charging station and taking full account of market factors, there are upper and lower limits for the electricity prices:

[0248]

[0249] S3: Based on the carbon flow theory, while considering the charging, power consumption and discharging processes of electric vehicles, an electric vehicle charging and discharging carbon emission model based on dynamic carbon emission factor measurement is established to calculate the carbon emission factor of the node and obtain the stored electrical energy and stored carbon flow expressions.

[0250] The electric vehicle charging and discharging carbon emission model established in step S3 considers two scenarios: bidirectional interaction between electric vehicles and the grid and bidirectional charging and discharging of electric vehicles. Specifically,

[0251] ① For the scenario of two-way interaction between electric vehicles and the power grid:

[0252] (1) When an electric vehicle is charging at a charging pile node in the power grid, the carbon potential at the charging pile node depends on the node where the charging pile is connected to the power grid, which is determined by the characteristics of the power unit in the power grid and the network topology, and is recorded as (n is the number of the electric vehicle, t is the corresponding time), and further through the electric vehicle power model established above, the carbon flow rate of the electric vehicle entering with the power during this period is obtained (n is the number of the electric car, t is the corresponding time):

[0253]

[0254] in, is the carbon potential of electric vehicles at the charging station node in the grid, in tCO2 / kWh; is the carbon flow rate corresponding to the charging power of electric vehicles at the charging pile node of the power grid, in tCO2 / h; P i,t is the charging power of the i-th electric vehicle at the charging pile network node at time t, in kW.

[0255] (2) At the same time, there is a corresponding charging loss in the process of charging electric vehicles from the power grid. The carbon flow rate corresponding to this part of the loss is:

[0256]

[0257] in, is the carbon potential of electric vehicles at the charging station node in the grid, in tCO2 / kWh; is the carbon flow rate corresponding to the charging loss of electric vehicles at the charging pile node of the power grid, in tCO2 / h; P lossc,i,t is the power loss of the i-th electric vehicle when charging at the charging pile from the initial time 0 to the time t, in kW.

[0258] (3) When an electric vehicle discharges back to the grid, it is equivalent to a power unit, and its corresponding equivalent carbon emission intensity is determined by the previous time period.

[0259] Taking the process of charging, driving, and supplying electricity to the grid as an example (the specific order is not limited and can be reversed, only the general principle is abstracted here), the theoretical calculation of carbon flow for discharging / using electricity to the grid is described. At time 0, the battery capacity of the i-th electric vehicle is 1% SOC. The initial carbon emissions of the battery during this period are classified as the production stage, that is, its node carbon potential is considered to be 0; from time 0 to t1, the charging power P i,t1 When the power is SOC2%, the corresponding node carbon potential is During the period from t1 to t2, the normal driving consumes power to SOC3%. After the driving process is completed, at the moment from t2 to t3, the power P is supplied to the grid. i,t2,disDischarge to SOC1%. At the same time, after the electric vehicle is charged at the grid charging pile node, the charging capacity of the electric vehicle is E store (t), the carbon flow corresponding to this part of electricity is F store (t). The power loss during the charging and discharging process is not differentiated from the driving power and is expressed uniformly.

[0260] During charging:

[0261]

[0262] in, is the carbon flow rate corresponding to the charging of the i-th electric vehicle at the grid charging pile node, in tCO2 / h; P i，t (t) is the charging power of the i-th electric vehicle at the charging pile network node at time t, in kW.

[0263] During driving and discharging, electric vehicles inject power into the grid as equivalent units, and their corresponding equivalent carbon emission intensity is determined by the previous period.

[0264] According to the power balance and carbon emission conservation relationship:

[0265]

[0266] P i，t (t)=[P i，t，dis (t)+P load′ (t)]

[0267] in, is the carbon flow rate corresponding to the charging of the i-th electric vehicle at the grid charging pile node, in tCO2 / h; P load′ (t) is the carbon flow rate corresponding to the charge and discharge loss and the electricity used during driving, in tCO2 / h; is the carbon flow rate corresponding to the discharge of the i-th electric vehicle at the charging pile node of the power grid, in tCO2 / h; P load′ (t) is the sum of charging and discharging power loss and driving power, and the unit is kW.

[0268] By combining the above four equations, the carbon emission factor of the electric vehicle during discharge is equal to the node carbon potential of the charging pile node during charging, so we have:

[0269]

[0270] Therefore, the carbon flow rate of an electric vehicle during discharge is:

[0271] The carbon flow rate of electric vehicles during driving is:

[0272] The carbon emission rate during electric vehicle charging is:

[0273] Therefore, the carbon emissions of electric vehicles in the above series of processes are:

[0274]

[0275] ②For the scenario of bidirectional charging of electric vehicles:

[0276] (1) When the i-th electric car charges the j-th electric car, the carbon potential of the node where the j-th electric car obtains the electricity is determined by a series of charging and discharging behaviors of the i-th electric car. The carbon potential of the i-th electric car is recorded as (n is the number of the electric vehicle, t is the corresponding time), and further, through the aforementioned power parameters, the carbon flow rate of the j-th electric vehicle entering during the period with the power provided by the i-th electric vehicle is obtained. (n is the number of the electric car, t is the corresponding time):

[0277]

[0278] (2) At the same time, there is a corresponding charging loss when the i-th electric car charges the j-th electric car. The carbon flow rate corresponding to this loss is

[0279]

[0280] (3) When the jth electric vehicle discharges to the i-th electric vehicle, the j-th electric vehicle is equivalent to a power unit, and its corresponding equivalent carbon emission intensity is jointly determined by the previous time period.

[0281] Taking the process of charging electric vehicle i to electric vehicle j, driving and consuming electricity, and then electric vehicle j to electric vehicle i as an example (the specific order is not limited and can be changed, only the general principle is abstracted here), the theoretical calculation of the carbon flow of bidirectional discharge / power consumption is described. The initial carbon emission of the battery during this period is classified as the production stage, that is, its node carbon potential is considered to be 0; from 0 to t1, the charging power P ij,Bicharge,t1 When the power is SOC2%, the corresponding node carbon potential is During the period from t1 to t2, the normal driving consumes the power until the SOC is 3%. After the driving process is completed, at the moment from t2 to t3, the power P of the i-th electric vehicle is supplied. ji,Bidischarge,t2 Discharge to SOC1%. At the same time, assuming that when the i-th electric car charges the j-th electric car, the energy stored by the electric car is E bistore (t), the carbon flow corresponding to this part of energy is F bistore (t).

[0282] During charging:

[0283]

[0284] During the driving and discharging process, since the i-th electric vehicle acts as an equivalent unit to inject power into the j-th electric vehicle, its corresponding equivalent carbon emission intensity is determined by the previous period, at this time:

[0285]

[0286] in, The carbon flow rate corresponding to the charging of the jth electric vehicle by the i-th electric vehicle is tCO2 / h; The carbon flow rate includes the power loss of bidirectional charging and discharging and the electricity used during driving, and the unit is tCO2 / h; The carbon flow rate corresponding to the j-th electric vehicle charging the i-th electric vehicle is tCO2 / h; This is the sum of the bidirectional charging and discharging power loss and driving power, and the unit is kW.

[0287] By combining the above four equations, we can obtain the carbon emission flow density during discharge under the bidirectional charging scenario:

[0288]

[0289] Therefore, the carbon flow rate during discharge in the bidirectional charging scenario is:

[0290] The carbon flow rate during driving under the bidirectional charging scenario is:

[0291] The carbon flow rate during the charging process in the bidirectional charging scenario is:

[0292] Therefore, the carbon emissions of electric vehicles in the above series of processes are:

[0293]

[0294] S4: Construct a baseline carbon emission model based on the conversion of equivalent mileage of fuel vehicles, quantify the carbon emissions under the baseline scenario, and then obtain the carbon emission reduction when electric vehicles replace fuel vehicles, and further calculate the carbon emission reduction benefits obtained by electric vehicles participating in carbon market transactions.

[0295] The baseline carbon emission model based on the conversion of equivalent mileage of fuel vehicles established in step S4 is as follows:

[0296] E ofc =e ofc *L M =S F *HV F*e F *ρ*L M

[0297]

[0298] Among them E ofc is the carbon emissions under the baseline; e ofc is the carbon emission factor per kilometer of fuel vehicles, which is determined by the fuel consumption rate S F , calorific value HV of fuel F , CO2 emission factor of fuel e F , and fuel density ρ are multiplied together to obtain; L M is the equivalent mileage of fuel vehicles and electric vehicles, α driving and β load are the correction coefficients for the effects of driving habits and vehicle load on the energy consumption of electric vehicles, E charge is the charge capacity of the electric vehicle, ω basic and ω air They are the basic energy consumption of electric vehicles and air conditioning energy consumption per kilometer respectively.

[0299] Therefore, the carbon emission reduction of electric vehicles is: E R =E ofc -E 总

[0300] The carbon emission reduction benefits obtained by electric vehicles participating in the carbon trading market are: R =p market *E R .p market is the price of the carbon market during the trading cycle.

[0301] For the carbon flow and carbon emission reduction model of electric vehicles, there are the following constraints:

[0302] 1. The charging and discharging of electric vehicles must meet the regional load balance constraints and system backup demand constraints:

[0303]

[0304] where u i,t represents the start and stop status of the i-th unit at time t, u i,t =1 means enabled, u i,t =0 means deactivation; the sum of the outputs of all units at time t is balanced with the base load and the electric vehicle load; the maximum output of the i-th unit at time t must be greater than or equal to the sum of the electric vehicle, base load and spare capacity.

[0305] 2. The electric vehicle load in each period must be less than the dispatchable electric vehicle load in that period:

[0306] 0≤P ev，t≤N*P charge,max

[0307] Among them, N is the number of electric vehicles being charged at time t, P charge,max The maximum charging power for each electric vehicle.

[0308] 3. In order to maintain battery life and prevent deep charging, the charging amount of each electric vehicle in each period should be less than the upper limit of its battery capacity:

[0309] E t ≤α*N*E max

[0310] Where N is the number of electric vehicles being charged at time t, α is the charging protection coefficient, which is 0.9, and E max is the maximum capacity of a single electric vehicle battery, E t is the remaining power of the electric vehicle battery at time t.

[0311] S5: Take the difference between the transportation cost of each kilowatt-hour of charging / discharging of electric vehicles and the carbon emission reduction benefits of electric vehicles as the objective function, establish a target model with the minimum objective function as the optimization goal, and output the results as the guidance for the optimized charging and discharging strategy of electric vehicle users under the constraints.

[0312] In step S5, the difference between the travel cost and the carbon emission reduction benefit of electric vehicles is used as the objective function, and an optimization target model is established with the optimization objectives of minimizing the travel cost per unit of charging and maximizing the carbon emission reduction benefit of electric vehicles. The output result is used as the optimized charging and discharging strategy of electric vehicle users under the constraint conditions, specifically:

[0313] Objective function:

[0314] Among them, C time The time cost incurred by electric vehicle users due to travel and charging and discharging behaviors; C dis,c The economic cost of charging and discharging electric vehicles; B R E is the income of electric vehicles participating in carbon market transactions, charge The amount of charge in an electric vehicle over a period of time.

[0315] Constraints:

[0316] Compared with the existing technology, this application has the following advantages:

[0317] 1. Compared with only using the carbon flow model as a charging and discharging strategy for electric vehicle users, this application further considers traffic flow, an important influencing factor for electric vehicle charging strategies. Through two indicators, the cost of electric vehicle users' travel and the benefits of electric vehicles participating in the carbon market, the unit charging cost / benefit of electric vehicle users is used as an incentive signal to guide the orderly charging and discharging behavior of electric vehicle users.

[0318] 2. This application expands the scenarios of mutual charging and discharging between electric vehicles, and based on the carbon flow theory, establishes a carbon emission model for bidirectional charging and discharging of electric vehicles, thereby providing a basis for the development of bidirectional charging and discharging technology for electric vehicles.

[0319] 3. The electric vehicle carbon emission reduction model established based on carbon flow theory accurately quantifies the emission reduction of electric vehicles compared with fuel vehicles during the use phase, thus providing a theoretical basis for the environmental benefit assessment of electric vehicles.

[0320] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0321] Based on the same inventive concept, the present application also provides an apparatus for implementing the aforementioned method for guiding electric vehicle charging and discharging based on traffic and carbon emissions. The solution provided by this apparatus is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more of the following embodiments of the apparatus for guiding electric vehicle charging and discharging based on traffic and carbon emissions can be found in the aforementioned method for guiding electric vehicle charging and discharging based on traffic and carbon emissions, and will not be further elaborated here.

[0322] In an exemplary embodiment, Figure 4 As shown, a method and apparatus for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions is provided, comprising: a data acquisition module 401, a first constraint construction module 402, a second constraint construction module 403, a target data acquisition module 404, and a charging and discharging guidance module 405, wherein:

[0323] Data acquisition module 401 is used to obtain fuel vehicle data of the road network-grid to be guided in any current time period, the time constraints, resource constraints, and carbon emission reduction constraints of the road network-grid to be guided in the current time period, and the charging capacity, time data, resource data, and carbon data of electric vehicles;

[0324] The first constraint construction module 402 is used to input time data into a pre-built time cost acquisition model to obtain the time cost in the current period, and input resource data into a pre-built resource cost acquisition model to obtain the resource cost in the current period;

[0325] The second constraint construction module 403 is used to input the carbon data and fuel vehicle data into a pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction in the current period and obtain the corresponding carbon emission reduction benefits based on the carbon emission reduction;

[0326] The target data acquisition module 404 is used to input the charging capacity, time cost, resource cost, and carbon emission reduction benefits into a pre-built target cost data acquisition model to obtain the target charging capacity, target time cost, target resource cost, and target carbon emission reduction benefits that meet the time constraints, resource constraints, and carbon emission reduction constraints;

[0327] The charging and discharging guidance module 405 is used to guide the charging and discharging of electric vehicles in the guided road network-grid according to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit.

[0328] In one embodiment, the data acquisition module 401 is further configured to acquire charging data, driving power consumption data, and discharge data of the electric vehicle in the current period.

[0329] The charging amount acquisition submodule is used to input the charging data, driving power consumption data and discharge data into the charging power acquisition module in the pre-built charging amount acquisition model, and obtain the first charging amount in the current time period through the charging power acquisition module; input the driving power consumption data into the power consumption acquisition module in the charging amount acquisition model, and obtain the power consumption in the current time period through the power consumption acquisition module; input the discharge data into the discharge power acquisition module in the charging amount acquisition model, and obtain the first discharge amount in the current time period through the discharge power acquisition module; and obtain the charging amount in the current time period based on the first charging amount, power consumption and first discharge amount.

[0330] In an exemplary embodiment, the time data includes charging time data, discharging time data and travel time data: the charging time data includes charging duration and charging waiting time, the discharging time data includes discharging duration and discharging waiting time, and the travel time data includes the road section delay time and intersection delay time along any road section. The first constraint construction module 402 is further used to input the charging duration, charging waiting time, road section delay time and intersection delay time into the charging time cost acquisition module in the time cost acquisition model to obtain the charging time cost in the current time period; input the discharging duration, discharging waiting time, road section delay time and intersection delay time into the discharging time cost acquisition module in the time cost acquisition module to obtain the discharging time cost in the current time period; and obtain the time cost in the current time period based on the charging time cost and the discharging time cost.

[0331] In one embodiment, the resource data includes charging resource data and discharging resource data: the charging resource data includes the charging electricity price and the second charging amount, and the discharging resource data includes the discharging electricity price and the second discharging amount; the constraint construction first module 402 is also used to input the charging electricity price, the second charging amount, the discharging electricity price and the second discharging amount into the resource cost acquisition model to obtain the resource cost in the current period.

[0332] In one embodiment, the second constraint construction module 403 further includes a constraint construction submodule, a carbon emission acquisition submodule, and a carbon emission reduction acquisition submodule, wherein:

[0333] The constraint construction submodule is used to input the carbon potential of the charging node and the carbon flow rate of the charging node loss into the carbon emission acquisition module in the carbon emission reduction acquisition model, and obtain the first carbon emissions and the second carbon emissions through the carbon emission acquisition module; the first carbon emissions are used to characterize the carbon emissions under the scenario of two-way interaction between electric vehicles and power grids, and the second carbon emissions are used to characterize the carbon emissions under the scenario of two-way charging of electric vehicles.

[0334] The carbon emission acquisition submodule is used to obtain the carbon emission in the current period according to the first carbon emission and the second carbon emission.

[0335] The carbon emission reduction acquisition submodule is used to obtain the carbon emission reduction in the current period based on carbon emissions and fuel vehicle data.

[0336] In an exemplary embodiment, the carbon emission reduction acquisition submodule is also used to input the fuel vehicle data into the baseline carbon emissions acquisition module in the carbon emission reduction acquisition model, and obtain the baseline carbon emissions in the current period through the baseline carbon emissions acquisition module; and subtract the carbon emissions from the baseline carbon emissions to obtain the carbon emission reduction.

[0337] Each module in the aforementioned device for guiding electric vehicle charging and discharging based on traffic and carbon emissions can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device's memory in software form, so that the processor can call and execute the corresponding operations of each module.

[0338] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store fuel vehicle data, charging amount, time data, resource data, carbon data, time cost, time constraint, resource cost, resource constraint, carbon emission reduction amount, carbon emission reduction constraint, and carbon emission reduction benefit data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions is implemented.

[0339] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0340] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the method for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions in the above embodiment.

[0341] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions in the above embodiment is implemented.

[0342] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions in the above embodiment is implemented.

[0343] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0344] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile memory and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a programmable logic unit (PLC), a data processing logic unit based on quantum computing, an artificial intelligence (AI) processor, and the like.

[0345] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0346] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions, characterized in that: The method comprises: Obtain fuel vehicle data of the road network-grid to be guided in any current time period, the time constraints, resource constraints and carbon emission reduction constraints of the road network-grid to be guided in the current time period, and obtain charging data, driving power consumption data and discharge data of electric vehicles in the current time period; input the charging data into the charging power acquisition module in the pre-built charging amount acquisition model, and obtain the first charging amount in the current time period through the charging power acquisition module; input the driving power consumption data into the power consumption acquisition module in the charging amount acquisition model, and obtain the power consumption in the current time period through the power consumption acquisition module; input the discharge data into the charging amount acquisition model a discharge power acquisition module, which obtains a first discharge amount in the current time period through the discharge power acquisition module; obtains the charge amount in the current time period based on the first charge amount, the power consumption, and the first discharge amount, and obtains time data, resource data, and carbon data of the electric vehicle; the time data includes charging time data, discharging time data, and travel time data: the charging time data includes charging duration and charging waiting time, the discharging time data includes discharging duration and discharging waiting time, and the travel time data includes road section delay time and intersection delay time for any road section; the carbon data includes charging node carbon potential and charging node loss carbon flow rate; Inputting the charging duration, charging waiting time, road section delay time, and intersection delay time into a charging time cost acquisition module in a pre-built time cost acquisition model to obtain the charging time cost in the current time period; inputting the discharging duration, discharging waiting time, road section delay time, and intersection delay time into a discharging time cost acquisition module in the time cost acquisition module to obtain the discharging time cost in the current time period; obtaining the time cost in the current time period based on the charging time cost and the discharging time cost; and inputting the resource data into a pre-built resource cost acquisition model to obtain the resource cost in the current time period; The carbon potential of the charging node and the carbon flow rate of the loss of the charging node are input into a carbon emission acquisition module in a pre-built carbon emission reduction acquisition model, and a first carbon emission and a second carbon emission are obtained through the carbon emission acquisition module; the first carbon emission is used to characterize the carbon emissions in a scenario of two-way interaction between the electric vehicle and the power grid, and the second carbon emission is used to characterize the carbon emissions in a scenario of two-way charging of the electric vehicle; the carbon emissions in the current time period are obtained based on the first carbon emissions and the second carbon emissions; based on the carbon emissions and the fuel vehicle data, the carbon emission reduction in the current time period is obtained, and the corresponding carbon emission reduction benefit is obtained based on the carbon emission reduction; Inputting the charging amount, the time cost, the resource cost, and the carbon emission reduction benefit into a pre-built target cost data acquisition model to obtain a target charging amount, target time cost, target resource cost, and target carbon emission reduction benefit that satisfy the time constraint, the resource constraint, and the carbon emission reduction constraint; According to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit, charging and discharging guidance is performed on the electric vehicles in the road network-power grid to be guided.

2. The method according to claim 1, characterized in that The resource data includes charging resource data and discharging resource data: the charging resource data includes a charging electricity price and a second charging amount, and the discharging resource data includes a discharging electricity price and a second discharging amount; Inputting the resource data into a pre-built resource cost acquisition model to obtain the resource cost in the current period includes: The charging electricity price, the second charging amount, the discharging electricity price, and the second discharging amount are input into the resource cost acquisition model to obtain the resource cost in the current time period.

3. The method according to claim 1, characterized in that The obtaining of the carbon emission reduction amount in the current period based on the carbon emission amount and the fuel vehicle data includes: Inputting the fuel vehicle data into a baseline carbon emissions acquisition module in the carbon emission reduction acquisition model, and obtaining the baseline carbon emissions in the current period through the baseline carbon emissions acquisition module; The carbon emission reduction is obtained by subtracting the carbon emission amount from the baseline carbon emission amount.

4. The method according to any one of claims 1 to 3, characterized in that The time constraints include a charging time constraint, a discharging time constraint, and a travel time constraint corresponding to the road network-power grid to be guided in the current time period.

5. The method according to any one of claims 1 to 3, characterized in that The resource constraints include charging resource constraints and discharging resource constraints corresponding to the road network-power grid to be guided in the current time period.

6. A device for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions, characterized in that: The device comprises: A data acquisition module is used to obtain fuel vehicle data of the road network-grid to be guided in any current time period, the time constraints, resource constraints and carbon emission reduction constraints of the road network-grid to be guided in the current time period, and the charging data, driving power consumption data and discharge data of the electric vehicle in the current time period; the charging data is input into the charging power acquisition module in the pre-built charging amount acquisition model, and the first charging amount in the current time period is obtained through the charging power acquisition module; the driving power consumption data is input into the power consumption acquisition module in the charging amount acquisition model, and the power consumption in the current time period is obtained through the power consumption acquisition module; the discharge data is input into the charging amount acquisition model A discharge power acquisition module in the model obtains a first discharge amount in the current time period through the discharge power acquisition module; obtains the charge amount in the current time period based on the first charge amount, the power consumption, and the first discharge amount, and obtains time data, resource data, and carbon data of the electric vehicle; the time data includes charging time data, discharge time data, and travel time data: the charging time data includes charging duration and charging waiting time, the discharge time data includes discharge duration and discharge waiting time, and the travel time data includes road section delay time and intersection delay time for any road section; the carbon data includes charging node carbon potential and charging node loss carbon flow rate; a first constraint construction module configured to input the charging duration, charging waiting time, road section delay time, and intersection delay time into a charging time cost acquisition module of a pre-constructed time cost acquisition model to obtain the charging time cost for the current time period; input the discharging duration, discharging waiting time, road section delay time, and intersection delay time into a discharging time cost acquisition module of the time cost acquisition module to obtain the discharging time cost for the current time period; obtain the time cost for the current time period based on the charging time cost and the discharging time cost; and input the resource data into a pre-constructed resource cost acquisition model to obtain the resource cost for the current time period; A second constraint construction module is used to input the charging node carbon potential and the charging node loss carbon flow rate into a carbon emission acquisition module in a pre-constructed carbon emission reduction acquisition model, and obtain a first carbon emission and a second carbon emission through the carbon emission acquisition module; the first carbon emission is used to characterize the carbon emissions in a scenario of two-way interaction between the electric vehicle and the power grid, and the second carbon emission is used to characterize the carbon emissions in a scenario of two-way charging of the electric vehicle; based on the first carbon emission and the second carbon emission, the carbon emissions in the current time period are obtained; based on the carbon emissions and the fuel vehicle data, the carbon emission reduction in the current time period is obtained, and the corresponding carbon emission reduction benefit is obtained according to the carbon emission reduction; a target data acquisition module, configured to input the charging amount, the time cost, the resource cost, and the carbon emission reduction benefit into a pre-built target cost data acquisition model, and obtain a target charging amount, target time cost, target resource cost, and target carbon emission reduction benefit that satisfy the time constraint, the resource constraint, and the carbon emission reduction constraint; The charging and discharging guidance module is used to guide the charging and discharging of electric vehicles in the road network-power grid to be guided according to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit.

7. The device according to claim 6, characterized in that The resource data includes charging resource data and discharging resource data: the charging resource data includes a charging electricity price and a second charging amount, and the discharging resource data includes a discharging electricity price and a second discharging amount; the device further includes: The constraint construction first module inputs the charging electricity price, the second charging amount, the discharging electricity price and the second discharging amount into the resource cost acquisition model to obtain the resource cost in the current time period.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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