Method and equipment for guiding electric vehicle to charge and discharge based on traffic and carbon emission

Abstract

The invention relates to a method and equipment for guiding charging and discharging of an electric vehicle based on traffic and carbon emission. The method comprises the steps that fuel vehicle data, time constraints, resource constraints and carbon emission reduction constraints of a road network-power grid to be guided in any current time period are acquired, the charging amount, time data, resource data and carbon data of an electric vehicle are acquired, the time data are input into a time cost acquisition model, time cost is obtained, and the time cost of the electric vehicle is acquired; and inputting the resource data into a resource cost acquisition model to obtain a resource cost, inputting the carbon data and the fuel vehicle data into a pre-constructed carbon emission reduction acquisition model to obtain a carbon emission reduction, obtaining a corresponding carbon emission reduction income according to the carbon emission reduction, and inputting the charging amount, the time cost, the resource cost and the carbon emission reduction income into a target model. And obtaining target data satisfying the constraint, and performing charging and discharging guidance on the to-be-guided road network-power grid according to the target data. By adopting the method, the system stability of the road network-power grid coupling system can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicle operation control, and in particular to 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. Background Art

[0002] In the context of accelerating the development of vehicle-grid integration and interactive models and the construction of new power systems, electric vehicles are both electricity consumption terminals and can be regarded as energy storage units at the end of the power grid, which can effectively improve the flexibility of the power system. On the one hand, under the premise of ensuring that the total load demand remains unchanged, electric vehicles are allowed to be interrupted during the power consumption period and the interruption time is not fixed, so as to achieve flexible adjustment of the charging amount in each time period; on the other hand, electric vehicles can be used as distributed energy storage during the period of high output of new energy to provide the power system with flexible resources of objective scale, 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 other aspects, and mainly consider price incentives under the electricity demand response mechanism to guide users' electricity consumption behavior.

[0004] However, the current electric vehicle charging and discharging strategy 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 of the road network-grid to be guided in any current period, time constraints, resource constraints and carbon emission reduction constraints of the road network-grid to be guided in the current period, and obtain 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 the carbon data and fuel vehicle data into the 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 target charging capacity, target time cost, target resource cost and target carbon emission reduction benefits that meet 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-grid.

[0012] In one embodiment, obtaining the charging amount 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, the driving power consumption data and the discharging 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] The driving power consumption data is input into the power consumption acquisition module in the charging capacity acquisition model, and the power consumption in the current period is obtained through the power consumption 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 amount in the current time period is obtained according to the first charge amount, the power consumption and the first discharge amount.

[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 the road section delay time and the 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] Inputting 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 in 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 the 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 the carbon data and fuel vehicle data into the pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction in 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 in 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 in the scenario of two-way charging of the electric vehicle;

[0029] According to the first carbon emission and the second carbon emission, the carbon emission in the current period is obtained;

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

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

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

[0033] Subtract carbon emissions from baseline carbon emissions to get carbon reduction.

[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 acquire fuel vehicle data of the road network-grid to be guided in any current period, time constraints, resource constraints and carbon emission reduction constraints of the road network-grid to be guided in the current period, and to acquire 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 module of constraint construction 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 according to the carbon emission reduction;

[0038] A target data acquisition module is used to 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;

[0039] The charging and discharging guidance module 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.

[0040] In a third aspect, the present application further provides a computer device, including 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 of the road network-grid to be guided in any current period, time constraints, resource constraints and carbon emission reduction constraints of the road network-grid to be guided in the current period, and obtain 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 the carbon data and fuel vehicle data into the 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 target charging capacity, target time cost, target resource cost and target carbon emission reduction benefits that meet 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-grid.

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

[0047] Obtain fuel vehicle data of the road network-grid to be guided in any current period, time constraints, resource constraints and carbon emission reduction constraints of the road network-grid to be guided in the current period, and obtain 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 the carbon data and fuel vehicle data into the 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 target charging capacity, target time cost, target resource cost and target carbon emission reduction benefits that meet 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-grid.

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

[0053] Obtain fuel vehicle data of the road network-grid to be guided in any current period, time constraints, resource constraints and carbon emission reduction constraints of the road network-grid to be guided in the current period, and obtain 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 the carbon data and fuel vehicle data into the 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-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-constructed time cost acquisition model to obtain the time cost in the current time period, and input the resource data into a pre-constructed resource cost acquisition model to obtain the resource cost in the current time period, and input the carbon data and the fuel vehicle data into a pre-constructed carbon emission reduction acquisition model to obtain the carbon emission reduction in the current time period, and obtain the corresponding carbon emission reduction benefits according to the carbon emission reduction, input the charging amount, time cost, resource cost and carbon emission reduction benefits into a pre-constructed 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, and guide the charging and discharging of the electric vehicles in the road network-grid to be guided according to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefits. 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 only considering the price incentive method under the electricity demand response mechanism to guide users' electricity consumption behavior. At the same time, carbon emission reduction constraints are added to improve the feasibility and scientificity 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 drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. 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 creative work.

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

[0061] Figure 2 A schematic diagram of 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. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present 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. Among them, the road network to be guided-the power grid is a coupling system 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 a power flow. The transportation network includes a traffic flow. The distribution network and the transportation network interact with each other through a distributed traffic information system. The road network to be guided-the power grid communicates with the server 102 through the network. The data storage system can store data that the server 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on 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 according to 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 according to 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 the charging and discharging of electric vehicles 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 method, which includes the following steps S201 to S205. Among them:

[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-grid can be understood as the coupling system of the transportation network and the grid, the fuel vehicle data can be understood as various types of data generated during the operation of a fuel-consuming vehicle, the charging amount can be understood as the amount of electricity obtained by the electric vehicle from the grid or other electric vehicles during use, the time data can be understood as the time consumed by the electric vehicle during charging, discharging and traveling, the resource data can be understood as the resource data consumed by the electric vehicle 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, the time constraint can be understood as the charging time constraint, discharging time constraint, travel time constraint, etc. corresponding to the road network-power grid to be guided in the current period. Similarly, the resource constraint can be understood as the charging resource constraint, discharging resource constraint, 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 power 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 power grid and the bidirectional charging and discharging of electric vehicles are both carried out in the charging station:

[0073] F(t)≤t n,max

[0074] t n,max is 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 the charging station and the driving route 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, in 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 for charging, the charging station that 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] Where 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 the transformer in area i.

[0083] 2. 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:

[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 reduction constraints:

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

[0090]

[0091] where u i,t represents the start / stop status of the i-th unit at time t, u i,t =1 means enabled, u i,t =0 means shutdown; 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 vehicles, base load and spare capacity.

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

[0093] 0≤P ev,t ≤N*P charge,max

[0094] Where 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 factor, which is taken as 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] Exemplarily, the server 102 obtains the fuel vehicle data of the road network-grid to be guided in any current period, the time constraints, resource constraints and carbon emission reduction constraints of the road network-grid to be guided in the current period, and obtains the charging amount, time data, resource data and carbon data of the electric vehicles therein. By obtaining multi-source data and corresponding constraints in the road network-grid to be guided, a solid data foundation is laid for the subsequent correct guidance of the charging and discharging behavior of the electric vehicles therein, and obtaining the constraints in the system in advance is conducive to taking into account the actual situation when adjusting the data later, 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, the server 102 inputs the time data into a pre-built time cost acquisition model, obtains the time cost in the current period through the time cost acquisition model, and inputs the resource data into a pre-built resource cost acquisition model, obtains the resource cost in the current period through the resource cost acquisition model. The time cost and resource cost are acquired through the time cost acquisition model and the resource cost acquisition model, and the calculated cost provides a prerequisite for subsequently obtaining the corresponding target time cost and target resource cost.

[0101] Step S203, inputting 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 obtaining the corresponding carbon emission reduction benefits according to 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] Exemplarily, the server 102 inputs the carbon data and fuel vehicle data into the pre-built carbon emission reduction acquisition model, obtains the carbon emission reduction after replacing fuel vehicles with electric vehicles in the current period through the carbon emission reduction acquisition model, and calculates the corresponding carbon emission reduction benefits according to the reduced carbon emissions and the carbon price of the carbon market in the current trading cycle. By organically integrating the traffic flow perspective and the power system carbon emission perspective, applying the traffic flow allocation and power system carbon emission flow theory, the cost of electric vehicles for realizing charging and discharging behavior and the benefits of participating in carbon market transactions are accurately quantified, and the corresponding carbon emission reduction benefits and carbon emission reduction constraints are constructed, laying a data foundation for the subsequent realization of electric vehicle charging and discharging guidance.

[0104] Step S204, input the charging amount, time cost, resource cost and carbon emission reduction benefit into a pre-constructed 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 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.

[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, and 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 of the constraints in the current period.

[0107] Optionally, the server 102 inputs the charging amount, time cost, resource cost and carbon emission reduction benefit into a pre-built target model, obtains the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit that meet the time constraint, resource constraint and carbon emission reduction constraint, and guides the electric vehicles in the guided road network-grid to charge and discharge according to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit, so that the charging amount in the current period is updated to the target charging amount, 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 benefit is updated to the target carbon emission reduction benefit. Through the two indicators of the cost of electric vehicle users' travel and the benefit 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, thereby improving the system operation stability of the road network-grid.

[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-constructed time cost acquisition model to obtain the time cost in the current time period, and the resource data is input into a pre-constructed resource cost acquisition model to obtain the resource cost in the current time period, and the carbon data and the fuel vehicle data are input into a pre-constructed 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-constructed 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, and the electric vehicles in the road network-grid to be guided are guided for charging and discharging according to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefits. 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 only considering the price incentive method under the electricity demand response mechanism to guide users' electricity consumption behavior. At the same time, carbon emission reduction constraints are added to improve the feasibility and scientificity 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 discharging data of the electric vehicle in a current time period; inputting the charging data, driving power consumption data and discharging data into a charging power acquisition module in a pre-constructed 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 discharging data into a discharging power acquisition module in the charging amount acquisition model, and obtaining a first discharging amount in the current time period through the discharging power acquisition module; and obtaining the charging amount in the current time period based on the first charging amount, the power consumption and the first discharging amount.

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

[0111] Exemplarily, the server 102 obtains the charging power and power loss of the electric vehicle at a charging pile Internet node at a certain moment in the current period, and the charging power and power loss output when the electric vehicle charges another electric vehicle at a certain moment, and the output power during driving at this moment. Similarly, the output power and discharge power loss of the electric vehicle at the charging pile Internet node at this moment, and the output power and discharge power loss of the electric vehicle when the electric vehicle discharges to another electric vehicle at this moment, and inputs the aforementioned data into the pre-built charging amount acquisition model to obtain the charging amount in the current period. Not only the power interaction information between the road network and the power grid is obtained, but also the power interaction between electric vehicles is considered, which improves the accuracy of charging amount acquisition, thereby 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 is the amount of electricity obtained by the i-th electric vehicle from the initial time 0 to time t of the power 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 charging power of the i-th electric car to 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 car charges the j-th electric car, 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 it is charged at the charging pile from time 0 to time t, in kW; E loss,ij,Bicharge P is the amount of electricity consumed by the i-th electric car charging the j-th electric car 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 the 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 the electric vehicle 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 is the amount of electricity discharged from the i-th electric vehicle to the charging pile network node from the initial time 0 to time t, in kWh; P 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 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; P ji,Bidischarge,t is the output power when the j-th electric car discharges to the ith electric car, in kW.

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

[0127] or

[0128] Among them, E loss,i,discharge is the power consumption of 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; P 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 electricity 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 loss power when the j-th electric vehicle discharges the i-th electric vehicle from the initial time 0 to the time t, in kW. Through the above formula, a charging and discharging power model considering the loss of electric vehicles can be established in two scenarios: bidirectional charging and discharging of electric vehicles and interaction between electric vehicles and the power grid.

[0129] The first discharge amount is obtained according to the results of (4) and (5). The first charge amount, the power consumption and the second discharge amount are added together to obtain the charge amount 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 the road section delay time and the intersection delay time of any road section;

[0132] Input the time data into a pre-built time cost acquisition model to obtain the time cost in the current time period, including: 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; obtain the time cost in the current time 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 behaviors (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 yuan / min, F(t) is the time cost of electric vehicle travel and charging and discharging, unit 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 the electric vehicle, 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 the electric vehicle speed v k,j It is calculated that c1, c2, c3, c4 are the road section parameters under different road levels, k1 is the road section traffic load coefficient; I j is the free flow travel time of vehicles 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, 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, in minutes; SOC f and SOC s They are the state of charge when the electric vehicle user expects to complete charging and the state of charge when starting charging, in %; C battery is the battery capacity in MWh; P i,t , P ij,Bicharge,t are the power of the electric vehicle 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 the electric vehicle user, operator, transportation department, and power grid department, 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 method can be used to comprehensively evaluate the time cost of electric vehicle users during charging and discharging. This evaluation helps optimize the layout of charging stations, improve charging efficiency, improve user experience, and achieve better energy management in the interaction between electric vehicles and the power 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 operation status of the traffic network and the operation status of the power grid jointly determine the economic cost model of the charging and discharging process of the electric vehicle (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 They 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 They are the charging and discharging amounts of the nth electric car at the sth charging station / electric car at time t, in kWh.

[0148] By dynamically adjusting the charging and discharging electricity prices, the economic burden on electric vehicle users can be reduced. By considering the grid's voltage transformation capacity and rationally scheduling the charging and discharging behavior of electric vehicles, the system operation stability of the road network-grid can be improved.

[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 time period, including: input 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 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 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; according to the first carbon emissions and the second carbon emissions, 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.

[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 node carbon potential at the charging pile 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 node carbon potential of electric vehicles at the charging pile node of the power 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 car at the charging pile Internet node at time t, in kW.

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

[0157]

[0158] in, is the node carbon potential of electric vehicles at the charging pile node of the power 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 It is the power loss of the i-th electric car 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 jointly determined by the previous time period.

[0160] Taking the process of charging-driving power consumption-supplying power to the grid as an example (the specific order is not limited, the order can be changed, and only the general principle is abstracted here), the theoretical calculation of carbon flow for discharging / using electricity to the grid is described. At the initial time 0, the power of the i-th electric vehicle is SOC1%. 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; at 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 power 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 car at the charging pile Internet access node at time t, in kW.

[0164] During driving and discharging, since electric vehicles inject power into the grid as equivalent units, 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 power consumption during charging and discharging and the power consumption during driving, and the unit is 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] 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] ② For 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 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 (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 this 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 now 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 power consumption - electric vehicle j to electric vehicle i as an example (the specific order is not limited, the order can be changed, and only the general principle is abstracted here), the theoretical calculation of 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 power to SOC3%. After the driving process is over, at the moment from t2 to t3, the i-th electric vehicle is supplied with power P 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 corresponding carbon flow of 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 injects power into the j-th electric vehicle as an equivalent unit, 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 i-th electric vehicle charging the j-th electric vehicle is tCO2 / h; The carbon flow rate corresponding to the bidirectional charging and discharging power loss and the electricity used during driving, in tCO2 / h; is the carbon flow rate corresponding to the j-th electric vehicle charging the i-th electric vehicle, in tCO2 / h; This is to take into account the sum of bidirectional charging and discharging power loss and driving power, and the unit is kW.

[0188] 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 carbon emissions in the current period are obtained by adding the first carbon emissions to the second carbon emissions, and then the carbon emission reduction in the current period is obtained based on the carbon emissions and fuel vehicle data. The carbon emissions during the charging process are dynamically monitored in the above manner, which ensures the accuracy of obtaining carbon emissions and thus improves the accuracy of obtaining carbon emission reduction.

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

[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:

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

[0199]

[0200] Where E ofc is the carbon emissions under the baseline; ofc is the carbon emission factor per kilometer of the fuel vehicle, 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 price of the carbon market during the trading cycle. Further considering traffic flow, an important factor affecting the charging strategy of electric vehicles, the cost of electric vehicle users' travel and the benefits of electric vehicles participating in the carbon market are used as two indicators. 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.

[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 only in this case to implement the solution of this application):

[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-connected node of the charging pile as the boundary.

[0205] Furthermore, the model of electric vehicle charging and bidirectional charging, charging loss, driving power consumption, discharging, and discharging 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 is the amount of electricity obtained by the i-th electric vehicle from the initial time 0 to time t of the power 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 charging power of the i-th electric car to 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 car charges the j-th electric car, 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 it is charged at the charging pile from time 0 to time t, in kW; E loss,ij,Bicharge P is the amount of electricity consumed by the i-th electric car charging the j-th electric car 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 the 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 is the amount of electricity discharged from the i-th electric vehicle to the charging pile network node from the initial time 0 to time t, in kWh; P 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 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; P ji,Bidischarge,t is the output power when the j-th electric car discharges to the ith electric car, in kW.

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

[0219] or

[0220] Among them, E loss,i,discharge is the power consumption of 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; P 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 electricity 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 loss power when the j-th electric vehicle discharges the i-th electric vehicle from the initial time 0 to the time t, in kW. Through the above formula, a charging and discharging power model considering the loss of electric vehicles can be established in two scenarios: bidirectional charging and discharging of electric vehicles and interaction between electric vehicles and the power 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 functions.

[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 yuan / min, F(t) is the time cost of electric vehicle travel and charging and discharging, unit 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 the electric vehicle, 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 the electric vehicle speed v k,j It is calculated that c1, c2, c3, c4 are the road section parameters under different road levels, k1 is the road section traffic load coefficient; I j is the free flow travel time of vehicles 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, 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, in minutes; SOC f and SOC s They are the state of charge when the electric vehicle user expects to complete charging and the state of charge when starting charging, in %; C battery is the battery capacity in MWh; P i,t , P ij,Bicharge,t are the power of the electric vehicle 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 the electric vehicle user, operator, transportation department, and power grid department, 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 operation status of the traffic 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 They 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 They are the charging and discharging amounts of the nth electric car at the sth charging station / electric car 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 in the charging station:

[0237] F(t)≤t n,max

[0238] t n,max is 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 the charging station and the driving route 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, in 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 for charging, the charging station that the electric vehicles go to should meet the capacity range of the regional transformer:

[0243]

[0244] Where 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 the transformer 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, the carbon emission factor of the node is calculated, and the stored electrical energy and stored carbon flow expressions are obtained.

[0250] The electric vehicle charging and discharging carbon emission model established in step S3 considers two scenarios: bidirectional interaction between electric vehicles and the power 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 node carbon potential at the charging pile 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 node carbon potential of electric vehicles at the charging pile node of the power 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 car at the charging pile Internet node at time t, in kW.

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

[0256]

[0257] in, is the node carbon potential of electric vehicles at the charging pile node of the power 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 It is the power loss of the i-th electric car 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 jointly determined by the previous time period.

[0259] Taking the process of charging-driving power consumption-supplying power to the grid as an example (the specific order is not limited, the order can be changed, and only the general principle is abstracted here), the theoretical calculation of carbon flow for discharging / using electricity to the grid is described. At the initial time 0, the power of the i-th electric vehicle is SOC1%. 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; at 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 power 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 car at the charging pile Internet access node at time t, in kW.

[0263] During driving and discharging, since electric vehicles inject power into the grid as equivalent units, 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 power consumption during charging and discharging and the power consumption during driving, and the unit is 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] 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] ② For 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 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 (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 this 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 vehicle charges the j-th electric vehicle. The carbon flow rate corresponding to this part of the loss is

[0279]

[0280] (3) When the jth electric vehicle discharges to the i-th electric vehicle, the j-th electric vehicle is now 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 power consumption - electric vehicle j to electric vehicle i as an example (the specific order is not limited, the order can be changed, and only the general principle is abstracted here), the theoretical calculation of 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 power to SOC3%. After the driving process is over, at the moment from t2 to t3, the i-th electric vehicle is supplied with power P 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 corresponding carbon flow of 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 injects power into the j-th electric vehicle as an equivalent unit, 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 i-th electric vehicle charging the j-th electric vehicle is tCO2 / h; The carbon flow rate corresponding to the bidirectional charging and discharging power loss and the electricity used during driving, in tCO2 / h; is the carbon flow rate corresponding to the j-th electric vehicle charging the i-th electric vehicle, in tCO2 / h; This is to take into account the sum of bidirectional charging and discharging power loss and driving power, and the unit is kW.

[0287] 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] Where E ofc is the carbon emissions under the baseline; ofc is the carbon emission factor per kilometer of the fuel vehicle, 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 reduction model of electric vehicles, there are the following constraints:

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

[0303]

[0304] where u i,t represents the start / stop status of the i-th unit at time t, u i,t =1 means enabled, u i,t =0 means shutdown; 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 vehicles, base load and spare capacity.

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

[0306] 0≤P ev，t≤N*P charge,max

[0307] Where 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 factor, which is taken as 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 benefit 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 optimized charging and discharging strategy for electric vehicle users under guiding constraints.

[0312] In step S5, the difference between the transportation cost and the carbon emission reduction benefit of electric vehicles is used as the objective function, and the optimization target model is established with the minimization of the transportation cost per unit charging amount and the maximization of the carbon emission reduction benefit of electric vehicles as the optimization target. 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 prior art, 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 the two indicators of the cost of electric vehicle users' travel and the benefits of electric vehicles' participation 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 involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0321] Based on the same inventive concept, the embodiment of the present application also provides a device for implementing the above-mentioned method of guiding the charging and discharging of electric vehicles based on traffic and carbon emissions. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in the embodiments of one or more methods and devices for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions provided below can be referred to the limitations of the methods for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions above, and will not be repeated here.

[0322] In an exemplary embodiment, Figure 4 As shown, a method and device 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] The data acquisition module 401 is used to acquire the fuel vehicle data of the road network-grid to be guided in any current period, the time constraints, resource constraints and carbon emission reduction constraints of the road network-grid to be guided in the current period, and the charging amount, time data, resource data and carbon data of the electric vehicle;

[0324] The first constraint construction module 402 is used to input the time data into a pre-constructed time cost acquisition model to obtain the time cost in the current period, and input the resource data into a pre-constructed 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-constructed carbon emission reduction acquisition model to obtain the carbon emission reduction in the current period, and obtain the corresponding carbon emission reduction benefits according to the carbon emission reduction;

[0326] The target data acquisition module 404 is used to 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 constraint, resource constraint and carbon emission reduction constraint;

[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 also used to acquire the 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; 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 passing through any section of the road. The first constraint construction module 402 is also 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 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.

[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; use the baseline carbon emissions to subtract the carbon emissions to obtain the carbon emission reduction.

[0337] Each module in the above-mentioned device for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[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, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, 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 the 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, 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 those shown in the figure, or combine certain components, or have a different arrangement of components.

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

[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, which, when executed by a processor, implements the method for guiding the charging and discharging of electric vehicles based on traffic and carbon emissions in the above embodiment.

[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 can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can 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 can include random access memory (RAM) or external cache memory, etc. As an 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 each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0345] The technical features of the above embodiments may be combined arbitrarily. 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 only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached 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, 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 capacity, time data, resource data and carbon data of electric vehicles; 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; Inputting the carbon data and the fuel vehicle data into a pre-built carbon emission reduction acquisition model to obtain the carbon emission reduction in the current period, and obtaining corresponding carbon emission reduction benefits according to the carbon emission reduction; Inputting the charging amount, the time cost, the resource cost and the carbon emission reduction benefit into a pre-constructed target cost data acquisition model to obtain a target charging amount, a target time cost, a target resource cost and a 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-grid to be guided.

2. The method according to claim 1, characterized in that The obtaining of the charging amount of the electric vehicle comprises: Acquiring charging data, driving power consumption data and discharging data of the electric vehicle in the current time period; Inputting the charging 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; Inputting the driving power consumption data into the power consumption acquisition module in the charging amount acquisition model, and obtaining the power consumption in the current time period through the power consumption acquisition module; Inputting the discharge data into a discharge power acquisition module in the charge capacity acquisition model, and obtaining a first discharge capacity in the current period through the discharge power acquisition module; The charge amount in the current time period is obtained according to the first charge amount, the power consumption and the first discharge amount.

3. The method according to claim 1, characterized in that 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 of any section of the road; The step of inputting the time data into a pre-built time cost acquisition model to obtain the time cost in the current period includes: Inputting the charging duration, charging waiting time, road section delay time and intersection delay time into a charging time cost acquisition module in the time cost acquisition model to obtain the charging time cost in the current time period; Inputting 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; Based on the charging time cost and the discharging time cost, the time cost in the current time period is obtained.

4. 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; The 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 period.

5. The method according to claim 1, characterized in that The carbon data includes the charging node carbon potential and the charging node loss carbon flow rate; The step of inputting 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 includes: Input 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 obtain the first carbon emission and the second carbon emission through the carbon emission acquisition module; the first carbon emission is used to characterize the carbon emission in 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 in the scenario of two-way charging of the electric vehicle; Obtaining the carbon emissions in the current period according to 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 period is obtained.

6. The method according to claim 5, characterized in that The obtaining 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 emission acquisition module in the carbon emission reduction acquisition model, and obtaining the baseline carbon emissions in the current period through the baseline carbon emission acquisition module; The carbon emission reduction is obtained by subtracting the carbon emission amount from the baseline carbon emission amount.

7. 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 acquire 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 to acquire charging capacity, time data, resource data and carbon data of electric vehicles; A first constraint construction module is used to input the time data into a pre-constructed time cost acquisition model to obtain the time cost in the current period, and input the resource data into a pre-constructed resource cost acquisition model to obtain the resource cost in the current period; The second constraint construction module is used to input the carbon data and the fuel vehicle data into a pre-constructed carbon emission reduction acquisition model to obtain the carbon emission reduction in the current period, and obtain the corresponding carbon emission reduction benefits 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, a target time cost, a target resource cost, and a 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-grid to be guided according to the target charging amount, target time cost, target resource cost and target carbon emission reduction benefit.

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 6 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 6 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 6 are implemented.

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