Space-time multi-dimensional low-carbon demand response method, device and equipment for electric vehicle

Abstract

The invention relates to the technical field of low-carbon electric power, in particular to a space-time multi-dimensional low-carbon demand response method, device and equipment for an electric vehicle, and the method comprises the steps: obtaining a power flow distribution prediction result of an electric power system; predicting dynamic carbon emission factors of each node in the power system in each future time period according to the trend distribution prediction result, and identifying target time periods corresponding to dynamic carbon emission factors smaller than a low carbon threshold in the dynamic carbon emission factors of each node in each future time period; and obtaining a trip chain model of the electric vehicle, simulating the charging and swapping demands of the electric vehicle in each future time period according to the trip chain model, and guiding the electric vehicle with the charging and swapping demands to execute the charging and swapping action in the target time period. Therefore, the problem that the traditional low-carbon demand response mainly guides the user to adjust the electric power at different time periods according to the time-varying carbon emission factor, and the charging of the electric vehicle is difficult to meet the time and space flexibility is solved.

Description

Technical Field

[0001] The present application relates to the field of low-carbon power technology, and in particular to a temporal and spatial multi-dimensional low-carbon demand response method, device and equipment for electric vehicles. Background Art

[0002] Electricity and land transportation are the two major industries that generate carbon emissions worldwide, accounting for 38.5% and 17.9% of the total carbon emissions of all industries in 2022, respectively. With the rapid growth of electric vehicles, the collaboration between the power and transportation sectors to achieve carbon reduction has attracted widespread attention. Although electric vehicles do not directly generate carbon emissions, the electricity used for charging may come from fossil fuel power generation, thus generating indirect carbon emissions. In order to achieve the reduction of carbon emissions from electric vehicles, the problem of electricity-transportation collaborative optimization has been widely studied.

[0003] However, there are data barriers between the power and transportation sectors, and it is difficult to achieve good coordinated optimization between the two sectors in actual engineering. Therefore, the demand response guidance method can be adopted, with the power system providing guidance factors, and the charging carbon emission responsibility information is transmitted to the user side to achieve user-side response. Traditional low-carbon demand response mainly guides users to adjust the power consumption in different periods based on time-varying carbon emission factors, but electric vehicle charging has both time and space flexibility, which is difficult to meet with traditional low-carbon demand response. Summary of the invention

[0004] The present application provides a temporal and spatial multi-dimensional low-carbon demand response method, device and equipment for electric vehicles to solve the problem that traditional low-carbon demand response mainly guides users to adjust electricity consumption in different time periods based on time-varying carbon emission factors, which is difficult to meet the time and space flexibility of electric vehicle charging.

[0005] The first aspect of the present application provides a spatiotemporal multi-dimensional low-carbon demand response method for electric vehicles, comprising the following steps: obtaining the power flow distribution prediction results of the power system; predicting the dynamic carbon emission factors of each node in the power system in each future time period based on the power flow distribution prediction results, and identifying the target time period corresponding to the dynamic carbon emission factors of each node in each future time period that are less than the low-carbon threshold; obtaining a travel chain model of the electric vehicle, simulating the charging and swapping needs of the electric vehicle in each future time period according to the travel chain model, and guiding the electric vehicles with charging and swapping needs to perform charging and swapping actions in the target time period.

[0006] Optionally, the calculation formula for the power flow distribution prediction result is:

[0007] min C=c T P G Δt

[0008] Where C is the total system cost; c is the vector of the power generation cost per unit time of each generator set, c T is the transpose of vector c; P G is the vector of the output power of each generator set during the scheduling period; Δt is the scheduling time interval.

[0009] The power flow distribution prediction meets the upper and lower limit constraints of unit output and power flow safety constraints:

[0010]

[0011]

[0012] in, For the generator set collection, is a collection of transmission lines, and are the upper and lower limits of the output of the i-th unit, and are the upper and lower limits of the power flow of the lth line respectively.

[0013] Optionally, the dynamic carbon emission factor is calculated as:

[0014]

[0015] Among them, E N is the dynamic carbon emission factor of node N, reflecting the carbon emissions generated by the electricity consumed by the node; P N is the power injection vector of node N; P B is a reference power injection vector of node B, where node B is a reference node or a balancing node; is the vector P G The transpose of E G is the carbon emission factor vector of the generator set.

[0016] Optionally, the travel chain model includes an electric private car simulation model and an electric taxi simulation model. The charging and swapping demands of electric vehicles in each future time period are simulated according to the travel chain model, including: simulating the charging and swapping demands of electric private cars in each future time period according to the electric private car simulation model; simulating the charging and swapping demands of electric taxis in each future time period according to the electric taxi simulation model.

[0017] Optionally, the charging and swapping needs of electric private cars in various future time periods are simulated according to the electric private car simulation model, including: simulating the travel chain type, vehicle parameters, battery status lower limit, initial battery status and departure time of each electric private car, and selecting a target travel chain from the travel chain types during simulation; determining the probability distribution matrix corresponding to the target travel chain, and generating the starting point and end point of each trip according to the probability distribution matrix and the initial starting point of the electric car; selecting a target path according to the starting point and end point of each trip, completing each trip based on the target path, and obtaining the actual driving time of each trip; deciding whether the electric private car should go to a fast charging station for charging according to the vehicle parameters, battery status lower limit, initial battery status, departure time and actual driving time, and determining the stay time of the electric private car at the destination and the charging behavior during the stay according to the decision result.

[0018] Optionally, the charging and battery replacement needs of the electric taxi in each future time period are simulated according to the electric taxi simulation model, including: obtaining the initial battery status of the electric taxi at the initial location; identifying the required power of the electric taxi order, and updating the battery power and vehicle spatiotemporal information of the electric taxi according to the required power and the initial battery status; deciding whether to charge the electric taxi according to the updated battery power and vehicle spatiotemporal information, and determining the charging behavior of the electric taxi according to the rental type of the electric taxi and the decision result.

[0019] Optionally, electric vehicles with charging and battery swapping needs are guided to perform charging and battery swapping actions during a target time period, including: identifying slow charging needs, fast charging needs and battery swapping needs among the charging and battery swapping needs; if the charging and battery swapping needs are slow charging needs, guiding the electric vehicle to perform slow charging according to a slow charging demand response model, wherein the charging amount in a time period with a lower carbon emission factor is greater than the charging amount in a time period with a higher carbon emission factor; if the charging and battery swapping needs are fast charging needs and battery swapping needs, guiding the electric vehicle to perform fast charging and battery swapping according to a fast charging and battery swapping demand model, wherein the electric vehicle is guided to charge or swap batteries at a node with the lowest carbon emission factor along the way; guiding the charging behavior of the battery swap station according to the battery swap station operation demand response model, wherein the charging amount of the battery swap station in a time period with a lower carbon emission factor is greater than the charging amount in a time period with a higher carbon emission factor.

[0020] Optionally, the slow charging demand response model is:

[0021]

[0022] in, is the electricity price at time t; C carbon is the carbon emission cost per unit of electricity consumption at time t; is the dynamic carbon emission factor of node i at time t; is the slow charging power of node i at time t; and Needs to be satisfied The constraints are as follows: begin and T depart They are the charging start time and end time respectively; Needs to be satisfied The constraints are: is the number of electric vehicles that are slow-charged at node i at time t, The maximum slow charging power of a single vehicle;

[0023] The fast charging and battery replacement demand model is:

[0024]

[0025]

[0026] in, is the charging power of the electric vehicle numbered j at time t from its current location to node i for fast charging; is the time cost of the electric vehicle to travel from the current location to node i for fast charging; The time to start charging; The time to end charging;

[0027] The demand response model of battery swap station operation is:

[0028]

[0029]

[0030] in, is the number of fully charged batteries at node i at time t; is the number of fully charged batteries at node i at time t-1; is the number of batteries replaced at node i at time t-1; is the charging power of the battery energy storage system at node i at time t-1; Capa is the rated capacity of a single battery.

[0031] The second aspect of the present application provides a spatiotemporal multi-dimensional low-carbon demand response device for electric vehicles, including: an acquisition module for acquiring the power flow distribution prediction results of the power system; a prediction module for predicting the dynamic carbon emission factors of each node in the power system in each future time period based on the power flow distribution prediction results, and identifying the target time period corresponding to the dynamic carbon emission factors of each node in each future time period that are less than the low-carbon threshold; a guidance module for acquiring a travel chain model of the electric vehicle, simulating the charging and swapping needs of the electric vehicle in each future time period according to the travel chain model, and guiding the electric vehicle with charging and swapping needs to perform charging and swapping actions in the target time period.

[0032] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the spatiotemporal multi-dimensional low-carbon demand response method for an electric vehicle of the first aspect.

[0033] Therefore, this application includes the following beneficial effects:

[0034] The embodiment of the present application obtains the power system's power flow distribution prediction results, predicts the dynamic carbon emission factors of each node in the power system in each future time period, and identifies the target time period corresponding to the dynamic carbon emission factors of each node in each future time period that are less than the low-carbon threshold, and then obtains the electric vehicle's travel chain model, performs simulation to obtain the electric vehicle's charging and swapping needs in each future time period, and finally guides the electric vehicle with charging and swapping needs to perform charging and swapping actions in the target time period. The travel chain model comprehensively considers the time and space flexibility of electric vehicle charging and swapping, and uses the dynamic carbon emission factor as a guiding signal to achieve low-carbon demand response for electric vehicles. As a result, the problem that the traditional low-carbon demand response mainly guides users to adjust the power consumption in different time periods based on the time-varying carbon emission factor is solved, and it is difficult to meet the time and space flexibility of electric vehicle charging.

[0035] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0037] Figure 1 A flowchart of a spatiotemporal multi-dimensional low-carbon demand response method for an electric vehicle provided according to an embodiment of the present application;

[0038] Figure 2 A flowchart of a spatiotemporal multi-dimensional low-carbon demand response method for an electric vehicle according to an embodiment of the present application;

[0039] Figure 3 A flow chart of a simulation of electric vehicle charging and battery replacement demand according to an embodiment of the present application;

[0040] Figure 4 This is an example diagram of a spatiotemporal multi-dimensional low-carbon demand response device for an electric vehicle according to an embodiment of the present application;

[0041] Figure 5 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0042] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0043] The following describes the time-space multi-dimensional low-carbon demand response method, device and equipment of the electric vehicle of the embodiment of the present application with reference to the accompanying drawings. In view of the traditional low-carbon demand response mentioned in the above background technology, which mainly guides users to adjust the power consumption in different time periods according to the time-varying carbon emission factor, it is difficult to meet the problem that the charging of electric vehicles has both time and space flexibility, the present application provides a time-space multi-dimensional low-carbon demand response method for electric vehicles, in which the dynamic carbon emission factors of each node in the power system in each future time period are predicted by obtaining the power flow distribution prediction results of the power system, and the target time period corresponding to the dynamic carbon emission factor of each node in each future time period that is less than the low-carbon threshold is identified, and then the travel chain model of the electric vehicle is obtained, and the charging and replacement demand of the electric vehicle in each future time period is simulated, and finally the electric vehicle with charging and replacement demand is guided to perform the charging and replacement action in the target time period, and the travel chain model comprehensively considers the time and space flexibility of the charging and replacement of electric vehicles, and uses the dynamic carbon emission factor as a guiding signal to realize the low-carbon demand response of electric vehicles. Thus, the problem that the traditional low-carbon demand response mainly guides users to adjust the power consumption in different time periods according to the time-varying carbon emission factor is difficult to meet the problem that the charging of electric vehicles has both time and space flexibility is solved.

[0044] Specifically, Figure 1 A schematic flow chart of a method for responding to a low-carbon demand in multiple dimensions in space and time for an electric vehicle provided in an embodiment of the present application.

[0045] like Figure 1 As shown, the temporal and spatial multi-dimensional low-carbon demand response method of the electric vehicle includes the following steps:

[0046] In step S101, a power flow distribution prediction result of the power system is obtained.

[0047] The calculation method of the power flow distribution prediction result will be described in detail below and will not be repeated here.

[0048] It is understandable that the embodiment of the present application can obtain the power flow distribution prediction result of the power system by calculation, and the calculation formula of the power flow distribution prediction result is:

[0049] min C=c T P G Δt

[0050] Where C is the total system cost; c is the vector of the power generation cost per unit time of each generator set, c T is the transpose of vector c; P G is the vector of the output power of each generator set in the dispatching cycle; Δt is the dispatching time interval. The power flow distribution prediction satisfies the upper and lower limit constraints of the unit output and the power flow safety constraints:

[0051]

[0052]

[0053] in, For the generator set collection, is a collection of transmission lines, and are the upper and lower limits of the output of the i-th unit, and are the upper and lower limits of the power flow of the lth line respectively.

[0054] It can be understood that the embodiments of the present application can calculate the flow distribution prediction result by the total system cost, the vector of the power generation cost of each generator set per unit time, the vector of the output power of each generator set in the scheduling cycle and the scheduling time interval, while satisfying the upper and lower limit constraints of the unit output and the flow safety constraints.

[0055] In step S102, the dynamic carbon emission factors of each node in the power system in each future time period are predicted based on the power flow distribution prediction results, and the target time period corresponding to the dynamic carbon emission factor of each node in each future time period that is less than the low carbon threshold is identified.

[0056] Among them, the calculation method of the dynamic carbon emission factor will be described in detail below and will not be repeated here; the low-carbon threshold is a threshold set according to actual needs and is not specifically limited here.

[0057] It can be understood that the embodiment of the present application can predict the dynamic carbon emission factor of each node in each future time period based on the power system's power flow distribution prediction results, and determine these time periods as target periods with lower carbon emissions by identifying the time periods where these dynamic carbon emission factors are less than a preset low-carbon threshold.

[0058] In the embodiment of the present application, the calculation formula of the dynamic carbon emission factor is:

[0059]

[0060] Among them, E N is the dynamic carbon emission factor of node N, reflecting the carbon emissions generated by the electricity consumed by the node; P N is the power injection vector of node N; PB is a reference power injection vector of node B, where node B is a reference node or a balancing node; is the vector P G The transpose of E G is the carbon emission factor vector of the generator set.

[0061] It can be understood that the embodiment of the present application can calculate the dynamic carbon emission factor through the power injection vector of the node, the baseline power injection vector of the reference node, the vector of the output power of each generator set during the scheduling period, and the carbon emission factor vector of the generator set.

[0062] In step S103, a travel chain model of the electric vehicle is obtained, and the charging and swapping needs of the electric vehicle in each future time period are simulated according to the travel chain model, so as to guide the electric vehicle with charging and swapping needs to perform charging and swapping actions in the target time period.

[0063] Among them, the travel chain model type of electric vehicles will be described in detail below and will not be repeated here.

[0064] It can be understood that the embodiments of the present application can obtain the travel chain model of the electric vehicle, simulate and predict the charging and swapping needs of the electric vehicle in each future time period, and based on the above prediction results, combined with the dynamic carbon emission factors of each node in the power system in each future time period, identify the target time period with lower carbon emissions, and guide the electric vehicles with charging and swapping needs to perform charging and swapping actions in these target time periods.

[0065] In an embodiment of the present application, the travel chain model includes an electric private car simulation model and an electric taxi simulation model. The travel chain model is used to simulate the charging and swapping needs of electric vehicles in each future time period, including: simulating the charging and swapping needs of electric private cars in each future time period according to the electric private car simulation model; simulating the charging and swapping needs of electric taxis in each future time period according to the electric taxi simulation model.

[0066] Among them, the electric private car simulation model is a model obtained by modeling the travel behavior of electric private cars; the electric taxi simulation model is a model obtained by modeling the operation behavior of electric taxis.

[0067] It can be understood that the travel chain model of the embodiment of the present application includes an electric private car simulation model and an electric taxi simulation model. Through the electric private car simulation model and the electric taxi simulation model, the charging and swapping needs of electric private cars and electric taxis in various future time periods can be simulated and predicted respectively.

[0068] In an embodiment of the present application, the charging and swapping needs of electric private cars in various future time periods are simulated according to an electric private car simulation model, including: simulating the travel chain type, vehicle parameters, battery status lower limit, initial battery status and departure time of each electric private car, and selecting a target travel chain from the travel chain types during simulation; determining a probability distribution matrix corresponding to the target travel chain, and generating a starting point and an end point for each trip according to the probability distribution matrix and the initial starting point of the electric car; selecting a target path according to the starting point and the end point of each trip, completing each trip based on the target path, and obtaining the actual driving time of each trip; deciding whether the electric private car should go to a fast charging station for charging according to the vehicle parameters, battery status lower limit, initial battery status, departure time and actual driving time, and determining the stay time of the electric private car at the destination and the charging behavior during the stay according to the decision result.

[0069] Among them, the vehicle parameters include the battery capacity, maximum charging power, energy consumption rate and other technical parameters of the electric private car, which can be obtained according to the vehicle model; the probability distribution matrix describes the selection probability of the electric private car in the target travel chain.

[0070] It can be understood that the embodiment of the present application uses an electric private car simulation model to first simulate the travel chain type, vehicle parameters, battery status lower limit, initial battery status and departure time of each electric private car. During the simulation process, a target travel chain is selected from the travel chain types, and the starting point and end point of each trip are generated according to the probability distribution matrix corresponding to the target travel chain and the initial starting point of the electric car. The target path is selected according to the starting point and end point of each trip, and each trip is completed based on the target path to obtain the actual driving time of each trip. Finally, based on the vehicle parameters, battery status lower limit, initial battery status, departure time and actual driving time, a decision is made as to whether the electric private car needs to go to a fast charging station for charging. According to the decision result, the stay time of the electric private car at the destination and the charging behavior during the stay are determined.

[0071] In an embodiment of the present application, the charging and battery replacement needs of the electric taxi in each future time period are simulated according to the electric taxi simulation model, including: obtaining the initial battery status of the electric taxi at the initial location; identifying the required power of the electric taxi order, and updating the battery power and vehicle spatiotemporal information of the electric taxi according to the required power and the initial battery status; deciding whether to charge the electric taxi according to the updated battery power and vehicle spatiotemporal information, and determining the charging behavior of the electric taxi according to the rental type of the electric taxi and the decision result.

[0072] Among them, the battery power and vehicle spatiotemporal information are the current battery power, location, time and other information of the electric taxi.

[0073] It can be understood that the embodiments of the present application can first obtain the initial battery status of the electric taxi at the initial location through the electric taxi simulation model, and at the same time obtain the required power of the received order, update the battery power and vehicle spatiotemporal information of the electric taxi according to the required power and the initial battery status, and decide whether the electric taxi needs to be charged according to the updated battery power and vehicle spatiotemporal information, and finally determine the charging behavior of the electric taxi according to the rental type of the electric taxi and the decision result, including whether to charge, the charging location and the charging time.

[0074] In the present embodiment, electric vehicles with charging and battery swapping needs are guided to perform charging and battery swapping actions in a target time period, including: identifying slow charging needs, fast charging needs and battery swapping needs in the charging and battery swapping needs; if the charging and battery swapping needs are slow charging needs, guiding the electric vehicle to perform slow charging behavior according to a slow charging demand response model, wherein the charging amount in a time period with a lower carbon emission factor is greater than the charging amount in a time period with a higher carbon emission factor; if the charging and battery swapping needs are fast charging needs and battery swapping needs, guiding the electric vehicle to perform fast charging and battery swapping behavior according to a fast charging and battery swapping demand model, wherein the electric vehicle is guided to charge or swap batteries at a node with the lowest carbon emission factor along the way; guiding the charging behavior of the battery swap station according to the battery swap station operation demand response model, wherein the charging amount of the battery swap station in a time period with a lower carbon emission factor is greater than the charging amount in a time period with a higher carbon emission factor.

[0075] Among them, slow charging demand refers to the demand for electric vehicles to charge in a longer period of time, usually at night or when parked for a long time; fast charging demand refers to the demand for electric vehicles to charge quickly in a shorter period of time, usually on the way or in emergency situations; battery swapping demand refers to the demand for electric vehicles to replenish energy by replacing batteries in a short period of time, usually at special battery swapping stations; the slow charging demand response model is used to guide electric vehicles to charge more during periods with lower carbon emission factors and reduce charging during periods with higher carbon emission factors; the fast charging and battery swapping demand model is used to guide electric vehicles to charge or swap batteries at nodes with the lowest carbon emission factors along the way to reduce carbon emissions; the battery swapping station operation demand response model is used to guide battery swapping stations to charge more during periods with lower carbon emission factors and reduce charging during periods with higher carbon emission factors.

[0076] It can be understood that the embodiments of the present application can identify whether the charging and battery replacement demand of an electric vehicle is a slow charging demand, a fast charging demand or a battery replacement demand. For the slow charging demand, the electric vehicle is guided to charge more during periods with lower carbon emission factors and reduce charging during periods with higher carbon emission factors according to the slow charging demand response model; for the fast charging demand and battery replacement demand, the electric vehicle is guided to charge or replace batteries at the node with the lowest carbon emission factor along the way according to the fast charging and battery replacement demand model; in addition, when charging at the battery replacement station, the battery replacement station is guided to charge more during periods with lower carbon emission factors and reduce charging during periods with higher carbon emission factors according to the battery replacement station operation demand response model. This method helps to optimize the charging and battery replacement behavior of electric vehicles and reduce overall carbon emissions.

[0077] In the embodiment of the present application, the slow charging demand response model is:

[0078]

[0079] in, is the electricity price at time t; C carbon is the carbon emission cost per unit of electricity consumption at time t; is the dynamic carbon emission factor of node i at time t; is the slow charging power of node i at time t; and Needs to be satisfied The constraints are as follows: begin and T depart They are the charging start time and end time respectively; Needs to be satisfied The constraints are: is the number of electric vehicles that are slow-charged at node i at time t, The maximum slow charging power of a single vehicle;

[0080] The fast charging and battery replacement demand model is:

[0081]

[0082]

[0083] in, is the charging power of the electric vehicle numbered j at time t from its current location to node i for fast charging; is the time cost of the electric vehicle to travel from the current location to node i for fast charging; The time to start charging; The time to end charging;

[0084] The demand response model of battery swap station operation is:

[0085]

[0086]

[0087] in, is the number of fully charged batteries at node i at time t; is the number of fully charged batteries at node i at time t-1; is the number of batteries replaced at node i at time t-1; is the charging power of the battery energy storage system at node i at time t-1; Capa is the rated capacity of a single battery.

[0088] Among them, in the embodiment of the present application, for the slow charging demand response model and the battery swap station operation demand response model, the model of electric private cars and electric taxis is the same, and for the fast charging demand model, it is divided into the following two types according to electric private cars and electric taxis:

[0089] The electric private car numbered j moves from its current position l j The sum of the fast charging loads transferred to all nodes along the way is equal to the charging demand obtained by the original simulation: The average charging power is

[0090] For the electric taxi numbered j, its charging demand can only be transferred to a station that is cheaper than the nearest fast charging station. The cost mainly includes time cost, charging electricity price and carbon emission cost of electricity consumption. Among them, C t is the unit time cost of a taxi going to other nodes for fast charging, Indicates that from the current position l j The time cost of going to node i, and Respectively represent nodes i and l j The electricity cost of charging and the carbon emission cost of charging.

[0091] For the battery swap demand model, the choice of battery swap stations by electric vehicle users is similar to the choice of fast charging stations. The model assumes that users will go to the battery swap station with the lowest carbon emissions and a total cost not higher than the nearest station. The model is specifically expressed as follows: s..t.iNlj,i,tD=1; in, is a non-negative variable, indicating the distance of electric car j from its current position l at time t. j The battery replacement demand transferred to node i, The battery replacement demand of electric vehicle j can only be transferred to one node. The total battery replacement cost is constrained to be no higher than the cost of replacing batteries at the nearest battery replacement station.

[0092] It can be understood that the slow charging demand response model, fast charging and battery replacement demand model and battery replacement station operation demand response model of the embodiment of the present application can be established by the above-mentioned calculation method. For the slow charging demand response model and the battery replacement station operation demand response model, the models of electric private cars and electric taxis are the same; for the fast charging and battery replacement demand model, the model is obtained according to the above-mentioned classification calculation.

[0093] According to the spatiotemporal multi-dimensional low-carbon demand response method for electric vehicles proposed in the embodiment of the present application, by obtaining the power system's power flow distribution prediction results, predicting the dynamic carbon emission factors of each node in the power system in each future time period, and identifying the target time period corresponding to the dynamic carbon emission factors of each node in each future time period that are less than the low-carbon threshold, then obtaining the electric vehicle's travel chain model, performing simulation to obtain the electric vehicle's charging and swapping needs in each future time period, and finally guiding the electric vehicles with charging and swapping needs to perform charging and swapping actions in the target time period. The travel chain model comprehensively considers the time and space flexibility of electric vehicle charging and swapping, and uses the dynamic carbon emission factor as a guiding signal to achieve low-carbon demand response for electric vehicles.

[0094] The following is a further description of the temporal and spatial multi-dimensional low-carbon demand response method for electric vehicles through a specific embodiment. Figure 2 As shown, this embodiment includes the following steps:

[0095] Step S201: Simulation of future operation of power system

[0096] The economic dispatch results are obtained based on the predicted values ​​of load and output of new energy units. The economic dispatch model aims to minimize the total system cost:

[0097] min C=c T P G Δt

[0098] The model needs to meet the upper and lower limits of unit output and power flow safety constraints:

[0099]

[0100]

[0101] in, For the generator set collection, is a collection of transmission lines, and are the upper and lower limits of the output of the i-th unit, and are the upper and lower limits of the power flow of the lth line respectively.

[0102] According to the economic dispatch model, the output of each unit and the line flow are obtained.

[0103] Step S202: Calculate the dynamic carbon emission factor based on the carbon emission flow calculation node

[0104] According to the future power flow distribution results of the system, the dynamic carbon emission factor of each node is calculated using the carbon emission flow theory. The specific calculation formula is as follows:

[0105]

[0106] Step S203: Simulation of electric vehicle charging and replacement demand based on travel chain model

[0107] In this step, the simulation of electric vehicle charging and swapping demand based on the travel chain model is divided into three parts: electric private car simulation, electric taxi simulation and electric vehicle swapping demand simulation. Among them, the simulation of electric private car charging demand is divided into 6 steps, as follows:

[0108] 1. Generate the type of travel chain, vehicle parameters, lower limit of electric vehicle battery status, initial battery status of electric vehicle and departure time. During simulation, a travel chain is selected for each electric vehicle according to the probability distribution. Vehicle parameters include parameters such as battery capacity and driving power consumption of electric vehicles. The lower limit of battery status means that when the battery power of the electric vehicle is lower than this value, the user will charge it.

[0109] 2. Generate the starting point and end point of each trip. Since the initial starting point of each day is set as the place of residence, this paper generates it based on the population distribution. After obtaining the starting point of the trip, the destination of each trip is determined according to the probability distribution matrix. Different types of trip chains correspond to different probability distribution matrices. For the kth trip chain, if the starting point of a trip is i, then the probability that its destination is j is.

[0110] 3. Complete this trip. For each trip, it is assumed that the driver will choose the shortest route. Therefore, the Dijkstra algorithm can be used to find the shortest route based on the starting point, destination and road network topology. The degree of road congestion varies at different travel times, so it is necessary to determine the average speed of electric vehicles based on this, and then calculate the total travel time based on the average speed, as shown. Where is the average speed of the electric vehicle at time t, is the length of each road segment in the trip, is the set of roads found by the Dijkstra algorithm, and is the total travel time.

[0111] 4. Decide whether to go to a fast charging station for charging. If the battery status of the electric vehicle is not enough to complete the trip, the user goes to the nearest fast charging station for charging, and the electric vehicle is considered to be charged until the battery is full. For the electric vehicle numbered j, its fast charging capacity and charging time are as shown. Where is the battery status of the electric vehicle numbered j at time t, is the battery capacity of the electric vehicle numbered j, and is the charging power of the fast charging station.

[0112] 5. Decide on the dwell time at the destination and the charging behavior during the dwell time. After the electric vehicle arrives at the destination, the dwell time is generated according to the type of destination. If the battery power is below the minimum limit, the electric vehicle will be charged at the stop. If the dwell time is long enough, the electric vehicle will be charged until the battery is fully charged; otherwise, the charging amount is as shown. Among them, is the slow charging amount, and is the slow charging power.

[0113] 6. Repeat steps 2 to 5 to simulate the remaining trips.

[0114] The simulation of electric taxi charging demand is divided into five steps, as follows:

[0115] 1. Generate the initial location. Unlike private cars, the initial battery level of a taxi is set to be fully charged, and a fixed departure time is set.

[0116] 2. Generate an order. Taking into account the time, population distribution, and distance, a probability distribution matrix is ​​obtained, and the start and end points of the order are generated from it. If the battery power of the electric vehicle is not enough to complete the order, the driver chooses to skip the order and go to a nearby fast charging station to charge. If the power is sufficient, the driving route and the corresponding driving time are calculated, and the battery power and vehicle time and space information are updated.

[0117] 3. Decision on whether to charge. After completing the order, if the battery level is below the lower limit or the driver decides to take a break such as eating, the electric taxi will go to a nearby fast charging station to charge until it is fully charged.

[0118] 4. Determine whether to end the trip for the day. For taxis that change shifts, if the time is close to the handover time after completing an order, the last leg of the trip is from the current location to the handover location, and the battery is fully charged before the handover. For taxis that do not change shifts, set a fixed off-duty time, and after get off work, the driver returns to the place where he lives for slow charging.

[0119] 5. Repeat steps 2 to 4 to complete the remaining simulations.

[0120] The generation of demand for electric vehicle battery replacement is similar to the generation of fast charging demand. The battery replacement station starts charging immediately after receiving the battery replaced by the electric vehicle.

[0121] Step S204: guiding electric vehicle demand response

[0122] 1. Modeling of slow charging demand response for electric vehicle users:

[0123] For slow charging of electric vehicles, the charging power can be adjusted under the premise of ensuring that the charging demand is met when leaving, so slow charging has time flexibility. This paper assumes that electric vehicle users only need to decide whether to participate in demand response and hand over the charging power adjustment authority to the charging station. The corresponding objective function of slow charging demand can be obtained. Among them, C carbon and Represent the carbon emission cost and electricity price per unit of electricity at time t, respectively, and is the slow charging power of node i at time t.

[0124]

[0125] Before the electric vehicle leaves, the battery should be fully charged. This constraint can be expressed as follows. begin and T depart They are the charging start time and end time respectively.

[0126]

[0127] The charging power constraint can be expressed as follows. is the number of electric vehicles that are slow-charged at node i at time t. It is the maximum slow charging power of a single vehicle.

[0128]

[0129] 2. Modeling of fast charging and battery replacement demand response for electric vehicle users

[0130] For fast charging of electric vehicles, users can flexibly choose fast charging stations for fast charging, so this type of charging has spatial flexibility. This paper believes that electric private car users will choose the cheapest fast charging station along the way; electric taxi users will choose a fast charging station by comprehensively considering the time cost and charging cost. From this, the objective functions for a single electric private car and taxi to choose a fast charging station can be obtained, which are the following two equations. Among them, is the charging power of the electric car numbered j at time t from its current location to node i for fast charging, is the time cost of a taxi traveling from its current location to node i for fast charging.

[0131]

[0132]

[0133] For the electric private car numbered j, it moves from the current position l j The sum of the fast charging loads transferred to all nodes along the way is equal to the charging demand obtained from the original simulation:

[0134]

[0135] The average charging power is:

[0136]

[0137] For electric taxi numbered j, its charging demand can only be transferred to a station that is cheaper than the nearest fast charging station. The cost mainly includes time cost, charging electricity price and carbon emission cost of electricity consumption. t is the unit time cost of a taxi going to other nodes for fast charging, Indicates that from the current position l j The time cost of going to node i, and Respectively represent nodes i and l j The electricity cost of charging and the carbon emission cost of charging.

[0138]

[0139] The choice of battery swap stations by electric vehicle users is similar to the choice of fast charging stations. The model assumes that users will go to the battery swap station with the lowest carbon emissions and a total cost not higher than the nearest station. The model is specifically expressed in the following three formulas. is a non-negative variable, indicating the distance of electric car j from its current position l at time t. j The battery swap demand transferred to node i. The second formula below constrains that the battery swap demand of electric vehicle j can only be transferred to one node. The third formula below constrains that the total battery swap cost should not be higher than the cost of swapping at the nearest battery swap station.

[0140]

[0141]

[0142]

[0143] 3. Modeling of demand response for battery swap station operation

[0144] When the battery swap station is in operation, the charging power can be flexibly adjusted according to the carbon emission factor, so it has time flexibility. The following formula shows that within the scope of service capacity, the battery swap station needs to meet the battery swap needs of electric vehicle users. Among them, and They respectively represent the battery swap demand and actual battery swap amount at the battery swap station at node i at time t. It is the total amount of batteries stored in the battery swap station.

[0145]

[0146]

[0147] The following formula constrains the number of fully charged batteries at time t to be no less than the number of batteries replaced. is the number of fully charged batteries at the battery swap station at node i at time t

[0148]

[0149] The following formula indicates that the number of fully charged batteries at the current moment is equal to the number of fully charged batteries at the previous moment minus the number of batteries replaced, plus the number of fully charged batteries at the previous moment.

[0150]

[0151]

[0152] The following formula constrains that the total charging power of the battery swap station cannot exceed the product of the number of uncharged batteries and the maximum charging power of a single battery.

[0153]

[0154] Step S205: Evaluate carbon emissions of electric vehicles and carbon reduction benefits of demand response

[0155] Using the above model, the carbon emissions from charging electric vehicles and the carbon reduction effect after low carbon demand are evaluated.

[0156] like Figure 3 As shown in the figure, the simulation of electric vehicle charging and replacement demand is carried out according to the following process:

[0157] Step S301: Single vehicle simulation starts.

[0158] Step S302, select the vehicle type as an electric private car or an electric taxi, and determine the vehicle parameters, the lower limit of the electric vehicle SOC (State of Charge), and the initial battery state of the electric vehicle. The vehicle parameters include the battery capacity of the electric vehicle, the driving power consumption and other parameters. The lower limit of the battery state means that when the battery power of the electric vehicle is lower than this value, the user will charge it.

[0159] Step S303: Select the type of travel chain. During simulation, a travel chain is selected for each electric vehicle according to the probability distribution.

[0160] Step S304: determine the initial departure point and departure time of the trip.

[0161] Step S305: determine the destination of the first trip / the first order.

[0162] Step S306: Determine whether the SOC is sufficient and decide whether to go to a fast charging station for charging. If the battery status of the electric vehicle is not sufficient to complete the trip, proceed to step S306; if the battery status of the electric vehicle is sufficient to complete the trip, proceed to step S307.

[0163] Step S306: The user goes to the nearest fast charging station to charge the electric vehicle, and the electric vehicle is charged until the battery is fully charged, and the SOC information and time information are updated.

[0164] Step S307, find the shortest route. For each trip, it is assumed that the driver will choose the shortest route. Therefore, the Dijkstra algorithm can be used to find the shortest route based on the starting point, destination and road network topology. Calculate the journey time. The road congestion level is different at different travel times. Therefore, it is necessary to determine the average driving speed of the electric vehicle, and then calculate the total driving time based on the average speed, and update the SOC state at the same time.

[0165] Step S308: Arrive at the destination, decide the stay time / complete the order.

[0166] Step S309, determine whether to charge, if charging is required, proceed to step S310; if charging is not required, proceed to step S311.

[0167] Step S310: determine the charging mode, calculate the charging power and update the SOC.

[0168] Step S311, determine whether it is the last leg of the trip, if yes, end; if not, proceed to step S312.

[0169] Step S312: After the stop, determine the destination of the next trip / next order, and return to step S305.

[0170] Next, the spatiotemporal multi-dimensional low-carbon demand response device for an electric vehicle proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0171] Figure 4 It is a block diagram of a spatiotemporal multi-dimensional low-carbon demand response device for an electric vehicle according to an embodiment of the present application.

[0172] like Figure 4 As shown, the spatiotemporal multi-dimensional low-carbon demand response device 10 of the electric vehicle includes: an acquisition module 401 , a prediction module 402 and a guidance module 403 .

[0173] Among them, the acquisition module 401 is used to obtain the power flow distribution prediction results of the power system; the prediction module 402 is used to predict the dynamic carbon emission factors of each node in the power system in each future time period based on the power flow distribution prediction results, and identify the target time period corresponding to the dynamic carbon emission factors of each node in each future time period that are less than the low carbon threshold; the guidance module 403 is used to obtain the travel chain model of the electric vehicle, simulate the charging and swapping needs of the electric vehicle in each future time period according to the travel chain model, and guide the electric vehicles with charging and swapping needs to perform charging and swapping actions in the target time period.

[0174] In the embodiment of the present application, the calculation formula of the power flow distribution prediction result is:

[0175] min C=c T P G Δt

[0176] Where C is the total system cost; c is the vector of the power generation cost per unit time of each generator set, c T is the transpose of vector c; P G is the vector of the output power of each generator set in the dispatching cycle; Δt is the dispatching time interval. The power flow distribution prediction satisfies the upper and lower limit constraints of the unit output and the power flow safety constraints:

[0177]

[0178]

[0179] in, For the generator set collection, is a collection of transmission lines, and P Gi,max are the upper and lower limits of the output of the i-th unit, and are the upper and lower limits of the power flow of the lth line respectively.

[0180] In the embodiment of the present application, the calculation formula of the dynamic carbon emission factor is:

[0181]

[0182] Among them, E N is the dynamic carbon emission factor of node N, reflecting the carbon emissions generated by the electricity consumed by the node; P N is the power injection vector of node N, representing the power absorbed by the node from the grid; P B is the reference power injection vector of node B, which is a reference node or a balancing node and is used to compare the power changes of other nodes; is the vector P G The transpose of E Gis the carbon emission factor vector of the generator set.

[0183] In an embodiment of the present application, the guidance module 403 is further used for: the travel chain model includes an electric private car simulation model and an electric taxi simulation model, and the charging and swapping needs of electric vehicles in each future time period are simulated according to the travel chain model, including simulating the charging and swapping needs of electric private cars in each future time period according to the electric private car simulation model; simulating the charging and swapping needs of electric taxis in each future time period according to the electric taxi simulation model.

[0184] In an embodiment of the present application, the guidance module 403 is further used to: simulate the charging and swapping needs of the electric private car in each future time period according to the electric private car simulation model, including simulating the travel chain type, vehicle parameters, battery status lower limit, initial battery status and departure time of each electric private car, and selecting the target travel chain from the travel chain type during simulation; determine the probability distribution matrix corresponding to the target travel chain, and generate the starting point and end point of each trip according to the probability distribution matrix and the initial starting point of the electric car; select the target path according to the starting point and end point of each trip, complete each trip based on the target path, and obtain the actual driving time of each trip; decide whether the electric private car should go to the fast charging station for charging according to the vehicle parameters, battery status lower limit, initial battery status, departure time and actual driving time, and determine the stay time of the electric private car at the destination and the charging behavior during the stay according to the decision result.

[0185] In an embodiment of the present application, the guidance module 403 is further used to: simulate the charging and battery replacement needs of the electric taxi in each future time period according to the electric taxi simulation model, including obtaining the initial battery status of the electric taxi at the initial location; identify the required power of the electric taxi order, and update the battery power and vehicle spatiotemporal information of the electric taxi according to the required power and the initial battery status; decide whether to charge the electric taxi according to the updated battery power and vehicle spatiotemporal information, and determine the charging behavior of the electric taxi according to the rental type of the electric taxi and the decision result.

[0186] In an embodiment of the present application, the guidance module 403 is further used to: guide electric vehicles with charging and battery swapping needs to perform charging and battery swapping actions during the target time period, including identifying slow charging needs, fast charging needs and battery swapping needs in the charging and battery swapping needs; if the charging and battery swapping needs are slow charging needs, then guiding the electric vehicle's slow charging behavior according to the slow charging demand response model, wherein the charging amount in the time period with a lower carbon emission factor is greater than the charging amount in the time period with a higher carbon emission factor; if the charging and battery swapping needs are fast charging needs and battery swapping needs, then guiding the electric vehicle's fast charging and battery swapping behavior according to the fast charging and battery swapping demand model, wherein the electric vehicle is guided to charge or swap batteries at the node with the lowest carbon emission factor along the way; guiding the charging behavior of the battery swap station according to the battery swap station operation demand response model, wherein the charging amount of the battery swap station in the time period with a lower carbon emission factor is greater than the charging amount in the time period with a higher carbon emission factor.

[0187] In the embodiment of the present application, the slow charging demand response model is:

[0188]

[0189] in, is the electricity price at time t; C carbon is the carbon emission cost per unit of electricity consumption at time t; is the dynamic carbon emission factor of node i at time t; is the slow charging power of node i at time t; and Needs to be satisfied The constraints are as follows: begin and T depart They are the charging start time and end time respectively; Needs to be satisfied The constraints are: is the number of electric vehicles that are slow-charged at node i at time t, is the maximum slow charging power of a single vehicle; the fast charging and battery replacement demand model is:

[0190]

[0191]

[0192] in, is the charging power of the electric vehicle numbered j at time t from its current location to node i for fast charging; is the time cost of the electric vehicle to travel from the current location to node i for fast charging; The time to start charging; is the time to end charging; the demand response model of the battery swap station is

[0193]

[0194]

[0195] in, is the number of fully charged batteries at node i at time t; is the number of fully charged batteries at node i at time t-1; is the number of batteries replaced at node i at time t-1; is the charging power of the battery energy storage system at node i at time t-1; Capa is the rated capacity of a single battery.

[0196] It should be noted that the aforementioned explanation of the embodiment of the spatiotemporal multi-dimensional low-carbon demand response method for an electric vehicle is also applicable to the spatiotemporal multi-dimensional low-carbon demand response device for an electric vehicle of this embodiment, and will not be repeated here.

[0197] According to the spatiotemporal multi-dimensional low-carbon demand response device for electric vehicles proposed in the embodiment of the present application, through the coordinated action of the acquisition module, the prediction module and the guidance module, it is possible to obtain the power system's power flow distribution prediction results, predict the dynamic carbon emission factors of each node in the power system in each future time period, and identify the target time period corresponding to the dynamic carbon emission factors of each node in each future time period that are less than the low-carbon threshold. Then, the travel chain model of the electric vehicle is obtained, and simulation is performed to obtain the charging and swapping needs of the electric vehicle in each future time period. Finally, the electric vehicle with charging and swapping needs is guided to perform charging and swapping actions in the target time period. The travel chain model comprehensively considers the time and space flexibility of charging and swapping of electric vehicles, and uses the dynamic carbon emission factor as a guidance signal to achieve low-carbon demand response of electric vehicles.

[0198] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0199] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0200] When the processor 502 executes the program, the temporal and spatial multi-dimensional low-carbon demand response method for the electric vehicle provided in the above embodiment is implemented.

[0201] Furthermore, the electronic device also includes:

[0202] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0203] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0204] The memory 501 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0205] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0206] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0207] The processor 502 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0208] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0209] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0210] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0211] It should be understood that the various parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, the steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0212] A person of ordinary skill in the art may understand that all or part of the steps carried by the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the above-mentioned program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0213] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A temporal and spatial multi-dimensional low-carbon demand response method for electric vehicles, characterized in that: The following steps are involved: Obtain power flow distribution prediction results of the power system; Predicting the dynamic carbon emission factors of each node in the power system in each future time period according to the power flow distribution prediction result, and identifying the target time period corresponding to the dynamic carbon emission factors of each node in each future time period that are less than the low carbon threshold; A travel chain model of an electric vehicle is obtained, and the charging and swapping demands of the electric vehicle in each future time period are simulated according to the travel chain model, so as to guide the electric vehicle with the charging and swapping demands to perform the charging and swapping actions in the target time period.

2. The temporal and spatial multi-dimensional low-carbon demand response method for electric vehicles according to claim 1, characterized in that: The calculation formula of the power flow distribution prediction result is: min C=c T P G Δt Where C is the total system cost; c is the vector of the power generation cost per unit time of each generator set, c T For vector c The transpose of G is the vector of the output power of each generator set in the scheduling period; Δt is the scheduling time interval; The power flow distribution prediction meets the upper and lower limit constraints of unit output and power flow safety constraints: in, For the generator set collection, is a collection of transmission lines, and are the upper and lower limits of the output of the i-th unit, and are the upper and lower limits of the power flow of the first line respectively.

3. The temporal and spatial multi-dimensional low-carbon demand response method for electric vehicles according to claim 1, characterized in that: The calculation formula of the dynamic carbon emission factor is: Among them, E N is the dynamic carbon emission factor of node N, reflecting the carbon emissions generated by the electricity consumed by the node; P N is the power injection vector of node N; P B is a reference power injection vector of node B, where node B is a reference node or a balancing node; is the vector P G The transpose of E G is the carbon emission factor vector of the generator set.

4. The temporal and spatial multi-dimensional low-carbon demand response method for electric vehicles according to claim 1, characterized in that: The travel chain model includes an electric private car simulation model and an electric taxi simulation model. The simulation of the charging and replacement demand of the electric vehicle in each future time period according to the travel chain model includes: Simulating the charging and battery replacement requirements of the electric private car in each future time period according to the electric private car simulation model; The charging and battery replacement requirements of the electric taxi in various future time periods are simulated according to the electric taxi simulation model.

5. The temporal and spatial multi-dimensional low-carbon demand response method of electric vehicles according to claim 4, characterized in that: The simulating the charging and replacement requirements of the electric private car in each future time period according to the electric private car simulation model includes: Simulate the travel chain type, vehicle parameters, battery status lower limit, initial battery status and departure time of each electric private car, and select a target travel chain from the travel chain types during simulation; Determine a probability distribution matrix corresponding to the target travel chain, and generate a starting point and an end point of each travel segment according to the probability distribution matrix and an initial starting point of the electric vehicle; Selecting a target path according to the starting point and the end point of each trip, completing each trip based on the target path, and obtaining the actual travel time of each trip; Whether the electric private car needs to go to a fast charging station for charging is determined based on the vehicle parameters, the lower limit of the battery status, the initial battery status, the departure time and the actual driving time, and the stay time of the electric private car at the destination and the charging behavior during the stay are determined based on the decision result.

6. The temporal and spatial multi-dimensional low-carbon demand response method of electric vehicles according to claim 4, characterized in that: The simulating the charging and battery replacement requirements of the electric taxi in each future time period according to the electric taxi simulation model includes: Obtaining the initial battery status of the electric taxi at the initial location; Identify the power demand of the order of the electric taxi, and update the battery power and vehicle time-space information of the electric taxi according to the power demand and the initial battery state; The electric taxi decides whether to charge according to the updated battery power and vehicle spatiotemporal information, and the charging behavior of the electric taxi is determined according to the rental type of the electric taxi and the decision result.

7. The temporal and spatial multi-dimensional low-carbon demand response method for electric vehicles according to claim 1, characterized in that: The step of guiding the electric vehicle having the charging and swapping demand to perform the charging and swapping action during the target time period includes: Identify the slow charging demand, fast charging demand and battery replacement demand among the charging and battery replacement demands; If the charging and battery replacement demand is a slow charging demand, the slow charging behavior of the electric vehicle is guided according to the slow charging demand response model, wherein the charging amount during the period with a lower carbon emission factor is greater than the charging amount during the period with a higher carbon emission factor; If the charging and battery replacement demand is a fast charging demand and a battery replacement demand, the fast charging and battery replacement behaviors of the electric vehicle are guided according to the fast charging and battery replacement demand model, wherein the electric vehicle is guided to charge or replace the battery at the node with the lowest carbon emission factor along the way; The charging behavior of the battery swap station is guided according to the battery swap station operation demand response model, wherein the charging amount of the battery swap station during the period with lower carbon emission factors is greater than that during the period with higher carbon emission factors.

8. The temporal and spatial multi-dimensional low-carbon demand response method of electric vehicles according to claim 7, characterized in that: The slow charging demand response model is: in, is the electricity price at time t; C carbon is the carbon emission cost per unit of electricity consumption at time t; is the dynamic carbon emission factor of node i at time t; is the slow charging power of node i at time t; and Needs to be satisfied The constraints are as follows: begin and T depart They are the charging start time and end time respectively; Needs to be satisfied The constraints are: is the number of electric vehicles that are slow-charged at node i at time t, The maximum slow charging power of a single vehicle; The fast charging and battery replacement demand model is: in, is the charging power of the electric vehicle numbered j at time t from its current location to node i for fast charging; is the time cost of the electric vehicle to travel from the current location to node i for fast charging; The time to start charging; The time to end charging; The demand response model of the battery swap station operation is: in, is the number of fully charged batteries at node i at time t; is the number of fully charged batteries at node i at time t-1; is the number of batteries replaced at node i at time t-1; is the charging power of the battery energy storage system at node i at time t-1; Capa is the rated capacity of a single battery.

9. A temporal and spatial multi-dimensional low-carbon demand response device for electric vehicles, characterized in that: include: An acquisition module is used to obtain the power flow distribution prediction results of the power system; A prediction module, used to predict the dynamic carbon emission factors of each node in the power system in each future time period according to the power flow distribution prediction result, and identify the target time period corresponding to the dynamic carbon emission factors of each node in each future time period that are less than the low carbon threshold; The guidance module is used to obtain a travel chain model of an electric vehicle, simulate the charging and swapping needs of the electric vehicle in each future time period according to the travel chain model, and guide the electric vehicle with the charging and swapping needs to perform charging and swapping actions in the target time period.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the spatiotemporal multi-dimensional low-carbon demand response method for an electric vehicle as described in any one of claims 1 to 8.

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