Electric Vehicle Charging and Discharging Load Prediction Method Based on Time-Space-Electricity Chain

Abstract

The present invention discloses a method for predicting the charging and discharging load of electric vehicles based on the time-space-electricity chain, which includes the following steps: simulating the driving energy consumption, charging and discharging behaviors, and travel behaviors of electric vehicles in a region, and performing vehicle time-space-electricity chain simulation according to the driving energy consumption, charging and discharging behaviors, and travel behaviors to correlate the changes in vehicle time and space with electricity; establishing a topological model of the traffic road network based on the region; using the topological model as the input, adopting Monte Carlo to simulate the electric vehicle time-space-electricity chain, and outputting the predicted value of the charging and discharging load of electric vehicles in the region. The beneficial effects of the present invention are: by analyzing the prediction results of the charging and discharging loads of electric vehicles in each functional region and node of the city under different scenarios and their spatio-temporal distribution, the problem of unclear distribution of vehicle storage load resources in the region is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging and discharging, and particularly relates to a method for predicting electric vehicle charging and discharging load based on a time-space-electricity chain. Background Art

[0002] With the progress and development of technology, the emissions of greenhouse gases in various countries around the world have increased year by year, and the resulting climate change and environmental threats have become global problems and challenges that humanity needs to face. As an important green means of transportation for reducing carbon emissions, electric vehicles have been widely used in recent years. With the rapid development of vehicle-to-grid (V2G) technology, electric vehicles have the characteristics of mobile and flexible energy storage resources, and can be connected to the grid to participate in peak shaving, frequency modulation, and consumption of new energy and other auxiliary services, and have broad application prospects in stabilizing the safe operation of the power grid and energy conservation and emission reduction.

[0003] With the increase in the penetration rate of electric vehicles, the large-scale connection of electric vehicles to the grid will have a significant impact on the safety and stability of the power grid operation. Affected by factors such as user travel behavior and the availability of V2G service facilities, the electric vehicle load resources show randomness in space-time distribution. Therefore, it is necessary to conduct short-term prediction of the electric vehicle charging and discharging load in the region to accurately grasp the volume and space-time distribution change law of the electric vehicle load resources.

[0004] The prediction of electric vehicle charging and discharging load is to reduce the impact of unclear electric vehicle energy storage resources on the power grid, and to provide methods and technical support for formulating control strategies for charging and discharging resources. Current technologies generally use intelligent multi-agent technology, fuzzy inference methods, etc., only considering the energy storage and load characteristics of electric vehicles, and ignoring the randomness of traffic road network factors during driving. In terms of travel simulation, the impact of traffic road network and user decision-making factors on the travel and charging and discharging characteristics of electric vehicles is ignored. Summary of the Invention

[0005] In view of the above problems, the present invention proposes a method for predicting electric vehicle charging and discharging load based on a time-space-electricity chain, mainly solving the problems in the background art.

[0006] To solve the above technical problems, the technical solution of the present invention is as follows:

[0007] A method for predicting electric vehicle charging and discharging load based on a time-space-electricity chain, comprising the following steps:

[0008] Simulate the driving energy consumption, charging and discharging behavior, and travel behavior of electric vehicles in a selected area, and perform vehicle time-space-electricity chain simulation according to the driving energy consumption, the charging and discharging behavior, and the travel behavior, and associate the vehicle space-time and electricity changes;

[0009] Build a topological model of the transportation road network based on the said area;

[0010] Taking the said topological model of the transportation road network as the input, adopt Monte Carlo to simulate the time-space-electricity chain of the electric vehicle, and output the predicted value of the charging and discharging load of the electric vehicle within the said area.

[0011] In some embodiments, the simulation conditions of the driving energy consumption include environmental temperature and road grade:

[0012] Under the condition of environmental temperature, simulate and calculate the charging and discharging efficiency of the electric vehicle;

[0013] Classify urban roads according to a predetermined rule, and considering the road conditions of urban roads, simulate and calculate the power consumption per unit mileage of each grade of urban roads;

[0014] According to the environmental temperature and the road conditions of urban roads, simulate and calculate the power consumption per unit mileage of the electric vehicle, and then infer the driving energy consumption of the electric vehicle from the starting point to the destination.

[0015] In some embodiments, the calculation method of the driving energy consumption is:

[0016]

[0017] where s γ-1，γ is the driving energy consumption, d γ-1，γ is the driving mileage, E la is the power consumption per unit mileage of the electric vehicle, η is the charging and discharging efficiency, and B is the battery capacity of the electric vehicle.

[0018] In some embodiments, the simulation process of the charging and discharging behavior includes: based on the charging and discharging model of the electric vehicle based on the hysteresis theory, perform the charging and discharging simulation of the electric vehicle, and output the average charging power value and the average charging curve function of the electric vehicle.

[0019] In some embodiments, judge the charging and discharging order of the electric vehicle according to the arriving electricity of the electric vehicle, and perform the charging and discharging operation:

[0020] If the electricity of the electric vehicle is higher than the guaranteed electricity when it arrives, the electric vehicle executes the strategy of first performing the charging operation and then the discharging operation. The average charging power value and the average charging curve function of the electric vehicle are respectively expressed as:

[0021]

[0022]

[0023] If the power of the electric vehicle is higher than the expected power when it arrives, implement the strategy that the electric vehicle first discharges and then charges. The average charging power and average charging curve of the electric vehicle are respectively expressed as:

[0024]

[0025]

[0026] Among them, is the average charging power of the electric vehicle, S e_γ is the expected off-grid power of the electric vehicle, S a_γ is the power of the electric vehicle when it arrives at the destination γ, B is the battery capacity of the electric vehicle, η c is the charging efficiency of the electric vehicle, t p_γ is the parking time of the electric vehicle at the destination γ, is the average charging curve function of the electric vehicle, t is the time variable, T a_γ is the time when the electric vehicle arrives at the destination γ, δ is the allowable range of charge and discharge error set, T e_γ is the expected off-grid power of the electric vehicle.

[0027] In some embodiments, the simulation process of the travel behavior includes simulating the travel of the electric vehicle with the travel probability function of the electric vehicle. The number of user trips follows a normal distribution. Select the travel situations of traveling 2 to 4 times from the number of user trips. Each travel situation contains at most 4 travel destinations, and the travel time of the first travel situation follows a normal distribution. The parking time of the electric vehicle follows a Gumbel distribution.

[0028] The association of the time-space-power chain is:

[0029]

[0030] Among them, B is the battery capacity of the electric vehicle, T a_γ is the time when the vehicle arrives at the destination γ, T s_γ is the time when the vehicle leaves the destination γ, O γ represents the destination γ, S a_γ is the power when the vehicle arrives, S s_γ is the off-grid power of the vehicle, N EV is the total number of electric vehicles in the area, Γ is the number of trips of the vehicle in a day.

[0031] In some embodiments, the establishment process of the topological model includes:

[0032] Select an area for spatial grid modeling, conduct basic urban spatial grid division, and evenly divide the selected area range into spatial grids according to the spatial scale as the research area;

[0033] Obtain the POI data within the selected range through an open map platform, identify and discriminate the functional area types to which the POI data belongs, and divide the selected range into residential areas, commercial areas, and working areas;

[0034] Obtain the road network vector map of the selected range through the OpenStreetMap open-source website, read the traffic road network information of the selected range, and use the graph theory analysis method to establish a topological model of the traffic road network:

[0035]

[0036] Among them, V represents the set of all nodes in the road network, that is, the traffic node set, with a total of N; S represents the set of road segments in the road network; W represents the set of road segment weights. v i represents the i-th road network node; v ix represents the road segment between node i and x in the road network; w ix represents the road segment v ix 's weight, which is used to describe the lengths of each road segment and the connection relationships of each node.

[0037] In some embodiments, the calculation method of the predicted value of the electric vehicle charging and discharging load is as follows:

[0038] Calculate the charging and discharging load P i (t) of node i in the selected urban area:

[0039]

[0040] According to the charging and discharging load P i (t), calculate the total charging and discharging load P(t) of the regional electric vehicle cluster:

[0041]

[0042] Among them, N is the number of regional traffic nodes, N EV is the number of electric vehicles in the region, respectively represent the charging load and discharging load of vehicle j at node i.

[0043] The beneficial effects of the present invention are as follows: By analyzing the spatio-temporal distribution of the predicted results of the charging and discharging loads of electric vehicles in each functional area and urban node under different scenarios, the problem of unclear distribution of electric vehicle storage load resources is solved, the distribution and change rules of electric vehicle storage load resources in the urban area are mastered, and a fast and reliable power response load can be provided during the peak shaving and valley filling of the power grid, making up for the deficiencies of the conventional prediction system that does not consider the real urban traffic conditions and temperature factors, and improving the prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flowchart of a method for predicting the charging and discharging loads of electric vehicles based on a time-space-electricity chain provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] To make the objectives, technical solutions and advantages of the present invention clearer and more definite, the content of the present invention will be further described in detail below with reference to the drawings and specific embodiments. It can be understood that the specific embodiments described herein are only for explaining the present invention, rather than limiting the present invention. Additionally, it should be noted that for the sake of description, only parts related to the present invention are shown in the drawings rather than all the content.

[0046] This embodiment proposes a method for predicting the charging and discharging loads of electric vehicles based on a time-space-electricity chain. By analyzing the spatio-temporal distribution of the predicted results of the charging and discharging loads of electric vehicles in each functional area and urban node under different scenarios, the problem of unclear distribution of electric vehicle storage load resources is solved, the distribution and change rules of electric vehicle storage load resources in the urban area are mastered, and a fast and reliable power response load can be provided during the peak shaving and valley filling of the power grid, making up for the deficiencies of the conventional prediction system that does not consider the real urban traffic conditions and temperature factors, and improving the prediction accuracy.

[0047] This method includes the following steps S1 - S3, which can be referred to the Figure 1 shown process:

[0048] S1. Simulate the driving energy consumption, charging and discharging behaviors, and travel behaviors of electric vehicles in the area, and perform vehicle time-space-electricity chain simulation based on the driving energy consumption, charging and discharging behaviors, and travel behaviors to associate the spatio-temporal and electricity changes of the vehicles.

[0049] S11. The simulation conditions for driving energy consumption include environmental temperature and road grade, that is, use the electric vehicle driving energy consumption model considering temperature and road grade to simulate the driving energy consumption of electric vehicles.

[0050] S111. Under the condition of environmental temperature, simulate and calculate the charging and discharging efficiency of electric vehicles;

[0051] The relationship between the air conditioner opening rate and temperature is as follows:

[0052] K on =α1U 3 +α2U 2 +α3U+β1

[0053] The ratio of the power consumption per unit mileage at different temperatures when the air conditioner is turned on to the power consumption per unit mileage when the air conditioner is turned off is:

[0054]

[0055] The charge and discharge efficiency calculation formula is:

[0056] η=-1.567×10 -5 U 2 +2.012×10 -3 U+0.8891

[0057] Among them, U is the ambient temperature, α1-α4 and β1-β3 are fitting parameters.

[0058] S112, classifying urban roads according to predetermined rules, and considering the road conditions of urban roads, simulating and calculating the power consumption per unit mileage of urban roads of each grade.

[0059] For example, urban roads are divided into four levels. Considering the real-time congestion of the roads, the power consumption per unit mileage of each level of roads is as follows:

[0060]

[0061] Among them, E ks 、E zg 、E cg 、E z They represent the power consumption per unit mileage of expressways, trunk roads, secondary trunk roads and branch roads respectively, and v represents the speed of the electric car when passing through this road section.

[0062] S113, comprehensively considering the ambient temperature and the road conditions of the city roads, simulating and calculating the power consumption per unit mileage of the electric vehicle, and then calculating the driving energy consumption of the electric vehicle from the starting point to the destination.

[0063] Considering the influence of traffic network and ambient temperature, the power consumption per unit mileage of electric vehicles can be expressed as

[0064] E la =K a E l

[0065] S114, therefore, the calculation method of the driving energy consumption of the electric vehicle from destination γ-1 to destination γ is:

[0066]

[0067] Among them, s γ-1，γ is the driving energy consumption, d γ-1，γ is the driving mileage, E la is the power consumption per unit mileage of the electric vehicle, η is the charge-discharge efficiency, and B is the battery capacity of the electric vehicle.

[0068] S12. The simulation process of the charge-discharge behavior includes: based on the charge-discharge model of the electric vehicle based on the hysteresis theory, performing the charge-discharge simulation of the electric vehicle, and outputting the average charging power and the average charging curve of the electric vehicle.

[0069] The charge-discharge process of a single electric vehicle in the parked state can be expressed as:

[0070]

[0071] Among them, η c and η d represent the charging efficiency and the discharging efficiency of the electric vehicle respectively, μ p_γ is the charge-discharge state variable of the electric vehicle, and B represents the battery capacity of the electric vehicle.

[0072] At the moment T a_γ when the electric vehicle arrives at location γ, it is connected to the power grid and starts the charge-discharge operation until the departure moment T s_γ when it leaves the destination γ. The initial power of the electric vehicle is S a_γ , and the user sets the expected departure moment T e_γ and the expected off-grid power S e_γ according to their own travel needs.

[0073] Calculate the shortest charging duration required by the electric vehicle:

[0074]

[0075] The parking time t p_γ of the electric vehicle at location γ has the discharging ability only when it is greater than , otherwise the electric vehicle starts charging at the rated power immediately when it is connected to the power grid until it leaves location γ.

[0076] Judge whether the electric vehicle can participate in discharging according to the parking time and the arriving power of the electric vehicle. If the power of the electric vehicle is lower than the guaranteed power when it arrives, the electric vehicle first charges to the guaranteed power S N at the rated power P ms_γ , and the electric vehicle cannot perform the charge-discharge switching during this stage; when S a_x > S ms_γ , the electric vehicle can use the rated power P N and -P NPerform charge and discharge operations.

[0077] Calculate the average charging power value of the electric vehicle:

[0078]

[0079] Calculate the time taken for the electric vehicle to charge to the guaranteed power S at the rated power P N : ms_γ The time used:

[0080]

[0081] Calculate the average charging curve function of the electric vehicle after the battery level reaches the guaranteed power and its corresponding upper and lower boundary curve functions:

[0082]

[0083]

[0084]

[0085] where T ms_γ represents the moment when the battery level of the electric vehicle reaches the guaranteed power, and t ms_γ = T ms_γ - T a_γ ; δ is the vertical distance between the upper and lower boundary curves and the average charging curve , and δ ≤ S max - S e_γ . Within the charge and discharge feasible region, the charge and discharge path of the electric vehicle changes within the upper and lower boundary curves S (t), S + (t) of the average charging curve. - (t).

[0086] Specifically, determine the charge and discharge sequence of the electric vehicle according to the battery level reached by the electric vehicle, and perform charge and discharge operations:

[0087] If the battery level of the electric vehicle is higher than the guaranteed power when it arrives, the electric vehicle executes the strategy of first performing a charging operation and then a discharging operation. The average charging power and average charging curve of the electric vehicle are respectively expressed as:

[0088]

[0089]

[0090] If the battery level of the electric vehicle is higher than the desired power when it arrives, the electric vehicle executes the strategy of first performing a discharging operation and then a charging operation. The average charging power and average charging curve of the electric vehicle are respectively expressed as:

[0091]

[0092]

[0093] Among them, is the average charging power of the electric vehicle, S e_γ is the expected off-grid power of the electric vehicle, S a_γ is the power of the electric vehicle when it reaches the destination γ, B is the battery capacity of the electric vehicle, η c is the charging efficiency of the electric vehicle, t p_γ is the parking time of the electric vehicle at the destination γ, is the average charging curve of the electric vehicle, t is the time variable, T a_γ is the time when the electric vehicle reaches the destination γ, δ is the allowable range of charge and discharge error set, T e_γ is the expected off-grid power of the electric vehicle.

[0094] The electric vehicle performs charge and discharge operations. When the actual charging curve of the electric vehicle intersects with the upper boundary curve, the electric vehicle switches from the charging state to the discharging state. When the actual charging curve of the electric vehicle intersects with the lower boundary curve or the guaranteed power line, the electric vehicle switches from the discharging state to the charging state.

[0095] S13, The simulation process of the travel behavior includes simulating the travel of the electric vehicle with the travel probability function of the electric vehicle. The number of user trips Γ follows a normal distribution. Select the travel situations of traveling 2 to 4 times from the number of user trips. Each travel situation contains at most 4 travel destinations, and the travel time of the first travel situation follows a normal distribution. The parking time of the electric vehicle follows a Gumbel distribution.

[0096] S14, The association of the time-space-power chain is:

[0097]

[0098] Among them, B is the battery capacity of the electric vehicle, T a_γ is the time when the vehicle reaches the destination γ, T s_γ is the time when the vehicle leaves the destination γ, O γ represents the destination γ, S a_γ is the power when the vehicle arrives, S s_γ is the off-grid power of the vehicle, N EV is the total number of electric vehicles in the area, Γ is the number of trips of the vehicle in a day.

[0099] The spatial chain of the electric vehicle user's travel includes the start and end point distribution of each trip and the traffic path traveled. The spatial starting point of the electric vehicle travel is O1, and the location at the destination γ is O γ, the Dijkstra algorithm is used to simulate the traffic path of electric vehicle trips, and the path with the shortest travel distance is selected as the optimal path.

[0100] The characteristic quantities included in the time chain of electric vehicle users' trips are: the start time T of the trip to destination γ s_γ-1 , the arrival time T of the trip a_γ , and the travel time parameter is obtained from the electric vehicle travel simulation step S13.

[0101] The characteristic quantities included in the power chain of electric vehicles are: the power S when arriving at destination γ a_γ , the power S when leaving destination γ s_γ , and the dynamic driving energy consumption s for driving from destination γ - 1 to destination γ γ-1，γ can be calculated by the driving energy consumption simulation step S11, and the total charge and discharge amount s in the parked state p_γ is calculated from the electric vehicle charge and discharge simulation step S12.

[0102] S2. Establish a topological model of the traffic road network based on regions;

[0103] The establishment process of the topological model includes:

[0104] Select a region for spatial grid modeling, perform basic grid division of the urban space, and evenly divide the selected regional scope into spatial grids as the research area according to the spatial scale;

[0105] Obtain the POI data within the selected range through an open map platform, identify and discriminate the functional area types to which the POI data belongs, and divide the selected range into residential areas, commercial areas, and working areas;

[0106] Obtain the road network vector map of the selected range through the OpenStreetMap open source website, read the traffic road network information of the selected range, and use the graph theory analysis method to establish a topological model of the traffic road network:

[0107]

[0108] Among them, V represents the set of all nodes in the road network, that is, the traffic node set, with a total of N; S represents the set of road segments in the road network; W represents the set of road segment weights. v i represents the i-th road network node; v ix represents the road segment between node i and x in the road network; w ix represents the weight of road segment v ix , which is used to describe the length of each road segment and the connection relationship of each node.

[0109] S3. Taking the topological model as the input, adopt Monte Carlo simulation for the time-space-electricity chain of electric vehicles, and output the predicted values of the charging and discharging loads of electric vehicles in the area, so as to realize the prediction of the charging and discharging loads of electric vehicles at each functional area and each road node.

[0110] S31. Specifically, taking the topological model as the input, conduct spatial grid modeling for the area, retrieve the POI information in the area, and conduct urban functional area division; read the traffic road network information of the area, calculate the road network topological model, with a total of N traffic nodes, among which there are N disc charging and discharging station nodes.

[0111] S32. Monte Carlo extraction of travel parameters: The number of electric vehicles in the area is N EV , randomly extract the number of daily trips Γ, the first departure time T s_0 and the starting and ending points of the trip for each vehicle.

[0112] S33. Driving path simulation and energy consumption calculation: Adopt the Dijkstra algorithm to simulate the travel path of the γ-th segment, calculate the corresponding driving distance and driving duration, and calculate the power consumption during the trip and the power S a_γ when arriving at the destination γ through the energy consumption model.

[0113] S34. Charging and discharging process simulation: Extract the parking time t p_γ at the destination γ, set the expected power S e_γ , and calculate the total charging and discharging amount of the electric vehicle at the destination γ and the power S s_γ when leaving the destination γ through the charging and discharging model.

[0114] S35. Start the next trip of the vehicle: Repeat the driving path simulation and energy consumption calculation, and the charging and discharging process simulation until all trips within a day are completed, and calculate the load conditions of the electric vehicle j at each time period within a day.

[0115] S36. Complete the daily travel and charging and discharging simulation of the next vehicle: Continue the travel simulation of the next vehicle until the travel and charging and discharging simulation of all electric vehicles in the area are completed.

[0116] S37. The calculation method of the predicted value of the charging and discharging load of electric vehicles is as follows:

[0117] Calculate the charging and discharging load P i (t) of node i:

[0118]

[0119] According to the charging and discharging load P i (t), calculate the total charging and discharging load P(t) of the regional electric vehicle cluster:

[0120]

[0121] where N is the number of regional traffic nodes, N EV is the number of electric vehicles in the region, respectively represent the charging load and discharging load of vehicle j at node i.

[0122] The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose is to enable those of ordinary skill in the art to understand the content of the present invention and implement it accordingly, and it cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the essence of the content of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for predicting the charging and discharging load of electric vehicles based on the time-space-electricity chain, characterized in that, The following steps are involved: Simulating the driving energy consumption, charging and discharging behavior and travel behavior of electric vehicles in the selected area, and simulating the time-space-electricity chain of electric vehicles based on the driving energy consumption, the charging and discharging behavior and the travel behavior, and associating the time-space and electricity changes of vehicles; Establishing a topological model of the traffic network based on the area; Taking the traffic network topology model as input, Monte Carlo simulation is used to simulate the electric vehicle time-space-electricity chain, and output the predicted value of the electric vehicle charging and discharging load in the area; The simulation conditions of the driving energy consumption include ambient temperature and road grade: Simulate and calculate the charging and discharging efficiency of electric vehicles under ambient temperature conditions; According to the predetermined rules, the urban roads are graded and the road conditions of the urban roads are taken into consideration to simulate and calculate the power consumption per unit mileage of urban roads of each grade; According to the ambient temperature and the urban road conditions, the power consumption per unit mileage of the electric vehicle is simulated and calculated, and then the driving energy consumption of the electric vehicle from the starting point to the destination is calculated; The calculation method of the driving energy consumption is: Among them, S γ-1,γ is the driving energy consumption, d γ-1,γ is the driving mileage, E la is the power consumption per unit mileage of the electric vehicle, η is the charge-discharge efficiency, and B is the battery capacity of the electric vehicle; The simulation process of the charging and discharging behavior includes: performing charging and discharging simulation of the electric vehicle based on the electric vehicle charging and discharging model of the hysteresis theory, and outputting the average charging power value and the average charging curve function of the electric vehicle; Determine the charging and discharging sequence according to the electric vehicle's reached power level, and perform the charging and discharging operations: If the electric vehicle's power level is higher than the guaranteed power level when it arrives, the strategy of charging the electric vehicle first and then discharging the electric vehicle is implemented. The average charging power value and the average charging curve function of the electric vehicle are expressed as: If the electric vehicle arrives with a power level higher than the expected power level, the strategy of discharging the electric vehicle first and then charging the electric vehicle is implemented. The average charging power and average charging curve of the electric vehicle are expressed as: Among them, is the average charging power of the electric vehicle, S e_γ is the expected off-grid power of the electric vehicle, S a_γ is the power of the electric vehicle when it reaches the destination γ, B is the battery capacity of the electric vehicle, η c is the charging efficiency of the electric vehicle, t p_γ is the parking time of the electric vehicle at the destination γ, is the average charging curve function of the electric vehicle, t is the time variable, T a_γ is the time when the electric vehicle reaches the destination γ, δ is the allowable range of charge and discharge error set, T e_γ is the expected off-grid power of the electric vehicle; The calculation method of the predicted value of the electric vehicle charging and discharging load is: Calculate the charge and discharge load \(P_{i}(t)\) of node \(i\) in the selected area i (t): According to the charging and discharging load P i (t), calculate the total charging and discharging load P(t) of the regional electric vehicle cluster: Among them, N is the number of regional traffic nodes, N EV is the number of electric vehicles in the region, respectively represent the charging load and discharging load of vehicle j at node i.

2. The electric vehicle charging and discharging load prediction method based on the time-space-electricity chain according to claim 1, characterized in that The simulation process of the travel behavior includes simulating electric vehicle travel with the travel probability function of the electric vehicle, the number of user trips obeys the normal distribution, and the travel situations of 2 to 4 trips are selected from the number of user trips. Each of the travel situations contains at most 4 travel destinations, and the travel time of the first travel situation obeys the normal distribution, and the parking time of the electric vehicle obeys the Gumbel distribution.

3. The electric vehicle charging and discharging load prediction method based on the time-space-electricity chain according to claim 1, wherein, The relationship between the time-space-electricity chain is: Among them, B is the battery capacity of the electric vehicle, T a_γ is the time when the vehicle arrives at the destination γ, T s_γ is the time when the vehicle departs from the destination γ, O γ represents the destination γ, S a_γ is the remaining battery level when the vehicle arrives, S s_γ is the off-grid battery level of the vehicle, N EV is the total number of electric vehicles in the area, and Γ is the number of trips made by the vehicle in a day.

4. The electric vehicle charging and discharging load prediction method based on the time-space-electricity chain according to claim 1, characterized in that, The process of establishing the topology model includes: Select an area for spatial grid modeling, perform urban space basic grid division, and evenly divide the selected area into spatial grids according to the spatial scale as the research area; Acquire POI data within the selected range through an open map platform, identify and distinguish the functional area types to which the POI data belongs, and divide the selected range into residential areas, commercial areas, and work areas; Obtain the road network vector diagram of the selected range through the OpenStreetMap open source website, read the traffic road network information of the selected range, and use the graph theory analysis method to establish the topological model of the traffic road network: Among them, V represents the set of all nodes in the road network, that is, the traffic node set, with a total of N; S represents the set of road segments in the road network; W represents the set of road segment weights; v i represents the i-th road network node; v ix represents the road segment between node i and x in the road network; w ix represents the weight of the road segment v ix , which is used to describe the lengths of each road segment and the connection relationships of each node.

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