Electric vehicle charging and discharging load prediction method based on time-space-electric quantity chain

Abstract

The invention discloses an electric vehicle charge and discharge load prediction method based on a time-space-electric quantity chain, and the method comprises the following steps: simulating the driving energy consumption, charge and discharge behaviors and travel behaviors of an electric vehicle in a region, and carrying out the vehicle time-space-electric quantity chain simulation according to the driving energy consumption, charge and discharge behaviors and travel behaviors, associating time-space and electric quantity changes of the vehicle; establishing a topological model of a traffic network on the basis of the region; and by taking the topological model as input, simulating the time-space-electric quantity chain of the electric vehicle by adopting Monte Carlo, and outputting a predicted value of the charging and discharging load of the electric vehicle in the region. The method has the beneficial effects that the problem of unclear distribution of regional vehicle storage and load resources is solved by analyzing electric vehicle charge and discharge load prediction results and spatial and temporal distribution conditions of each functional region and node of a city in different scenes.

Description

Technical Field

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

[0002] With the advancement of science and technology, the emission of greenhouse gases in countries around the world has increased year by year, and the climate change and environmental threats brought about have become global problems and challenges that mankind needs to face. Electric vehicles, as an important green means of transportation to reduce carbon emissions, 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 load storage resources, can be connected to the grid to participate in peak and frequency regulation, and absorb 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] As the penetration rate of electric vehicles increases, large-scale electric vehicle grid connection will have a significant impact on the safety and stability of power grid operation. Affected by factors such as user travel behavior and the availability of V2G service facilities, electric vehicle load resources show randomness in temporal and spatial distribution. Therefore, it is necessary to make short-term predictions on the charging and discharging loads of electric vehicles in the region to accurately grasp the volume and temporal and spatial distribution of electric vehicle load resources.

[0004] Electric vehicle charging and discharging load forecasting is to reduce the impact of unclear electric vehicle storage resources on the power grid, and to provide methods and technical support for formulating charging and discharging resource regulation strategies. Current technologies generally use intelligent multi-agent technology, fuzzy reasoning methods, etc., which only consider the energy storage and load characteristics of electric vehicles, ignoring the randomness of the driving process that is susceptible to traffic network factors. In terms of travel simulation, the impact of traffic network and user decision factors on electric vehicle travel and charging and discharging characteristics is ignored. Summary of the invention

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

[0006] In order 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 comprises the following steps:

[0008] Simulating the driving energy consumption, charging and discharging behavior and travel behavior of electric vehicles in the selected area, and performing a vehicle time-space-electricity chain simulation based on the driving energy consumption, the charging and discharging behavior and the travel behavior, and associating the vehicle time-space and electricity changes;

[0009] Establishing a topological model of the traffic network based on the area;

[0010] Taking the traffic network topology model as input, Monte Carlo simulation is adopted to simulate the electric vehicle time-space-electricity chain, and the predicted value of the charging and discharging load of the electric vehicle in the area is output.

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

[0012] Simulate and calculate the charging and discharging efficiency of electric vehicles under ambient temperature conditions;

[0013] 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;

[0014] 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.

[0015] In some embodiments, the driving energy consumption is calculated as follows:

[0016]

[0017] Among them, s γ-1，γ is the driving energy consumption, d γ-1，γ is the mileage, E la is the power consumption per unit mileage of the electric vehicle, η is the charge and discharge 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: performing an electric vehicle charging and discharging simulation based on an electric vehicle charging and discharging model of hysteresis theory, and outputting an average charging power value and an average charging curve function of the electric vehicle.

[0019] In some implementations, the charging and discharging sequence of the electric vehicle is determined according to the amount of electricity reached, and the charging and discharging operations are performed:

[0020] If the electric vehicle's power level is higher than the guaranteed power level when it arrives, the electric vehicle will perform a charging operation first and then a discharging operation. The average charging power value and the average charging curve function of the electric vehicle are expressed as follows:

[0021]

[0022]

[0023] 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:

[0024]

[0025]

[0026] in, is the average charging power of electric vehicles, S e_γ is the expected off-grid power of the electric vehicle, S a_γ is the amount of electricity required for the electric vehicle to reach the destination γ, B is the battery capacity of the electric vehicle, η c is the electric vehicle charging efficiency, t p_γ is the dwell time of the electric vehicle at the destination γ, is the average charging curve function of electric vehicles, t is the time variable, T a_γ is the time it takes for the electric vehicle to reach the destination γ, δ is the set allowable range of charging and discharging error, T e_γ is the expected off-grid capacity of the electric vehicle.

[0027] In some embodiments, 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 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 stop time of the electric vehicle obeys the Gumbel distribution.

[0028] The relationship between the time-space-electricity chain is:

[0029]

[0030] Where B is the battery capacity of the electric vehicle, T a_γ is the time it takes for the vehicle to reach its destination, T s_γ is the time when the vehicle leaves the destination γ, O γ represents the destination γ, S a_γ is the vehicle’s arrival charge, S s_γ is the vehicle’s off-grid power, N EV is the total number of electric vehicles in the area, and Γ is the number of vehicle trips in a day.

[0031] In some implementations, the process of establishing the topology model includes:

[0032] 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;

[0033] 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;

[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 the 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 set of traffic nodes, a total of N; S represents the set of road sections in the road network; W represents the set of road section weights. i represents the i-th road network node; v ix represents the road segment between nodes i and x in the road network; w ix Representative road segment v ix The weight is used to describe the length of each road section and the connection relationship of each node.

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

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

[0039]

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

[0041]

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

[0043] The beneficial effects of the present invention are as follows: by analyzing the spatiotemporal distribution of electric vehicle charging and discharging load prediction results in various functional areas and urban nodes under different scenarios, the problem of unclear distribution of electric vehicle load storage resources is solved, the distribution and change rules of electric vehicle load storage resources in urban areas are mastered, and fast and reliable power response load can be provided when the power grid is peak shaving and valley filling, which makes up for the deficiency of conventional prediction systems that do not consider real urban traffic conditions and temperature factors, and improves the prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of a method for predicting electric vehicle charging and discharging load based on a time-space-electricity chain provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the content of the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It is understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all the contents.

[0046] This embodiment proposes a method for predicting the charging and discharging load of electric vehicles based on a time-space-electricity chain. By analyzing the spatiotemporal distribution of the prediction results of the charging and discharging load of electric vehicles in various functional areas and urban nodes under different scenarios, the problem of unclear distribution of electric vehicle storage resources is solved, and the distribution and change law of electric vehicle storage resources in urban areas are mastered. It can provide fast and reliable power response load when the power grid is shaving peaks and filling valleys, which makes up for the deficiency of conventional prediction systems that do not consider real urban traffic conditions and temperature factors, and improves the prediction accuracy.

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

[0048] S1, simulates the driving energy consumption, charging and discharging behavior and travel behavior of electric vehicles in the area, and simulates the vehicle time-space-electricity chain based on the driving energy consumption, charging and discharging behavior and travel behavior, and associates the vehicle time-space and electricity changes.

[0049] S11, the simulation conditions of driving energy consumption include ambient temperature and road grade, that is, the driving energy consumption simulation of the electric vehicle is performed using an electric vehicle form energy consumption model that takes temperature and road grade into consideration.

[0050] S111, simulate and calculate the charging and discharging efficiency of electric vehicles under ambient temperature conditions;

[0051] The relationship between the air conditioning 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 mileage, E la is the power consumption per unit mileage of the electric vehicle, η is the charge and discharge efficiency, and B is the battery capacity of the electric vehicle.

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

[0069] The charging and discharging process of a single electric vehicle in a parked state can be expressed as:

[0070]

[0071] Among them, η c , η d Represent the charging efficiency and discharging efficiency of electric vehicles, μ p_γ is the charging and discharging state variable of the electric vehicle, and B represents the battery capacity of the electric vehicle.

[0072] The electric car arrives at location γ at time T a_γ That is, it is connected to the grid and starts charging and discharging operations until the time T s_γ Leave the destination γ. The starting power of the electric car is S a_γ , users set their desired departure time T according to their travel needs e_γ and expected off-grid power S e_γ .

[0073] Calculate the minimum charging time required for an electric vehicle:

[0074]

[0075] The parking time t of the electric vehicle at location γ p_γ In greater than Otherwise, the electric vehicle starts charging at rated power when it is connected to the grid until it leaves the location γ.

[0076] The electric vehicle can be judged whether it can participate in the discharge according to the parking time and the arrival power of the electric vehicle. If the electric vehicle’s power is lower than the guaranteed power when it arrives, the electric vehicle will first be discharged at the rated power P N Charge to the minimum charge S ms_γ At this stage, the electric vehicle cannot switch between charging and discharging; when S a_x >S ms_γ When the electric vehicle can reach the rated power P N and -P NPerform charge and discharge operations.

[0077] Calculate the average charging power value for electric vehicles:

[0078]

[0079] Calculate the rated power P of an electric vehicle N Charge to the minimum charge S ms_γ Time taken:

[0080]

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

[0082]

[0083]

[0084]

[0085] Among them, T ms_γ Represents the time when the electric vehicle reaches the guaranteed minimum power, t ms_γ =T ms_γ -T a_γ ;δ is the upper and lower boundary curves and the average charging curve The vertical distance between them, δ≤S max -S e_γ In the feasible domain of charge and discharge, the charge and discharge path of electric vehicles is in the average charge curve The upper and lower boundary curves S + (t), S - (t) Internal changes.

[0086] Specifically, the charging and discharging sequence of the electric vehicle is determined according to the amount of electricity it has reached, and the charging and discharging operations are performed:

[0087] If the electric vehicle arrives with a power level higher than the guaranteed power level, the electric vehicle will first charge and then discharge. The average charging power and average charging curve of the electric vehicle are expressed as:

[0088]

[0089]

[0090] If the electric vehicle arrives with a power level higher than the expected power level, the electric vehicle executes the strategy of first discharging and then charging. The average charging power and average charging curve of the electric vehicle are expressed as:

[0091]

[0092]

[0093] in, is the average charging power of electric vehicles, S e_γ is the expected off-grid power of the electric vehicle, S a_γ is the amount of electricity required for the electric vehicle to reach the destination γ, B is the battery capacity of the electric vehicle, η c is the electric vehicle charging efficiency, t p_γ is the dwell time of the electric vehicle at the destination γ, is the average charging curve of electric vehicles, t is the time variable, T a_γ is the time it takes for the electric vehicle to reach the destination γ, δ is the set allowable range of charging and discharging error, T e_γ is the expected off-grid capacity of the electric vehicle.

[0094] The electric vehicle performs charging and discharging 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 travel behavior includes simulating electric vehicle travel with the travel probability function of the electric vehicle, the user's travel times Γ obeys the normal distribution, and the travel situations of 2 to 4 trips are selected from the user's travel times. Each travel situation contains at most 4 travel destinations, and the travel time of the first trip situation obeys the normal distribution, and the parking time of the electric vehicle obeys the Gumbel distribution.

[0096] S14, the relationship between time-space-electricity chain is:

[0097]

[0098] Where B is the battery capacity of the electric vehicle, T a_γ is the time it takes for the vehicle to reach its destination, T s_γ is the time when the vehicle leaves the destination γ, O γ represents the destination γ, S a_γ is the vehicle’s arrival charge, S s_γ is the vehicle’s off-grid power, N EV is the total number of electric vehicles in the area, and Γ is the number of vehicle trips in a day.

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

[0100] The characteristic quantities contained in the time chain of electric vehicle users’ travel include: the starting time T of the trip to the destination γ s_γ-1 , Trip arrival time T a_γ , the travel time parameter is obtained by the electric vehicle travel simulation step S13.

[0101] The characteristic quantities of the electric vehicle's power chain include: the power S when arriving at the destination γ a_γ , the amount of electricity S when leaving the destination γ s_γ , the dynamic driving energy consumption s from destination γ-1 to destination γ γ-1，γ The total amount of charge and discharge in the parking state s can be calculated by the driving energy consumption simulation step S11. p_γ Calculated by the electric vehicle charging and discharging simulation step S12.

[0102] S2, establish a topological model of the transportation network based on the region;

[0103] The process of establishing a topology model includes:

[0104] 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;

[0105] Obtain 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;

[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 the 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 set of traffic nodes, a total of N; S represents the set of road sections in the road network; W represents the set of road section weights. i represents the i-th road network node; v ix represents the road segment between nodes i and x in the road network; w ix Representative road segment v ix The weight is used to describe the length of each road section and the connection relationship of each node.

[0109] S3, taking the topological model as input, adopts Monte Carlo simulation of the electric vehicle time-space-electricity chain, and outputs the predicted value of the electric vehicle charging and discharging load in the area, thereby realizing the prediction of the electric vehicle charging and discharging load in each functional area and each road node.

[0110] S31, specifically, taking the topological model as input, spatial grid modeling is performed on the area, POI information in the area is retrieved, and the urban functional area is divided; the traffic road network information of the area is read, and the road network topological model is calculated, with a total of N traffic nodes, of which there are N disc A charging and discharging station node.

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

[0112] S33, driving route simulation and energy consumption calculation: Use Dijkstra algorithm to simulate the travel route of the γth segment, calculate the corresponding driving distance and driving time, and calculate the power consumption during the travel and the power consumption when arriving at the destination γ through the energy consumption model S a_γ .

[0113] S34, charging and discharging process simulation: extracting the dwell time t at the destination γ p_γ , set the expected power S e_γ The total charge and discharge amount of the electric vehicle at the destination γ and the amount of electricity S when leaving the destination γ are calculated by the charge and discharge model. s_γ .

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

[0115] S36, completing the daily travel and charging and discharging simulation of the next vehicle: continuing 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 electric vehicle charging and discharging load is:

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

[0118]

[0119] According to the charge and discharge 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, They represent the charging load and discharging load of vehicle j at node i respectively.

[0122] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting electric vehicle charging and discharging load based on 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 adopted to simulate the electric vehicle time-space-electricity chain, and the predicted value of the charging and discharging load of the electric vehicle in the area is output.

2. The electric vehicle charging and discharging load prediction method based on time-space-electricity chain according to claim 1 is characterized in that: 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.

3. The electric vehicle charging and discharging load prediction method based on time-space-electricity chain as claimed in claim 2 is characterized in that: 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 mileage, E la is the power consumption per unit mileage of the electric vehicle, η is the charge and discharge efficiency, and B is the battery capacity of the electric vehicle.

4. The electric vehicle charging and discharging load prediction method based on time-space-electricity chain as claimed in claim 1 is characterized in that: 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.

5. The electric vehicle charging and discharging load prediction method based on time-space-electricity chain as claimed in claim 4 is characterized in that: 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: in, 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 amount of electricity required for the electric vehicle to reach the destination γ, B is the battery capacity of the electric vehicle, η c is the electric vehicle charging efficiency, t p_γ is the dwell time of the electric vehicle at the destination γ, is the average charging curve function of electric vehicles, t is the time variable, T a_γ is the time it takes for the electric vehicle to reach the destination γ, δ is the set allowable range of charging and discharging error, T e_γ is the expected off-grid capacity of the electric vehicle.

6. The electric vehicle charging and discharging load prediction method based on time-space-electricity chain as claimed in claim 1 is 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.

7. The electric vehicle charging and discharging load prediction method based on time-space-electricity chain as claimed in claim 1 is characterized in that: The relationship between the time-space-electricity chain is: Where B is the battery capacity of the electric vehicle, T a_γ is the time it takes for the vehicle to reach its destination, T s_γ is the time when the vehicle leaves the destination γ, O γ represents the destination γ, S a_γ is the vehicle’s arrival charge, S s_γ is the vehicle’s off-grid power, N EV is the total number of electric vehicles in the area, and Γ is the number of vehicle trips in a day.

8. The method for predicting electric vehicle charging and discharging load based on 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 set of traffic nodes, a total of N; S represents the set of road sections in the road network; W represents the set of road section weights. i represents the i-th road network node; v ix represents the road segment between nodes i and x in the road network; w ix Representative road segment v ix The weight is used to describe the length of each road section and the connection relationship of each node.

9. The method for predicting electric vehicle charging and discharging load based on time-space-electricity chain according to claim 1, characterized in that: The calculation method of the predicted value of the electric vehicle charging and discharging load is: Calculate the charge and discharge load P of node i in the selected area i (t): According to the charge and discharge load P i (t) Calculate the total charging and discharging load P(t) of the regional electric vehicle cluster: Where N is the number of regional traffic nodes, N EV is the number of electric vehicles in the region, They represent the charging load and discharging load of vehicle j at node i respectively.

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