CN111199300A - Electric vehicle charging load space-time prediction method under vehicle-road-network mode - Google Patents
Electric vehicle charging load space-time prediction method under vehicle-road-network mode Download PDFInfo
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Abstract
The invention discloses a method for predicting charging load space-time of an electric automobile in a vehicle-road-network mode, and belongs to the field of electric automobile load prediction. The method simulates the traffic driving characteristics of the electric automobile under the constraint condition of the urban regional road network through the electric automobile (EV) state parameters, the speed-flow practical relation model and the road traffic model, further accurately simulates the driving path of the electric automobile by using a traffic starting and stopping point analysis method, then predicts the charging load of various electric automobiles by using a Monte Carlo method, and finally calculates to the corresponding power distribution network node through the urban road network and the charging load. The method simulates the driving condition of the electric automobile according to the urban road traffic network model, improves the prediction precision of the urban area charging load space-time distribution, and is beneficial to reasonably planning the layout and capacity allocation of the charging facilities in the city.
Description
Technical Field
The invention relates to a method for predicting charging load of an electric automobile, in particular to a method for predicting the charging load space-time of the electric automobile in a vehicle-road-network mode, and belongs to the field of load prediction of the electric automobile.
Background
Compared with the traditional fuel oil automobile with high pollution and high energy consumption, the electric automobile (EV) driven by electric power instead of petroleum or natural gas has the advantages of low noise, high energy efficiency, no pollutant discharge, obvious advantages in the aspects of energy conservation, environmental protection, cleanness and the like, is considered as an effective solution for reducing the dependence on fossil energy and reducing the emission of carbon dioxide, and is highly concerned by governments and related enterprises in various countries. The access of a large number of electric vehicles to the power grid provides development opportunities for the aspects of system safety, stability, economic operation and the like, and simultaneously brings important challenges. The charging behavior of the electric automobile, which is a new electric load, shows strong randomness and disorder in distribution of time and space. Therefore, in order to effectively analyze the charging demand of a large-scale electric vehicle after the electric vehicle is connected in the future and the influence of the charging demand on the power distribution network trend, the coupling characteristics of the electric vehicle (vehicle), the urban traffic network (road) and the urban power distribution network (network) and the influence of the coupling characteristics on the charging load space-time distribution characteristic need to be comprehensively considered.
The large-scale use of the electric automobile is closely coupled with both a power grid and a road network, so the influence of vehicle-road-network triple factors needs to be considered in a synergic manner in the research. At present, an OD analysis method introducing traffic start points is adopted, a space-time distribution model STM combining a traffic system and a power distribution system is established, but a specific traffic network model is not involved, and the prediction precision is yet to be improved; in addition, a charging station load prediction method is researched by using cloud computing and big data analysis means, and the spatial distribution characteristics of the electric vehicles are considered, but the influence of a traffic network is not considered, and the prediction accuracy of the method is still to be improved. In addition, with the continuous improvement of the permeability of the electric automobile, the large-scale construction of charging facilities is imperative. The reasonable planning of the layout and the capacity configuration of the charging facilities is vital to the efficient use and operation of the future charging facilities, and has an important guiding function on the upgrading and transformation and the safe operation of the power distribution network. The reasonable planning of the charging facility needs specific time-space distribution data of the charging load of the electric automobile, but related data are lacked at present, and historical data resources and real-time data resources are difficult to obtain.
Disclosure of Invention
The invention provides a method for predicting the charging load space-time of an electric automobile in a vehicle-road-network mode in order to meet the planning requirement of a charging facility. A method for predicting charging load space-time of an electric automobile in a vehicle-road-network mode comprises the following steps:
step 1: and reading the type of the electric automobile, and obtaining the parameter and state information of the electric automobile.
1) The state parameters of the electric vehicle are defined as follows:
Oi: an electric vehicle initial position; t is ts: the electric automobile is in an initial trip moment; l ist: the electric automobile is located at the time t; capr: electric vehicle capacity; cap0: initial electric quantity of the electric vehicle; captThe residual electric quantity of the electric automobile at the moment t, △ Cap is the electric consumption per kilometer, and R is the real-time endurance mileage.
2) The electric automobile t moment residual capacity is as follows:
Capt＝η(Capt-1-△l·△Cap) (1)
in the formula, Capt-1The residual electric quantity of the automobile is the residual electric quantity at the last sampling moment t-1, △ l is the distance from t-1 to t, η is an efficiency coefficient, represents the electric quantity loss caused by starting and braking in the actual driving process, and generally takes the value of 0.9-1.
Step 2: and simulating a driving path by random sampling according to the OD probability matrix corresponding to the type of the electric automobile, and generating a travel destination according to the corresponding state parameter. Representing simulation time by t, firstly, allocating an initial position O to each electric automobile by Monte Carlo sampling according to the automobile typeiAnd an initial trip time tsRe-combination of tsAnd generating a travel destination d of the electric vehicle by randomly sampling an OD probability matrix corresponding to the type of the electric vehicle in the time period.
The OD probability matrix is expressed as:
in the formula: matrix elements
(1 ≦ i ≦ m,1 ≦ j ≦ m) represents the number of electric vehicles that have been addressed to node j (i.e., from node i to node j) starting at node i during the time period; by transformation of formula (2), elements
Represents the period from T to T +1, the probability of the electric vehicle taking the node j as the destination in the electric vehicles taking the node i as the initial node,
and the probability that the electric automobile stops in place and does not travel in the period is represented.
And step 3: determining a driving path, a road section number s and a total mileage l according to a Floyd algorithmijAnd calculating the running speed v of each section according to the path and the speed-flow model.
1) The electric automobile has the advantages that multiple routes can be selected between a starting place i and a destination j of the electric automobile under the normal condition, and the driver is assumed to select the shortest route to drive. The shortest travel path set R ═ i,. e, f,. and j, and the total traveled distance l between i and jijThe method can be obtained by a Floyd shortest path method;
2) and calculating the running speed v of each section according to the path and speed-flow model, wherein the process is as follows:
the running speed v of the vehicle on a straight-connected road section (i, j) taking i and j as endpoints at the time tijThe expression of (t) is:
in the formula, vij-mZero stream velocity for a direct link segment (i, j); cijIs the traffic capacity of the road (i, j) and is related to the road grade; q. q.sij(t) is the section flow of the road (i, j) at the time t; q. q.sij(t) and CijThe ratio of (a) to (b) is road saturation at the time t; a. b and n are adaptive coefficients under different road grades, and for the main road, a, b and n respectively take values of 1.726, 3.15 and 3; for the secondary main road, values of a, b and n are 2.076, 2.870 and 3 respectively.
And 4, step 4: and calculating the travelable time of the residual capacity to ensure the destination, and if the destination cannot be reached, selecting the nearest charging station for charging and calculating according to the destination. The travel time calculation process is as follows:
1) if the set R comprises s direct-connected road sections, the driving speed V of the h direct-connected road section can be calculated according to a speed-flow modelh(T) its travel time △ ThCan be derived from formula (5), and dhThe assignment rule of equation (4) is followed.
Wherein inf is a section without direct connection between two nodes (or called as the two nodes are not adjacent), and e (g) is a road network graph.
2) And obtaining the total driving time between i and j as follows:
and 5: when the electric automobile arrives at the destination, updating the Cap of the electric automobiletR, judging whether charging is needed, and if not, repeating the process from the step 2 to the step 4; and if the charging is needed, determining a charging mode according to the type of the electric automobile, and updating the space-time information of the electric automobile after the charging is finished. The charging modes of different electric vehicles are determined as follows:
1) for the taxi, the charging time is relatively urgent due to the demand for profit, and a near quick charging mode is adopted when the residual electric quantity is reduced to a threshold value, and the charging power is 45-60 kW.
2) For a private car for working, it is assumed that it has sufficient time to trickle charge both at the residence and at the workplace, and the charging power is 7 kW. If the remaining endurance mileage after arriving at the working place is not enough to support the return journey requirement, slowly charging at the working place; otherwise, the slow charging is carried out after the user returns to the place of residence.
3) For other functional vehicles, the travel of the functional vehicles is discontinuously distributed in time and has certain randomness in space. According to the factors such as the residual electric quantity, the destination mileage, the trip time interval and the like, the vehicle can be quickly charged nearby when the residual electric quantity is reduced to a threshold value in the trip so as to meet the requirement of the trip mileage; and after the vehicle arrives at the destination, the vehicle can select quick charging or slow charging according to the travel time condition so as to meet the next travel requirement.
Step 6: determining charging load space-time information of the jth electric automobile, judging whether the jth electric automobile exceeds the total number of the electric automobiles, and if not, returning to determine the charging load space-time information of the jth electric automobile; if the voltage exceeds the preset value, counting the voltage load P of the node of the power distribution networkk(t), simultaneously, after completing one Monte Carlo simulation, taking 15 minutes as a step length, and charging load P of each node of the power distribution network for 24 hoursk(t) storing as a power distribution network node charging power matrix Li；
1) The node voltage of the power distribution network:
in the formula, Pk(t) is the total charging load at the time t, r is the number of electric vehicles accessed to the node k at the time t,
Pi kand (t) is charging power of the ith electric vehicle accessed by the node k. And (7) calculating the slow charging total load and the fast charging total load of the node k at the time t respectively.
And 7: if the Monte Carlo simulation termination condition is met, ending; if not, returning to the step 1;
the Monte Carlo simulation terminates when one of the following conditions is met:
1) the maximum number of Monte Carlo simulations is reached;
2) the condition of formula (8) is satisfied.
In the formula: max is the maximum value of the matrix elements; l isiAnd charging the power matrix for the power distribution network nodes stored after the ith Monte Carlo simulation. N is the Monte Carlo simulation times; ε is the convergence accuracy of the simulation.Let ε be 0.1 and the maximum number of Monte Carlo simulations be 1000.
The invention has the beneficial effects that:
the method for predicting the charging load space-time of the electric automobile in the vehicle-road-network mode provided by the invention considers the road constraint, can simulate the driving condition of the electric automobile in an urban road network, and effectively improves the prediction precision of the charging load space-time distribution characteristic in an urban area. Meanwhile, the invention is beneficial to reasonably planning the layout and capacity configuration of the charging facilities and is also beneficial to the maintenance and capacity expansion of the power distribution network.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 shows a part of a main road in a city.
Fig. 3 shows the number of quick charges of the electric vehicle.
Fig. 4 is an electric vehicle initial position distribution.
FIG. 5 is a fast charge spatiotemporal distribution from 7:00 to 24: 00.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
A method for predicting charging load space-time of an electric automobile in a vehicle-road-network mode comprises the following steps:
step 1: and reading the type of the electric automobile, and obtaining the parameter and state information of the electric automobile.
1) The state parameters of the electric vehicle are defined as follows:
Oi: an electric vehicle initial position; t is ts: the electric automobile is in an initial trip moment; l ist: the electric automobile is located at the time t; capr: electric vehicle capacity; cap0: initial electric quantity of the electric vehicle; captThe residual electric quantity of the electric automobile at the moment t, △ Cap is the electric consumption per kilometer, and R is the real-time endurance mileage.
2) The electric automobile t moment residual capacity is as follows:
Capt＝η(Capt-1-△l·△Cap) (1)
in the formula, Capt-1Is the last oneThe method comprises the steps of sampling residual electric quantity at a time t-1, △ l representing a driving route from the time t-1 to the time t, η representing an efficiency coefficient, representing electric quantity loss caused by starting and braking in the actual driving process, and generally taking the value of 0.9-1.
Step 2: and simulating a driving path by random sampling according to the OD probability matrix corresponding to the type of the electric automobile, and generating a travel destination according to the corresponding state parameter. Representing simulation time by t, firstly, allocating an initial position O to each electric automobile by Monte Carlo sampling according to the automobile typeiAnd an initial trip time tsRe-combination of tsAnd generating a travel destination d of the electric vehicle by randomly sampling an OD probability matrix corresponding to the type of the electric vehicle in the time period.
The OD probability matrix is expressed as:
in the formula: matrix elements
(1 ≦ i ≦ m,1 ≦ j ≦ m) represents the number of electric vehicles that have been addressed to node j (i.e., from node i to node j) starting at node i during the time period; by transformation of formula (2), elements
Represents the period from T to T +1, the probability of the electric vehicle taking the node j as the destination in the electric vehicles taking the node i as the initial node,
and the probability that the electric automobile stops in place and does not travel in the period is represented.
And step 3: determining a driving path, a road section number s and a total mileage l according to a Floyd algorithmijAnd calculating the running speed v of each section according to the path and the speed-flow model.
1) The electric automobile has departure place i and destination j, and multiple routes between i and j are selectable under normal conditionsAnd (6) driving. The shortest travel path set R ═ i,. e, f,. and j, and the total traveled distance l between i and jijThe method can be obtained by a Floyd shortest path method;
2) and calculating the running speed v of each section according to the path and speed-flow model, wherein the process is as follows:
the running speed v of the vehicle on a straight-connected road section (i, j) taking i and j as endpoints at the time tijThe expression of (t) is:
in the formula, vij-mZero stream velocity for a direct link segment (i, j); cijIs the traffic capacity of the road (i, j) and is related to the road grade; q. q.sij(t) is the section flow of the road (i, j) at the time t; q. q.sij(t) and CijThe ratio of (a) to (b) is road saturation at the time t; a. b and n are adaptive coefficients under different road grades, and for the main road, a, b and n respectively take values of 1.726, 3.15 and 3; for the secondary main road, values of a, b and n are 2.076, 2.870 and 3 respectively.
And 4, step 4: and calculating the travelable time of the residual capacity to ensure the destination, and if the destination cannot be reached, selecting the nearest charging station for charging and calculating according to the destination. The travel time calculation process is as follows:
1) if the set R comprises s direct-connected road sections, the driving speed V of the h direct-connected road section can be calculated according to a speed-flow modelh(T) its travel time △ ThCan be derived from formula (5), and dhThe assignment rule of equation (4) is followed.
Wherein inf is a section without direct connection between two nodes (or called as the two nodes are not adjacent), and e (g) is a road network graph.
2) And obtaining the total driving time between i and j as follows:
and 5: when the electric automobile arrives at the destination, updating the Cap of the electric automobiletR, judging whether charging is needed, and if not, repeating the process from the step 2 to the step 4; and if the charging is needed, determining a charging mode according to the type of the electric automobile, and updating the space-time information of the electric automobile after the charging is finished. The charging modes of different electric vehicles are determined as follows:
1) for the taxi, the charging time is relatively urgent due to the demand for profit, and a near quick charging mode is adopted when the residual electric quantity is reduced to a threshold value, and the charging power is 45-60 kW.
2) For a private car for working, it is assumed that it has sufficient time to trickle charge both at the residence and at the workplace, and the charging power is 7 kW. If the remaining endurance mileage after arriving at the working place is not enough to support the return journey requirement, slowly charging at the working place; otherwise, the slow charging is carried out after the user returns to the place of residence.
3) For other functional vehicles, the travel of the functional vehicles is discontinuously distributed in time and has certain randomness in space. According to the factors such as the residual electric quantity, the destination mileage, the trip time interval and the like, the vehicle can be quickly charged nearby when the residual electric quantity is reduced to a threshold value in the trip so as to meet the requirement of the trip mileage; and after the vehicle arrives at the destination, the vehicle can select quick charging or slow charging according to the travel time condition so as to meet the next travel requirement.
Step 6: determining charging load space-time information of the jth electric automobile, judging whether the jth electric automobile exceeds the total number of the electric automobiles, and if not, returning to determine the charging load space-time information of the jth electric automobile; if the voltage exceeds the preset value, counting the voltage load P of the node of the power distribution networkk(t), simultaneously, after completing one Monte Carlo simulation, taking 15 minutes as a step length, and charging load P of each node of the power distribution network for 24 hoursk(t) storing as a power distribution network node charging power matrix Li；
1) The node voltage of the power distribution network:
in the formula, Pk(t) is the total charging load at the time t, r is the number of electric vehicles accessed to the node k at the time t,
Pi kand (t) is charging power of the ith electric vehicle accessed by the node k. And (7) calculating the slow charging total load and the fast charging total load of the node k at the time t respectively.
And 7: if the Monte Carlo simulation termination condition is met, ending; if not, returning to the step 1;
the Monte Carlo simulation terminates when one of the following conditions is met:
1) the maximum number of Monte Carlo simulations is reached;
2) the condition of formula (8) is satisfied.
In the formula: max is the maximum value of the matrix elements; l isiAnd charging the power matrix for the power distribution network nodes stored after the ith Monte Carlo simulation. N is the Monte Carlo simulation times; ε is the convergence accuracy of the simulation. Let ε be 0.1 and the maximum number of Monte Carlo simulations be 1000.
In the embodiment, a part of a trunk road in an urban area of a certain city is taken as an example, and the charging load of the electric vehicle in the area on a typical working day is subjected to simulation analysis. The road network comprises 29 nodes and 49 roads, the average road length is 2.92km, and the road network is sequentially divided into a residential area 1 (comprising nodes 1-11), a residential area 2 (comprising nodes 12-16), a working area (comprising nodes 17-20) and a business area (comprising nodes 21-29), as shown in fig. 2. Suppose there are 12000 private cars on duty, 4000 taxi meters and 4000 other functional vehicles in the area.
Step 1: reading the types of the electric automobiles (taxi, private car for working and other functional cars) to obtain the parameters and state information of the electric automobiles; take fast charge as an example, adopt the electric automobile of the fast charge mode, it fillsThe time profile of the electrical demand is shown in fig. 3, reaching a peak of 682 vehicles at 13:00-14:00 and another small peak at 17:00-18: 00. Step 2: determining each electric vehicle to be allocated with an initial position O by Monte Carlo sampling according to vehicle typesiAnd an initial trip time tsObtaining initial position distribution curves of different types of electric automobiles as shown in FIG. 4; recombination of tsAnd generating a travel destination d of the electric vehicle by randomly sampling an OD probability matrix corresponding to the type of the electric vehicle in the time period.
And step 3: determining a driving path, a road section number s and a total mileage l by using a Floyd algorithmijAnd calculating the running speed v of each section according to the path and the speed-flow model.
And 4, step 4: and calculating the travelable time of the residual electric quantity.
And 5: when the electric automobile reaches the destination, updating the residual electric quantity and the real-time endurance mileage of the electric automobile at the moment t of the electric automobile, and judging whether the electric automobile needs to be charged;
step 6: determining charging load space-time information of the jth electric automobile, and judging whether the jth electric automobile exceeds the total number of the electric automobiles; recording the charging starting time t of each vehiclescAnd the charging load and the charging duration are reduced to the corresponding power distribution network nodes according to the coupling relation between the road network and the power distribution network nodes, so that a power distribution network node charging power matrix is obtained. Taking 15min as the step size, the spatiotemporal distribution of the fast-charging load of the test region 07:00-24:00 is shown in fig. 5. The space-time distribution characteristics of the fast charging load can be seen more intuitively through the graph 5, the load is most concentrated in the nodes such as the power distribution networks 10, 11 and 14, and the road network areas corresponding to the nodes are mainly traffic hubs connecting residential areas and commercial areas; in addition, loads are concentrated at nodes such as the distribution networks 17, 18, and 19, and the road network areas corresponding to the nodes are business districts. The above areas are the main destinations and passenger carrying areas for taxi and other functions. This is in full agreement with the actual situation, and shows that the present invention is effective and feasible.
The above is only one embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (13)
1. A method for predicting charging load space-time of an electric automobile in a vehicle-road-network mode is characterized by comprising the following steps: the method comprises the following steps:
step 1: reading the type of the electric automobile, and obtaining parameters and state information of the electric automobile;
step 2: initial position O is distributed to each electric automobile by Monte Carlo sampling according to automobile typesiAnd an initial trip time tsRe-combination of tsGenerating a travel destination d of the electric vehicle by randomly sampling an OD probability matrix corresponding to the type of the electric vehicle in the time period;
and step 3: determining a driving path, a road section number s and a total mileage l by using a Floyd algorithmijCalculating the running speed v of each section according to the path and the speed-flow model;
and 4, step 4: calculating the travelable time of the residual electric quantity to ensure that the destination is reached; if the destination can not be reached, selecting a near charging station for charging, and meanwhile, calculating according to the destination;
and 5: after the electric automobile reaches the destination, updating the residual electric quantity and the real-time endurance mileage of the electric automobile at the moment t, judging whether the electric automobile needs to be charged, if the electric automobile does not need to be charged, continuing to use the electric automobile, and repeating the processes of the step 2 to the step 4; if the charging is needed, determining a charging mode according to the type of the electric automobile, and updating the space-time information of the electric automobile after the charging is finished;
step 6: determining charging load space-time information of the jth electric automobile, judging whether the jth electric automobile exceeds the total number of the electric automobiles, and if not, returning to determine the charging load space-time information of the jth electric automobile; if the voltage exceeds the preset value, counting the voltage load P of the node of the power distribution networkk(t) generating a charging power matrix L of each node of the power distribution networki(ii) a Completing Monte Carlo simulation once for n electric vehicles, repeating the steps for n times, and recording the charging starting time t of each vehiclescCharging load and charging durationAnd load is reduced to the corresponding distribution network node according to the coupling relation between the road network and the distribution network node.
And 7: judging whether Monte Carlo simulation termination conditions are met, and if yes, ending; and if not, returning to repeat the processes from the step 1 to the step 6.
2. The space-time prediction method for the charging load of the electric automobile in the vehicle-road-network mode according to claim 1 is characterized in that: the state parameters of the electric automobile in the step 1 mainly comprise: initial trip time t of electric automobiles(ii) a Electric automobile position L at time tt(ii) a Capacity Cap of electric vehicler(ii) a Initial electric quantity Cap of electric vehicle0(ii) a Electric automobile residual capacity Cap at time ttPower consumption △ Cap per kilometer, real time endurance mileage R.
3. The space-time prediction method for the charging load of the electric automobile in the vehicle-road-network mode according to claim 2 is characterized in that: the calculation formula of the electric automobile residual capacity at the time t in the step 1 is as follows:
Capt＝η(Capt-1-△l·△Cap) (1)
in the formula, Capi-1The energy consumption of the automobile is the residual energy of the last sampling moment t-1, △ l is the distance from t-1 to t, η is an efficiency coefficient representing the energy loss caused by starting and braking in the actual driving process, and the value is generally 0.9-1.
4. The space-time prediction method for the charging load of the electric automobile in the vehicle-road-network mode according to claim 1 is characterized in that: in the step 2, the OD probability matrix solving expression is as follows:
in the formula: matrix elements
Indicating the number of electric vehicles that are initially addressed to node i and are addressed to node j during the time period.
5. The space-time prediction method for the charging load of the electric automobile in the vehicle-road-network mode according to claim 1 is characterized in that: in the step 3, a shortest driving path set R ═ i,... e, f.... j } and a total driving distance l between the departure place i and the destination j are obtained by a Floyd shortest path methodij。
6. The space-time prediction method for the charging load of the electric automobile in the vehicle-road-network mode according to claim 5 is characterized in that: in the step 3, the running speed v of each section is calculated according to the path and the speed-flow model, and the process is as follows:
the running speed v of the vehicle on a straight-connected road section (i, j) taking i and j as endpoints at the time tijThe expression of (t) is:
in the formula, vij-mZero stream velocity for a direct link segment (i, j); cijIs the traffic capacity of the road (i, j) and is related to the road grade; q. q.sij(t) is the section flow of the road (i, j) at the time t; q. q.sij(t) and CijThe ratio of (a) to (b) is road saturation at the time t; a. and b and n are adaptive coefficients under different road grades.
7. The space-time prediction method for the charging load of the electric automobile in the vehicle-road-network mode according to claim 6 is characterized in that: wherein, for the main road, a, b and n respectively take values of 1.726, 3.15 and 3; for the secondary main road, values of a, b and n are 2.076, 2.870 and 3 respectively.
8. The space-time prediction method for the charging load of the electric automobile in the vehicle-road-network mode according to claim 1 is characterized in that: in the step 4, the meter is measured according to the speed-flow modelCalculating the driving speed V of the h-th direct connection road sectionh(T) its travel time △ ThComprises the following steps:
wherein d ishSatisfies the following conditions:
wherein inf is the section without direct connection between two nodes, E (G) is the road network graph,
further, the total travel time between i and j is found to be:
9. the space-time prediction method for the charging load of the electric automobile in the vehicle-road-network mode according to claim 1 is characterized in that: the electric vehicle types include: taxi, private car and other functional vehicles.
10. The space-time prediction method for the charging load of the electric vehicle in the vehicle-road-network mode according to claim 9, characterized in that: in the step 5, the charging modes of the electric automobiles of different types are as follows:
(a) for the taxi, when the residual electric quantity is reduced to a threshold value, a near quick charging mode is adopted, and the charging power is 45-60 kW;
(b) for a private car, assuming that the private car has enough time to carry out slow charging in a residential place and a working place, the charging power is 7kW, and if the residual endurance mileage after arriving at the working place is not enough to support the return requirement, the private car starts slow charging at the working place; otherwise, slowly charging after returning to the residence;
(c) for other functional vehicles, the vehicle can be quickly charged nearby immediately when the residual electric quantity is reduced to a threshold value in the driving process, and can also be quickly charged or slowly charged according to the traveling time condition after arriving at a destination.
11. The method for predicting the charging load space-time of the electric automobile in the vehicle-road-network mode according to claim 1, wherein the time-space prediction is carried out by a vehicle-road-network model; in the step 6, after one Monte Carlo simulation is completed, the charging load P of each node of the power distribution network for 24 hours is calculated by taking 15 minutes as a step lengthk(t) storing as a power distribution network node charging power matrix Li。
12. The method for spatiotemporal prediction of the charging load of the electric vehicle in the vehicle-road-network mode according to claim 11, wherein the time interval is a time interval between the charging load and the charging load; the node voltage load P of the power distribution networkk(t) is:
in the formula, Pk(t) is the total charging load at the time t, r is the number of electric vehicles accessed to the node k at the time t,
Pi kand (t) is the charging power of the ith electric vehicle accessed by the node k, and the equation (7) can respectively calculate the slow charging total load and the fast charging total load of the node k at the moment t.
13. The method for predicting the charging load space-time of the electric automobile in the vehicle-road-network mode according to claim 1, wherein the time-space prediction is carried out by a vehicle-road-network model; the monte carlo simulation terminates in said step 7 when one of the following conditions is met:
(a) the maximum number of Monte Carlo simulations is reached;
(b) the condition of formula (8) is satisfied.
In the formula: max is the maximum value of the matrix elements; l isiCharging a power matrix for the power distribution network node stored after the ith Monte Carlo simulation; n is the Monte Carlo simulation times; ε is an emulationThe convergence accuracy of (2).
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