Electric vehicle charging load space-time distribution prediction method and system based on OD analysis method

Abstract

The invention belongs to the technical field of electric power system loads, and particularly relates to an electric vehicle charging load space-time distribution prediction method and system based on an OD analysis method.The method comprises the steps that initial travel space-time parameters and a destination are generated based on the type of an electric vehicle, and then the travel path of the electric vehicle is planned; the current electric quantity of the electric vehicle is updated in real time according to the energy consumption model, the moments when the electric vehicle travels to different road nodes are updated in real time according to the time model considering the dynamic road network, corresponding charging behaviors are described according to the type of the electric vehicle in the traveling period, and the fast charging load, the charging node and the charging moment of the electric vehicle are recorded; and after calculation of all the electric vehicles is completed, a prediction result of charging load space-time distribution of the electric vehicles is obtained through statistics. According to the method, the types of the electric vehicles are divided, so that travel paths and charging behaviors of different types of electric vehicles can be accurately described, and finally, the prediction precision of the time-space distribution of the charging load is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system load, and particularly relates to a method and system for predicting the spatio-temporal distribution of electric vehicle charging load based on OD analysis method. Background Art

[0002] With the global emphasis on clean energy and carbon emission reduction, the electric vehicle market has grown rapidly. Many countries and regions have introduced policies to support the popularization of electric vehicles and promote the transformation from traditional fuel vehicles to electric vehicles. The popularization of electric vehicles has not only changed people's travel modes, but also had a profound impact on the power system, especially on the distribution of grid load. Since electric vehicles need to be charged regularly, the distribution of their charging load is uneven in time and space, which is likely to cause local load peaks in the power grid, thereby affecting the stability and security of the power grid. At present, the concentrated charging peak of electric vehicles may increase the burden on the distribution system and even lead to grid overload or local power outages. Therefore, how to predict the spatio-temporal distribution of electric vehicle load is of great significance for the dispatching, planning and load management of the power system.

[0003] The invention patent with application number 202010013815.9 provides a method for predicting the spatio-temporal distribution of electric vehicle charging load based on the travel probability matrix. First, a probability model of influencing factors is established, then an electric vehicle travel probability matrix is established according to the network topology of electric vehicles and the travel of electric vehicles between cities, and finally, the spatio-temporal distribution of electric vehicle charging load in a day is predicted by using the Monte Carlo method based on the probability model of influencing factors and the electric vehicle travel probability matrix. Although this method can realize the prediction of electric vehicle charging load under spatio-temporal distribution, due to the lack of division of electric vehicle users, it is difficult to accurately depict the travel behavior and charging behavior of electric vehicles, and finally the prediction accuracy is low. Summary of the Invention

[0004] The object of the present invention is to provide a method and system for predicting the spatio-temporal distribution of electric vehicle charging load based on OD analysis method, which can accurately depict the travel behavior and charging behavior of different types of electric vehicles by classifying electric vehicle types, so as to improve the prediction accuracy, aiming at the above problems existing in the prior art.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] In the first aspect, the present invention provides a method for predicting the spatio-temporal distribution of electric vehicle charging load based on OD analysis method, characterized in that:

[0007] The prediction method includes:

[0008] S1. Acquire initial data of electric vehicles and initial data of road networks, wherein the initial data of electric vehicles includes the number of electric vehicles in use, and the initial data of road networks includes a dynamic road network adjacency matrix;

[0009] S2, first read the type of a single electric vehicle, and generate initial travel time and space parameters based on the type of the electric vehicle, wherein the initial travel time and space parameters include initial travel time, battery capacity, starting position, and initial power; then generate the destination of the electric vehicle based on the starting position and the OD probability matrix;

[0010] S3, using the dynamic road network adjacency matrix to plan the travel path of the electric vehicle, and updating the current power of the electric vehicle in real time according to the energy consumption model, and updating the time when the electric vehicle travels to different road nodes in real time according to the time model considering the dynamic road network; if the electric vehicle type is a private car, then enter S4, if the electric vehicle type is a taxi, then enter S5, if the electric vehicle type is a vehicle for other functions, then enter S6;

[0011] S4, after the electric vehicle arrives at the destination, the return route of the electric vehicle is planned using the dynamic road network adjacency matrix. After the return, the electric vehicle is charged in a slow charging mode, and the slow charging load, charging node, and charging time of the electric vehicle are recorded; enter S7;

[0012] S5. Determine in real time whether the current power of the electric vehicle meets the taxi charging conditions. If so, select the nearest charging node based on the current position of the electric vehicle to charge in fast charging mode, and record the fast charging load, charging node, and charging time of the electric vehicle; after charging, update the current power of the electric vehicle and continue driving. After arriving at the destination, use the current destination as the starting position to generate the next destination, return to S3 to continue calculation, until the taxi operation time is reached, and enter S7;

[0013] S6. Determine in real time whether the current power of the electric vehicle meets the charging conditions of other functional vehicles. If so, a charging demand is generated. Select the nearest charging node for charging based on the current location of the electric vehicle in the road network, and determine whether the slow charging conditions are met. If so, charge in slow charging mode, otherwise charge in fast charging mode; record the charging load, charging node, and charging time of the electric vehicle in the selected charging mode; update the current power of the electric vehicle after charging is completed and continue driving. After arriving at the destination, use the current destination as the starting position to generate the next destination, return to S3 to continue calculation, until the operation time of other functional vehicles is reached, and enter S7;

[0014] S7. Determine whether the calculation of all electric vehicles has been completed. If not, return to S2 to perform the calculation of the next electric vehicle. If completed, obtain the prediction result of the spatiotemporal distribution of the charging load of the electric vehicles.

[0015] The energy consumption model includes:

[0016]

[0017]

[0018] In the above formula, represents the remaining battery power; represents the initial battery power of the electric vehicle; d ij represents the driving distance of section ij, where section ij refers to the section between node i and node j in the road network; E p represents the energy consumption per unit mileage; C t represents the battery capacity of the electric vehicle; represents the air-conditioning power consumption of the electric vehicle when driving at speed V ij , driving distance d ij , and environmental temperature T; represents the power consumption generated when the electric vehicle drives on Class I or Class II roads; respectively represent the power consumption generated when the electric vehicle drives on Class I and Class II roads. Class I roads are main roads, and Class II roads are secondary roads; respectively represent the air-conditioning cooling and heating powers; V ij represents the driving speed of the electric vehicle on section ij; T represents the environmental temperature; T k-max , T k-min respectively represent the cold and hot temperature limits; V ij-m represents the zero-flow speed corresponding to the road category; N ij represents the traffic capacity of the section between node i and node j corresponding to the road category; S represents the section saturation degree; q ij (t) represents the traffic flow passing through section ij at time t; β represents the road traffic coefficient; a, b, and γ are all adaptive coefficients.

[0019] The time model considering the dynamic road network includes:

[0020]

[0021] λ = t green，yellow / c;

[0022] S = q ij (t) / N ij ;

[0023] In the above formula, t ij represents the time required for the electric vehicle to pass through section ij; n represents the set of road nodes; represents the driving time required for the electric vehicle to pass through section ij; Denote the delay time caused by signal control on road section ij; V ij Denote the driving speed of the electric vehicle on road section ij; x ij Denote the path decision variable. If the electric vehicle travels from node i to node j, then x ij Takes the value of 1, otherwise 0; λ denotes the ratio of the total duration of the green and yellow lights to the signal cycle; t green，yellow Denote the total duration of the green and yellow lights; c denotes the entire red-yellow-green signal cycle; S denotes the road section saturation degree; q ij q(t) denotes the traffic flow passing through road section ij at time t; N ij Denote the traffic capacity of road section ij corresponding to the road category; q denotes the vehicle arrival rate.

[0024] When selecting the nearest charging node according to the current position of the electric vehicle for fast charging, determine whether the electric vehicle needs to queue for charging. If not, directly charge; otherwise, first calculate the charging waiting duration according to the following formula, and then calculate the charging time of the electric vehicle based on the time when the electric vehicle arrives at the nearest charging node and the charging waiting duration:

[0025] t sc c(i) = t a c(i) + T wait c(i);

[0026]

[0027] In the above formula, t sc c(i) denotes the charging time of the electric vehicle; t a a(i) denotes the time when the electric vehicle arrives at the nearest charging node; T wait c(i) denotes the charging waiting duration; Cr(k) denotes the remaining battery power of the kth electric vehicle waiting for charging at the charging node at time t a c(i); C t C(k) denotes the battery capacity of the kth electric vehicle waiting for charging at the charging node at time t a c(i); N denotes the total number of electric vehicles waiting for charging at the charging node at time t a N station Denotes the total number of charging piles at the charging node; P fc Denotes the fast charging power of the electric vehicle.

[0028] The expression of the prediction result of the spatio-temporal distribution of the charging load of the electric vehicle is:

[0029]

[0030] In the above formula, P k(t) represents the charging load at charging node k at time t; represents the charging power of the i-th electric vehicle at the charging node k at time t under the selected charging mode; m is the number of electric vehicles connected to the charging node k at time t.

[0031] In a second aspect, the present invention provides a prediction system for the spatiotemporal distribution of electric vehicle charging load based on the OD analysis method, wherein the prediction method system comprises a data acquisition module, an initialization module, a dynamic update module, a recording module, and an output module, wherein the recording module comprises a private car recording module, a taxi recording module, and a vehicle recording module for other functions;

[0032] The data acquisition module is used to acquire initial data of electric vehicles and initial data of road networks, wherein the initial data of electric vehicles includes the number of electric vehicles in use, and the initial data of road networks includes a dynamic road network adjacency matrix;

[0033] The initialization module is used to first read the type of a single electric vehicle, generate initial travel time and space parameters based on the type of the electric vehicle, and the initial travel time and space parameters include initial travel time, battery capacity, starting position, and initial power; and then generate the destination of the electric vehicle based on the starting position and the OD probability matrix;

[0034] The dynamic update module is used to plan the travel path of the electric vehicle using the dynamic road network adjacency matrix, and to update the current power of the electric vehicle in real time according to the energy consumption model, and to update the time when the electric vehicle travels to different road nodes in real time according to the time model considering the dynamic road network;

[0035] The private car recording module is used to plan the return route of the electric car using the dynamic road network adjacency matrix after the electric car arrives at the destination. After the return, the electric car is charged in the slow charging mode, and the slow charging load, charging node, and charging time of the electric car are recorded;

[0036] The taxi recording module is used to determine in real time whether the current power of the electric vehicle meets the taxi charging conditions. If so, the nearest charging node is selected based on the current position of the electric vehicle to charge in fast charging mode, and the fast charging load, charging node, and charging time of the electric vehicle are recorded; after charging is completed, the current power of the electric vehicle is updated and the vehicle continues to travel. After arriving at the destination, the next destination is generated with the current destination as the starting position and sent to the dynamic update module for further calculation until the taxi operation time is reached;

[0037] The other functional vehicle usage recording module is used to determine in real time whether the current battery level of the electric vehicle meets the charging conditions for other functional vehicle usages. If it meets the conditions, a charging demand is generated. Based on the current location of the electric vehicle in the road network, the nearest charging node is selected for charging, and it is determined whether the slow charging conditions are met. If they are met, charging is carried out in the slow charging mode; otherwise, charging is carried out in the fast charging mode. The charging load, charging node, and charging time of the electric vehicle in the selected charging mode are recorded. After charging is completed, the current battery level of the electric vehicle is updated and it continues to travel. After arriving at the destination, a new destination is generated with the current destination as the starting position and sent to the dynamic update module for continued calculation until the operating time for other functional vehicle usages is reached.

[0038] The output module is used to obtain the predicted result of the spatio-temporal distribution of the charging load of electric vehicles after completing the calculation for all electric vehicles.

[0039] The energy consumption model includes:

[0040]

[0041] In the above formula, represents the remaining battery level; represents the initial battery level of the electric vehicle; d ij represents the driving distance of section ij, where section ij refers to the section between node i and node j in the road network; E p represents the energy consumption per unit mileage; C t represents the battery capacity of the electric vehicle; represents the power consumption of the air conditioner of the electric vehicle at driving speed V ij , driving distance d ij , and environmental temperature T; represents the power consumption generated when the electric vehicle is driving on class I or class II roads; respectively represent the power consumption generated when the electric vehicle is driving on class I and class II roads. Class I roads are main roads and class II roads are secondary roads; respectively represent the air conditioner cooling and heating powers; V ij represents the driving speed of the electric vehicle on section ij; T represents the environmental temperature; T k-max , T k-min respectively represent the cold and hot temperature limits; V ij-m represents the zero-flow speed corresponding to the road category; N ij represents the traffic capacity between node i and node j of the section corresponding to the road category; S represents the section saturation degree; q ij (t) represents the traffic flow passing through section ij at time t; β represents the road traffic coefficient; a, b, and γ are all adaptive coefficients.

[0042] The time model considering the dynamic road network includes:

[0043]

[0044] λ = t green，yellow / c;

[0045] S = q ij (t) / N ij ;

[0046] In the above formula, t ij represents the time required for the electric vehicle to pass through section ij; n represents the set of road nodes; represents the driving time required for the electric vehicle to pass through section ij; represents the delay time caused by signal control on section ij; V ij represents the driving speed of the electric vehicle on section ij; x ij represents the path decision variable. If the electric vehicle travels from node i to node j, then x ij takes the value of 1, otherwise 0; λ represents the ratio of the total duration of the green and yellow lights to the signal cycle; t green，yellow represents the total duration of the green and yellow lights; c represents the entire red - yellow - green signal cycle; S represents the degree of saturation of the section; q ij (t) represents the traffic flow passing through section ij at time t; N ij represents the traffic capacity of section ij corresponding to the road category; q represents the vehicle arrival rate.

[0047] The private car record module is also used to judge whether the electric vehicle needs to queue for charging when selecting the nearest charging node to charge in fast - charge mode according to the current position of the electric vehicle. If not, it charges directly; otherwise, it first calculates the charging waiting duration according to the following formula, and then calculates the charging time of the electric vehicle based on the time when the electric vehicle arrives at the nearest charging node and the charging waiting duration:

[0048] t sc (i) = t a (i) + T wait (i);

[0049]

[0050] In the above formula, t sc (i) represents the charging time of the electric vehicle; t a (i) represents the time when the electric vehicle arrives at the nearest charging node; T wait (i) represents the charging waiting duration; Cr(k) represents the remaining power of the k - th electric vehicle waiting for charging at the charging node at t a (i); C t (k) represents t a(i) The battery capacity of the k-th electric vehicle waiting to be charged at the charging node at time t; N represents t a (i) The total number of electric vehicles waiting to be charged at the charging node at time t; N station represents the total number of charging piles at the charging node; P fc represents the fast charging power of the electric vehicle.

[0051] The expression of the prediction result of the spatio-temporal distribution of the charging load of the electric vehicle is:

[0052]

[0053] In the above formula, P k (t) represents the charging load at the charging node k at time t; represents the charging power of the i-th electric vehicle at the charging node k at time t under the selected charging mode; m is the number of electric vehicles connected at the charging node k at time t.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] 1. The present invention relates to a method for predicting the spatio-temporal distribution of electric vehicle charging load based on OD analysis. First, the type of a single electric vehicle is read, and initial travel spatio-temporal parameters are generated based on the type of the electric vehicle. The initial travel spatio-temporal parameters include the initial travel time, battery capacity, starting position, and initial battery charge. The destination of the electric vehicle is generated based on the starting position and the OD probability matrix. Then, the travel path of the electric vehicle is planned using the dynamic road network adjacency matrix, and the current battery charge of the electric vehicle is updated in real time according to the energy consumption model. The time when the electric vehicle reaches different road nodes is updated in real time according to the time model considering the dynamic road network. During the travel, the corresponding charging behavior is characterized according to the type of the electric vehicle. For private cars, after the electric vehicle arrives at the destination, the return path of the electric vehicle is planned using the dynamic road network adjacency matrix. After returning, the electric vehicle is charged in the slow charging mode, and the slow charging load, charging node, and charging time of the electric vehicle are recorded. For taxis, it is judged in real time whether the current battery charge of the electric vehicle meets the taxi charging condition. If it meets the condition, the nearest charging node is selected based on the current position of the electric vehicle for fast charging, and the fast charging load, charging node, and charging time of the electric vehicle are recorded. After charging is completed, the current battery charge of the electric vehicle is updated and it continues to travel. After arriving at the destination, the next destination is generated with the current destination as the starting position and the calculation continues until the taxi operation time is reached. For other functional vehicles, it is judged in real time whether the current battery charge of the electric vehicle meets the charging condition for other functional vehicles. If it meets the condition, a charging demand is generated, the nearest charging node is selected based on the position of the electric vehicle in the road network for charging, and it is judged whether the slow charging condition is met. If it meets the condition, it is charged in the slow charging mode, otherwise it is charged in the fast charging mode. The charging load, charging node, and charging time of the electric vehicle in the selected charging mode are recorded. After charging is completed, the current battery charge of the electric vehicle is updated and it continues to travel. After arriving at the destination, the next destination is generated with the current destination as the starting position and the calculation continues until the operation time of other functional vehicles is reached. When the calculation of all electric vehicles is completed, the prediction result of the spatio-temporal distribution of the electric vehicle charging load is obtained. The above design divides the types of electric vehicles, accurately characterizes the travel paths and charging behaviors of different types of electric vehicles, and finally improves the prediction accuracy of the spatio-temporal distribution of the charging load, which helps to realize the orderly control of electric vehicle charging behaviors, the optimization of charging station layout planning, and the safe and stable operation of the distribution network.

[0056] 2. In the method for predicting the spatio-temporal distribution of electric vehicle charging load based on OD analysis of the present invention, when calculating the time required to pass through a section in the time model considering the dynamic road network, in addition to considering the influence of road impedance, the influence of signal control is also considered. By combining the two, the travel behavior of electric vehicles is analyzed, which is more in line with the travel rules of electric vehicles in actual traffic, thereby further improving the prediction accuracy of the spatio-temporal distribution of the electric vehicle charging load. Brief Description of the Drawings

[0057] Figure 1 This is a flowchart of the prediction method described in the present invention.

[0058] Figure 2 This is a schematic structural diagram of the prediction system described in the present invention. Detailed Embodiments

[0059] The present invention will be further described in detail below in conjunction with the detailed embodiments and the drawings.

[0060] Embodiment 1:

[0061] Refer to Figure 1 , a prediction method for spatio-temporal distribution of electric vehicle charging load based on OD analysis method, and the specific steps include:

[0062] S1. Obtain the initial data of electric vehicles and the initial data of the road network. The initial data of electric vehicles includes the ownership of electric vehicles, and the initial data of the road network includes the dynamic road network adjacency matrix;

[0063] S2. First, read the type of a single electric vehicle. The types of electric vehicles include private cars, taxis, and other functional vehicles; generate initial travel spatio-temporal parameters based on the type of electric vehicle. The initial travel spatio-temporal parameters include the initial travel time, battery capacity, starting position, and initial power; then obtain the OD matrix of electric vehicles. The OD matrix is used to describe the travel characteristics of electric vehicles. The elements in this matrix represent the traffic volume between each node of the traffic road network in each time period, and use TransCAD software to inversely deduce the OD probability matrix. The elements in this matrix represent the probability that an electric vehicle departs from one node and travels to another node from time t to time t + 1, and generate the destination of the electric vehicle based on the starting position and the OD probability matrix;

[0064] The probability density function of the initial travel time of the electric vehicle is obtained by curve fitting using matlab, and the expression is:

[0065]

[0066] In the above formula, f personal (t s ) represents the probability density of the initial travel time; t s represents the initial travel time; α 1 , α 2 , α 3 , β 1 , β 2 , β 3 , λ 1 , λ 2 , λ 3All represent fitting parameters; for private cars, λ 1 is taken as 0.018, α 1 is taken as 7.483, β 1 is taken as 0.307; for taxis, λ 2 is taken as 0.112, α 2 is taken as 7.280, β 2 is taken as 1.572; for other functional vehicles, λ 3 is taken as 0.017, α 3 is taken as 12.750, β 3 is taken as 8.069;

[0067] S3. Use the dynamic road network adjacency matrix, starting position, and destination to plan the shortest travel path of the electric vehicle using the Floyd algorithm, and determine the number of road segments included in the shortest travel path and the initial road segment h. Then, during the driving process, update the current battery level of the electric vehicle in real time according to the energy consumption model, and update the time when the electric vehicle travels to different nodes in the road network in real time according to the time model considering the dynamic road network; each time the electric vehicle passes through a road segment, update the current battery level, current time, and the node position at this time, and let h = h + 1;

[0068] Since the power consumption of electric vehicles is affected by many factors, such as the driving speed of the vehicle, environmental temperature, etc., a dynamic energy consumption model is designed; the energy consumption model includes:

[0069]

[0070] In the above formula, represents the remaining battery level; represents the initial battery level of the electric vehicle; d ij represents the driving distance of road segment ij, where road segment ij refers to the road segment between node i and node j in the road network; E p represents the energy consumption per unit mileage; C t represents the battery capacity of the electric vehicle; represents the air-conditioning power consumption of the electric vehicle when driving at speed V ij , driving distance d ij , and environmental temperature T; represents the power consumption generated when the electric vehicle travels on Class I or Class II roads; respectively represent the power consumption generated when the electric vehicle travels on Class I and Class II roads. Class I roads are main roads, and Class II roads are secondary roads; respectively represent the air-conditioning cooling and heating powers; V ij represents the driving speed of the electric vehicle on road segment ij; T represents the environmental temperature; T k-max , T k-min respectively represent the cold and hot temperature limits; V ij-mDenote the zero-flow speed corresponding to the road category; N ij Denote the traffic capacity of the section between node i and node j corresponding to the road category; S denotes the saturation degree of the section; q ij (t) represents the traffic flow passing through section ij at time t; β represents the road passing coefficient; a, b, γ are all adaptive coefficients; for Class I roads, N ij , V ij-m , a, b, γ are 1200 vehicles per hour, 40 - 60 kilometers per hour, 1.726, 3.15, 3 respectively; for Class II roads, N ij , V ij-m , a, b, γ are 900 vehicles per hour, 40 - 50 kilometers per hour, 2.076, 2.87, 3 respectively;

[0071] The time model considering the dynamic road network includes:

[0072]

[0073] λ = t green，yellow / c;

[0074] S = q ij (t) / N ij ;

[0075] In the above formula, t ij represents the time required for the electric vehicle to pass through section ij, and n represents the set of road nodes; represents the driving time required for the electric vehicle to pass through section ij; represents the delay time generated by signal control on section ij; V ij represents the driving speed of the electric vehicle on section ij; x ij represents the path decision variable. If the electric vehicle travels from node i to node j, then x ij takes the value of 1, otherwise 0; λ represents the ratio of the total duration of the green and yellow lights to the signal cycle; t green，yellow represents the total duration of the green and yellow lights; c represents the entire red - yellow - green signal cycle; s represents the saturation degree of the section; q ij (t) represents the traffic flow passing through section ij at time t; N ij represents the traffic capacity of section ij corresponding to the road category; q represents the vehicle arrival rate;

[0076] If the type of the electric vehicle is a private car, then enter S4. If the type of the electric vehicle is a taxi, then enter S5. If the type of the electric vehicle is other functional vehicles, then enter S6;

[0077] S4. After the electric vehicle arrives at the destination, use the dynamic road network adjacency matrix to plan the return path of the electric vehicle and determine the return time; the expression of the probability density function of the return time of the private car is:

[0078]

[0079] In the above formula, f(t f ) represents the probability density of the return time; μ f , σ f Take 17.6 and 3.4 respectively;

[0080] After returning, update the current time and the current remaining power. The electric vehicle is charged in slow charge mode, and record the slow charge load, charging node, start charging time, and charging duration of this electric vehicle; enter S7;

[0081] S5. Continuously judge whether the current power of the electric vehicle meets the charging conditions of the taxi. If it meets, select the nearest charging node based on the current position of the electric vehicle and charge it in fast charge mode, and record the fast charge load, charging node, start charging time, and charging duration of this electric vehicle; when selecting the nearest charging node based on the current position of the electric vehicle and charging it in fast charge mode, judge whether the electric vehicle needs to queue for charging. If not, charge directly. Otherwise, first calculate the charging waiting time according to the following formula, and then calculate the charging time of the electric vehicle based on the time when the electric vehicle arrives at the nearest charging node and the charging waiting time:

[0082] t sc (i) = t a (i) + T wait (i);

[0083]

[0084] In the above formula, t sc (i) represents the charging time of the electric vehicle; t a (i) represents the time when the electric vehicle arrives at the nearest charging node; T wait (i) represents the charging waiting time; Cr(k) represents the remaining power of the kth electric vehicle waiting for charging at the charging node at t a (i); C t (k) represents the battery capacity of the kth electric vehicle waiting for charging at the charging node at t a (i); N represents the total number of electric vehicles waiting for charging at the charging node at t a (i); N station represents the total number of charging piles at the charging node; P fc represents the fast charging power of the electric vehicle;

[0085] After charging is completed, the current power of the electric vehicle is updated and the vehicle continues to travel. After arriving at the destination, the next destination is generated with the current destination as the starting position, and the calculation is returned to S3 to continue until the taxi operation time (generally 24 hours) is reached, and then S7 is entered;

[0086] S6. Determine in real time whether the current power of the electric vehicle meets the charging conditions for other functional vehicles. The charging conditions for other functional vehicles refer to whether the distance between the current position of the electric vehicle and the destination exceeds the cruising range. The cruising range is calculated based on the current power and driving speed of the electric vehicle. If it meets the requirements, a charging demand is generated. The nearest charging node is selected for charging based on the current position of the electric vehicle in the road network, and it is determined whether the slow charging conditions are met. The slow charging conditions refer to the stay time being greater than 30 minutes and the current power being higher than 35% of the battery capacity. If it meets the requirements, it is charged in slow charging mode, otherwise it is charged in fast charging mode; record the charging load, charging node, charging start time, and charging time of the electric vehicle in the selected charging mode; update the current power of the electric vehicle after charging is completed and continue to drive. After arriving at the destination, the next destination is generated with the current destination as the starting position, and return to S3 to continue calculation until the operation time of other functional vehicles is reached (generally set to 24h), and enter S7;

[0087] S7. Determine whether the calculation of all electric vehicles has been completed. If not, return to S2 to perform the calculation of the next electric vehicle. If completed, obtain the prediction result of the spatiotemporal distribution of the charging load of the electric vehicles.

[0088] The expression of the prediction result of the spatiotemporal distribution of the electric vehicle charging load is:

[0089]

[0090] In the above formula, P k (t) represents the charging load at charging node k at time t; represents the charging power of the i-th electric vehicle at the charging node k at time t under the selected charging mode; m is the number of electric vehicles connected to the charging node k at time t.

[0091] Embodiment 2:

[0092] See also Figure 2A system for predicting the spatiotemporal distribution of charging load of electric vehicles based on OD analysis method includes a data acquisition module, an initialization module, a dynamic update module, a recording module, and an output module. The recording module includes a private car recording module, a taxi recording module, and a vehicle recording module for other functions; the data acquisition module is used to obtain the initial data of electric vehicles and the initial data of the road network. The initial data of electric vehicles includes the number of electric vehicles in use, and the initial data of the road network includes a dynamic road network adjacency matrix; the initialization module is used to first read the type of a single electric vehicle, and generate initial travel spatiotemporal parameters based on the type of the electric vehicle. The initial travel spatiotemporal parameters include initial travel time, battery capacity, starting position, and initial power; then generate the destination of the electric vehicle based on the starting position and the OD probability matrix; the dynamic update module is used to plan the travel path of the electric vehicle using the dynamic road network adjacency matrix, and update the current power of the electric vehicle in real time according to the energy consumption model, and update the time when the electric vehicle travels to different road nodes in real time according to the time model considering the dynamic road network; the energy consumption model includes:

[0093]

[0094] In the above formula, Indicates the remaining power; Indicates the initial charge of the electric vehicle; d ij represents the driving distance of road segment ij, where road segment ij refers to the road segment between nodes i and j in the road network; E p Indicates energy consumption per unit mileage; C t Indicates the battery capacity of electric vehicles; Indicates that the electric vehicle is traveling at a speed V ij , driving distance d ij , air conditioning power consumption at ambient temperature T; It indicates the power consumption of electric vehicles when driving on Class I or Class II roads; They represent the power consumption of electric vehicles when driving on Class I and Class II roads, respectively. Class I roads are main roads, and Class II roads are secondary roads. Respectively represent the cooling and heating power of air conditioner; V ij represents the driving speed of the electric vehicle on road section ij; T represents the ambient temperature; T k-max , T k-min Respectively represent the cold and hot temperature limits; V ij-m Indicates the zero flow velocity corresponding to the road category; N ij represents the traffic capacity of the road section between node i and node j corresponding to the road category; S represents the saturation degree of the road section; q ij (t) represents the traffic flow through the road section ij at time t; β represents the road traffic coefficient; a, b, and γ are all adaptive coefficients;

[0095] The time model considering the dynamic road network includes:

[0096]

[0097] λ = t green，yellow / c;

[0098] S = q ij (t) / N ij ;

[0099] In the above formula, t ij represents the time required for the electric vehicle to pass through section ij; n represents the set of road nodes; represents the driving time required for the electric vehicle to pass through section ij; represents the delay time caused by signal control on section ij; V ij represents the driving speed of the electric vehicle on section ij; x ij represents the path decision variable. If the electric vehicle travels from node i to node j, then x ij takes the value of 1, otherwise 0; λ represents the ratio of the total duration of the green and yellow lights to the signal cycle; t green，yellow represents the total duration of the green and yellow lights; c represents the entire red-yellow-green signal cycle; S represents the saturation degree of the section; q ij (t) represents the traffic flow passing through section ij at time t; N ij represents the traffic capacity of section ij corresponding to the road category; q represents the vehicle arrival rate;

[0100] The private car recording module is used to plan the return path of the electric vehicle using the dynamic road network adjacency matrix after the electric vehicle arrives at the destination. After returning, the electric vehicle charges in the slow charging mode, and records the slow charging load, charging node, and charging time of this electric vehicle. Specifically, when charging in the fast charging mode by selecting the nearest charging node according to the current position of the electric vehicle, the private car recording module determines whether the electric vehicle needs to queue for charging. If not, it directly charges. Otherwise, it first calculates the charging waiting duration according to the following formula, and then calculates the charging time of the electric vehicle based on the time when the electric vehicle arrives at the nearest charging node and the charging waiting duration:

[0101] t sc (i) = t a (i) + T wait (i);

[0102]

[0103] In the above formula, t sc (i) represents the charging time of the electric vehicle; t a(i) represents the moment when the electric vehicle arrives at the nearest charging node; T wait (i) represents the charging waiting duration; Cr(k) represents t a (i) represents the remaining power of the k-th electric vehicle waiting to be charged at the charging node at time t; C t (k) represents t a (i) represents the battery capacity of the k-th electric vehicle waiting to be charged at the charging node at time t; N represents t a (i) represents the total number of electric vehicles waiting to be charged at the charging node at time t; N station represents the total number of charging piles at the charging node; p fc represents the fast charging power of the electric vehicle;

[0104] The taxi record module is used to determine in real time whether the current power of the electric vehicle meets the taxi charging condition. If it meets, it selects the nearest charging node based on the current location of the electric vehicle and charges it in fast charging mode, records the fast charging load, charging node, and charging time of the electric vehicle; after charging is completed, it updates the current power of the electric vehicle and continues to drive. After arriving at the destination, it generates the next destination with the current destination as the starting location and sends it to the dynamic update module for continued calculation until the taxi operation time is reached; the other functional vehicle record module is used to determine in real time whether the current power of the electric vehicle meets the charging condition of other functional vehicles. If it meets, it selects the nearest charging node based on the current location of the electric vehicle in the road network and charges it in slow charging mode, records the slow charging load, charging node, and charging time of the electric vehicle; after charging is completed, it updates the current power of the electric vehicle and continues to drive. After arriving at the destination, it generates the next destination with the current destination as the starting location and sends it to the dynamic update module for continued calculation until the operation time of other functional vehicles is reached; the output module is used to statistically obtain the predicted result of the spatio-temporal distribution of the electric vehicle charging load after completing the calculation of all electric vehicles; the expression of the predicted result of the spatio-temporal distribution of the electric vehicle charging load is:

[0105]

[0106] In the above formula, P k (t) represents the charging load at charging node k at time t; represents the charging power of the i-th electric vehicle at charging node k at time t under the selected charging mode; m is the number of electric vehicles connected to charging node k at time t.

[0107] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0108] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0109] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method for predicting the spatiotemporal distribution of electric vehicle charging load based on OD analysis, characterized by: The prediction method comprises: S1. Acquire initial data of electric vehicles and initial data of road networks, wherein the initial data of electric vehicles includes the number of electric vehicles in use, and the initial data of road networks includes a dynamic road network adjacency matrix; S2, first read the type of a single electric vehicle, and generate initial travel time and space parameters based on the type of the electric vehicle, wherein the initial travel time and space parameters include initial travel time, battery capacity, starting position, and initial power; then generate the destination of the electric vehicle based on the starting position and the OD probability matrix; S3, using the dynamic road network adjacency matrix to plan the travel path of the electric vehicle, and updating the current power of the electric vehicle in real time according to the energy consumption model, and updating the time when the electric vehicle travels to different road nodes in real time according to the time model considering the dynamic road network; if the electric vehicle type is a private car, then enter S4, if the electric vehicle type is a taxi, then enter S5, if the electric vehicle type is a vehicle for other functions, then enter S6; S4, after the electric vehicle arrives at the destination, the return route of the electric vehicle is planned using the dynamic road network adjacency matrix. After the return, the electric vehicle is charged in a slow charging mode, and the slow charging load, charging node, and charging time of the electric vehicle are recorded; enter S7; S5. Determine in real time whether the current power of the electric vehicle meets the taxi charging conditions. If so, select the nearest charging node based on the current position of the electric vehicle to charge in fast charging mode, and record the fast charging load, charging node, and charging time of the electric vehicle; after charging, update the current power of the electric vehicle and continue driving. After arriving at the destination, use the current destination as the starting position to generate the next destination, return to S3 to continue calculation, until the taxi operation time is reached, and enter S7; S6. Determine in real time whether the current power of the electric vehicle meets the charging conditions of other functional vehicles. If so, a charging demand is generated. Select the nearest charging node for charging based on the current location of the electric vehicle in the road network, and determine whether the slow charging conditions are met. If so, charge in slow charging mode, otherwise charge in fast charging mode; record the charging load, charging node, and charging time of the electric vehicle in the selected charging mode; update the current power of the electric vehicle after charging is completed and continue driving. After arriving at the destination, use the current destination as the starting position to generate the next destination, return to S3 to continue calculation, until the operation time of other functional vehicles is reached, and enter S7; S7. Determine whether the calculation of all electric vehicles has been completed. If not, return to S2 to perform the calculation of the next electric vehicle. If completed, obtain the prediction result of the spatiotemporal distribution of the charging load of the electric vehicles.

2. The method for predicting the spatiotemporal distribution of electric vehicle charging load based on OD analysis method according to claim 1 is characterized in that: The energy consumption model includes: In the above formula, Indicates the remaining power; Indicates the initial charge of the electric vehicle; d ij represents the driving distance of road segment ij, where road segment ij refers to the road segment between nodes i and j in the road network; E p Indicates energy consumption per unit mileage; C t Indicates the battery capacity of electric vehicles; Indicates that the electric vehicle is traveling at a speed V ij , driving distance d ij , air conditioning power consumption at ambient temperature T; It indicates the power consumption of electric vehicles when driving on Class I or Class II roads; They represent the power consumption of electric vehicles when driving on Class I and Class II roads, respectively. Class I roads are main roads, and Class II roads are secondary roads. Respectively represent the cooling and heating power of air conditioner; V ij represents the driving speed of the electric vehicle on road section ij; T represents the ambient temperature; T k-max 、T k-min Respectively represent the cold and hot temperature limits; V ij-m Indicates the zero flow velocity corresponding to the road category; N ij represents the traffic capacity of the road section between node i and node j corresponding to the road category; S represents the saturation degree of the road section; q ij (t) represents the traffic flow through the road section ij at time t; β represents the road traffic coefficient; a, b, and γ are all adaptive coefficients.

3. The method for predicting the spatiotemporal distribution of electric vehicle charging load based on OD analysis method according to claim 2 is characterized in that: The time model considering the dynamic road network includes: λ=t green，yellow / c; S=q ij (t) / N ij ; In the above formula, t ij represents the time required for an electric vehicle to pass through road section ij, and n represents the set of road nodes; represents the driving time required for an electric vehicle to pass through road section ij; Indicates the delay time caused by the traffic light control on the road section ij; V ij represents the driving speed of the electric vehicle on road section ij; x ij represents the path decision variable. If an electric vehicle travels from node i to node j, then x ij The value is 1, otherwise it is 0; λ represents the ratio of the total duration of the green light and the yellow light to the signal period; t green，yellow represents the total duration of green and yellow lights; c represents the entire red, yellow and green signal cycle; S represents the saturation level of the road section; q ij (t) represents the traffic flow through the road section ij at time t; N ij represents the traffic capacity of road section ij corresponding to the road category; q represents the vehicle arrival rate.

4. The method for predicting the spatiotemporal distribution of electric vehicle charging load based on OD analysis method according to claim 3 is characterized in that: When the nearest charging node is selected according to the current location of the electric vehicle for fast charging mode, it is determined whether the electric vehicle needs to queue up for charging. If not, it is charged directly. Otherwise, the charging waiting time is calculated according to the following formula, and then the charging time of the electric vehicle is calculated based on the time when the electric vehicle arrives at the nearest charging node and the charging waiting time: t sc (i)=t a (i)+T wait (i); In the above formula, t sc (i) represents the charging time of the electric vehicle; t a (i) represents the time when the electric vehicle arrives at the nearest charging node; T wait (i) represents the charging waiting time; Cr(k) represents t a (i) The remaining power of the kth electric vehicle waiting to be charged at the charging node at that moment; C t (k) represents t a (i) The battery capacity of the kth electric vehicle waiting to be charged at the charging node at the moment; N represents t a (i) The total number of electric vehicles waiting to be charged at the charging node at any given moment; N station Represents the total number of charging piles at the charging node; P fc Indicates the fast charging power of electric vehicles.

5. The method for predicting the spatiotemporal distribution of electric vehicle charging load based on OD analysis method according to claim 4 is characterized in that: The expression of the prediction result of the spatiotemporal distribution of the electric vehicle charging load is: In the above formula, P k (t) represents the charging load at charging node k at time t; represents the charging power of the i-th electric vehicle at the charging node k at time t under the selected charging mode; m is the number of electric vehicles connected to the charging node k at time t.

6. The electric vehicle charging load spatiotemporal distribution prediction system based on OD analysis method is characterized by: The prediction method system includes a data acquisition module, an initialization module, a dynamic update module, a recording module, and an output module. The recording module includes a private car recording module, a taxi recording module, and other functional vehicle recording modules; The data acquisition module is used to acquire initial data of electric vehicles and initial data of road networks, wherein the initial data of electric vehicles includes the number of electric vehicles in use, and the initial data of road networks includes a dynamic road network adjacency matrix; The initialization module is used to first read the type of a single electric vehicle, and generate initial travel time and space parameters based on the type of the electric vehicle, wherein the initial travel time and space parameters include initial travel time, battery capacity, starting position, and initial power; Then the destination of the electric vehicle is generated based on the starting location and the OD probability matrix; The dynamic update module is used to plan the travel path of the electric vehicle using the dynamic road network adjacency matrix, and to update the current power of the electric vehicle in real time according to the energy consumption model, and to update the time when the electric vehicle travels to different road nodes in real time according to the time model considering the dynamic road network; The private car recording module is used to plan the return route of the electric car using the dynamic road network adjacency matrix after the electric car arrives at the destination. After the return, the electric car is charged in the slow charging mode, and the slow charging load, charging node, and charging time of the electric car are recorded; The taxi recording module is used to determine in real time whether the current power of the electric vehicle meets the taxi charging conditions. If so, the nearest charging node is selected based on the current position of the electric vehicle to charge in fast charging mode, and the fast charging load, charging node, and charging time of the electric vehicle are recorded; after charging is completed, the current power of the electric vehicle is updated and the vehicle continues to travel. After arriving at the destination, the next destination is generated with the current destination as the starting position and sent to the dynamic update module for further calculation until the taxi operation time is reached; The other function vehicle recording module is used to determine in real time whether the current power of the electric vehicle meets the charging conditions of other function vehicles. If so, a charging demand is generated, and the nearest charging node is selected for charging based on the current location of the electric vehicle in the road network, and whether the slow charging conditions are met. If so, it is charged in slow charging mode, otherwise it is charged in fast charging mode, and the charging load, charging node, and charging time of the electric vehicle in the selected charging mode are recorded; after charging is completed, the current power of the electric vehicle is updated and the vehicle continues to travel. After arriving at the destination, the next destination is generated with the current destination as the starting position and sent to the dynamic update module for continued calculation until the operation time of other function vehicles is reached; The output module is used to obtain the prediction results of the time-space distribution of the charging load of the electric vehicles after completing the calculation of all the electric vehicles.

7. The electric vehicle charging load spatiotemporal distribution prediction system based on OD analysis method according to claim 6 is characterized by: The energy consumption model includes: In the above formula, Indicates the remaining power; Indicates the initial charge of the electric vehicle; d ij represents the driving distance of road segment ij, where road segment ij refers to the road segment between nodes i and j in the road network; E p Indicates energy consumption per unit mileage; C t Indicates the battery capacity of electric vehicles; Indicates that the electric vehicle is traveling at a speed V ij , driving distance d ij , air conditioning power consumption at ambient temperature T; It indicates the power consumption of electric vehicles when driving on Class I or Class II roads; They represent the power consumption of electric vehicles when driving on Class I and Class II roads, respectively. Class I roads are main roads, and Class II roads are secondary roads. Respectively represent the cooling and heating power of air conditioner; V ij represents the driving speed of the electric vehicle on road section ij; T represents the ambient temperature; T k-max , T k-min Respectively represent the cold and hot temperature limits; V ij-m Indicates the zero flow velocity corresponding to the road category; N ij represents the traffic capacity of the road section between node i and node j corresponding to the road category; S represents the saturation degree of the road section; q ij (t) represents the traffic flow through the road section ij at time t; β represents the road traffic coefficient; a, b, and γ are all adaptive coefficients.

8. The electric vehicle charging load spatiotemporal distribution prediction system based on OD analysis method according to claim 7 is characterized by: The time model considering the dynamic road network includes: λ=t green，yellow / c; S=q ij (t) / N ij ; In the above formula, t ij represents the time required for an electric vehicle to pass through road section ij; n represents the set of road nodes; represents the driving time required for an electric vehicle to pass through road section ij; Indicates the delay time caused by the traffic light control on the road section ij; V ij represents the driving speed of the electric vehicle on road section ij; x ij represents the path decision variable. If an electric vehicle travels from node i to node j, then x ij The value is 1, otherwise it is 0; λ represents the ratio of the total duration of the green light and the yellow light to the signal period; t green，yellow represents the total duration of green and yellow lights; c represents the entire red, yellow and green signal cycle; S represents the saturation level of the road section; q ij (t) represents the traffic flow through the road section ij at time t; N ij represents the traffic capacity of road section ij corresponding to the road category; q represents the vehicle arrival rate.

9. The electric vehicle charging load spatiotemporal distribution prediction system based on OD analysis method according to claim 8 is characterized by: The private car recording module is also used to determine whether the electric car needs to queue up for charging when the nearest charging node is selected according to the current position of the electric car for charging in the fast charging mode. If not, it is charged directly. Otherwise, the charging waiting time is first calculated according to the following formula, and then the charging time of the electric car is calculated based on the time when the electric car arrives at the nearest charging node and the charging waiting time: t sc (i)=t a (i)+T wait (i); In the above formula, t sc (i) represents the charging time of the electric vehicle; t a (i) represents the time when the electric vehicle arrives at the nearest charging node; T wait (i) represents the charging waiting time; Cr(k) represents t a (i) The remaining power of the kth electric vehicle waiting to be charged at the charging node at that moment; C t (k) represents t a (i) The battery capacity of the kth electric vehicle waiting to be charged at the charging node at the moment; N represents t a (i) The total number of electric vehicles waiting to be charged at the charging node at any given moment; N station Represents the total number of charging piles at the charging node; P fc Indicates the fast charging power of electric vehicles.

10. The electric vehicle charging load spatiotemporal distribution prediction system based on OD analysis method according to claim 9 is characterized in that: The expression of the prediction result of the spatiotemporal distribution of the electric vehicle charging load is: In the above formula, P k (t) represents the charging load at charging node k at time t; represents the charging power of the i-th electric vehicle at the charging node k at time t under the selected charging mode; m is the number of electric vehicles connected to the charging node k at time t.

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