CN111242403B - Charging load prediction method, device equipment and storage medium for charging station - Google Patents
Charging load prediction method, device equipment and storage medium for charging station Download PDFInfo
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Abstract
According to the charging station charging load prediction method, the charging station computer equipment and the storage medium, the charging station selection behaviors of the user are simulated by calculating the time required by the user to go to different stations for charging, so that the vehicles to be charged of all charging stations in an area at different moments are determined, the accurate prediction of the charging load of all the charging stations is completed, firstly, the optimal path selection based on the traffic information characteristic quantity in a pre-extracted target area and the pre-established shortest running distance is performed, after the total charging time of the users of the electric vehicles to be charged is determined, the charging station selection behaviors of the electric vehicles to be charged are simulated, a charging station load function matrix is constructed, and therefore the charging load curve of all the charging stations in the target area is obtained, so that a basis is provided for real-time scheduling of the charging behaviors of the electric vehicles.
Description
Technical Field
The invention relates to a method, device equipment and storage medium for predicting charging loads of all electric vehicle charging stations of an in-area power distribution network.
Background
As a novel transportation tool, the electric automobile can effectively reduce environmental pollution, reduce the exhaust emission and the consumption of fossil energy of the traditional fuel oil automobile, and has important strategic significance in the aspect of building environment-friendly and sustainable development society.
With the popularization of electric vehicles, charging is a rigid requirement of electric vehicle users, and the construction of infrastructure such as charging stations is also a foundation for the development of electric vehicle industry. However, because the charging power of the electric automobile is larger, when the large-scale electric automobile is randomly connected into the power grid for charging, serious influence can be brought to the safe and stable operation of the power grid. If the charging behavior of the electric vehicle user can be predicted, the charging load of each charging station in the target power grid area at different moments is determined, and on the basis of combining the conventional load fluctuation condition, the real-time scheduling and global control strategy of the charging behavior of the electric vehicle is formulated, so that the ordering of the charging behavior of the electric vehicle can be expected to be realized, the impact caused by the charging load connected to the power grid is reduced, the tide distribution of the power distribution network is improved, and the electric energy quality and the power supply reliability are improved.
At present, research on charge load prediction mainly focuses on load modeling based on a travel chain, space-time distribution rules of electric vehicle charge load obtained through Monte Carnot simulation and the like. However, the above research analysis is to analyze the overall characteristics of the charging load of the distribution network in the area, and when there are a plurality of charging stations in the target area, there are differences in the positions of the selected charging stations for electric vehicles having different geographic positions and different amounts of remaining Charge (SOC) at different times, so the charging load conditions of the respective charging stations are also different. For this reason, it is necessary to make predictions of the charging loads of different electric vehicle charging stations.
Disclosure of Invention
The invention aims to provide a charging load prediction method, device equipment and storage medium for a charging station, which are used for solving the problems existing in the prior art.
In order to achieve the above purpose, the specific technical scheme of the invention is as follows:
the invention provides a charging load prediction method of a charging station, which comprises the following steps:
(1) Extracting traffic information characteristic quantity in a target area: acquiring the number and geographical position information of traffic nodes and charging stations in a target area road network, and establishing a mapping relationship between the charging stations and corresponding nearest traffic nodes in the target area according to the principle that the charging stations are nearest to the traffic nodes so as to determine a traffic node n corresponding to any charging station r in the target area r Wherein, saidThe total number of charging stations in the target area is P, r epsilon [1, P]The total number of the traffic nodes is N, each traffic node in the target area is numbered from 1 to N, N r ∈[1.N]；
(2) And (3) selecting an optimal driving path: establishing a navigation set of an optimal travel path between any traffic node i and any traffic node j in a target area by taking the shortest travel path as a principle, wherein i, j epsilon [1, N ];
(3) Determining the number of electric vehicles to be charged: acquiring the current geographic positions, the battery residual quantity SOC and the driving destination position of all electric vehicles in a target area, defining the electric vehicles with the SOC lower than a preset value as electric vehicles to be charged, and determining the total number M of the electric vehicles to be charged in the target area;
(4) Optimal charging station selection and charging load function construction: the method comprises the steps of executing the following circulation processing on a vehicle k to be charged until optimal charging station selection and charging load function construction of M electric vehicles to be charged in a target area are completed, wherein k is the number of the electric vehicles to be charged;
(4-1) determining the traffic node number i of the automobile k to be charged in the road network according to the current geographic position and the driving destination position of the automobile k to be charged k Destination traffic node number j k ，
(4-2) respectively carrying out accessibility judgment on P charging stations in the target area according to the navigation set, the mapping relation and the accessibility criterion so as to determine all the reachable charging stations A of the vehicle k to be charged w The reachability criterion is as follows:
wherein the S is path·ir For traffic node i to traffic node n r The optimal path length of the vehicle is C is the consumption percentage of the residual electric quantity of the k running unit path length of the vehicle to be charged, and SOC k The remaining capacity of the battery of the automobile k to be charged is obtained;
(4-3) determining the automobile to be chargedk to the respective reachable charging stations A w Total set of charging times required for charging
And the reachable charging station corresponding to the minimum value of the total charging time is used as the target charging station of the automobile k to be charged,
Wherein t is sum·k ＝＝t d·k +t w·k +t c·k
Wherein t is d·k For the time required for an electric vehicle to travel from the current location to a charging station and then from the charging station to a destination, t w·k For the queuing time of the electric vehicle in the charging station, t ck The charging time of the electric automobile in the charging station is set;
(4-4) constructing a load function matrix of the target charging station of the vehicle k to be charged with t=0 as the current moment:
M load·k ＝[0，…，0，p(k)，0，…0]
wherein M is load·k And P (k) is a load characteristic function corresponding to the target charging station when the vehicle k to be charged is charged to the target charging station, and P is a number corresponding to the target charging station:
wherein t is start For the charging start time, t, of the vehicle k to be charged w·k And t c·k Respectively charging queuing time and charging time of automobile k to be charged, P c The charging electric energy percentage of the electric automobile is calculated;
(4-5) making k=k+1, repeating the steps (4-1) - (4-4) until k=M, and completing optimal charging station selection and charging load function construction of all electric vehicles to be charged in the target area to obtain M charging station load function matrixes { M } load·1 ，M load·2 ，…，M load·M} ；
(5) Determining a charging total load function of each charging station in a target area, and comparingThe M charging station load function matrixes are summed to obtain a charging total load function matrix M of each charging station in the target area sum ：
Wherein M is sum The P-th column function of the matrix is the charging load prediction condition of the P-th charging station in the target area.
In the step (3), the real-time information of the current geographic position, the battery residual capacity SOC and the driving destination of all the electric vehicles in the area is obtained from the electric vehicle-mounted positioning navigation system and the area distribution network vehicle information monitoring system by one of the wireless communication modes of 5G, microwave and frequency modulation.
As a preferred embodiment, for said A w Each of the charging stations is able to determine the vehicle k to be charged by the following formula
To determine the value of t d·k Is of the size of (2):
As a preferred embodiment of the present invention, 
Wherein SOC is end The battery power of the electric automobile when the electric automobile is charged is obtained.
Further, the SOC end The value of (2) is 0.9-0.95 of rated battery power.
As a preferred embodiment, the queuing time t of the electric vehicle k at the charging station w·k Is a preset value.
As a preferable mode, in the step (4-2), S path·ir For traffic node i to traffic node n r The determination of the path length of the optimal path of (c) comprises the steps of:
determining a traffic node n corresponding to the charging station r according to the mapping relation r ；
Determining traffic node i to the traffic node n via the navigation set r Is provided.
The invention also provides a charging load prediction device of the charging station, which comprises the following components:
the traffic information feature quantity extraction module is used for acquiring the quantity and geographic position information of traffic nodes and charging stations in the target area road network, and establishing a mapping relationship between the charging stations and the corresponding nearest traffic nodes in the target area according to the principle that the charging stations are nearest to the traffic nodes so as to determine the traffic node n corresponding to any charging station r in the target area r Wherein the total number of charging stations in the target area is P, r epsilon [1, P]The total number of the traffic nodes is N, each traffic node in the target area is numbered from 1 to N, N r ∈[1.N]；
The optimal driving path selection module is used for establishing a navigation set of an optimal driving path between any traffic node i and any traffic node j in the target area on the basis of the shortest driving path, wherein i, j epsilon [1, N ];
the system comprises a to-be-charged electric automobile quantity determining module, a charging control module and a charging control module, wherein the to-be-charged electric automobile quantity determining module is used for acquiring the current geographic positions, the battery residual quantity SOC and the driving destination position of all electric automobiles in a target area, defining the electric automobiles with the SOC lower than a preset value as to-be-charged electric automobiles, and determining the total number M of the to-be-charged electric automobiles in the target area;
the optimal charging station selection and charging load function construction module is used for performing the following circulation processing on the to-be-charged automobile k until the optimal charging station selection and charging load function construction of M to-be-charged electric automobiles in the target area are completed, wherein k is the number of the to-be-charged electric automobiles and comprises
The target traffic node acquisition sub-module is used for determining the traffic node number i of the automobile k to be charged in the road network according to the current geographic position and the driving destination position of the automobile k to be charged k Destination traffic node number j k 。
The reachable charging station acquisition sub-module is used for respectively carrying out reachability judgment on P charging stations in the target area according to the navigation set, the mapping relation and the reachability criterion so as to determine all reachable charging stations A of the vehicle k to be charged w The reachability criterion is as follows:
wherein the S is path·ir For traffic node i to traffic node n r The distance length and C of the optimal path of the vehicle k to be charged are the remaining power consumption percentages of the vehicle k to be charged in the unit distance length of running;
target charging station determination submodule for determining a vehicle k to be charged to each of the available charging stations a w Total set of charging times required for charging
And the total charging time is maximizedThe reachable charging station corresponding to the decimal value serves as the target charging station for the vehicle k to be charged, wherein,
t sum·k ＝＝t d·k +t w·k +t c·k
t d·k for the time required for an electric vehicle to travel from the current location to a charging station and then from the charging station to a destination, t w·k For the queuing time of the electric vehicle in the charging station, t c·k The charging time of the electric automobile in the charging station is set;
the target charging station load function matrix construction submodule is used for constructing a load function matrix of a target charging station of the automobile k to be charged by taking t=0 as the current moment:
M load·k ＝[0，…，0,p(k)，0，…0]
wherein M is load·k And the matrix is 1 row and P column, and P is the total number of charging stations in the target area. p (k) is a load characteristic function corresponding to the target charging station when the vehicle k to be charged is charged to the target charging station, and p is a number corresponding to the target charging station:
wherein t is start For the charging start time, t, of the vehicle k to be charged wk And t ck Respectively charging queuing time and charging time of automobile k to be charged, P c The charging electric energy percentage of the electric automobile is calculated;
the charging station load function matrix construction submodule is used for completing optimal charging station selection and charging load function construction of all electric vehicles to be charged in a target area to obtain M charging station load function matrixes { M } load·1 ，M load·2 ，…，M load·M }；
The charging station charging total load function determining module is used for summing the M charging station load function matrixes to obtain a charging total load function matrix M of each charging station in the target area sum ：
Wherein M is sum And the function of the P-th column is the charging load prediction condition of the P-th charging station in the target area, wherein the P-th column is a matrix of 1 row and P columns.
As a preferred solution, in the module for determining the number of electric vehicles to be charged, real-time information of the current geographic position, the remaining battery power SOC and the driving destination of all electric vehicles in the area is obtained from the vehicle-mounted positioning navigation system of the electric vehicles and the area distribution network vehicle information monitoring system by one of 5G, microwave and frequency modulation.
As a preferred aspect, in the target charging station determination submodule, for the a w Each of the charging stations is able to determine t of the vehicle k to be charged by the following formula d·kAw To determine the value of t d·k Is of the size of (2):
In a preferred embodiment, the target charging station determination sub-module,
wherein SOC is end The battery power of the electric automobile when the electric automobile is charged is obtained.
Further, the SOC end The value of (2) is 0.9-0.95 of rated battery power.
As a preferred embodiment, in the target charging station determination submodule, the electric vehicle k is in the charging station for a queuing time t w·k Is a preset value.
As a preferred solution, in the reachable charging station acquisition sub-module, S path·ir For traffic node i to traffic node n r The determination of the path length of the optimal path of (a) comprises:
Determining a traffic node n corresponding to the charging station r according to the mapping relation r ；
Determining traffic node i to the traffic node n via the navigation set r Is provided.
According to the charging station charging load prediction method, device equipment and storage medium provided by the invention, after the total charging time of an electric vehicle user to be charged is determined based on the extracted traffic information characteristic quantity in a target area and the pre-established optimal path selection of the shortest driving distance, a charging station load function matrix is constructed by simulating the charging station selection behavior of the electric vehicle to be charged; therefore, the charging load curve of each charging station in the target area is obtained, and a basis is provided for real-time scheduling of the charging behavior of the electric vehicle. Specifically, when a plurality of selectable charging stations exist in a target area, through simulating charging behaviors of electric vehicles at different positions in a road network traffic system, running time spent by the electric vehicles to be charged on the road, queuing time at the charging stations, service time required by full charge and running time from the charging stations to a destination are calculated, all the time are summed to obtain total charging time required by the electric vehicles to finish charging at the different charging stations, a station with the least time consumption is selected from the total charging time to serve as an optimal charging station of the electric vehicles, and after the corresponding optimal charging station is formulated for each electric vehicle, a charging load curve of each charging station is obtained to provide basis for real-time scheduling of charging behaviors of the electric vehicles.
Drawings
Fig. 1 is a schematic workflow diagram of a charging load prediction method of a charging station according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a charge load prediction device of a question real charging station according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to facilitate the understanding of the structure of the present invention, the following description is made with reference to the drawings and embodiments.
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The invention provides a charging load prediction method for charging stations of an electric vehicle of an electric distribution network in an area.
The charging load prediction method of the charging station comprises the following steps:
(1) Traffic information feature quantity extraction, obtaining traffic nodes in a target area road network and the respective quantity and geographic positions of charging stations, and establishing the charging stations in the target area according to the principle that the charging stations are nearest to the traffic nodesMapping relation between the charging station r and the corresponding nearest traffic node to determine the traffic node n corresponding to any charging station r in the target area r Wherein the total number of charging stations in the target area is P, r epsilon [1, P]The total number of the traffic nodes is N, each traffic node in the target area is numbered from 1 to N, N r ∈[1，N]The method comprises the steps of carrying out a first treatment on the surface of the In this embodiment, the real-time map and the vehicle navigation system that can be directly applied acquire the number of traffic nodes and charging stations in the target area road network and the geographic position information.
(2) Selecting an optimal running path, and establishing a navigation set of the optimal running path between any traffic node i and any traffic node j in a target area by taking the shortest running path as a principle, wherein i, j epsilon [1, N ]; in this embodiment, the optimal travel path is determined by calculating the distance between the traffic node i and any traffic node j, that is, the route with the shortest travel path between the traffic node i and any traffic node j is used as the optimal path by using the real-time map and the vehicle navigation system.
(3) Determining the number of electric vehicles to be charged, acquiring the current geographic positions, the battery residual capacity SOC and the driving destination positions of all the electric vehicles in a target area, defining the electric vehicles with the battery residual capacity SOC lower than a preset value as electric vehicles to be charged, and determining the total number M of the electric vehicles to be charged in the target area;
(4) The optimal charging station selection and the charging load function construction are carried out on the automobile k to be charged, and the following circulation processing is carried out until the optimal charging station selection and the charging load function construction of M electric automobiles to be charged in a target area are completed, wherein k is the number of the electric automobiles to be charged, and k is [1, M ];
(4-1) determining the traffic node number i of the automobile k to be charged in the road network according to the current geographic position and the driving destination position of the automobile k to be charged k Destination traffic node number j k ，
(4-2) respectively carrying out accessibility judgment on P charging stations in the target area according to the navigation set, the mapping relation and the accessibility criterion so as to determine all accessible charges of the vehicle k to be chargedPower station A w Wherein the reachability criterion is:
the S is path·ir Corresponding traffic node n from traffic node i to charging station r r Path length, SOC of the optimal path of (a) k The remaining battery power C of the automobile k to be charged is a remaining power consumption percentage of the running unit distance length of the automobile k to be charged, the percentage is a determined value for a certain type of electric automobile, when the automobile is delivered from the factory, the manufacturer determines that the north automobile E150EV electric automobile is taken as an example, the power consumption is 15 kW.h for hundred kilometers, and the battery capacity is 25.6 kW.h, so that the automobile is provided with the following functions
I.e. consume 0.586% of electricity per kilometer;
(4-3) determining the vehicle k to be charged to the respective reachable charging station A w Total set of charging times required for charging
And taking the reachable charging station corresponding to the minimum value of the total charging time as the target charging station of the automobile k to be charged, #>
Wherein t is sum·k ＝＝t d·k +t w·k +t c·k
t d·k For driving the vehicle k to be charged from the current position to the reachable charging station a w Then from the reachable charging station A w Time required for traveling to destination, t w·k A is a reachable charging station for an electric automobile w Queuing time in t c·k A is a reachable charging station for an electric automobile w The charging time in the battery;
(4-4) constructing a load function matrix of the target charging station of the vehicle k to be charged with t=0 as the current moment:
M load·k ＝[0，…，0，p(k)，0，…0]
wherein M is load·k The method comprises the steps that the matrix is 1 row and P column, P is the total number of charging stations in a target area, P (k) is a load characteristic function corresponding to a target charging station when a vehicle k to be charged is charged to the target charging station, and P is a number corresponding to the target charging station:
Wherein t is start For the charging start time, t, of the vehicle k to be charged wk And t c·k Respectively charging queuing time and charging time of automobile k to be charged, P c The charging electric energy percentage of the automobile k to be charged is calculated;
(4-5) making k=k+1, repeating the steps (4-1) - (4-4) until k=M, and completing target charging station selection and charging load function construction of all the vehicles to be charged in the target area to obtain M charging station load function matrixes { M } load·1 ，M load·2 ，...，M load·M }；
(5) Determining charging total load function of charging stations, summing the M charging station load function matrixes, and obtaining a charging total load function matrix M of each charging station in a target area sum ：
Wherein M is sum And the function of the P-th column is the charging load prediction condition of the P-th charging station in the target area, wherein the P-th column is a matrix of 1 row and P columns.
According to the method for predicting the charging load of each electric vehicle charging station of the power distribution network in the area, when a plurality of selectable charging stations exist in a target area, charging behavior simulation is conducted on electric vehicles in different positions in a road network traffic system, when the residual electric quantity of the electric vehicles reaches a charging requirement, charging stations in the range of the residual electric quantity are screened out, running time consumed by the electric vehicles on the way, queuing time of the charging stations, service time required by full charge and running time from the charging stations to a destination are calculated, all the time are summed, and charging total time required by the electric vehicles to finish charging at different charging stations is obtained, and a station with the minimum time consumption is selected as an optimal charging station of the electric vehicles. After the charging stations of all the electric vehicles in the target area are determined, the charging load curve of each charging station can be obtained.
In step 3, the current geographic position, the battery residual capacity (SOC) and the driving destination information of all the electric vehicles in the area are obtained from the electric vehicle-mounted positioning navigation system and the area distribution network vehicle information monitoring system by one wireless communication mode of 5G, microwave and frequency modulation.
In a preferred embodiment, in the step 3, the step S path·ir The acquisition of (1) comprises the steps of: firstly, determining a traffic node n corresponding to a charging station r according to the mapping relation r Determining traffic node i to the traffic node n via the navigation set r The path length of the optimal path between the traffic node i and the charging station r.
In a preferred embodiment, in the step 3, vehicles with SOC lower than 20% are screened out, which is defined as vehicles requiring charging.
In a preferred embodiment, in the step 4, the t d·k The calculation of (2) is based on the following:
wherein the method comprises the steps of
For the vehicle k to be charged to travel from its current position to a w The number node charging station charges and then from A w The time required for the number node charging station to travel to the destination; i.e k And j k Respectively numbering traffic nodes at the current position and the destination position of the automobile k to be charged; / >
For node i k And the path length of the optimal path between nodes Aw; />
For node Aw and node j k The distance of the optimal path is obtained from the navigation set in the step (2); v is the average speed of the electric vehicle for a total of A w The charging stations are individually calculated for the +.>
Then with i c·k And i w·k Summing up to determine the vehicle k to be charged to the respective reachable charging station a w Total set of charging times required for charging
Taking the charging illustration of the vehicle k to be charged from the current position to the node 1 charging station, the time t required for the vehicle k to be charged to travel from the current position to the node 1 charging station and then travel from the node 1 charging station to the destination dk1 The values are as follows:
wherein i is k And j k Respectively numbering traffic nodes at the current position and the destination position of the automobile k to be charged;
for node i k And the path length of the optimal path between nodes 1; />
For node 1 and node j k The distance of the optimal path between the two paths; v is the average speed of the electric automobile, then t sum·1 ＝t d·k1 +t c·k +t w·k 。
In a preferred embodiment, in the step 4, the t c·k The calculation of (2) is based on the following:
wherein SOC is end For the battery power of the electric vehicle when charging is completed, in this embodiment, the SOC end Taking 0.9-0.95 rated power of the battery; p (P) c The percentage of electrical energy replenished per hour at the charging station; SOC (State of Charge) k And the battery residual quantity SOC of the automobile k to be charged is obtained.
In this embodiment, it is set that the automobile to be charged arrives according to poisson flow with parameter λ (λ > 0), where the poisson flow is also called poisson process, and is a distribution mode of discrete random variable of probability theory, and X obeys poisson distribution with parameter λ, and then the probability function of X is:
wherein Z represents the number of vehicles to be charged reaching the target charging station, namely Z vehicles to be charged reach the same charging station to be charged, the charging service time required by each vehicle k to be charged is independent and obeys the negative exponential distribution with the parameter mu (mu > 0), and the charging time t of the vehicle k to be charged is c·k The probability distribution function of (2) is:
wherein the battery electric quantity of the automobile k to be charged when the charging is completed is SOC end The method comprises the steps of carrying out a first treatment on the surface of the The charging station supplements electric energy with the percentage P per hour c Taking a North automobile E150EV electric automobile as an example, the battery is charged slowly by 2.7kW.h per hour, and the battery capacity is 25.6kW.h, so that the battery is suitable for the automobile
I.e. 10.55% of electric energy can be charged per hour, and at this time, the calculation formula of the parameter μ is:
then, in determining the charging time t of the electric automobile k c·k And taking the expected value of the probability distribution function, namely:
in a preferred embodiment, in the step 4, the t w·k The value of (2) is a preset value, such as a constant of 15 minutes.
According to the charging station charging load prediction method, after the total charging time of the electric vehicle user to be charged is determined based on the traffic information characteristic quantity in the target area extracted in advance and the optimal path selection of the shortest running distance established in advance, a charging station load function matrix is constructed by simulating the charging station selection behavior of the electric vehicle to be charged, so that the charging load curve of each charging station in the target area is obtained, and a basis is provided for real-time scheduling of the charging behavior of the electric vehicle. Specifically, when a plurality of selectable charging stations exist in a target area, through simulating charging behaviors of electric vehicles at different positions in a road network traffic system, running time spent by the electric vehicles to be charged on the road, queuing time at the charging stations, service time required by full charge and running time from the charging stations to a destination are calculated, all the time are summed to obtain total charging time required by the electric vehicles to finish charging at the different charging stations, then a station with the minimum time consumption is selected from the total charging time to serve as an optimal charging station of the electric vehicles, and after corresponding optimal charging stations are formulated for the electric vehicles, charging load curves of the charging stations are obtained to provide basis for real-time scheduling of charging behaviors of the electric vehicles.
Example two
With continued reference to fig. 2, the present invention shows a charging station charging load prediction device 10, which is based on the first embodiment, and is used to implement the charging station charging load prediction method of the first embodiment, and includes the functions of each program module: in this embodiment, the charging station charging load prediction apparatus 10 may include or be divided into one or more program modules, and the one or more program modules are stored in a storage medium and executed by one or more processors to complete the present invention and may implement the charging station charging load prediction method described above. Program modules in the present invention refer to a series of computer program instruction segments capable of performing a specific function, which are more suitable than the program itself for describing the execution of the charging station charging load prediction device in a storage medium. The following description will specifically describe functions of each program module of the present embodiment:
the present invention provides a charging station charging load prediction apparatus 10, comprising:
the traffic information feature extraction module 11 is configured to obtain the number of traffic nodes and charging stations in the target area road network and geographic location information, and establish a mapping relationship between the charging stations in the target area and the corresponding nearest traffic nodes according to the principle that the charging stations are nearest to the traffic nodes, so as to determine a traffic node n corresponding to any charging station r in the target area r Wherein the total number of charging stations in the target area is P, r epsilon [1, P]The total number of the traffic nodes is N, each traffic node in the target area is numbered from 1 to N, N r ∈[1.N]；
The optimal driving path selection module 12 is configured to establish a navigation set of an optimal driving path between any traffic node i to any traffic node j in the target area based on a principle that the driving path is shortest, where i, j e [1, n ];
the number determining module 13 of electric vehicles to be charged is configured to obtain current geographic positions, battery remaining capacity SOC, and driving destination positions of all electric vehicles in a target area, define vehicles with SOC lower than a preset value as electric vehicles to be charged, and determine total number M of the electric vehicles to be charged in the target area;
the optimal charging station selection and charging load function construction module 14 is configured to perform the following cycle processing on the vehicle k to be charged until the optimal charging station selection and charging load function construction of the M electric vehicles to be charged in the target area are completed, where k is the number of the electric vehicles to be charged, and includes
The target traffic node acquisition sub-module is used for determining the traffic node number i of the automobile k to be charged in the road network according to the current geographic position and the driving destination position of the automobile k to be charged k Bow j is compiled to destination traffic node k ，
The reachable charging station acquisition sub-module is used for respectively carrying out reachability judgment on P charging stations in the target area according to the navigation set, the mapping relation and the reachability criterion so as to determine all reachable charging stations A of the vehicle k to be charged w The reachability criterion is as follows:
wherein the S is path·ir For traffic node i to traffic node n r The distance length and C of the optimal path of the vehicle k to be charged are the remaining power consumption percentages of the vehicle k to be charged in the unit distance length of running;
target charging station determination submodule for determining a vehicle k to be charged to each of the available charging stations a w Total set of charging times required for charging
And the reachable charging station corresponding to the minimum value of the total charging time is used as the target charging station of the automobile k to be charged, wherein,
t sum·k ＝＝t d·k +t w·k +t c·k
t d·k for the time required for an electric vehicle to travel from the current location to a charging station and then from the charging station to a destination, t w·k In charging stations for electric vehiclesQueuing time, t c·k The charging time of the electric automobile in the charging station is set;
the target charging station load function matrix construction submodule is used for constructing a load function matrix of a target charging station of the automobile k to be charged by taking t=0 as the current moment:
M load·k ＝[0，…，0，p(k)，0，…0]
Wherein M is load·k And the matrix is 1 row and P column, and P is the total number of charging stations in the target area. p (k) is a load characteristic function corresponding to the target charging station when the vehicle k to be charged is charged to the target charging station, and p is a number corresponding to the target charging station:
wherein t is start For the charging start time, t, of the vehicle k to be charged wk And t ck Respectively charging queuing time and charging time of automobile k to be charged, P c The charging electric energy percentage of the electric automobile is calculated;
the charging station load function matrix construction submodule is used for completing optimal charging station selection and charging load function construction of all electric vehicles to be charged in a target area to obtain M charging station load function matrixes { M } load·1 ,M load·2 ,…,M load·M }；
The charging station charging total load function determining module 15 is configured to sum the M charging station load function matrices to obtain a charging total load function matrix M of each charging station in the target area sum ：
Wherein M is sum And the function of the P-th column is the charging load prediction condition of the P-th charging station in the target area, wherein the P-th column is a matrix of 1 row and P columns.
As a preferred solution, in the to-be-charged electric vehicle number determining module 12, real-time information of the current geographic position, the battery remaining capacity SOC and the driving destination of all electric vehicles in the area is obtained from the electric vehicle-mounted positioning navigation system and the area distribution network vehicle information monitoring system through one of 5G, microwave and frequency modulation.
As a preferred aspect, in the target charging station determination submodule, for the a w Each of the charging stations is able to determine the vehicle k to be charged by the following formula
To determine the value of t d·k Is of the size of (2):
respectively numbering traffic nodes at the current position and the destination position of the automobile k to be charged;
for node i k And the path length of the optimal path between nodes Aw; />
For node Aw and node j k The distance of the optimal path is obtained from the navigation set in the step (2); v is the average speed of the electric automobile.
In a preferred embodiment, the target charging station determination sub-module,
wherein SOC is end Is electric powerAnd the battery power of the motor car when the motor car is charged.
Further, the SOC end The value of (2) is 0.9-0.95 of rated battery power.
As a preferred embodiment, in the target charging station determination submodule, the electric vehicle k is in the charging station for a queuing time t w·k Is a preset value.
As a preferred solution, in the reachable charging station acquisition sub-module, S path·ir For traffic node i to traffic node n r The determination of the path length of the optimal path of (a) comprises:
determining a traffic node n corresponding to the charging station r according to the mapping relation r ；
Determining traffic node i to the traffic node n via the navigation set r The path length of the optimal path of (a) is S path·ir And (5) taking a value.
The charging station charging load prediction device 10 disclosed by the invention constructs a charging station load function matrix by simulating the charging station selection behavior of the electric vehicle to be charged after determining the total charging time of the electric vehicle user to be charged based on the pre-extracted traffic information characteristic quantity in the target area and the pre-established optimal path selection of the shortest driving distance, thereby acquiring the charging load curve of each charging station in the target area so as to provide a basis for real-time scheduling of the charging behavior of the electric vehicle. Specifically, when a plurality of selectable charging stations exist in a target area, through simulating charging behaviors of electric vehicles at different positions in a road network traffic system, running time spent by the electric vehicles to be charged on the road, queuing time at the charging stations, service time required by full charge and running time from the charging stations to a destination are calculated, all the time are summed to obtain total charging time required by the electric vehicles to finish charging at the different charging stations, then a station with the minimum time consumption is selected from the total charging time to serve as an optimal charging station of the electric vehicles, and after corresponding optimal charging stations are formulated for the electric vehicles, charging load curves of the charging stations are obtained to provide basis for real-time scheduling of charging behaviors of the electric vehicles.
Example III
The invention also provides a computer device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack-mounted server, a blade server, a tower server or a cabinet server (comprising independent servers or a server cluster formed by a plurality of servers) and the like which can execute programs. The computer device 20 of the present embodiment includes at least, but is not limited to: a memory 21, a processor 22, which may be communicatively coupled to each other via a system bus, as shown in fig. 3. It should be noted that fig. 3 only shows a computer device 20 having components 21-22, but it should be understood that not all of the illustrated components are required to be implemented, and that more or fewer components may be implemented instead.
In the present embodiment, the memory 21 (i.e., readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 21 may be an internal storage unit of the computer device 20, such as a hard disk or memory of the computer device 20. In other embodiments, the memory 21 may also be an external storage device of the computer device 20, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the computer device 20. Of course, the memory 21 may also include both internal storage units of the computer device 20 and external storage devices. In this embodiment, the memory 21 is generally used to store an operating system and various types of application software installed in the computer device 20, for example, program codes of the charging station charging load prediction apparatus 10 of the first embodiment. Further, the memory 21 may be used to temporarily store various types of data that have been output or are to be output.
The processor 22 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 22 is generally used to control the overall operation of the computer device 20. In this embodiment, the processor 22 is configured to execute the program code or the processing data stored in the memory 21, for example, to execute the charging station charging load prediction device 10, so as to implement the charging station charging load prediction method of the first embodiment.
Example IV
The present invention also provides a computer readable storage medium such as a flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored that when executed by a processor performs a corresponding function. The computer-readable storage medium of the present embodiment is used for storing the charging station charging load prediction device 10, and when executed by the processor, implements the charging station charging load prediction method of the first embodiment.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment.
Claims (9)
1. A charging load prediction method for a charging station, comprising:
(1) Extracting traffic information characteristic quantity in a target area: acquiring the number and geographical position information of traffic nodes and charging stations in a road network of a target area, and establishing a mapping relationship between the charging stations in the target area and the corresponding nearest traffic nodes according to the principle that the charging stations are nearest to the traffic nodes so as to determine a traffic node n corresponding to any charging station r in the target area r Wherein the total number of charging stations in the target area is P, r epsilon [1, P]The total number of traffic nodes isN, each traffic node in the target area is numbered from 1 to N respectively, N r ∈[1.N]；
(2) And (3) selecting an optimal driving path: establishing a navigation set of an optimal travel path between any traffic node i and any traffic node j in a target area by taking the shortest travel path as a principle, wherein i, j epsilon [1, N ];
(3) Determining the number of electric vehicles to be charged: acquiring the current geographic positions, the battery residual quantity SOC and the driving destination position of all electric vehicles in a target area, defining the electric vehicles with the SOC lower than a preset value as electric vehicles to be charged, and determining the total number M of the electric vehicles to be charged in the target area;
(4) Optimal charging station selection and charging load function construction: the method comprises the steps of executing the following circulation processing on a vehicle k to be charged until optimal charging station selection and charging load function construction of M electric vehicles to be charged in a target area are completed, wherein k is the number of the electric vehicles to be charged;
(4-1) determining the traffic node number i of the automobile k to be charged in the road network according to the current geographic position and the driving destination position of the automobile k to be charged k Destination traffic node number j k ,
(4-2) respectively carrying out accessibility judgment on P charging stations in the target area according to the navigation set, the mapping relation and the accessibility criterion so as to determine all the reachable charging stations A of the vehicle k to be charged w The reachability criterion is as follows:
wherein the S is path·ir For the optimal path length from the traffic node i to the charging station r, C is the residual electricity consumption percentage of the unit path length of the vehicle k to be charged, and SOC k The remaining capacity of the battery of the automobile k to be charged is obtained; the S is path·ir The acquisition of (1) comprises the steps of: firstly, determining a traffic node n corresponding to a charging station r according to the mapping relation r At the transit stationThe navigation set determines traffic node i to the traffic node n r The path length of the optimal path between the traffic node i and the charging station r is the path length of the optimal path;
(4-3) determining the vehicle k to be charged to the respective reachable charging station A w Total set of charging times required for charging
And the reachable charging station corresponding to the minimum value of the total charging time is used as the target charging station of the automobile k to be charged,
wherein t is sum·k ＝t d·k +t w·k +t c·k
Wherein t is d·k For the time required for an electric vehicle to travel from the current location to a charging station and then from the charging station to a destination, t w·k For the queuing time of the electric vehicle in the charging station, t c·k The charging time of the electric automobile in the charging station is set;
(4-4) constructing a load function matrix of the target charging station of the vehicle k to be charged with t=0 as the current moment:
M load·k ＝[0，…，0，p(k)，0，…0]
wherein M is load·k And P (k) is a load characteristic function corresponding to the target charging station when the vehicle k to be charged is charged to the target charging station, and P is a number corresponding to the target charging station:
Wherein t is start For the charging start time, t, of the vehicle k to be charged w·k And t c·k Respectively charging queuing time and charging time of automobile k to be charged, P c The charging electric energy percentage of the electric automobile is v, which is the average speed of the electric automobile; s is S ik.p The optimal path length of the optimal path from the traffic node i to the target charging station p for the vehicle k to be charged;
(4-5) making k=k+1, repeating the steps (4-1) - (4-4) until k=M, and completing optimal charging station selection and charging load function construction of all electric vehicles to be charged in the target area to obtain M charging station load function matrixes { M } load·1 ,M load·2 ,…,M load·M }；
(5) Determining the charging total load function of each charging station in the target area, summing the M charging station load function matrixes according to columns to obtain a charging total load function matrix M of each charging station in the target area sum ：
Wherein M is sum The P-th column function of the matrix is the charging load prediction condition of the P-th charging station in the target area.
2. The charging load prediction method according to claim 1, wherein in the step (3), the current geographic position, the battery remaining capacity SOC, and the real-time information of the driving destination of all the electric vehicles in the area are obtained from the electric vehicle-mounted positioning navigation system and the area distribution network vehicle information monitoring system via one of 5G, microwave, and frequency modulation.
3. The charging station charging load prediction method according to claim 1, wherein: for said A w Personal (S)
A charging station is reached, and the vehicle k to be charged is determined by the following formula
To determine the value of t d·k Is larger than (1)The size is small:
wherein t is d·kAw The method comprises the steps of charging a vehicle k to be charged from the current position to an Aw node charging station, and then, driving from the Aw node charging station to a destination; i.e k And j k Respectively numbering traffic nodes at the current position and the destination position of the automobile k to be charged;
for node i k And the path length of the optimal path between nodes Aw; />
For node Aw and node j k The distance of the optimal path is obtained from the navigation set in the step (2); v is the average speed of the electric automobile.
4. The charging station charging load prediction method according to claim 1, wherein,
wherein SOC is end Battery power when electric automobile is charged, P c The percentage of electrical energy replenished per hour at the charging station; SOC (State of Charge) k And the remaining capacity of the battery of the automobile k to be charged is obtained.
5. The charging station charging load prediction method according to claim 4, wherein the SOC end The value of (2) is 0.9-0.95 of rated battery power.
6. The charging station charging load prediction method according to claim 1, wherein the queuing time t of the electric vehicle k at the charging station w·k Is a preset value.
7. A charging station charging load prediction device, characterized in that: comprising the following steps:
the traffic information feature quantity extraction module is used for acquiring the quantity and geographic position information of traffic nodes and charging stations in the target area road network, and establishing a mapping relationship between the charging stations in the target area and the corresponding nearest traffic nodes according to the principle that the charging stations are nearest to the traffic nodes so as to determine the traffic node n corresponding to any charging station r in the target area r Wherein the total number of charging stations in the target area is P, r epsilon [1, P]The total number of the traffic nodes is N, each traffic node in the target area is numbered from 1 to N, N r ∈[1.N]；
The optimal driving path selection module is used for establishing a navigation set of an optimal driving path between any traffic node i and any traffic node j in the target area on the basis of the shortest driving path, wherein i, j epsilon [1, N ];
the system comprises a to-be-charged electric automobile quantity determining module, a charging control module and a charging control module, wherein the to-be-charged electric automobile quantity determining module is used for acquiring the current geographic positions, the battery residual quantity SOC and the driving destination position of all electric automobiles in a target area, defining the electric automobiles with the SOC lower than a preset value as to-be-charged electric automobiles, and determining the total number M of the to-be-charged electric automobiles in the target area;
The optimal charging station selection and charging load function construction module is used for executing the following circulation processing on the to-be-charged automobile k until the optimal charging station selection and charging load function construction of M to-be-charged electric automobiles in the target area are completed, wherein k is the number of the to-be-charged electric automobiles, and the optimal charging station selection and charging load function construction module comprises the following steps:
the target traffic node acquisition sub-module is used for determining the traffic node number i of the automobile k to be charged in the road network according to the current geographic position and the driving destination position of the automobile k to be charged k Destination traffic node number j k ；
The reachable charging station acquisition sub-module is used for respectively carrying out reachability judgment on P charging stations in the target area according to the navigation set, the mapping relation and the reachability criterion so as to determine all reachable charging stations A of the vehicle k to be charged w The reachability criterion is as follows:
wherein the S is path·ir The remaining power consumption percentage of the automobile k to be charged for the optimal path length from the traffic node i to the charging station r and the unit path length of the running C; the S is path·ir The acquisition of (1) comprises the steps of: firstly, determining a traffic node n corresponding to a charging station r according to the mapping relation r Determining traffic node i to the traffic node n via the navigation set r The path length of the optimal path between the traffic node i and the charging station r is the path length of the optimal path;
target charging station determination submodule for determining a vehicle k to be charged to each of the available charging stations a w Total set of charging times required for charging
And the reachable charging station corresponding to the minimum value of the total charging time is used as the target charging station of the automobile k to be charged, wherein,
t sum·k ＝t d·k +t w·k +t c·k
t d·k for the time required for an electric vehicle to travel from the current location to a charging station and then from the charging station to a destination, t w·k For the queuing time of the electric vehicle in the charging station, t c·k The charging time of the electric automobile in the charging station is set;
the target charging station load function matrix construction submodule is used for constructing a load function matrix of a target charging station of the automobile k to be charged by taking t=0 as the current moment:
M load-k ＝[0，…，0，p(k)，0，…0]
wherein M is load·k Is a matrix of 1 row and P column, P is the total number of charging stations in a target area, P (k) is a load characteristic function corresponding to the target charging station when the automobile k to be charged is charged to the target charging station,p is the number corresponding to the target charging station:
wherein t is start For the charging start time, t, of the vehicle k to be charged w·k And t c·k Respectively charging queuing time and charging time of automobile k to be charged, P c The charging electric energy percentage of the electric automobile is calculated; v is the average speed of the electric automobile; s is S ik.p The optimal path length of the optimal path from the traffic node i to the target charging station p for the vehicle k to be charged;
the charging station load function matrix construction submodule is used for completing optimal charging station selection and charging load function construction of all electric vehicles to be charged in a target area to obtain M charging station load function matrixes { M } load·1 ,M load·2 ,…,M load·M }；
The charging station charging total load function determining module is used for summing the M charging station load function matrixes to obtain a charging total load function matrix M of each charging station in the target area sum ：
Wherein M is sum And the function of the P-th column is the charging load prediction condition of the P-th charging station in the target area, wherein the P-th column is a matrix of 1 row and P columns.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1 to 6 when the computer program is executed.
9. A computer-readable storage medium having stored thereon a computer program, characterized by: which computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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