CN111242403A - Charging load prediction method and device for charging station and storage medium - Google Patents
Charging load prediction method and device for charging station and storage medium Download PDFInfo
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Abstract
According to the charging station charging load prediction method, the device, the computer equipment and the storage medium, the time required by a user to go to different stations for charging is calculated, the charging station selection behavior of the user is simulated, vehicles to be charged at different moments of each charging station in an area are determined, the accurate prediction of the charging load of each charging station is completed, firstly, based on the pre-extracted traffic information characteristic quantity in a target area and the pre-established optimal path selection of the shortest driving distance, after the total charging time of the electric vehicle user to be charged is determined, the charging station selection behavior of the electric vehicle to be charged is simulated, a charging station load function matrix is constructed, 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.
Description
Technical Field
The invention relates to a method, device and storage medium for predicting charging load of each electric vehicle charging station of a power distribution network in an area.
Background
As a novel transportation tool, the electric automobile can effectively reduce environmental pollution, reduce the exhaust emission of the traditional fuel oil automobile and the consumption of fossil energy, and has important strategic significance in the aspect of building an environment-friendly and sustainable development society for popularizing the electric automobile.
With the great popularization of electric vehicles, charging is a rigid demand for electric vehicle users, and the construction of infrastructure such as charging stations is also the foundation of the development of the electric vehicle industry. However, because the charging power of the electric vehicle is high, when the large-scale electric vehicle is connected to the power grid for charging in an unordered mode, the safe and stable operation of the power grid is seriously affected. If the charging behavior of the electric automobile user can be predicted, the size of the charging load of each charging station in the target power grid area at different moments is determined, and the real-time scheduling and global control strategy of the charging behavior of the electric automobile is formulated on the basis of combining the conventional load fluctuation situation, so that the orderliness of the charging behavior of the electric automobile is expected to be realized, the impact caused by the fact that the charging load is connected into the power grid is reduced, the power flow distribution of the power distribution network is improved, and the power quality and the power supply reliability are improved.
At present, research on the aspect of charging load prediction mainly focuses on the aspects of load modeling based on a trip chain, obtaining a space-time distribution rule of the charging load of the electric vehicle through Monte Carnot simulation and the like. However, the above research analyzes the general characteristics of the charging loads of the distribution network in the area, and when a plurality of charging stations exist in the target area, the charging load conditions of the respective charging stations are different because the positions of the selected charging stations are different for electric vehicles having different residual capacities (SOC) and different geographical positions at different times. For this reason, it is necessary to predict the charging load of different electric vehicle charging stations.
Disclosure of Invention
The invention aims to provide a charging load prediction method, charging load prediction device equipment and a storage medium of a charging station, which are used for solving the problems in the prior art.
In order to achieve the purpose, the specific technical scheme of the invention is as follows:
the invention provides a charging load prediction method for a charging station, which comprises the following steps:
(1) extracting traffic information characteristic quantity in the target area: acquiring the quantity and the geographical position information of traffic nodes and charging stations in a road network of a target area, and establishing a mapping relation between the charging stations and corresponding nearest traffic nodes in the target area according to the principle that the charging stations are closest to the traffic nodes so as to determine a traffic node n corresponding to any charging station r in the target arearWherein the charging station in the target area isThe number is P, r is equal to [1, P ∈]The total number of the traffic nodes is N, the traffic nodes in the target area are numbered from 1 to N respectively, and Nr∈[1.N]；
(2) Selecting an optimal running path: establishing a navigation set of an optimal driving path from any traffic node i to any traffic node j in a target area by taking the shortest driving path as a principle, wherein i, j belongs to [1, N ];
(3) determining the number of the electric vehicles to be charged: acquiring current geographic positions, battery remaining capacity SOC and driving destination positions of all electric vehicles in a target area, defining the 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) selecting an optimal charging station and constructing a charging load function: executing the following cyclic processing on an automobile k to be charged until the optimal charging station selection and the charging load function construction of M electric automobiles to be charged in the target area are completed, wherein k is the number of the electric automobile to be charged;
(4-1) determining the number i of the traffic node 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 chargedkAnd destination traffic node number jk,
(4-2) 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 automobile k to be chargedwThe reachability judgment data is as follows:
wherein, the Spath·irFrom traffic node i to traffic node nrThe distance length of the optimal path C is the residual electric quantity consumption percentage of the k-running unit distance length of the automobile to be charged, and the SOC iskThe residual battery capacity of the automobile k to be charged is obtained;
(4-3) determining that the vehicle k to be charged reaches each reachable charging station AWCharging is carried outSet of total required charging times tsum·1,tsum·2,tsum·3,…,tsum·AwAnd the reachable charging station corresponding to the minimum value of the total charging time is taken as the target charging station of the automobile k to be charged,
wherein, tsum·k＝＝td·k+tw·k+tc·k
Wherein t isd·kThe time required for the electric vehicle to travel from the current position to the charging station and then from the charging station to the destination, tw·kFor the queuing time of the electric vehicle in the charging station, tc·kCharging time of the electric vehicle in a charging station;
(4-4) constructing a load function matrix of the target charging station of the vehicle k to be charged by taking t as 0 at the current moment:
Mload·k＝[0，…，0，p(k)，0，…0]
wherein M isload·kA matrix with 1 row and P columns, where 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, tstartFor the charging start time, t, of the vehicle k to be chargedw·kAnd tc·kCharging queuing time and charging time, P, of the vehicle k to be charged respectivelycThe percentage of charging electric energy of the electric vehicle;
(4-5) repeating the steps (4-1) - (4-4) until k is equal to M, completing the optimal charging station selection and charging load function construction of all electric vehicles to be charged in the target area, and obtaining M charging station load function matrixes { M { (M) } Mload·1,Mload·2,…,Mload·M}；
(5) Determining the charging total load function of each charging station in the target area, summing the load function matrixes of the M charging stations, and acquiring the charging total load function matrix M of each charging station in the target areasum：
Wherein M issumThe matrix is a matrix with 1 row and P columns, and the function of the P-th column is the charging load prediction condition of the P-th charging station in the target area.
As a preferable scheme, in the step (3), the current geographic positions, the battery remaining capacities SOC, and the real-time information of the driving destinations of all the electric vehicles in the area are acquired from the vehicle-mounted positioning navigation system of the electric vehicles and the regional distribution network vehicle information monitoring system through one wireless communication mode of 5G, microwave, and frequency modulation.
As a preferable mode, for the AwThe reachable charging stations respectively determine t of the vehicle k to be charged through the following formulad·kAwTo determine said td·kThe size of (2):
td·kAwthe time required for the vehicle k to be charged to travel from the current position to the Aw node charging station for charging and then travel from the Aw node charging station to the destination is obtained; i.e. ikAnd jkRespectively numbering the traffic nodes of the current position and the destination position of the automobile k to be charged;
is a node ikAnd the distance of the optimal path between the nodes Aw;
is node Aw and node jkThe optimal path distance between the navigation sets is obtained from the navigation set in the step (2); and v is the average speed of the electric automobile.
As a preferred embodiment of the method, the method comprises the following steps,
therein, SOCendAnd the battery capacity when the electric automobile is charged is achieved.
Further, the SOCendThe value of (a) is 0.9-0.95 of rated electric quantity of the battery.
Preferably, the queuing time t of the electric vehicle k at the charging stationw·kIs a preset value.
As a preferable mode, in the step (4-2), Spath·irFrom traffic node i to traffic node nrThe determination of the path length of the optimal path comprises the following steps:
determining the traffic node n corresponding to the charging station r according to the mapping relationr；
Determining a traffic node i to the traffic node n via the navigation setrThe path length of the optimal path.
The invention also provides a charging load prediction device of the charging station, which comprises the following components:
a traffic information characteristic quantity extraction module, configured to obtain the number of traffic nodes and charging stations in a road network of a target area and geographical location information, and establish a mapping relationship between the charging stations and corresponding nearest traffic nodes in the target area according to a principle that the charging stations are closest to the traffic nodes, so as to determine a traffic node n corresponding to any charging station r in the target arearWherein the total number of charging stations in the target area is P, r is equal to [1, P ∈]The total number of the traffic nodes is N, the traffic nodes in the target area are numbered from 1 to N respectively, and Nr∈[1.N]；
The optimal running path selection module is used for establishing a navigation set of an optimal running path between any traffic node i and any traffic node j in a target area on the basis of the shortest running path, wherein i, j belongs to [1, N ];
the system comprises a to-be-charged electric vehicle quantity determining module, a charging management module and a charging management module, wherein the to-be-charged electric vehicle quantity determining module is used for acquiring the current geographic positions, the battery residual electric quantity SOC and the driving destination positions of all electric vehicles in a target area, defining the vehicles with the SOC lower than a preset value as to-be-charged electric vehicles and determining the total number M of the to-be-charged electric vehicles in the target area;
the optimal charging station selection and charging load function construction module is used for executing the following cyclic processing on the vehicles k to be charged until the optimal charging station selection and charging load function construction of M electric vehicles to be charged in the target area are completed, wherein k is the serial number of the electric vehicles to be charged and comprises
A target traffic node obtaining submodule for determining the current traffic node number i of the vehicle k to be charged in the road network according to the current geographic position and the position of the driving destination of the vehicle k to be chargedkAnd destination traffic node number jk。
The reachable charging station acquisition submodule 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 automobile k to be chargedwThe reachability criterion is:
wherein, the Spath·irFrom traffic node i to traffic node nrThe distance length C of the optimal path is the residual electricity consumption percentage of the automobile k to be charged in the distance length of the driving unit;
a target charging station determination submodule for determining whether the vehicle k to be charged reaches each reachable charging station AwSet of total charging times required for charging { t }sum·1,tsum·2,tsum·3,…,tsum·AwAnd taking the reachable charging station corresponding to the minimum value of the total charging time as a target charging station of the vehicle k to be charged,
tsum·k＝＝td·k+tw·k+tc·k
td·kthe time required for the electric vehicle to travel from the current position to the charging station and then from the charging station to the destination, tw·kFor the queuing time of the electric vehicle in the charging station, tc·kCharging the electric automobileCharging time within the station;
the target charging station load function matrix construction submodule is used for constructing a load function matrix of a target charging station of the vehicle k to be charged by taking t as 0 as the current moment:
Mlood·k＝[0，…，0，p(k)，0，…0]
wherein M isload·kIs a matrix of 1 row and P columns, where 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, tstartFor the charging start time, t, of the vehicle k to be chargedw·kAnd tc·kCharging queuing time and charging time, P, of the vehicle k to be charged respectivelycThe percentage of charging electric energy of the electric vehicle;
the charging station load function matrix construction submodule is used for completing the optimal charging station selection and the charging load function construction of all the electric vehicles to be charged in the target area to obtain M charging station load function matrixes { Mload·1,Mload·2,…,Mload·M}；
A charging station charging total load function determining module, 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 areasum：
Wherein M issumThe matrix is a matrix with 1 row and P columns, and the function of the P column is the charging load prediction condition of the P charging station in the target area.
As a preferable scheme, in the to-be-charged electric vehicle quantity determining module, the current geographic positions, the battery remaining capacities SOC, and the real-time information of the driving destinations of all electric vehicles in the area are acquired from an electric vehicle-mounted positioning navigation system and an area distribution network vehicle information monitoring system through one wireless communication mode of 5G, microwave, and frequency modulation.
Preferably, in the target charging station determination submodule, for awThe reachable charging stations respectively determine t of the vehicle k to be charged through the following formulad·kAwTo determine said td·kThe size of (2):
td·kAwthe time required for the vehicle k to be charged to travel from the current position to the Aw node charging station for charging and then travel from the Aw node charging station to the destination is obtained; i.e. ikAnd jkRespectively numbering the traffic nodes of the current position and the destination position of the automobile k to be charged;
is a node ikAnd the distance of the optimal path between the nodes Aw;
is node Aw and node jkThe optimal path distance between the navigation sets is obtained from the navigation set in the step (2); and v is the average speed of the electric automobile.
Preferably, in the target charging station determination submodule,
therein, SOCendAnd the battery capacity when the electric automobile is charged is achieved.
Further, the SOCendThe value of (a) is 0.9-0.95 of rated electric quantity of the battery.
Preferably, in the target charging station determination submodule, the queuing time t of the electric vehicle k at the charging stationw·kIs a preset value.
As a preferred solution, in the reachable charging station acquisition submodule, Spath·irFrom traffic node i to traffic node nrThe determining of the path length of the optimal path comprises:
determining the traffic node n corresponding to the charging station r according to the mapping relationr；
Determining a traffic node i to the traffic node n via the navigation setrThe path length of the optimal path.
The invention provides a charging station charging load prediction method, device and storage medium, wherein a charging station load function matrix is constructed by simulating charging station selection behaviors of an electric vehicle to be charged after total charging time of the electric vehicle to be charged is determined based on extracted traffic information characteristic quantity in a target area and pre-established optimal path selection of a shortest driving distance; 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 automobile. Specifically, when a plurality of optional charging stations exist in a target area, charging behavior simulation is performed on electric vehicles at different positions in a road network traffic system, the running time of the electric vehicle to be charged on the road, the queuing time of the electric vehicle at the charging stations, the service time required by full charge and the running time from the charging stations to a destination are calculated, all the time is summed to obtain the total charging time required by the electric vehicle to complete charging at different charging stations, the station with the minimum time consumption is selected as the optimal charging station of the electric vehicle, and after the corresponding optimal charging station is established for each electric vehicle, the charging load curve of each charging station is obtained to provide a basis for real-time scheduling of the charging behavior of the electric vehicle.
Drawings
Fig. 1 is a schematic workflow diagram of a charging load prediction method for a charging station according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a charging load prediction apparatus of a question-practice 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
To facilitate an understanding of the structure of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
The invention provides a charging load prediction method for charging stations of electric vehicles of a power distribution network in an area, which simulates the selection behavior of the charging stations of a user by calculating the time required by the user to go to different stations for charging so as to determine vehicles to be charged at different moments of each charging station in the area and finish the accurate prediction of the charging load of each charging station.
The charging load prediction method for the charging station comprises the following steps:
(1) extracting traffic information characteristic quantity, acquiring respective quantity and geographical position of traffic nodes and charging stations in a road network of a target area, and establishing a mapping relation 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 the traffic node n corresponding to any charging station r in the target arearWherein the total number of charging stations in the target area is P, r is equal to [1, P ∈]The total number of the traffic nodes is N, the traffic nodes in the target area are numbered from 1 to N respectively, and Nr∈[1，N](ii) a In this embodiment, the directly applicable real-time map and vehicle navigation system obtains the number of traffic nodes and charging stations in the road network of the target area and the geographical location information.
(2) Selecting an optimal running path, and establishing a navigation set of the optimal running path from any traffic node i to any traffic node j in a target area on the basis of the shortest running path, wherein i, j belongs to [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 shortest route between the traffic node i and any traffic node j is used as the optimal path by using a real-time map and a vehicle navigation system.
(3) Determining the number of electric vehicles to be charged, acquiring the current geographic positions, the battery remaining capacity SOC and the driving destination positions of all the electric vehicles in a target area, defining the vehicles with the battery remaining capacity SOC lower than a preset value as the electric vehicles to be charged, and determining the total number M of the electric vehicles to be charged in the target area;
(4) selecting an optimal charging station and constructing a charging load function, wherein the following cyclic processing is executed on a vehicle k to be charged until the optimal charging station selection and the construction of the charging load function 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, and belongs to [1, M ];
(4-1) determining the number i of the traffic node 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 chargedkAnd destination traffic node number jk,
(4-2) 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 automobile k to be chargedwWherein the reachability criterion is:
said Spath·irFor the traffic node n corresponding to the traffic node i to the charging station rrLength of path, SOC of optimal pathkThe residual battery capacity C of the automobile k to be charged is the residual capacity consumption percentage of the automobile k to be charged in unit distance, the percentage is a determined value for a certain type of electric automobile, and the north of the percentage is determined by a manufacturer when the automobile leaves a factoryThe E150EV electric automobile has the power consumption of 15 kW.h hundred kilometers and the battery capacity of 25.6 kW.h, so the automobile has the advantages of high power consumption, high power consumption and low cost
Namely, the electricity consumption of 0.586 percent is consumed per kilometer when the vehicle runs;
(4-3) determining that the vehicle k to be charged reaches each reachable charging station AwSet of total charging times required for charging { t }sum·1,tsum·2,tsum·3,…,tsum·AwAnd the reachable charging station corresponding to the minimum value of the total charging time is taken as the target charging station of the vehicle k to be charged,
wherein, tsum·k＝＝td·k+tw·k+tc·k
td·kFor a vehicle k to be charged to travel from a current position to an accessible charging station AwThen, from the reachable charging station AwTime required to travel to destination, tw·kAccessible charging station A for electric vehiclewInner queuing time, tc·kAccessible charging station A for electric vehiclewInternal charging time;
(4-4) constructing a load function matrix of the target charging station of the vehicle k to be charged by taking t as 0 at the current moment:
Mlccd·k＝[0，…，0，p(k)，0，…0]
wherein M isload·kThe method comprises the following steps of (1) a matrix with rows and columns, wherein 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, tstartFor the charging start time, t, of the vehicle k to be chargedw·kAnd tc·kCharging queuing time and charging time, P, of the vehicle k to be charged respectivelycThe charging electric energy percentage of the automobile k to be charged is calculated;
(4-5) repeating the steps (4-1) - (4-4) until k is equal to M, completing target charging station selection and charging load function construction of all vehicles to be charged in the target area, and obtaining M charging station load function matrixes { M { (M) } Mload·1,Mload·2,…,Mload·M}；
(5) Determining charging total load function of the charging stations, summing the load function matrixes of the M charging stations to obtain a charging total load function matrix M of each charging station in a target areasum：
Wherein M issumThe matrix is a matrix with 1 row and P columns, and the function of the P column is the charging load prediction condition of the P charging station in the target area.
The invention discloses a method for predicting charging load of each electric vehicle charging station of an in-region power distribution network, which comprises the steps of simulating charging behaviors of electric vehicles at different positions in a road network traffic system when a plurality of optional charging stations exist in a target region, screening out the charging stations within the reachable range of the residual electric quantity when the residual electric quantity of the electric vehicle meets the charging requirement, calculating the running time of the electric vehicle on the road, the queuing time of the electric vehicle at the charging stations, the service time required by full charging and the running time of the electric vehicle from the charging stations to the destination, summing all the time to obtain the total charging time required by the electric vehicle to complete charging at different charging stations, and selecting the station with the minimum consumed time as the optimal charging station of the electric vehicle. After the charging stations of all electric vehicles in the target area are determined, the charging load curves of all the charging stations can be obtained.
Preferably, in step 3, the current geographic positions, battery remaining capacities (SOCs), and driving destination information of all electric vehicles in the area are acquired from the vehicle-mounted positioning navigation system of the electric vehicles and the regional distribution vehicle information monitoring system through one of a wireless communication mode of 5G, microwave, and frequency modulation.
Preferably, in the step 3, the S ispath·irThe acquisition comprises the following steps: firstly, the traffic node n corresponding to the charging station r is determined according to the mapping relationrDetermining a traffic node i to the traffic node n via the navigation setrThe path length of the optimal path is the path length of the optimal path between the traffic node i and the charging station r.
Preferably, in step 3, the vehicle with the SOC lower than 20% is selected, and is defined as the vehicle that needs to be charged.
Preferably, in the step 4, the t isd·kThe calculation of (c) is based on:
wherein t isd·kAwFor a vehicle k to be charged to travel from its current position to AwCharging from charging station AwThe time required for the number node charging station to travel to the destination; i.e. ikAnd jkRespectively numbering the current position of the automobile k to be charged and the traffic node of the destination position;
is a node ikAnd the distance of the optimal path between the nodes Aw;
is node Aw and node jkThe optimal path distance between the navigation sets is obtained from the navigation set in the step (2); v is the average speed of the electric vehicle for a totalwReachable charging stations for respectively calculating t of the vehicle k to be chargedd·kAwThen, with tc·kAnd tw·kAdding and summing to determine the vehicles k to be charged to each reachable charging station AwSet of total charging time required for charging
Taking the example that the vehicle k to be charged travels from the current position to the charging station of node 1 for charging, the time t required for the vehicle k to travel from the current position to the charging station of node 1 for charging and then from the charging station of node 1 to the destinationd·k1The values are as follows:
wherein ikAnd jkRespectively numbering the traffic nodes of the current position and the destination position of the automobile k to be charged;
is a node ikAnd node 1;
is node 1 and node jkThe distance of the optimal path therebetween; v is the average speed of the electric vehicle, tsum·1＝td·k1+tc·k+tw·k。
Preferably, in the step 4, the t isc·kThe calculation of (c) is based on:
therein, SOCendFor the battery capacity of the electric vehicle when charging is completed, in this embodiment, the SOCendTaking 0.9-0.95 rated electric quantity of the battery; pcPercentage of electrical energy replenished by the charging station per hour; SOCkAnd the residual battery capacity SOC of the automobile k to be charged is obtained.
In this embodiment, it is set that an automobile to be charged arrives according to a poisson flow with a parameter λ (λ >0), where the poisson flow is also referred to as a poisson process, and is a distribution mode of a discrete random variable in probability theory, and X obeys poisson distribution with a parameter λ, and then a probability function of X is:
wherein Z represents the number of the automobiles to be charged reaching the target charging station, namely Z automobiles to be charged reach the same charging station for charging, the charging service time required by each automobile k to be charged is independent, and the obedience parameter is mu (mu)>0) Negative exponential distribution of (c), the charging time t of the vehicle k to be chargedc·kThe probability distribution function of (a) is:
wherein the battery electric quantity of the vehicle k to be charged when the charging is finished is SOCend(ii) a The percentage of the electric energy replenished by the charging station per hour is PcFor example, in the case of an electric automobile with a North gasoline E150EV, the battery is charged at a rate of 2.7 kW.h/hour with slow charging, and the battery capacity is 25.6 kW.h, so that the battery is charged at a rate of 2.7 kW.h/hour
That is, 10.55% of the electric energy can be charged per hour, and at this time, the calculation formula of the parameter μ is:
then determining the charging time t of the electric automobile kc·kThen, take the expected value of its probability distribution function, i.e.:
preferably, in the step 4, the t isw·kIs a predetermined value, such as a predetermined constant of 15 minutes.
According to the charging load prediction method for the charging station, after the total charging time of the electric vehicle user to be charged is determined 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, the 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 optional charging stations exist in a target area, charging behavior simulation is performed on electric vehicles at different positions in a road network traffic system, the running time of the electric vehicle to be charged on the road, the queuing time of the electric vehicle at the charging stations, the service time required by full charge and the running time from the charging stations to a destination are calculated, all the time is summed to obtain the total charging time required by the electric vehicle to be charged at different charging stations, then the station with the minimum time consumption is selected as the optimal charging station of the electric vehicle, and after the corresponding optimal charging station is established for each electric vehicle, the charging load curve of each charging station is obtained to provide a basis for real-time scheduling of the charging behavior of the electric vehicle.
Example two
Referring to fig. 2, the present invention shows a charging load prediction apparatus 10 of a charging station, which is based on the first embodiment and is used to implement the charging load prediction method of the charging station of the first embodiment, and the charging load prediction apparatus includes the following program modules: 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 implement the present invention and implement the charging station charging load prediction method. The program module referred to in the present invention means a series of instruction segments of a computer program capable of performing a specific function, and is more suitable than the program itself for describing the execution process of the charging station charging load prediction means in a storage medium. The following description will specifically describe the functions of the program modules of the present embodiment:
the present invention provides a charging load prediction device 10 for a charging station, including:
a traffic information feature quantity extraction module 11, configured to obtain the number of traffic nodes and charging stations in a road network of a target area and the geographyThe position information is obtained, and the mapping relation between the charging stations in the target area and the corresponding nearest traffic node is established according to the principle that the charging stations are closest to the traffic node, so that the traffic node n corresponding to any charging station r in the target area is determinedrWherein the total number of charging stations in the target area is P, r is equal to [1, P ∈]The total number of the traffic nodes is N, the traffic nodes in the target area are numbered from 1 to N respectively, and Nr∈[1.N]；
The optimal running path selection module 12 is used for establishing a navigation set of an optimal running path from any traffic node i to any traffic node j in the target area on the basis of the shortest running path, wherein i, j belongs to [1, N ];
the number determining module 13 of the electric vehicles to be charged is used for acquiring the current geographic positions, the battery remaining capacity SOC and the driving destination positions of all the electric vehicles in the target area, defining the vehicles with the SOC lower than a preset value as the electric vehicles to be charged, and determining the total number M of the electric vehicles to be charged in the target area;
an optimal charging station selection and charging load function construction module 14, configured to perform the following cyclic processing on a to-be-charged vehicle k until the optimal charging station selection and charging load function construction of M to-be-charged electric vehicles in the target area are completed, where k is a serial number of the to-be-charged electric vehicle, and includes
A target traffic node obtaining submodule for determining the current traffic node number i of the vehicle k to be charged in the road network according to the current geographic position and the position of the driving destination of the vehicle k to be chargedkAnd destination traffic node number jk,
The reachable charging station acquisition submodule 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 automobile k to be chargedwThe reachability criterion is:
wherein, the Spath·irFrom traffic node i to traffic node nrThe distance length C of the optimal path is the residual electricity consumption percentage of the automobile k to be charged in the distance length of the driving unit;
a target charging station determination submodule for determining whether the vehicle k to be charged reaches each reachable charging station AwSet of total charging times required for charging { t }sum·1,tsum·2,tsum·3,…,tsum·AwAnd taking the reachable charging station corresponding to the minimum value of the total charging time as a target charging station of the vehicle k to be charged,
tsum·k＝＝td·k+tw·k+tc·k
td·kthe time required for the electric vehicle to travel from the current position to the charging station and then from the charging station to the destination, tw·kFor the queuing time of the electric vehicle in the charging station, tc·kCharging time of the electric vehicle in a charging station;
the target charging station load function matrix construction submodule is used for constructing a load function matrix of a target charging station of the vehicle k to be charged by taking t as 0 as the current moment:
Mlood·k＝[0，…，0，p(k)，0，…0]
wherein M isload·kIs a matrix of 1 row and P columns, where 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, tstartFor the charging start time, t, of the vehicle k to be chargedw·kAnd tc·kCharging queuing time and charging time, P, of the vehicle k to be charged respectivelycThe percentage of charging electric energy of the electric vehicle;
charging station load function matrix construction submoduleAnd the method is used for completing the optimal charging station selection and the charging load function construction of all the electric vehicles to be charged in the target area to obtain M charging station load function matrixes { M }load·1,Mload·2,…,Mload·M}；
A charging station charging total load function determining module 15, 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 areasum：
Wherein M issumThe matrix is a matrix with 1 row and P columns, and the function of the P column is the charging load prediction condition of the P charging station in the target area.
As a preferable scheme, in the to-be-charged electric vehicle quantity determining module 12, the current geographic positions, the battery remaining capacities SOC, and the real-time information of the driving destinations of all electric vehicles in the area are acquired from an electric vehicle-mounted positioning navigation system and an area distribution network vehicle information monitoring system through one wireless communication mode of 5G, microwave, and frequency modulation.
Preferably, in the target charging station determination submodule, for awThe reachable charging stations respectively determine t of the vehicle k to be charged through the following formulad·kAwTo determine said td·kThe size of (2):
td·kAwthe time required for the vehicle k to be charged to travel from the current position to the Aw node charging station for charging and then travel from the Aw node charging station to the destination is obtained;
respectively numbering the traffic nodes of the current position and the destination position of the automobile k to be charged;
is a node ikAnd the distance of the optimal path between the nodes Aw;
is node Aw and node jkThe optimal path distance between the navigation sets is obtained from the navigation set in the step (2); and v is the average speed of the electric automobile.
Preferably, in the target charging station determination submodule,
therein, SOCendAnd the battery capacity when the electric automobile is charged is achieved.
Further, the SOCendThe value of (a) is 0.9-0.95 of rated electric quantity of the battery.
Preferably, in the target charging station determination submodule, the queuing time t of the electric vehicle k at the charging stationw·kIs a preset value.
As a preferred solution, in the reachable charging station acquisition submodule, Spath·irFrom traffic node i to traffic node nrThe determining of the path length of the optimal path comprises:
determining the traffic node n corresponding to the charging station r according to the mapping relationr；
Determining a traffic node i to the traffic node n via the navigation setrIs the path length of the optimal path Spath·irAnd (4) taking values.
The charging load prediction device 10 of the charging station disclosed by the invention is used for establishing 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, 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 optional charging stations exist in a target area, charging behavior simulation is performed on electric vehicles at different positions in a road network traffic system, the running time of the electric vehicle to be charged on the road, the queuing time of the electric vehicle at the charging stations, the service time required by full charge and the running time from the charging stations to a destination are calculated, all the time is summed to obtain the total charging time required by the electric vehicle to complete charging at the different charging stations, then the station with the minimum time consumption is selected as the optimal charging station of the electric vehicle, and after the corresponding optimal charging station is established for each electric vehicle, the charging load curve of each charging station is obtained to provide a basis for real-time scheduling of the charging behavior of the electric vehicle.
EXAMPLE III
The present invention also provides a computer device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server or a rack server (including a stand-alone server or a server cluster composed of a plurality of servers) capable of executing programs, and the like. 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 is noted that fig. 3 only shows a computer device 20 with components 21-22, but it is to be understood that not all shown components need be implemented, and that more or less components may be implemented instead.
In the present embodiment, the memory 21 (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type 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 storage 21 may be an internal storage unit of the computer device 20, such as a hard disk or a memory of the computer device 20. In other embodiments, the memory 21 may 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, provided on the computer device 20. Of course, the memory 21 may also include both internal and external storage devices of the computer device 20. In this embodiment, the memory 21 is generally used for storing an operating system and various application software installed in the computer device 20, such as the program codes of the charging station charging load prediction apparatus 10 in the first embodiment. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 22 is typically used to control the overall operation of the computer device 20. In this embodiment, the processor 22 is configured to execute the program codes stored in the memory 21 or process data, for example, to operate the charging station charging load prediction apparatus 10, so as to implement the charging station charging load prediction method according to the first embodiment.
Example four
The present invention also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type 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 mall, etc., on which a computer program is stored, which when executed by a processor implements a corresponding function. The computer-readable storage medium of the present embodiment is used for storing the charging station charging load prediction apparatus 10, and when being executed by a processor, the computer-readable storage medium implements the charging station charging load prediction method of the first embodiment.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
Claims (10)
1. A charging load prediction method for a charging station, comprising:
(1) extracting traffic information characteristic quantity in the target area: acquiring the quantity and the geographical position information of traffic nodes and charging stations in a road network of a target area, and establishing a mapping relation between the charging stations and corresponding nearest traffic nodes in the target area according to the principle that the charging stations are closest to the traffic nodes so as to determine a traffic node n corresponding to any charging station r in the target arearWherein the total number of charging stations in the target area is P, r is equal to [1, P ∈]The total number of the traffic nodes is N, the traffic nodes in the target area are numbered from 1 to N respectively, and Nr∈[1.N]；
(2) Selecting an optimal running path: establishing a navigation set of an optimal driving path between any traffic node i and any traffic node j in a target area on the basis of the shortest driving path, wherein i, j belongs to [1, N ];
(3) determining the number of the electric vehicles to be charged: acquiring current geographic positions, battery remaining capacity SOC and driving destination positions of all electric vehicles in a target area, defining the 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) selecting an optimal charging station and constructing a charging load function: executing the following cyclic processing on an automobile k to be charged until the optimal charging station selection and the charging load function construction of M electric automobiles to be charged in the target area are completed, wherein k is the number of the electric automobile to be charged;
(4-1) determining the number i of the traffic node 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 chargedkAnd destination traffic node number jk,
(4-2) 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 automobile k to be chargedwSaid reachability criterionComprises the following steps:
wherein, the Spath·irFrom traffic node i to traffic node nrThe distance length of the optimal path C is the residual electric quantity consumption percentage of the k-running unit distance length of the automobile to be charged, SOCkThe residual battery capacity of the automobile k to be charged is obtained;
(4-3) determining that the vehicle k to be charged reaches each reachable charging station AwSet of total charging times required for charging { t }sum·1,tsum·2,tsum·3,…,tsum·AwAnd the reachable charging station corresponding to the minimum value of the total charging time is taken as the target charging station of the vehicle k to be charged,
wherein, tsum·k＝＝td·k+tw·k+tc·k
Wherein t isd·kThe time required for the electric vehicle to travel from the current position to the charging station and then from the charging station to the destination, tw·kFor the queuing time of the electric vehicle in the charging station, tc·kCharging time of the electric vehicle in a charging station;
(4-4) constructing a load function matrix of the target charging station of the vehicle k to be charged by taking t as 0 at the current moment:
Mload·k＝[0，…，0，p(k)，0，…0]
wherein M isload·kA matrix with 1 row and P columns, where 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, tstartFor the charging start time, t, of the vehicle k to be chargedw·kAnd tc·kRespectively charging queue time and charging time, P, of the vehicle k to be chargedcThe percentage of charging electric energy of the electric vehicle;
(4-5) repeating the steps (4-1) - (4-4) by making k equal to k +1 until k equal to M, completing the optimal charging station selection and charging load function construction of all electric vehicles to be charged in the target area, and obtaining M charging station load function matrixes { M { (M) }load·1,Mload·2,…,Mload·M}；
(5) Determining the charging total load function of each charging station in the target area, summing the load function matrixes of the M charging stations, and acquiring the charging total load function matrix M of each charging station in the target areasum：
Wherein M issumThe matrix is a matrix with 1 row and P columns, and the function of the P-th column is the charging load prediction condition of the P-th charging station in the target area.
2. The charging station charging load prediction method according to claim 1, wherein in the step (3), the current geographic positions, the battery remaining capacities SOC, and the real-time information of the driving destinations of all electric vehicles in the area are obtained from an electric vehicle-mounted positioning navigation system and an area distribution network vehicle information monitoring system through one of a wireless communication mode of 5G, microwave, and frequency modulation.
3. The charging station charging load prediction method of claim 1, wherein: for the AwReachable charging stations, which determine the vehicles k to be charged respectively by the following formula
To determine said td·kThe size of (2):
wherein,
the time required for the vehicle k to be charged to travel from the current position to the Aw node charging station for charging and then travel from the Aw node charging station to the destination is obtained; i.e. ikAnd jkRespectively numbering the traffic nodes of the current position and the destination position of the automobile k to be charged;
is a node ikAnd the distance of the optimal path between the nodes Aw;
is node Aw and node jkThe optimal path distance between the navigation sets is obtained from the navigation set in the step (2); and v is the average speed of the electric automobile.
4. The charging station charging load prediction method of claim 1,
therein, SOCendAnd the battery capacity when the electric automobile is charged is achieved.
5. The charging station charging load prediction method of claim 4, wherein the SOC isendThe value of (a) is 0.9-0.95 of rated electric quantity of the battery.
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 stationw·kIs a preset value.
7. The charging station charging load prediction method according to claim 1, wherein in the step (4-2), Spath·irFrom traffic node i to traffic node nrThe determination of the path length of the optimal path comprises the following steps:
determining the traffic node n corresponding to the charging station r according to the mapping relationr；
Determining a traffic node i to the traffic node n via the navigation setrThe path length of the optimal path.
8. A charging load prediction device for a charging station, characterized in that: the method comprises the following steps:
a traffic information characteristic quantity extraction module, configured to obtain the number of traffic nodes and charging stations in a road network of a target area and geographical location information, and establish a mapping relationship between the charging stations and corresponding nearest traffic nodes in the target area according to a principle that the charging stations are closest to the traffic nodes, so as to determine a traffic node n corresponding to any charging station r in the target arearWherein the total number of charging stations in the target area is P, r is equal to [1, P ∈]The total number of the traffic nodes is N, the traffic nodes in the target area are numbered from 1 to N respectively, and Nr∈[1.N]；
The optimal running path selection module is used for establishing a navigation set of an optimal running path from any traffic node i to any traffic node j in a target area on the basis of the shortest running path, wherein i, j belongs to [1, N ];
the system comprises a to-be-charged electric vehicle quantity determining module, a charging management module and a charging management module, wherein the to-be-charged electric vehicle quantity determining module is used for acquiring the current geographic positions, the battery residual electric quantity SOC and the driving destination positions of all electric vehicles in a target area, defining the vehicles with the SOC lower than a preset value as to-be-charged electric vehicles and determining the total number M of the to-be-charged electric vehicles in the target area;
the optimal charging station selection and charging load function construction module is used for executing the following cyclic processing on a to-be-charged automobile k until the optimal charging station selection and charging load function construction of M to-be-charged electric automobiles in a target area are completed, wherein the k is the serial number of the to-be-charged electric automobile and comprises the following steps:
a target traffic node acquisition submodule for acquiring the steam to be charged according to the target traffic nodeDetermining the current geographic position and the driving destination position of the vehicle k, and determining the number i of the traffic node of the vehicle k to be charged in the road networkkAnd destination traffic node number jk；
The reachable charging station acquisition submodule 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 automobile k to be chargedwThe reachability criterion is:
wherein, the Spath·irFrom traffic node i to traffic node nrThe distance length C of the optimal path is the residual electricity consumption percentage of the automobile k to be charged in the running unit distance length;
a target charging station determination submodule for determining whether the vehicle k to be charged reaches each reachable charging station AwSet of total charging times required for charging { t }sum·1,tsum·2,tsum·3,…,tsum·AwAnd the reachable charging station corresponding to the minimum value of the total charging time is taken as the target charging station of the vehicle k to be charged,
tsum·k＝＝td·k+tw·k+tc·k
td·kthe time required for the electric vehicle to travel from the current position to the charging station and then from the charging station to the destination, tw·kFor the queuing time of the electric vehicle in the charging station, tc·kCharging time of the electric vehicle in a charging station;
the target charging station load function matrix construction submodule is used for constructing a load function matrix of a target charging station of the vehicle k to be charged by taking t as 0 as the current moment:
Mload·k＝[0，…，0，p(k)，0，…0]
wherein M isload·kIs a matrix of 1 row and P columns, P is the total number of charging stations in the target area, and P (k) isWhen the vehicle k to be charged is charged to the target charging station, the load characteristic function corresponding to the target charging station, p is the number corresponding to the target charging station:
wherein, tstartFor the charging start time, t, of the vehicle k to be chargedw·kAnd tc·kRespectively charging queue time and charging time, P, of the vehicle k to be chargedcThe percentage of charging electric energy of the electric vehicle;
the charging station load function matrix construction submodule is used for completing the optimal charging station selection and the charging load function construction of all the electric vehicles to be charged in the target area to obtain M charging station load function matrixes { Mload·1,Mload·2,…,Mload·M}；
A charging station charging total load function determining module, 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 areasum：
Wherein M issumThe matrix is a matrix with 1 row and P columns, and the function of the P column is the charging load prediction condition of the P charging station in the target area.
9. 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 7 when executing the computer program.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when executed by a processor implements the steps of the method of any one of claims 1 to 7.
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| 黄小庆;陈颉;陈永新;杨夯;曹一家;江磊;: "大数据背景下的充电站负荷预测方法" * |
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| CN113326883A (en) * | 2021-06-03 | 2021-08-31 | 中创三优（北京）科技有限公司 | Training method, device and medium for power utilization rate prediction model of charging station |
| CN113326883B (en) * | 2021-06-03 | 2022-08-30 | 中创三优（北京）科技有限公司 | Training method, device and medium for power utilization rate prediction model of charging station |
| CN114611811A (en) * | 2022-03-21 | 2022-06-10 | 特斯联科技集团有限公司 | Low-carbon park optimal scheduling method and system based on EV load participation |
| CN114611811B (en) * | 2022-03-21 | 2022-10-25 | 特斯联科技集团有限公司 | Low-carbon park optimal scheduling method and system based on EV load participation |
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