CN110119841B - Charging station load prediction method based on multiple choices of users - Google Patents

Charging station load prediction method based on multiple choices of users Download PDF

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CN110119841B
CN110119841B CN201910350903.5A CN201910350903A CN110119841B CN 110119841 B CN110119841 B CN 110119841B CN 201910350903 A CN201910350903 A CN 201910350903A CN 110119841 B CN110119841 B CN 110119841B
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金锋
吴杰康
康丽
赵俊浩
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Abstract

The invention discloses a charging station load prediction method based on multiple choices of users, which comprises the following steps: s1, determining a site range for prediction, and acquiring relevant parameters of a charging station and an electric vehicle to be charged in the site range; s2, in the site range, acquiring influences of different factors on charging station selection of the corresponding users of the electric automobile, and calculating the attraction of each charging station to the corresponding users of the electric automobile; s3, calculating the number of the electric vehicles in the charging station at any moment and the leaving probability of the electric vehicles in the charging station at present according to the attraction force; and S4, calculating the charging load of the charging station through a Monte Carlo method. The invention also discloses a charging station load forecasting method and a charging station load forecasting system.

Description

Charging station load prediction method based on multiple choices of users
Technical Field
The invention relates to the technical field of electric vehicle charging load prediction, in particular to a charging station load prediction method based on multiple choices of users.
Background
The charging infrastructure serves as an important supporting system for electric automobile development, and reasonable planning and construction of the charging infrastructure are of great significance to development of the electric automobile industry. At present, research on charging infrastructure planning needs to be carried out on the basis of electric vehicle charging load prediction. In general, factors such as the scale, the charging mode, the operation rule, the battery characteristics, the electricity price system and the like of the electric automobile are considered, and a load prediction model is established.
Generally speaking, the charging pile load is mainly influenced by two factors, namely the charging power of the electric automobile and the charging time of the electric automobile, wherein the charging time comprises the charging time of a single electric automobile and the overall charging time of all the electric automobiles, and the overall charging time depends on the queuing number of the charging automobiles. However, the current research on the number of queues aims at load prediction of a single charging station, and influence of distance and other influence factors around a charging pile on charging selection of a user is not considered, so that the influence degree of the distance and other influence factors on the charging load cannot be quantitatively analyzed.
Disclosure of Invention
The invention provides a charging station load prediction method based on multiple choices of users, aiming at solving the problem that a plurality of influence factors are not combined in the process of the conventional charging station load prediction.
In order to achieve the above purpose, the technical means adopted is as follows:
a charging station load prediction method based on multiple user selections comprises the following steps:
s1, determining a location range for prediction, and acquiring relevant parameters of charging stations and electric vehicles to be charged in the location range, wherein the relevant parameters comprise the number of the charging stations in the location range, the number of charging piles in each charging station and the number of the electric vehicles;
s2, in the site range, acquiring influences of different factors on charging station selection of the corresponding users of the electric automobile, and calculating the attraction of each charging station to the corresponding users of the electric automobile;
s3, calculating the number of the electric vehicles in the charging station at any moment and the leaving probability of the electric vehicles in the charging station at present according to the attraction force;
and S4, calculating the charging load of the charging station through a Monte Carlo method.
According to the scheme, in the load prediction process of the charging station, the influence of different factors on the charging station selection of the corresponding user of the electric vehicle is introduced, and the load prediction is performed on the plurality of charging stations in the site range, so that a more accurate charging station load prediction result is obtained.
Preferably, the different factors described in step S2 include a distance between the electric vehicle and the charging station, a distribution of stores around the charging station, a distribution of schools around the charging station, and a distribution of hotels around the charging station. In the preferred embodiment, since these factors are closely related to the interests of the user of the electric vehicle and daily life, the selection of the charging station by the user is directly affected, and thus the load prediction result of the charging station is affected.
Preferably, the step S2 specifically includes:
s21, in the site range, obtaining the distance between the electric automobile and each charging station, and obtaining a distance parameter D as follows:
Figure BDA0002043909730000021
wherein i represents the ith charging station, n represents the nth electric vehicle, Di,nIndicating the distance between the ith charging station and the nth electric vehicle; wherein i and n are positive integers;
s22, in the site range, acquiring influence factors of store distribution around each charging station on users corresponding to each electric vehicle, wherein the influence factors F of the store distribution around the charging stations on the electric vehicles are as follows:
Figure BDA0002043909730000022
where i denotes the shop distribution around the ith charging station, n denotes the nth electric vehicle, Fi,nRepresenting influence factors of store distribution around the ith charging station on users corresponding to the nth electric vehicle; wherein i and n are positive integers;
s23, in the site range, obtaining influence factors of school distribution around each charging station on users corresponding to each electric vehicle, wherein the influence factors S of the school distribution around the charging stations on the electric vehicles are as follows:
Figure BDA0002043909730000023
wherein i represents the school distribution around the ith charging station, n represents the nth electric vehicle, Si,nRepresenting school distribution around the ith charging station to corresponding users of the nth electric vehicleThe influence factor of (c); wherein i and n are positive integers;
s24, in the site range, obtaining influence factors of hotel distribution around each charging station on users corresponding to each electric automobile, wherein the influence factors H of the hotel distribution around each charging station on the electric automobiles are as follows:
Figure BDA0002043909730000031
where i represents the hotel distribution around the ith charging station, n represents the nth electric vehicle, Hi,nRepresenting influence factors of hotel distribution around the ith charging station on users corresponding to the nth electric vehicle; wherein i and n are positive integers;
s25, calculating the attraction a of each charging station to the corresponding user of each electric automobilei,nThe calculation formula is as follows:
Figure BDA0002043909730000032
wherein a isi,nThe attraction of the ith charging station to the user corresponding to the nth electric vehicle; wherein i represents the ith charging station, n represents the user corresponding to the nth electric vehicle, and K is a preset non-negative constant.
In the preferred scheme, each influence factor and the K value are artificially specified according to the actual situation and are expressed by random numbers; in the invention, K is 2.
Preferably, the step S3 specifically includes:
s31, according to the attraction force ai,nCalculating the number of electric vehicles in the charging station at any moment:
Figure BDA0002043909730000033
Figure BDA0002043909730000034
Figure BDA0002043909730000035
wherein
Figure BDA0002043909730000036
Representing the number of electric vehicles in the charging station at any time t,
Figure BDA0002043909730000037
representing the number of electric vehicles in the charging station at the moment of t-1; pφIs a preset probability threshold value when leaving the probability Pt-1If the value is larger than the preset value, the electric automobile is selected to leave, otherwise, the electric automobile is selected to leave;
Figure BDA0002043909730000038
the system is used for recording the leaving condition of the electric automobile, wherein 1 represents that the electric automobile is selected to leave, and 0 represents that the electric automobile is selected to leave; epsilonb,nThe system is used for recording the attracted condition of the electric vehicle user, and the attracted condition represents that the attraction of the b-th charging station to the n-th electric vehicle user is the maximum; wherein
Figure BDA0002043909730000039
Is a preset assumed value;
s32, calculating the leaving probability of the electric vehicle in the charging station:
Figure BDA0002043909730000041
Figure BDA0002043909730000042
wherein P istFor the probability of leaving of an electric vehicle in the charging station at time t,/t-1The length of a queuing team at the time of t-1, r is a preset value and represents the influence of weather and other factors on corresponding users of the electric automobile, and n2Indicating the charging stationThe number of charging piles in the system. In the preferred embodiment, r is randomly selected from (0.3, 0.5).
Preferably, the step S4 specifically includes:
Figure BDA0002043909730000043
wherein
Figure BDA0002043909730000044
Representing the charging power of the charging station at the moment t; pdThe value is a preset value and represents the charging power of the electric automobile;
Figure BDA0002043909730000045
representing the number of electric vehicles in the charging station at the current moment t; n is2The number of charging piles in the charging station is represented.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
according to the method, in the load prediction process of the charging station, different factors are introduced to influence the charging station selection of the user corresponding to the electric automobile, and the factors are closely related to the benefits of the user of the electric automobile and daily life, so that the selection of the user on the charging station is directly influenced, namely the charging load of the charging station is influenced; and meanwhile, load prediction is carried out on a plurality of charging stations in the site range, so that a more accurate charging station load prediction result is obtained.
The charging station load prediction method based on the multi-user selection is higher in accuracy rate, and solves the problem that a plurality of influence factors are not combined in the existing charging load prediction process.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic view of a charging station distribution with multiple user selections according to the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the present embodiments, certain elements of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
A charging station load prediction method based on multiple user selections, applied to a scenario shown in fig. 2, the method comprising the following steps shown in fig. 1:
s1, determining a location range for prediction, and acquiring relevant parameters of charging stations and electric vehicles to be charged in the location range, wherein the relevant parameters comprise the number of the charging stations in the location range, the number of charging piles in each charging station and the number of the electric vehicles;
s2, in the place range, acquiring influences of different factors on charging station selection of the corresponding users of the electric automobile, and calculating the attraction a of each charging station to the corresponding users of the electric automobilei,n；
S3, according to the attraction ai,nCalculating the number of electric vehicles in the charging station at any moment and the leaving probability of the electric vehicles in the charging station at present;
and S4, calculating the charging load of the charging station through a Monte Carlo method.
Example 2
A charging station load prediction method based on multiple user selections is applied to a scene shown in figure 2, and comprises the following steps:
s1, determining a location range for prediction, and acquiring relevant parameters of charging stations and electric vehicles to be charged in the location range, wherein the relevant parameters comprise the number of the charging stations in the location range, the number of charging piles in each charging station and the number of the electric vehicles;
s2, in the site range, acquiring different factors to carry out processing on corresponding users of the electric automobileInfluence of charging station selection is calculated, and attraction a of each charging station to corresponding user of the electric automobile is calculatedi,n；
The method comprises the following specific steps:
s21, in the site range, obtaining the distance between the electric automobile and each charging station, and obtaining a distance parameter D as follows:
Figure BDA0002043909730000051
wherein i represents the ith charging station, n represents the nth electric vehicle, Di,nIndicating the distance between the ith charging station and the nth electric vehicle; wherein i and n are positive integers;
s22, in the site range, acquiring influence factors of store distribution around each charging station on users corresponding to each electric vehicle, wherein the influence factors F of the store distribution around the charging stations on the electric vehicles are as follows:
Figure BDA0002043909730000061
where i denotes the shop distribution around the ith charging station, n denotes the nth electric vehicle, Fi,nRepresenting influence factors of store distribution around the ith charging station on users corresponding to the nth electric vehicle; if no shop exists around the charging station, F is equal to 0, wherein i and n are positive integers;
s23, in the site range, obtaining influence factors of school distribution around each charging station on users corresponding to each electric vehicle, wherein the influence factors S of the school distribution around the charging stations on the electric vehicles are as follows:
Figure BDA0002043909730000062
wherein i represents the school distribution around the ith charging station, n represents the nth electric vehicle, Si,nShows the influence factors of school distribution around the ith charging station on the corresponding users of the nth electric vehicleA seed; if no school exists around the charging station, S is 0, wherein i and n are positive integers;
s24, in the site range, obtaining influence factors of hotel distribution around each charging station on users corresponding to each electric automobile, wherein the influence factors H of the hotel distribution around each charging station on the electric automobiles are as follows:
Figure BDA0002043909730000063
where i represents the hotel distribution around the ith charging station, n represents the nth electric vehicle, Hi,nRepresenting influence factors of hotel distribution around the ith charging station on users corresponding to the nth electric vehicle, wherein if no hotel exists around the charging station, H is 0, and i and n are positive integers;
in embodiment 2, the distribution of stores, schools, and hotels around each charging station refers to stores, schools, and hotels within 200 meters of the circumference of each charging station. Since the distance can be determined according to actual conditions, the invention does not limit the distance;
s25, calculating the attraction a of each charging station to the corresponding user of each electric automobilei,nThe calculation formula is as follows:
Figure BDA0002043909730000064
wherein a isi,nThe attraction of the ith charging station to the user corresponding to the nth electric vehicle; wherein i represents the ith charging station, n represents the user that nth electric automobile corresponds, and K is predetermined nonnegative constant, and the K value is artificially injectd according to actual conditions, and in this embodiment 2, the value of K takes 2.
S3, according to the attraction ai,nCalculating the number of electric vehicles in the charging station at any moment and the leaving probability of the electric vehicles in the charging station at present;
the method comprises the following specific steps:
s31, according to the attraction force ai,nCalculating arbitrary timeThe number of electric vehicles engraved in the charging station:
Figure BDA0002043909730000071
Figure BDA0002043909730000072
Figure BDA0002043909730000073
wherein
Figure BDA0002043909730000074
Representing the number of electric vehicles in the charging station at any time t,
Figure BDA0002043909730000075
representing the number of electric vehicles in the charging station at the moment of t-1; pφIs a preset probability threshold value when leaving the probability Pt-1If the value is larger than the preset value, the electric automobile is selected to leave, otherwise, the electric automobile is selected to leave;
Figure BDA0002043909730000076
the system is used for recording the leaving condition of the electric automobile, wherein 1 represents that the electric automobile is selected to leave, and 0 represents that the electric automobile is selected to leave; epsilonb,nThe system is used for recording the attracted condition of the electric vehicle user, and the attracted condition represents that the attraction of the b-th charging station to the n-th electric vehicle user is the maximum; wherein
Figure BDA0002043909730000077
Is a preset assumed value;
s32, calculating the leaving probability of the electric vehicle in the charging station:
Figure BDA0002043909730000078
Figure BDA0002043909730000079
wherein P istFor the probability of leaving of an electric vehicle in the charging station at time t,/t-1The length of a queuing team at the time of t-1, r is a preset value and represents the influence of weather and other factors on corresponding users of the electric automobile, and n2Number of charging piles, n, in the charging station2From step S1.
S4, calculating the charging load of the charging station through a Monte Carlo method:
Figure BDA00020439097300000710
wherein
Figure BDA00020439097300000711
Representing the charging power of the charging station at the moment t; pdThe value is a preset value and represents the charging power of the electric automobile;
Figure BDA00020439097300000712
representing the number of electric vehicles in the charging station at the current moment t; n is2The number of charging piles in the charging station is represented.
The terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (3)

1. A charging station load prediction method based on multiple user selections is characterized by comprising the following steps:
s1, determining a location range for prediction, and acquiring relevant parameters of charging stations and electric vehicles to be charged in the location range, wherein the relevant parameters comprise the number of the charging stations in the location range, the number of charging piles in each charging station and the number of the electric vehicles;
s2, in the site range, acquiring influences of different factors on charging station selection of the corresponding users of the electric automobile, and calculating the attraction of each charging station to the corresponding users of the electric automobile;
s3, calculating the number of the electric vehicles in the charging station at any moment and the leaving probability of the electric vehicles in the charging station according to the attraction force;
s4, calculating the charging load of the charging station through a Monte Carlo method;
the different factors described in step S2 include the distance between the electric vehicle and the charging station, the store distribution around the charging station, the school distribution around the charging station, and the hotel distribution around the charging station;
the step S2 specifically includes:
s21, in the site range, obtaining the distance between the electric automobile and each charging station, and obtaining a distance parameter D as follows:
Figure FDA0003656571900000011
wherein i represents the ith charging station, n represents the nth electric vehicle, Di,nIndicating the distance between the ith charging station and the nth electric vehicle; wherein i and n are positive integers;
s22, in the site range, acquiring influence factors of store distribution around each charging station on users corresponding to each electric vehicle, wherein the influence factors F of the store distribution around the charging stations on the electric vehicles are as follows:
Figure FDA0003656571900000012
where m denotes the shop distribution around the ith charging station, n denotes the nth electric vehicle, Fm,nRepresenting influence factors of store distribution around the ith charging station on users corresponding to the nth electric vehicle; wherein m and n are positive integers;
s23, in the site range, obtaining influence factors of school distribution around each charging station on users corresponding to each electric vehicle, wherein the influence factors S of the school distribution around the charging stations on the electric vehicles are as follows:
Figure FDA0003656571900000021
where v denotes the school distribution around the ith charging station, n denotes the nth electric vehicle, Sv,nRepresenting influence factors of school distribution around the ith charging station on users corresponding to the nth electric vehicle; wherein v and n are positive integers;
s24, in the site range, obtaining influence factors of hotel distribution around each charging station on users corresponding to each electric automobile, wherein the influence factors H of the hotel distribution around each charging station on the electric automobiles are as follows:
Figure FDA0003656571900000022
where e denotes the hotel distribution around the ith charging station, n denotes the nth electric vehicle, He,nRepresenting influence factors of hotel distribution around the ith charging station on users corresponding to the nth electric vehicle; wherein e and n are positive integers;
s25, calculating attraction a of each charging station to corresponding users of each electric automobilei,nThe calculation formula is as follows:
Figure FDA0003656571900000023
wherein a isi,nThe attraction of the ith charging station to the user corresponding to the nth electric vehicle; wherein i represents the ith charging station, n represents the user corresponding to the nth electric vehicle, and K is a preset non-negative constant.
2. The charging station load prediction method according to claim 1, wherein the step S3 specifically includes:
s31, according to the attraction force ai1,nCalculating the number of electric vehicles in the charging station at any moment:
Figure FDA0003656571900000024
Figure FDA0003656571900000031
Figure FDA0003656571900000032
wherein
Figure FDA0003656571900000033
Representing the number of electric vehicles in the charging station at any time t,
Figure FDA0003656571900000034
representing the number of electric vehicles in the charging station at the moment of t-1; pφIs a preset probability threshold value when leaving the probability Pt-1Greater than PφIf so, the electric automobile is selected to leave, otherwise, the electric automobile is selected to be left;
Figure FDA0003656571900000035
the system is used for recording the leaving condition of the electric automobile, wherein 1 represents that the electric automobile is selected to leave, and 0 represents that the electric automobile is selected to leave; epsilonb,nFor recording electric motorsThe attraction condition of the automobile user represents that the attraction of the b-th charging station to the n-th electric automobile user is maximum; wherein
Figure FDA0003656571900000036
Is a preset assumed value;
s32, calculating the leaving probability of the electric vehicle in the charging station:
Figure FDA0003656571900000037
Figure FDA0003656571900000038
wherein P istFor the probability of leaving of an electric vehicle in the charging station at time t,/t-1The length of the queue at the time t-1; r is a preset value and represents the influence of weather and other factors on corresponding users of the electric automobile; n is2The number of charging piles in the charging station is represented.
3. The charging station load prediction method according to claim 2, wherein the step S4 specifically includes:
Figure FDA0003656571900000039
wherein
Figure FDA00036565719000000310
Representing the charging power of the charging station at the moment t; pdThe value is a preset value and represents the charging power of the electric automobile;
Figure FDA00036565719000000311
representing the number of electric vehicles in the charging station at time t; n is2The number of charging piles in the charging station is represented.
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