CN111597690A - Method for establishing electric vehicle charging equipment demand coefficient calculation model - Google Patents
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Abstract
The invention relates to a method for establishing a coefficient calculation model required by electric vehicle charging equipment, which comprises the following steps of: step one, data processing; secondly, setting model input and output parameters; fitting out probability distribution curves of access time and charging duration according to the access event data set; step four, defining a charging matrix; and (5) obtaining a required coefficient. The value of the adjusted required coefficient Kx is more targeted and closer to the actual use condition, the calculation result is more in line with the actual condition, the capacity selection of the transformer of the power distribution system designed according to the algorithm is much smaller than that of the traditional rough algorithm, the requirements of the corresponding switch breaking capacity and the short-circuit tolerance capacity of the power distribution cabinet are reduced, and the manufacturing cost is saved.
Description
Technical Field
The invention relates to the field of electric vehicle charging technology design, in particular to a method for establishing a coefficient calculation model required by electric vehicle charging equipment.
Background
At present, the existing charging pile power distribution load calculation generally adopts the following formula:
in the calculation mode, the input power of all charging equipment in a place is directly accumulated, and then multiplied by a uniform required coefficient Kx value to obtain the calculation power of the charging equipment.
The charging piles of different types have different use conditions, the probability of large-area simultaneous use of a large number of 7kW alternating-current charging piles is very low, and the requirement coefficient is also very low; the possibility of using a small amount of high-power direct-current charging piles at the same time is very high.
In most cases, the value of this coefficient is suggested to be 0.8, but there is no proper theory or practical measure for this value.
For parking lots with different properties, the charging rules are different, and it is not reasonable to determine the Kx value as a uniform value.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the method for establishing the calculation model of the required coefficient of the electric vehicle charging equipment, which has more pertinence in the value of the required coefficient and is closer to the actual use condition.
The technical scheme adopted by the invention for solving the technical problems is as follows: the method for establishing the coefficient calculation model required by the electric vehicle charging equipment comprises the following steps:
step 1, data processing: converting the access time and the charging time in the original access event data set into minutes;
step 2, setting model input and output parameters, wherein:
model input parameters: the access time interval is: a is1(ii) a The charging time interval is: a is2(ii) a The access time segmentation points are: b1，b2，b3(ii) a The number n of charging piles;
the model output is: the required coefficients corresponding to the n charging piles;
and 3, fitting an access time and charging duration probability distribution curve according to the access event data set, wherein:
the access time probability distribution curve fitting step is as follows:
step 311: to b is1:00～b2:00，b2:00～b3:00，b3:00～b100 the frequency of access events in the three interval periods is carried out according to a unimodal lognormal distributionFitting to obtain three functional relations: f. of1，f2，f3；
f1，f2，f3Each represents b1:00～b2:00，b2:00～b3:00，b3:00～b100 an access event frequency fitting function of the interval;
step 312: obtaining the i-th section i.a of the vehicle1～(i+1)·a1Access probability function f (i);
the charging time length probability distribution curve fitting step is as follows:
step 321: fitting the charging time lengths of all charging events according to a single-peak Gaussian to obtain a charging time length fitting function relation f4；
Step 322: the charging time period is obtained to belong to the jth category, namely: j.a2～(j+1)·a2The probability function g (j);
step 4, defining a charging matrix: the method comprises the following steps:
step 41, the access probability of each time slot obtained in step 312 is:
step 42, ranking the access probability of each time slot in step 41 to obtain:
in the above ordering, the following are satisfied:
p represents an access probability value of a certain access time period; t represents a corresponding certain access time period;
step 43, calculating coefficients of the n charging piles: the ordered access probability is normalized and corrected to obtain the corrected access probability
The charging matrix of the n charging piles is recorded as M:
in the above formula, M is n × 1440D charging matrix, MjAn n × 1440-dimensional charging matrix corresponding to the j charging mode;
e is an n-dimensional row vector of all 1, y-e.M is a 1440-dimensional row vector, and the elements are the sum of each column of the matrix M.
In a preferred embodiment of the method for establishing the model of calculating the required coefficient of the electric vehicle charging device, in step 312, the access time fitting function is specifically:
in a preferred embodiment of the method for establishing a model of calculating a required coefficient of an electric vehicle charging device, in step 322, the charging duration correction probability specifically includes:
in the above formula: jmax represents the total number of charging modes,
dmax represents the maximum charge duration in the data set.
In a preferred embodiment of the method for establishing the model for calculating the required coefficient of the electric vehicle charging equipment, in the step 1, the event frequency is obtained by segmentation to obtain sample points, and then the probability distribution curve of the access time and the charging duration is fitted by performing gaussian fitting according to the sample points.
In a preferred embodiment of the method for establishing the model of calculating the demand coefficient of the electric vehicle charging equipment, in step 322, the charging duration x of the jth charging mode isjCan be set to the maximum value: x is the number ofj＝(j+1)·a2。
In a preferred embodiment of the method for establishing the model of calculating the required coefficient of the charging device of the electric vehicle according to the present invention, in the step 43, m is setrcIs a matrix MjRow r and column c of (1), where r ∈ {0, 1, 2, …, n }, c ∈ {0, 1, 2, … 1439}, are defined as follows:
when r.a1+xj<At 1440:
when r.a1+xjAt more than or equal to 1440:
compared with the prior art, the method for establishing the coefficient calculation model required by the electric vehicle charging equipment has the beneficial effects that: the value of the adjusted required coefficient Kx is more targeted and closer to the actual use condition, the calculation result is more in line with the actual condition, the capacity of the transformer of the power distribution system designed according to the algorithm is more selected than that of the traditional rough algorithm, the corresponding requirements on the switch breaking capacity and the short-circuit tolerance capacity of the power distribution cabinet are reduced, and the manufacturing cost is saved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below.
Fig. 1 is a value curve diagram of required coefficient Kx obtained by using the calculation model of the present invention for the actual data of a parking lot provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The method for establishing the coefficient calculation model required by the electric vehicle charging equipment comprises the following steps:
step 1, data processing: converting the Access time and the charging Duration in the original Access event data set into minutes, and simultaneously respectively modifying the list into Access and Duration as identification marks of software;
step 2, setting model input and output parameters, wherein:
model input parameters: the access time interval is: a is1(ii) a The charging time interval is: a is2(ii) a The access time segmentation points are: b1，b2，b3(ii) a The number n of charging piles;
the model output is: the required coefficients corresponding to the n charging piles;
and 3, fitting an access time and charging duration probability distribution curve according to the access event data set, wherein:
the fitting method of the embodiment is as follows: firstly, the event frequency is calculated in a segmented manner to obtain a sample point, and then Gaussian fitting is carried out according to the sample point, wherein the charging time is specifically taken as an example:
calculating frequency freq of the charging time length according to the charging time length interval, such as: 0 to a2Interval frequency is freq0If the number 0 sample point is (0, freq)0)；
Accordingly, the ith sample point represents the ith interval number and the ith interval i · a1～(i+1)·a1Frequency of (d): (i, freq)i) (ii) a For these sample points { (i, freq)i) And fitting unimodal normal distribution.
The access time probability distribution curve fitting step is as follows:
step 311: to b is1:00～b2:00，b2:00～b3:00，b3:00～b100 the frequency of the access events in the time periods of the three intervals is fitted according to unimodal lognormal distribution to obtain three functional relations: f. of1，f2，f3；
f1，f2，f3Each represents b1:00～b2:00，b2:00～b3:00，b3:00～b100 an access event frequency fitting function of the interval;
step 312: obtaining the i-th section i.a of the vehicle1～(i+1)·a1Is (i) the access probability function of (f) is:
the charging time length probability distribution curve fitting step is as follows:
step 321: fitting the charging time lengths of all charging events according to a single-peak Gaussian to obtain a charging time length fitting function relation f4；
Step 322: the charging time period is obtained to belong to the jth category, namely: j.a2～(j+1)·a2Is (required to make the sum of probabilities 1):
in the above formula: jmax represents the total number of charging modes,
dmax represents the maximum charge duration in the dataset;
can be charged in the j charging modeLength xjSet to the maximum value: x is the number ofj＝(j+1)·a2The unimodal Gaussian distribution is normal distribution.
Step 4, defining a charging matrix: the charging model mathematical matrix is manufactured according to the embodiment, and the specific idea is as follows:
assuming that the charging pile is a charging pile, namely selecting the highest point on a probability curve of an access time point as the access time point, wherein the matrix has only one row at the moment, redistributing the probability of the access time point, calculating the matrix for P times (P is the total number of charging modes), multiplying each time by the probability distribution of corresponding access time length, and then summing to obtain a final coefficient;
assuming that two charging piles are adopted, namely, the highest point and the next highest point on the probability curve of the access time point are selected as the access time points, the matrix has only two rows at the moment, the probability of the access time points is redistributed, P times (P is the total number of charging modes) are calculated for the matrix, and the probability distribution of each time multiplied by the corresponding access time duration is summed to obtain a final coefficient;
and the rest of the charging piles are analogized in sequence.
The defining a charging matrix comprises the steps of:
step 41, the access probability of each time slot obtained in step 312 is:
step 42, ranking the access probability of each time slot in step 41 to obtain:
in the above ordering, the following are satisfied:
p represents the access probability value of accessing a certain access time period; t represents a corresponding certain access time period;
step 43, calculating the series of n charging pilesNumber: the ordered access probability is normalized and corrected to obtain the corrected access probability
The charging matrix of the n charging piles is recorded as M:
in the above formula, M is n × 1440D charging matrix, MjAn n × 1440-dimensional charging matrix corresponding to the j charging mode;
let mrcIs a matrix MjRow r, column c; wherein:
r ∈ {0, 1, 2, …, n }, c ∈ {0, 1, 2, … 1439}, defined as follows:
when r.a1+xj<At 1440:
when r.a1+xjAt more than or equal to 1440:
step 4, obtaining a required coefficient: let the row vector e be (1,1, …,1)1×nIf the row vector y is equal to e · M, the sum of each column of the matrix M, and then k is equal to max { y }, which is the estimation coefficient of n piles;
e is an n-dimensional row vector of all 1, y-e.M is a 1440-dimensional row vector, and the elements are the sum of each column of the matrix M.
According to the method, two probability distribution curves of an access time point and a charging time are fitted through a mathematical model according to an existing access event data set (the more the data is, the better the data is), and then a series of required coefficient values are obtained through calculation. This conclusion is unique for a particular sample library and is very targeted, i.e. for charging events of a certain type of parking lot (when the number of samples is sufficient, the same type of parking lot can be considered to obey the same probability distribution), if the charging event sample data set of another type of parking lot is replaced, conclusions for other types of parking lots can also be drawn.
Specifically, according to the actual data of a certain parking lot, the required coefficient Kx obtained by using the calculation model of the present invention takes the following values:
fitting was performed according to the data in the table above and the curve is shown in figure 1.
The comparison between the traditional required coefficient value and the required coefficient value of the invention is as follows:
A. and taking values according to the demand coefficient before modification, wherein the final calculation load of a parking lot provided with 200 7kW alternating-current charging piles is 1120kW (assuming that the efficiency is 1). The calculation process is as follows:
total Kx ═ 0.8
Then Pjs-0.8-200-7-1120 kW
The required distribution equipment parameters are as follows
A transformer: 1600kVA
Bus bar: 3150A
Breaking capacity of the circuit breaker: 40kA
Setting value of the incoming line breaker: 3200A
B. According to the modified demand coefficient value, the final calculation load of a parking lot provided with 200 7kW alternating-current charging piles is 280kW (assuming that the efficiency is 1). The calculation process is as follows:
Kx＝0.2
then Pjs-0.2-200-7-280 kW
The required distribution equipment parameters are as follows
A transformer: 400kVA
Bus bar: 800A
Breaking capacity of the circuit breaker: 15kA
Setting value of the incoming line breaker: 800A
The value of the adjusted required coefficient Kx is more targeted and closer to the use condition, the calculation result is more practical, the capacity of the transformer of the power distribution system designed according to the algorithm is much smaller than that of the transformer in the prior art, the corresponding requirements on the switch breaking capacity and the short-circuit tolerance capacity of the power distribution cabinet are reduced, and the manufacturing cost is saved.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by the present specification, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (6)
1. A method for establishing a coefficient calculation model required by electric vehicle charging equipment is characterized by comprising the following steps of: the method comprises the following steps:
step 1, data processing: converting the access time and the charging time in the original access event data set into minutes;
step 2, setting model input and output parameters, wherein:
model input parameters: the access time interval is: a is1(ii) a The charging time interval is: a is2(ii) a The access time segmentation points are: b1，b2，b3(ii) a The number n of charging piles;
the model output is: the required coefficients corresponding to the n charging piles;
and 3, fitting an access time and charging duration probability distribution curve according to the access event data set, wherein:
the access time probability distribution curve fitting step is as follows:
step 311: to b is1:00～b2:00，b2:00～b3:00，b3:00～b100 the frequency of the access events in the three interval time periods is fitted according to unimodal lognormal distribution to obtain three functional relations: f. of1，f2，f3；
f1，f2，f3Each represents b1:00～b2:00，b2:00～b3:00，b3:00～b100 an access event frequency fitting function of the interval;
step 312: obtaining the i-th section i.a of the vehicle1～(i+1)·a1Access probability function f (i);
the charging time length probability distribution curve fitting step is as follows:
step 321: fitting the charging time lengths of all charging events according to a single-peak Gaussian to obtain a charging time length fitting function relation f4；
Step 322: the charging time period is obtained to belong to the jth category, namely: j.a2～(j+1)·a2The probability function g (j);
step 4, defining a charging matrix: the method comprises the following steps:
step 41, the access probability of each time slot obtained in step 312 is:
step 42, ranking the access probability of each time slot in step 41 to obtain:
in the above ordering, the following are satisfied:
p represents an access probability value of a certain access time period; t represents a corresponding certain access time period;
step 43, calculating coefficients of the n charging piles: to the orderedThe access probability is normalized and corrected to obtain the corrected access probability
The charging matrix of the n charging piles is recorded as M:
in the above formula, M is n × 1440D charging matrix, MjAn n × 1440-dimensional charging matrix corresponding to the j charging mode;
step 5, obtaining a required coefficient: let the row vector e be (1,1, …,1)1×nIf the row vector y is equal to e · M, the sum of each column of the matrix M, and then k is equal to max { y }, which is the estimation coefficient of n piles;
e is an n-dimensional row vector of all 1, y-e.M is a 1440-dimensional row vector, and the elements are the sum of each column of the matrix M.
2. The electric vehicle charging equipment demand coefficient calculation model of claim 1, characterized in that: in step 312, the access time fitting function specifically includes:
3. the electric vehicle charging equipment demand coefficient calculation model of claim 1, characterized in that: in step 322, the charging duration correction probability specifically includes:
in the above formula: jmax represents the total number of charging modes,
dmax represents the maximum charge duration in the data set.
4. The electric vehicle charging equipment demand coefficient calculation model of claim 1, characterized in that: in the step 1, the event frequency is calculated in a segmented manner to obtain sample points, and then the probability distribution curves of the access time and the charging duration are fitted by a Gaussian fitting method according to the sample points.
5. The electric vehicle charging equipment demand coefficient calculation model of claim 1, characterized in that: in step 322, the charging time length x of the jth charging modejCan be set to the maximum value: x is the number ofj＝(j+1)·a2。
6. The electric vehicle charging equipment demand coefficient calculation model of claim 1, characterized in that: in the step 43, let mrcIs a matrix MjRow r and column c of (1), where r ∈ {0, 1, 2, …, n }, c ∈ {0, 1, 2, … 1439}, are defined as follows:
when r.a1+xj<At 1440:
when r.a1+xjAt more than or equal to 1440:
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