CN109285039B - Electric vehicle charging station electricity price making method considering wind and light uncertainty - Google Patents

Electric vehicle charging station electricity price making method considering wind and light uncertainty Download PDF

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CN109285039B
CN109285039B CN201811397282.8A CN201811397282A CN109285039B CN 109285039 B CN109285039 B CN 109285039B CN 201811397282 A CN201811397282 A CN 201811397282A CN 109285039 B CN109285039 B CN 109285039B
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蒋浩
张雄义
周凯帆
罗皓
姜维伊
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Abstract

The invention discloses an electric vehicle charging station electricity price making method considering wind and light uncertainty, which comprises the following steps: acquiring a regional historical meteorological data set, and clustering the daily wind speed and the illumination intensity by adopting a clustering algorithm to obtain a clustering center and a weight; building a wind power generation and photovoltaic power generation output model of a charging station; constructing a charging time model of a single electric vehicle, and calculating the chargeable duration of the single electric vehicle; constructing a response model and a probability transfer matrix of the single charging price to obtain a single charging probability distribution model after time-of-use price; simulating charging, and acquiring a daily load demand of an electric automobile group, a total charging power model of a charging station and a load demand model of the charging station on a power grid; constructing a charging station benefit model of the time-of-use charging electricity price, and solving the charging electricity price of each time period in the model by adopting a genetic algorithm; and (4) taking the optimal individual to decode, and obtaining the time-of-use electricity price of the electric vehicle charging station. According to the invention, the economic benefit target function of the charging station is constructed, and the optimal solution is solved, so that the electricity price formulation process is more accurate.

Description

Electric vehicle charging station electricity price making method considering wind and light uncertainty
Technical Field
The invention relates to a method for formulating the electricity price of an electric vehicle charging station considering wind and light uncertainty, and belongs to the technical field of power system scheduling.
Background
In recent years, various new energy power generation is increasingly developed and applied to power systems, and the pressure of energy requirements of the current society is relieved to a certain extent. The development of new energy power generation technology accelerates the change of life style of people, and wind power generation, photovoltaic cells, electric vehicles and the like are gradually popularized and developed. However, compared with the traditional power generation, the wind power and photovoltaic output have large uncertainty, the prediction difficulty is high, as the popularization rate of electric vehicles is gradually increased in the future, the economic dispatching and operation of a power distribution network containing a distributed power supply also face problems and challenges, and a power distribution network optimal dispatching and operation method needs to be provided for the uncertainty of the distributed power generation and the charging response of the electric vehicles.
In consideration of uncertainty of wind power and photovoltaic output, a reasonable model or processing method needs to be provided for wind speed and illumination intensity in the region. Most studies at present consider that the wind speed approximately obeys Weibull (Weibull) distribution, the illumination intensity obeys Beta (Beta) distribution, and the method for constructing the wind-solar output model by simulating the wind speed and the illumination intensity through a determined probability density function has high representativeness, but the model may lack precision for some small or extreme power supply regions. Some random algorithms, such as a Monte Carlo method, are adopted for simulating various distributed power generation output aiming at uncertainty of wind speed and illumination, but the random algorithms have high randomness, distribution characteristics of wind speed and illumination intensity in an area can not be reflected, and the calculation amount in the simulation process is too large. The power distribution network containing the electric vehicles is more complex, a large number of electric vehicles are connected, and the large fluctuation of the load of the power distribution network area is inevitably caused, so that the power supply safety and reliability are influenced. At present, part of research on methods for formulating the electricity price of an electric vehicle charging station in a power distribution network has the following defects:
1. the existence of new energy power generation in the electric automobile station is not considered. Some electric automobile stations are internally provided with new energy sources such as wind power and photovoltaic power generation, the trend is more and more obvious in the future, and the wind power generation, the photovoltaic power generation and the like are considered in the current fresh research.
2. A reasonable output model is not effectively established for wind power generation and photovoltaic power generation in the station. Wind power and photovoltaic are greatly influenced by meteorological factors, the amount of regional meteorological historical data is large, and due to the fact that certain interconnectivity of wind and photovoltaic is considered, wind power and illumination intensity need to be combined, modeling is difficult, data with high value amount are extracted from mass data, and therefore a reasonable wind power and photovoltaic output model is built.
3. The charging behavior of a regional electric vehicle fleet is not reasonably simulated. The charging behavior of the electric vehicle is influenced by time and charging electricity price factors, so a charging behavior simulation on a time scale needs to be established, and price response needs to be considered.
In summary, it is necessary to consider establishing a reasonable model of the charging behavior of the electric vehicle, consider the existence of wind power generation and photovoltaic power generation in the station, and formulate a reasonable time-sharing charging price on a larger time scale to achieve the optimum with a certain objective function.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a method for establishing the electricity price of an electric vehicle charging station by considering wind and light uncertainty, and solves the problems that the existing method cannot establish a reasonable model of the charging behavior of the electric vehicle and needs to consider the existence of wind power generation and photovoltaic power generation in the station, so that the electricity price is determined inaccurately.
The invention specifically adopts the following technical scheme to solve the technical problems:
an electric vehicle charging station electricity price making method considering wind and light uncertainty comprises the following steps:
step 1, acquiring a regional historical meteorological data set of an electric vehicle charging station, clustering wind speed and illumination intensity distribution curves in the regional historical meteorological data set by adopting a k-means clustering algorithm to obtain a representative distribution curve taking a clustering center as each class, and solving the weight of each class;
step 2, establishing a wind power generation and photovoltaic power generation output model of the electric vehicle charging station in regional grid-connected operation;
step 3, constructing a charging time model of the single electric automobile, and calculating the number of chargeable time segments of the single electric automobile;
step 4, combining the number of chargeable periods of the single electric vehicle, constructing a response model of the charging price of the single electric vehicle and a probability transfer matrix of the charging price at the initial charging time, and acquiring a charging probability distribution model of the single electric vehicle after time-of-use price;
step 5, simulating a charging behavior scene of the electric automobile group, acquiring daily load requirements of the electric automobile group, and then acquiring a total charging power model of each time period of the electric automobile charging station and a load requirement model of the electric automobile charging station on a power grid by combining an established wind power generation and photovoltaic power generation output model in the electric automobile charging station and a single electric automobile charging probability distribution model after time-of-use electricity price;
step 6, constructing an electric vehicle charging station benefit model of the time-of-use charging price by combining the clustering center and the weight thereof, a total charging power model of each time interval of the electric vehicle charging station and a load demand model of the electric vehicle charging station on the power grid, wherein the electric vehicle charging station benefit model comprises the electricity selling benefit of the electric vehicle charging station and the basic benefit obtained by participating in power grid peak shaving;
7, solving the charging electricity price of each time period of the electric vehicle charging station in the constructed benefit model of the electric vehicle charging station by adopting a genetic algorithm;
and 8, obtaining the optimal individual decoding according to the solved charging electricity prices of the electric vehicle charging station at each time period, and obtaining the time-of-use electricity price of the electric vehicle charging station.
Further, as a preferred technical solution of the present invention, the step 1 adopts a k-means clustering algorithm to cluster the wind speed and illumination intensity distribution curves, specifically:
1-1, respectively carrying out normalization processing on the historical meteorological data concentrated wind speed and illumination intensity data by adopting a formula:
Figure GDA0003018436240000031
wherein v and s are vectors of N years of full-time wind speed and illumination intensity respectively; v. of*、s*The matrix is formed by normalized vectors of N years of full-time wind speed and illumination intensity respectively, and the size of the matrix is Nd×24，NdTotal number of samples; v. ofmax、vminMaximum and minimum wind speed, s, respectively, over full timemax、sminMaximum and minimum light intensities in full time sequence;
step 1-2, constructing a k-means clustering sample x of wind speed and illumination intensity, wherein x is NdX 48 matrix, one cluster sample per row：
x＝[v* s*]
Step 1-3, randomly selecting k non-repeated row vectors from a clustering sample x, initializing k clustering centers, xc1、xc2..xck；
Step 1-4, calculating the distance from each clustering sample to each clustering center, and classifying the clustering samples into each clustering center; and calculating each new cluster center, the updating formula of the ith cluster center is as follows:
Figure GDA0003018436240000032
wherein x isciRepresenting the ith cluster center; n isiThe number of samples contained for the ith class; x is the number ofi,mIs the mth sample in the ith class;
and 1-5, repeating the steps 1-4 until the clustering center is converged.
Further, as a preferred technical solution of the present invention, the step 1 finds the weight of each class, and adopts a formula:
ρci＝ni/Nd
wherein N isdIs the total number of samples of the cluster.
Further, as a preferred technical solution of the present invention, the wind power generation and photovoltaic power generation output models established in step 2 are respectively:
Figure GDA0003018436240000041
Ps(t)＝A*γ(t)*η
wherein, Pw(t) is the output of the wind driven generator in the time period t, v (t) is the wind speed in the electric vehicle charging station in the time period t, vciCutting into wind speed, v, for wind generatorscrRated wind speed, vcoFor cutting out the wind speed, PrThe rated power of the wind driven generator; t represents the chargeable time period number d of the electric automobileThe t-th period; psAnd (t) is the active power output of the photovoltaic cell in the period of t, A is the area of the cell, gamma (t) is the illumination intensity in the period of t, and eta is the photoelectric conversion efficiency.
By adopting the technical scheme, the invention can produce the following technical effects:
the invention provides a reference for formulating reasonable electricity price for an electric vehicle charging station, and provides an electric vehicle charging station electricity price formulating method considering wind and light uncertainty. The method considers new energy power generation equipment commonly existing in the future electric vehicle charging station, such as wind power generation and photovoltaic power generation, establishes a reasonable model for wind power and photovoltaic output of the charging station area, and extracts data information from massive historical data by using a k-means clustering algorithm, so that the algorithm workload is reduced; then, performing mathematical simulation on the charging behavior of the electric automobile, including starting trip, traveling distance, ending trip, charging time and the like, establishing a demand response model of the electric automobile for the charging electricity price, providing a charging probability transfer matrix, and reflecting the response of the electric automobile to the charging price; then, charging simulation is carried out on the electric automobile group, and the load demand of the charging station on the power grid is calculated by combining an in-station wind power and photovoltaic output model; and finally, constructing a charging station economic benefit objective function by using the improved genetic algorithm and taking the charging station economic benefit as a target, and solving the time-of-use electricity price of the electric vehicle charging station by using the improved genetic algorithm.
According to the method, the k-means clustering method is adopted to process the regional meteorological data set, so that the calculation complexity caused by mass data can be reduced; the electric vehicle charging model comprises simulation of a trip starting time, a trip ending time and trip duration, and is more practical; the electric automobile charging probability transfer matrix reflects the offset response of a user to the charging starting time generated by price change; the charging station benefit model comprehensively considers the electricity selling income and the basic income participating in peak clipping and valley filling; the improved genetic algorithm realizes the self-adaptation of the cross rate and the variation rate and can quickly converge in a larger feasible domain.
Drawings
Fig. 1 is a flow chart of a method for establishing an electricity price of an electric vehicle charging station, which takes wind and light uncertainty into account.
FIG. 2 is a schematic flow chart of the genetic algorithm of the present invention.
Detailed Description
The following describes embodiments of the present invention with reference to the drawings.
As shown in fig. 1, the invention designs a method for making electricity prices of electric vehicle charging stations by taking wind and light uncertainty into account, which specifically comprises the following steps:
step 1, obtaining a regional historical meteorological data set of an electric vehicle charging station, clustering daily wind speed and illumination intensity distribution curves in the regional historical meteorological data set by adopting a k-means clustering algorithm to obtain a representative distribution curve taking a clustering center as each class, and solving the weight of each class, wherein the specific steps are as follows:
and extracting a data set of the wind speed and the illumination intensity in N years from the regional historical meteorological database of the electric vehicle charging station, and performing k-means clustering on curves of the wind speed and the illumination intensity by taking the day as a basic unit. The method comprises the following basic steps:
1-1, respectively carrying out normalization processing on the N years of full-time distribution data of wind speed and illumination intensity, wherein the normalization formula is as follows:
Figure GDA0003018436240000051
in the formula (1), v and s are vectors of N years of full-time wind speed and illumination intensity respectively, and v*、s*The matrix is formed by normalized vectors of N years of full-time wind speed and illumination intensity respectively, and the size of the matrix is Nd×24，NdTotal number of samples, vmax、vminMaximum and minimum wind speed, s, respectively, over full timemax、sminMaximum and minimum light intensities in full time sequence;
step 1-2, constructing a k-means clustering sample x of wind speed and illumination intensity, wherein x is NdX 48 matrix, one clustered sample per row:
x＝[v* s*] (2)
step 1-3, randomly selecting k non-repeated row vectors from x, initializing k clustering centers, and xc1、xc2..xck；
Step 1-4, calculating the distance from each sample to each clustering center, and classifying the samples into the clustering centers, wherein the distance formula adopts an Euclidean distance formula:
Figure GDA0003018436240000061
equation (3) is the ith sample xiAnd a cluster center xcThe distance calculation formula of (2);
then, calculating each new cluster center, and the updating formula of the ith cluster center is as follows:
Figure GDA0003018436240000062
wherein n isiNumber of samples, x, contained for the ith classi,mIs the mth sample in the ith class;
step 1-5, repeating the step 1-4 until the clustering center is converged;
taking each clustering center as a typical meteorological scene of the electric vehicle charging station, and calculating the weight of each scene:
ρci＝ni/Nd (5)
wherein n isiFor class i containing the number of samples, NdIs the total number of samples.
Step 2, establishing a wind power generation and photovoltaic power generation output model of the electric vehicle charging station during regional grid-connected operation:
Figure GDA0003018436240000063
Ps(t)＝A*γ(t)*η (7)
in the formula (6), Pw(t) the output of the wind turbine during the period t, v (t)Wind speed, v, of the distribution network region for a period of tciCutting into wind speed, v, for wind generatorscrRated wind speed, vcoFor cutting out the wind speed, PrThe rated power of the wind driven generator; t represents the t-th period in the chargeable period number d of the electric automobile; in the formula (7), PsAnd (t) is the active power output of the photovoltaic cell in the period of t, A is the area of the cell, gamma (t) is the illumination intensity, and eta is the photoelectric conversion efficiency.
Substituting time sequence wind speed and illumination intensity distribution data of k meteorological clustering centers, calculating wind power and photovoltaic output of the clustering centers according to formulas (6) and (7), and constructing the following set:
[Pcwi(k,t),Pcsi(k,t)] (8)
in the formula, Pcwi(k,t)、Pcsi(k, t) respectively represent the wind generator output and the photovoltaic output of the kth time period of the kth clustering center.
Step 3, constructing a charging time model of the single electric automobile, and calculating the chargeable duration of the single electric automobile, wherein the construction of the charging time model of the single electric automobile is as follows:
Figure GDA0003018436240000071
wherein, TCThe charging time for a single electric vehicle; u is the power consumption per 100 kilometers, l is the daily mileage of a single electric vehicle, P is the charging power of a single electric vehicle, etaevFor charging efficiency, the round function is an integer function, and the number of the selectable charging sections of a single automobile is as follows:
Figure GDA0003018436240000072
wherein, TSAnd TEAnd respectively taking integers for the starting trip time and the ending trip time of the automobile.
And 4, combining the chargeable time period number d of the single electric vehicle, constructing a response model of the charging price of the single electric vehicle, constructing a probability transfer matrix F of the charging price, and obtaining a charging probability distribution model of the single electric vehicle after the time-of-use price. The method specifically comprises the following steps:
at the price of electricity p0Then, the electric vehicle is at TEThe d time intervals after the trip is finished at the moment select the charging probability approximately to obey the semi-normal distribution. Selection of Standard Normal distribution quantiles (u)e,φe) (2,0.9545), and assuming that the charging probability of the ith electric vehicle in the t-th period in d after the trip is finished is:
Figure GDA0003018436240000073
where φ (. eta.) is a standard normal distribution function. t represents the tth period in the chargeable duration d.
And then constructing a probability transfer matrix F of the initial charging time of the electric automobile to the charging electricity price:
Figure GDA0003018436240000074
wherein the content of the first and second substances,
Figure GDA0003018436240000075
representing elements in the probability transition matrix F, i.e.
Figure GDA0003018436240000076
When t is1＝t2When d is equal to d, there is
Figure GDA0003018436240000077
ΔPi(t1) For the ith electric vehicle at the t1Variation of probability of one possible charging period, Δ p (t)2) Is at the t2The change of the charging electricity price in each feasible charging time period, wherein d is the number of chargeable time periods of the electric automobile; p is a radical of0Is the basic charging electricity price; pi(t2) For the ith electric vehicle at t2A charging power of a period;
the probability after the time-of-use electricity price of the ith electric vehicle meets the following conditions:
Figure GDA0003018436240000081
updating the probability after the time of use electricity price:
Figure GDA0003018436240000082
and 5, simulating a charging behavior scene of the electric automobile group, acquiring daily load requirements of the electric automobile group, and then combining the built wind power generation and photovoltaic power generation output models in the electric automobile charging station to acquire a total charging power model of each time period of the electric automobile charging station and a load requirement model of the electric automobile charging station on a power grid. The method comprises the following specific steps:
the electric automobile starts to travel and follows normal distribution of N (8.92, 3.2422), the last trip finishes and follows normal distribution of N (17.47, 3.4122), and the daily mileage l in km follows log normal distribution, namely: lnl-N (3.46, 1.1422). Suppose that the electric vehicle has N in the charging peripheryCarWhen the number is 2000, random numbers are generated according to the 3 normal distribution functions, and the cycle N is repeatedCarNext, parameters d and T are updated each cycleEWherein d represents the number of chargeable periods of the electric vehicle, TERepresenting the time when the electric vehicle arrives at the charging station, and taking the integral point moment; in each cycle, the charging probability P of the ith vehicle in each time period can be obtained according to the model established in the step 4i(t) obtaining N by using a wheel methodCarCalculating the total charging power of the charging station in each time interval according to the following formula:
Figure GDA0003018436240000083
in the formula (15), PsumEV(t) represents the total charging power of the electric vehicle charging station in the t-th time period, Pi(t) charging power of ith electric vehicle in the t period, NCarNumber of electric vehicles(ii) a P is the charging power of a single electric automobile; psum(k, t) is the load demand of the electric vehicle charging station on the power grid in the t-th time period under the condition of the k-th clustering center; pcwi(k,t)、Pcsi(k, t) respectively represent the wind generator output and the photovoltaic output of the kth time period of the kth clustering center.
And 6, establishing a time-of-use charging electricity price electric vehicle charging station benefit model by combining the clustering center and the weight thereof, the charging electricity price model and the total charging power model of each time period of the electric vehicle charging station and the load demand model of the electric vehicle charging station on the power grid, wherein the electric vehicle charging station benefit model comprises the electricity selling benefits of the electric vehicle charging station and the basic benefits obtained by participating in power grid peak shaving.
The constructed charging station benefit model of the time-of-use charging electricity price is as follows:
Figure GDA0003018436240000091
in the formula, object F is the comprehensive benefit of the electric vehicle charging station; rhociThe weight of the ith clustering center is shown, the object F is the comprehensive benefit and is mainly divided into two parts, the first part is the electricity selling income of the charging station, p (t) is the electricity selling price of the electric vehicle charging station in the t time period, and p (t) is the electricity selling price of the electric vehicle charging station in the t time periodgrid(t) is the purchase price of the electric vehicle charging station in the t-th time period, Psum(k, t) is the load demand of the electric vehicle charging station on the power grid in the t-th time period under the k-th meteorological clustering center; the second part is extra income obtained by the charging station participating in peak clipping and valley filling, Psum(k) Vectors required to be formed by the time sequences of the charging stations 24 in the kth class and the lower charging stations for the power grid; j is an artificially defined peak-to-valley difference benefit function.
Step 7, solving the charging electricity prices p (t) of the electric vehicle charging stations in each time period in the constructed electric vehicle charging station benefit model by adopting a genetic algorithm, wherein the process is shown in fig. 2, and the specific sub-steps are as follows:
step 7-1. give population number Nz200, the following chromosomes were constructed:
Figure GDA0003018436240000092
wherein, TzThe current generation number of the population is shown,
Figure GDA0003018436240000093
is the chromosome of the nth individual in the current generation,
Figure GDA0003018436240000094
is the t-th gene on the chromosome of the nth individual. Initializing the population by adopting a random assignment method according to constraint conditions in the formula, wherein the current population generation number Tz1, maximum algebraic T of populationmaxThe maximum variation rate p is set at 50m,maxAnd minimum rate of variation pm,minSetting the maximum crossing rate pc,maxAnd minimum crossing rate pc,min；
Step 7-2, calculating the individual fitness, wherein the fitness is obtained according to the comprehensive benefit objectF calculation formula in the step 6;
7-3, selecting the elite individual with the optimal fitness to not participate in crossing, crossing every two other individuals, and self-adapting the crossing probability pcThe following were used:
pc＝pc,max-(pc,max-pc,min)*Tz/Tmax (18)
wherein, TmaxIs the maximum algebra of the population, pc,maxIs the maximum cross probability and pc,minIs the minimum cross probability;
adopting a wheel disc method for judging, directly copying two individuals to enter the next step if the judgment is not carried out, otherwise, operating as follows:
Figure GDA0003018436240000101
wherein theta is a random number between 0 and 1,
Figure GDA0003018436240000102
is the T thzGroup of generationsThe t gene of the nth individual;
7-4, except for the fact that the elite individuals do not participate in variation operation, other individuals are subjected to probabilistic variation and the probability p of adaptive variationmThe following were used:
Figure GDA0003018436240000103
in the formula, TmaxIs the maximum algebra of the population, pm,maxIs the maximum mutation probability and pm,minThe probability of the minimum variation is the minimum,
Figure GDA0003018436240000104
to the current average fitness, fmaxThe maximum fitness of the current algebra is obtained. The formula realizes that the variation rate is reduced along with the genetic algebra and the population average fitness is improved, so that the algorithm convergence is accelerated;
step 7-5, let Tz＝Tz+1, if Tz＝TmaxStopping the operation and outputting the optimal individual, otherwise, returning to the step 7-2.
And 8, obtaining the optimal individual decoding according to the solved charging electricity prices of the electric vehicle charging station at each time period, and obtaining the time-of-use electricity price of the electric vehicle charging station.
In summary, the invention considers the establishment of a reasonable model of the charging behavior of the electric automobile, and simultaneously needs to consider the existence of wind power generation and photovoltaic power generation in the station, constructs an economic benefit objective function of the charging station, and utilizes an improved genetic algorithm to solve the time-of-use electricity price of the electric automobile charging station, so as to achieve the optimization by the electricity price objective function, and enable the electricity price formulation process to be more accurate.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.

Claims (10)

1. An electric vehicle charging station electricity price making method considering wind and light uncertainty is characterized by comprising the following steps:
step 1, acquiring a regional historical meteorological data set of an electric vehicle charging station, clustering wind speed and illumination intensity distribution curves in the regional historical meteorological data set by adopting a k-means clustering algorithm to obtain a representative distribution curve taking a clustering center as each class, and solving the weight of each class;
step 2, establishing a wind power generation and photovoltaic power generation output model of the electric vehicle charging station in regional grid-connected operation;
step 3, constructing a charging time model of the single electric automobile, and calculating the number of chargeable time segments of the single electric automobile;
step 4, combining the chargeable time period number of the single electric vehicle, constructing a response model of the charging electricity price of the single electric vehicle and a probability transfer matrix of the electric vehicle to the charging electricity price at the initial charging moment, and acquiring a charging probability distribution model of the single electric vehicle after time-of-use electricity price;
step 5, simulating a charging behavior scene of the electric automobile group, acquiring daily load requirements of the electric automobile group, and then acquiring a total charging power model of each time period of the electric automobile charging station and a load requirement model of the electric automobile charging station on a power grid by combining an established wind power generation and photovoltaic power generation output model in the electric automobile charging station and a single electric automobile charging probability distribution model after time-of-use electricity price;
step 6, constructing an electric vehicle charging station benefit model of the time-of-use charging price by combining the clustering center and the weight thereof, a total charging power model of each time interval of the electric vehicle charging station and a load demand model of the electric vehicle charging station on the power grid, wherein the electric vehicle charging station benefit model comprises the electricity selling benefit of the electric vehicle charging station and the basic benefit obtained by participating in power grid peak shaving;
7, solving the charging electricity price of each time period of the electric vehicle charging station in the constructed benefit model of the electric vehicle charging station by adopting a genetic algorithm;
and 8, obtaining the optimal individual decoding according to the solved charging electricity prices of the electric vehicle charging station at each time period, and obtaining the time-of-use electricity price of the electric vehicle charging station.
2. The method for formulating the electricity price of the electric vehicle charging station considering the wind and light uncertainty according to claim 1, wherein a k-means clustering algorithm is adopted in the step 1 to cluster the wind speed and illumination intensity distribution curves, and specifically comprises the following steps:
step 1-1, respectively carrying out normalization processing on the wind speed data and the illumination intensity data of the historical meteorological data set by adopting a formula:
Figure FDA0003018436230000011
wherein v and s are vectors of N years of full-time wind speed and illumination intensity respectively; v. of*、s*The matrix is formed by normalized vectors of N years of full-time wind speed and illumination intensity respectively, and the size of the matrix is Nd×24，NdIs the total number of samples; v. ofmax、vminMaximum and minimum wind speed, s, respectively, over full timemax、sminMaximum and minimum light intensities in full time sequence;
step 1-2, constructing a k-means clustering sample x of wind speed and illumination intensity, wherein x is NdX 48 matrix, one clustered sample per row:
x＝[v* s*]
step 1-3, randomly selecting k non-repetitive row vectors from a clustering sample x, and initializing k clustering centers: x is the number ofc1、xc2..xck；
Step 1-4, calculating the distance from each clustering sample to each clustering center, and classifying the clustering samples into each clustering center; and calculating each new cluster center, the updating formula of the ith cluster center is as follows:
Figure FDA0003018436230000021
wherein x isciRepresenting the ith cluster center; n isiThe number of samples contained for the ith class; x is the number ofi,mIs the mth sample in the ith class;
and 1-5, repeating the steps 1-4 until the clustering center is converged.
3. The method for formulating the electricity price of the electric vehicle charging station considering the wind and light uncertainty as claimed in claim 1, wherein the weight p of each class is obtained in the step 1ciThe formula is adopted:
ρci＝ni/Nd
wherein n isiNumber of samples included for ith class, NdIs the total number of samples of the cluster.
4. The method for formulating the electricity price of the electric vehicle charging station considering the wind and light uncertainty as claimed in claim 1, wherein the wind power generation and photovoltaic power generation output models established in the step 2 are respectively as follows:
Figure FDA0003018436230000022
Ps(t)＝A*γ(t)*η
wherein, Pw(t) is the output of the wind driven generator in the time period t, v (t) is the wind speed in the electric vehicle charging station in the time period t, vciCutting into wind speed, v, for wind generatorscrRated wind speed, vcoFor cutting out the wind speed, PrThe rated power of the wind driven generator; psAnd (t) is the active power output of the photovoltaic cell in the period of t, A is the area of the cell, gamma (t) is the illumination intensity in the period of t, and eta is the photoelectric conversion efficiency.
5. The method for formulating the electricity price of the electric vehicle charging station considering the wind and light uncertainty according to claim 1, wherein the charging time model of the single electric vehicle constructed in the step 3 is as follows:
Figure FDA0003018436230000031
wherein, TCThe charging time for a single electric vehicle; u is the power consumption per 100 kilometers, l is the daily mileage of a single electric vehicle, P is the charging power of a single electric vehicle, etaevFor charging efficiency, the round function is a rounding function,
and calculating the chargeable time period number d of the single electric automobile by adopting a formula:
Figure FDA0003018436230000032
wherein, TSAnd TEAnd respectively taking integers for the starting trip time and the ending trip time of the electric automobile.
6. The method for formulating the electricity price of the electric vehicle charging station considering the wind and light uncertainty as recited in claim 1, wherein the response model of the charging electricity price of the single electric vehicle constructed in the step 4 has charging power of an ith electric vehicle in a period t as follows:
Figure FDA0003018436230000033
wherein phi () is a standard normal distribution function; (u)e,φe) Standard normal distribution quantile; t represents the t-th period in the chargeable period number d of the electric automobile.
7. The method for formulating the electricity price of the electric vehicle charging station considering the wind and light uncertainty according to claim 1, wherein the step 4 is to construct a probability transfer matrix F of the initial charging time to the charging electricity price, specifically:
Figure FDA0003018436230000034
wherein the content of the first and second substances,
Figure FDA0003018436230000035
represents an element in a probability transition matrix F, and
Figure FDA0003018436230000036
ΔPi(t1) For the ith electric vehicle at the t1Variation of probability of one possible charging period, Δ p (t)2) Is at the t2The change of the charging electricity price in each feasible charging time period, wherein d is the number of chargeable time periods of the electric automobile; p is a radical of0Is the basic charging electricity price; pi(t2) For the ith electric vehicle at t2Charging power of a period.
8. The method for formulating the electricity price of the electric vehicle charging station considering the wind and light uncertainty according to claim 1, wherein the model of the total charging power of the electric vehicle charging station in each time period and the model of the load demand of the electric vehicle charging station on the power grid, which are obtained in the step 5, specifically comprise:
Figure FDA0003018436230000041
wherein, PsumEV(t) represents the total charging power of the electric vehicle charging station in the period t, Pi(t) charging power of ith electric vehicle in t period, NCarThe number of the electric automobiles is P, and the charging power of a single electric automobile is P; d represents the chargeable time period number of the electric automobile; t isERepresenting the time when the electric vehicle arrives at the charging station, and taking the integral point moment; psum(k, t) is the load demand of the electric vehicle charging station on the power grid in the t-th time period under the condition of the k-th clustering center; pcwi(k,t)、Pcsi(k, t) respectively represent the wind generator output and the photovoltaic output of the kth time period of the kth clustering center.
9. The method for formulating the electricity price of the electric vehicle charging station considering the wind and light uncertainty according to claim 1, wherein the electric vehicle charging station benefit model of the time-of-use charging electricity price constructed in the step 6 is as follows:
Figure FDA0003018436230000042
wherein, the object F is the comprehensive benefit of the electric vehicle charging station; rhociIs the weight of the ith clustering center, p (t) is the selling price of the electric vehicle charging station at the t time period, pgrid(t) the purchase price of the electric vehicle charging station at the t-th time period; psum(k, t) is the load demand of the electric vehicle charging station on the power grid in the kth time period under the kth clustering center; psum(k) And J is an artificially defined peak-valley difference benefit function which is a vector formed by the time sequence of the lower charging station 24 in the kth class and the power grid.
10. The method for formulating the electricity price of the electric vehicle charging station with consideration of wind and light uncertainty as claimed in claim 1, wherein the step 7 adopts a genetic algorithm to solve the cross probability p when the electricity price of the electric vehicle charging station is charged in each time periodcAnd the probability of variation pmRealizing self-adaptation:
pc＝pc,max-(pc,max-pc,min)*Tz/Tmax
Figure FDA0003018436230000051
wherein, TmaxIs the maximum algebra of the population, TzFor the current generation of the population, pm,maxIs the maximum mutation probability and pm,minTo the minimum mutation probability, pc,maxIs the maximum cross probability and pc,minIn order to minimize the probability of a cross-over,
Figure FDA0003018436230000052
to the current average fitness, fmaxThe maximum fitness of the current algebra is obtained.
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