CN111400662B - Space load prediction method considering charging requirements of electric automobile - Google Patents
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Abstract
The invention discloses a space load prediction method considering the charging requirement of an electric automobile, which comprises the following steps: step 1: according to the operation characteristics and the charging mode, the electric automobiles are divided into three types; step 2: fitting probability distribution of the journey information according to the survey data to obtain a probability density function of the journey information; step 3: according to the type of the travel destination of the electric automobile, a travel chain model is built, and the probability proportion of the travel chain model is counted; step 4: and according to the probability density function of the travel information and the probability proportion of the travel chain model, calculating the charging load variance coefficients of three types of electric vehicles based on a Monte Carlo algorithm in a simulation mode, and superposing to complete space load prediction. The method solves the problem that the large-scale electric vehicle access power grid has uncertain influence on the space load prediction result, comprehensively considers various factors, simulates the space-time distribution of the electric vehicle charging demand, and provides reference for planning of the urban power distribution network in the future.
Description
Technical Field
The invention relates to the technical field of space load prediction, in particular to a space load prediction method considering the charging requirement of an electric automobile.
Background
The space load prediction is also called cell load prediction, can predict the geographical position distribution and numerical value of the load in a planning area, is an important reference for planning and constructing an urban power distribution network, and an electric automobile is a main development direction of a future new energy automobile, becomes a hot spot for the development of automobile industry at home and abroad in recent years, has obvious national policy guidance and increasingly accelerated development speed, and is gradually put into an industrialization stage.
The large-scale electric vehicle charging load has the characteristics of time and space randomness, intermittence, volatility and the like, and can greatly influence the space load prediction result of the power distribution network, so that a space load prediction method considering the space-time distribution of the electric vehicle charging load is needed, the traditional research mostly neglects the space-time randomness of the electric vehicle charging load, and the problems of travel distribution prediction, charging place diversity and the like of the electric vehicle are not careful enough, and the prediction method is not mature enough.
Disclosure of Invention
The invention aims to provide a space load prediction method considering the charging requirement of an electric automobile. The system and the method aim to solve the problem that the large-scale electric vehicle access power grid has uncertain influence on the space load prediction result, comprehensively consider the space-time distribution of the electric vehicle charging demand simulated by multiple factors, and provide reference for planning of a future urban power distribution network.
In order to achieve the above object, the present invention provides a space load prediction method considering the charging requirement of an electric vehicle, comprising the following steps:
step 1: according to the running characteristics and the charging mode of the electric automobile, the electric automobile is divided into three types of private automobiles, taxis and buses;
step 2: fitting probability distribution of the travel information of the electric automobile according to the survey data to obtain a probability density function of the travel information of the electric automobile;
step 3: according to the type of the travel destination of the electric automobile, a travel chain model of the electric automobile is built, and the probability proportion of the travel chain model is counted;
step 4: and according to the probability density function of the journey information and the probability proportion of the travel chain model, calculating the charging load variance coefficients of three types of electric vehicles in a simulation mode based on a Monte Carlo algorithm, and superposing to complete the space load prediction.
Most preferably, the step of calculating the charge load variance coefficient of the electric automobile in a simulation manner further comprises the following steps:
step 4.1: setting the total quantity of electric vehicles as N, setting the total simulation times of a Monte Carnot algorithm as M, and defining the initial number N of the nth (n=1, 2, …, N) electric vehicles 0 Number of initial simulations M calculated by M (m=1, 2, …, M) th simulation 0 ＝1；
Step 4.2: randomly extracting probability proportion of a travel chain model of the nth electric automobile and probability density function of travel information during the mth simulation calculation, and calculating to obtain mth simulation calculation and charging information of the nth electric automobile;
step 4.3: according to the mth simulation calculation and the charging information of the nth electric automobile, obtaining the mth simulation calculation and the space load distribution of the nth electric automobile;
step 4.4: the first judgment is carried out on the simulation times m=M, if the simulation times m=M are not met, 1 is added on the basis of the simulation times M, and the steps 4.2-4.3 are continuously repeated; if so, carrying out second judgment on the vehicle quantity n=N of the electric vehicle, if not, adding 1 on the basis of the vehicle quantity N of the electric vehicle, and continuously repeating the steps 4.2-4.4; if so, completing simulation calculation;
step 4.5: and superposing the space load distribution of n electric vehicles under m times of simulation calculation to obtain the charging load variance coefficients of three types of electric vehicles, and superposing the charging load variance coefficients by combining the traditional space loads of the functional cells to finish the space load prediction.
Most preferably, the charging information of the electric vehicle includes a charging location, a charging mode, and a charging duration of the electric vehicle.
Most preferably, the charging load variance coefficient of the electric automobile is beta i And satisfies:
max(β i )≤0.05％
wherein,standard deviation of charging load at the ith moment, P i And (3) the expected value of the charging load at the ith moment, wherein M is the total simulation times.
Most preferably, the travel information of the electric automobile comprises travel starting time, travel mileage and parking duration of the electric automobile; the parking time period further comprises the parking time period of the electric vehicle in the working area and the parking time period of the electric vehicle in the non-working area.
Most preferably, fitting the probability distribution of the travel information of the electric automobile further includes the following cases:
case 1: fitting probability distribution of journey starting time of the electric automobile based on survey data to obtain probability density function f of journey starting time 1 (x) And satisfies:
wherein mu 1 Sigma, the expected value of the stroke start time 1 Is the standard deviation of the trip start time;
case 2: based on survey data, fitting probability distribution of driving mileage of the electric automobile to obtain probability density function f of the driving mileage 2 (x) And satisfies:
wherein mu 2 Sigma, the expected value of the mileage 2 Is the standard deviation of the driving mileage;
case 3: based on survey data, parking time t for working area of electric automobile 1 Probability distribution and non-operating region parking duration t 2 Respectively fitting probability distribution of the working area to respectively obtain parking time length t of the working area 1 Probability density function f of (2) 1 (z) and non-working area parking time period t 2 Probability density function f of (2) 2 (z) and respectively satisfy:
most preferably, the probability distribution of the trip start time of the electric automobile approximately follows a normal distribution; the probability distribution of the driving mileage approximately follows the lognormal distribution; working area parking duration t 1 Probability distribution and non-operating region parking duration t 2 Respectively, take the probability distributions of different generalized extrema.
Most preferably, the travel chain model of the electric automobile includes one of any combination of a home H model, a work W model, and a recreational O model.
Most preferably, the charging modes of the electric automobile include a slow charging L1 mode, a normal charging L2 mode, and a fast charging L3 mode; the slow charging L1 mode and the normal charging L2 mode are alternating-current charging; the fast charge L3 mode is a direct current charge.
By the aid of the method, the influence of uncertainty of a large-scale electric vehicle access power grid on a space load prediction result is solved, space-time distribution of electric vehicle charging requirements is simulated by comprehensively considering various factors, and references are provided for planning of future urban power distribution networks.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a space load prediction method for simulating the charging demand of an electric vehicle by comprehensively considering various factors, solves the problem that the charging load of a traditional large-scale electric vehicle has temporal and spatial randomness, intermittence and fluctuation and has great influence on the space load prediction result of a power distribution network, and provides reference for planning of a future urban power distribution network.
Drawings
FIG. 1 is a flow chart of a space load prediction method considering the charging requirement of an electric automobile;
fig. 2 is a probability distribution diagram of a trip start time of an electric vehicle;
FIG. 3 is a diagram of a typical travel chain model provided by the present invention;
FIG. 4 is a flowchart of a method for predicting a space load according to the present invention;
FIG. 5 is a schematic diagram of daily load in a residential area in accordance with an embodiment of the present invention;
FIG. 6 is a schematic diagram of daily load of a work area according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of daily load of a commercial area in an embodiment provided by the invention.
Detailed Description
The invention is further described by the following examples, which are given by way of illustration only and are not limiting of the scope of the invention.
The invention provides a space load prediction method considering the charging requirement of an electric automobile, which is shown in fig. 1 and comprises the following steps:
step 1: according to the running characteristics and the charging mode of the electric automobile, the electric automobile is divided into three types of private automobiles, taxis and buses.
According to investigation data of electric automobile conduction interface passing 2010 in China, the charging modes of the electric automobile comprise a slow charging L1 mode, a conventional charging L2 mode and a quick charging L3 mode; the slow charging L1 mode and the normal charging L2 mode are alternating current charging; the rapid charging L3 mode is direct current charging; the conventional charging L2 mode can be divided into three modes of large, medium and small according to the charging power, as shown in the following table 1:
table 1 electric vehicle replenishment method
Step 2: according to survey data of the travel of the American family in 2017 of the United states department of transportation, probability distribution of the travel information of the electric automobile is fitted, and a probability density function of the travel information of the electric automobile is obtained.
The travel information of the electric automobile comprises travel starting time, travel mileage and parking time of the electric automobile; and the parking time period comprises the parking time period of the electric vehicle in the working area and the parking time period of the electric vehicle in the non-working area.
Fitting the probability distribution of the travel information of the electric automobile also comprises the following cases:
case 1: as shown in fig. 2, the probability distribution of the trip start time of the electric vehicle is analyzed based on the survey data of the trip of the united states department of transportation in 2017 in all-united states, and the probability distribution of the trip start time of the electric vehicle approximately follows the normal distribution;
therefore, the probability distribution of the travel starting time of the electric automobile is fitted to obtain a probability density function f of the travel starting time of the electric automobile 1 (x) And satisfies:
wherein mu 1 Is the expected value of the travel starting time sigma of the electric automobile 1 Is the standard deviation of the travel start time of the electric vehicle.
Case 2: based on investigation data of the American family trip in 2017 of the United states department of transportation, the probability distribution of the driving mileage of the electric automobile is analyzed, and the probability distribution of the driving mileage of the electric automobile approximately obeys the lognormal distribution;
therefore, the probability distribution of the driving mileage of the electric automobile is fitted to obtain the probability density function f of the driving mileage of the electric automobile 2 (x) And satisfies:
wherein mu 2 Is the expected value of the driving mileage of the electric automobile, sigma 2 Is the standard deviation of the driving mileage of the electric automobile.
Case 3: based on investigation data of the American household trip of 2017 of the United states department of transportation, the parking time t of the working area of the electric automobile 1 Probability distribution of (2) and non-operating region parking time t of electric vehicle 2 Respectively analyzing probability distribution of the electric vehicle, and stopping time t of the working area of the electric vehicle 1 Probability distribution of (2) and non-operating region parking time t of electric vehicle 2 Respectively taking different generalized extremum distributions;
thus, the parking time period t is respectively set for the working areas of the electric vehicles 1 Probability distribution of (2) and non-operating region parking time t of electric vehicle 2 Fitting the probability distribution of the electric vehicle, and respectively obtaining the parking time t of the working area of the electric vehicle 1 Probability density function f of (2) 1 (z) and non-operating region parking time period t of electric vehicle 2 Probability density function f of (2) 2 (z) and respectively satisfy:
step 3: according to survey data of the united states department of transportation 2017 full-beauty family trip, an electric automobile has mobility in space, and uncertainty in the moving process can influence the prediction of the space-time distribution of the charging power of the electric automobile, wherein a private car and a taxi trip place in the electric automobile have typical destinations. Therefore, the travel chain model of the electric automobile is constructed according to the type of the travel destination, the travel chain model can be used for simulating the transfer characteristic of the electric automobile in space in one day, and the probability proportion of the travel chain model of the electric automobile is counted.
The travel chain model comprises any combination of a home H model, a work W model and a leisure and entertainment O model; the travel chain model is a charging place of the electric automobile, and the travel chain model can be used for comprising a travel starting point and a destination.
Meanwhile, the trip chain model is greatly connected with the parking time of the electric automobile at a trip destination; when the travel destination of the electric automobile is a work place, namely, when the travel chain model is a work W model, the parking time of the electric automobile can reach about 8 hours due to work requirements; when the electric automobile travel destination is a recreational entertainment place, namely, the travel chain model is a recreational entertainment O model, the parking duration of the electric automobile can be only about 2 hours.
According to investigation data of the travel of the American family in 2017 of the United states department of transportation, a typical travel chain model of private cars per day is counted as shown in fig. 3; and probability ratios of a typical daily travel chain model of private cars are shown in table 2 below:
TABLE 2 probability proportion of different travel chain models
Step 4: and according to the probability density function of the travel information of the electric automobile and the probability proportion of the travel chain model of the electric automobile, calculating the charging load variance coefficients of three types of electric automobiles based on a Monte Carlo algorithm in a simulation mode, and superposing to finish space load prediction.
Wherein, as shown in fig. 4, the simulation calculation of the charge load variance coefficient further comprises the following steps:
step 4.1: setting the total quantity of electric vehicles as N, respectively setting the quantity of private cars, taxis and buses, setting the total simulation frequency of a Monte Carnot algorithm as M, and defining the initial number N of N (n=1, 2, …, N) electric vehicles 0 Number of initial simulations M calculated by M (m=1, 2, …, M) th simulation 0 ＝1。
In this embodiment, the total number of simulations M is 1000.
In the embodiment, the total amount of electric private cars in a certain area is 2 ten thousand, the total amount of electric taxis is 1 ten thousand, the total amount of electric buses is 1.2 ten thousand, the charging power of a residential area is set to be 3kW, and the charging power of a working area and a commercial area is set to be 14kW; when the electric vehicle starts traveling every day, the electric vehicle is fully charged, that is, the initial State of Charge (SOC) of the battery is 1, and relevant battery parameters of various electric vehicles are set as shown in the following table 3:
table 3 various electric automobile battery parameter tables
Step 4.2: randomly extracting probability proportion of a travel chain model of the nth electric automobile and probability density function of travel information during the mth simulation calculation, and calculating to obtain mth simulation calculation and charging information of the nth electric automobile;
the charging information of the electric automobile comprises a charging place, a charging mode and a charging duration of the electric automobile; and the charging time is not longer than the parking time of the electric automobile. In an embodiment, the charging sites are various functional cells.
Step 4.3: according to the m-th simulation calculation and the charging information of the nth electric automobile, the space load distribution of the m-th simulation calculation and the nth electric automobile is obtained.
Step 4.4: the first judgment is carried out on the simulation times m=M, if the simulation times m=M are not met, 1 is added on the basis of the simulation times M, and the steps 4.2-4.3 are continuously repeated; if so, carrying out second judgment on the vehicle quantity n=N of the electric vehicle, if not, adding 1 on the basis of the vehicle quantity N of the electric vehicle, and continuously repeating the steps 4.2-4.4; if so, the simulation calculation is completed.
Step 4.5: superposing the spatial load distribution of n electric vehicles under m times of simulation calculation, combining the traditional spatial loads of various functional cells to obtain a charging load variance coefficient of the electric vehicles, and finishing spatial load prediction; the charging load variance coefficient of the electric automobile is beta i And satisfies:
max(β i )≤0.05％
wherein,standard deviation of charging load at the ith moment, P i And (3) the expected value of the charging load at the ith moment, wherein M is the total simulation times.
And obtaining the electric vehicle charging loads of the residential area, the working area and the commercial area through simulation calculation by using a Monte Carlo algorithm, and overlapping the electric vehicle charging loads with the traditional space loads of all the functional areas to obtain the prediction results of the space loads, wherein the prediction results are shown in figures 5-7. As can be seen from fig. 5 to 7, the charging behaviors of the electric vehicles in the different functional cells show obvious differences, and the charging behaviors of the electric vehicles in different types are different.
Because the randomness of the travelling path of the private car is larger, the private car needs to be charged in the working area or the business area in the daytime, and the private car needs to be charged in the working area and the business area in the daytime because the electric quantity is required to be replenished, the private car is charged in the daytime, the charging load of the private car is concentrated in the working area and the business area, and the private car returns to the home at night, so that the travelling requirement in the next day can be met, the private car is charged in the residential area at night.
The taxi has long daily driving mileage, and the battery capacity is insufficient to meet the driving requirement of one day, so the taxi can be charged rapidly in the running course of the taxi, and the taxi cannot be charged in a residential area, and the charging load is mainly concentrated in a working area and a commercial area according to the selection of a trip chain model.
The public traffic operation mode is generally double-shift, the public traffic operation of the early shift and the afternoon shift is finished, the public traffic operation is uniformly managed and charged by the company, and the charging places are business areas, so that the charging load of the bus has two peaks, and the charging load respectively appears at about 10 am and about 8 pm.
From the predicted overall result, under the condition of the space-time distribution of the charging load of the electric automobile, the daily load change of each cell is greatly different from the traditional space load, and the load fluctuation is mainly represented to be increased, and the peak-valley difference of the load of each cell is increased. If the existing space load prediction method is continuously adopted, the influence of electric vehicle access on the power grid load is not considered, the accuracy of space load prediction is affected, the rationality of future power grid planning is further affected, and the safe and stable operation of the power grid is not facilitated.
The working principle of the invention is as follows:
according to the running characteristics and the charging mode of the electric automobile, the electric automobile is divided into three types of private automobiles, taxis and buses; fitting probability distribution of the travel information of the electric automobile according to the survey data to obtain a probability density function of the travel information of the electric automobile; according to the type of the travel destination of the electric automobile, a travel chain model of the electric automobile is built, and the probability proportion of the travel chain model is counted; and according to the probability density function of the journey information and the probability proportion of the travel chain model, simulating and calculating a charging load variance coefficient of the electric automobile based on a Monte Carlo algorithm, and completing space load prediction.
In summary, the spatial load prediction method considering the charging requirements of the electric vehicle solves the problem that the large-scale electric vehicle access power grid has uncertain influence on the spatial load prediction result, comprehensively considers various factors to simulate the space-time distribution of the charging requirements of the electric vehicle, and provides reference for planning of a future urban power distribution network.
While the present invention has been described in detail through the foregoing description of the preferred embodiment, it should be understood that the foregoing description is not to be considered as limiting the invention. Many modifications and substitutions of the present invention will become apparent to those of ordinary skill in the art upon reading the foregoing. Accordingly, the scope of the invention should be limited only by the attached claims.
Claims (6)
1. The space load prediction method considering the charging requirement of the electric automobile is characterized by comprising the following steps of:
step 1: according to the running characteristics and the charging mode of the electric automobile, the electric automobile is divided into three types of private automobiles, taxis and buses;
step 2: fitting probability distribution of the travel information of the electric automobile according to the survey data to obtain a probability density function of the travel information of the electric automobile;
step 3: according to the type of the travel destination of the electric automobile, a travel chain model of the electric automobile is built, and the probability proportion of the travel chain model is counted;
step 4: according to the probability density function of the travel information and the probability proportion of the travel chain model, based on a Monte Carlo algorithm, simulating and calculating the charging load variance coefficients of three types of electric vehicles, and superposing to complete space load prediction;
the simulation calculation of the charging load variance coefficient further comprises the following steps:
step 4.1: setting the total quantity of electric vehicles as N, setting the total simulation times of a Monte Carnot algorithm as M, and defining the initial number N of the nth (n=1, 2, …, N) electric vehicles 0 Number of initial simulations M calculated by M (m=1, 2, …, M) th simulation 0 ＝1；
Step 4.2: randomly extracting probability proportion of a travel chain model of the nth electric automobile and probability density function of travel information during the mth simulation calculation, and calculating to obtain charging information of the mth simulation calculation and the nth electric automobile;
step 4.3: according to the mth simulation calculation and the charging information of the nth electric automobile, obtaining the mth simulation calculation and the space load distribution of the nth electric automobile;
step 4.4: the first judgment is carried out on the simulation times m=M, if the simulation times m=M are not met, 1 is added on the basis of the simulation times M, and the steps 4.2-4.3 are continuously repeated; if so, carrying out second judgment on the number n=N of the electric vehicles, and if not, adding 1 on the basis of the number N of the electric vehicles, and continuously repeating the steps 4.2-4.4; if so, completing simulation calculation;
step 4.5: superposing the space load distribution of n electric vehicles under m times of simulation calculation to obtain the charging load variance coefficients of three types of electric vehicles, and superposing the charging load variance coefficients by combining the traditional space loads of the functional cells to finish the space load prediction;
the travel information comprises travel starting time, travel mileage and parking duration of the electric automobile; the parking time length comprises the parking time length of the electric automobile in a working area and the parking time length of the electric automobile in a non-working area;
wherein, the fitting of the probability distribution of the travel information further comprises the following cases:
case 1: fitting probability distribution of the trip start time of the electric automobile based on survey data to obtain a probability density function f of the trip start time 1 (x) And satisfies:
wherein mu 1 Sigma, the expected value of the stroke start time 1 Is the standard deviation of the trip start time; case 2: fitting probability distribution of the driving mileage of the electric automobile based on survey data to obtain probability density function f of the driving mileage 2 (x) And satisfies:
wherein mu 2 Sigma, which is the expected value of the driving mileage 2 Is the standard deviation of the mileage;
case 3: based on survey data, parking time t for the working area of the electric automobile 1 Probability distribution of (c) and said non-operating region parking duration t 2 Respectively fitting probability distribution of the working area to respectively obtain parking time length t of the working area 1 Probability density function f of (2) 1 (z) and the non-working area parking time period t 2 Probability density function f of (2) 2 (z) and respectively satisfy:
2. the method for predicting the space load considering the charging demand of an electric vehicle according to claim 1, wherein the charging information includes a charging place, a charging mode and a charging duration of the electric vehicle.
3. The spatial load prediction method considering electric vehicle charging demand according to claim 1, wherein the charging load variance coefficient is β i And satisfies:
max(β i )≤0.05％
wherein,standard deviation of charging load at the ith moment, P i And (3) the expected value of the charging load at the ith moment, wherein M is the total simulation times.
4. The spatial load prediction method considering electric vehicle charging demand according to claim 1, wherein the probability distribution of the trip start time approximately follows a normal distribution; the probability distribution of the driving mileage approximately obeys the lognormal distribution; the working area parking time length t 1 Probability distribution of (c) and said non-operating region parking duration t 2 Respectively, take the probability distributions of different generalized extrema.
5. The method for predicting space load considering electric vehicle charging demand as claimed in claim 1, wherein the travel chain model is comprised of one of any combination of a home H model, a work W model and a recreational O model.
6. The spatial load prediction method considering the charging demand of an electric vehicle according to claim 1, wherein the charging modes of the electric vehicle include a slow charging L1 mode, a normal charging L2 mode, and a fast charging L3 mode; the slow charging L1 mode and the normal charging L2 mode are alternating-current charging; the fast charge L3 mode is a direct current charge.
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| CN111967686B (en) * | 2020-08-31 | 2022-10-11 | 国网河南省电力公司经济技术研究院 | Random load prediction method based on power utilization probability distribution function |
| CN112364293B (en) * | 2020-10-14 | 2024-04-12 | 国电南瑞南京控制系统有限公司 | Electric vehicle required charge quantity prediction method and device considering urban functional areas |
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