CN110674575A

CN110674575A - Electric vehicle cluster charging demand and discharging capacity model modeling method based on time sequence traveling set

Info

Publication number: CN110674575A
Application number: CN201910890387.5A
Authority: CN
Inventors: 梁海峰; 李梓杨
Original assignee: North China Electric Power University
Current assignee: North China Electric Power University
Priority date: 2019-09-20
Filing date: 2019-09-20
Publication date: 2020-01-10

Abstract

A time sequence travel set-based electric vehicle cluster charging demand and discharging capacity model modeling method belongs to the technical field of electric vehicle modeling, and comprises the steps of firstly performing mathematical fitting on distribution parameters of time and space characteristic quantities in urban resident travel statistical data, then extracting time sequence travel set characteristic quantities through Monte Carlo simulation, constructing a travel chain to simulate a large-scale EV cluster travel scene in a continuous multi-week time scale, then calculating the time-space distribution characteristic of electric vehicle cluster charging demand according to different charging demand criteria of electric vehicle users, further calculating the cluster vehicle network interaction participation rate and safe charging and discharging controllable area constraint conditions, and calculating the time-space distribution characteristic of electric vehicle cluster discharging capacity. The model can embody the charging aggregation characteristic and the cluster discharging potential of the large-scale electric automobile widely and randomly accessed to the power grid. The invention lays a foundation for realizing the ordered charging and discharging of the electric automobile through policy guidance or related incentive measures.

Description

Electric vehicle cluster charging demand and discharging capacity model modeling method based on time sequence traveling set

Technical Field

The invention relates to a modeling method for electric vehicle cluster charging demand and discharging capacity, which is a modeling method for calculating the time-space characteristics of charging load and discharging capacity of an electric vehicle based on a time sequence travel set and belongs to the technical field of electric vehicle modeling.

Background

In recent years, Electric Vehicles (EVs) have become a necessary choice for alleviating energy crisis and improving ecological environment in the transportation field, and are widely popularized worldwide. The well-developed Vehicle network interaction technology (V2G) can effectively control charging and discharging when a large-scale electric Vehicle is connected into a power grid, not only can improve the supply and demand balance relation of a power market, but also is beneficial to solving the problems of power grid voltage reduction, harmonic wave increase and the like under disordered charging, and reduces the operation risk of the power grid. The method is used as a basic work for researching the participation of the electric automobile in the power system dispatching, and has important significance for accurately modeling the discharging capability of the electric automobile.

Disclosure of Invention

The invention aims to provide an electric vehicle cluster charging demand and discharging capacity model modeling method based on a time sequence travel set and considering car network interaction safety controllable area constraint.

The problem of the invention is realized by the following technical scheme:

a time sequence trip set based electric vehicle charging demand and discharging capacity model modeling method can embody the time-space distribution characteristics of the charging demand and the discharging capacity of a large-scale electric vehicle cluster which is widely connected to a power grid within a continuous multi-week time scale by establishing the time sequence trip set and considering the charging and discharging constraints of vehicle grid interaction. Firstly, modeling cluster battery parameters of the electric automobile, relating to information such as occupation ratios, endurance mileage, power consumption speed and the like of various types of automobiles; then, determining the selection of characteristic quantities of time sequence travel concentration time and space, analyzing the distribution characteristics of all characteristic quantities in urban travel statistical data, and fitting the distribution parameters according to different mathematical models; extracting time sequence trip set characteristic quantity through Monte Carlo random simulation, and constructing a trip chain to simulate an EV cluster trip scene; according to different electric automobile charging and discharging requirement criteria, the time-space characteristics of the electric automobile cluster charging requirement and the discharging capacity in the urban area are calculated under the condition that the charging and discharging constraint condition of vehicle network interaction is met.

The electric vehicle cluster charging demand and discharging capacity model modeling method based on the time sequence traveling set comprises the following steps:

(1) vehicle battery parameters for EV cluster

The invention fully considers the actual situation of the electric automobile market in China. The method comprises the steps of collecting sales information of domestic electric vehicles in China in nearly 6 years, and selecting electric vehicle models with accumulated sales reaching 80% of the total market amount as research objects. According to information such as the endurance mileage of a corresponding vehicle type, the power consumption of hundreds of kilometers under working conditions and the like provided by the industrial and informatization department of China, factors such as a vehicle-mounted air conditioner, road congestion and the like are considered, and information such as the battery capacity, the power consumption speed and the like of each vehicle type is utilized for modeling.

(2) Structure for determining electric automobile time sequence traveling set

For a single trip of the electric vehicle, the main characteristic quantities are extracted as shown in table 1:

TABLE 1 Main characteristic quantities for single trip

Each trip is characterized by the above 8 characteristic quantities, where T_s，t、t_v，i、T_a，i、t_p，i、T_g，iRepresenting the characteristics of the trip on time, wherein capital letters represent time, and lower case letters represent time duration; d_i、p_i、p_i-1Representing the characteristics of the trip in space. For the ith and (i + 1) th trips that are chronologically adjacent, there is a relationship:

T_s，i+1＝T_g，i(1)

this relationship may group time-sequential trips into a continuous trip chain, with each trip being referred to as a loop in the trip chain. For example, someone was 05: 00 leave home to park and jog in 06: 00 go home, then at 07: 30 driving for work, and on the day 17: 00 driving the car home, and going out in one day, the trip chain is composed of 3 trip rings, and the trip chain can be represented as H-R-H-W-H. The invention only takes the trip which occurs in the city and drives the private vehicle as a research object, and the trip chain can be simply expressed as H-W-H and has the chain length of 2.

(3) Acquisition of travel statistical data

The invention adopts the latest National Household Travel statistical Survey data (NHTS) released by Federal high road Administration (FHA) in 2017 as the research basis of resident Travel data. NHTS originally conducted the first total american Personal travel Survey (NPTS) in 1969 and was renamed NHTS in 2001. The FHA releases latest trip survey data in 2017, mainly comprises information such as vehicle types, trip starting time, trip arrival time, trip parking time, trip traveling mileage, whether trips occur at weekends, trip parking areas, trip departure areas, whether trips are transferred, trip areas and trip serial numbers of drivers on the same day, and provides a detailed data basis for mining trip laws.

(4) Data screening

According to the invention, the charging load and the discharging capacity are calculated by simulating the trip in the city range, so that the original data needs to be screened, the conditions are set as that the trip in the city area, the personal motor vehicle is driven, the driving time and the distance are positive, and the trip of the working day and the trip of the rest day are screened in a distinguishing way. Considering the probability of double-holiday trip relative to working day, it should be 0.4 times of the ratio of the number of records of double-holiday trip to the number of records of working sunday trip. In order to simplify the travel model, the proportion of all travel purposes in the total travel records is calculated, and Q categories with the cumulative probability higher than 90% are selected.

(5) Fitting of travel parameters

The main characteristic quantities of a single trip have different characteristics, and the distribution characteristics and the fitting method of each main characteristic quantity are explained as follows:

(5-1) departure time of first trip of day

Analysis of NHTS survey data shows that the departure time of the vehicle owner's first trip every day does not exhibit an obvious single distribution characteristic, and can be regarded as a gaussian mixture model formed by weighted superposition of a plurality of gaussian distributions, so that a maximum Expectation algorithm (EM) is adopted to solve the component parameters.

The probability density function of a one-dimensional gaussian mixture distribution formed by N samples can be expressed as:

θ_k＝(π_k，μ_k，σ_k) Distribution parameter called kth Gaussian component, where_kIs the weight of this component, μ_k，σ_kThe mean and standard deviation of the components, respectively; n (x; mu)_k，σ_k) Is a probability density function of the kth gaussian component.

E-step: according to Bayes formula, sample data x_iThe probability generated by the kth gaussian component is:

in the formula_k、μ_k、σ_kFrom the initialization value or the last iteration.

M-step: and (3) iteratively solving the distribution parameter theta (pi, mu, sigma) of each Gaussian component:

iteration criterion:

where epsilon is some specified smaller quantity.

(5-2) travel duration of trip

The driving time of the vehicle owner in a single trip has obvious correlation with the types of the starting place and the destination, and for the trip between the determined starting place and the destination, the driving time of the vehicle owner presents a log-normal distribution characteristic, so that a log-normal distribution model is adopted to perform parameter fitting on the driving time.

(5-3) arrival time of trip

Obtaining the departure time T of a trip_s，iAnd a running time period t_v，iThen, the arrival time of this trip can be calculated:

T_a，i＝T_s，i+t_v，i(9)

(5-4) parking duration at destination

The parking time of a trip is closely related to the destination of the trip, and the distribution characteristics of the parking time of the trip are analyzed and parameter fitting is carried out respectively according to the parking time of different purposes.

(5-5) end time of trip

After the time quantum is obtained, the end time of the trip can be calculated according to the continuity of the trip characteristic quantity:

T_g，i＝T_a，t+t_p，t(10)

(5-6) probability of travel transition between different regions

Probability of travel P [ P ] between different regions_i|p_i-1，T_s，i]The method is influenced by travel time, so that a day is divided into T time intervals, frequency statistics is carried out on travel records in each time interval, and a T multiplied by Q transition probability matrix is obtained. An element P in the matrix_k，i，jIs represented by [ t_k-1，t_k]Within a period from p_iRegion to p_jThe trip probability of the region. It should be noted that for a trip with a home as a trip purpose, it is necessary to distinguish whether the trip is the last trip in the day or the trip is a temporary trip.

(5-7) mileage

The invention considers that the speed in the driving process has randomnessProperty, when driving for a period of time t_vWhen determined, the speed v (t) of travel_v) Obey normal distribution

Due to the fact that

d＝v(t_v)·t_v(11)

The traveled mileage d also follows a normal distribution

Analyzing the data set to find mu_d(t_v) And t_v、σ_d(t_v) And t_vAll approximately satisfy the power function characteristic, so the power function form y (t) is adopted_v)＝a×t_v ^bFitting was performed and Mean Absolute Error (MAE) was calculated.

(6) Generating a simulation trip chain

According to the method, the characteristic quantity of the travel set can be simulated according to the following process so as to obtain the travel chain:

1) extracting a first trip time;

2) extracting a trip purpose according to the type of the departure place and the trip time;

3) extracting the running time and the parking time at the destination according to the travel purpose, and calculating the time of arriving at the destination;

4) extracting the driving mileage according to the driving time length;

5) calculating the trip ending time when the user leaves the destination, and ending the trip;

6) judging whether the trip of the current day is finished, if so, entering 1), and if not, entering 2).

(7) Calculating the charging requirement and the discharging capability

The calculation of the charging requirement and the discharging capability needs to be carried out on the following premise:

1) only two charging modes of alternating current slow charging and direct current fast charging are considered, and battery replacement is not considered.

2) Considering that the charging process of the electric vehicle battery is mainly a constant power charging stage, and the time proportion of the pre-charging stage and the constant voltage charging stage is low, the charging and discharging of the electric vehicle after being connected to the power grid is considered to be constant power.

3) The running power consumption of the electric automobile is only generated in the running period, the charging action is only generated in the parking period, and only the electric automobile participating in the vehicle network interaction is allowed to discharge to the power network.

The invention provides two strategies for calculating the charging requirement and the discharging capacity, which are introduced respectively.

(7-1) strategy 1: irrespective of electric vehicle discharging to the grid

Under the strategy, the electric automobile does not participate in power grid dispatching, the discharge capacity is zero, and only the charging requirement is calculated, wherein the method comprises the following steps:

1) when the electric automobile arrives at the destination, if the residual electric quantity meets the requirement of the user for next trip, the user can choose to not charge or slowly charge. If the residual electric quantity is higher than the initial charging electric quantity ratio which is habituated to the user, the user does not select charging; if the remaining capacity is lower than the initial charging capacity ratio to which the user is accustomed, the user selects slow charging, at which time the charging is stopped when leaving the destination if the battery is not fully charged during the parking period.

2) When the electric automobile arrives at the destination, if the residual electric quantity cannot meet the requirement of the user for next trip, the user can select slow charging or quick charging. If the travel demand can be met by adopting slow charging in the parking period, the user selects slow charging; and if the travel demand cannot be met by adopting slow charging in the parking time period, the user selects fast charging.

The State of Charge (SOC) update process of the battery during charging can be expressed as:

in the formula: SOC_tRepresenting the state of charge of the battery at the t-th moment; eta_cRepresents the charging efficiency; c_bBattery capacity (kWh); p_cRepresenting the measured charging power on the grid side; Δ t represents the time interval over which changes in SOC and area load are observed.

(7-2) strategy 2: considering electric vehicles discharging into the grid

Under this strategy, K is set_pThe remaining 1-K is the proportion of N electric vehicles participating in the vehicle network interaction in the cluster_pThe proportional electric vehicle charging demand calculation method is described in strategy 1. The charging and discharging strategy of the electric vehicle participating in the vehicle network interaction is introduced as follows:

and determining the charge and discharge requirements of the power grid at the moment t. Selecting a typical daily per unit load curve in a certain area, and calculating the average load P_avDifference P between peak load and average load_uDifference P between minimum load and average load_l. Let the original load at time t be P (t), and calculate the difference Δ P (t) -P from the average load_av. If Δ P < 0, then Δ P/P in the cluster is required_lCharging the electric automobile in proportion; if Δ P > 0, then Δ P/P in the cluster is required_uDischarging the proportional electric vehicle; if delta P is 0, no charge and discharge is needed. The criterion determines the degree of the demand while judging the charging and discharging demand of the electric network on the electric automobile cluster.

And (4) vehicle network interaction based on the maximum safe charging and discharging area. For an electric vehicle which is connected to a power grid and participates in vehicle-grid interaction, factors such as battery safety, travel demand and power grid demand are comprehensively considered, and a maximum safe charging and discharging area needs to be set for the electric vehicle in a parking period. First, a parking period of the electric vehicle on a certain trip is equally divided into a plurality of control periods Δ T. For each time interval Δ T_iFirstly, judging the charging and discharging requirements of the power grid, and when the power grid needs to be charged by the electric automobile cluster in the period, ensuring that the maximum electric quantity SOC of the battery of the electric automobile is not more than the allowable SOC_uCharging is carried out in the interval, and when the electric quantity ratio SOC is increased to the SOC_uStopping charging when the charging is finished; when the electric network needs to discharge the electric automobile cluster at the time, whether forced charging is needed before the electric automobile leaves a parking place in the time period or not is judged first to meet the requirement of next trip, and if the forced charging is not needed, the electric automobile is enabled to be not lower than the lowest allowable battery charge SOC_lWhen the electric quantity ratio SOC is reduced to SOC_lAnd then stopping charging. After the current time interval is finished, the next time interval is judged again according to the process until the electric automobile is driven awayA parking place.

The representation mode of the battery state of charge SOC updating process in the charging process is shown as a formula (12). The process of updating the state of charge SOC of the battery during discharge can be expressed as:

in the formula: eta_dIndicating the discharge efficiency; p_dRepresenting the grid-side measured discharge power.

Drawings

FIG. 1 is a schematic diagram of a travel chain

FIG. 2 is a flow chart of a simulation trip chain

FIG. 3 is a graph of the mileage distribution in the raw statistics

FIG. 4 is a graph of a trip distance distribution in trip simulation data

FIG. 5 shows the charging requirements of electric vehicle cluster in working day 1

FIG. 6 shows an example 1 of the demand for charging the electric vehicle cluster in two holidays

FIG. 7 is a typical load per day curve for a certain area

FIG. 8 shows example 2K_p0.6-hour working day electric automobile cluster charging demand

FIG. 9 shows example 2K_pElectric automobile cluster discharge capacity in 0.6 hour working day

FIG. 10 shows example 2K_p0.6-hour double-holiday electric automobile cluster charging demand

FIG. 11 shows example 2K_p0.6-hour double-holiday electric automobile cluster discharge capacity

FIG. 12 shows example 2K_p0.3 hour workday electric automobile cluster charging demand

FIG. 13 shows example 2K_pElectric automobile cluster discharge capacity in 0.3 hour working day

FIG. 14 shows example 2K_p0.3-hour double-holiday electric automobile cluster charging demand

FIG. 15 shows example 2K_p0.3-hour double-holiday electric automobile cluster discharge capacity

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings.

(1) Initializing basic properties of each electric vehicle

According to the invention, sales data of major manufacturers in the electric automobile market in China in recent 6 years are counted, and the data shows that the total sales accounts for 20 models of which the percentage is up to 80%. According to the information of the endurance mileage of 20 types of vehicles, the power consumption of hundreds of kilometers under working conditions and the like issued by the industrial and informatization department of China, the data of all the vehicles are summarized in a table 2.

Table 2 information of various vehicle types in the present embodiment

Considering the factors of air conditioner power consumption, traffic jam, frequent start and stop and the like, the hundred kilometer power consumption and the endurance mileage of the electric automobile under the actual condition are worse than the performances under the ideal working condition. In this embodiment, the power consumption of each vehicle model per hundred kilometers in the simulation process is set to be 1.2 times of the ideal value.

In the present embodiment, the electric vehicle cluster size is set to 5000 vehicles, and the electric vehicle models are randomly extracted according to the market share in table 2. According to the energy efficiency and renewable energy Office (Office of energy and yeffeffeffecificiency) of Nissan and the United states&RENEWABLE ENERGY) combined issued electric vehicle charging data summary, initial charging electric quantity ratio SOC that electric vehicle owner is accustomed to_sObeying a normal distribution with a mean of 0.48 and a standard deviation of 0.15, i.e., SOC_s～N(0.48，0.15²) And extracting a random value for each electric automobile model as the customary initial charging capacity ratio.

(2) Trip original data screening and sorting

The original data in the invention are from latest full-American family trip statistical survey data NHTS2017 released by the Federal Highway administration FHA in 2017, and trip data are screened according to the following conditions: 1) the travel vehicle is a private motor vehicle; 2) belonging to urban trip; 3) the driving time and the driving distance are effective values; 4) travel occurs on weekdays or double holidays.

After screening the trip record that accords with the condition, record key feature: 1) the departure time of the trip; 2) a type of origin; 3) the time of arrival at the destination; 4) a destination type; 5) the length of travel; 6) the parking time length; 7) driving mileage; 8) a departure time; 9) travel occurs on weekdays or double holidays; 10) whether it is the last trip of the day with the home as the destination.

Consider the probability of a double holiday trip relative to a weekday. After the screening, 463904 trip records are generated on the working day, 144031 trip records are generated on the double holidays, and the probability of the trip on the working day is 1, so that the probability of the trip on the double holidays relative to the working day is as follows:

and counting the main travel purpose of the resident driving. The total of 6 types of travel purposes with 90% of the cumulative percentage in working days and double holidays are respectively home-returning, working, shopping, entertainment, delivery and dining, and the detailed percentage of each travel purpose is shown in table 3. In the present embodiment, only trips for the purpose of these 6 categories are considered.

TABLE 3 ratio of working day to double-holiday 6-class main trip purpose

Purpose of trip

Go home

Work by

Shopping

Entertainment system

Receiving and delivering

Dining with food

Total of

Working day

32.34％

16.90％

16.05％

8.67％

7.73％

7.36％

89.05％

Double holidays

37.31％

3.88％

20.34％

14.50％

4.50％

10.77％

91.30％

(3) Fitting of travel parameters

The main feature quantities of a single trip have different characteristics, and the fitting process of each main feature quantity in the embodiment is as follows:

(3-1) departure time of first trip of day

Analysis of NHTS survey data shows that the departure time of the vehicle owner's first trip every day does not exhibit an obvious single distribution characteristic, and the histogram of the departure time can be regarded as 3 normal distribution weighted gaussian mixture models, so that the maximum expectation algorithm described above is adopted to solve the distribution parameters, and the result is shown in table 4.

TABLE 4 distribution of departure time parameters for first trip in day

(3-2) travel duration of trip

For the trip of the determined departure place and destination, a lognormal distribution model is adopted to perform parameter fitting on the travel time to obtain corresponding mean and variance parameters, and random numbers obeying corresponding distribution are extracted to be used as the travel time t of the trip_v，i。

(3-3) arrival time of trip

Obtaining the departure time T of a trip_s，iAnd a running time period t_v，iAnd then, calculating the arrival time of the trip according to the formula (9).

(3-4) parking duration at destination

The parking duration of a trip is closely related to the destination of the trip, and distribution characteristics of travel records of different purposes are analyzed respectively and parameter fitting is carried out on the parking duration. The analysis of the parking time histogram of various trips in the NHTS data shows that the parking time of four trips of home returning, shopping, entertainment and delivering obeys the lognormal distribution, the parking time in a workplace can be regarded as 3 normal distribution weighted Gaussian mixed models, the parking time of dining can be regarded as 2 normal distribution weighted Gaussian mixed models, and the parking time distribution of working days and double-holidays has a certain difference. The corresponding models were used to fit the parking duration distribution parameters for the working day and the double holidays, and the fitting results are shown in table 5.

TABLE 5 parking duration distribution parameters corresponding to various trips

(3-5) end time of trip

After the time quantum is obtained, the end time of the trip can be calculated according to the expression (10) by combining the continuity of the trip characteristic quantity.

(3-6) probability of travel transition between different regions

The trip probability between different areas is affected by the trip time, and in the embodiment, a day is divided into 24 time intervals, and frequency statistics is performed on trip records in each time interval respectively, so that a 24 × Q transition probability matrix is obtained. It should be noted that for a trip with a home as a trip purpose, it is necessary to distinguish whether the trip is the last trip in the day or the trip is a temporary trip.

(3-7) mileage

Analyzing the data set to find mu_d(t_v) And t_v、σ_d(t_v) And t_vThe power function characteristics are approximately satisfied, and the invention adopts the power function form y (t)_v)＝a×t_v ^bFitting is performed and the mean absolute error MAE is calculated. The results of the functional relationship fitting of the mean value and standard deviation of the mileage and the running time length in this embodiment are shown in table 6.

TABLE 6 functional relationship fitting result of mean value and standard deviation of mileage and driving duration

After obtaining the parameters, the mileage on a working day can be expressed as compliance t_vNormal distribution under the conditions N [0.2622 × t_v ^1.2602，(0.1500×t_v ^1.2476)²]The mileage on double holidays can be expressed as obedience t_vNormal distribution under the conditions N [0.1677 × t_v ^1.3956，(0.2019×t_v ^1.1571)²]。

(4) Generating a simulation trip chain

The structure of the trip chain is shown in fig. 1. After the distribution parameters of the trip characteristic quantities are obtained, corresponding characteristic quantities are extracted from the electric vehicle cluster according to the distribution characteristics to simulate and generate a trip chain, and the flow is shown in fig. 2.

(5) Trip chain accuracy verification

After the trip chain of the electric vehicle cluster is obtained through the process, the travel distance of a single trip is selected as a verification object, the probability distribution in the original statistical data and the trip simulation data is shown in fig. 3 and 4, and the simulated trip rule is consistent with the actual situation.

(6) Simulation example

And obtaining the travel demand of the electric automobile cluster according to the steps, and analyzing the charging characteristics of the cluster. Considering that the travel parking time for receiving and delivering and dining is short, the travel probability is low, and the difference between the construction conditions of the parking lot and the charging pile is large, the charging demand and the discharging capacity of a residential area, a working area, a shopping area and an entertainment area are only analyzed.

(6-1) example 1: electric vehicle cluster charging demand calculation

In this embodiment, the slow charging power is set to 5kW, the fast charging power is set to 50kW, the cluster scale of the electric vehicles is 5000 vehicles, and the information of the battery capacity, the hundred kilometers of power consumption, and the like of the 20 models is used. The time interval Δ t for observing changes in SOC and regional load was set to 1 minute and the simulation time period was set to 3 weeks. In order to reduce the influence of simulation initialization and distinguish the travel characteristics of working days and weekdays, in this embodiment, the charging loads of each region of the second and third weeks are extracted, and are calculated and analyzed by using the strategy 1, and the charging demand results of the electric vehicles in different functional regions are shown in fig. 5 and 6.

Compared with the charging demands of electric automobiles in different functional areas of a city, the daily charging load peak of a residential area occurs at 20: about 00, and the peak load is obviously higher than other areas; the peak charging load occurs 10: about 00, the charging amount accounts for a higher percentage in each area, which indicates the importance of the working area to meet the charging requirement of the citizen electric automobile, the charging load of the working area on two holidays is obviously reduced, which accords with the conventional cognition, and as the shunting action of the charging requirement of the working area on the two holidays is reduced, the peak value of the charging load of the residential area on the night of the two holidays is obviously higher than that of the working day; the charging demand in the shopping and entertainment areas in the day is gentle, the load peak appears in the afternoon and in the evening, the charging demand in the shopping and entertainment areas in the double-holidays is obviously higher than that in the working day, but the load value is still smaller relative to other areas, and the shopping and entertainment areas accord with conventional cognition.

The peak-valley difference of the total daily charge demand of each functional area appears, the charge demand is high in daytime and evening time periods, the demand is low in early morning time periods, the peak value of the total charge demand appears 1.5 to 2 hours later than the working day on double-holidays, the charge demand of the residential area and the working area accounts for the highest in the total demand, and the load peak-valley difference of the charge demand is improved because the charge demand of the working area on the double-holidays is obviously reduced, and the total daily load demand is changed from the double-peak value of the working day to the single-peak value of the double-holidays.

(6-2) example 2: electric vehicle cluster discharge capacity calculation

Considering that the car owners have different preference degrees for participating in the car network interaction, the embodiment sets the electric car proportion K participating in the car network interaction in the cluster formed by the N electric cars_pThe discharge power is set to be 0.6 and 0.3 respectively, the typical per day load curve in a certain area is shown in FIG. 7, and other preset values are the same as those in the example 1. Extracting the charging demand and the discharging capacity of each area of the second week two and the third week six, adopting a strategy 2 to calculate and analyze, K_pThe charging demand and the discharging capacity of different functional regions of the 0.6-hour working day and the two-holiday are shown in fig. 8, 9, 10 and 11, and K is_pThe charging requirements and the discharging capabilities of different functional regions on the 0.3-hour working day and the two-holiday are shown in fig. 12, 13, 14 and 15.

Electric automobile proportion K participating in vehicle network interaction_pAt 0.6, the electric vehicle cluster charging demand is mostly shifted to the valley period 22 of the original load: 00-day 06: 00, while the cluster discharge power peak period is centered on the original load peak period 08: 00-20: 00, the number of the parked electric automobiles in the shopping and entertainment areas is small, the parking time is short, and therefore the charging and discharging power is low. In the peak period of the original load, the charging demand of the electric automobile cluster is greatly reduced compared with that of example 1, the discharging capacity of a working area on a double-break day is obviously lower than that of the working area, and the discharging capacity of the working area on the morning is higher than that of the working area in the afternoon, because a large number of electric automobiles arrive at the working area in the morning, the residual electric energy of the batteries is returned to the power grid, and in the evening, the residual electric energy of the batteries is returned to the powerThe time interval is that the trip demand of the next shift is met, and the discharging power is obviously reduced due to the increase of the charging load; the discharge capacity of residential areas is characterized by both morning and evening peaks, because some owners travel late in the morning and with short trips, and some owners return home and do not travel any longer, and therefore respond to the grid to send back electric energy. In the valley period of the original load, the electric vehicles participating in the vehicle network interaction are charged in the period so as to meet the travel demands of the vehicle owners in the daytime, and most of the vehicle owners are at home in the period, so that the charging demands of the residential area are remarkably improved compared with the charging demands of the residential area in the following example 1.

Electric automobile proportion K participating in vehicle network interaction_p0.3, the discharge capacity of the electric vehicle cluster is compared to K during peak periods of the original load_pAt 0.6, the charging demand of the electric vehicle cluster is also significantly reduced during the valley period of the original load.

The calculation results of the examples show that the modeling method provided by the invention can effectively calculate the space-time distribution of the charging demand and the discharging capacity of the electric vehicle cluster by establishing the electric vehicle cluster, simulating the time sequence trip model according to the real statistical data, transferring the charging demand of the cluster to the load valley period under the condition of meeting the constraint condition of the vehicle network interaction safety controllable area, and feeding back electric energy to the power network at the load peak period. The peak clipping and valley filling of the system load need further orderly charge and discharge optimization control on the method.

Claims

1. A modeling method of an electric vehicle cluster charging demand and discharging capacity model based on a time sequence travel set belongs to the technical field of electric vehicle modeling, and is characterized in that the method firstly performs mathematical fitting on distribution parameters of time and space characteristic quantities in urban resident travel statistical data; then, extracting time sequence trip set characteristic quantities through Monte Carlo simulation, and constructing a trip chain to simulate a large-scale electric vehicle cluster trip scene within continuous weeks of time scale; next, calculating the space-time distribution characteristic of the charging requirements of the electric automobile cluster according to different charging requirement criteria of electric automobile users; the interaction participation rate of the electric automobile cluster network and the constraint conditions of the safe charging and discharging controllable area are further considered, the space-time distribution characteristic of the electric automobile cluster discharging capacity is calculated, and the model can embody the charging aggregation characteristic and the cluster discharging potential of the large-scale electric automobile widely and randomly accessed to the electric network.

2. The electric vehicle cluster charging demand and discharging capacity model modeling method based on the time sequence traveling set according to claim 1, characterized by comprising the following steps:

a. determining the number ratio and the battery parameters of various types of electric automobiles in the electric automobile cluster;

b. carrying out mathematical fitting on distribution parameters of the characteristic quantity of the urban resident trip statistical data;

c. monte Carlo simulation is carried out to extract characteristic quantities, and a trip chain is constructed to simulate a large-scale electric automobile cluster trip scene;

d. calculating the space-time distribution characteristic of the charging requirements of the electric automobile cluster according to different charging requirement criteria of electric automobile users;

e. and calculating the interaction participation rate of the cluster vehicle network and the constraint conditions of the safe charge and discharge controllable area, and calculating the time-space distribution characteristic of the electric vehicle cluster discharge capacity.

3. The electric vehicle cluster charging demand and discharging capacity model modeling method based on the time sequence traveling set as claimed in claim 2, wherein the specific steps of determining the quantity ratio and the battery parameters of each type of electric vehicle in the electric vehicle cluster are as follows:

a. acquiring sales information of domestic electric vehicles in China in nearly 6 years, and selecting a plurality of electric vehicle models with accumulated sales reaching 80% of the total market amount as research objects;

b. according to information such as the endurance mileage of corresponding vehicle types, the power consumption of hundreds of kilometers under working condition and the like provided by the industrial and informatization department of China, the factors such as vehicle-mounted air conditioners, road congestion and the like are considered, and the information such as the battery capacity, the power consumption speed and the like of the electric vehicle cluster is modeled according to the proportions of various vehicle types;

c. respect to the charging habit of users and each electricitySetting a habitual initial charge capacity ratio of the electric vehicle, wherein the value follows normal distribution SOC_s～N(0.48，0.15²)。

4. The electric vehicle cluster charging demand and discharging capacity model modeling method based on the time sequence travel set as claimed in claim 2, characterized in that the specific steps of mathematically fitting the distribution parameters of the statistical data characteristic quantities of urban resident travel are as follows:

a. the original data is screened and sorted, and the following conditions are required to be met: 1) the travel vehicle is a private motor vehicle, 2) belongs to urban travel, 3) the travel time and the travel distance are effective values, and 4) travel occurs on working days or double-holidays;

b. calculating the trip probability of the double-holidays relative to the working day:

in the formula: n is₁Number of working sunrise entries, n, indicating compliance₂Representing the number of the double-holiday trip records meeting the conditions;

c. adopting a maximum expectation algorithm to fit distribution parameters of the first trip time of the working day and the double-holiday day;

d. carrying out parameter fitting on travel running time of the trips with the same starting point type and end point type by adopting a lognormal distribution model;

e. performing parameter fitting on the parking time lengths of different purposes by adopting a lognormal distribution model and a maximum expectation algorithm;

f. dividing one day into a plurality of time intervals, counting the travel transition probability among different destinations in each time interval, and considering whether the travel taking a home as a destination is the last travel of the day;

g. when driving for a period of time t_vThe traveled mileage d is determined to follow a normal distribution

In the form of a power function y (t)_v)＝a×t_v ^bAnd fitting the relation between the driving mileage normal parameter and the driving time length, and calculating the average absolute error.

5. The electric vehicle cluster charging demand and discharging capacity model modeling method based on the time sequence trip set as claimed in claim 2, wherein the specific steps of extracting characteristic quantities to construct a trip chain to simulate a large-scale electric vehicle cluster trip scene by Monte Carlo simulation are as follows:

a. extracting a first trip moment according to the one-dimensional Gaussian mixture distribution characteristic;

b. extracting travel purposes according to the types of departure places and the travel transition probabilities among various regions at different moments;

c. extracting the running time and the parking time at the destination according to the travel purpose, and calculating the time of arriving at the destination;

d. extracting the travel mileage according to the travel time length, and calculating the travel finish time of leaving the destination;

e. and judging whether the trip of the current day is finished, if so, finishing the simulation of the trip chain, and otherwise, continuing the simulation.

6. The electric vehicle cluster charging demand and discharging capacity model modeling method based on the time sequence traveling set as claimed in claim 2, wherein the concrete steps of calculating the space-time distribution characteristic of the electric vehicle cluster charging demand are as follows:

a. setting two charging modes of slow charging and fast charging, and considering the charging as a constant power process;

b. when the electric automobile arrives at a destination, if the residual electric quantity meets the requirement of the user for next trip, the user can choose to not charge or slowly charge, and if the residual electric quantity is higher than the initial charging electric quantity ratio which the user is accustomed to, the user can not choose to charge; if the residual electric quantity is lower than the initial charging electric quantity ratio which is habituated to the user, the user selects slow charging, and at the moment, if the battery cannot be fully charged in the parking period, the charging is stopped when the user leaves the destination;

c. when the electric automobile arrives at a destination, if the residual electric quantity cannot meet the next trip of the user, the user can select slow charging or fast charging, and if the trip demand can be met by adopting slow charging in a parking period, the user selects slow charging; if the travel demand cannot be met by adopting slow charging in the parking period, the user selects fast charging;

d. the battery state of charge SOC update process during charging can be expressed as:

in the formula: SOC_tIndicating the state of charge of the battery at time t, η_cIndicates the charging efficiency, C_bAs the battery capacity (kWh), P_cRepresents the charging power measured on the grid side, and Δ t represents the time interval for observing the SOC and the regional load change;

e. and accumulating the charging requirements of the electric automobile cluster at each moment and each area to obtain the space-time distribution characteristic of the charging requirements of the electric automobile cluster.

7. The electric vehicle cluster charging demand and discharging capacity model modeling method based on the time sequence traveling set as claimed in claim 2, wherein the concrete steps of calculating the space-time distribution characteristic of the discharging capacity of the electric vehicle cluster are as follows:

a. setting the proportion of the number of the electric vehicles participating in the vehicle network interaction to the number of the clusters to be K_pAllowing discharge to the power grid, wherein the charge and discharge are constant power processes, and the rest is 1-K_pThe method for calculating the charging requirement of the proportional electric vehicle is as set forth in claim 6;

b. considering the vehicle network interaction in the maximum safe charging and discharging area, when a single electric vehicle responds to the power network requirement for charging, the maximum allowable battery electric quantity SOC is not exceeded_uWhen a single electric vehicle discharges in response to the power grid requirement, whether forced charging is required before the electric vehicle leaves a parking place in the period is judged first to meet the requirement of next trip, and if the forced charging is not required, the electric vehicle is required to be charged at the SOC not lower than the lowest allowable battery electric quantity SOC_lCharging is carried out in the interval;

c. the battery state of charge SOC update process during discharge can be expressed as:

in the formula: eta_dIndicating discharge efficiency, P_dRepresenting the grid-side measured discharge power;

d. and accumulating the charging requirements and the discharging power of the electric automobile cluster at each moment and each area to obtain the space-time distribution characteristics of the charging requirements and the discharging capability of the electric automobile cluster.