CN108288112A - Region electric automobile charging station load forecasting method based on user's trip simulation - Google Patents
Region electric automobile charging station load forecasting method based on user's trip simulation Download PDFInfo
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- H—ELECTRICITY
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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Abstract
The invention discloses a kind of region electric automobile charging station load forecasting methods based on user's trip simulation, according to user's trip purpose and the type of residing region division electric automobile charging station, using travel activity of the discrete markoff process analog subscriber between different zones, Trip chain is constructed；Extracting influences the space-time characteristic amount of electric automobile charging station charging load in Trip chain, and uses Probability Distribution Fitting；Charge condition is set according to the trip requirements of user, establishes electric automobile charging station load forecasting model in region；The load prediction curve of different types of electric automobile charging station is calculated using Monte Carlo simulation approach.The present invention can effectively reflect the randomness of automobile user trip, accurately predict different types of electric automobile charging station power load distributing, powerful guarantee is provided to study influence and orderly charging of the extensive electric vehicle access to power grid, there is good practicability.
Description
Technical Field
The invention relates to the field of load prediction of electric vehicle charging stations, in particular to a regional electric vehicle charging station load prediction method based on user travel simulation.
Background
In the current society, the environmental crisis is continuously increased, and the traditional fuel oil automobile generates a large amount of harmful gas, so that the global warming effect is more and more obvious. The electric automobile has the advantages of low noise, low cost, environmental protection and the like, and becomes a research hotspot in the automobile research and development field in recent years. Supporting facilities such as the electric automobile industry and the electric automobile charging station are rapidly popularized and developed in a short time.
The electric vehicle charging station is used as an important facility for large-scale popularization of electric vehicles, and load distribution of the electric vehicle charging station is very important for stable operation and optimized dispatching of a power grid. At present, the load prediction method for the electric vehicle charging station includes a probability modeling method, a process simulation method and a regression algorithm based on a support vector machine. However, the charging behavior of the electric vehicle is influenced by the trip demand of the user and other environmental factors, and has randomness in time and space. Although the accuracy is guaranteed to a certain extent, the algorithm is still limited to a simple probability distribution rule and specific charging station historical data, the charging station type of the electric vehicle is not considered comprehensively, and the randomness of the travel rule of an electric vehicle user cannot be effectively reflected.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems, the invention provides a regional electric vehicle charging station load prediction method based on user travel simulation.
The technical scheme is as follows: in order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows: a regional electric vehicle charging station load prediction method based on user travel simulation comprises the following steps:
(1) dividing the types of the electric vehicle charging stations according to the trip purpose and the region where the user is located;
(2) simulating the travel activities of the user in different areas by adopting a discrete Markov process, and constructing a travel chain;
(3) calculating the probability of the user staying at different types of charging stations according to the statistical data of each section of travel in the trip chain;
(4) setting a charging condition according to the driving requirement of a user, and establishing a load prediction model of an electric vehicle charging station in an area;
(5) and calculating load prediction curves of different types of electric vehicle charging stations by using a Monte Carlo simulation method, and evaluating the precision of a load prediction result by using a variance coefficient.
In the step (1), the area includes a residential area (H), a work area (W), a public leisure area (P), and another area (O), and the type of the electric vehicle charging station includes a residential area charging station, a work area charging station, a public leisure area charging station, and another area charging station.
And (3) extracting the space-time characteristic quantity affecting the charging load of the electric vehicle charging station in the trip chain, and fitting the time characteristic quantity and the space characteristic quantity by respectively adopting a normal distribution function and a lognormal distribution function. The time characteristic quantity is a starting time, an arrival time and a stay time, and the space characteristic quantity is a driving mileage.
In the step (4), the charging conditions are as follows:
therein, SOCtRepresenting the state of charge of the electric vehicle at time t;representing the warning value of the battery of the electric automobile; l (SOC)t) The driving mileage of the electric automobile can be completed in the current state; l isnextIndicating the mileage that needs to be traveled from the current area to the next area.
The load prediction model is as follows:
wherein, PA,tRepresenting the total charging power of the electric vehicle charging station in the area A at the moment t; n is a radical ofAIndicates the number of station service vehicles in the area A, pcRepresenting charging power, x, of a single vehiclei,tIndicating the state of charge of the electric vehicle.
Has the advantages that: according to the load prediction method for the electric vehicle charging station, disclosed by the invention, resident travel investigation data are deeply mined, the influence of user behaviors on the charging demand of the electric vehicle is analyzed, the randomness of the charging load in time and space and the difference of the geographic positions of the electric vehicle charging stations are considered, and the obtained prediction result is more in line with the actual situation. The method provided by the invention has strong practicability and provides a reliable basis for researching the influence of large-scale electric automobile access on a power grid.
Drawings
FIG. 1 is a flow chart of regional electric vehicle charging station load prediction based on user travel simulation in accordance with the present invention;
FIG. 2 is a load prediction curve for different types of electric vehicle charging stations.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
As shown in fig. 1, the method for predicting the load of the charging station of the regional electric vehicle based on the user travel simulation according to the present invention includes the steps of:
(1) dividing the types of the electric vehicle charging stations according to the travel purpose and the region of the user;
the region comprises a residential area (H), a work area (W), a public leisure area (P) and other areas (O), and the type of the electric vehicle charging station comprises a residential area charging station, a work area charging station, a public leisure area charging station and other area charging stations.
(2) Simulating the travel activities of the user among different areas by adopting a discrete Markov process, and constructing a travel chain;
the trip chain refers to the process of time and space change generated by residents in a certain space-time range to complete one or more activities, and the process comprises a large number of characteristic quantities related to space-time.
According to the invention, on the assumption that the electric automobile has similar traveling characteristics with the traditional fuel automobile, the average traveling chain length of the private automobile is 3.02 according to data statistics, and the total ratio of home returning, work and leisure and entertainment in the traveling purpose of residents is close to 80%, so that the charging behavior is considered to occur in the four types of electric automobile charging stations.
The discrete Markov process refers to a discrete event stochastic process with Markov property in mathematics, in which each state transition is related to the state at the previous moment and not to the past state.
The transition of the electric automobile between different regions can be represented by conditional probability:
P(Ei→Ej)＝P(Ei|Ej)＝pij
wherein E isiIs the state at the present moment, EjIs the state of the next moment, pijIs in slave state EiTransition to state EjThe transition probability of (2).
Considering each travel area as a state, the spatial transition probability matrix of the electric vehicle between different areas can be represented as:
wherein p isijThe following conditions are satisfied:
wherein M is the total number of the regions, the invention divides the travel regions into four categories, namely M is 4, pijAnd obtaining the probability of each trip chain according to the statistical data and calculating through the combination of different trips.
(3) Calculating the probability of the user staying at different types of charging stations according to the statistical data of each section of travel in the trip chain;
extracting space-time characteristic quantities affecting the charging load of the electric vehicle charging station in a trip chain, and fitting time characteristic quantities and space characteristic quantities by adopting a normal distribution function and a lognormal distribution function respectively, wherein the time characteristic quantities correspond to a departure time, an arrival time and a stay time; the spatial feature amount corresponds to the mileage.
The invention assumes that the electric vehicle starts to be charged after reaching the charging station without queuing, so the reaching time is the charging starting time, and the probability density expressions of normal distribution and log-normal distribution are respectively as follows:
wherein, muSThe expected value of the time for starting charging; sigmaSAs standard deviation, h is derived from the statistical data.
Wherein, muDThe expected value of the driving mileage is obtained; sigmaDKm is the standard deviation from the statistical data.
(4) Setting a charging condition according to the driving requirement of a user, and establishing a load prediction model of an electric vehicle charging station in an area;
the charging conditions were as follows:
wherein,SOCtrepresenting the state of charge of the electric vehicle at time t;representing the warning value of the battery of the electric automobile; l (SOC)t) The driving mileage of the electric automobile can be completed in the current state; l isnextIndicating the mileage that needs to be traveled from the current area to the next area.
Wherein L isnextRandomly generated by a fitted probability distribution, L (SOC)t) The calculation formula of (a) is as follows:
wherein E is100The power consumption is hundred km, and the fixed value is 15 kW.h.
The load prediction model is as follows:
wherein, PA,tRepresenting the total charging power of the electric vehicle charging station in an area A at the moment t, wherein A belongs to { H, W, P, O }; n is a radical ofAIndicates the number of station service vehicles in the area A, pcRepresenting charging power, x, of a single vehiclei,tThe charging state of the electric vehicle is represented by 1 when the electric vehicle is charged, and 0 when the electric vehicle is not charged.
Wherein N isAThe calculation formula of (a) is as follows:
NA＝NEV*∑PA
wherein N isEVThe total scale of the electric automobile is represented; pAAnd the travel chain transfer probability related to the electric vehicle charging station in the area A is represented and is obtained through statistics of resident travel research data.
(5) And calculating by using a Monte Carlo simulation method to obtain load prediction curves of different types of electric vehicle charging stations, and evaluating the precision of a load prediction result by using a variance coefficient.
The method comprises the following specific steps:
(1) inputting original data including battery capacity Bc and total automobile scale N of the electric automobileEV；
(2) Setting Monte Carlo simulation times K and convergence precision;
(3) selecting a charging area, setting K to 1, and setting charging power Pc and charging efficiency η;
(4) let n equal to 1;
(5) according to the probability distribution, randomly extracting an initial SOC, a driving mileage and a charging starting time;
(6) judging whether the charging condition is met, if so, continuing, otherwise, turning to the step 8;
(7) calculating the charging time length and accumulating the charging load;
(8) judging N is larger than N, if so, continuing, otherwise, enabling N to be N +1, and turning to the step 5;
(9) judging whether the convergence precision is met, if so, turning to the step 11, otherwise, continuing;
(10) judging that K is larger than K, if so, continuing, otherwise, changing K to K +1, and turning to the step 4;
(11) and outputting a load curve of the electric vehicle charging station in the area.
The initial SOC is the state of charge of the electric vehicle when the electric vehicle reaches an electric vehicle charging station, and obeys normal distribution N (0.5, 0.1); the calculation formulas of the charging time and the variance coefficient are respectively as follows:
wherein, TCIs the charging time; sSOCIs a firstThe SOC is started.
Wherein, βtThe variance coefficient of the charging load at the moment t;variance of charging load at time t;the expected value of the charging load at the moment t;max (β) is the maximum value β of the square difference coefficient at each time pointt) As a basis for accuracy evaluation.
The following description is given with reference to a specific embodiment.
In this embodiment, the resident trip investigation data adopts a NHTS2009 database of united states family trip investigation data issued by the united states department of transportation, the prediction area includes four categories of H area, W area, P area, and O area, the total scale of the electric vehicle is 10000, the charging mode of the electric vehicle charging station includes fast charging and slow charging, and because in most cases, the electric vehicle has a longer time in the H area and the W area and a shorter dwell time in the P area and the O area, it is assumed that the charging mode in the H area and the W area is fast charging and the charging mode in the P area and the O area is slow charging. The method of the invention is used for predicting the loads of the electric vehicle charging stations in the region, and finally daily load distribution curves of different types of electric vehicle charging stations are obtained, as shown in fig. 2.
As can be seen from fig. 2, the load distributions of different types of electric vehicle charging stations have obvious differences, which are mainly caused by different driving laws and charging behaviors of users in different areas. In general, most users choose to go home for charging after traveling on a day, and therefore, the charging station in the residential area bears the most charging load, and the charging loads in other areas are relatively dispersed. The load curves of the electric vehicle charging stations in the public leisure areas and other areas are not smooth in other areas, mainly because the two areas adopt a quick charging mode, the charging power is high, and the load fluctuation is large.
The load distribution of the electric vehicle charging stations in different areas reflects different charging requirements of users, and the influence on the total load of the system after the system is connected into a power grid is different. Therefore, the travel demand of the user is analyzed, the random characteristics of the charging behaviors are considered, the load prediction of different types of electric vehicle charging stations is carried out, and the method has very important significance for the economic operation and the optimized dispatching of the power grid.
Claims (6)
1. A regional electric vehicle charging station load prediction method based on user travel simulation is characterized by comprising the following steps: the method comprises the following steps:
(1) dividing the types of the electric vehicle charging stations according to the trip purpose and the region where the user is located;
(2) simulating the travel activities of the user in different areas by adopting a discrete Markov process, and constructing a travel chain;
(3) calculating the probability of the user staying at different types of charging stations according to the statistical data of each section of travel in the trip chain;
(4) setting a charging condition according to the driving requirement of a user, and establishing a load prediction model of an electric vehicle charging station in an area;
(5) and calculating load prediction curves of different types of electric vehicle charging stations by using a Monte Carlo simulation method, and evaluating the precision of a load prediction result by using a variance coefficient.
2. The regional electric vehicle charging station load prediction method based on user travel simulation according to claim 1, characterized in that: in the step (1), the area includes a residential area (H), a work area (W), a public leisure area (P), and another area (O), and the type of the electric vehicle charging station includes a residential area charging station, a work area charging station, a public leisure area charging station, and another area charging station.
3. The regional electric vehicle charging station load prediction method based on user travel simulation according to claim 1, characterized in that: and (3) extracting the space-time characteristic quantity affecting the charging load of the electric vehicle charging station in the trip chain, and fitting the time characteristic quantity and the space characteristic quantity by respectively adopting a normal distribution function and a lognormal distribution function.
4. The regional electric vehicle charging station load prediction method based on user travel simulation of claim 3, characterized in that: the time characteristic quantity is a starting time, an arrival time and a stay time, and the space characteristic quantity is a driving mileage.
5. The regional electric vehicle charging station load prediction method based on user travel simulation according to claim 1, characterized in that: in the step (4), the charging conditions are as follows:
therein, SOCtRepresenting the state of charge of the electric vehicle at time t;representing the warning value of the battery of the electric automobile; l (SOC)t) The driving mileage of the electric automobile can be completed in the current state; l isnextIndicating the mileage that needs to be traveled from the current area to the next area.
6. The regional electric vehicle charging station load prediction method based on user travel simulation of claim 5, wherein: in the step (4), the load prediction model is as follows:
wherein, PA,tRepresenting the total charging power of the electric vehicle charging station in the area A at the moment t; n is a radical ofAIndicates the number of station service vehicles in the area A, pcRepresenting charging power, x, of a single vehiclei,tIndicating the state of charge of the electric vehicle.
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Cited By (13)
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| CN110363332A (en) * | 2019-06-21 | 2019-10-22 | 国网天津市电力公司电力科学研究院 | A kind of electric car charging load spatial and temporal distributions prediction technique based on individual behavior characteristic |
| CN110826813A (en) * | 2019-11-13 | 2020-02-21 | 上海恒能泰企业管理有限公司璞能电力科技工程分公司 | Power grid optimization method based on charging difference requirements of household electric vehicle users |
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| CN110956329A (en) * | 2019-12-02 | 2020-04-03 | 国网浙江省电力有限公司绍兴供电公司 | Load prediction method based on distributed photovoltaic and electric vehicle space-time distribution |
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| CN112036624A (en) * | 2020-08-21 | 2020-12-04 | 上海电力大学 | Power grid dispatching method based on charging load prediction of electric vehicles in region |
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| CN112380664A (en) * | 2020-08-27 | 2021-02-19 | 国电南瑞南京控制系统有限公司 | Characteristic simulation method and system for electric vehicle virtual energy storage to participate in power grid regulation |
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| CN110956329B (en) * | 2019-12-02 | 2023-04-25 | 国网浙江省电力有限公司绍兴供电公司 | Load prediction method based on distributed photovoltaic and electric automobile space-time distribution |
| CN110956329A (en) * | 2019-12-02 | 2020-04-03 | 国网浙江省电力有限公司绍兴供电公司 | Load prediction method based on distributed photovoltaic and electric vehicle space-time distribution |
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| CN112036624A (en) * | 2020-08-21 | 2020-12-04 | 上海电力大学 | Power grid dispatching method based on charging load prediction of electric vehicles in region |
| CN112380664A (en) * | 2020-08-27 | 2021-02-19 | 国电南瑞南京控制系统有限公司 | Characteristic simulation method and system for electric vehicle virtual energy storage to participate in power grid regulation |
| CN112348387A (en) * | 2020-11-16 | 2021-02-09 | 中原工学院 | Dynamic power dispatching method for assisting user in traveling through charging and discharging strategies |
| CN113361587B (en) * | 2021-06-02 | 2022-11-01 | 东南大学 | Electric vehicle charging station load characteristic clustering modeling method based on POI information |
| CN113361587A (en) * | 2021-06-02 | 2021-09-07 | 东南大学 | Electric vehicle charging station load characteristic clustering modeling method based on POI information |
| CN113592152A (en) * | 2021-07-07 | 2021-11-02 | 国网上海市电力公司 | Residential community multi-type charging facility configuration method |
| CN113592152B (en) * | 2021-07-07 | 2023-08-22 | 国网上海市电力公司 | Configuration method for multi-type charging facilities of residential area |
| CN114971067A (en) * | 2022-06-17 | 2022-08-30 | 长沙理工大学 | Building-new energy automobile load prediction method considering personnel characteristics |
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