CN115409325A - Electric vehicle grid-connected risk assessment method and system based on multi-scene generation - Google Patents

Abstract

The invention provides an electric vehicle grid-connected risk assessment method and system based on multi-scene generation, which comprises the following steps: acquiring charging data of the electric automobile at each time interval; obtaining the charging position and the average charging power of the electric automobile in each time period based on the charging data of the electric automobile in each time period by combining a Latin hypercube algorithm; obtaining a plurality of electric automobile charging scene risk values based on the charging positions and the average charging power of the electric automobiles in each time period; and evaluating the grid-connected risk of the electric automobile based on the plurality of electric automobile charging scene risk values. The problem that the actual charging environment is complex and the risk condition of electric vehicle charging cannot be accurately and effectively evaluated is solved, the risk scenes under different probabilities are obtained by performing layered sampling through a Latin hypercube algorithm, and the risk conditions under different scenes are calculated through multiple times of sampling. In consideration of risks of different types of electric automobiles to the power grid in different time, places and charging modes under different complex scenes, the risk calculation result has objectivity.

Description

A risk assessment method and system for grid-connected electric vehicles based on multi-scenario generation

technical field

The invention relates to the field of electricity consumption, in particular to a multi-scenario-based grid connection risk assessment method and system for electric vehicles.

Background technique

With the rapid development of new energy technologies, the penetration rate of electric vehicles is gradually increasing. The charging modes and charging laws of different types of electric vehicles are quite different, and the randomness of charging affects the safe and stable operation of the power system. Electric vehicle charging is a dynamic and random process. The actual charging environment is relatively complex and is affected by factors such as vehicle type, charging mode, weather, time, and distribution of charging piles. The static risk assessment model only considers a simple scenario and simulates the actual environment. There are deficiencies that cannot be evaluated objectively and accurately. Therefore, how to establish a more realistic risk scenario of electric vehicles connecting to the distribution network is an urgent problem to be solved.

Contents of the invention

In order to solve the problem, the present invention provides a multi-scenario-based grid-connected risk assessment method for electric vehicles, including:

Obtain the charging data of electric vehicles at each time period;

Obtain the charging position and average charging power of the electric vehicle in each time period based on the charging data of the electric vehicle in each period in combination with the Latin hypercube algorithm;

Obtaining a plurality of electric vehicle charging scene risk values based on the charging position and average charging power of the electric vehicle in each time period;

The electric vehicle grid connection risk is evaluated based on the multiple electric vehicle charging scenario risk values.

Preferably, the charging position and average charging power of the electric vehicle in each time period are obtained based on the charging data of the electric vehicle in each time period combined with the Latin hypercube algorithm, including:

Based on the charging data of the electric vehicle in each time period combined with time sampling, the cumulative density function of the charging position of the electric vehicle is obtained;

Based on the charging data of the electric vehicle in each time period combined with time sampling, the cumulative density function of the charging power of the electric vehicle is obtained;

Based on the cumulative density function of the charging position and charging power of the electric vehicle combined with the Latin hypercube algorithm, the charging position and average charging power of the electric vehicle in each time period are obtained.

Preferably, the cumulative density function based on the charging position and charging power of the electric vehicle is combined with the Latin hypercube algorithm to obtain the charging position and average charging power of the electric vehicle in each time period, including:

Based on the cumulative density function of the charging position and charging power of the electric vehicle, stratified sampling is carried out on the basis that the sampling points cover the entire distribution interval to obtain sampling points in each interval;

Based on the sampling points in each interval, the charging position and average charging power of the electric vehicle in each time period are obtained by using an inverse transformation function.

Preferably, the multiple electric vehicle charging scene risk values are obtained based on the charging position and average charging power of the electric vehicle in each time period, including:

Based on the charging position of the electric vehicle in each time period and the average charging power combined with the probability product formula, the distribution position of the electric vehicle and the probability product of the average charging power are obtained;

Based on the probability product of the charging position of the electric vehicle and the average charging power combined with the risk value formula to obtain the risk value of the plurality of electric vehicle charging scenarios;

Preferably, the probability product formula is as follows:

In _the formula, _{P T} ( _{D Tm} ) is the probability that the mth electric vehicle appears at the position D _Tm in the time period T; ; K is the time period; P is the probability product.

Preferably, the risk value formula is as follows:

R ₀ =P ₀ ×ΔU;

In the formula, P ₀ is the occurrence probability of this scenario; ΔU is the accumulated node voltage deviation; R ₀ is the risk value.

Preferably, the assessing the grid-connected risk of electric vehicles based on the multiple electric vehicle charging scene risk values includes:

Performing an expected summation of the multiple electric vehicle charging scene risk values to obtain a comprehensive risk value;

Based on the comprehensive risk value, the grid-connected risk of the electric vehicle is evaluated.

Preferably, the charging data of the electric vehicle includes:

The number of electric vehicles, charging frequency, charging time and charging location.

Based on the same inventive concept, the present invention also provides a multi-scenario-based electric vehicle grid connection risk assessment system, which is characterized in that it includes:

The data acquisition module is used to acquire the charging data of the electric vehicle at each time period;

Calculation module, for obtaining the charging position and average charging power of the electric vehicle in each time period based on the charging data of the electric vehicle in each time period combined with the Latin hypercube algorithm;

A risk value calculation module, configured to obtain a plurality of electric vehicle charging scene risk values based on the charging position and average charging power of the electric vehicle in each time period;

An assessment module, configured to assess the grid connection risk of electric vehicles based on the risk values of the plurality of electric vehicle charging scenarios.

Preferably, the calculation module includes:

The charging position density calculation sub-module is used to obtain the cumulative density function of the charging position of the electric vehicle based on the charging data of the electric vehicle in each time period combined with time sampling;

The charging power density calculation sub-module is used to obtain the cumulative density function of the charging power of the electric vehicle based on the charging data of the electric vehicle in each time period combined with time sampling;

The Latin hypercube calculation sub-module is used to obtain the charging position and average charging power of the electric vehicle in each time period based on the cumulative density function of the charging position and charging power of the electric vehicle combined with the Latin hypercube algorithm.

Preferably, the Latin hypercube calculation submodule is specifically used for:

Based on the cumulative density function of the charging position and charging power of the electric vehicle, stratified sampling is carried out on the basis that the sampling points cover the entire distribution interval to obtain sampling points in each interval;

Based on the sampling points in each interval, the charging position and average charging power of the electric vehicle in each time period are obtained by using an inverse transformation function.

Preferably, the risk value calculation module is specifically used for:

Based on the charging position of the electric vehicle in each time period and the average charging power combined with the probability product formula, the distribution position of the electric vehicle and the probability product of the average charging power are obtained;

Based on the probability product of the charging location of the electric vehicle and the average charging power combined with a risk value formula to obtain the risk values of the plurality of electric vehicle charging scenarios.

Preferably, the evaluation module is specifically used for:

Performing an expected summation of the multiple electric vehicle charging scene risk values to obtain a comprehensive risk value;

Based on the comprehensive risk value, the grid-connected risk of the electric vehicle is evaluated.

Preferably, the electric vehicle charging data includes:

The number of electric vehicles, charging frequency, charging time and charging location.

Compared with prior art, the beneficial effect of the present invention is:

The present invention provides a multi-scenario-based grid-connected risk assessment method for electric vehicles, comprising: obtaining charging data of electric vehicles at each time period; The charging position and average charging power of the time period; multiple electric vehicle charging scene risk values are obtained based on the charging position and average charging power of the electric vehicle in each time period; the electric vehicle is evaluated based on the multiple electric vehicle charging scene risk values On-grid risk. To solve the problem that the actual charging environment is complex and the risk status of electric vehicle charging cannot be accurately and effectively evaluated, stratified sampling is carried out through the Latin hypercube algorithm to obtain risk scenarios under different probabilities, and the risk status under different scenarios is calculated after multiple sampling. Considering the risks of different types of electric vehicles to the power grid at different times, locations, and charging modes under different complex scenarios, the calculated risk results are objective.

Description of drawings

Fig. 1 is a flow chart of a multi-scenario-based grid-connected risk assessment method for electric vehicles provided by the present invention;

Fig. 2 is the LHS layered sampling schematic diagram of the present invention;

Fig. 3 is a kind of work flowchart of the present invention.

Detailed ways

In order to better understand the present invention, the content of the present invention will be further described below in conjunction with the accompanying drawings and examples.

Example 1:

The present invention provides a multi-scenario-based grid-connected risk assessment method for electric vehicles, as shown in Figure 1, including:

Step 1: Obtain the charging data of electric vehicles at each time period;

Step 2: Obtain the charging position and average charging power of the electric vehicle in each time period based on the charging data of the electric vehicle in each time period combined with the Latin hypercube algorithm;

Step 3: Obtain multiple electric vehicle charging scene risk values based on the charging position and average charging power of the electric vehicle in each time period;

Step 4: Evaluate the grid connection risk of electric vehicles based on the risk values of the multiple electric vehicle charging scenarios.

In step 1, the charging data of the electric vehicle at each time period is acquired, specifically including:

Step A1, under the known historical data such as the number of different types of electric vehicles, charging frequency, charging time, charging location, etc., obtain the distribution position of electric vehicles in each time period and the cumulative probability density function of the average charging power.

In step 2, the charging position and average charging power of the electric vehicle in each time period are obtained by combining the charging data of the electric vehicle based on the described each time period with the Latin hypercube algorithm, specifically including:

Based on the charging data of the electric vehicle in each time period combined with time sampling, the cumulative density function of the charging position of the electric vehicle is obtained;

Based on the charging data of the electric vehicle in each time period combined with time sampling, the cumulative density function of the charging power of the electric vehicle is obtained;

Assuming that there are M electric vehicles in the distribution network area, D _Tm and C _Tm respectively represent the position of the mth (m=1, 2,....M) charging vehicle in the distribution network under the time period T, The average charging power, F _m (D _Tm ) and F _m (C _Tm ) are the cumulative probability density functions of D _Tm and C _Tm respectively.

Based on the cumulative density function of the charging position and charging power of the electric vehicle combined with the Latin hypercube algorithm, the charging position and average charging power of the electric vehicle in each time period are obtained.

Step A2, considering the risk status of different time periods, set up K time periods, divide 24 hours into K sections, and each period is 24/K. Use simple random sampling to obtain time period T. Under the condition of time period T, use Latin Hypercube Algorithm (LHS) for scene sampling. LHS includes two steps: ① Sampling: perform stratified sampling on each input variable according to the sampling scale N , so that the sampling points of each input variable can cover its entire distribution interval; ② Inverse transformation: According to the cumulative probability density function, the sampling value is brought into the inverse function to obtain the actual required variable value. The principle of LHS stratified sampling is shown in Figure 2.

Based on the cumulative density function of the charging position and charging power of the electric vehicle, stratified sampling is carried out on the basis that the sampling points cover the entire distribution interval to obtain sampling points in each interval;

For the distribution location and average charging power of electric vehicles, the specific sampling process is as shown in Figure 3,

Set the sampling scale to N, which is the number of electric vehicles in this scene. First, divide the interval [0,1] into N equal parts, then the probability of each interval is 1/N; in the interval [0,1/N), [1/N,2/N), ... , [N-1/N,1] randomly select sampling points respectively. Let the sampling point of the (r=1,2,...N)th interval be α _r , and α _r satisfies

Based on the sampling points in each interval, the charging position and average charging power of the electric vehicle in each time period are obtained by using an inverse transformation function.

其中

为F_m(g)的反变换。Finally, use the inverse transformation to obtain the variable value corresponding to the rth sampling interval

in

It is the inverse transformation of F _m (g).

According to the above steps, N electric vehicles are sampled on F _m (D _m ) and F _m (C _m ), respectively, and the positions and average charging of N electric vehicles distributed in the distribution network during the time period T are obtained power.

In step 3, based on the charging position and average charging power of the electric vehicle in each time period, multiple electric vehicle charging scene risk values are obtained, specifically including:

Based on the charging position of the electric vehicle in each time period and the average charging power combined with the probability product formula, the distribution position of the electric vehicle and the probability product of the average charging power are obtained;

Step A3, calculate the risk value of the charging scene in the time period T, the probability of the occurrence of the risk scene is the probability product of all electric vehicles in their corresponding distribution position and the average charging power condition,

Among them, P _T (D _Tm ) is the probability that the mth electric vehicle appears at the position D _Tm in the time period T, and P _T (C _Tm ) is the probability that the charging power of the mth electric vehicle is C _Tm in the time period T. The generation probability of the corresponding position and charging power of the mth car can be expressed by the integral on the extraction interval where the electric car is located, then

u_i为第i(i＝1,2,……,N_G)个节点的电压偏差值。The risk index L can be set as the cumulative node voltage deviation value, set the position of the mth electric vehicle as D _Tm = (x _m , y _m ), the number of nodes in the power grid is N _G , and use the Euclidean distance to judge the electric vehicle The node to which the car belongs, the voltage of each node is obtained through power flow calculation, and the accumulated node voltage deviation

u _i is the voltage deviation value of the ith (i=1, 2,..., N _G ) node.

Based on the probability product of the charging location of the electric vehicle and the average charging power combined with a risk value formula to obtain the risk values of the plurality of electric vehicle charging scenarios.

The risk value R ₀ in the current scenario = P ₀ ×ΔU, P ₀ is the occurrence probability of this scenario, since P ₀ is the occurrence probability of scenarios considering multiple electric vehicle locations and charging power, the probability value is relatively small, and the calculated The risk value is only the risk in the current scenario. When it is necessary to comprehensively evaluate the risk of electric vehicles connecting to the grid, more scenarios need to be generated.

In step 4, assess the grid-connected risk of electric vehicles based on the risk values of the multiple electric vehicle charging scenarios, including:

Performing an expected summation of the multiple electric vehicle charging scene risk values to obtain a comprehensive risk value;

Based on the comprehensive risk value, the grid-connected risk of the electric vehicle is evaluated.

其中，P_i、L_i分别为第i个场景的出现概率和损失值。Step A4, repeat step A2 and step A3 to generate S different charging scenarios. When the number of scenarios continues to increase, the number of samples that can participate in the risk calculation increases, which can more truly reflect the charging situation of the electric vehicle after it is connected to the grid, so as to comprehensively calculate all value at risk in the scenario

Among them, P _i and L _i are the occurrence probability and loss value of the i-th scene, respectively.

Example 2:

Based on the same inventive concept, the present invention also provides a grid-connected risk assessment system for electric vehicles based on multi-scenario generation, including:

The data acquisition module is used to acquire the charging data of the electric vehicle at each time period;

Calculation module, for obtaining the charging position and average charging power of the electric vehicle in each time period based on the charging data of the electric vehicle in each time period combined with the Latin hypercube algorithm;

A risk value calculation module, configured to obtain a plurality of electric vehicle charging scene risk values based on the charging position and average charging power of the electric vehicle in each time period;

An assessment module, configured to assess the grid connection risk of electric vehicles based on the risk values of the plurality of electric vehicle charging scenarios.

The data acquisition module is specifically used for:

Obtain historical data such as the number of different types of electric vehicles, charging frequency, charging time, and charging location.

The calculation module is used specifically for:

The charging position density calculation sub-module is used to obtain the cumulative density function of the charging position of the electric vehicle based on the charging data of the electric vehicle in each time period combined with time sampling;

The charging power density calculation sub-module is used to obtain the cumulative density function of the charging power of the electric vehicle based on the charging data of the electric vehicle in each time period combined with time sampling;

Given historical data such as the number of different types of electric vehicles, charging frequency, charging time, and charging location, the cumulative probability density function of the distribution position and average charging power of electric vehicles in each time period is obtained.

The Latin hypercube calculation sub-module is specifically used for:

Based on the cumulative density function of the charging position and charging power of the electric vehicle, stratified sampling is carried out on the basis that the sampling points cover the entire distribution interval to obtain sampling points in each interval;

Set the sampling scale to N, which is the number of electric vehicles in this scene. First, divide the interval [0,1] into N equal parts, then the probability of each interval is 1/N; in the interval [0,1/N), [1/N,2/N), ... , [N-1/N,1] randomly select sampling points respectively. Let the sampling point of the (r=1,2,...N)th interval be α _r , and α _r satisfies

According to the above steps, N electric vehicles are sampled on F _m (D _m ) and F _m (C _m ), respectively, and the positions and average charging of N electric vehicles distributed in the distribution network during the time period T are obtained power.

Based on the sampling points in each interval, the charging position and average charging power of the electric vehicle in each time period are obtained by using an inverse transformation function.

其中

为F_m(g)的反变换。Finally, use the inverse transformation to obtain the variable value corresponding to the rth sampling interval

in

It is the inverse transformation of F _m (g).

The value-at-risk calculation module is specifically used for:

Based on the charging position of the electric vehicle in each time period and the average charging power combined with the probability product formula, the distribution position of the electric vehicle and the probability product of the average charging power are obtained;

Calculate the risk value of the charging scene in the time period T, the probability of the occurrence of the risk scene is the probability product of all electric vehicles in their corresponding distribution position and the average charging power condition,

Among them, P _T (D _Tm ) is the probability that the mth electric vehicle appears at the position D _Tm in the time period T, and P _T (C _Tm ) is the probability that the charging power of the mth electric vehicle is C _Tm in the time period T. The generation probability of the corresponding position and charging power of the mth car can be expressed by the integral on the extraction interval where the electric car is located, then

Based on the probability product of the charging location of the electric vehicle and the average charging power combined with a risk value formula to obtain the risk values of the plurality of electric vehicle charging scenarios.

The risk value R ₀ in the current scenario = P ₀ ×ΔU, P ₀ is the occurrence probability of this scenario, since P ₀ is the occurrence probability of scenarios considering multiple electric vehicle locations and charging power, the probability value is relatively small, and the calculated The risk value is only the risk in the current scenario. When it is necessary to comprehensively evaluate the risk of electric vehicles connecting to the grid, more scenarios need to be generated.

The evaluation modules are used specifically for:

Performing an expected summation of the multiple electric vehicle charging scene risk values to obtain a comprehensive risk value;

Based on the comprehensive risk value, the grid-connected risk of the electric vehicle is evaluated.

其中，P_i、L_i分别为第i个场景的出现概率和损失值。Generate S different charging scenarios. When the number of scenarios continues to increase, the number of samples that can participate in risk calculation increases, which can more truly reflect the charging situation of electric vehicles after they are connected to the grid, so as to comprehensively calculate the risk value in all scenarios

Among them, P _i and L _i are the occurrence probability and loss value of the i-th scene, respectively.

Example 3:

Taking a certain regional power grid as an example, the following is a detailed introduction to the multi-scenario generation-based risk assessment method for grid-connected electric vehicles:

Step S1: Assuming that there are 10 electric vehicles in a certain regional power grid, 10 scenarios are generated, and the whole day is divided into 12 periods, which are 00:00-02:00, 02:00-04:00, ... , 22:00-24:00. According to the historical data of electric vehicles, the accumulative probability distribution functions of the position of electric vehicles in the distribution network and the charging power of 12 periods are respectively obtained.

Step S2: Carry out simple random sampling on the time period, the selected time period is 10:00-12:00, find the cumulative probability density function of the electric vehicle position and charging power corresponding to the time period 10:00-12:00, assuming two The functions are all normally distributed, and the interval [0,1] is divided into 10 equal parts on average, then the probability of each interval is 0.1, and a point is randomly selected in each interval, and the point extracted this time is each interval The middle points of , respectively, are 0.05, 0.15, 0.25, ..., 0.95, and the above values are brought into the inverse function of the cumulative probability density function, and the distribution positions (0.1, 0.5), (0.4, 0.2) of 10 electric vehicles are calculated respectively , ..., the average charging power is 8kW, 27kW, ..., the distribution and charging status of these 10 electric vehicles in the time period 10:00-12:00 constitute a charging scene.

Step S3: Calculate the occurrence probability of the above-mentioned generated scenarios, and calculate the integral of the cumulative probability density function (with the horizontal The area of the graph enclosed by the axis) indicates that the probability of scene occurrence P is obtained by multiplying the corresponding probabilities. Taking the node voltage deviation as the risk index, and the accumulated voltage deviation value of each node is VU, then the risk value R=PVU in this scenario.

Step S4: Repeat Step S2 and Step S3 9 times to generate the remaining 9 scenarios and calculate the risk value. The risk value of all scenarios is accumulated to obtain the risk value of electric vehicles connected to the grid in multiple scenarios.

Apparently, the described embodiments are some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowcharts and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow diagram procedure or procedures and/or block diagram procedures or blocks.

The above are only embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included in the pending application of the present invention. within the scope of the claims.

Claims (14)

1. The electric vehicle grid-connected risk assessment method based on multi-scene generation is characterized by comprising the following steps of:

acquiring charging data of the electric automobile at each time interval;

obtaining the charging position and the average charging power of the electric automobile in each time period based on the charging data of the electric automobile in each time period by combining a Latin hypercube algorithm;

obtaining a plurality of electric automobile charging scene risk values based on the charging positions and the average charging power of the electric automobiles in each time period;

and evaluating the grid-connected risk of the electric automobile based on the plurality of electric automobile charging scene risk values.

2. The method of claim 1, wherein the obtaining of the charging position and the average charging power of the electric vehicle in each time period based on the charging data of the electric vehicle in each time period and the Latin hypercube algorithm comprises:

obtaining an accumulated density function of the charging position of the electric automobile based on the charging data of the electric automobile in each time period and time sampling;

obtaining an accumulated density function of the charging power of the electric automobile based on the charging data of the electric automobile in each time period and time sampling;

and obtaining the charging position and the average charging power of the electric automobile in each time period based on the accumulated density function of the charging position and the charging power of the electric automobile and the Latin hypercube algorithm.

3. The method of claim 2, wherein the obtaining the charging location and the average charging power of the electric vehicle at each time period based on the cumulative density function of the charging location and the charging power of the electric vehicle in combination with the latin hypercube algorithm comprises:

based on the accumulated density function of the charging position and the charging power of the electric automobile, carrying out layered sampling by taking the sampling points covering the whole distribution interval as a standard to obtain the sampling points of each interval;

and obtaining the charging position and the average charging power of the electric automobile in each time period by using an inverse transformation function based on the sampling points of each interval.

4. The method of claim 1, wherein the deriving a plurality of electric vehicle charging scenario risk values based on the charging location and the average charging power of the electric vehicle over time periods comprises:

obtaining a probability product of the distribution position and the average charging power of the electric vehicle based on a probability product formula combining the charging position and the average charging power of the electric vehicle in each time period;

and obtaining a plurality of electric automobile charging scene risk values based on the probability product of the charging positions and the average charging power of the electric automobiles and a risk value formula.

5. The method of claim 4, wherein the probability product formula is as follows:

in the formula, P _T (D _Tm ) For the mth electric vehicle to appear at the position D in the time period T _Tm The probability of (d); p _T (C _Tm ) Is m-th electricThe charging power of the automobile is C in the time period T _Tm The probability of (d); k is a time period; p is the probability product.

6. The method of claim 5, wherein the risk value formula is as follows:

R ₀ ＝P ₀ ×ΔU；

in the formula, P ₀ The occurrence probability of the scene is; Δ U is the accumulated node voltage deviation; r ₀ Is the risk value.

7. The method of claim 1, wherein the assessing electric vehicle grid-connection risk based on the plurality of electric vehicle charging scenario risk values comprises:

carrying out expected summation on the plurality of electric automobile charging scene risk values to obtain a comprehensive risk value;

and evaluating the grid-connected risk of the electric automobile based on the comprehensive risk value.

8. The method of claim 1, wherein the charging data for the electric vehicle comprises:

the holding capacity, the charging frequency, the charging time and the charging place of the electric vehicle.

9. The utility model provides an electric automobile risk assessment system that is incorporated into power networks based on multi-scenario generation which characterized in that includes:

the data acquisition module is used for acquiring the charging data of the electric automobile at each time interval;

the calculation module is used for obtaining the charging position and the average charging power of the electric automobile in each time period based on the charging data of the electric automobile in each time period by combining the Latin hypercube algorithm;

the risk value calculation module is used for obtaining a plurality of electric automobile charging scene risk values based on the charging positions and the average charging power of the electric automobiles in each time period;

and the evaluation module is used for evaluating the grid-connected risk of the electric automobile based on the plurality of electric automobile charging scene risk values.

10. The system of claim 9, wherein the computation module comprises:

the charging position density calculation sub-module is used for obtaining an accumulated density function of the charging position of the electric automobile based on the charging data of the electric automobile in each time period and time sampling;

the charging power density calculation submodule is used for obtaining an accumulated density function of the charging power of the electric automobile based on the charging data of the electric automobile in each time period and time sampling;

and the Latin hypercube computation submodule is used for combining the accumulative density function based on the charging position and the charging power of the electric automobile with the Latin hypercube algorithm to obtain the charging position and the average charging power of the electric automobile in each time period.

11. The system of claim 10, wherein the latin hypercube computation submodule is specifically configured to:

based on the accumulated density function of the charging position and the charging power of the electric automobile, carrying out layered sampling by taking the sampling points covering the whole distribution interval as a standard to obtain the sampling points of each interval;

and obtaining the charging position and the average charging power of the electric automobile in each time period by using an inverse transformation function based on the sampling points of each interval.

12. The system of claim 11, wherein the risk value calculation module is specifically configured to:

obtaining a probability product of the distribution position and the average charging power of the electric vehicle based on a probability product formula combining the charging position and the average charging power of the electric vehicle in each time period;

and obtaining the plurality of electric automobile charging scene risk values based on the probability product of the charging position and the average charging power of the electric automobile and a risk value formula.

13. The system of claim 9, wherein the evaluation module is specifically configured to:

carrying out expected summation on the risk values of the plurality of electric automobile charging scenes to obtain a comprehensive risk value;

and evaluating the grid-connected risk of the electric automobile based on the comprehensive risk value.

14. The system of claim 9, wherein the electric vehicle charging data comprises:

the holding capacity, the charging frequency, the charging time and the charging place of the electric vehicle.

Images