CN115409325A - Electric vehicle grid-connected risk assessment method and system based on multi-scene generation - Google Patents
Electric vehicle grid-connected risk assessment method and system based on multi-scene generation Download PDFInfo
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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
Technical Field
The invention relates to the field of power utilization, in particular to an electric vehicle grid-connected risk assessment method and system based on multi-scene generation.
Background
With the rapid development of new energy technologies, the permeability of electric vehicles is gradually increased. The charging modes and the charging rules of different types of electric automobiles are greatly different, and the charging randomness influences the safe and stable operation of a power system. The electric automobile charging is a dynamic random process, the actual charging environment is relatively complex and is influenced by factors such as vehicle type, charging mode, weather, time, charging pile distribution and the like, and the static risk assessment model only considers a simple scene, so that the actual environment simulation is insufficient, and the assessment cannot be objectively and accurately carried out. Therefore, how to establish a more real risk scene of the electric vehicle accessing the power distribution network is an urgent problem to be solved.
Disclosure of Invention
In order to solve the problem, the invention provides an electric vehicle grid-connected risk assessment method 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.
Preferably, 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 in combination with the latin hypercube algorithm includes:
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.
Preferably, the obtaining of the charging position and the average charging power of the electric vehicle in each time period based on the cumulative density function of the charging position and the charging power of the electric vehicle and the latin hypercube algorithm includes:
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.
Preferably, the obtaining a plurality of risk values of the charging scenario of the electric vehicle based on the charging position and the average charging power of the electric vehicle in each time period includes:
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;
obtaining a 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;
preferably, the probability product formula is shown 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 ) Charging power C for the mth electric vehicle in the time period T Tm The probability of (d); k is a 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 The occurrence probability of the scene is; Δ U is the accumulated node voltage deviation; r 0 Is the risk value.
Preferably, the evaluating the grid-connected risk of the electric vehicle based on the plurality of electric vehicle charging scene risk values includes:
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.
Preferably, the charging data of the electric vehicle includes:
the holding capacity, the charging frequency, the charging time and the charging place of the electric vehicle.
The invention also provides an electric vehicle grid-connected risk assessment system based on multi-scene generation, which is characterized by comprising the following steps:
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.
Preferably, the computing module includes:
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 calculation submodule is used for 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.
Preferably, 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.
Preferably, 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 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.
Preferably, 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.
Preferably, the electric vehicle charging data includes:
the holding capacity, the charging frequency, the charging time and the charging place of the electric vehicle.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an electric vehicle grid-connected risk assessment method 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.
Drawings
FIG. 1 is a flow chart of a multi-scenario generation-based electric vehicle grid-connected risk assessment method provided by the invention;
FIG. 2 is a schematic diagram of the present invention for LHS layered sampling;
fig. 3 is a flow chart of an operation of the present invention.
Detailed Description
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
Example 1:
the invention provides a multi-scene generation-based electric vehicle grid-connected risk assessment method, which comprises the following steps of:
step 1: acquiring charging data of the electric automobile at each time interval;
step 2: 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;
and step 3: 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 4, step 4: and evaluating the grid-connected risk of the electric automobile based on the plurality of electric automobile charging scene risk values.
In step 1, the step of obtaining the charging data of the electric vehicle at each time interval specifically comprises the following steps:
a1, acquiring a cumulative probability density function of the distribution position and the average charging power of the electric automobile in each time period under the known historical data of the holding capacity, the charging frequency, the charging time, the charging place and the like of different types of electric automobiles.
In step 2, the charging position and the average charging power of the electric automobile in each time period are obtained by combining the charging data of the electric automobile in each time period with a Latin hypercube algorithm, and the method specifically comprises the following steps:
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;
suppose there are M electric vehicles in the distribution network area, D Tm 、C Tm Respectively, the position of the M (M =1,2.. M) charging vehicle in the distribution grid during time period T, the average charging power, F m (D Tm )、F m (C Tm ) Are respectively D Tm 、C Tm Is calculated as a function of the cumulative probability density of (1).
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.
And step A2, considering risk conditions in different periods, setting K time periods, equally dividing 24 hours into K periods, wherein each period is 24/K. Obtaining a time period T by using simple random sampling, and performing scene sampling by using a Latin hypercube algorithm (LHS) under the condition of the time period T, wherein the LHS comprises two steps: (1) sampling: carrying out layered sampling on each input variable according to the sampling scale N, so that the sampling point of each input variable can cover the whole distribution interval; (2) inverse transformation: and according to the cumulative probability density function, the sampling value is substituted into the inverse function to obtain the actually required variable value. The LHS hierarchical sampling principle is shown in fig. 2.
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;
for the distributed location and the average charging power of the electric vehicle, the specific sampling process is, as shown in fig. 3,
and setting the sampling scale as N, namely the number of the electric automobiles in the scene. First, the interval [0,1]The average division is N equal divisions, and the probability of each interval is 1/N; in the interval [0,1/N ], [1/N, 2/N), … …, [ N-1/N,1]Respectively randomly sampling points. Is provided with the first(r =1,2, … N) intervals of sampling points are α r ，α r Satisfy the requirements of
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.
Finally, the variable value corresponding to the r sampling interval is obtained by inverse transformation
Wherein
Is F m (g) Inverse transformation of (3).
According to the above steps, respectively at F m (D m )、F m (C m ) The N electric vehicles are sampled, and the positions of the N electric vehicles distributed in the power distribution network and the average charging power are obtained when the N electric vehicles are in the time period T.
In step 3, a plurality of electric vehicle charging scene risk values are obtained based on the charging positions and the average charging power of the electric vehicles in each time period, and the method specifically comprises the following steps:
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;
step A3, calculating a risk value of the charging scene in the time period T, wherein the probability of the occurrence of the risk scene is the probability product of all the electric vehicles under the conditions of corresponding distribution positions and average charging power,
wherein, P T (D Tm ) For the mth electric vehicle to appear at the position D in the time period T Tm Probability of (P) T (C Tm ) Charging the mth electric vehicle in the time period TPower is C Tm The probability of (c). The generation probability of the corresponding position and the charging power of the mth automobile can be represented by the integral on the extraction interval where the mth automobile is located, and then
Setting the risk index L as the accumulated node voltage deviation value, and setting the position of the mth electric vehicle as D Tm ＝(x m ,y m ) The number of nodes of the power grid is N G Judging the node to which the electric automobile belongs by using the Euclidean distance, obtaining the voltage of each node through load flow calculation, and accumulating the voltage deviation of the nodes
u i Is the ith (i =1,2, … …, N G ) Voltage deviation values of the individual nodes.
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.
Risk value R in current scene 0 ＝P 0 ×ΔU，P 0 Is the probability of occurrence of the scene, due to P 0 The scene occurrence probability of the positions and the charging power of the electric automobiles is considered, so the probability value is small, the calculated risk value is only the risk in the current scene, and when the risk of the electric automobiles accessing the power grid needs to be comprehensively evaluated, more scenes need to be generated.
In step 4, evaluating the grid-connected risk of the electric vehicle based on the risk values of the plurality of electric vehicle charging scenes, specifically comprising:
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.
Step A4, repeating the step A2 and the step A3 to generate S different charging scenes, and when the number of the scenes is increased continuously, samples which can participate in risk calculation are increased, and the number of the samples can be increasedThe charging condition of the electric automobile after being connected into the power grid is truly reflected, so that the risk values under all scenes are comprehensively calculated
Wherein, P i 、L i Respectively, the occurrence probability and the loss value of the ith scene.
Example 2:
based on the same invention concept, the invention also provides an electric vehicle grid-connected risk evaluation system based on multi-scene generation, which comprises:
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.
The data acquisition module is specifically configured to:
and acquiring historical data of the holding capacity, the charging frequency, the charging time, the charging place and the like of different types of electric automobiles.
The calculation module is specifically configured to:
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;
under the known historical data of the holding capacity, the charging frequency, the charging time, the charging place and the like of different types of electric automobiles, the cumulative probability density function of the distribution position and the average charging power of the electric automobiles in each time period is obtained.
The Latin hypercube calculation submodule is specifically used for:
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 setting the sampling scale as N, namely the number of the electric automobiles in the scene. First, the interval [0,1]The average division is N equal divisions, and the probability of each interval is 1/N; in the interval [0,1/N ], [1/N, 2/N), [ … …, [ N-1/N, 1]]Respectively randomly extracting sampling points. Let the sampling point in the (r =1,2, … N) th interval be alpha r ，α r Satisfy the requirement of
According to the above steps, respectively at F m (D m )、F m (C m ) The N electric vehicles are sampled, and the positions of the N electric vehicles distributed in the power distribution network and the average charging power are obtained when the N electric vehicles are in the time period T.
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.
Finally, the variable value corresponding to the r sampling interval is obtained by inverse transformation
Wherein
Is F m (g) Inverse transformation of (3).
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 charging position and average charging power of the electric vehicle in each time period and a probability product formula;
calculating the risk value of the charging scene in the time period T, wherein the probability of occurrence of the risk scene is the probability product of all the electric automobiles under the conditions of corresponding distribution positions and average charging power,
wherein, P T (D Tm ) For the mth electric vehicle to appear at the position D in the time period T Tm Probability of (P) T (C Tm ) Charging power C for the mth electric vehicle in the time period T Tm The probability of (c). The generation probability of the corresponding position and the charging power of the mth automobile can be represented by the integral on the extraction interval where the mth automobile is located, and then
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.
Risk value R in current scene 0 ＝P 0 ×ΔU，P 0 Is the probability of occurrence of the scene, due to P 0 The scene occurrence probability of the positions and the charging power of the electric vehicles is considered, so the probability value is small, the calculated risk value is only the risk in the current scene, and when the risk of the electric vehicles accessing the power grid needs to be comprehensively evaluated, more scenes need to be generated.
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.
S different charging scenes are generated, when the number of the scenes is increased continuously, samples capable of participating in risk calculation are increased, the charging condition of the electric automobile after being connected into the power grid can be reflected more truly, and therefore risk values under all the scenes are calculated comprehensively
Wherein, P i 、L i Respectively, the occurrence probability and the loss value of the ith scene.
Example 3:
the electric vehicle grid-connected risk assessment method based on multi-scenario generation is described in detail below by taking a certain regional power grid as an example:
step S1: assuming that 10 electric vehicles are contained in a regional power grid, 10 scenes are generated, and the whole day period is divided into 12, namely 00-02. And respectively obtaining the cumulative probability distribution functions of the electric vehicles in the power distribution network and the charging power in 12 time periods according to the historical data of the electric vehicles.
Step S2: the method comprises the following steps of performing simple random sampling on time periods, wherein the sampling time period is 10-12, finding an accumulated probability density function of electric vehicle positions and charging power corresponding to the time periods 10-12, assuming that both functions are normal distributions, averagely dividing a section [0,1] into 10 equal parts, wherein the probability of each section is 0.1, randomly sampling one point in each section, setting the sampled point as a middle point of each section, namely 0.05, 0.15, 0.25, … and 0.95, substituting the values into an inverse function of the accumulated probability density function, and calculating distribution positions (0.1,0.5), (0.4,0.2), …, and the average charging power 8, 27kW and … of 10 electric vehicles in the time periods 10-12 to form a charging scene.
And step S3: the probability of occurrence of the generated scene is calculated, and the sampling point extraction probability is expressed by the integral of the cumulative probability density function (area of a graph formed by enclosing the horizontal axis) calculated in each of the sections [0,0.1], [0.1,0.2], … and [0.9,1], and the corresponding probability is multiplied to obtain the scene occurrence probability P. And taking the node voltage deviation value as a risk index, and taking the accumulated voltage deviation value of each node as VU, wherein the risk value R = PVU of the scene.
And step S4: and repeating the step S2 and the step S3 for 9 times, generating the remaining 9 scenes, calculating risk values, and accumulating the risk values of all the scenes to obtain the risk value of the electric automobile accessed to the power grid in multiple scenes.
It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. 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, and so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention are included in the scope of the claims of the present invention as filed.
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.
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