CN114707292A - Voltage stability analysis method for power distribution network containing electric automobile - Google Patents

Voltage stability analysis method for power distribution network containing electric automobile Download PDF

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CN114707292A
CN114707292A CN202210071063.0A CN202210071063A CN114707292A CN 114707292 A CN114707292 A CN 114707292A CN 202210071063 A CN202210071063 A CN 202210071063A CN 114707292 A CN114707292 A CN 114707292A
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黄南天
胡乾坤
王日俊
贺庆奎
杨冬锋
刘闯
孔令国
张良
蔡国伟
高旭
姜雨晴
郭笑林
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Northeast Electric Power University
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Abstract

The invention discloses a method for analyzing voltage stability of a power distribution network containing an electric automobile, which is implemented according to the following steps: firstly, a trip chain model is established by considering the correlation between the destination type and the time, and a Monte Carlo simulation method is combined to realize trip simulation and produce a large-amount charging load scene; secondly, considering multi-node spatial correlation, generating a countermeasure network based on conditional Wasserstein gradient penalty to establish a conventional load generation model, and generating a large number of conventional load scenes; then, screening out a typical and extreme charging load scene and a typical and extreme conventional load scene, and constructing a potential small-probability extreme load scene and a typical load scene; finally, the voltage stability of the power distribution network is analyzed based on the rapid voltage stability index and the voltage stability margin index, the problem that the capacity of a charging station is redundant due to the fact that a planning result is unbalanced with an actual demand is solved, and the voltage stability analysis result of the power distribution network of the electric automobile can be more reliable.

Description

Voltage stability analysis method for power distribution network containing electric automobile
Technical Field
The invention belongs to the technical field of voltage stability analysis of power distribution networks, and particularly relates to a voltage stability analysis method of a power distribution network containing an electric automobile.
Background
Under the aim of 'double carbon', the electric automobile is further applied and popularized by an energy transformation strategy and the construction of a smart city. In 2030, the stock of Chinese electric vehicles is estimated to be 8000 thousands of vehicles, and the existing urban charging facilities cannot meet the rapidly-increasing electric vehicle charging requirements. With the continuous development of electric vehicles, the proportion of charging loads to the total load of a power distribution network is increased continuously, but the time-space characteristics of the charging loads and the conventional loads (other loads of the power distribution network except the charging loads of the electric vehicles) are obviously different, so that the planning result is unbalanced with the actual demand, and the problem of redundant capacity of a charging station is caused.
Disclosure of Invention
The invention aims to provide a method for analyzing the voltage stability of a power distribution network containing an electric automobile, and solves the problem of redundant capacity of a charging station caused by unbalance of a planning result and an actual demand.
The invention adopts the technical scheme that the voltage stability analysis method of the power distribution network containing the electric automobile is implemented according to the following steps:
step 1, establishing a trip chain model considering the correlation between destination types and time, and generating an electric vehicle charging load scene set by combining Monte Carlo simulation;
step 2, establishing a conventional load generation model considering node spatial correlation, and generating a conventional load scene set;
step 3, constructing a potential small-probability extreme load scene and a typical load scene based on the electric vehicle charging load scene set and the conventional load scene set;
and 4, analyzing the voltage stability of the power distribution network after the electric automobile is connected based on the rapid voltage stability index and the voltage stability margin.
The invention is also characterized in that:
the specific process of the step 1 is as follows:
step 1.1, carrying out statistical analysis on the travel data of the electric automobile to obtain the probability distribution of characteristic variables representing a travel chain;
step 1.2, when the type of the current position of the electric automobile and the current time are known, calculating the conditional probability p (l) of going to the next destinationnext|lcur,tcur) Establishing a trip chain model considering the correlation between the destination type and the time;
and step 1.3, based on Monte Carlo simulation, carrying out travel simulation on a plurality of electric vehicles to form a travel chain, and calculating the charging load of the electric vehicles at each node.
The electric automobile trip data in step 1.1 includes: initial departure location LdInitial departure time TdTime t of traveldDestination LeStopping time tpThe travel number N and the sunrise line number N, wherein the sunrise line number represents the userHow many times a day goes out; the travel progression represents the number of destinations passed by the user per trip.
Step 1.2 calculate the conditional probability p (l) of a departure to the next destinationnext|lcur,tcur) The process is as follows: acquiring function area travel conditional probability p (l) considering destination type and time correlationnext|lcur,tcur) By using LcurAnd LnextIndicating the current location and the next destination, TcurWhen the departure time of the current position is shown, the current position is lcurDeparture time of tcurWhen the destination of the next trip is lnextThe probability of (c) is:
Figure BDA0003482099860000031
s.t.0≤tcur≤96
in the formula Icur、lnextFor the current position and the next destination, t, in the experimental areacurIs the current time, Tra1And Tra is the number of strokes satisfying the condition;
the specific process of the step 1.3 is as follows:
step 1.3.1, numbering bus nodes of a power distribution network system, and defining the number of electric vehicles as M;
step 1.3.2, defining a variable j and initializing to 0;
step 1.3.3, let j equal to j +1, extract the jth electric vehicle EVjGenerating a jth electric vehicle EV according to a trip chain model by using the initial state of charge (SOC), the number of sunrise trips, the travel progression, the initial trip time, the initial departure point and the stopping timejTrip chain Ttrip，TtripIs composed of
Figure BDA0003482099860000032
Dimension row vector, comprising
Figure BDA0003482099860000033
A stage stroke;
step 1.3.4, planning an optimal driving path based on Floyd algorithm and calculating EV of the electric vehiclejState of charge at k-node
Figure BDA0003482099860000034
Expressed as:
Figure BDA0003482099860000035
in the formula (3), QcIndicating that the battery charge before departure is equal to the battery capacity,
Figure BDA0003482099860000036
representing the state of charge before departure, and the product of the unit power consumption e and the travel distance d represents the distance power consumption;
step 1.3.5, set threshold S for starting charging2If, if
Figure BDA0003482099860000037
Calculating the charging quantity deltas of the jth electric automobile at the k node at the time tj,kAnd charging power Pj,k(t), expressed as:
Figure BDA0003482099860000038
wherein the charging time period is
Figure BDA0003482099860000039
Is the time to reach node k;
Pj,k(t) containing slow charging power P1And fast charging power P2Calculating the parking time at node k
Figure BDA00034820998600000310
Time t required for charging to full charge in slow charging modec,1Time t required for charging to full charge in quick charge modec,2Charging power Pj,k(t), expressed as:
Figure BDA0003482099860000041
Figure BDA0003482099860000042
setting a charging threshold S in consideration of mileage-anxiety and influence of charging/discharging depth1、S2I.e. SOC is less than or equal to S2Charging is started, SOC is greater than or equal to S1Ending the charging;
Figure BDA0003482099860000043
continuing to drive until the whole trip chain is completed;
step 1.3.6, judging whether j is equal to M, if so, executing step 1.3.7, otherwise, returning to step 1.3.3;
step 1.3.7, calculating charging power of each node at each moment
Figure BDA0003482099860000044
I.e. the load;
Figure BDA0003482099860000045
m represents the number of EVs charged at node k,
and 1.3.8, performing simulation for multiple times to generate a scene of a mass charging load.
The specific process of the step 2 is as follows:
step 2.1, generating a countermeasure network by adopting conditional Wasserstein gradient penalty, wherein the countermeasure network comprises a generator and a discriminator, a condition variable is set to be a date type comprising working days and non-working days, and is represented by 0 (non-working days) and 1 (working days), and the conditional Wasserstein gradient penalty generation countermeasure network based on data driving, namely a conventional load scene generation model, is constructed;
and 2.2, acquiring other loads except the charging load of the electric automobile in each node of the power distribution network as original conventional load data through an ammeter, remolding the original conventional load data of the nodes into a multidimensional matrix input condition Wasserstein gradient punishment to generate a countermeasure network, and generating a massive conventional load scene.
Step 2.2, the process of remolding the original conventional load data of a plurality of nodes into a multidimensional matrix is as follows: and taking the original conventional load data acquired every day as a row and the days for acquiring the original conventional load data as a column to form a multi-dimensional matrix.
The specific process of the step 3 is as follows:
step 3.1, dividing the electric vehicle charging load scene set obtained in the step 1 into a working day charging load scene set and a non-working day charging load scene set, respectively selecting the average value of the charging loads of all nodes as a typical charging scene and selecting the upper boundary of the charging loads of all nodes as an extreme charging scene for the working day charging load scene set and the non-working day charging load scene set;
3.2, dividing the conventional load generation scene set generated in the step 2 into a working daily regular load scene set and a non-working daily regular load scene set, clustering the conventional load generation scene sets by adopting a K-means clustering algorithm, selecting the clustering center of each clustering cluster under the optimal clustering number as a typical conventional load scene of each node, and taking the scene with the maximum peak-valley difference in each clustering cluster as an extreme conventional load scene of each node;
and 3.3, correspondingly superposing the charging load scenes of the nodes corresponding to the working days and the non-working days and the conventional load scenes respectively, taking the scenes which contain the extreme charging scenes or the extreme conventional load scenes as potential small-probability extreme load scenes after superposition, and taking the scenes which do not contain the extreme charging scenes or the extreme conventional load scenes as typical load scenes after superposition.
The specific process of the step 4 is as follows: respectively calculating branch stability and node stability of each potential small-probability extreme load scene and each typical load scene through a rapid voltage stability index and a voltage stability margin:
fast voltage stability indicator FVSIikThe calculation formula is as follows:
Figure BDA0003482099860000051
wherein i and k are the first and last nodes of the line, QkFor receiving reactive power at terminals, ViFor line head end voltage, XikIs the line reactance value, ZikFor the line impedance value, the closer FVSI is to 0, the higher the system voltage stability, conversely, if the FVSI value is larger, the lower the voltage stability, when FVSI is close to 1, the system will lose stability;
the voltage stability margin calculation formula is as follows:
VSM(k)＝Vi 4-4(P(k)Xik-Q(k)Rik)2-4Vi 2(P(k)Rik+Q(k)Xik) (9)
s.t.i,k＝2,3,…,33
in the formula, RikFor the line resistance value, p (k) is the sum of the active load of all nodes except the k node, the active load of the k node, and the active loss of all branches except the k node; q (k) is the sum of the reactive load of all nodes except the k node, the reactive load of the k node and the reactive loss of all branches except the k node, VSM is an important index for judging whether the voltage of the node is close to breakdown, the value of VSM is between 0 and 1, and the smaller VSM is, the more the node reaches the breakdown critical value.
The invention has the beneficial effects that:
according to the voltage stability analysis method of the power distribution network comprising the electric automobile, a travel chain model is established, and the relevance between the destination type and the time is calculated, so that the simulation of the travel behavior is more in line with the actual travel behavior, and the charging load simulation result is more accurate; the established conventional load generation model takes into account the multi-node spatial correlation, so that the conventional load generation result is more real; and finally, analyzing the stability of the branch and the stability of the node based on the rapid voltage stability index and the voltage stability margin index, so that the analysis result is more reliable.
Drawings
FIG. 1 is a flow chart of a voltage stability analysis method for a power distribution network including an electric vehicle according to the present invention;
FIG. 2 is a network diagram of a traffic network and distribution network coupling according to an embodiment of the present invention;
FIG. 3 is a distribution diagram of travel times and travel stage number probability of the electric vehicle in the embodiment of the invention;
FIG. 4 is a probability distribution diagram of an initial travel time of an electric vehicle according to an embodiment of the present invention;
fig. 5 is a travel probability distribution diagram in an embodiment of the present invention, taking into account the correlation between the destination type and the time of day;
FIG. 6 is a probability distribution diagram of a driving distance of an electric vehicle according to an embodiment of the present invention;
FIG. 7 is a diagram of a conventional load experiment data set construction process in an embodiment of the present invention;
FIG. 8 is a probability distribution diagram for a conventional load generation scenario versus a historical scenario in an embodiment of the present invention;
FIG. 9 is a diagram illustrating timing characteristics and coverage capability analysis of a conventional load generation scenario in an embodiment of the present invention;
FIG. 10 is a diagram of a typical, extreme load scenario for a workday in an embodiment of the present invention;
FIG. 11 is a diagram of a non-workday typical, extreme load scenario in an embodiment of the present invention;
FIG. 12 is a graph illustrating fast voltage stabilization indicator results for various scenarios in accordance with an embodiment of the present invention;
FIG. 13 is a graph of voltage stability margin indicator results for various scenarios in an embodiment of the invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention relates to a voltage stability analysis method for a power distribution network of an electric automobile, which is implemented according to the following steps:
step 1, establishing a trip chain model considering the correlation between destination types and time, and generating an electric vehicle charging load scene set by combining Monte Carlo simulation; the specific process is as follows:
step 1.1, carrying out statistical analysis on the travel data of the electric automobile, wherein the travel data of the electric automobile comprises an initial starting place LdInitial departure time TdTime t of traveldDestination LeStopping time tpThe travel number N and the number N of sunrise rows, wherein the number of sunrise rows indicates how many times the user travels every day; the travel progression represents the number of destinations passed by the user during each trip, and the probability distribution of characteristic variables representing a trip chain is obtained;
step 1.2, when the current position type and the current time of the electric automobile are known, obtaining the function area travel condition probability p (l) considering the destination type and the time correlationnext|lcur,tcur) By the use of LcurAnd LnextIndicating the current location and the next destination, TcurWhen the departure time of the current position is shown, the current position is lcurThe departure time is tcurWhen the destination of the next trip is lnextThe probability of (c) is:
Figure BDA0003482099860000081
s.t.0≤tcur≤96
in the formula Icur、lnextFor the current position and the next destination, t, in the experimental areacurIs the current time, Tra1And Tra is the number of strokes that satisfy the condition;
establishing a travel chain model considering the correlation between the destination type and the time according to a travel variable formed by the travel data of the electric vehicle;
step 1.3, based on Monte Carlo simulation, the Ld、Td、td、tpN, n as independently distributed characteristic variables, randomly drawn and then based on p (l)next|lcur,tcur) Selecting a destination of each stage of travel, performing travel simulation on a plurality of electric vehicles to form a travel chain, and calculating the charging load of the electric vehicles at each node; the specific process is as follows:
step 1.3.1, numbering bus nodes of a power distribution network system, and defining the number of electric vehicles as M;
step 1.3.2, defining a variable j and initializing to 0;
step 1.3.3, let j equal j+1, extract the jth electric vehicle EVjGenerating a jth electric vehicle EV according to a trip chain model by using the initial state of charge (SOC), the number of sunrise trips, the travel progression, the initial trip time, the initial departure point and the stopping timejTrip chain Ttrip，TtripIs composed of
Figure BDA0003482099860000082
Dimension row vectors, collectively comprising
Figure BDA0003482099860000083
A step stroke;
first, the
Figure BDA0003482099860000084
When going out again
Figure BDA0003482099860000085
Step travel chain
Figure BDA0003482099860000086
As shown in equation (2), each EV produces n trip chains per day as shown in equation (2).
Figure BDA0003482099860000087
Step 1.3.4, planning an optimal driving path based on the Floyd algorithm (for example, assuming that 4 routes are total from the functional area A to the functional area B, the selected route is the route with the shortest distance in the 4 routes), and calculating the EV of the electric vehiclejState of charge at k-node
Figure BDA0003482099860000091
The ratio of the result obtained by subtracting the power consumption of the route from the battery power before departure to the battery capacity is expressed as follows:
Figure BDA0003482099860000092
in the formula (3), QcTo representThe battery charge before departure is equal to the battery capacity,
Figure BDA0003482099860000093
representing the state of charge before departure, and the product of the unit power consumption e and the travel distance d represents the distance power consumption;
step 1.3.5, set threshold S for starting charging2If, if
Figure BDA0003482099860000094
Calculating the charging quantity deltas of the jth electric automobile at the k node at the time tj,kAnd charging power Pj,k(t), expressed as:
Figure BDA0003482099860000095
wherein the charging time period is
Figure BDA0003482099860000096
Is the time to reach node k;
Pj,k(t) containing slow charging power P1And a fast charging power P2Calculating the parking time at node k
Figure BDA0003482099860000097
Time t required for charging to full charge in slow charging modec,1Time t required for charging to full charge in quick charge modec,2Charging power Pj,k(t), expressed as:
Figure BDA0003482099860000098
Figure BDA0003482099860000099
setting a charging threshold S in consideration of mileage-anxiety and influence of charging/discharging depth1、S2I.e. SOC is less than or equal to S2Charging is started, and the SOC is more than or equal to S1When the charging is finished;
Figure BDA00034820998600000910
continuing to drive until the whole trip chain is completed;
step 1.3.6, judging whether j is equal to M, if so, executing step 1.3.7, otherwise, returning to step 1.3.3;
step 1.3.7, calculating the charging power of each node at each moment
Figure BDA00034820998600000911
I.e. the load;
Figure BDA0003482099860000101
m represents the number of EVs charged at node k,
and 1.3.8, performing simulation for multiple times to generate a scene of a mass charging load.
Step 2, establishing a conventional load generation model considering node spatial correlation, and generating a conventional load scene set; the specific process is as follows:
step 2.1, generating a countermeasure network by adopting conditional Wasserstein gradient penalty, wherein the countermeasure network comprises a generator and a discriminator, a condition variable is set to be a date type comprising working days and non-working days, and is represented by 0 (non-working days) and 1 (working days), and the conditional Wasserstein gradient penalty generation countermeasure network based on data driving, namely a conventional load scene generation model, is constructed;
and 2.2, acquiring other loads except the charging load of the electric automobile in each node of the power distribution network as original conventional load data through an ammeter, taking the original conventional load data acquired every day as a row, and taking the number of days for acquiring the original conventional load data as a column to form a multi-dimensional matrix. And inputting the multidimensional matrix into a condition Wasserstein gradient penalty to generate a countermeasure network, and generating a massive conventional load scene.
Step 3, constructing a potential small-probability extreme load scene and a typical load scene based on the electric vehicle charging load scene set and the conventional load scene set; the specific process is as follows:
step 3.1, dividing the electric vehicle charging load scene set obtained in the step 1 into a working day charging load scene set and a non-working day charging load scene set, respectively selecting the average value of the charging loads of all nodes as a typical charging scene and selecting the upper boundary of the charging loads of all nodes as an extreme charging scene for the working day charging load scene set and the non-working day charging load scene set;
3.2, dividing the conventional load generation scene set generated in the step 2 into a working daily regular load scene set and a non-working daily regular load scene set, clustering the conventional load generation scene sets by adopting a K-means clustering algorithm, selecting the clustering center of each clustering cluster under the optimal clustering number as a typical conventional load scene of each node, and taking the scene with the maximum peak-valley difference in each clustering cluster as an extreme conventional load scene of each node;
and 3.3, correspondingly superposing the charging load scenes of the nodes corresponding to the working days and the non-working days and the conventional load scenes respectively, taking the scenes which contain the extreme charging scenes or the extreme conventional load scenes as potential small-probability extreme load scenes after superposition, and taking the scenes which do not contain the extreme charging scenes or the extreme conventional load scenes as typical load scenes after superposition.
Step 4, analyzing the voltage stability of the power distribution network after the electric automobile is connected based on the rapid voltage stability index and the voltage stability margin; the specific process is as follows: respectively calculating branch stability and node stability of each potential small-probability extreme load scene and each typical load scene through a rapid voltage stability index and a voltage stability margin:
fast voltage stability indicator FVSIikThe calculation formula is as follows:
Figure BDA0003482099860000111
wherein i and k are the first and last nodes of the line, QkFor receiving reactive power at terminals, ViFor line head end voltage, XikIs the line reactance value, ZikFor line impedance values, the closer to 0 the FVSI, the system voltage stabilityThe higher, conversely, the lower the voltage stability if the FVSI value is larger, and the system will lose stability when FVSI approaches 1;
the voltage stability margin calculation formula is as follows:
VSM(k)＝Vi 4-4(P(k)Xik-Q(k)Rik)2-4Vi 2(P(k)Rik+Q(k)Xik) (9)
s.t.i,k＝2,3,…,33
in the formula, RikFor the line resistance value, p (k) is the sum of the active load of all nodes except the k node, the active load of the k node, and the active loss of all branches except the k node; q (k) is the sum of the reactive load of all nodes except the k node, the reactive load of the k node and the reactive loss of all branches except the k node, VSM is an important index for judging whether the voltage of the node is close to breakdown, the value of VSM is between 0 and 1, and the smaller VSM is, the more the node reaches the breakdown critical value.
Examples
1. Charging load scene production of electric automobile
The embodiment selects a coupling network obtained by coupling a certain urban actual traffic network with an IEEE 33 node standard system as a basis, as shown in figure 2.
A travel chain model considering the relevance between the destination type and the time is established based on the 2017 family travel survey data in a certain area, and the travel of the user is simulated. The probability distribution of the daily travel times and the travel progression is shown in fig. 3, and as can be seen from a) a sub-graph a), the daily travel times can reach 9 times at most, and are generally concentrated in 1-4 times; as can be seen from sub-graph b), the number of the travel stages can reach 11 stages, and is generally concentrated on 2-7 stages.
As shown in table 1, for example, the initial departure point type ratio indicates that the probability of departure from a residential area is 61.03%, the probability of departure from a commercial area is 24.54%, and the probability of departure from a work area is 14.43% when a user travels on a weekday; the probability of departure from residential areas and business areas in non-working days is obviously increased compared with working days, and the probability of departure from working areas is obviously reduced.
TABLE 1
Figure BDA0003482099860000121
The initial travel time probability distribution at different initial departure points is shown in fig. 4. And counting initial departure time discrete probability distribution of users from each functional area by adopting 1h time resolution through a probability quality function, and utilizing a probability density function to serialize a statistical result so as to obtain the departure probability at any moment.
From the sub-graph a), it can be known that the peak time of the working day from the residential area is concentrated in 6:00 to 10:00, the working area is basically the same as the residential area, the commercial area is concentrated in 7:00 to 12:00, and the peak time is slightly later than the residential area and the working area; as shown in sub-graph b), the peak hours of the non-working day from the residential area and the working area are concentrated in the range of 7:00 to 17:00, the peak hours from the commercial area are concentrated in the range of 8:00 to 12:00, and the time is obviously later than the working day, wherein the non-working day is more entertaining or played and more travels to and from the home and the commercial area. The comparison shows that the data conclusion conforms to the actual life cognition.
With the 1h time resolution, the probability distribution of departure time of the user from each functional area is counted by the PMF, so the statistical results are serialized by the PDF, and the result is shown in fig. 5. In each sub-graph, at the same time, the sum of 3 solid lines is 1, and the sum of 3 dashed lines is 1, that is, the sum of probabilities of going to 3 functional regions is 1 (the case of not going out is considered as going to a functional region of the same type as the current position). From the sub-graph a), the probability that the residential area goes to the working area is highest in the early peak period of the working day (6:00-8:00), the probability that the residential area goes to the business area is second, the probability that the residential area goes to the business area is lowest, and the probability that the residential area goes to the residential area is increased continuously after 20: 00; the probability of the non-working day going to the working area is obviously reduced compared with the working day; from sub-graph b), the probability of the working area going to the working area is highest, the probability of going to the business area is lowest, the probability of going to the residential area is highest, the probability of going to the business area is lowest, the probability of going to the residential area is lowest, and the probability of going to the residential area is lowest during the late peak period (17:00-19: 00); the variation trend of the non-working days is not much different from the working days; from sub-graph c), the business district has the highest probability of going to the work district, the lowest probability of going to the residential district, the highest probability of going to the business, the highest probability of going to the residential district, the lowest probability of going to the business district, and the lowest probability of going to the work district, in the early peak period (6:00-8:00) of the working day; the non-working day and the working day are basically consistent in trend.
And (4) checking the travel type proportion and daily mileage of the generated trip chain to verify the effectiveness of the trip chain model established by the invention. 12000 electric automobiles (after a plurality of experiments, the effect tends to be stable when 12000 electric automobiles are carried out) are simulated to operate within 360 continuous days. 12000 EV was included in the set experiment region, and the charging efficiency eta wasc90%, battery capacity Qc40kWh, 0.188kWh/km power consumption per mileage, two-stage charging power, wherein the slow charging power P is used13.7kW, fast charging power P230kW, threshold S is completed to charge10.9, threshold S for starting charging2＝0.2。
The simulation result of the journey type proportion and the comparison result of the real data are shown in table 2, the relative error mean values of the trip chain simulation result considering the correlation are 3.88% and 5.94%, the relative error mean values of each variable independent modeling are 13.42% and 16.32%, and the quality of the trip chain model established by the method is obviously improved.
TABLE 2
Figure BDA0003482099860000141
The simulation results of the mileage of the trip are shown in fig. 6. Compared with independent modeling, the probability distribution result of the trip chain model considering the correlation is closer to real data; and quantifying the average deviation and similarity degree of the simulation result and real data by Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC), wherein the RMSE of the trip chain model takes the working day and the correlation into accountCAnd PCCCRMSE modeled independently for each variable at 8.74% and 98.87%IAnd PCCI16.27% and 88.12% non-workday instrumentsRMSE of relevance trip chain modelCAnd PCCCRMSE modeled independently for each variable at 2.30% and 98.76%IAnd PCCI12.52% and 89.03%, further verifying the accuracy of the relevance-considered trip chain model for simulating user trips.
2. Conventional load scenario generation
The experimental dataset construction process is shown in figure 7. The method includes the steps that data of the intelligent electric meters in a region from 2009 to 2010 in 12 months are adopted in an experiment, the data include load data of 6435 users for 730 days, the sampling period is 30min, 48 load values are included in each day, the load data of 32 nodes in the experiment region for 1 day are reshaped into a matrix of 48 multiplied by 32 load values serving as a condition Wasserstein gradient penalty to generate confrontation network input, 730 groups of data samples are used, and the data are divided into a training set and a testing set according to the proportion of 4: 1. 5000 sets of data were generated containing 3570 weekdays and 1430 non-weekdays.
The high-quality multi-node conventional load generation scene has the following characteristics: 1) the probability distribution characteristics are similar to those of the historical scene; 2) the method can embody the conventional load time sequence characteristics of the single node and can effectively cover unknown scenes.
An Empirical Cumulative Distribution Function (ECDF) is used to analyze the probability distribution characteristics of the generated scene, and the ECDF of each node of the generated scene set and the historical scene set is shown in fig. 8. Compared with the situation generation of each node independently, the ECDF curve of the multi-node combined situation generation data set is almost highly fitted, the generated situation has the probability distribution characteristic similar to that of the historical situation, the model has the learning capacity of realizing the probability modeling of the situation, and the probability characteristic, the change trend, the short-term characteristic (peak-valley change) and the long-term characteristic (power probability distribution) of the historical data can be captured more effectively.
The coverage effect of the generated scene on the unknown scene is shown in fig. 9, and it can be known from the graph that the load mean curve of the generated scene can better reflect the curve characteristics (increase and decrease trend, peak-valley change) of the historical scene set, is similar to the load mean curve of the historical scene, has basically consistent time sequence change, and has stronger time sequence similarity with the historical data; the upper edge of each node conventional load generation scene is higher, the lower edge is lower, the interval formed by the upper edge and the lower edge is larger, the coverage range is wider, the test set scene can be wrapped completely, and the unknown scene coverage capability is stronger.
Fig. 10 is a workday scenario, combination 1 represents a workday typical charging scenario superimposed with a workday typical normal load, combination 2 represents a workday extreme charging scenario superimposed with a workday typical normal load, combination 3 represents a workday typical charging load superimposed with a workday extreme normal load, and combination 4 represents a workday extreme charging load superimposed with a workday extreme normal load. EV charging loads of electric vehicles significantly increase the distribution network load during the early morning hours and further increase the late peak (18: 00-22: 00) load, with charging loads increasing the distribution network load by 20.12% (EL-2) to 92.63% (EL-7).
Fig. 11 is a non-workday scenario, where combination 1 represents a non-workday typical charging scenario superimposed on a non-workday typical normal load, combination 2 represents a non-workday extreme charging scenario superimposed on a non-workday typical normal load, combination 3 represents a non-workday typical charging load superimposed on a non-workday extreme normal load, and combination 4 represents a non-workday extreme charging load superimposed on a non-workday extreme normal load. The charging load significantly increases the early morning and late peak hours loads, substantially consistent with the workday effects, by 22.83% (EL-26) to 93.72% (EL-43) of the distribution grid load, compared to the workday, the charging load increases in proportion to the total load.
The results of the respective scenario FVSI indexes are shown in fig. 12. The figure shows that the FVSI value of each branch circuit before the charging load of the electric automobile is superposed is less than 0.6, most branch circuits are stabilized between 0 and 0.3, the voltage of the branch circuit of the power distribution network is in a stable state as a whole, the load is larger and the fluctuation is stronger compared with the working day scene, the FVSI index is gradually increased compared with the working day scene, and unstable branch circuits are concentrated in branch circuits of 2, 5, 7, 8, 9, 27 and the like; due to the increase of the EV charging load, the FVSI of each branch is increased, the maximum value reaches 0.998(EL-12 scene, branch 5), and instability is approached, so that important consideration is needed; taking branch 2 as an example, comparing 44 load scenes, and after charging loads are calculated, the FVSI value of each scene in working days and non-working days is changed greatly, the fluctuation is stronger, and the index characteristic that the working day scene is generally lower than the non-working day scene is changed to a certain extent; the unstable branches are concentrated in branches 2, 3, 4, 5, 7, 8, 9, 27, 28, 29, 30 and the like after the charging load is considered, and the unstable branch ratio is increased when only the conventional load is considered.
The node VSM indexes of each scene are as shown in fig. 13, and after the charging load is switched in, the VSM value of each node is significantly reduced, that is, the voltage stability is reduced; the voltage stability of different nodes is different, and the change of the nodes 21, 22, 23, 24, 25 and the like before and after being connected to the charging load is smaller, namely the nodes are more stable. By synthesizing VSM results of all nodes in all scenes, the weakest nodes are nodes 7, 14, 17, 18, 29, 32, 33 and the like, and VSM indexes of other nodes are higher than 0.243 and are relatively stable.
By the mode, the invention discloses a method for analyzing the voltage stability of the power distribution network containing the electric automobile, and load scene generation of the space correlation among multi-node loads is considered.

Claims (9)

1. The method for analyzing the voltage stability of the power distribution network containing the electric automobile is characterized by comprising the following steps:
step 1, establishing a trip chain model considering the correlation between destination types and time, and generating an electric vehicle charging load scene set by combining Monte Carlo simulation;
step 2, establishing a conventional load generation model considering node spatial correlation, and generating a conventional load scene set;
step 3, constructing a potential small-probability extreme load scene and a typical load scene based on the electric vehicle charging load scene set and the conventional load scene set;
and 4, analyzing the voltage stability of the power distribution network after the electric automobile is connected based on the rapid voltage stability index and the voltage stability margin.
2. The analysis method for the voltage stability of the distribution network containing the electric vehicles based on the multi-node load generation as claimed in claim 1, wherein the specific process of the step 1 is as follows:
step 1.1, carrying out statistical analysis on the travel data of the electric automobile to obtain the probability distribution of characteristic variables representing a travel chain;
step 1.2, when the type of the current position of the electric automobile and the current time are known, calculating the conditional probability p (l) of going to the next destinationnext|lcur,tcur) Establishing a trip chain model considering the correlation between the destination type and the time;
and step 1.3, based on Monte Carlo simulation, carrying out travel simulation on a plurality of electric vehicles to form a travel chain, and calculating the charging load of the electric vehicles at each node.
3. The analysis on the voltage stability of the distribution network containing the electric vehicles based on the multi-node load generation as claimed in claim 1, wherein the electric vehicle travel data in step 1.1 includes: initial departure location LdInitial departure time TdTime t of traveldDestination LeStopping time tpThe travel number N and the number N of sunrise rows, wherein the number of sunrise rows indicates how many times the user travels every day; the travel progression represents the number of destinations passed by the user per trip.
4. The multi-node load generation-based voltage stability analysis method for distribution network of electric vehicles according to claim 1, wherein step 1.2 is implemented by calculating conditional probability p (l) of departure to next destinationnext|lcur,tcur) The process is as follows: acquiring function area travel conditional probability p (l) considering destination type and time correlationnext|lcur,tcur) By the use of LcurAnd LnextIndicating the current location and the next destination, TcurWhen the departure time of the current position is shown, the current position is lcurThe departure time is tcurWhen the destination of the next trip is lnextThe probability of (c) is:
Figure RE-FDA0003671940880000021
in the formula Icur、lnextFor the current position and the next destination, t, in the experimental areacurIs the current time, Tra1And Tra is the number of strokes that satisfy the condition.
5. The analysis on the voltage stability of the distribution network containing the electric vehicles based on the multi-node load generation as claimed in claim 2 is characterized in that the step 1.3 comprises the following specific processes:
step 1.3.1, numbering bus nodes of a power distribution network system, and defining the number of electric vehicles as M;
step 1.3.2, defining a variable j and initializing to 0;
step 1.3.3, let j equal to j +1, extract the jth electric vehicle EVjGenerating a jth electric vehicle EV according to a trip chain model by using the initial state of charge (SOC), the number of sunrise trips, the travel progression, the initial trip time, the initial departure point and the stopping timejTrip chain Ttrip，TtripIs composed of
Figure RE-FDA0003671940880000022
Dimension row vector, comprising
Figure RE-FDA0003671940880000023
A step stroke;
step 1.3.4, planning an optimal driving path based on Floyd algorithm and calculating EV of the electric vehiclejState of charge at k-node
Figure RE-FDA0003671940880000024
Expressed as:
Figure RE-FDA0003671940880000031
in the formula (3), QcIndicating that the battery charge before the departure is equal to the battery capacity,
Figure RE-FDA0003671940880000032
representing the state of charge before departure, and the product of the unit power consumption e and the travel distance d represents the distance power consumption;
step 1.3.5, set threshold S for starting charging2If, if
Figure RE-FDA0003671940880000033
Calculating the charging quantity deltas of the jth electric automobile at the k node at the time tj,kAnd charging power Pj,k(t), expressed as:
Figure RE-FDA0003671940880000034
wherein the charging time period is
Figure RE-FDA0003671940880000035
Figure RE-FDA0003671940880000036
Is the time to reach node k;
Pj,k(t) containing slow charging power P1And fast charging power P2Calculating the parking time at node k
Figure RE-FDA0003671940880000037
Time t required for charging to full charge in slow charging modec,1Time t required for charging to full charge in quick charge modec,2Charging power Pj,k(t), expressed as:
Figure RE-FDA0003671940880000038
Figure RE-FDA0003671940880000039
setting a charging threshold S in consideration of mileage concern and influence of charging/discharging depth1、S2I.e. SOC is less than or equal to S2Charging is started, and the SOC is more than or equal to S1Ending the charging;
Figure RE-FDA00036719408800000310
continuing to drive until the whole trip chain is completed;
step 1.3.6, judging whether j is equal to M, if so, executing step 1.3.7, otherwise, returning to step 1.3.3;
step 1.3.7, calculating the charging power of each node at each moment
Figure RE-FDA00036719408800000311
I.e. the load;
Figure RE-FDA00036719408800000312
m represents the number of EVs charged at node k,
and 1.3.8, performing simulation for multiple times to generate a scene of a mass charging load.
6. The analysis on the voltage stability of the distribution network containing the electric vehicles based on the multi-node load generation as claimed in claim 1 is characterized in that the specific process of the step 2 is as follows:
step 2.1, generating a countermeasure network by adopting conditional Wasserstein gradient penalty, wherein the countermeasure network comprises a generator and a discriminator, a condition variable is set to be a date type comprising working days and non-working days, and is represented by 0 (non-working days) and 1 (working days), and the conditional Wasserstein gradient penalty generation countermeasure network based on data driving, namely a conventional load scene generation model, is constructed;
and 2.2, acquiring other loads except the charging load of the electric automobile in each node of the power distribution network as original conventional load data through an ammeter, remolding the original conventional load data of the nodes into a multidimensional matrix input condition Wasserstein gradient punishment to generate a countermeasure network, and generating a massive conventional load scene.
7. The analysis on the voltage stability of the distribution network of the electric vehicle based on the multi-node load generation as recited in claim 1, wherein the step 2.2 of remolding the original normal load data of the plurality of nodes into the multi-dimensional matrix comprises the following steps: and taking the original conventional load data acquired every day as a row and the days for acquiring the original conventional load data as a column to form a multi-dimensional matrix.
8. The analysis method for the voltage stability of the distribution network comprising the electric automobile based on the multi-node load generation as claimed in claim 1 is characterized in that the specific process of the step 3 is as follows:
step 3.1, dividing the electric vehicle charging load scene set obtained in the step 1 into a working day charging load scene set and a non-working day charging load scene set, respectively selecting the average value of the charging loads of all nodes as a typical charging scene and selecting the upper boundary of the charging loads of all nodes as an extreme charging scene for the working day charging load scene set and the non-working day charging load scene set;
3.2, dividing the conventional load generation scene set generated in the step 2 into a working daily regular load scene set and a non-working daily regular load scene set, clustering the conventional load generation scene sets by adopting a K-means clustering algorithm, selecting the clustering center of each clustering cluster under the optimal clustering number as a typical conventional load scene of each node, and taking the scene with the maximum peak-valley difference in each clustering cluster as an extreme conventional load scene of each node;
and 3.3, correspondingly superposing the charging load scenes of the nodes corresponding to the working days and the non-working days and the conventional load scenes respectively, taking the scenes which contain the extreme charging scenes or the extreme conventional load scenes as potential small-probability extreme load scenes after superposition, and taking the scenes which do not contain the extreme charging scenes or the extreme conventional load scenes as typical load scenes after superposition.
9. The analysis on the voltage stability of the distribution network containing the electric vehicles based on the multi-node load generation as claimed in claim 1 is characterized in that the specific process of step 4 is as follows: respectively calculating branch stability and node stability of each potential small-probability extreme load scene and each typical load scene through a rapid voltage stability index and a voltage stability margin:
fast voltage stability indicator FVSIikThe calculation formula is as follows:
Figure RE-FDA0003671940880000051
wherein i and k are the first and last nodes of the line, QkFor receiving reactive power at terminals, ViFor line head end voltage, XikIs the line reactance value, ZikFor the line impedance value, the closer FVSI is to 0, the higher the system voltage stability, conversely, if the FVSI value is larger, the lower the voltage stability, when FVSI is close to 1, the system will lose stability;
the voltage stability margin calculation formula is as follows:
Figure RE-FDA0003671940880000052
in the formula, RikFor the line resistance value, p (k) is the sum of the active load of all nodes except the k node, the active load of the k node, and the active loss of all branches except the k node; q (k) is the sum of the reactive load of all nodes except the k node, the reactive load of the k node and the reactive loss of all branches except the k node, VSM is an important index for judging whether the voltage of the node is close to breakdown, the value of VSM is between 0 and 1, and the smaller VSM is, the more the node reaches the breakdown critical value.
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