CN113468719A - Power distribution network electric vehicle acceptance capacity evaluation method based on data driving - Google Patents

Power distribution network electric vehicle acceptance capacity evaluation method based on data driving Download PDF

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CN113468719A
CN113468719A CN202110615882.2A CN202110615882A CN113468719A CN 113468719 A CN113468719 A CN 113468719A CN 202110615882 A CN202110615882 A CN 202110615882A CN 113468719 A CN113468719 A CN 113468719A
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distribution network
electric vehicle
power distribution
electric
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陈忠华
陈致远
杨翾
陈琳
黄帅
叶刚进
王骏海
尹建兵
叶奕
姜奕晖
徐强
商佳宜
俞容江
高振宇
王梦涵
孙婧卓
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Hangzhou Electric Power Design Institute Co ltd
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention discloses a power distribution network electric automobile acceptance capacity evaluation method based on data driving. The method can scientifically evaluate the acceptance capacity of the electric automobiles of the power distribution network for the urban power distribution network accessed by the future large-scale electric automobiles. Firstly, probability modeling is carried out on 3 characteristic variables of three types of urban electric vehicle charging stations by a nonparametric kernel density estimation method, namely charging quantity, charging starting time and charging duration. And secondly, modeling three types of electric vehicle charging loads through Monte Carlo rejection-acceptance sampling. On the basis, a power distribution network electric vehicle admission capacity evaluation model is established by taking the maximum number of electric vehicle admission in the urban power distribution network as a target, can be applied to the actual power distribution network regulation and control of the electric vehicle access scale, so that the safety and reliability of the system are ensured, and can also be used as a reference for future power distribution network planning.

Description

Power distribution network electric vehicle acceptance capacity evaluation method based on data driving
Technical Field
The invention belongs to the technical field of operation, simulation, analysis and scheduling of a power system, and particularly relates to a power distribution network electric vehicle admission capacity evaluation method.
Background
Electric Vehicles (EVs) are actively being developed as a more environmentally friendly and economical alternative to conventional vehicles. In addition to higher efficiency compared to internal combustion engines, electric vehicles can also charge electrical energy generated from renewable energy sources to further reduce greenhouse gas emissions.
According to the related new energy automobile industry development planning, in 2025 years, the average power consumption of a new pure electric passenger automobile is reduced to 12.0 kilowatt-hour/hundred kilometers, the new energy automobile sales volume reaches about 20% of the total new automobile sales volume, and in 2035 years, a pure electric automobile becomes the mainstream of the new automobile sales volume, the automobile in the public field is fully electrified, the fuel cell automobile is commercially applied, the highly automatic driving automobile is applied in a large scale, and the energy-saving emission-reducing level and the social operation efficiency are effectively promoted to be improved.
The large-scale deployment of electric vehicles will have a significant impact on future power grids. The large amount of energy required by the electric automobile, together with uncertainty of charging time and duration, brings serious technical and economic challenges to the power quality of a power distribution network, local power supply capacity, safe and stable operation and the like. How to evaluate the acceptance capability of the electric automobile of the power distribution network is one of the technical problems to be solved urgently at present.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a power distribution network electric automobile acceptance capacity evaluation method.
The technical scheme adopted by the invention is as follows:
a power distribution network electric vehicle admission capacity evaluation method based on data driving comprises the following steps:
s1, dividing the urban electric vehicles into three types of buses, taxis and private cars, performing probability modeling on 3 characteristic variables in the charging station of each type of urban electric vehicles by using an nonparametric kernel density estimation method, and establishing a charging amount PcAnd a time T for starting chargingsAnd a charging period TcA probability density model of (a);
s2, modeling the load of each type of electric automobile by using a Monte Carlo (MC) rejection-acceptance sampling method according to the probability density model obtained in S1, accumulating and averaging the charging load curves of each electric automobile belonging to the same type to obtain the expected charging power value of each type of single electric automobile in each time interval, and carrying out weighted averaging on the expected charging power values of the three types of single electric automobiles in each time interval according to the ratios of the three types of electric automobiles to obtain the expected charging power value of each type of single electric automobile in each time interval;
s3, aiming at a power distribution network in a target evaluation area, proportionally and supposing that the electric automobiles are connected into the power distribution network according to the distribution of actual charging piles in the area, and calculating the charging power of each electric automobile in the power distribution network according to the expected charging power value of each period of the single electric automobile which is not in the type of distinguishing; the maximum access quantity of the electric automobiles in the power distribution network is an objective function, meanwhile, an optimization model is constructed by taking the operation safety of the power distribution network as a constraint, and the optimization model is solved to obtain the electric automobile acceptance capacity of the power distribution network.
Preferably, in S1, the kernel function is a gaussian kernel function when the non-parametric kernel density estimation method is used.
Preferably, in S2, the following three assumptions are made when modeling each type of electric vehicle load:
1) the charging modes of all types of electric automobiles are regarded as constant-current charging, and the influence of other random factors is ignored;
2) the total load of the electric automobile is the superposition of the charging loads of the independent vehicles, namely the charging loads of different vehicle types at the same time are summed;
3) electric vehicles which are charged in the power distribution network are supposed to be of only three types, namely electric buses, electric taxis and electric private cars.
Preferably, in S2, the expected charging power value calculation method for each period of the individual electric vehicle of the type not distinguished is as follows:
s21, modeling each type of electric vehicle load by using a Monte Carlo rejection-acceptance sampling method, and extracting a charging starting time and a charging duration according to probability density distribution of 3 characteristic variables obtained in S1 during modeling so as to obtain a daily charging load curve of a single electric vehicle;
s22, accumulating daily charging load curves of each electric vehicle in the type aiming at each type of electric vehicle to obtain a total charging load curve taking days as a calculation unit, dividing the total charging load curve by taking hours as time intervals, and averaging according to the total number N of the electric vehicle types to obtain the expected charging power value of each type of single electric vehicle in each hour; charging power expected value L of single electric automobile with type f in ith hourf,iThe calculation formula is as follows:
Figure BDA0003097994640000021
in the formula:
Figure BDA0003097994640000022
charging power of the jth electric automobile with the type f at the ith hour;
s23, calculating the expected charging power value of each type of electric vehicle in each time period in a weighted average mode according to the proportion of the three types of electric vehicles in the target evaluation area:
Li＝αLbus,i+βLtaxi,i+γLcar,i
in the formula: l isiThe expected charging power value of a single electric automobile of an undistinguished type in the ith hour; l isbus,i、Ltaxi,i、Lcar,iCharging expected values L of single bus, taxi and private car respectivelyf,iAnd alpha, beta and gamma are the proportion of three electric automobiles, namely a bus, a taxi and a private car respectively.
Preferably, in S3, the construction optimization model constructed for the electric vehicle acceptance of the power distribution network is as follows:
the objective function of the optimization model is:
EVAC＝max NEV
in the formula, NEVFor the number of electric vehicles accessed in the power grid, EVAC represents the electric vehicle acceptance capacity of the power distribution grid;
the power distribution network operation safety constraints comprise power balance constraints, node voltage constraints, line current constraints and transformer capacity constraints.
Further, the power balance constraint is:
the linearized DistFlow equation is adopted to describe the node power balance relation of the power distribution system, namely, the condition that
Figure BDA0003097994640000031
In the formula Pij、QijRespectively the active power and the reactive power flow of a line l (i, j) between a node i and a node j;
Figure BDA0003097994640000032
respectively the active power and the reactive power generation capacity of the node j generator set;
Figure BDA0003097994640000033
respectively the active power and the reactive demand of the load at the node j; pEV,jThe node j is the charging load of the electric vehicle; n is a radical ofEV,jThe number of electric vehicles charging node j; and pi (j) is the total number of branches connected by the node j.
Further, the node voltage constraint is:
Figure BDA0003097994640000034
Ui l≤Ui≤Ui u
in the formula, rij、xijRespectively the resistance and reactance of the line l (i, j), zijIs the impedance of line l (i, j); u shapeiIs the square of the voltage amplitude of the node i;
Figure BDA0003097994640000041
respectively an upper limit value and a lower limit value of the voltage of the node i; u shape0To balance the node voltages.
Further, the line flow constraint is:
Pl≤Pij≤Pu
Ql≤Qij≤Qu
in the formula: pl、QlRespectively an active power lower limit and a reactive power flow lower limit of the line l (i, j); pu、QuThe upper limit of the active power and the upper limit of the reactive power flow of the line l (i, j) are respectively.
Further, the transformer capacity constraint is as follows:
PT≤ST
in the formula: pTIs the sum of the loads connected to the transformer, STThe rated capacity of the transformer.
Preferably, the optimization model is solved by programming under Matlab and calling Cplex optimization toolkit.
The invention has the beneficial effects that: the invention provides a power distribution network electric vehicle acceptance assessment method based on data driving, which comprises the steps of firstly carrying out probability modeling on 3 characteristic variables of three types of urban electric vehicle charging stations by a nonparametric kernel density estimation method, namely, a charging amount, charging starting time and charging duration; secondly, modeling three types of electric vehicle charging loads through Monte Carlo rejection-acceptance sampling; on the basis, a power distribution network electric vehicle admission capacity evaluation model is established by taking the maximum number of electric vehicle admission in the urban power distribution network as a target, and the power distribution network electric vehicle admission capacity evaluation model can be applied to the actual power distribution network regulation and control of the electric vehicle access scale so as to ensure the safety and reliability of the system.
Drawings
Fig. 1 is a schematic diagram of monte carlo reject-accept sampling.
FIG. 2 is a flow chart for electric vehicle load modeling by Monte Carlo.
Fig. 3 is a Hangzhou regional distribution network topology diagram.
Fig. 4 is a typical daily load graph.
Fig. 5 is a graph showing the calculation results of the expected power value of a single electric vehicle.
Fig. 6 shows the actual values of the power exchange between the distribution network and the large power grid within 24h a day.
Fig. 7 is a graph of distribution network voltage over 24 hours a day.
Fig. 8 is a power distribution network line tidal current graph within 24h of a day.
Detailed Description
The invention will be further elucidated and described with reference to the drawings and the detailed description.
In a preferred embodiment of the present invention, as shown in fig. 1, there is provided a method for evaluating the acceptance capability of an electric vehicle in a power distribution network based on data driving, the method comprising the following steps:
the method comprises the steps of firstly, dividing urban electric vehicles into three types of buses, taxis and private cars, carrying out probability modeling on 3 characteristic variables in a charging station of each type of urban electric vehicles by using a nonparametric nuclear density estimation method, and establishing a charging amount PcAnd a time T for starting chargingsAnd a charging period TcThe probability density model of (2).
The non-parametric kernel density estimation method belongs to the prior art, and the form of the non-parametric kernel density estimation method used in the present embodiment is:
suppose Pc1,Pc2，…，PcnCharging electric quantity P for electric automobilecA set of n samples of data after the segmentation process. f (P)c) An original probability density function of the charging capacity of the electric vehicle
Figure BDA0003097994640000051
Is f (P)c) Is expressed as follows:
Figure BDA0003097994640000052
where h is the bandwidth and K (.) is the kernel function.
Although the kernel functions have different forms, the accuracy of the non-parameter estimation is less affected by the different kernel functions, so the present embodiment selects a commonly used gaussian kernel function, and the expression is:
Figure BDA0003097994640000053
and secondly, modeling the load of each type of electric vehicle by using a Monte Carlo (MC) rejection-acceptance sampling method according to the probability density model obtained in the first step, averaging the charging load curves of each electric vehicle belonging to the same type after accumulation to obtain the expected charging power value of each type of single electric vehicle in each time period, and then carrying out weighted averaging on the expected charging power values of the three types of single electric vehicles in each time period according to the ratio of the three types of electric vehicles to obtain the expected charging power value of each type of single electric vehicle in each time period.
The following assumptions are made in modeling each type of electric vehicle load:
(1) in the charging process of most batteries, constant current charging occupies a dominant position, all types of charging modes are regarded as constant current charging, and the influence of other random factors is ignored;
(2) the total load of the electric automobile is the superposition of the charging loads of the independent vehicles, namely the charging loads of different vehicle types at the same time are summed.
(3) Electric vehicles charged in the power distribution network are assumed to have three types: electric buses, electric taxis, electric private cars.
Thus, in the present embodiment, the expected charging power value calculation method for each period of a single electric vehicle of an indistinguishable type is as follows:
s21, modeling each type of electric vehicle load by using a Monte Carlo rejection-acceptance sampling method, and extracting the charging starting time and the charging duration according to the probability density distribution of the 3 characteristic variables obtained in the first step during modeling, so as to obtain a daily charging load curve of a single electric vehicle.
The reject-accept sampling method also belongs to the prior art, and for the convenience of understanding, the specific process thereof is described as follows:
a common probability distribution function q (x) that is convenient to sample, and a constant k are set such that p (x) is always below kq (x). First, a sample z of p (x) is obtained by sampling0Then a value u is sampled from the uniform distribution. If u falls in the gray area of the upper graph, the sample is rejected, otherwise the sample z is accepted0. Repeating the above process to obtain n accepted samples z0,z1,...,zn-1And then the final solution result of the Monte Carlo method is as follows:
Figure BDA0003097994640000061
the whole process achieves the aim of simulating the probability distribution of p (x) by q (x) through a series of acceptance and rejection decisions.
In this embodiment, specific steps of simulation modeling by using the monte carlo method may be shown in fig. 2, where the number of iterative cycles may be optimized according to an algorithm.
And S22, accumulating daily charging load curves of each electric vehicle in the type to obtain a total charging load curve taking days as a calculation unit, dividing the total charging load curve by taking hours as time intervals, and averaging according to the total number N of the electric vehicle types to obtain the expected charging power value of each type of single electric vehicle in each hour, namely representing the expected average charging power value of one electric vehicle in one hour.
Charging power expected value L of single electric automobile with type f in ith hourf,iThe calculation formula is as follows:
Figure BDA0003097994640000062
in the formula:
Figure BDA0003097994640000063
charging power of the jth electric automobile with the type f at the ith hour.
S23, calculating the expected charging power value of each type of electric vehicle in each time period in a weighted average mode according to the proportion of the three types of electric vehicles in the target evaluation area:
Li＝αLbus,i+βLtaxi,i+γLcar,i
in the formula: l isiThe expected charging power value of a single electric automobile of an undistinguished type in the ith hour; l isbus,i、Ltaxi,i、Lcar,iCharging expected values L of single bus, taxi and private car respectivelyf,iThe alpha, the beta and the gamma are respectively the proportion of three electric automobiles of a bus, a taxi and a private car, the proportion can be determined according to actual investigation or related reports, and the sum of the alpha, the beta and the gamma is 1.
And thirdly, aiming at a power distribution network of a target evaluation area, connecting the electric vehicles into the power distribution network according to a proportional assumption according to the distribution of actual charging piles in the area, wherein the charging power of each electric vehicle in the power distribution network is calculated according to the expected charging power value of each electric vehicle in each time period of the single electric vehicle of the type which is not distinguished obtained in the previous step. Therefore, the maximum access quantity of the electric vehicles of the power distribution network is an objective function, meanwhile, an optimization model is constructed by taking the operation safety of the power distribution network as constraint, and the optimization model is solved to obtain the electric vehicle acceptance capacity of the power distribution network. The solution of the optimization model is not limited.
In this embodiment, the construction optimization model constructed for the electric vehicle acceptance of the power distribution network is as follows:
the objective function of the optimization model is:
EVAC＝max NEV
in the formula, NEVFor the number of electric vehicles connected to the grid, EVAC represents the electric vehicle acceptance of the distribution grid, and max represents the maximum value.
The operation safety constraints of the power distribution network comprise power balance constraints, node voltage constraints, line power flow constraints and transformer capacity constraints, and specifically comprise the following steps:
1) the power balance constraint is:
the linearized DistFlow equation is adopted to describe the node power balance relation of the power distribution system, namely, the condition that
Figure BDA0003097994640000071
In the formula Pij、QijRespectively the active power and the reactive power flow of a line l (i, j) between a node i and a node j;
Figure BDA0003097994640000072
respectively the active power and the reactive power generation capacity of the node j generator set;
Figure BDA0003097994640000073
respectively the active power and the reactive demand of the load at the node j; pEV,jThe node j is the charging load of the electric vehicle; n is a radical ofEV,jThe number of electric vehicles charging node j; and pi (j) is the total number of branches connected by the node j.
2) The node voltage constraint is:
Figure BDA0003097994640000081
Ui l≤Ui≤Ui u
in the formula, rij、xijRespectively the resistance and reactance of the line l (i, j), zijIs the impedance of line l (i, j); u shapeiIs the square of the voltage amplitude of the node i;
Figure BDA0003097994640000083
respectively an upper limit value and a lower limit value of the voltage of the node i; u shape0To balance the node voltages.
3) The line power flow constraint is as follows:
Pl≤Pij≤Pu
Ql≤Qij≤Qu
in the formula: pl、QlRespectively an active power lower limit and a reactive power flow lower limit of the line l (i, j); pu、QuThe upper limit of the active power and the upper limit of the reactive power flow of the line l (i, j) are respectively.
4) The transformer capacity constraint is:
for safe reliability of grid operation, the transformer load needs to be within the transformer capacity allowed range:
PT≤ST
in the formula: pTIs the sum of the loads connected to the transformer, STThe rated capacity of the transformer.
When the optimization model is obtained through solving, the number N of the electric vehicles connected to the power grid can be obtainedEVI.e. the electric vehicle acceptance of the distribution network.
The following description will be made in detail with reference to the accompanying drawings, wherein the methods from S1 to S3 are applied to an embodiment of the present invention.
Examples
And selecting a power supply area of a 220KV coin pool transformer substation in the Hangzhou city area as a research object, and calculating the electric automobile acceptance capacity of the Hangzhou area power distribution network. The topology of the area network is shown in fig. 3, and the area comprises a 220KV substation change and 11 110KV substations, and the data of the substations and lines are shown in table 1.
TABLE 1 substation data
Figure BDA0003097994640000082
Figure BDA0003097994640000091
The position information of the electric automobile charging station comes from Hangzhou electric automobile charging app (Hangzhou e charging), and the app can acquire the position, the power and the use condition information of the electric automobile charging pile. For convenience of calculation, the scattered electric vehicle charging piles are combined into a large electric vehicle charging station and connected to the most adjacent node. The electric vehicle charging stations are respectively located at the nodes of the power distribution network 4, 11, 12, 15, 16, 18, 23, 28 and 33.
The charging of the electric automobile is divided into fast charging and slow charging. According to the Hangzhou e charging app, the fast charging power of the electric automobile in the Hangzhou city is mostly 60KW, and the slow charging power is 7 KW. Because the slow charging power is small, the influence on the power grid is small, the influence is ignored in the text, the fast charging of the electric automobile is only considered, and the fast charging power of the electric automobile is assumed to be 60 KW.
According to the prediction results of the automobile holding amounts in the Chinese automobile industry development report (2008), the number ratio of buses, taxis and private cars is about 1: 2.4: 95.2, and the proportion of three electric automobiles in Hangzhou city is calculated according to the ratio.
And taking 24 hours as the time length of the optimization problem, taking 1h as a scheduling period, and solving an electric vehicle admission capacity evaluation model by calling a Cplex optimization toolbox by programming under Matlab by adopting a typical daily load curve shown in FIG. 4 for the load of the power distribution network.
As shown in fig. 5, it can be seen that most electric vehicles are charged in 15 hours or so, the charging load is not uniformly distributed, and if a large-scale electric vehicle is connected to a power distribution network, impact is caused to the power distribution network.
The calculation result of the electric vehicle acceptance capacity of the power distribution network is that the number of the electric vehicles accepted by the maximum energy of the power distribution network is 30532. In this case, the power value of the distribution network interacting with the large power grid 24 hours a day is shown in fig. 6, and it can be seen that the power value of the distribution network interacting with the large power grid rises to a limit value during the charging peak period of the electric vehicle, so that the capacity limit is an important factor for limiting the electric vehicle acceptance capacity of the distribution network. The voltage and current distribution of the distribution network at the load peak and valley is shown in fig. 7 and 8. The calculated acceptance capacity value of the electric automobile can be applied to regulating and controlling the access scale of the electric automobile by an actual power grid so as to ensure the safety and reliability of the system, and can also be used as a reference for planning the future power grid.
The above-described embodiments are merely preferred embodiments of the present invention, which should not be construed as limiting the invention. Various changes and modifications may be made by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present invention. Therefore, the technical scheme obtained by adopting the mode of equivalent replacement or equivalent transformation is within the protection scope of the invention.

Claims (10)

1. A power distribution network electric vehicle admission capacity evaluation method based on data driving is characterized by comprising the following steps:
s1, dividing the urban electric vehicles into three types of buses, taxis and private cars, performing probability modeling on 3 characteristic variables in the charging station of each type of urban electric vehicles by using an nonparametric kernel density estimation method, and establishing a charging amount PcAnd a time T for starting chargingsAnd a charging period TcA probability density model of (a);
s2, modeling the load of each type of electric automobile by using a Monte Carlo (MC) rejection-acceptance sampling method according to the probability density model obtained in S1, accumulating and averaging the charging load curves of each electric automobile belonging to the same type to obtain the expected charging power value of each type of single electric automobile in each time interval, and carrying out weighted averaging on the expected charging power values of the three types of single electric automobiles in each time interval according to the ratios of the three types of electric automobiles to obtain the expected charging power value of each type of single electric automobile in each time interval;
s3, aiming at a power distribution network in a target evaluation area, proportionally and supposing that the electric automobiles are connected into the power distribution network according to the distribution of actual charging piles in the area, and calculating the charging power of each electric automobile in the power distribution network according to the expected charging power value of each period of the single electric automobile which is not in the type of distinguishing; the maximum access quantity of the electric automobiles in the power distribution network is an objective function, meanwhile, an optimization model is constructed by taking the operation safety of the power distribution network as a constraint, and the optimization model is solved to obtain the electric automobile acceptance capacity of the power distribution network.
2. The method for evaluating the electric vehicle acceptance capability of the power distribution network according to claim 1, wherein the method comprises the following steps: in S1, the kernel function is a gaussian kernel function when the non-parametric kernel density estimation method is used.
3. The method for evaluating the electric vehicle acceptance capability of the power distribution network according to claim 1, wherein the method comprises the following steps: in S2, the following three assumptions are made when modeling each type of electric vehicle load:
1) the charging modes of all types of electric automobiles are regarded as constant-current charging, and the influence of other random factors is ignored;
2) the total load of the electric automobile is the superposition of the charging loads of the independent vehicles, namely the charging loads of different vehicle types at the same time are summed;
3) electric vehicles which are charged in the power distribution network are supposed to be of only three types, namely electric buses, electric taxis and electric private cars.
4. The method for evaluating the electric vehicle acceptance capability of the power distribution network according to claim 1, wherein the method comprises the following steps: in S2, the calculation method of the expected charging power value for each period of the single electric vehicle of the type that is not distinguished is as follows:
s21, modeling each type of electric vehicle load by using a Monte Carlo rejection-acceptance sampling method, and extracting a charging starting time and a charging duration according to probability density distribution of 3 characteristic variables obtained in S1 during modeling so as to obtain a daily charging load curve of a single electric vehicle;
s22, accumulating daily charging load curves of each electric vehicle in the type aiming at each type of electric vehicle to obtain a total charging load curve taking days as a calculation unit, dividing the total charging load curve by taking hours as time intervals, and averaging according to the total number N of the electric vehicle types to obtain the expected charging power value of each type of single electric vehicle in each hour; charging power expected value L of single electric automobile with type f in ith hourf,iThe calculation formula is as follows:
Figure FDA0003097994630000021
in the formula:
Figure FDA0003097994630000022
charging power of the jth electric automobile with the type f at the ith hour;
s23, calculating the expected charging power value of each type of electric vehicle in each time period in a weighted average mode according to the proportion of the three types of electric vehicles in the target evaluation area:
Li＝αLbus,i+βLtaxi,i+γLcar,i
in the formula: l isiThe expected charging power value of a single electric automobile of an undistinguished type in the ith hour; l isbus,i、Ltaxi,i、Lcar,iCharging expected values L of single bus, taxi and private car respectivelyf,iAnd alpha, beta and gamma are the proportion of three electric automobiles, namely a bus, a taxi and a private car respectively.
5. The method for evaluating the electric vehicle acceptance capability of the power distribution network according to claim 1, wherein the method comprises the following steps: in S3, the construction optimization model constructed for the electric vehicle acceptance of the power distribution network is as follows:
the objective function of the optimization model is:
EVAC＝max NEV
in the formula, NEVFor the number of electric vehicles accessed in the power grid, EVAC represents the electric vehicle acceptance capacity of the power distribution grid;
the power distribution network operation safety constraints comprise power balance constraints, node voltage constraints, line current constraints and transformer capacity constraints.
6. The method for evaluating the electric vehicle acceptance capability of the power distribution network according to claim 5, wherein the method comprises the following steps: the power balance constraint is:
the linearized DistFlow equation is adopted to describe the node power balance relation of the power distribution system, namely, the condition that
Figure FDA0003097994630000031
In the formula Pij、QijRespectively the active power and the reactive power flow of a line l (i, j) between a node i and a node j;
Figure FDA0003097994630000032
respectively the active power and the reactive power generation capacity of the node j generator set;
Figure FDA0003097994630000033
respectively the active power and the reactive demand of the load at the node j; pEV,jThe node j is the charging load of the electric vehicle; n is a radical ofEV,jThe number of electric vehicles charging node j; and pi (j) is the total number of branches connected by the node j.
7. The method for evaluating the electric vehicle acceptance capability of the power distribution network according to claim 6, wherein the method comprises the following steps: the node voltage constraint is:
Figure FDA0003097994630000034
Ui l≤Ui≤Ui u
in the formula, rij、xijRespectively the resistance and reactance of the line l (i, j), zijIs the impedance of line l (i, j); u shapeiIs the square of the voltage amplitude of the node i;
Figure FDA0003097994630000035
respectively an upper limit value and a lower limit value of the voltage of the node i; u shape0To balance the node voltages.
8. The method for evaluating the electric vehicle acceptance capability of the power distribution network according to claim 7, wherein the method comprises the following steps: the line flow constraint is as follows:
Pl≤Pij≤Pu
Ql≤Qij≤Qu
in the formula: pl、QlRespectively an active power lower limit and a reactive power flow lower limit of the line l (i, j); pu、QuThe upper limit of the active power and the upper limit of the reactive power flow of the line l (i, j) are respectively.
9. The method for evaluating the electric vehicle acceptance capability of the power distribution network according to claim 8, wherein: the transformer capacity constraint is as follows:
PT≤ST
in the formula: pTIs the sum of the loads connected to the transformer, STThe rated capacity of the transformer.
10. The method for evaluating the electric vehicle acceptance capability of the power distribution network according to claim 1, wherein the method comprises the following steps: the optimization model is programmed under Matlab, and a Cplex optimization tool box is called to solve.
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