CN117913824A - Charging potential evaluation method based on residential electric vehicle charging management mode - Google Patents
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Abstract
The invention discloses a charging potential evaluation method based on a residential area electric automobile charging management mode, which comprises the following steps: s1, based on a resident community electric automobile charging management mode, taking the traveling characteristics of users in the community into consideration, and establishing a community charging potential evaluation model; s2, according to the cell charging potential evaluation model, the charging potential evaluation based on the residential cell electric vehicle charging management mode is completed, and the cell charging potential under the charging management mode is quantitatively evaluated by taking the cell load characteristics and the traveling behavior habits of users into consideration, so that the number of chargeable electric vehicles in the cell can be effectively increased, and the charging cost of the users is reduced.
Description
Technical Field
The invention relates to the technical field of power grid dispatching, in particular to a charging potential evaluation method based on a residential area electric automobile charging management mode.
Background
With the rapid development of electric vehicles, the national pure electric vehicles have been kept up to 1401 ten thousand by the end of 9 months in 2023. By 10 months of 2023, the number of charging piles in China is 795.4 ten thousand, wherein the quantity of the public charging piles is 252.5 ten thousand, and one pile is not achieved. At present, private charging piles of a community mainly have two situations, namely, after residents obtain permission, a power grid company installs an electric energy meter to singly charge, and the power grid company directly charges electricity according to the electricity consumption charging mode of the residents; the other is that the electric energy meter is installed by the property company, and the user pays the electric charge to the property company according to the price (not divided into time periods and higher than the electricity price of residents) agreed by the two parties. For private charging piles charged according to fixed price, users generally start charging when going home and stopping, so that the superposition of charging load and conventional peak load of residents is formed, and peak-to-peak peaking is caused; and users who enjoy the time-of-use electricity prices often respond to the time-of-use electricity price policy, and charging at the beginning of the valley electricity price results in the formation of a new early morning peak.
The time-sharing electricity price is utilized to guide the vehicles to charge orderly, so that the bearing capacity of the cell for accessing the high-permeability electric automobile can be improved. However, the user response time-sharing electricity price has uncertainty, and the uncertainty of the user response needs to be considered when the charging electricity price of the electric automobile is manufactured, so that the existing solution can be roughly divided into:
By setting a dynamic electricity price strategy, the electric vehicle charging transfer in a late peak period is stimulated, the load peak-valley difference of residential communities is reduced, and the vehicle access quantity is enlarged;
dividing users into different groups according to the response depth and response willingness of the users, and making corresponding charging scheduling modes among the different groups so as to reduce the influence of uncertainty of the user response and the charging cost of the users;
the electric automobile collaborative charging strategy based on the constraint planning algorithm ensures the enthusiasm of users to participate in ordered charging;
on the basis of the electric vehicle load prediction technology, a cell power supply capacity planning method is provided. The distribution transformer expansion planning method considering ordered charging is provided, the capacity increasing investment of the transformer is reduced, and the charging requirement of a user is met;
and setting the district charging scene as shared charging, and completing the replacement of the charging vehicle at night by an unmanned technology.
In general, the ordered charging of the electric vehicles and the expansion of the district transformers can be conducted to relieve the charging requirements of a certain number of electric vehicles, but the ordered charging and the expansion of the district transformers are established on the basis of autonomous decision charging behaviors of users, and the problem that the spare distribution capacity of the conventional electricity utilization valley period at night cannot be fully utilized is essentially solved, so that the number of the charged electric vehicles which can be accommodated is low, and a lifting space exists physically. The capacity expansion of the district transformer relates to the whole owners, and the requirements are difficult to unify, and the implementation difficulty is high.
Disclosure of Invention
Aiming at the defects in the prior art, the charging potential evaluation method based on the residential electric vehicle charging management mode solves the problem that the conventional residential electric vehicle charging management mode cannot fully utilize the spare power distribution capacity of the conventional electricity consumption valley period at night, and maximizes the quantity of electric vehicles capable of being in the residential area.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: the charging potential evaluation method based on the residential area electric automobile charging management mode comprises the following steps:
S1, based on a resident community electric automobile charging management mode, taking the traveling characteristics of users in the community into consideration, and establishing a community charging potential evaluation model;
And S2, according to the community charging potential evaluation model, completing charging potential evaluation based on the residential community electric automobile charging management mode.
Further: in the step S1, in the electric automobile charging management mode of the residential community, the resident charging fee comprises charging electric fee and charging service fee;
The charging electricity charge is determined by the electricity price of resident cells;
the expression of the charging service charge Co is as follows:
Wherein, C S represents the construction cost of the charging management platform, C OM represents the operation maintenance cost, C p represents the charging electricity price of the peak period cell, P EV,p represents the charging load of the peak period cell, C s represents the charging electricity price of the flat period cell, P EV,s represents the charging load of the flat period cell, and C v represents the charging electricity price of the valley period cell.
Further: the cell user travel characteristics are represented by a cell user travel model, and the expression of the cell user travel model is as follows:
where f (-) represents the probability density function, t c is the return cell time, To return to the expected value of the time,/>For the standard deviation of return time, t dic is the time to leave the cell,/>Is the expected value of travel time,/>For the standard deviation of the travel time, d is the daily driving mileage of the automobile, mu D is the expected value of the daily driving mileage, sigma D is the standard deviation of the daily driving mileage, s start is the charging start SOC value of the electric automobile, mu soc is the expected value of the charging start SOC value of the electric automobile, and sigma soc is the standard deviation of the charging start SOC value of the electric automobile.
Further: the step S2 includes the steps of:
S21, determining an initial value N min of the cell bearing capacity according to the daily load level of the cell, and setting the number of electric vehicles as N min;
s22, generating n electric vehicle data by utilizing Monte Carlo simulation based on the travel characteristics of users in the cell;
s23, carrying out data processing on the generated n pieces of electric vehicle data to obtain electric vehicle data after data processing;
S24, inputting the data of the electric automobile after data processing into a charging optimization model to obtain a return value of the charging optimization model, and storing the return value into a return value matrix;
s25, repeating the steps S22-S24 until the preset cycle times are reached;
s26, judging whether the passing rate standard is met according to the return value matrix:
if yes, increasing the value of n by a preset step length, and returning to the step S22;
If not, outputting the value of n, and completing the charging potential evaluation based on the residential electric vehicle charging management mode.
Further: the step S23 includes the following sub-steps:
S231, eliminating and supplementing vehicle data in the daytime at a parking period in the generated n pieces of electric vehicle data, and storing the supplemented electric vehicle data into a list EV;
And S232, performing time correction on the stored list EV to obtain a time-corrected list EV' as data of the electric vehicle after data processing.
Further: in the step S231, the expression of the list EV is:
EV＝[Tj,arrive,Tj,leave,SOCj,arrive,CUIj]N×4
Wherein, T j,arrive represents the time when the jth electric automobile arrives at the cell, T j,leave represents the time when the jth electric automobile leaves the cell, SOC j,arrive is the initial electric quantity when the jth electric automobile arrives at the cell, and N is the total number of electric automobiles parked in the cell; CUI j represents an electric vehicle charging emergency coefficient, and the expression of CUI j is:
wherein, P ch,j represents the charging power of the jth electric automobile, and C j represents the battery capacity of the jth electric automobile.
Further: in step S232, the time correction includes two cases when the electric vehicle leaves the cell earlier than the time of reaching the cell and when the electric vehicle returns to the cell in the early morning;
when the electric vehicle leaves the cell time earlier than the arrival cell time, the electric vehicle leaving time correction is expressed as:
when the electric automobile returns to the cell in the early morning, the arrival time and the departure time of the electric automobile are corrected as follows:
further: in the step S24, the method for processing the data of the electric vehicle by the charge optimization model after the data processing includes the following steps:
A1, removing a part of CUI j <0 in the data of the electric automobile after data processing, and incorporating a basic load to obtain the removed data of the electric automobile;
a2, taking the minimum sum of the charging costs of the users of the electric automobile as an objective function;
a3, establishing constraint conditions to constrain the objective function;
A4, solving an objective function according to the constraint conditions and the removed electric vehicle data;
A5, judging whether solutions exist:
if yes, the return value is 1;
If not, the return value is 0.
Further: in the step A2, the expression of the objective function is:
Wherein, C t is the charging electricity price of the electric automobile in the T period, y j,t represents the charging state of the j-th electric automobile in the T period, and DeltaT represents the charging time slot.
Further: in the step A3, the constraint condition has the following expression:
naction≤4
naction＝naction+1,yj,t≠yj,t+1
t∈[Tj,arrive,Tj,leave-1]
Tj,arrive≤t≤Tj,leave
Wherein η j is the charging efficiency of the jth electric vehicle, SOC end is the expected electric quantity after the electric vehicle is charged, n action represents the total number of actions of the charging pile in one day, β is the transformer load factor, and C tra is the rated capacity of the transformer.
The beneficial effects of the invention are as follows:
1. compared with the time-of-use electricity price responded by the user, the cell charging management mode provided by the invention can improve the cell charging potential on the premise of ensuring the economic operation of the cell transformer, expand the carrying capacity of the cell for charging the electric automobile and increase the installation quantity of cell charging facilities;
2. the district charging management mode provided by the invention has the advantages that under the same load rate, after the number of the chargeable vehicles is increased, the property charge management fee for a single user is reduced, and the total charging cost of the user is reduced;
3. The night transformer runs in an economic interval after the charging management mode of the district electric automobile is subjected to charging management, so that overload running or other electricity safety accidents of the district transformer are effectively avoided, meanwhile, the charging cost of an access charging management user is reduced, and the user satisfaction degree is improved;
4. The invention can provide a certain effectiveness and economy for the property to carry out district charging load management and charging pile configuration.
Drawings
Fig. 1 is a flowchart of a charging potential evaluation method based on a residential electric vehicle charging management mode.
Fig. 2 is a graph comparing cell load utilization before and after dividing a charging period.
Fig. 3 is a load curve of the user response time-of-use electricity price at different load rates in the embodiment.
Fig. 4 is a graph of the overall load of the cell after charge management in the embodiment.
FIG. 5 is a plot of the daily range probability density function of a resident in an embodiment.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, in one embodiment of the present invention, a charging potential evaluation method based on a residential-area electric vehicle charging management mode is provided, including the steps of:
S1, based on a resident community electric automobile charging management mode, taking the traveling characteristics of users in the community into consideration, and establishing a community charging potential evaluation model;
And S2, according to the community charging potential evaluation model, completing charging potential evaluation based on the residential community electric automobile charging management mode.
In one embodiment of the present invention, in the step S1, in the residential area electric car charging management mode, the resident charging fee includes a charging electric fee and a charging service fee;
The charging electricity charge is determined by the electricity price of resident cells;
the expression of the charging service charge Co is as follows:
Wherein, C S represents the construction cost of the charge management platform, C OM represents the operation maintenance cost, C p represents the charge price of the peak period district, P EV,p represents the charge load of the peak period district, C s represents the charge price of the flat period district, P EV,s represents the charge load of the flat period district, C v represents the charge price of the valley period district, N is the total number of electric vehicles in the charge management mode of the electric vehicles in the residential district, and N is the total number of electric vehicles in the charge management mode of the electric vehicles in the residential district.
In one embodiment of the invention, the cell user travel characteristic is represented by a cell user travel model, and the expression of the cell user travel model is:
where f (-) represents the probability density function, t c is the return cell time, To return to the expected value of the time,/>For the standard deviation of return time, t dic is the time to leave the cell,/>Is the expected value of travel time,/>For the standard deviation of the travel time, d is the daily driving mileage of the automobile, mu D is the expected value of the daily driving mileage, sigma D is the standard deviation of the daily driving mileage, s start is the charging start SOC value of the electric automobile, mu soc is the expected value of the charging start SOC value of the electric automobile, and sigma soc is the standard deviation of the charging start SOC value of the electric automobile.
In one embodiment of the present invention, considering the characteristics of cell users traveling, the number of parked vehicles in the daytime of the cell is small, and the cell transformer can accommodate charging vehicles in the daytime, so only the charging potential of the cell at night is evaluated herein, said step S2 comprises the steps of:
S21, determining an initial value N min of the cell bearing capacity according to the daily load level of the cell, and setting the number of electric vehicles as N min;
s22, generating n electric vehicle data by utilizing Monte Carlo simulation based on the travel characteristics of users in the cell;
The electric automobile data are user travel data, and comprise a time for driving away from a cell, a time for reaching the cell, a charging start SOC and a daily driving mileage;
s23, carrying out data processing on the generated n pieces of electric vehicle data to obtain electric vehicle data after data processing;
S24, inputting the data of the electric automobile after data processing into a charging optimization model to obtain a return value of the charging optimization model, and storing the return value into a return value matrix;
s25, repeating the steps S22-S24 until the preset cycle times are reached;
s26, judging whether the passing rate standard is met according to the return value matrix:
if yes, increasing the value of n by a preset step length, and returning to the step S22;
If not, outputting the value of n, and completing the charging potential evaluation based on the residential electric vehicle charging management mode.
In one embodiment of the present invention, the step S23 includes the following sub-steps:
S231, eliminating and supplementing vehicle data in the daytime at a parking period in the generated n pieces of electric vehicle data, and storing the supplemented electric vehicle data into a list EV;
And S232, performing time correction on the stored list EV to obtain a time-corrected list EV' as data of the electric vehicle after data processing.
In one embodiment of the present invention, in the step S231, the expression of the list EV is:
EV＝[Tj,arrive,Tj,leave,SOCj,arrive,CUIj]N×4
Wherein, T j,arrive represents the time when the jth electric automobile arrives at the cell, T j,leave represents the time when the jth electric automobile leaves the cell, SOC j,arrive is the initial electric quantity when the jth electric automobile arrives at the cell, and N is the total number of electric automobiles parked in the cell; CUI j represents an electric vehicle charging emergency coefficient, and when CUI j is less than 0, it represents that the j-th electric vehicle battery cannot be fully charged even if the electric vehicle is charged in a parking period, and the expression of CUI j is as follows:
wherein, P ch,j represents the charging power of the jth electric automobile, and C j represents the battery capacity of the jth electric automobile.
In one embodiment of the present invention, in the step S232, the time correction includes two cases that the electric vehicle leaves the cell earlier than the time of reaching the cell and the electric vehicle returns to the cell in the early morning;
when the electric vehicle leaves the cell time earlier than the arrival cell time, the electric vehicle leaving time correction is expressed as:
when the electric automobile returns to the cell in the early morning, the arrival time and the departure time of the electric automobile are corrected as follows:
In one embodiment of the present invention, in the step S24, the method for processing the data of the electric vehicle by the charge optimization model after the data processing includes the following steps:
A1, removing a part of CUI j <0 in the data of the electric automobile after data processing, and incorporating a basic load to obtain the removed data of the electric automobile;
a2, taking the minimum sum of the charging costs of the users of the electric automobile as an objective function;
a3, establishing constraint conditions to constrain the objective function;
A4, solving an objective function according to the constraint conditions and the removed electric vehicle data;
A5, judging whether solutions exist:
if yes, the return value is 1;
If not, returning to the value of 0;
The return value of 1 indicates that the cell can bear a corresponding number of electric vehicles; a return value of 0 indicates that the cell cannot bear a corresponding number of electric vehicles;
In one embodiment of the present invention, in the step A1, for an electric vehicle whose CUI j <0 in the electric vehicle data cannot meet its charging requirement in the parking period, for this reason, it is set herein to be a direct access grid charging in the parking period, and its charging load is incorporated into a base load, where the expression of the base load is: p 0,t＝P0,t+Pch,j,∈[Tj,arrive,Tj,leave ];
Where P 0,t is the residential base power of the t period.
In one embodiment of the present invention, in the step A2, the expression of the objective function is:
Wherein C t is the charging electricity price of the electric vehicle at the T period, y j,t represents the charging state of the jth electric vehicle at the T period, Δt represents the charging time slot, and Δt=15min in this embodiment.
In one embodiment of the present invention, in the step A3, the constraint condition includes:
electric automobile charging quantity constraint:
For the electric automobile with CUI j >0, the charging period is divided into two sections, which is helpful for improving the night load utilization rate of the cell and stabilizing the load fluctuation. Assuming that the load of the cell only bears two electric vehicles to charge at the same time, but the two electric vehicles are not charged in the whole process in a charging period, the idle capacity of the cell is wasted, after the charging period of the two electric vehicles is divided, the two electric vehicles can be connected into one electric vehicle to charge, the idle load utilization rate of the cell is improved, and a night load utilization rate schematic diagram of the cell before the charging period is divided is shown in fig. 2 (a); the cell night load utilization after the charging period is divided is schematically shown in fig. 2 (b).
As can be seen from fig. 2, when the vehicle charging period is not divided, the cell distribution capacity still has a certain margin and is not fully utilized, but after the vehicle charging period is divided, the cell distribution capacity is more effectively utilized, and the stability of the cell load is more facilitated. Therefore, the vehicle charging time period is divided, meanwhile, the maximum action of 4 times in one day of the charging pile is set in consideration of the service life of the start-stop switch of the charging pile, and the action times in one day of the charging pile are restrained:
naction≤4
naction＝naction+1,yj,t≠yj,t+1
t∈[Tj,arrive,Tj,leave-1]
electric automobile inserts electric wire netting charging time constraint:
Tj,arrive≤t≤Tj,leave
Residential district power supply capacity constraint:
Wherein η j is the charging efficiency of the jth electric vehicle, SOC end is the expected electric quantity after the electric vehicle is charged, n action represents the total number of actions of the charging pile in one day, β is the transformer load factor, and C tra is the rated capacity of the transformer.
In one embodiment of the invention, in order to verify the effectiveness of the charging potential evaluation method based on the charging management mode of the residential electric vehicle, taking a certain residential area as an example, 1000 residents are shared in the residential area, the transformer capacity is 4MVA, the battery capacity of the electric vehicle is assumed to be 40kWh, the power of a charging pile is 7kW, the power consumption of the charging pile for running hundred kilometers is 20kWh, the simulation step length is 15min, the total cost of the ordered charging system of the residential electric vehicle is 30 ten thousand yuan, and the total system operation maintenance cost and personnel management cost are 9000 yuan/month. The build model herein calls the CPLEX solver in MATLAB using Yalmip toolkit for solving.
When all the electric car users in the district respond to the time-of-use electricity price charging, and the property does not participate in the charging scheduling of the electric car users in the district, the property can limit the installation of the charging piles for the users in the district in order to ensure that the load of the district does not exceed the capacity limit of the transformer and the working energy of the transformer is in an economic operation interval. The cell load curve in response to the time-of-use price charging is shown in fig. 3.
In fig. 3, when the load rate of the physical limiting transformer is 65%, the district night charging potential enables the district to bear 202 electric vehicles for charging, and when the load rate of the physical limiting transformer is 75%, the district night charging potential enables the district to bear 259 electric vehicles for charging. As can be seen from fig. 3, the night load does not risk power exceeding the transformer capacity, although the transformer is operating in an economy interval. However, the user charging is not scheduled, so that the night idle load of the cell is not fully utilized, the number of electric vehicles which can be connected to the cell power grid for charging at night is reduced, and the popularization of the electric vehicles is limited.
And on the basis of the load rate, solving and evaluating the night charging potential of the cell in the cell charging management mode. The charging management mode provided by the invention has two forms, namely, a charging period is limited in a valley period (mode one), and the charging period comprises a peak period, a flat period and a valley period (mode two). When the charging period is limited in the valley period and the maximum load rate of the cell is not more than 65%, 455 electric vehicles can be charged at night after the electric vehicles are charged through the model management. When the maximum load rate of the cell is not more than 75%, the cell can bear 615 electric automobile charges at night. When the charging period comprises peak, flat and valley periods and the maximum load rate of the cell is not more than 65%, 590 electric vehicles can be carried by the cell at night to be connected into charging. When the maximum load rate of the cell is not more than 75%, the cell can bear 840 electric automobile charging at night. After the charge management, the overall load curves of the cells in the above four cases are shown in fig. 4.
As can be seen from fig. 4, after the charge management, the night load of the cell is more fully utilized, and the number of chargeable electric vehicles in the cell is greatly increased. In order to verify the reliability of the number of the electric vehicles carried by the obtained cell, the Monte Carlo is utilized to simulate a plurality of times of verification, and whether the obtained result meets the charging requirements of users under different travel conditions is judged. The 1000 Monte Carlo simulation results are shown in Table 1 for the number of electric vehicles.
TABLE 1 multiple Monte Carlo simulation results
As can be seen from table 1, in most cases, when the load rate is 65%, the charging period is a valley period, the cell can bear 455 electric vehicle charges, the charging period includes peak, flat and valley periods, the cell can bear 590 electric vehicle charges, when the load rate is 75%, the charging period is a valley period, the cell can bear 615 electric vehicle charges, the charging period includes peak, flat and valley periods, the cell can bear 840 electric vehicle charges, and the effectiveness of the charging quantity of the electric vehicle can be borne by the cell obtained in the mentioned cell charging management mode is proved.
In the actual travel process, most users consume less electric quantity after traveling in one day, and the charging interval period is longer. To determine the charge costs of the user in the charge management mode presented herein, the amount of electricity consumed by the resident after a trip on a daily basis is determined on a resident daily basis, and a generated resident daily mileage probability density function curve is shown in fig. 5.
As can be seen from fig. 5, the one-day travel distance of the residents is mostly concentrated at about 20km, so under the two load factors, the embodiment assumes that the property is recovering the software system construction cost for three years, the daily travel mileage of the residents is 20km, and by calculation, the property charges the user with service fees of 38 yuan/month and 28 yuan/month when the charging period is defined as the valley period, and the property charges the user with service fees of 41 yuan/month and 34 yuan/month when the charging period includes the peak, flat and valley periods. When the user adopts a public charging mode, the monthly charging cost is approximately 180 yuan, and when the user adopts the charging management mode provided by the text, the monthly charging electric charge is approximately 31 yuan, so that the charging cost of the user is reduced.
The present embodiment also evaluates the charging potential of the cell when the user charging behavior is more extreme. The two situations that the users can be charged when the electric quantity is lower than 30% and the users can be charged immediately after going back to the cell after going out on the same day are discussed, and the cell charging potential under different charging behaviors is evaluated. The cell charging potential and the total charge for the user in the different charging behavior modes are shown in table 2.
Table 2 cell charging potential under different charging behaviors
From table 2, it can be seen that the change of the charging potential of the cell under different load rates and different charging behaviors can be found that the charging potential of the cell is better utilized after charging management, the number of accessible charging vehicles is obviously improved, and the total charging cost of the user is greatly reduced. Under the same load rate, the number of accessible charging vehicles in the cell is improved by at least more than 1.4 times no matter how the charging behaviors of the users change. When the daily travel distance of the user is greater than 20km, the more the user saves the charging cost as the travel distance is increased. At the same load rate, when the number of chargeable vehicles increases, the property charge management fee for a single user decreases, and the total charge cost of the user decreases.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. The charging potential evaluation method based on the residential area electric automobile charging management mode is characterized by comprising the following steps of:
S1, based on a resident community electric automobile charging management mode, taking the traveling characteristics of users in the community into consideration, and establishing a community charging potential evaluation model;
And S2, according to the community charging potential evaluation model, completing charging potential evaluation based on the residential community electric automobile charging management mode.
2. The method for evaluating the charging potential based on the residential electric vehicle charging management mode according to claim 1, wherein in the step S1, the residential electric vehicle charging management mode, the resident charging fee includes a charging electric fee and a charging service fee;
The charging electricity charge is determined by the electricity price of resident cells;
the expression of the charging service charge Co is as follows:
Wherein, C S represents the construction cost of the charge management platform, C OM represents the operation maintenance cost, C p represents the charge price of the peak period cell, P EV,p represents the charge load of the peak period cell, C s represents the charge price of the flat period cell, P EV,s represents the charge load of the flat period cell, C v represents the charge price of the valley period cell, and N is the total number of electric vehicles in the electric vehicle charge management mode of the residential community.
3. The method for evaluating the charging potential based on the residential electric vehicle charging management mode according to claim 1, wherein the cell user travel characteristics are represented by a cell user travel model, and the expression of the cell user travel model is as follows:
where f (-) represents the probability density function, t c is the return cell time, To return to the expected value of the time,/>For the standard deviation of return time, t dic is the time to leave the cell,/>Is the expected value of travel time,/>For the standard deviation of the travel time, d is the daily driving mileage of the automobile, mu D is the expected value of the daily driving mileage, sigma D is the standard deviation of the daily driving mileage, s start is the charging start SOC value of the electric automobile, mu soc is the expected value of the charging start SOC value of the electric automobile, and sigma soc is the standard deviation of the charging start SOC value of the electric automobile.
4. The method for evaluating the charging potential based on the residential electric vehicle charging management mode according to claim 1, wherein the step S2 comprises the steps of:
S21, determining an initial value N min of the cell bearing capacity according to the daily load level of the cell, and setting the number of electric vehicles as N min;
s22, generating n electric vehicle data by utilizing Monte Carlo simulation based on the travel characteristics of users in the cell;
s23, carrying out data processing on the generated n pieces of electric vehicle data to obtain electric vehicle data after data processing;
S24, inputting the data of the electric automobile after data processing into a charging optimization model to obtain a return value of the charging optimization model, and storing the return value into a return value matrix;
s25, repeating the steps S22-S24 until the preset cycle times are reached;
s26, judging whether the passing rate standard is met according to the return value matrix:
if yes, increasing the value of n by a preset step length, and returning to the step S22;
If not, outputting the value of n, and completing the charging potential evaluation based on the residential electric vehicle charging management mode.
5. The method for evaluating the charging potential based on the residential electric vehicle charging management mode according to claim 4, wherein the step S23 comprises the following sub-steps:
S231, eliminating and supplementing vehicle data in the daytime at a parking period in the generated n pieces of electric vehicle data, and storing the supplemented electric vehicle data into a list EV;
And S232, performing time correction on the stored list EV to obtain a time-corrected list EV' as data of the electric vehicle after data processing.
6. The method for evaluating the charging potential based on the residential electric vehicle charging management mode according to claim 5, wherein in the step S231, the expression of the list EV is:
EV＝[Tj,arrive,Tj,leave,SOCj,arrive,CUIj]N×4
Wherein, T j,arrive represents the time when the jth electric automobile arrives at the cell, T j,leave represents the time when the jth electric automobile leaves the cell, SOC j,arrive is the initial electric quantity when the jth electric automobile arrives at the cell, and N is the total number of electric automobiles parked in the cell; CUI j represents an electric vehicle charging emergency coefficient, and the expression of CUI j is:
wherein, P ch,j represents the charging power of the jth electric automobile, and C j represents the battery capacity of the jth electric automobile.
7. The method for evaluating the charging potential of the electric vehicle in the residential area according to claim 5, wherein in the step S232, the time correction includes two cases that the electric vehicle leaves the cell earlier than the time of reaching the cell and that the electric vehicle returns to the cell in the early morning;
when the electric vehicle leaves the cell time earlier than the arrival cell time, the electric vehicle leaving time correction is expressed as:
when the electric automobile returns to the cell in the early morning, the arrival time and the departure time of the electric automobile are corrected as follows:
8. The method for evaluating the charging potential based on the residential electric vehicle charging management mode according to claim 4, wherein in the step S24, the method for processing the data of the electric vehicle by the charging optimization model after the data processing comprises the following steps:
A1, removing a part of CUI j <0 in the data of the electric automobile after data processing, and incorporating a basic load to obtain the removed data of the electric automobile;
a2, taking the minimum sum of the charging costs of the users of the electric automobile as an objective function;
a3, establishing constraint conditions to constrain the objective function;
A4, solving an objective function according to the constraint conditions and the removed electric vehicle data;
A5, judging whether solutions exist:
if yes, the return value is 1;
If not, the return value is 0.
9. The method for evaluating the charging potential based on the residential electric vehicle charging management mode according to claim 8, wherein in the step A2, the expression of the objective function is:
Wherein, C t is the charging electricity price of the electric automobile in the T period, y j,t represents the charging state of the j-th electric automobile in the T period, and DeltaT represents the charging time slot.
10. The method for evaluating the charging potential based on the residential electric vehicle charging management mode according to claim 8, wherein in the step A3, the constraint condition is expressed as:
naction≤4
naction＝naction+1,yj,t≠yj,t+1
t∈[Tj,arrive,Tj,leave-1]
Tj,arrive≤t≤Tj,leave
Wherein η j is the charging efficiency of the jth electric vehicle, SOC end is the expected electric quantity after the electric vehicle is charged, n action represents the total number of actions of the charging pile in one day, β is the transformer load factor, and C tra is the rated capacity of the transformer.
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