CN114156927A - Electric vehicle cluster scheduling potential evaluation method considering variable power charge-discharge model - Google Patents

Electric vehicle cluster scheduling potential evaluation method considering variable power charge-discharge model Download PDF

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CN114156927A
CN114156927A CN202111490897.7A CN202111490897A CN114156927A CN 114156927 A CN114156927 A CN 114156927A CN 202111490897 A CN202111490897 A CN 202111490897A CN 114156927 A CN114156927 A CN 114156927A
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power
charging
scheduling
electric automobile
electric
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CN114156927B (en
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刘杰
吴长龙
于浩明
马雷
魏莘
康伟
林芳
杨喆
沈盛
何佳安
林英俊
殷德惠
董豆
张雷
孙晋
孙韬
张谦
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Chongqing University
Yantai Power Supply Co of State Grid Shandong Electric Power Co Ltd
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Yantai Power Supply Co of State Grid Shandong Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Abstract

The invention relates to an electric vehicle cluster scheduling potential evaluation method considering a variable power charge-discharge model, and belongs to the field of intelligent power grids. The method comprises the following steps: s1: analyzing the electric steam dispatching potential under the constant power model; s2: establishing a constant-voltage-constant-current two-stage charge and discharge model of the electric automobile; s3: estimating the dispatching potential of the electric automobile cluster; s4: and (5) correcting the user honesty. The charging and discharging peak values calculated by the electric vehicle cluster scheduling potential estimation method based on the variable power model are higher than the result under the constant power, and the scheduling time period of the discharging scheduling is more consistent with the actual situation than the latter, so that more effective support can be provided for making a related scheduling strategy.

Description

Electric vehicle cluster scheduling potential evaluation method considering variable power charge-discharge model
Technical Field
The invention belongs to the field of intelligent power grids, and relates to an electric vehicle cluster scheduling potential evaluation method considering a variable power charge-discharge model.
Background
With the continuous deterioration of energy and environmental problems, new energy technologies are being actively developed and utilized in various countries. Electric vehicles, as new energy vehicles, can reduce fossil fuel consumption and environmental pollution, and have received wide attention from various countries. By reasonably scheduling the charging and discharging processes, auxiliary services such as frequency modulation, peak shaving, spare capacity providing and the like can be provided for the power grid. With the continuous increase of the cluster scale of the electric automobile, the evaluation of the cluster scheduling potential in different areas becomes an important premise for guaranteeing the stability and reliability of scheduling.
Much research has been done on the operation of electric vehicles in dispatch, but there are few achievements on the potential of electric vehicle dispatch. Document [1] defines a chargeable and dischargeable period based on the SOC of a user on the network, and obtains the tunable potential of the electric vehicle cluster based on the chargeable and dischargeable period. The document [2] defines a reverse power supply capacity index through the relation between the user stop time and the time required for charging to the expected electric quantity, and introduces user reliability for weighting to obtain an adjustable potential evaluation function. Document [3] establishes an electric vehicle charging boundary model, and based on the model, a corresponding scheduling strategy is formulated, and the influence of various factors on the cluster charging load is analyzed. Document [4] constructs an evaluation model of the available capacity of V2G based on the configuration parameters and the management mode of the battery, and analyzes the charge and discharge potential of the electric automobile bus. Document [5] establishes an analysis model of the electric vehicle dispatching capability based on the electric vehicle battery loss degree, the user credit, the reverse power supply capability and the like, and analyzes a threshold value of the electric vehicle dispatching capability with the minimum power distribution network peak-valley difference as a target. Document [6] analyzes probability distribution of the number of scheduling degrees of the electric vehicle based on a queue network theory, and further evaluates the scheduling potential of the electric vehicle, but does not consider travel demands of users. The above documents adopt a constant power model to estimate the scheduling potential, and in practice, the charging and discharging power of the electric vehicle changes along with the change of the SOC, so that a certain error is generated; moreover, the discharge scheduling of the document has a large duration of the schedulable period, and some periods of time cannot be discharged in practice.
[1] Zhang Dong, Aixin, Pan seal Ann. load aggregator optimization scheduling strategy considering user schedulable potential [ J/OL ]. North China university of electric Power journal (Nature science edition): 1-16[2021-11-04].
[2] Li Dongdong, Wangbolon, Zhouzuan electric automobile in aggregator model consumes wind power research [ J ] renewable energy source, 2019,37(09):1346 one 1355.
[3] Suqiang, a scholar garden, Tangjia, Wangdan, Luo Feng Chao.
[4] V2G strategy (J) of battery clusters of electric buses participating in island microgrid energy dispatching in West national celebration, yellow Feiteng, Zhang soldier, Xielie, chiffon, 2016,36(10):31-37.
[5] Yangxiangdong, minister, zhang soldier, zhao wave, huang feiteng, xieliao, electric vehicle dispatching ability model and day priority dispatching strategy [ J ] power system automation, 2017,41(02):84-93.
[6]A.Y.S.Lam,K.Leung and V.O.K.Li,"Capacity Estimation for Vehicle-to-Grid Frequency Regulation Services With Smart Charging Mechanism,"in IEEE Transactions on Smart Grid,vol.7,no.1,pp.156-166,Jan.2016.
Disclosure of Invention
In view of the above, the present invention provides an electric vehicle cluster scheduling potential evaluation method considering a variable power charge-discharge model. The method comprises the steps of classifying electric vehicle clusters according to whether schedulable or not, obtaining a more accurate electric vehicle charging and discharging power boundary by using a constant-voltage and constant-current two-stage variable power model, and correcting based on user honesty (whether the electric vehicle clusters are off-grid in advance) so as to obtain the scheduling potential of the electric vehicle clusters in each period. The method effectively considers the charge and discharge power change situation in practice, and further improves the reliability of the scheduling process by introducing the user honesty degree, so that the accuracy and the rationality of the electric vehicle cluster scheduling potential evaluation are improved.
In order to achieve the purpose, the invention provides the following technical scheme:
the electric vehicle cluster scheduling potential evaluation method considering the variable power charge-discharge model comprises the following steps:
s1: analyzing the electric steam dispatching potential under the constant power model;
s2: establishing a constant-voltage-constant-current two-stage charge and discharge model of the electric automobile;
s3: estimating the dispatching potential of the electric automobile cluster;
s4: and (5) correcting the user honesty.
Optionally, the S1 specifically includes: and summing the charge and discharge power of all users in each time interval to obtain the scheduling potential of the electric automobile cluster, wherein the calculation formula is as follows:
Figure BDA0003399304430000021
Figure BDA0003399304430000022
wherein P isec,total、Ped,totalFor the total charging and discharging power, P, of the electric vehicle clusterec,j、Ped,jThe charging and discharging power of the jth electric automobile is generally considered as the maximum value; m is a set of schedulable electric vehicles, and T is the online time period of the electric vehicles; the advantage of this approach is that it is computationally simple, but it ignores the process of changing the charging power of the electric vehicle, and the discharge period does not exist throughout the on-grid period, thus generating a large error compared to the actual situation.
Optionally, the S2 specifically includes: setting the battery terminal voltage of the electric automobile as UebMaximum value of UebmaxThe equations of the charging and discharging processes under the variable power charging model are as follows:
Figure BDA0003399304430000031
wherein E is the constant potential of the battery, K is the polarization constant of the battery, C is the nominal capacity of the battery, i is the charging and discharging current of the battery, A, B is the constant of the exponential section charging and discharging process, R is the internal resistance of the battery, and SOC (t)0) The battery electric quantity at the beginning of charging and discharging; the charge and discharge power of the electric automobile in the constant current mode is represented as follows:
Figure BDA0003399304430000032
since the duration of the constant voltage mode is short in practice, it is considered that the battery is completely charged and discharged in the constant current mode.
Optionally, the S3 specifically includes:
(1) electric vehicle schedulable state analysis
After the electric automobile is connected to a power grid, firstly, whether the electric automobile is scheduled or not is judged, and the original charging time of the electric automobile needs to be solved; assuming that the off-network target SOC set by the user is SOCaThen the original charging time is calculated by the following formula;
Figure BDA0003399304430000033
firstly, assuming that the electric automobile is connected into a power grid and immediately starts charging, t0I.e. the network access time ta(ii) a The time t to reach the target SOC is obtained by calculationcThen, judging whether the electric vehicle is dispatched or not according to a formula (4);
tc-ta+ts＜tl-ta (6)
wherein t islPredetermined off-network time for the user, tsScheduling the duration for the minimum; when the original charging time and the minimum scheduling duration are less than the total on-line time of the user, the electric vehicle is considered to be capable of taking part in scheduling;
(2) calculating charge-discharge power boundary of electric automobile
According to the formula (1), when the electric automobile is connected into a power grid for charging, the battery voltage of the electric automobile is increased along with the increase of the SOC, and finally the maximum value is reached; the charging power curve when the electric automobile starts to be charged when the electric automobile is connected to the network and reaches the target SOC is the upper limit of the charging power in the period, and the upper limit of the power of the electric automobile is constant to the charging power when the electric automobile reaches the target SOC after the period; the battery voltage is continuously reduced during discharging, the voltage is larger when the initial discharging SOC is larger, and the maximum discharging power is the discharging power after charging; pcmaxIn order to achieve the upper limit of the charging power,Pdmaxis the upper discharge power limit;
solving a charge-discharge power curve of each electric automobile through a formula (2), and finally accumulating to obtain a total charge-discharge power boundary of the electric automobile cluster, wherein the total charge-discharge power boundary is shown as the following formula;
Figure BDA0003399304430000041
wherein P isec,total、Ped,totalM is the total charging and discharging power boundary of the electric vehicles, and P is the number of the adjustable electric vehiclesec,j、Ped,jAnd (4) setting a charging and discharging power boundary curve of the jth electric automobile.
Optionally, the S4 specifically includes:
when a user accesses the power grid, an agent reads the reported information or the intelligent terminal to obtain the access time and the preset off-grid time t of the agentaAnd tl(ii) a Assuming that the agent can obtain the number of times N that the user charges in its charging station, the integrity h (j) of the jth user is defined as follows:
Figure BDA0003399304430000042
wherein t isrlActual off-network time for the user; when a user leaves the network in advance, the integrity of the user is reduced, and the scheduling time of the user is shortened in subsequent scheduling, so that the user is ensured to have enough SOC when leaving the network; the modified scheduling time is as follows;
T′s(j)＝Ts(j)·H(j) (9)
wherein T iss(j)、T′s(j) The original scheduling time length and the modified scheduling time length of the jth user are obtained.
The invention has the beneficial effects that:
(1) the charging and discharging peak values calculated by the electric vehicle cluster scheduling potential estimation method based on the variable power model are higher than the result under the constant power, and the scheduling time period of the discharging scheduling is more consistent with the actual situation than the latter, so that more effective support can be provided for making a related scheduling strategy.
(2) Considering that the early off-grid behavior of the user can influence the scheduling stability, the concept of user honesty degree is introduced to correct the discharging scheduling time interval, so that the reliability of the discharging scheduling process can be further improved, and the SOC of the battery when the user is off-grid in advance is increased.
(3) Aiming at the problems that the power change condition caused by the voltage during battery charging, the division of the discharging scheduling time interval and the like are not considered in the conventional electric vehicle cluster scheduling potential evaluation method, the electric vehicle scheduling potential evaluation method considering the variable power charging model is provided. The method has the advantages that the practical charging and discharging process, the scheduling time period and the user integrity degree are considered, so that the estimation accuracy of the cluster scheduling electric potential is improved, and more accurate data support is provided for the related scheduling mechanism to make the charging and discharging scheduling plan.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
Drawings
For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a state diagram of an electric vehicle after being networked;
FIG. 2 is a schematic view of a "charge-discharge-charge" mode;
FIG. 3 is a schematic diagram of the charging and discharging power boundary of the electric vehicle;
FIG. 4 shows the number of users of the electric vehicle accessing the network at each time interval;
FIG. 5 shows the number of off-line users of the electric vehicle at each time interval;
FIG. 6 is a graph of electric vehicle cluster dispatching potential in constant power mode;
FIG. 7 is a graph of electric vehicle dispatching potential in a variable power mode.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.
1. Electric steam dispatching potential analysis under constant power model
In most of the current researches on the dispatching potential of the electric automobile, a constant-power charging and discharging model is adopted, and the electric automobile can be charged and discharged at any time interval when being connected to a power grid. The scheduling potential of the electric automobile cluster can be obtained by summing the charge and discharge power of all users in each time interval, and the calculation formula is as follows:
Figure BDA0003399304430000061
Figure BDA0003399304430000062
wherein P isec,total、Ped,totalFor the total charging and discharging power, P, of the electric vehicle clusterec,j、Ped,jThe charging and discharging power of the jth electric vehicle is generally considered to be a maximum value. M is the set of schedulable electric vehicles, and T is the online time period of the electric vehicles. The advantage of this approach is that it is computationally simple, but it ignores the process of changing the charging power of the electric vehicle, and the discharge period does not exist throughout the on-grid period, thus generating a large error compared to the actual situation.
2. Constant-voltage-constant-current two-stage charging and discharging model of electric automobile
In practice, the charging and discharging power of the electric automobile generally goes through two stages of constant current and constant voltage. Taking charging as an example, constant current charging is performed in the early stage, the voltage of the battery gradually rises, and enters a constant voltage charging stage after reaching the maximum value, and the current is rapidly reduced to zero. This prevents excessive current in the initial stage of charging and excessive charging in the later stage.
Setting the battery terminal voltage of the electric automobile as UebMaximum value of UebmaxThe equations of the charging and discharging processes under the variable power charging model are as follows:
Figure BDA0003399304430000063
wherein E is the constant potential of the battery, K is the polarization constant of the battery, and C is the standard of the batteryThe capacity is called, i is the charging and discharging current of the battery, A, B is the constant of the exponential section charging and discharging process, R is the internal resistance of the battery, SOC (t)0) The battery power is the battery power at the beginning of charging and discharging. Therefore, the charging and discharging power of the electric vehicle in the constant current mode can be expressed as:
Figure BDA0003399304430000064
since the duration of the constant voltage mode is short in practice, it is considered that the battery completes charge and discharge mainly in the constant current mode.
3. Electric vehicle cluster scheduling potential estimation method
(1) Electric vehicle schedulable state analysis
After the electric vehicle is connected to the power grid, whether the electric vehicle is scheduled or not is judged, so that the original charging time of the electric vehicle needs to be solved. Assuming that the off-network target SOC set by the user is SOCaThen the raw charge time can be calculated by the following equation.
Figure BDA0003399304430000065
At the moment, the electric automobile is firstly supposed to be connected into the power grid to immediately start charging, and t0I.e. the network access time ta. The time t to reach the target SOC is tcThen, whether the electric vehicle is dispatched or not can be judged according to the formula (4).
tc-ta+ts＜tl-ta (6)
Wherein t islPredetermined off-network time for the user, tsIs the minimum scheduling duration. When the original charging time and the minimum scheduling duration are less than the total on-line time of the user, the electric automobile can be considered to be scheduled.
(2) Calculating charge-discharge power boundary of electric automobile
The state of the electric vehicle after the network access is assumed as shown in fig. 1.
To ensure that the user has a sufficient SOC when off-grid, the schedulable period is arranged in front and the charging period is arranged in the back. The blue time interval is a schedulable time interval, and is divided into two equally divided sections for maximum discharge electric quantity, and charging and discharging are respectively carried out. Here, the time difference due to the variable power charge-discharge model is small, and the charge and discharge times are considered to be equal for the sake of simplicity of calculation. The red period is an original charging period, and only charging can be performed. The dischargeable period in this case exists only in the schedulable period, which is relatively small, and therefore the following is considered.
The charging period of the schedulable period is moved to the last, and the discharging period can be moved in any period except the former, such as the "charging-discharging-charging" mode in fig. 2, so that the length of the schedulable period can be relatively expanded, thereby increasing the scheduling flexibility.
According to the formula (1), when the electric automobile is connected to a power grid for charging, the battery voltage of the electric automobile increases along with the increase of the SOC, and finally reaches the maximum value. Therefore, the charging power curve when the electric vehicle starts to be charged when the electric vehicle is connected to the network and reaches the target SOC is the upper limit of the charging power in the period, and the upper limit of the power of the electric vehicle is constant to the charging power when the target SOC is reached after the period. And the battery voltage is continuously decreased at the time of discharging, and the larger the initial discharging SOC is, the larger the voltage thereof is, and thus the maximum discharging power thereof is the discharging power after the charging is performed, as shown at the dotted line in fig. 1. FIG. 3 is a schematic diagram of the charging and discharging power boundary of the electric vehicle, wherein PcmaxTo the upper limit of charging power, PdmaxIs the upper discharge power limit.
And (3) solving the charge-discharge power curve of each electric automobile through the formula (2), and finally accumulating to obtain the total charge-discharge power boundary of the electric automobile cluster, wherein the total charge-discharge power boundary is shown in the following formula.
Figure BDA0003399304430000071
Wherein P isec,total、Ped,totalM is the total charging and discharging power boundary of the electric vehicles, and P is the number of the adjustable electric vehiclesec,j、Ped,jCharging for jth electric automobileAnd a discharge power boundary curve.
4. User integrity correction
The state of the electric vehicle during charging is controlled by the agent, and it is considered that the target SOC is reached when the user is off-grid. However, in practice, a user may select to leave the network in advance due to his own reasons, so that the vehicle may not reach the predetermined SOC when leaving the network, and the scheduling stability of the electric vehicle may be affected. Therefore, the scheduled time period of the electric vehicle needs to be further corrected.
When a user accesses the power grid, an agent can read the reported information or the intelligent terminal to obtain the access time and the preset off-grid time t of the agentaAnd tl. Assuming that the agent is able to obtain the number of times N that the user is charged in his charging station, the integrity h (j) of the jth user can be defined as follows:
Figure BDA0003399304430000081
wherein t isrlIs the actual off-network time of the user. When the user leaves the network in advance, the integrity of the user is reduced, so that the scheduling time of the user is shortened in the subsequent scheduling, and the user is ensured to have enough SOC when leaving the network. The modified scheduled time is as follows.
T′s(j)＝Ts(j)·H(j) (9)
Wherein T iss(j)、T′s(j) The original scheduling time length and the modified scheduling time length of the jth user are obtained.
5. Example simulation
5.1 simulation parameters
Some parameters of the simulation are given below. Assuming that 500 electric vehicles are shared in a certain area, all users can charge and discharge at home and at work, and the parameters of the relevant electric vehicles and batteries are shown in tables 1 and 2 below. Fig. 4 and 5 show the number of online and offline users of the electric vehicle users in each time period, which is obtained by sampling monte carlo. The blue bar represents the number of people when the user leaves home in the morning and comes to the work place for online, and the yellow bar represents the number of people when the user leaves the net in the afternoon and comes to the home for online. Table 2 shows the charge and discharge electricity rates of the electric vehicle at a certain place.
TABLE 1 parameters of electric vehicles
Figure BDA0003399304430000082
TABLE 2 Battery parameters
Figure BDA0003399304430000091
5.2 example analysis
Electric automobile cluster dispatching potential curve under 5.2.1 constant power mode
Fig. 6 is a potential curve of electric vehicle cluster dispatching under a constant power model. In this case, it is considered that the electric vehicle can be charged and discharged in any time period from the network access to the network disconnection, so the curve represents the maximum charge and discharge power that the electric vehicle cluster can reach at each moment, and the actual charge and discharge power is less than or equal to the value.
The simulation time interval is 15 minutes, and 8 am is the simulation start time. After 8:00, the user leaving home starts to continuously arrive at the working place, the scheduling charging and discharging power of the electric automobile cluster continuously rises, and the power is about 10: a maximum of 1706kW is reached after 00. And at about 16:00-21:00, the user gradually starts to leave the power grid during work, and continues to access the power grid after returning home, and the dispatching power shows the trend of descending first and then ascending. And finally, starting to leave for work at 6:00, wherein the change trend of the scheduling power is basically the same as that of the scheduling power from 16:00 to 21: 00.
Electric vehicle dispatching potential curve in 5.2.2 variable power mode
FIG. 7 is a graph of electric vehicle cluster scheduling potential under a variable power model. The charging power margin in this case has a tendency to change substantially in accordance with that at constant power, but its peak value is 1851kW, which is about 8.5% higher than the latter, because the default battery voltage is constant at the rated voltage in the constant power mode, and the battery voltage in the variable power mode increases as the SOC increases, and thus its charging power peak value also increases accordingly. And due to the limitation of the scheduling time period, the discharging power begins to decrease at about 13:00 and 24:00, and is earlier compared with a constant power model, and more zero-power time periods appear, so that the situation is more practical, namely, the closer to the off-grid time, the more reluctant the user is to participate in discharging scheduling. While the maximum value of the discharge power margin is about 1830kW, 7.3% higher than the former. In addition, due to the influence that users may leave the network in advance, the scheduling periods of different users are correspondingly reduced according to respective honesty degrees, and therefore the boundary of the discharge power is lower.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (5)

1. The electric vehicle cluster scheduling potential evaluation method considering the variable power charge-discharge model is characterized by comprising the following steps of: the method comprises the following steps:
s1: analyzing the electric steam dispatching potential under the constant power model;
s2: establishing a constant-voltage-constant-current two-stage charge and discharge model of the electric automobile;
s3: estimating the dispatching potential of the electric automobile cluster;
s4: and (5) correcting the user honesty.
2. The electric vehicle cluster scheduling potential evaluation method considering the variable power charge-discharge model according to claim 1, characterized in that: the S1 specifically includes: and summing the charge and discharge power of all users in each time interval to obtain the scheduling potential of the electric automobile cluster, wherein the calculation formula is as follows:
Figure FDA0003399304420000011
Figure FDA0003399304420000012
wherein P isec,total、Ped,totalFor the total charging and discharging power, P, of the electric vehicle clusterec,j、Ped,jThe charging and discharging power of the jth electric automobile is generally considered as the maximum value; m is a set of schedulable electric vehicles, and T is the online time period of the electric vehicles; the advantage of this approach is that it is computationally simple, but it ignores the process of changing the charging power of the electric vehicle, and the discharge period does not exist throughout the on-grid period, thus generating a large error compared to the actual situation.
3. The electric vehicle cluster scheduling potential evaluation method considering the variable power charge-discharge model according to claim 2, characterized in that: the S2 specifically includes: setting the battery terminal voltage of the electric automobile as UebMaximum value of UebmaxThe equations of the charging and discharging processes under the variable power charging model are as follows:
Figure FDA0003399304420000013
wherein E is the constant potential of the battery, K is the polarization constant of the battery, C is the nominal capacity of the battery, i is the charging and discharging current of the battery, A, B is the constant of the exponential section charging and discharging process, R is the internal resistance of the battery, and SOC (t)0) The battery electric quantity at the beginning of charging and discharging; the charge and discharge power of the electric automobile in the constant current mode is represented as follows:
Figure FDA0003399304420000014
since the duration of the constant voltage mode is short in practice, it is considered that the battery is completely charged and discharged in the constant current mode.
4. The electric vehicle cluster scheduling potential evaluation method considering the variable power charge-discharge model according to claim 3, characterized in that: the S3 specifically includes:
(1) electric vehicle schedulable state analysis
After the electric automobile is connected to a power grid, firstly, whether the electric automobile is scheduled or not is judged, and the original charging time of the electric automobile needs to be solved; assuming that the off-network target SOC set by the user is SOCaThen the original charging time is calculated by the following formula;
Figure FDA0003399304420000021
firstly, assuming that the electric automobile is connected into a power grid and immediately starts charging, t0I.e. the network access time ta(ii) a The time t to reach the target SOC is obtained by calculationcThen, judging whether the electric vehicle is dispatched or not according to a formula (4);
tc-ta+ts＜tl-ta (6)
wherein t islPredetermined off-network time for the user, tsScheduling the duration for the minimum; when the original charging time and the minimum scheduling duration are less than the total on-line time of the user, the electric vehicle is considered to be capable of taking part in scheduling;
(2) calculating charge-discharge power boundary of electric automobile
According to the formula (1), when the electric automobile is connected into a power grid for charging, the battery voltage of the electric automobile is increased along with the increase of the SOC, and finally the maximum value is reached; the charging power curve when the electric automobile starts to be charged when the electric automobile is connected to the network and reaches the target SOC is the upper limit of the charging power in the period, and the upper limit of the power of the electric automobile is constant to the charging power when the electric automobile reaches the target SOC after the period; the battery voltage is continuously reduced during discharging, the voltage is larger when the initial discharging SOC is larger, and the maximum discharging power is the discharging power after charging; pcmaxTo the upper limit of charging power, PdmaxIs the upper discharge power limit;
solving a charge-discharge power curve of each electric automobile through a formula (2), and finally accumulating to obtain a total charge-discharge power boundary of the electric automobile cluster, wherein the total charge-discharge power boundary is shown as the following formula;
Figure FDA0003399304420000022
wherein P isec,total、Ped,totalM is the total charging and discharging power boundary of the electric vehicles, and P is the number of the adjustable electric vehiclesec,j、Ped,jAnd (4) setting a charging and discharging power boundary curve of the jth electric automobile.
5. The electric vehicle cluster scheduling potential evaluation method considering the variable power charge-discharge model according to claim 4, characterized in that: the S4 specifically includes:
when a user accesses the power grid, an agent reads the reported information or the intelligent terminal to obtain the access time and the preset off-grid time t of the agentaAnd tl(ii) a Assuming that the agent can obtain the number of times N that the user charges in its charging station, the integrity h (j) of the jth user is defined as follows:
Figure FDA0003399304420000023
wherein t isrlActual off-network time for the user; when a user leaves the network in advance, the integrity of the user is reduced, and the scheduling time of the user is shortened in subsequent scheduling, so that the user is ensured to have enough SOC when leaving the network; the modified scheduling time is as follows;
T′s(j)＝Ts(j)·H(j) (9)
wherein T iss(j)、T′s(j) The original scheduling time length and the modified scheduling time length of the jth user are obtained.
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