CN113541168A - Electric vehicle cluster controllability determining method, scheduling method and system - Google Patents
Electric vehicle cluster controllability determining method, scheduling method and system Download PDFInfo
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Abstract
A method for determining the adjustable and controllable capacity of an electric automobile cluster, a scheduling method and a system comprise the following steps: at each scheduling moment, according to the remaining charging duration, the current SOC and the target SOC of each EV, calculating the available charging/discharging capacity and the available charging/discharging power of each scheduling moment of the electric vehicle by using the single electric vehicle controllability evaluation model; superposing the available charging/discharging capacity and the available charging/discharging power at each scheduling moment of each electric automobile to determine the adjustable and controllable capacity of each scheduling time period of the electric automobile cluster; respectively distributing power of each electric automobile in the electric automobile cluster at each moment in a scheduling period by taking supply and demand balance as a target based on the power grid demand and the adjustable and controllable capacity of the electric automobile cluster at each moment; by adopting the scheme of the patent, the controllability of the EV cluster can be accurately evaluated, the real-time updating of the EV cluster power and capacity boundaries is realized by reasonably distributing power to the EVs in different states, and the method has important significance for balancing load power and making a real-time scheduling plan by a power grid.
Description
Technical Field
The invention relates to the field of electric automobile charging, in particular to a method for determining the adjustable and controllable capacity of an electric automobile cluster, a scheduling method and a system.
Background
Document [1] considers the constraint of EV transportation travel demand of the electric vehicle, performs statistical analysis on travel characteristics of EV users and battery parameters, and evaluates the day-ahead response capability of an electric vehicle cluster by combining a monte carlo algorithm, but does not realize accurate modeling of the controllability of each time period by combining the actual conditions of the EV travel within a day. Document [2] estimates the controllable capacity of an electric vehicle cluster based on a markov process, but the model is established without considering the influence of grid interactive scheduling. Document [3] evaluates the available capacity of large-scale EVs based on an aggregation queuing network model, and although an intelligent charging strategy is proposed on the basis of power grid interactive scheduling, it assumes that the charging or discharging time of an EV cluster is exponentially distributed, and has no typicality. Document [4] proposes an EV capacity assessment algorithm based on real-time intelligent charging scheduling, and the adjustable capacity of each EV is calculated respectively and then the adjustable potential assessment is achieved through aggregation. Document [5] proposes a private electric vehicle participation power grid regulation and control capability assessment method based on a trip chain and participation willingness, and discloses a single electric vehicle regulation and control capability model, but the method considers that EV aggregate modeling based on a trip location and assessment user satisfaction is only carried out on the overall estimation of the regulation and control capability of a cluster, specific analysis is not carried out on a charging process, and a fine modeling method is not proposed based on real-time scheduling, so that at present, research on the regulation and control capability of an EV cluster needs to be further carried out.
[1]M.Wang et al.,"Load curve smoothing strategy based on unified state model of different demand side resources,"in Journal of Modern Power Systems and Clean Energy,vol.6,no.3,pp.540-554,May 2018,doi:10.1007/s40565-017-0358-0.
[2]B.Zhang and M.Kezunovic,“Impact on power system flexibility by electric vehicle participation in ramp market,”IEEE Trans.Smart Grid,vol.7,no.3,pp.1285–1294,May 2016.
[3]A.Y.S.Lam,K.-C.Leung,and V.O.K.Li,“Capacity estimation for vehicle-to-grid frequency regulation services with smart charging mechanism,”IEEE Trans.Smart Grid,vol.7,no.1,pp.156–166,Jan.2016.
[4]K.N.Kumar,B.Sivaneasan,P.H.Cheah,P.L.So and D.Z.W.Wang,"V2G Capacity Estimation Using Dynamic EV Scheduling,"in IEEE Transactions on Smart Grid,vol.5,no.2,pp.1051-1060,March 2014,doi:10.1109/TSG.2013.2279681.
[5] Private electric vehicle participation power grid regulation and control capability assessment method based on travel chain and participation willingness, application number 202010920803.4
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for determining the cluster controllability of an electric vehicle, which comprises the following steps:
calculating available charge/discharge capacity and available charge/discharge power of each electric automobile at the current scheduling time by utilizing a pre-constructed single electric automobile controllability evaluation model according to the residual charge duration, the current SOC and the target SOC of each electric automobile at the current scheduling time;
and overlapping the available charging/discharging capacity and the available charging/discharging power of each electric automobile at the current moment, determining the charging/discharging capacity boundary and the power boundary of the electric automobile cluster at the current dispatching moment, and further determining the controllability of the electric automobile cluster at the dispatching time period corresponding to the current dispatching moment.
Preferably, the construction of the model for evaluating the controllability of the single electric vehicle includes:
determining a running state point X corresponding to the electric automobile at the current moment by taking a scheduling time interval T as a constraint according to a charging and discharging curve of the electric automobile after the electric automobile is accessed;
according to the operation state point X corresponding to the current moment, respectively determining a charging boundary point Y and a discharging boundary point Z which reach the maximum operation area most quickly by taking the maximum operation area at the current moment as a constraint;
respectively determining an available charge/discharge capacity boundary and an available charge/discharge power boundary of the electric automobile at the current moment based on the point X and the point Y as well as the point X and the point Z;
the current maximum operating region includes: a first boundary corresponding to a charging behavior with highest charging priority and charging priority by taking the maximum charge state allowed by the electric vehicle as a target; a second boundary corresponding to a charging behavior with lowest charging priority and discharging priority in view of the target state of charge of the electric vehicle; point Y is located at the first boundary and point Z is located at the second boundary;
the scheduling period is a time period between two scheduling instants.
Preferably, the determination of the maximum operation region includes:
determining a plurality of state of charge time points according to the initial SOC, the target SOC and the stop time when the electric automobile is connected into the charging pile; wherein the plurality of state of charge time points comprises: the time when the electric automobile is connected into the charging pile is a point A, the time when the maximum charging operation reaches the maximum charge state allowed by the electric automobile is a point B based on the initial SOC, the time when the electric automobile reaches the stop time based on the maximum available charge state is a point C, the time when the electric automobile reaches the stop time based on the target charge state is a point D, the time when the maximum discharging operation reaches the minimum charge state allowed to be regulated by the electric automobile is a point F based on the initial SOC, and the maximum time when the electric automobile reaches the target charge state at the stop time based on the maximum charging operation is a point E in the minimum charge state allowed to be regulated;
the edges AB, BC and CD, which are sequentially connected by the point A, B, C, D, form a first boundary corresponding to a charging behavior that is preferentially charged with the maximum available state of charge as a goal and has the highest charging priority;
the edges AF, FE, and ED sequentially connected by the point A, F, E, D form a second boundary corresponding to a charging behavior that preferentially discharges and has the lowest charging priority with the target state of charge as the target;
a maximum operating zone is determined based on the first and second boundaries.
Preferably, the operating states X, Y and Z respectively include: the state of charge and the time corresponding to point X, point Y and point Z.
Preferably, the relationship between the state of charge and the charge and discharge power and the time of the electric vehicle corresponding to each side in the maximum operation region is as follows:
in the formula, QchgTThe maximum available charging capacity of the electric automobile under the constraint of the scheduling time period T is obtained; qdchgTThe maximum available discharge capacity of the electric automobile under the constraint of a scheduling time period T is obtained; qchgBCThe maximum charging capacity under the constraint of BC at the edge of the electric automobile is indicated; qchgCDMeans maximum charging capacity, Q, of the electric vehicle under the limit of side CDdchgCDThe maximum discharge capacity of the electric automobile under the limit of side CD; qdchgEDThe maximum discharge capacity of the electric automobile under the limit of edge ED is defined; qdchgFEThe maximum discharge capacity of the electric automobile under the limit of side FE is indicated; pmaxThe maximum charging power of the electric automobile is obtained; -PmaxThe maximum discharge power of the electric automobile; SOC0The initial charge state of the electric vehicle when the electric vehicle is connected to a power grid; SOCmaxThe maximum state of charge allowed for the electric vehicle battery; SOCnowThe current charge state of the electric vehicle; SOCminA minimum state of charge for the electric vehicle to allow regulation; SOCobjA target state of charge set for an electric vehicle user; qnThe rated capacity of the electric automobile; t is t0Accessing the moment for the electric automobile; t is tnowIs the current scheduling time; t is tleaveThe leaving time of the electric automobile.
Preferably, the determining of the charge/discharge capacity boundary and the charge/discharge power boundary of the electric vehicle at the current scheduling time based on the point X and the point Y and the operating state X and the point Z includes the following calculation formula:
in the formula, QchgIs an available charge capacity boundary for the electric vehicle; qdchgIs the usable discharge capacity edge of the single electric automobileA boundary; pchgAn available charging power boundary for the electric vehicle; pdischgIs the available discharge power boundary of the electric automobile.
Based on the same invention concept, the invention also provides a system for determining the cluster controllability of the electric vehicle, which comprises the following steps:
the single electric vehicle calculation module is used for calculating the available charging/discharging capacity and the available charging/discharging power of the electric vehicle at the current scheduling time by utilizing a pre-constructed single electric vehicle controllability evaluation model according to the residual charging time, the current SOC and the target SOC of each electric vehicle at each scheduling time;
and the electric automobile cluster calculation module is used for superposing the available charging/discharging capacity and the available charging/discharging power of each electric automobile at the current scheduling moment, determining the charging/discharging capacity boundary and the power boundary of the electric automobile cluster at the current scheduling moment, and further determining the current adjustable and controllable capacity of the electric automobile cluster.
Based on the same inventive concept, the invention also provides a dispatching method of the electric automobile cluster, which comprises the following steps:
according to the initial SOC, the target SOC and the stop time of each electric automobile in the electric automobile cluster, the regulatability of the electric automobile cluster in each scheduling period is determined by the method for determining the regulatability of the electric automobile cluster;
and respectively distributing power of each electric automobile in the electric automobile cluster in each scheduling period based on the power grid demand time sequence and the controllability of each scheduling period electric automobile cluster to aim at supply and demand balance.
Preferably, the determining the controllable capacity of the electric vehicle cluster at each scheduling time according to the initial SOC, the target SOC and the stop time of each electric vehicle in the electric vehicle cluster includes:
at each scheduling time, sequentially aiming at the stop time, the accessed charge-discharge curve and the accessed time of each electric automobile in the electric automobile cluster, calculating the residual charge duration and the current SOC of the electric automobile at the current scheduling time;
determining the state type of each electric automobile according to the remaining charging time, the current SOC and the target SOC of each electric automobile at the current scheduling moment; wherein the electric vehicle status types include rigid electric vehicles and flexible electric vehicles;
based on all flexible electric vehicles, the method for determining the adjustability of the electric vehicle cluster calculates the adjustability of the electric vehicle cluster in the corresponding scheduling time period at the current scheduling time.
Preferably, the determining the state type of the electric vehicle according to the remaining charging time, the current SOC and the target SOC at the current scheduling time of each electric vehicle includes:
when the electric automobile cannot reach the target SOC even after being charged with the maximum charging power within the residual charging time, the state type of the electric automobile is a rigid electric automobile;
when the time length required by charging the electric automobile with the maximum charging power is less than the remaining charging time length of the electric automobile, the electric automobile is a flexible electric automobile.
Preferably, the power distribution of each rigid electric vehicle in the electric vehicle cluster at each time within the scheduling time period T includes:
charging is performed at maximum power.
Preferably, the power distribution of each flexible electric vehicle in the electric vehicle cluster in each scheduling period includes:
determining a charging and discharging priority based on the shortest charging time for the scheduling time corresponding to each scheduling time interval of each flexible electric vehicle to reach the target charge state;
and performing power distribution based on the charging and discharging priority of the electric automobile.
Preferably, the power distribution based on the charging and discharging priority of the electric vehicle includes:
and sequentially executing the electric automobiles based on the priority order:
calculating whether the power of the flexible electric vehicle which is distributed in response to the dispatching is smaller than the total required power; when the current charging power is less than the preset charging power, the electric automobile is charged and discharged based on the charging and discharging power of the current electric automobile in the current scheduling period; otherwise, the power allocation is ended.
Preferably, the charge and discharge priority is determined according to the following formula:
PRIj(tsch) For the jth flexible electric automobile at tschA charge-discharge priority index at a moment; t is tschIs a scheduling time;
for the jth flexible electric automobile at tschThe shortest charging time to reach the target state of charge at the moment;
the stopping time reached by the jth flexible electric automobile.
Preferably, the calculation formula of the charge and discharge power of the current electric vehicle in the current scheduling period is as follows:
in the formula (I), the compound is shown in the specification,
charging and discharging power of the jth flexible electric automobile in the current scheduling time period T; qnj is the battery capacity of the jth flexible electric automobile;
the maximum charge state allowed for the jth flexible electric vehicle;
for the jth flexible electric automobile at tschA state of charge at a time;
the minimum charge state allowed for the jth flexible electric vehicle; pflexAnd distributing the total required power of the flexible electric vehicle for dispatching.
Preferably, the power distribution is respectively performed on each electric vehicle in the electric vehicle cluster in each scheduling period based on the power grid demand time sequence and the controllability of the electric vehicle cluster at each moment, with the supply and demand balance as a target, and then the method further includes:
and according to the charging and discharging curve of each electric automobile after responding to the power distribution in the current scheduling period.
Based on the same inventive concept, the invention also provides a dispatching system of the electric automobile cluster, which comprises the following steps:
the system comprises an adjustable and controllable capacity determining module, a dispatching time determining module and a dispatching time determining module, wherein the adjustable and controllable capacity determining module is used for determining the adjustable and controllable capacity of an electric automobile cluster in each dispatching time period by utilizing the method for determining the adjustable and controllable capacity of the electric automobile cluster according to the initial SOC, the target SOC and the stop time of each electric automobile in the electric automobile cluster;
and the scheduling module is used for respectively performing power distribution on each electric automobile in the electric automobile cluster in each scheduling period by taking supply and demand balance as a target based on the power grid demand time sequence and the adjustable and controllable capacity of the electric automobile cluster in each scheduling period.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides a method and a system for determining the cluster controllability of an electric vehicle, which comprise the following steps: calculating available charge/discharge capacity and available charge/discharge power of each electric automobile at the current scheduling time by utilizing a pre-constructed single electric automobile controllability evaluation model according to the residual charge duration, the current SOC and the target SOC of each electric automobile at the current scheduling time; the available charging/discharging capacity and the available charging/discharging power of each electric automobile at the current moment are superposed, the charging/discharging capacity boundary and the power boundary of the electric automobile cluster at the current dispatching moment are determined, the controllability of the electric automobile cluster at the corresponding dispatching time interval at the current dispatching moment is further determined, and the controllability of the EV cluster can be accurately evaluated by adopting real-time calculation at each moment.
2. The invention provides a dispatching method and a dispatching system of an electric automobile cluster, which comprise the following steps: according to the initial SOC, the target SOC and the stop time of each electric automobile in the electric automobile cluster, the regulatable capacity of the electric automobile cluster in each scheduling period is determined by using the method for determining the regulatable capacity of the electric automobile cluster provided by the invention; respectively carrying out power distribution on each electric automobile in the electric automobile cluster in each scheduling period by taking supply and demand balance as a target based on the power grid demand time sequence and the adjustable and controllable capacity of the electric automobile cluster at each moment; on the basis of accurately evaluating the controllability of the EV cluster, reasonable power distribution is carried out on each EV to realize real-time dynamic update of the power and capacity boundaries of the EV cluster, and the method has important significance for balancing load power and making a real-time scheduling plan for a power grid.
Drawings
FIG. 1 is a flowchart of a method for determining an electric vehicle cluster controllability according to the present invention;
FIG. 2 is a schematic diagram of the regulation and control capability of a single electric vehicle;
FIG. 3 is a flow chart of a scheduling method of an electric vehicle cluster according to the present invention;
FIG. 4 shows the number of EV's in a given office area during a day;
FIG. 5 shows scheduling results of EV cluster participating systems;
FIG. 6 is an EV cluster response capability;
FIG. 7 is a comparison SOC plot before and after charging;
FIG. 8 is a partial monomer EV response;
FIG. 9 is a block diagram of a system for determining the controllability of an electric vehicle cluster;
fig. 10 is a block diagram of a scheduling system of an electric vehicle cluster.
Detailed Description
Aiming at the problems that in the prior art, accurate modeling of the controllability of each time interval is not realized by combining actual traveling conditions of the EV within a day, the influence of interactive scheduling with a power grid is not considered, and the research on the controllability of the EV cluster at present needs to be further deepened, fine modeling is carried out on the controllability of the EV cluster, and an EV cluster regulatable power and capacity boundary model is established by taking the capacity of an EV battery and the charging requirement of a vehicle owner as constraints. The method comprises the steps of classifying the states of electric vehicles at stations, and providing an EV cluster optimization scheduling method based on time margin and state of charge priority sequencing. The model can analyze the influence of the state change of the electric vehicle on the controllability of the EV cluster, and provides a basis for a power grid to make a scheduling plan according to the energy storage potential of the power grid.
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
Example 1:
the invention provides a method for determining the cluster controllability of an electric automobile, which comprises the following steps of:
s1: calculating available charge/discharge capacity and available charge/discharge power of each electric automobile at the current scheduling time by utilizing a pre-constructed single electric automobile controllability evaluation model according to the residual charge duration, the current SOC and the target SOC of each electric automobile at the current scheduling time;
s2: and overlapping the available charging/discharging capacity and the available charging/discharging power of each electric automobile at the current moment, determining the charging/discharging capacity boundary and the power boundary of the electric automobile cluster at the current dispatching moment, and further determining the controllability of the electric automobile cluster at the dispatching time period corresponding to the current dispatching moment.
In step S1, the model for evaluating the individual EV controllability is constructed as follows:
when a user accesses the electric automobile in the stop state into the power grid, the aggregator in the charging station can read parameters such as the initial state of charge (SOC), the target SOC and the stop time period of the electric automobile, so that the available charging and discharging power and capacity of the electric automobile are evaluated. The controllability of a single-body electric vehicle is shown in fig. 2.
Define electric vehicle in monomer at t0Constantly connected to the charging pile and at tleaveAnd when the connection is disconnected at the moment, the area A-B-C-D-E-F is the maximum operation area of the single electric automobile in the time period.
The relation between the SOC and the time t in the charging and discharging process of the electric automobile is
In the formula, SOCtThe state of charge of the electric automobile at the moment t;
for electric vehicles at t0A state of charge at a time; ptThe charging power of the electric automobile at the moment t; qnThe rated capacity of the electric automobile.
Since the rated capacity of the individual electric vehicle remains unchanged, the slope of the curve in fig. 2 can characterize the charging power of the electric vehicle. The area enclosed by the AB-BC-CD-DE-EF-FA is an EV operation feasible area, and the boundary can be represented as:
in the formula, PmaxThe maximum charging power of the electric automobile is obtained; -PmaxThe maximum discharge power of the electric automobile; SOC0The initial charge state of the electric vehicle when the electric vehicle is connected to a power grid; SOCmaxThe maximum state of charge allowed for the electric vehicle battery; SOCminA minimum state of charge for the electric vehicle to allow regulation; SOCobjAnd setting a target charge state for an electric vehicle user. In particular, when SOC0＜SOCminWhen the SOC is more than or equal to the SOC, the battery needs to be forcibly charged firstlyminAnd is allowed to participate in regulation. t is tnowIs the current scheduling time; t is tleaveThe leaving time of the electric automobile.
The point a is the time when the electric vehicle is connected into the charging pile, the point B is the time when the maximum charging operation reaches the maximum state of charge allowed by the electric vehicle based on the initial SOC, the point C is the time when the electric vehicle reaches the stop time based on the maximum available state of charge, the point D is the time when the electric vehicle reaches the stop time based on the target state of charge, the point F is the time when the maximum discharging operation reaches the minimum state of charge allowed to be regulated by the electric vehicle based on the initial SOC, and the point E is the maximum time when the electric vehicle reaches the target state of charge at the stop time based on the minimum state of charge allowed to be regulated by the electric vehicle;
the edges AB, BC and CD, which are sequentially connected by the point A, B, C, D, form a first boundary corresponding to a charging behavior that is preferentially charged with the maximum available state of charge as a goal and has the highest charging priority;
the edges AF, FE, and ED sequentially connected by the point A, F, E, D form a second boundary corresponding to a charging behavior that preferentially discharges and has the lowest charging priority with the target state of charge as the target;
defining an A-X curve as a charge-discharge operation curve of the electric automobile after self-access, wherein the electric automobile is at t0～tnowUndergoes a charge-discharge process in a time period tnowWhen the time reaches the operation state X, curves operated by the maximum charge-discharge power are XY and XZ respectively, a point Y is a charge boundary, a point Z is a discharge boundary, and the expression is as follows:
the adjustable capacity of the single electric automobile is influenced by scheduling of the EV cluster control center and the battery capacity of the single electric automobile. Defining the scheduling period of the EV cluster as T, wherein T refers to time, and is equivalent to an independent variable (horizontal axis) in the formula, limiting the available charge and discharge capacity of the electric vehicle in the X running state by T and boundaries BC, CD, DE and EF in the figure 2, and respectively calculating the intersection points of XY and XZ and the feasible region boundary so as to obtain the intersection point of the electric vehicle at TnowThe charge/discharge capacity boundary at the time is as shown in equation (4).
In the formula, QchgT、QdchgTThe maximum available charge-discharge capacity of the monomer EV under the constraint of the scheduling time T is referred to; qchgBCRefers to the maximum charge capacity of the monomer EV under the constraint of BC; qchgCD、QdchgCDThe maximum charge-discharge capacity of a monomer EV under the constraint of CD; qdchgEDRefers to the maximum discharge capacity of the monomer EV under the constraint of ED; qdchgFERefers to the maximum discharge capacity of the monomer EV under FE constraints.
The maximum available charge-discharge capacity of the monomer EV in the future scheduling period T under the constraint of each boundary of the formula (4) is shown as the formula (5). The maximum available charge-discharge power of the monomer EV is not only influenced by the maximum charge-discharge power parameter P of the self batterymaxAnd the calculation mode is shown as the formula (6) and is limited by the maximum available charge and discharge capacity.
In the formula, Qchg、QdchgIs the available charge and discharge capacity boundary of the monomer EV; pchg、PdischgIs the usable charge and discharge power margin of the individual EV. Because the maximum power limit in the scheduling time is considered in the calculation of the available charge and discharge capacity boundary, the available charge and discharge power boundary can not exceed Pmax。
After the model for evaluating the controllability of the single electric vehicle is constructed, the available charge/discharge capacity and the available charge/discharge power of the electric vehicle at the current scheduling moment can be calculated.
S2: the method includes the steps that the available charging/discharging capacity and the available charging/discharging power of each electric automobile at the current moment are superposed, the charging/discharging capacity boundary and the power boundary of the electric automobile cluster at the current dispatching moment are determined, and then the controllability of the electric automobile cluster at the corresponding dispatching time interval at the current dispatching moment is determined, and the method specifically comprises the following steps:
the electric automobile cluster controllability is the superposition of the individual electric automobile controllability on a time axis. Defining a set n (t) ([ 1, …, k, k +1, …, n ]) as a number of a certain cluster EV at time t, wherein the controllability of the electric vehicle k can be obtained according to a single EV energy storage capacity model, and then the cluster controllability at time t is:
in the formula, Qclu(t) is the available capacity boundary of the EV cluster at the time t, including the charging and discharging capacity boundaries; pcluAnd (t) is an available power boundary of the EV cluster at the time t, including a charging power boundary and a discharging power boundary.
Example 2:
based on the same inventive concept, the invention further provides a scheduling method of an electric vehicle cluster, as shown in fig. 3, including:
step 1: according to the initial SOC, the target SOC and the stop time of each electric automobile in the electric automobile cluster, the regulatable and controllable capacity of the electric automobile cluster in each scheduling period is determined by using the method for determining the regulatable and controllable capacity of the electric automobile cluster (the implementation method can refer to the specific example of embodiment 1, and the description is not repeated herein);
step 2: and respectively distributing power of each electric automobile in the electric automobile cluster in each scheduling period based on the power grid demand time sequence and the controllability of each scheduling period electric automobile cluster to aim at supply and demand balance.
Wherein, step 1 specifically includes:
1) at each scheduling time tschCalculating the remaining charging duration and the current SOC of the electric automobile at the current scheduling moment according to the stop time, the accessed charging and discharging curve and the accessed time of each electric automobile in the electric automobile cluster;
2) determining the state type of each electric automobile according to the remaining charging time, the current SOC and the target SOC of each electric automobile at the current scheduling moment; wherein the electric vehicle status types include rigid electric vehicles and flexible electric vehicles;
3) based on all flexible electric vehicles, the method for determining the adjustability of the electric vehicle cluster calculates the adjustability of the electric vehicle cluster in the corresponding scheduling time period at the current scheduling time. Wherein each scheduling time interval is a time interval between adjacent scheduling time instants.
The specific process for determining the state type of each electric vehicle according to the current time remaining charging time, the current SOC and the target SOC of each electric vehicle is as follows:
in the time period that the electric automobile in the state of stopping driving inserts the electric pile, its state can be divided into rigidity electric automobile and flexible electric automobile two kinds. If the EV is short in parking time and high in target SOC, the EV cannot reach the target SOC set by the user at the user leaving time even if charging is performed with the maximum charging power, and such an EV is called a rigid EV. If EV stopping time and target SOC are reasonable, with some margin for regulation of EV charging and discharging, such charging is referred to as a flexible EV.
Shortest charging time of kth vehicle EV
The residence time can be calculated by the formula (9)
Can be calculated by equation (10). If the grid is at tschAnd issuing an EV cluster output instruction at the moment, wherein the EV classification needs to be judged at the moment. If it is
The kth EV is a rigid electric automobile; if it is
The kth EV is a flexible electric vehicle.
In the formula (I), the compound is shown in the specification,
for the k-th EV at tschA state of charge at a time;
a target state of charge for the kth EV;
rated capacity of the kth EV;
the maximum charging power of the kth EV.
Step 2 comprises the following specific procedures
At EV cluster scheduling time tschThe EV cluster control center evaluates the controllability of the cluster, the power grid optimizes and obtains the output curve of the EV cluster by taking system supply and demand balance as a target according to the controllability boundary provided by the EV cluster control center, and the EV cluster control center performs corresponding power optimized distribution on each EV through an internal scheduling strategy to realize accurate tracking of the output curve of the EV cluster issued by the power grid.
First, the EV cluster control center pair tschElectric vehicles connected into the charging pile at any moment are classified, and the rigid EV is charged with the maximum power preferentially, [ t [ [ t ]sch,tsch+T]The charging power of the ith rigid EV in the time period is as shown in equation (11).
The total charging power of the rigid EV cluster is then:
in the formula, m is tschThe number of rigid EVs in an EV cluster at time;
charging power for the ith rigid EV; prigidTotal charging power for a rigid EV cluster.
The total charging and discharging power of the flexible electric vehicle cluster in the scheduling period T can be represented as:
Pflex＝Pdem-Prigid(13)
in the formula, PdemOutput command, P, issued to EV cluster by power gridflexIs the total charge and discharge power of the flexible EV cluster.
The internal charging and discharging sequence of the flexible EV cluster is according to the monomer electric vehicle tschA time margin at time and a state of charge margin are determined. PRI (t) according to the priority index of formula (14)sch) And the size carries out charging and discharging priority sequencing on the interior of the flexible EV cluster. PRI (t)sch) The larger the margin of adjustability representing a flexible EV, if tschFlexible EV cluster output instruction P issued at any momentflex<0, this portion EV is discharged preferentially; PRI (t)sch) The smaller the tunable margin representing a flexible EV, if tschFlexible EV cluster output instruction P issued at any momentflex>0, this portion EV is charged with priority.
In the formula, PRIj(tsch) At t for jth flexibility EVschA charge-discharge priority index at a moment;
at t for jth flexibility EVschThe shortest charging time when the time reaches the target SOC can be calculated by the formula (9);
time to leave after reaching the stop time, t, for the jth Flexible EVschIs the scheduling time.
When the flexible EV cluster receives a charging instruction, the principle of meeting the charging requirement of the flexible EV with higher charging priority is taken as the principle, and the adjustment margin is arranged first, the PRI is small, and the PRI is setj(tsch) Charging the EV with a lower value; when the flexible EV receives a discharge command, the PRI with large adjustment margin is arranged firstj(tsch) The higher value EV is discharged. Considering the EV's own battery capacity limit, [ t [sch,tsch+T]The charging and discharging power of the jth flexible EV in the time period is shown as the formula (15).
In the formula (I), the compound is shown in the specification,
battery capacity of jth flexible EV;
the maximum state of charge allowed for the jth flexible EV;
at t for jth flexibility EVschA state of charge at a time;
the minimum state of charge allowed for the jth compliant EV. Wherein the content of the first and second substances,
the maximum power limit of EV self-charging and discharging should be satisfied, i.e.
Flexible EV cluster responded power Pal-flexAs shown in equation (16).
In the formula, l represents the number of flexible EVs which respond to the output instruction;
at [ t ] for the jth Flexible EVsch,tsch+T]Charge and discharge power over a time period. PflexThe total required power of the flexible electric vehicle distributed for dispatching, if Pal-flex＜PflexAnd continuing power distribution on the flexible EV cluster according to the charge-discharge priority index. If Pal-flex＜PflexAnd is
Then
When P is presental-flex＝PflexWhen this is the case, it represents the end of the power allocation to the flexible EV cluster.
When the flexible electric vehicles respond according to the charging and discharging instructions, the flexible electric vehicles meet the flexible EV charging requirements with higher charging priority as the principle, and after the power response of each flexible electric vehicle is completed, the flexible electric vehicles all meet the power requirement PflexComparing, if the responded flexible electric vehicle power Pal-flexIf the power demand is not reached, the next flexible electric vehicle is allocated, and if the power demand is exceeded, the power demand is determined according to the following steps
And setting the power of the last electric automobile so that the power of the correspondingly scheduled flexible electric automobile is consistent with the total demand of the flexible electric automobile cluster.
Example 3:
the method for determining the cluster controllability of the electric vehicles and the method for scheduling the cluster controllability of the electric vehicles are explained by performing simulation modeling based on real time data of private vehicles entering and leaving an office at a certain day.
In the embodiment, the simulation time is set to be 0-24 points and adjustedThe degree period is set to 10 min. 169 electric vehicles are participated in the grid regulation on the same day, and the number of the electric vehicles in different time periods is shown in fig. 4. Setting the initial state of charge of the electric vehicle cluster as SOC0U (0.2,0.4), maximum state of charge SOCmax0.9, minimum state of charge SOCmin0.15, target state of charge SOCobj0.85, battery capacity Qn70 kW.h, maximum charging power Pmax60kW, the maximum discharge power is-60 kW.
The simulation process is specifically as follows, and the evaluation result of the cluster controllability is analyzed according to the simulation process:
the controllability of the EV cluster is affected by the power instruction issued by the power grid, and fig. 5 shows a scheduling result of the EV cluster participating in the system. As shown in fig. 5, in a 60-65 scheduling period, the EV cluster responds to a power grid charging scheduling instruction, the charging response capability of the EV cluster at the next scheduling time is decreased, the discharging response capability is increased, and the cluster adjustable boundary is integrally moved down; in contrast, in the 65-80 scheduling period, the EV cluster responds to the power grid discharging scheduling instruction, the charging response capacity of the EV cluster at the next scheduling moment is increased, the discharging response capacity of the EV cluster is decreased, and the cluster adjustable boundary moves upwards integrally.
The EV cluster output instruction issued by the power grid within the cluster controllable boundary can be responded. The response of the EV cluster after receiving the scheduling instruction is shown in fig. 6-8. As can be seen from the simulation result in fig. 6, the electric vehicle cluster can accurately track the given power requirement within the boundary of the response capability, but there is a very small fluctuation due to the fact that some electric vehicles leave the station within the dispatching interval.
FIG. 7 is a graph of output response of a single EV under a dispatching instruction, and it can be seen from the graph that flexible electric vehicles can be charged to [ SOC ] at an off-site timeobj,SOCmax]The section meets the trip requirements of the car owner; in 5 electric vehicles, the initial power is too low, the residence time is too short (belonging to rigid EV), and even if maximum power charging is adopted, the target SOC cannot be achieved.
Fig. 8 shows the response of EV 2, 8, 20, 156, and it can be seen that the individual EVs charge/discharge power and state of charge during station time follow the EV cluster output command. Where the 20 th EV is a rigid EV that maintains maximum charging power at station time regardless of changes in scheduling instructions, but still does not reach the target SOC when off-station.
Therefore, the method can determine the running state (rigidity or flexibility) of the electric automobile according to the charging process of the electric automobile, realize real-time dynamic update of EV cluster power and capacity boundaries by reasonably distributing the power of the EVs in different states, and realize fine modeling of the controllability of the EV cluster.
Example 4:
in order to implement the method for determining the controllability of the electric vehicle cluster, the present invention further provides a system for determining the controllability of the electric vehicle cluster, as shown in fig. 9, including:
the single electric vehicle calculation module is used for calculating the available charging/discharging capacity and the available charging/discharging power of the electric vehicle at the current moment by utilizing a pre-constructed single electric vehicle adjustable capacity evaluation model according to the residual charging duration, the current SOC and the target SOC of each electric vehicle at each moment in the scheduling period T;
and the electric automobile cluster calculation module is used for superposing the available charging/discharging capacity and the available charging/discharging power of each electric automobile at the current moment, determining the charging/discharging capacity boundary and the power boundary of the electric automobile cluster at the current moment, and further determining the current controllability of the electric automobile cluster.
Each functional module of this embodiment is designed to implement the method for determining the cluster controllability of the electric vehicle, and specific reference is made to the foregoing embodiments, which will not be described herein again.
Example 5:
in order to implement the scheduling method of the electric vehicle cluster of the present invention, this embodiment provides a scheduling system of an electric vehicle cluster, as shown in fig. 10, including:
the adjustable and controllable capacity determining module is used for determining the adjustable and controllable capacity of the electric automobile cluster by using the method for determining the adjustable and controllable capacity of the electric automobile cluster, provided by the invention, according to the initial SOC, the target SOC and the stop time of each electric automobile in the electric automobile cluster in the scheduling time period T;
and the scheduling module is used for respectively performing power distribution on each electric automobile in the electric automobile cluster at each moment in the scheduling time period T by taking supply and demand balance as a target based on the power grid demand time sequence and the controllability of the electric automobile cluster at each moment.
Each functional module of this embodiment is designed to implement a scheduling method of an electric vehicle cluster, and reference is specifically made to the above embodiments, which are not described herein again.
It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (17)
1. A method for determining the cluster controllability of an electric vehicle is characterized by comprising the following steps:
calculating available charge/discharge capacity and available charge/discharge power of each electric automobile at the current scheduling time by utilizing a pre-constructed single electric automobile controllability evaluation model according to the residual charge duration, the current SOC and the target SOC of each electric automobile at the current scheduling time;
and overlapping the available charging/discharging capacity and the available charging/discharging power of each electric automobile at the current moment, determining the charging/discharging capacity boundary and the power boundary of the electric automobile cluster at the current dispatching moment, and determining the controllability of the electric automobile cluster at the corresponding dispatching time interval at the current dispatching moment.
2. The method of claim 1, wherein the construction of the model for evaluating the controllability of the single electric vehicle comprises:
determining a running state point X corresponding to the electric automobile at the current moment by taking a scheduling time interval T as a constraint according to a charging and discharging curve of the electric automobile after the electric automobile is accessed;
according to the operation state point X corresponding to the current moment, respectively determining a charging boundary point Y and a discharging boundary point Z which reach the maximum operation area most quickly by taking the maximum operation area at the current moment as a constraint;
respectively determining an available charge/discharge capacity boundary and an available charge/discharge power boundary of the electric automobile at the current moment based on the point X and the point Y as well as the point X and the point Z;
the current maximum operating region includes: a first boundary corresponding to a charging behavior with highest charging priority and charging priority by taking the maximum charge state allowed by the electric vehicle as a target; a second boundary corresponding to a charging behavior with lowest charging priority and discharging priority in view of the target state of charge of the electric vehicle; point Y is located at the first boundary and point Z is located at the second boundary;
the scheduling period is a time period between two scheduling instants.
3. The method of claim 2, wherein the determining of the maximum operating region comprises:
determining a plurality of state of charge time points according to the initial SOC, the target SOC and the stop time when the electric automobile is connected into the charging pile; wherein the plurality of state of charge time points comprises: the time when the electric automobile is connected into the charging pile is a point A, the time when the maximum charging operation reaches the maximum charge state allowed by the electric automobile is a point B based on the initial SOC, the time when the electric automobile reaches the stop time based on the maximum available charge state is a point C, the time when the electric automobile reaches the stop time based on the target charge state is a point D, the time when the maximum discharging operation reaches the minimum charge state allowed to be regulated by the electric automobile is a point F based on the initial SOC, and the maximum time when the electric automobile reaches the target charge state at the stop time based on the maximum charging operation is a point E in the minimum charge state allowed to be regulated;
the edges AB, BC and CD, which are sequentially connected by the point A, B, C, D, form a first boundary corresponding to a charging behavior that is preferentially charged with the maximum available state of charge as a goal and has the highest charging priority;
the edges AF, FE, and ED sequentially connected by the point A, F, E, D form a second boundary corresponding to a charging behavior that preferentially discharges and has the lowest charging priority with the target state of charge as the target;
a maximum operating zone is determined based on the first and second boundaries.
4. The method of claim 2, wherein the operating states X, Y, and Z comprise, respectively: the state of charge and the time corresponding to point X, point Y and point Z.
5. The method of claim 3, wherein the relation between the state of charge and the charge and discharge power and the time of the electric vehicle during the charge and discharge process corresponding to each side in the maximum operation area is as follows:
in the formula, QchgTThe maximum available charging capacity of the electric automobile under the constraint of the scheduling time period T is obtained; qdchgTThe maximum available discharge capacity of the electric automobile under the constraint of a scheduling time period T is obtained; qchgBCThe maximum charging capacity under the constraint of BC at the edge of the electric automobile is indicated; qchgCDMeans maximum charging capacity, Q, of the electric vehicle under the limit of side CDdchgCDThe maximum discharge capacity of the electric automobile under the limit of side CD; qdchgEDThe maximum discharge capacity of the electric automobile under the limit of edge ED is defined; qdchgFEThe maximum discharge capacity of the electric automobile under the limit of side FE is indicated; pmaxThe maximum charging power of the electric automobile is obtained; -PmaxThe maximum discharge power of the electric automobile; SOC0The initial charge state of the electric vehicle when the electric vehicle is connected to a power grid; SOCmaxThe maximum state of charge allowed for the electric vehicle battery; SOCnowThe current charge state of the electric vehicle; SOCminA minimum state of charge for the electric vehicle to allow regulation;SOCobja target state of charge set for an electric vehicle user; qnThe rated capacity of the electric automobile; t is t0Accessing the moment for the electric automobile; t is tnowIs the current scheduling time; t is tleaveThe leaving time of the electric automobile.
6. The method as claimed in claim 5, wherein the determining of the charge/discharge capacity boundary and the charge/discharge power boundary of the electric vehicle at the current scheduling time based on the point X and the point Y and the point X between the operating state X and the point Z comprises the following calculation formula:
in the formula, QchgIs an available charge capacity boundary for the electric vehicle; qdchgIs the usable discharge capacity boundary of the single electric vehicle; pchgAn available charging power boundary for the electric vehicle; pdischgIs the available discharge power boundary of the electric automobile.
7. An electric vehicle cluster controllability determination system, comprising:
the single electric vehicle calculation module is used for calculating the available charging/discharging capacity and the available charging/discharging power of the electric vehicle at the current scheduling time by utilizing a pre-constructed single electric vehicle controllability evaluation model according to the residual charging time, the current SOC and the target SOC of each electric vehicle at each scheduling time;
and the electric automobile cluster calculation module is used for superposing the available charging/discharging capacity and the available charging/discharging power of each electric automobile at the current scheduling moment, determining the charging/discharging capacity boundary and the power boundary of the electric automobile cluster at the current scheduling moment, and further determining the current adjustable and controllable capacity of the electric automobile cluster.
8. A dispatching method of an electric automobile cluster is characterized by comprising the following steps:
determining the controllability of the electric automobile cluster in each scheduling period by using the controllability determination method of claims 1 to 6 according to the initial SOC, the target SOC and the stop time of each electric automobile in the electric automobile cluster;
and respectively distributing power of each electric automobile in the electric automobile cluster in each scheduling period based on the power grid demand time sequence and the controllability of each scheduling period electric automobile cluster to aim at supply and demand balance.
9. The method of claim 8, wherein determining the controllability of the electric vehicle cluster in each scheduling period at each moment according to the initial SOC, the target SOC and the stop time of each electric vehicle in the electric vehicle cluster comprises:
at each scheduling time, sequentially aiming at the stop time, the accessed charge-discharge curve and the accessed time of each electric automobile in the electric automobile cluster, calculating the residual charge duration and the current SOC of the electric automobile at the current scheduling time;
determining the state type of each electric automobile according to the remaining charging time, the current SOC and the target SOC of each electric automobile at the current scheduling moment; wherein the electric vehicle status types include rigid electric vehicles and flexible electric vehicles;
calculating the controllability of the electric vehicle cluster in the corresponding scheduling period at the current scheduling moment based on all flexible electric vehicles by using the controllability determination method in claims 1 to 6.
10. The method of claim 9, wherein the determining the electric vehicle state type according to the remaining charge duration at the current scheduling time of each electric vehicle, the current SOC, and the target SOC comprises;
when the electric automobile cannot reach the target SOC even after being charged with the maximum charging power within the residual charging time, the state type of the electric automobile is a rigid electric automobile;
when the time length required by charging the electric automobile with the maximum charging power is less than the remaining charging time length of the electric automobile, the electric automobile is a flexible electric automobile.
11. The method of claim 9, wherein allocating power to each rigid electric vehicle in the cluster of electric vehicles at each time within the scheduled time period T comprises:
charging is performed at maximum power.
12. The method of claim 9, wherein allocating power to each flexible electric vehicle in the electric vehicle cluster during the scheduled time period comprises:
determining a charging and discharging priority based on the shortest charging time for the scheduling time corresponding to each scheduling time interval of each flexible electric vehicle to reach the target charge state;
and performing power distribution based on the charging and discharging priority of the electric automobile.
13. The method of claim 12, wherein the power distribution based on charge-discharge priorities of the electric vehicles comprises:
and sequentially executing the electric automobiles based on the priority order:
calculating whether the power of the flexible electric vehicle which is distributed in response to the dispatching is smaller than the total required power; when the current charging power is less than the preset charging power, the electric automobile is charged and discharged based on the charging and discharging power of the current electric automobile in the current scheduling period; otherwise, the power allocation is ended.
14. The method of claim 12, wherein the charge and discharge priority is determined according to the following equation:
PRIj(tsch) For the jth flexible electric automobile at tschA charge-discharge priority index at a moment; t is tschIs a scheduling time;
for the jth flexible electric automobile at tschThe shortest charging time to reach the target state of charge at the moment;
the stopping time reached by the jth flexible electric automobile.
15. The method of claim 13, wherein the calculation formula of the charge and discharge power of the current electric vehicle in the current scheduling period is as follows:
in the formula (I), the compound is shown in the specification,
charging and discharging power of the jth flexible electric automobile in the current scheduling time period T;
the battery capacity of the jth flexible electric automobile;
the maximum charge state allowed for the jth flexible electric vehicle;
for the jth flexible electric automobile at tschA state of charge at a time;
the minimum charge state allowed for the jth flexible electric vehicle; pflexAnd distributing the total required power of the flexible electric vehicle for dispatching.
16. The method of claim 8, wherein the electric vehicle cluster controllability based on the grid demand time series and each time is used for respectively performing power distribution on each electric vehicle in the electric vehicle cluster in each scheduling period with the supply and demand balance as a target, and then further comprising:
and according to the charging and discharging curve of each electric automobile after responding to the power distribution in the current scheduling period.
17. A dispatching system of an electric automobile cluster is characterized by comprising:
the control ability determining module is used for determining the control ability of the electric automobile cluster in each scheduling period by using the method of claims 1 to 6 according to the initial SOC, the target SOC and the stop time of each electric automobile in the electric automobile cluster;
and the scheduling module is used for respectively performing power distribution on each electric automobile in the electric automobile cluster in each scheduling period by taking supply and demand balance as a target based on the power grid demand time sequence and the adjustable and controllable capacity of the electric automobile cluster in each scheduling period.
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| CN112003312A (en) * | 2020-09-04 | 2020-11-27 | 河海大学 | Private electric vehicle participation power grid regulation and control capability assessment method based on trip chain and participation willingness |
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2021
- 2021-05-31 CN CN202110597136.5A patent/CN113541168A/en active Pending
- 2021-09-07 WO PCT/CN2021/117054 patent/WO2022252426A1/en active Application Filing
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| CN116278915B (en) * | 2023-05-16 | 2023-10-13 | 国网信息通信产业集团有限公司 | Electric automobile load online optimization method, system, equipment and medium |
| CN118171966A (en) * | 2024-05-15 | 2024-06-11 | 国网上海市电力公司 | Electric vehicle response capability estimation method based on charge and discharge degrees of freedom |
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| WO2022252426A1 (en) | 2022-12-08 |
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