CN112909979A - Multi-mode frequency modulation method, device, equipment and medium for cluster electric automobile - Google Patents

Multi-mode frequency modulation method, device, equipment and medium for cluster electric automobile Download PDF

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CN112909979A
CN112909979A CN202110419399.7A CN202110419399A CN112909979A CN 112909979 A CN112909979 A CN 112909979A CN 202110419399 A CN202110419399 A CN 202110419399A CN 112909979 A CN112909979 A CN 112909979A
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power
control
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frequency modulation
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CN112909979B (en
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崔艳林
蔡新雷
董锴
孟子杰
黎嘉明
吴龙腾
郝文焕
杨民京
谢文超
王勇超
栾添瑞
郭俊宏
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Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
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Electric Power Dispatch Control Center of Guangdong Power Grid 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

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Abstract

The invention discloses a multi-mode frequency modulation method, a multi-mode frequency modulation device, a multi-mode frequency modulation equipment and a multi-mode frequency modulation medium for a cluster electric automobile, wherein the method comprises the steps of constructing a multi-state switching control model of the cluster electric automobile based on a load dynamic transfer model in a charging, discharging and idle state of the electric automobile and a switching process among the charging, discharging and idle states; calculating the real-time adjustable power of the cluster electric automobile according to the multi-state switching control model, and performing sectional processing on the range of the real-time adjustable power; and judging the section to which the AGC command belongs according to the result of the section processing, designing a corresponding control mode, and constructing a multi-mode frequency modulation mode of the cluster electric automobile according to the control mode. The invention widens the real-time adjustable power range by considering the direct conversion of the charge-discharge state; and the charging and discharging power of the cluster electric automobile can be adjusted in real time, the direct switching times of the charging and discharging states are reduced, the charging and discharging power is prevented from being offset, and the AGC instruction is accurately and quickly tracked.

Description

Multi-mode frequency modulation method, device, equipment and medium for cluster electric automobile
Technical Field
The invention relates to the technical field of vehicle network interconnection, in particular to a multi-mode frequency modulation method, a multi-mode frequency modulation device, multi-mode frequency modulation equipment and a multi-mode frequency modulation medium for a cluster electric vehicle.
Background
With the rapid development of battery technology and vehicle-electricity interconnection technology, the mobile energy storage characteristic of the electric vehicle and the rapid response capability of the battery are utilized to make up for the deficiency of the frequency modulation resource of the new energy power system, and the method becomes a hot spot of current research. Different from a centralized energy storage power station, a single electric vehicle has low power and low capacity, so that the user using behavior is uncertain, and the difficulty of directly controlling the single electric vehicle is very high, so that the cluster electric vehicle is more suitable for participating in power grid regulation and control. However, the cluster electric vehicles have a large number of groups and wide distribution, and the disordered charging of the cluster electric vehicles can increase peak-valley difference, reduce the power quality due to too many charging and discharging conversion times, and even have negative effects on the safe and stable operation of the system. Therefore, how to provide a frequency modulation method to reasonably adjust and control multiple modes of the cluster electric vehicle is a technical problem to be solved urgently in the field.
Disclosure of Invention
The invention aims to provide a multi-mode frequency modulation method, a multi-mode frequency modulation device, multi-mode frequency modulation equipment and a multi-mode frequency modulation medium for a cluster electric automobile, and aims to solve the problems that in the prior art, the frequency modulation difficulty of the electric automobile is high, the electric energy quality is influenced, and the safety is low.
In order to overcome the defects in the prior art, the invention provides a multi-mode frequency modulation method for a cluster electric automobile, which comprises the following steps:
constructing a multi-state switching control model of the cluster electric automobile based on a load dynamic transfer model of the electric automobile in a charging, discharging and idle state and a switching process between the charging, discharging and idle states;
calculating the real-time adjustable power of the cluster electric automobile according to the multi-state switching control model, and performing sectional processing on the range of the real-time adjustable power;
and judging the section to which the AGC command belongs according to the result of the section processing, designing a corresponding control mode, and constructing a multi-mode frequency modulation mode of the cluster electric automobile according to the control mode.
Further, the load dynamic transfer model in the charging state of the electric vehicle is as follows:
xc(k+1)＝Acxc(k)+vc(k)
in the formula, xc(k) The N-dimensional column vector represents the load quantity in each SOC interval under the charging state at the moment k; v. ofc(k) The N-dimensional column vector represents the load amount newly added or separated from the charging state in each interval at the moment k; a. thecA state transition matrix in a charging state;
wherein x isc(k)，vc(k) And AcRespectively satisfy:
xc(k)＝[xc(k,1),…,xc(k,i),…,xc(k,N)]T
vc(k)＝[vc(k,1),vc(k,2),…,vc(k,N)]T
Figure BDA0003027217220000021
in the formula (I), the compound is shown in the specification,
Figure BDA0003027217220000022
indicates SOC interval i during chargingcAnd icA transition probability between + 1;
Figure BDA0003027217220000023
the load amount representing the charge completion is a proportion of the total load amount in the section N.
Further, the load dynamic transfer model in the electric vehicle discharge state is as follows:
xd(k+1)＝Adxd(k)+vd(k)
in the formula, xd(k) The N-dimensional column vector represents the load quantity in each SOC interval in the discharge state at the moment k; v. ofd(k) The N-dimensional column vector represents the load quantity newly added or separated from the discharge state in each interval at the moment k; a. thedState transition matrix for discharge state:
Figure BDA0003027217220000024
in the formula (I), the compound is shown in the specification,
Figure BDA0003027217220000031
indicates the SOC interval i in the discharging processd+1 and idTransition probabilities between;
Figure BDA0003027217220000032
the load amount indicating completion of discharge is a proportion of the total load amount in the section 1.
Further, the load dynamic transfer model in the idle state of the electric vehicle is as follows:
xl(k+1)＝Alxl(k)+vl(k)
in the formula, xl(k) The N-dimensional column vector represents the load quantity in each SOC interval under the idle state at the moment k; v. ofl(k) The N-dimensional column vector represents the load quantity newly added or separated from the idle state in each interval at the moment k; a. thelIs an N-dimensional unit matrix and represents a state transition matrix in an idle state.
Further, the multi-state switching control model of the cluster electric vehicle is as follows:
Figure BDA0003027217220000033
in the formula u1(k)、u2(k)、u3(k) Are all N-dimensional column vectors; a is a 3 Nx 3N-dimensional state transition matrix; b is a 3 Nx 3N-dimensional state transition matrix used for describing a state switching process; c is an output row vector with 3N dimensions and is used for describing a power output part; v (k) ═ vc(k),vl(k),vd(k)]T(ii) a Y (k) is the total output power.
Wherein A, B, C is:
Figure BDA0003027217220000034
Figure BDA0003027217220000035
Figure BDA0003027217220000036
in the formula, 0 represents an N-dimensional zero matrix; i represents an N-dimensional unit array;
Figure BDA0003027217220000037
representing that the cluster electric vehicle adopts maximum power charging;
Figure BDA0003027217220000038
indicating that the cluster electric automobile adopts maximum power discharge.
Further, the step of performing segmentation processing on the range of the real-time adjustable power includes dividing the real-time adjustable power into a real-time down-adjustable power and a real-time up-adjustable power; wherein the content of the first and second substances,
the real-time down-tunable power is divided into a first section, a second section and a third section:
when u is1(k+1)＝xc(k)，u2(k +1) ═ 0 and u3When (k +1) is equal to 0, the power is adjusted downwards by P1 -To obtain the interval [0, P1 -]Is a first section;
when u is1(k+1)＝xc(k)，u2(k +1) ═ 0 and u3(k+1)＝xl(k) When the power is adjusted downwards by P2 -Obtaining a section (P)1 -,P2 -]Is a second section;
when u is1(k+1)＝0，u2(k+1)＝xc(k) And u is3(k+1)＝xl(k) Make the power down regulated by P3 -To obtain a section
Figure BDA0003027217220000041
Is a third stage;
the real-time adjustable power is divided into a fourth section, a fifth section and a sixth section:
when u is1(k+1)＝0，u2(k +1) ═ 0 and u3(k+1)＝-xd(k) While making the power up-regulated by P1 +To obtain the interval [0, P1 +]Is a fourth segment;
when u is1(k+1)＝-xl(k)，u2(k +1) ═ 0 and u3(k+1)＝-xd(k) While making the power up-regulated by P2 +Obtaining a section (P)1 +,P2 +]Is a fifth section;
when u is1(k+1)＝-xl(k)，u2(k+1)＝-xd(k)And u is3When (k +1) is 0, the power is adjusted up by P3 +To obtain a section
Figure BDA0003027217220000042
Is the sixth stage.
Further, the multi-mode frequency modulation mode of the cluster electric vehicle comprises:
a first control mode, a second control mode, a third control mode, a fourth control mode, a fifth control mode and a sixth control mode;
when the cluster electric automobile is in the first section, the first control mode is adopted, and the corresponding state space expression, the control target and the constraint condition are respectively as follows:
Figure BDA0003027217220000043
min J＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)
s.t.0≤u1(k+i|k)≤xc(k),i＝0,1,2…m-1
in the formula, x1(k) The total state variable in the first control mode is the sum of three state variables of charging, idling and discharging in the first control mode, i.e. the total state variable is
Figure BDA0003027217220000051
Y1(k) The output power of the system in the first control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control;
when the cluster electric automobile is in the second section, the second control mode is adopted, and the corresponding state space expression, the control target and the constraint condition are respectively as follows:
Figure BDA0003027217220000052
min J＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)+Qo(Cx(k+1))2
s.t.0≤u1(k+i|k)≤xc(k),i＝0,1,2…m-1
0≤u3(k+i|k)≤xl(k),i＝0,1,2…m-1
in the formula, x2(k) The total state variable in the second control mode is the sum of three state variables of charging, idling and discharging in the mode, namely
Figure BDA0003027217220000053
Y2(k) The output power of the system in the second control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control; qo(Cx (k +1)) is a mutually offsetting term for increasing the idle state ratio and reducing the charge and discharge power of the electric automobile in the cluster, QoIs QoA weight coefficient of the (Cx (k +1)) term;
when the cluster electric automobile is in the third section, the third control mode is adopted, and the corresponding state space expression, the control target and the constraint condition are respectively as follows:
Figure BDA0003027217220000061
min J＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)+Qo(Cx(k+1))2
s.t.u1(k+i|k),u2(k+i|k),u3(k+i|k)≥0
u1(k+i|k)+u2(k+i|k)≤xc(k)
u3(k+i|k)≤xl(k),i＝1,2,3…m
in the formula, x3(k) The total state variable in the third control mode is the sum of the three state variables of charging, idling and discharging in the mode, i.e. the total state variable
Figure BDA0003027217220000062
Y3(k) The output power of the system in the third control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control; qo(Cx (k +1)) is a mutually offsetting term for increasing the idle state ratio and reducing the charge and discharge power of the electric automobile in the cluster, QoIs QoA weight coefficient of the (Cx (k +1)) term;
when the cluster electric automobile is in the fourth section, the fourth control mode is adopted, and the corresponding state space expression, the control target and the constraint condition are respectively as follows:
Figure BDA0003027217220000063
minJ＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)
s.t.-xd(k)≤u3(k+i|k)≤0,i＝0,1,2…m-1
in the formula, x4(k) The total state variable in the fourth control mode is the sum of three state variables of charging, idling and discharging in the fourth control mode, namely
Figure BDA0003027217220000071
Y4(k) The output power of the system in the fourth control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control;
when the cluster electric automobile is in the fifth section, the fifth control mode is adopted, and the corresponding state space expression, the control target and the constraint condition are respectively as follows:
Figure BDA0003027217220000072
min J＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)+Qo(Cx(k+1))2
s.t.-xl(k)≤u1(k+i|k)≤0,i＝0,1,2…m-1
-xd(k)≤u3(k+i|k)≤0,i＝0,1,2…m-1
in the formula, x5(k) The total state variable in the fifth control mode is the sum of the three state variables of charging, idling and discharging in the fifth control mode, i.e. the total state variable is
Figure BDA0003027217220000073
Y5(k) The output power of the system in the fifth control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control; qo(Cx (k +1)) is a mutually offsetting term for increasing the idle state ratio and reducing the charge and discharge power of the electric automobile in the cluster, QoIs QoA weight coefficient of the (Cx (k +1)) term;
when the cluster electric automobile is in the sixth section, the sixth control mode is adopted, and the corresponding state space expression, the control target and the constraint condition are respectively as follows:
Figure BDA0003027217220000081
min J＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)+Qo(Cx(k+1))2
s.t.u1(k+i|k),u2(k+i|k),u3(k+i|k)≤0
u2(k+i|k)+u3(k+i|k)≥-xd(k)
u1(k+i|k)≥-xl(k),i＝1,2,3…m
in the formula, x6(k) The total state variable in the sixth control mode is the sum of the three state variables of charging, idling and discharging in the sixth control mode, namely
Figure BDA0003027217220000082
Y6(k) The output power of the system in the sixth control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control; qo(Cx (k +1)) is a mutually offsetting term for increasing the idle state ratio and reducing the charge and discharge power of the electric automobile in the cluster, QoIs QoThe weight coefficient of the (Cx (k +1)) term.
The invention also provides a multi-mode frequency modulation device of the cluster electric automobile, which comprises the following components:
the control model building unit is used for building a multi-state switching control model of the cluster electric automobile based on a load dynamic transfer model of the electric automobile in a charging, discharging and idle state and a switching process between the charging, discharging and idle states;
the segmentation processing unit is used for calculating the real-time adjustable power of the cluster electric automobile according to the multi-state switching control model and carrying out segmentation processing on the range of the real-time adjustable power;
and the frequency modulation mode establishing unit is used for judging the section to which the AGC command belongs according to the result of the section processing, designing a corresponding control mode and establishing a multi-mode frequency modulation mode of the cluster electric automobile according to the control mode.
The present invention also provides a terminal device, including:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, the one or more programs cause the one or more processors to implement the method for multi-mode frequency modulation for trunked electric vehicles as described in any of the above.
The invention also provides a computer readable storage medium, on which a computer program is stored, the computer program being executed by a processor to implement the multi-mode frequency modulation method for the cluster electric vehicle as described in any one of the above.
Compared with the prior art, the invention has the beneficial effects that:
the invention constructs a cluster electric vehicle multi-state switching control model by combining the switching process of three states based on the load dynamic transfer model of three states of charging, discharging and idling of the electric vehicle. The model considers the direct conversion between the charging state and the discharging state, and can widen the real-time adjustable power range of the cluster electric automobile. Based on the multi-state switching control model, the invention also carries out sectional processing on the real-time adjustable power, designs a corresponding control mode for each power section, and provides a multi-mode frequency modulation mode. The mode can effectively reduce the direct charge-discharge conversion times of the cluster electric automobile, avoid the offset of charge-discharge power and quickly and accurately track the AGC instruction of the power grid.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a multi-mode frequency modulation method for a cluster electric vehicle according to an embodiment of the present invention;
fig. 2 is a schematic diagram of probability transition between adjacent SOC intervals in a charging process according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a probability transition between adjacent SOC regions during a discharging process according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating state switching according to an embodiment of the present invention;
FIG. 5 is a graph comparing real-time adjustable power ranges provided by an embodiment of the present invention;
FIG. 6 is a graph illustrating a comparison of direct charge-discharge conversion times in different manners according to an embodiment of the present invention;
FIG. 7 is a graph comparing the number of electric vehicles charged and discharged in different manners according to an embodiment of the present invention;
fig. 8 is a diagram of the tracking result of AGC commands provided by an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a multi-mode frequency modulation device of a cluster electric vehicle according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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.
It should be understood that the step numbers used herein are for convenience of description only and are not intended as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
In a first aspect:
referring to fig. 1, an embodiment of the present invention provides a multi-mode frequency modulation method for a cluster electric vehicle, including:
s10, constructing a multi-state switching control model of the cluster electric automobile based on a load dynamic transfer model of the electric automobile in a charging, discharging and idle state and a switching process between the charging, discharging and idle states;
in this embodiment, first, a load dynamic transfer model in a charging, discharging, and idle state of the electric vehicle needs to be constructed, where SOC of the electric vehicle is discretized into N intervals, and x represents a load amount of the corresponding interval, and then the load dynamic transfer model in the charging state of the electric vehicle is:
xc(k+1)＝Acxc(k)+vc(k) (1)
in the formula, xc(k) Is an N-dimensional column vector and satisfies xc(k)＝[xc(k,1),…,xc(k,i),…,xc(k,N)]TRepresenting the load quantity in each SOC interval under the charging state at the moment k; v. ofc(k) Is an N-dimensional column vector and satisfies vc(k)＝[vc(k,1),vc(k,2),…,vc(k,N)]TIndicating the load amount newly added or separated from the charging state in each interval at the moment k; a. thecState transition matrix for charged state:
Figure BDA0003027217220000111
in the formula (I), the compound is shown in the specification,
Figure BDA0003027217220000112
indicates SOC interval i during chargingcAnd icA transition probability between + 1;
Figure BDA0003027217220000113
the load amount representing the charge completion is a proportion of the total load amount in the section N.
It should be noted that, in two adjacent time intervals, the load in each interval has only two transfer paths: remain in the original state interval or move to the next adjacent interval. At this time, the section icTo the interval ic+1 amount of load transferred
Figure BDA0003027217220000121
Represents; while remaining in the interval icThe load of (2) is
Figure BDA0003027217220000122
And (4) showing.
In one embodiment, the transition probability in equation (2)
Figure BDA0003027217220000123
The specific solving process of (2) is as follows:
assuming that the battery capacity of the electric automobile participating in the aggregation meets a certain probability density distribution f (C)P) From the inherent charging characteristics of the battery, one can derive:
Figure BDA0003027217220000124
wherein, PchRepresents a charging power; etachRepresents the charging efficiency; Δ t represents a time interval; s (k +1) and S (k) respectively represent the charge states of the electric vehicle at the k +1 moment and the k moment;
Figure BDA0003027217220000125
the critical capacity is the maximum allowable capacity capable of realizing the transition from S (k) to S (k +1) in one step. Thus, a relationship between the random process SOC and the probability distribution of the battery capacity can be established:
Figure BDA0003027217220000126
in the formula, PrRepresenting the conditional probability.
As shown in FIG. 2, the transition probabilities are solved for convenience and accuracy
Figure BDA0003027217220000127
Section icEqually divided into n cells.Firstly, calculating the inter-cell directional interval ic+1 transition probability
Figure BDA0003027217220000128
Further, find the interval icTo the interval ic+1 desired transition probability.
Figure BDA0003027217220000129
Wherein the content of the first and second substances,
Figure BDA00030272172200001210
represents a section icMiddle jcInter-cell directional interval ic+1 probability of transition; sdown(i +1) represents the i-thcA lower bound value of +1 interval; sdown(i, j) represents a section icMiddle jcSOC lower bound value of each small interval;
Figure BDA00030272172200001211
and
Figure BDA00030272172200001212
respectively represent the interval icThe minimum battery capacity C can be obtained by using the 0 probability critical value and the 1 probability critical valueminAnd maximum battery capacity CmaxThe results are obtained by the following formula (3). Probability of expected transition
Figure BDA0003027217220000131
Figure BDA0003027217220000132
Further, the discharge process is similar to the charging process in modeling, and the SOC is equally divided into N parts from low to high, so that the load dynamic transfer model in the discharge state of the electric vehicle is as follows:
xd(k+1)＝Adxd(k)+vd(k) (7)
in the formula (I), the compound is shown in the specification,xd(k) the N-dimensional column vector represents the load quantity in each SOC interval in the discharge state at the moment k; v. ofd(k) The N-dimensional column vector represents the load quantity newly added or separated from the discharge state in each interval at the moment k; a. thedState transition matrix for discharge state:
Figure BDA0003027217220000133
in the formula (I), the compound is shown in the specification,
Figure BDA0003027217220000134
indicates the SOC interval i in the discharging processd+1 and idTransition probabilities between;
Figure BDA0003027217220000135
the load amount indicating completion of discharge is a proportion of the total load amount in the section 1.
In contrast to the charging process, since the discharging is a load shift from the high SOC to the low SOC, the section i is defined asd+1 to the interval idThe transition probability of (2). At this time, the section id+1 to the interval idFor transferred load capacity
Figure BDA0003027217220000136
Represents; while remaining in the interval idThe load of (2) is
Figure BDA0003027217220000137
And (4) showing.
In one embodiment, the transition probability in equation (8)
Figure BDA0003027217220000138
The specific solving process of (2) is as follows:
first, it is noted that the critical capacity during discharge
Figure BDA0003027217220000139
The following can be obtained from the inherent discharge characteristics of the battery:
Figure BDA00030272172200001310
in the formula, PdisRepresents the discharge power; etadisIndicating the discharge efficiency; Δ t represents a time interval; s (k +1) and S (k) respectively represent the charge states of the electric vehicle at the k +1 moment and the k moment;
Figure BDA0003027217220000141
the critical capacity is the maximum allowable capacity capable of realizing the transition from S (k) to S (k +1) in one step.
As shown in FIG. 3, the transition probabilities are solved for convenience and accuracy
Figure BDA0003027217220000142
Section id+1 is equally divided into n sub-intervals. Firstly, calculating the inter-cell directional interval idTransition probability p ofd (i+1,j)→iFurther, the section i is obtainedd+1 to the interval idDesired transition probability of (2):
Figure BDA0003027217220000143
wherein the content of the first and second substances,
Figure BDA0003027217220000144
represents a section id+1 th jdInter-cell directional interval idThe probability of a transition; sup(i) Denotes the ithdUpper bound values of the intervals; sup(i +1, j) represents a section id+1 th jdSOC upper bound value among the cells;
Figure BDA0003027217220000145
and
Figure BDA0003027217220000146
respectively represent the interval idThe 0 probability threshold and the 1 probability threshold in +1 may be set to the minimum battery capacity CminAnd maximum battery capacity CmaxDetermination of the expected transition probability by means of the respective equations (9)
Figure BDA0003027217220000147
Figure BDA0003027217220000148
Further, the load dynamic transfer model in the idle state of the electric vehicle is as follows:
xl(k+1)＝Alxl(k)+vl(k) (12)
in the formula, xl(k) And the N-dimensional column vector represents the load quantity in each SOC interval in the idle state at the moment k. v. ofl(k) The N-dimensional column vector represents the load quantity newly added or separated from the idle state in each interval at the moment k; a. thelThe state transition matrix is in an idle state, and in the idle state, the electric vehicle has neither charging power nor discharging power, and the SOC of the electric vehicle does not change at all, so that the load quantity in each SOC interval does not change, namely AlIs an N-dimensional unit matrix.
Further, in this embodiment, according to the established dynamic transition models of the three states of the electric vehicle, a dynamic switching model between the three states of the electric vehicle can be developed. In the dynamic transfer model of the three states, the SOC discretization methods are the same, namely the SOC interval N is equally divided and sorted according to the increasing sequence. The state switching process means that the loads in the same SOC interval are reasonably switched among 3 states of charging, discharging and idling. As shown in FIG. 4, FIG. 4 shows a schematic diagram of the status switching process u1Indicating the amount of load transfer between the charging and idle states; u. of2Indicating the amount of load transfer between the charged and discharged states; u. of3Indicating the load transfer amount between the idle state and the discharge state; the superscript "+" indicates power up; "-" indicates power down.
Based on the above process analysis, in combination with the load dynamic transfer model of the charging, discharging and idle states that has been established in the foregoing, the expression of the multi-state switching control model of the clustered electric vehicle is as follows:
Figure BDA0003027217220000151
in the formula u1(k)、u2(k)、u3(k) Are all N-dimensional column vectors; a is a 3 Nx 3N-dimensional state transition matrix; b is a 3 Nx 3N-dimensional state transition matrix used for describing a state switching process; c is an output row vector with 3N dimensions and is used for describing a power output part; v (k) ═ vc(k),vl(k),vd(k)]T(ii) a Y (k) is the total output power.
It should be emphasized that, after considering the dynamic load transfer process of charging, discharging and idling, a in the multi-state switching control model is:
Figure BDA0003027217220000152
wherein A isc、Al、AdAs already mentioned above, 0 then represents an N-dimensional zero matrix.
Because the state switching process should satisfy the quantity conservation of the electric automobiles, only the running state of the electric automobiles is changed, and the quantity of the electric automobiles is not changed, namely the total net load change quantity is zero during each state switching. For this reason, the control matrix B can be derived as:
Figure BDA0003027217220000161
wherein I represents an N-dimensional unit matrix.
Making the cluster electric vehicles all adopt maximum power charging and discharging, and the idle state power is 0, so as to obtain C:
Figure BDA0003027217220000162
in the formula (I), the compound is shown in the specification,
Figure BDA0003027217220000163
representing that the cluster electric vehicle adopts maximum power charging;
Figure BDA0003027217220000164
indicating that the cluster electric automobile adopts maximum power discharge.
S20, calculating the real-time adjustable power of the cluster electric automobile according to the multi-state switching control model, and performing sectional processing on the range of the real-time adjustable power;
in this embodiment, in the multi-state switching control model, the electric vehicles at time k are divided into 3 different groups, that is, a charging state group, a discharging state group and an idle state group, and the load number of each state group at time k +1 can be obtained by combining the control quantity at time k, so that the real-time adjustable power of the electric vehicles at the next time can be predicted.
In particular, the power P may be adjusted down in real time-Can be divided into three sections, namely I-, II-and III-sections;
i-section: when u is1(k+1)＝xc(k)，u2(k+1)＝0，u3When (k +1) ═ 0, namely the electric vehicles in the charging state group are all stopped charging and are switched to the idle state, the power can be reduced by P1 -Then [0, P1 -]To down-regulate the power I-section.
II-section: when u is1(k+1)＝xc(k)，u2(k+1)＝0，u3(k+1)＝xl(k) When is at P1 -On the basis of the power regulation, all the electric vehicles in the idle state group are converted into a discharge state, so that the power is regulated down by P2 -Then (P)1 -,P2 -]In order to adjust the power II-section downward.
III-stage: when u is1(k+1)＝0，u2(k+1)＝xc(k)，u3(k+1)＝xl(k) That is, all the electric vehicles in the charging state group and the idle state group are converted into the discharging state, so as to obtain the maximum power reduction P3 -Then (P)2 -,P3 -]For down-regulation of power III-section, greater than P3 -The frequency modulation instruction cluster electric automobile reduces the power P at the maximum3 -A response is made.
Further, the power P can be adjusted down in real time+Can be divided into three sections, i.e. I+、II+And III+A segment;
I+section (2): when u is1(k+1)＝0，u2(k+1)＝0，u3(k+1)＝-xd(k) When the electric vehicle in the discharging state group stops discharging and is converted into an idle state, the power can be adjusted up to P1 +Then [0, P1 +]For up-regulating power I+And (4) section.
II+Section (2): when u is1(k+1)＝-xl(k)，u2(k+1)＝0，u3(k+1)＝-xd(k) When is at P1 +On the basis of the power control method, all the electric vehicles in the idle state group are converted into a charging state, so that the power is up-regulated by P2 +Then (P)1 +,P2 +]For up-regulating power II+And (4) section.
III+Section (2): when u is1(k+1)＝-xl(k)，u2(k+1)＝-xd(k)，u3(k +1) ═ 0, that is, all electric vehicles in the discharging state group and the idle state group are converted into the charging state, and the maximum up-regulated power P can be obtained3 +Then (P)2 +,P3 +]For up-regulating power III+Segment greater than P3 +The frequency modulation instruction cluster electric automobile adjusts the power P at the maximum3 +A response is made.
And S30, judging the segment to which the AGC command belongs according to the result of the segment processing, designing a corresponding control mode, and constructing a multi-mode frequency modulation mode of the cluster electric automobile according to the control mode.
In this embodiment, a multi-mode control method based on model predictive control is proposed by determining an applicable control mode of the frequency modulation command in combination with the foregoing real-time adjustable power segmentation method. The 6 power segments correspond to 6 control modes in total, and a state space expression, a control target and a constraint condition corresponding to each control mode are as follows:
control mode 1: the method is suitable for real-time power down regulation I-section, and the corresponding state space expression, control target and constraint condition are respectively as follows:
Figure BDA0003027217220000171
minJ＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)
s.t.0≤u1(k+i|k)≤xc(k),i＝0,1,2…m-1 (18)
in the formula, x1(k) The total state variable in the first control mode is the sum of three state variables of charging, idling and discharging in the first control mode, i.e. the total state variable is
Figure BDA0003027217220000181
Y1(k) The output power of the system in the first control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control;
control mode 2: the method is suitable for real-time power down regulation II-section, and the corresponding state space expression, control target and constraint condition are respectively as follows:
Figure BDA0003027217220000182
minJ＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)+Qo(Cx(k+1))2
s.t.0≤u1(k+i|k)≤xc(k),i＝0,1,2…m-1
0≤u3(k+i|k)≤xl(k),i＝0,1,2…m-1 (20)
in the formula, x2(k) The total state variable in the second control mode is the sum of three state variables of charging, idling and discharging in the mode, namely
Figure BDA0003027217220000183
Y2(k) The output power of the system in the second control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control; qo(Cx (k +1)) is a mutually offsetting term for increasing the idle state ratio and reducing the charge and discharge power of the electric automobile in the cluster, QoIs QoA weight coefficient of the (Cx (k +1)) term;
control mode 3: the method is suitable for real-time power down regulation III-section, and the corresponding state space expression, control target and constraint condition are respectively as follows:
Figure BDA0003027217220000191
minJ＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)+Qo(Cx(k+1))2
s.t.u1(k+i|k),u2(k+i|k),u3(k+i|k)≥0
u1(k+i|k)+u2(k+i|k)≤xc(k)
u3(k+i|k)≤xl(k),i＝1,2,3…m (22)
in the formula, x3(k) The total state variable in the third control mode is the sum of the three state variables of charging, idling and discharging in the mode, i.e. the total state variable
Figure BDA0003027217220000192
Y3(k) The output power of the system in the third control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control; qo(Cx (k +1)) is a mutually offsetting term for increasing the idle state ratio and reducing the charge and discharge power of the electric automobile in the cluster, QoIs QoA weight coefficient of the (Cx (k +1)) term; it is emphasized that Q is introduced hereoThe term (Cx (k +1)) has a function of reducing the number of times of direct switching of the charge/discharge state in addition to preventing the charge/discharge power from being cancelled out.
Control mode 4: adapted to up-regulate power I in real time+And the corresponding state space expression, control target and constraint condition are respectively:
Figure BDA0003027217220000193
min J＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)
s.t.-xd(k)≤u3(k+i|k)≤0,i＝0,1,2…m-1 (24)
in the formula, x4(k) The total state variable in the fourth control mode isSummary of three state variables of charging, idling, and discharging in this mode, i.e.
Figure BDA0003027217220000201
Y4(k) The output power of the system in the fourth control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control;
control mode 5: adapted to up-regulate power II in real time+And the corresponding state space expression, control target and constraint condition are respectively:
Figure BDA0003027217220000202
minJ＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)+Qo(Cx(k+1))2
s.t.-xl(k)≤u1(k+i|k)≤0,i＝0,1,2…m-1
-xd(k)≤u3(k+i|k)≤0,i＝0,1,2…m-1 (26)
in the formula, x5(k) The total state variable in the fifth control mode is the sum of the three state variables of charging, idling and discharging in the fifth control mode, i.e. the total state variable is
Figure BDA0003027217220000203
Y5(k) The output power of the system in the fifth control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2To output the quantityError term of predicted value and reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control; qo(Cx (k +1)) is a mutually offsetting term for increasing the idle state ratio and reducing the charge and discharge power of the electric automobile in the cluster, QoIs QoA weight coefficient of the (Cx (k +1)) term;
control mode 6: adapted to up-regulate power III in real time+And the corresponding state space expression, control target and constraint condition are respectively:
Figure BDA0003027217220000211
minJ＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)+Qo(Cx(k+1))2
s.t.u1(k+i|k),u2(k+i|k),u3(k+i|k)≤0
u2(k+i|k)+u3(k+i|k)≥-xd(k)
u1(k+i|k)≥-xl(k),i＝1,2,3…m (28)
in the formula, x6(k) The total state variable in the sixth control mode is the sum of the three state variables of charging, idling and discharging in the sixth control mode, namely
Figure BDA0003027217220000212
Y6(k) The output power of the system in the sixth control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is introduced for reducing the number of state switching times of the electric vehicleControl constraint term of, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control; qo(Cx (k +1)) is a mutually offsetting term for increasing the idle state ratio and reducing the charge and discharge power of the electric automobile in the cluster, QoIs QoThe weight coefficient of the (Cx (k +1)) term.
The embodiment of the invention considers the direct conversion between the charging state and the discharging state, and can widen the real-time adjustable power range of the cluster electric automobile. Meanwhile, based on a multi-state switching control model, the real-time adjustable power is processed in a segmented mode, a corresponding control mode is designed for each power segment, a multi-mode frequency modulation mode is provided, the direct charge-discharge conversion times of the cluster electric vehicle can be effectively reduced, the offset of charge-discharge power is avoided, and the AGC (automatic gain control) instruction of the power grid is quickly and accurately tracked.
To further understand the present invention and verify the effectiveness of the multimode fm method based on the multi-state switching control model, in one embodiment, the actual AGC commands in a certain area from 18:00 to 18:30 are selected for simulation verification. The simulation step length is 0.1s, and the cluster parameters of 1000 electric vehicles participating in aggregation are shown in table 1.
TABLE 1 electric vehicle Cluster parameter settings
Figure BDA0003027217220000221
To illustrate that the multi-state switching control model provided by the present invention has a wider real-time adjustable power range, simulation comparison is performed on the multi-state switching control model with the switching model without considering direct charge-discharge state conversion, and the result is shown in fig. 5. Therefore, in the face of the same regulation and control instruction, after direct charge-discharge conversion is considered in the regulation and control process, the same number of electric vehicles is switched, the power change value of the multi-state switching control model is larger, and a wider real-time adjustable power range is obtained really.
In one embodiment, to analyze the advantages of the multimode frequency modulation method, comparative simulation was performed in the following two ways: mode 1 multi-mode frequency modulation control mode; mode 2 does not distinguish between AGC commands and real-time adjustable power, and a single control mode is adopted, i.e. it is considered that 3 states can be switched at each time. As shown in fig. 6, fig. 6 shows the comparison result of the number of times of direct charge and discharge switching of the battery in 2 modes. It can be seen that the frequency of direct switching in mode 2 is significantly higher than in mode 1, while under the multi-mode regulation of mode 1, direct switching occurs only in modes 3 and 6, subject to constraints. It was calculated that the same AGC command was switched 926 times in the mode 1 and the number of times of switching in the mode 2 was as high as 2925 times. The multi-mode frequency modulation method provided by the invention can effectively reduce the direct switching frequency of battery charging and discharging, and reduce the loss of electric energy.
Referring to fig. 7, in one embodiment, the results of comparing the sum of the number of electric vehicles in the charged and discharged states in 2 ways are shown. It can be seen that the sum of the number of electric vehicles in the charging and discharging states in the mode 2 is obviously greater than that in the mode 1, and since the two track the same AGC instruction in the time interval, the sum of the power of the electric vehicles in the mode 2 which is greater than that in the mode 1 is inevitably zero at each moment, that is, the charging and discharging power in the mode 2 is cancelled out, and the mode 1 effectively avoids the phenomenon, so that the more efficient dispatching of the clustered electric vehicles is realized.
Referring to fig. 8, in one embodiment, the tracking result of the AGC command in the multi-mode control mode is shown. In the period of 18:00-18:30, except for the circled part in fig. 8, the cluster electric automobiles can accurately and quickly follow the change of the AGC power instruction. This is because the AGC command exceeds the real-time adjustable power limit of the cluster electric vehicle during the circled period, resulting in incomplete response of the cluster electric vehicle.
In a second aspect:
referring to fig. 9, in an embodiment, there is further provided a multi-mode frequency modulation apparatus for a cluster electric vehicle, including:
the control model building unit 01 is used for building a multi-state switching control model of the cluster electric vehicle based on a load dynamic transfer model of the electric vehicle in a charging, discharging and idle state and a switching process between the charging, discharging and idle states;
the segmentation processing unit 02 is used for calculating the real-time adjustable power of the cluster electric automobile according to the multi-state switching control model and performing segmentation processing on the range of the real-time adjustable power;
and the frequency modulation mode establishing unit 03 is configured to judge the segment to which the AGC instruction belongs according to the result of the segment processing, design a corresponding control mode, and establish a multi-mode frequency modulation mode of the cluster electric vehicle according to the control mode.
It can be understood that the functional modules 01 to 03 of the device are respectively used for executing the steps S10 to S30, and direct conversion between charging and discharging states is considered when the steps are executed, so that the real-time adjustable power range of the cluster electric vehicle is widened. Meanwhile, the direct charge-discharge conversion times of the cluster electric automobile can be effectively reduced, the offset of charge-discharge power is avoided, and the AGC instruction of the power grid is quickly and accurately tracked.
In a third aspect:
an embodiment of the present invention further provides a terminal device, including: one or more processors; a memory coupled to the processor for storing one or more programs; when executed by the one or more processors, the one or more programs cause the one or more processors to implement the method for multi-mode frequency modulation for a cluster electric vehicle as described above.
The processor is used for controlling the overall operation of the terminal equipment so as to complete all or part of the steps of the multi-mode frequency modulation method of the cluster electric automobile. The memory is used to store various types of data to support operation at the terminal device, and these data may include, for example, instructions for any application or method operating on the terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
The terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is configured to perform the method for multi-mode frequency modulation of the cluster electric vehicle according to any of the embodiments described above, and achieve technical effects consistent with the method described above.
An embodiment of the present invention further provides a computer readable storage medium including program instructions, which when executed by a processor, implement the steps of the method for multi-mode frequency modulation of the cluster electric vehicle according to any one of the above embodiments. For example, the computer readable storage medium may be the above memory including program instructions, which are executable by the processor of the terminal device to perform the method for multi-mode frequency modulation of the cluster electric vehicle according to any one of the above embodiments, and achieve the technical effects consistent with the above method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.

Claims (10)

1. A multi-mode frequency modulation method of a cluster electric automobile is characterized by comprising the following steps:
constructing a multi-state switching control model of the cluster electric automobile based on a load dynamic transfer model of the electric automobile in a charging, discharging and idle state and a switching process between the charging, discharging and idle states;
calculating the real-time adjustable power of the cluster electric automobile according to the multi-state switching control model, and performing sectional processing on the range of the real-time adjustable power;
and judging the section to which the AGC command belongs according to the result of the section processing, designing a corresponding control mode, and constructing a multi-mode frequency modulation mode of the cluster electric automobile according to the control mode.
2. The multi-mode frequency modulation method for the cluster electric vehicles according to claim 1, wherein the load dynamic transfer model in the charging state of the electric vehicles is as follows:
xc(k+1)＝Acxc(k)+vc(k)
in the formula, xc(k) The N-dimensional column vector represents the load quantity in each SOC interval under the charging state at the moment k; v. ofc(k) The N-dimensional column vector represents the load amount newly added or separated from the charging state in each interval at the moment k; a. thecA state transition matrix in a charging state;
wherein x isc(k)，vc(k) And AcRespectively satisfy:
xc(k)＝[xc(k,1),…,xc(k,i),…,xc(k,N)]T
vc(k)＝[vc(k,1),vc(k,2),…,vc(k,N)]T
Figure FDA0003027217210000011
in the formula (I), the compound is shown in the specification,
Figure FDA0003027217210000012
indicates SOC interval i during chargingcAnd icA transition probability between + 1;
Figure FDA0003027217210000013
the proportion of the load representing the completion of charging to the total load of the section N。
3. The multi-mode frequency modulation method of the cluster electric vehicle as claimed in claim 2, wherein the load dynamic transfer model in the electric vehicle discharging state is as follows:
xd(k+1)＝Adxd(k)+vd(k)
in the formula, xd(k) The N-dimensional column vector represents the load quantity in each SOC interval in the discharge state at the moment k; v. ofd(k) The N-dimensional column vector represents the load quantity newly added or separated from the discharge state in each interval at the moment k; a. thedState transition matrix for discharge state:
Figure FDA0003027217210000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003027217210000022
indicates the SOC interval i in the discharging processd+1 and idTransition probabilities between;
Figure FDA0003027217210000023
the load amount indicating completion of discharge is a proportion of the total load amount in the section 1.
4. The multi-mode frequency modulation method for the cluster electric vehicle as claimed in claim 3, wherein the load dynamic transfer model in the idle state of the electric vehicle is as follows:
xl(k+1)＝Alxl(k)+vl(k)
in the formula, xl(k) The N-dimensional column vector represents the load quantity in each SOC interval under the idle state at the moment k; v. ofl(k) The N-dimensional column vector represents the load quantity newly added or separated from the idle state in each interval at the moment k; a. thelIs an N-dimensional unit matrix and represents a state transition matrix in an idle state.
5. The multi-mode frequency modulation method of the cluster electric vehicle as claimed in claim 4, wherein the multi-state switching control model of the cluster electric vehicle is:
Figure FDA0003027217210000031
in the formula u1(k)、u2(k)、u3(k) Are all N-dimensional column vectors; a is a 3 Nx 3N-dimensional state transition matrix; b is a 3 Nx 3N-dimensional state transition matrix used for describing a state switching process; c is an output row vector with 3N dimensions and is used for describing a power output part; v (k) ═ vc(k),vl(k),vd(k)]T(ii) a Y (k) is the total output power;
wherein A, B, C is:
Figure FDA0003027217210000032
Figure FDA0003027217210000033
Figure FDA0003027217210000034
in the formula, 0 represents an N-dimensional zero matrix; i represents an N-dimensional unit array;
Figure FDA0003027217210000035
representing that the cluster electric vehicle adopts maximum power charging;
Figure FDA0003027217210000036
indicating that the cluster electric automobile adopts maximum power discharge.
6. The method according to claim 5, wherein the step of segmenting the range of the real-time adjustable power comprises dividing the real-time adjustable power into a real-time down-adjustable power and a real-time up-adjustable power; wherein the content of the first and second substances,
the real-time down-tunable power is divided into a first section, a second section and a third section:
when u is1(k+1)＝xc(k)，u2(k +1) ═ 0 and u3When (k +1) is equal to 0, the power is adjusted downwards by P1 -To obtain the interval [0, P1 -]Is a first section;
when u is1(k+1)＝xc(k)，u2(k +1) ═ 0 and u3(k+1)＝xl(k) When time, the power is adjusted downwards
Figure FDA0003027217210000037
Obtaining the interval
Figure FDA0003027217210000041
Is a second section;
when u is1(k+1)＝0，u2(k+1)＝xc(k) And u is3(k+1)＝xl(k) To down-regulate power
Figure FDA0003027217210000042
Obtaining the interval
Figure FDA0003027217210000043
Is a third stage;
the real-time adjustable power is divided into a fourth section, a fifth section and a sixth section:
when u is1(k+1)＝0，u2(k +1) ═ 0 and u3(k+1)＝-xd(k) While making the power up-regulated by P1 +To obtain the interval [0, P1 +]Is a fourth segment;
when u is1(k+1)＝-xl(k)，u2(k +1) ═ 0 and u3(k+1)＝-xd(k) Time-of-day, power up-regulation
Figure FDA0003027217210000044
Obtaining the interval
Figure FDA0003027217210000045
Is a fifth section;
when u is1(k+1)＝-xl(k)，u2(k+1)＝-xd(k) And u is3When (k +1) is 0, the power is adjusted up
Figure FDA0003027217210000046
Obtaining the interval
Figure FDA0003027217210000047
Is the sixth stage.
7. The method for multi-mode frequency modulation of the clustered electric vehicles according to claim 6, wherein the multi-mode frequency modulation mode of the clustered electric vehicles comprises:
a first control mode, a second control mode, a third control mode, a fourth control mode, a fifth control mode and a sixth control mode;
when the cluster electric automobile is in the first section, the first control mode is adopted, and the corresponding state space expression, the control target and the constraint condition are respectively as follows:
Figure FDA0003027217210000048
minJ＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)
s.t.0≤u1(k+i|k)≤xc(k),i＝0,1,2…m-1
in the formula, x1(k) The total state variable in the first control mode is the sum of three state variables of charging, idling and discharging in the first control mode, i.e. the total state variable is
Figure FDA0003027217210000049
Y1(k) The output power of the system in the first control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control;
when the cluster electric automobile is in the second section, the second control mode is adopted, and the corresponding state space expression, the control target and the constraint condition are respectively as follows:
Figure FDA0003027217210000051
minJ＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)+Qo(Cx(k+1))2
s.t.0≤u1(k+i|k)≤xc(k),i＝0,1,2…m-1
0≤u3(k+i|k)≤xl(k),i＝0,1,2…m-1
in the formula, x2(k) The total state variable in the second control mode is the sum of three state variables of charging, idling and discharging in the mode, namely
Figure FDA0003027217210000052
Y2(k) The output power of the system in the second control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtIs the error termWeight occupation; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control; qo(Cx (k +1)) is a mutually offsetting term for increasing the idle state ratio and reducing the charge and discharge power of the electric automobile in the cluster, QoIs QoA weight coefficient of the (Cx (k +1)) term;
when the cluster electric automobile is in the third section, the third control mode is adopted, and the corresponding state space expression, the control target and the constraint condition are respectively as follows:
Figure FDA0003027217210000061
minJ＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)+Qo(Cx(k+1))2
s.t.u1(k+i|k),u2(k+i|k),u3(k+i|k)≥0
u1(k+i|k)+u2(k+i|k)≤xc(k)
u3(k+i|k)≤xl(k),i＝1,2,3…m
in the formula, x3(k) The total state variable in the third control mode is the sum of the three state variables of charging, idling and discharging in the mode, i.e. the total state variable
Figure FDA0003027217210000062
Y3(k) The output power of the system in the third control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U(k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control; qo(Cx (k +1)) is a mutually offsetting term for increasing the idle state ratio and reducing the charge and discharge power of the electric automobile in the cluster, QoIs QoA weight coefficient of the (Cx (k +1)) term;
when the cluster electric automobile is in the fourth section, the fourth control mode is adopted, and the corresponding state space expression, the control target and the constraint condition are respectively as follows:
Figure FDA0003027217210000063
minJ＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)
s.t.-xd(k)≤u3(k+i|k)≤0,i＝0,1,2…m-1
in the formula, x4(k) The total state variable in the fourth control mode is the sum of three state variables of charging, idling and discharging in the fourth control mode, namely
Figure FDA0003027217210000064
Y4(k) The output power of the system in the fourth control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control;
when the cluster electric automobile is in the fifth section, the fifth control mode is adopted, and the corresponding state space expression, the control target and the constraint condition are respectively as follows:
Figure FDA0003027217210000071
minJ＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)+Qo(Cx(k+1))2
s.t.-xl(k)≤u1(k+i|k)≤0,i＝0,1,2…m-1
-xd(k)≤u3(k+i|k)≤0,i＝0,1,2…m-1
in the formula, x5(k) The total state variable in the fifth control mode is the sum of the three state variables of charging, idling and discharging in the fifth control mode, i.e. the total state variable is
Figure FDA0003027217210000072
Y5(k) The output power of the system in the fifth control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control; qo(Cx (k +1)) is a mutually offsetting term for increasing the idle state ratio and reducing the charge and discharge power of the electric automobile in the cluster, QoIs QoA weight coefficient of the (Cx (k +1)) term;
when the cluster electric automobile is in the sixth section, the sixth control mode is adopted, and the corresponding state space expression, the control target and the constraint condition are respectively as follows:
Figure FDA0003027217210000081
minJ＝Qt||Yp(k)-Pt(k)||2+U(k)TQuU(k)+Qo(Cx(k+1))2
s.t.u1(k+i|k),u2(k+i|k),u3(k+i|k)≤0
u2(k+i|k)+u3(k+i|k)≥-xd(k)
u1(k+i|k)≥-xl(k),i＝1,2,3…m
in the formula, x6(k) The total state variable in the sixth control mode is the sum of the three state variables of charging, idling and discharging in the sixth control mode, namely
Figure FDA0003027217210000082
Y6(k) The output power of the system in the sixth control mode; y isp(k) An output sequence of the prediction model at the time k; pt(k) A sequence formed by frequency modulation power instructions at the time k; | | Yp(k)-Pt(k)||2Is an error term of the output quantity predicted value and the reference signal, and QtThe weight of the error term is taken; u (k)TQuU (k) is a control constraint term introduced to reduce the number of state switching times of the electric vehicle, QuIs U (k)TQuA weight matrix of the U (k) term; m represents a control step in model predictive control; qo(Cx (k +1)) is a mutually offsetting term for increasing the idle state ratio and reducing the charge and discharge power of the electric automobile in the cluster, QoIs QoThe weight coefficient of the (Cx (k +1)) term.
8. A multi-mode frequency modulation device of a cluster electric automobile is characterized by comprising:
the control model building unit is used for building a multi-state switching control model of the cluster electric automobile based on a load dynamic transfer model of the electric automobile in a charging, discharging and idle state and a switching process between the charging, discharging and idle states;
the segmentation processing unit is used for calculating the real-time adjustable power of the cluster electric automobile according to the multi-state switching control model and carrying out segmentation processing on the range of the real-time adjustable power;
and the frequency modulation mode establishing unit is used for judging the section to which the AGC command belongs according to the result of the section processing, designing a corresponding control mode and establishing a multi-mode frequency modulation mode of the cluster electric automobile according to the control mode.
9. A terminal device, comprising:
one or more processors;
a memory coupled to the processor for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method of multi-mode frequency modulation for a cluster electric vehicle as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program is executed by a processor to implement a method of multi-mode frequency modulation of a trunked electric vehicle according to any of claims 1 to 7.
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