CN108964031B - Model prediction control method for charging and participating in voltage regulation of electric automobile - Google Patents

Model prediction control method for charging and participating in voltage regulation of electric automobile Download PDF

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CN108964031B
CN108964031B CN201810764467.1A CN201810764467A CN108964031B CN 108964031 B CN108964031 B CN 108964031B CN 201810764467 A CN201810764467 A CN 201810764467A CN 108964031 B CN108964031 B CN 108964031B
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CN108964031A (en
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李立英
李瑶
邹见效
徐红兵
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University of Electronic Science and Technology of China
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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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Abstract

The invention discloses a model predictive control method for charging and participating in voltage regulation of an electric automobile, which comprises the steps of firstly establishing a linear state space model of a power grid system for charging and participating in voltage regulation of the electric automobile, then calculating according to a preset voltage target value to obtain an optimal reference curve in a prediction stage, then establishing an optimization problem model in a first stage by taking the deviation of the voltage of the power grid system from a rated value and the change of a control quantity as a target function, and obtaining an optimal reachable curve which minimizes the target function of the optimization problem model according to the optimal reference curve; and finally, taking the deviation of the minimized power grid voltage, the controlled variable and a given reference value as a target function, establishing a predictive controller model in the second stage, obtaining control output according to the optimal reachable reference curve, converting the control output into a control increment of the power grid system, inputting the control increment into a state space model of the power grid system, and performing parameter control on the power grid system. The invention can simultaneously meet the power consumption requirements of the electric automobile and maintain the voltage stability of the power grid.

Description

Model prediction control method for charging and participating in voltage regulation of electric automobile
Technical Field
The invention belongs to the technical field of electric vehicle charging and control, and particularly relates to a model predictive control method for electric vehicle charging and voltage regulation participation.
Background
Along with increasingly prominent contradictions between economic development and environmental pollution and energy crisis, reducing energy consumption and dependence on petroleum resources is an urgent problem to be solved. The development of electric vehicles with obvious advantages in the aspects of environmental protection, cleanness, energy conservation and the like is an important measure for relieving the contradictions. Electric vehicles have increasingly taken up an increasing share of the automotive market. Although the development of electric vehicles can play a role in alleviating the problems of energy crisis, environmental pollution and the like, a large number of electric vehicles are charged by being connected to a power grid, and inevitably affect the operation, planning and electric power market operation of the power grid. Based on the technology (V2G) of the electric automobile interacting with the smart grid, a plurality of auxiliary services such as peak clipping and valley filling, frequency regulation, voltage regulation and the like can be provided for the grid by utilizing the flexibility of the electric automobile so as to maintain the stability of the grid.
At present, research on participation of electric vehicles in grid voltage regulation to schedule charging processes of electric vehicles and perform reactive power optimization is heavily focused, and some control algorithms have been researched in the industry to better achieve control targets. Model Predictive Control (MPC) is a feedback control strategy that has been widely discussed in recent years. It has several distinct advantages: the model is simple, and the requirement on the precision of the model is low; the dynamic performance is better by adopting a rolling optimization strategy; it can effectively deal with multivariable and constraint problems. The model predictive control can be regarded as a linear/nonlinear dynamic optimization problem with constraints, and compared with a linear controller, the online optimization process is feasible, and a better control effect can be realized. The MPC controller calculates future control sequences and minimizes performance metrics, reflecting the optimization objective equations and dynamic constraints of the system. In recent years, a large number of scholars have begun to incorporate this theory into the control strategy of electric vehicles, in combination with the results and experiences of model predictive control in industrial production.
In the research of combining model predictive control and electric automobile at present, what is considered mostly is to charge and energy management, the inside temperature management of battery etc. to electric automobile, and the characteristics such as flexibility and the parking time that utilize electric automobile very little provide the pressure regulating service for the electric wire netting, only rely on the effect of traditional reactive power compensator, have caused unnecessary wasting of resources. Therefore, current research lacks effective guidance for practical applications.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a model predictive control method for charging and participating in voltage regulation of an electric automobile, provides reactive compensation for a power grid on the basis of meeting the power consumption requirements of electric automobile users, establishes a mathematical model and completes the control process by taking the deviation of the power grid voltage and a rated value as a target, and simultaneously meets the power consumption requirements of the electric automobile and the target of maintaining the stability of the power grid voltage.
In order to achieve the purpose, the model predictive control method for charging and participating in voltage regulation of the electric automobile comprises the following specific steps:
s1: constructing a linear state space model of the power grid system needing model prediction control:
x(k+1)＝Ax(k)+Bu(k)
y(k)＝Cu(k)
Figure BDA0001728695320000021
where k denotes an operation time of the grid system, x (k) and x (k +1) denote SOC vectors of electric vehicles in the grid system at time k and k +1, respectively, and x (k) ═ e1(k),…,eM(k))T，x(k+1)＝(e1(k+1),…,eM(k+1))TThe superscript T denotes transposition, em(k)、em(k +1) represents the SOC of the mth electric vehicle in the grid system at time k and k +1, respectively, where M is 1,2, …, and M represents the number of electric vehicles in the grid system;
u(k)＝[Q(k)uc(k)]Tq (k) represents the reactive power compensation amount at time k,
Figure BDA0001728695320000022
Figure BDA0001728695320000023
the reactive compensation quantity of the electric vehicle group connected to the bus i at the time k is shown,
Figure BDA0001728695320000024
the reactive compensation amount of a reactive compensation device installed at the bus i at the time k is shown, i is 1,2, …, N, and N is the number of buses in the power grid system; u. ofc(k)＝(c1(k),…,cM(k))，cm(k) Representing the grid at time kCharging power of the mth electric vehicle in the system;
Figure BDA0001728695320000025
v (k) represents the voltage of the grid at time k,
Figure BDA0001728695320000026
Figure BDA0001728695320000027
a vector of a dimension N is represented,
Figure BDA0001728695320000028
v0a constant value representing the substation voltage; r represents an NxN bus resistor matrix, the element R of whichijRepresents the resistance between the busbars i and j, j being 1,2, …, N; x represents an NxN bus reactance matrix, the element X of whichijRepresents the reactance between busbars i and j; pl(k) The active consumption vector representing the base load in the grid system at time k,
Figure BDA0001728695320000029
Figure BDA00017286953200000210
the active power consumption of the basic load at the bus i at the moment k is represented; ql(k) Representing the vector of reactive consumption of the base load in the grid system at time k,
Figure BDA00017286953200000211
Figure BDA00017286953200000212
representing the reactive power consumption of the base load at the bus i at time k;
a represents an identity matrix, B ═ 0Tf]，TfRepresents a sampling time; c ═ X-RK]K represents a connection matrix of the NxM electric vehicles and the bus, and the corresponding element K is connected to the bus i when the mth electric vehicle is connected to the bus i im1, otherwise Kim＝0；
Figure BDA0001728695320000031
A constraint space representing the linear state space model, defined as follows:
Figure BDA0001728695320000032
wherein e (k) represents the SOC and SOC of the battery of the electric vehicle at the time kmaxAnd SOCminRespectively representing the upper limit and the lower limit of the battery SOC; c. Cm(k) Represents the charging power of the mth electric automobile in the power grid system at the time k,
Figure BDA0001728695320000033
is the reactive power of the mth electric vehicle, SmaxRepresents the maximum apparent power; c. CmaxRepresents the maximum charging power;
Figure BDA0001728695320000034
representing the maximum injected reactive power of the electric vehicle; q. q.sminAnd q ismaxRespectively representing the minimum value and the maximum value of the reactive power compensation quantity; v. ofnomRepresenting a preset nominal value of the target voltage, vmaxAnd vminRespectively representing the upper limit and the lower limit of a safety and stability range of the power grid voltage;
s2: according to the set nominal value v of the target voltagenomAnd calculating a prediction curve of the active power and the reactive power of the basic load in the power grid system to obtain an optimal reference curve r (k) in a prediction stage:
Figure BDA0001728695320000035
s3: and establishing an optimization problem model in a first stage by taking the minimum deviation of the voltage of the power grid system and the rated value and the change of the control quantity as objective functions:
Figure BDA0001728695320000036
Figure BDA0001728695320000037
Figure BDA0001728695320000038
Figure BDA0001728695320000039
Figure BDA00017286953200000310
Figure BDA00017286953200000311
wherein x isr(k)、ur(k)、yr(k) Respectively representing the best reachable curves of the minimized objective function obtained by solving according to the reference curve R (k), the superscript "-" represents that the corresponding parameters are the parameters obtained by solving according to the optimization problem model, | | | | represents the norm of the solution, R1And R2Representing a predetermined weight matrix for controlling the weight, T, of the corresponding two variablessRepresenting a prediction time domain in the phase model predictive control;
according to the optimal reference curve r (k) obtained in the step S2, solving the optimization problem model to obtain the optimal reachable curve x which minimizes the objective functionr(k)、ur(k)、yr(k)；
S4: and establishing a second-stage predictive controller model by taking the deviation of the minimized grid voltage and the controlled variable from a given reference value as an objective function:
Figure BDA0001728695320000041
Figure BDA0001728695320000042
Figure BDA0001728695320000043
Figure BDA0001728695320000044
Figure BDA0001728695320000045
Figure BDA0001728695320000046
Figure BDA0001728695320000047
wherein R is3And R4Representing a preset weight matrix for controlling the weight of the corresponding two variables, the superscript "-" representing that the corresponding parameter is a parameter solved according to the predictive controller model, TdA prediction horizon representing the model predictive control in that phase;
using the predictive controller model, the best achievable reference curve x obtained in step S3 is trackedr(k)、ur(k)、yr(k) Then, an optimal control increment Δ U (k) satisfying the above target is obtained, and then Δ U (k) ═ I0 … 0 is calculated]ΔU*(k)；
S5: and (5) inputting the control increment delta u (k) into the state space model in the step S1 to obtain the corresponding SOC vector x (k) and the voltage parameter y (k) of the electric automobile, and performing parameter control on the power grid system.
The invention relates to a model predictive control method for electric vehicle charging and voltage regulation, which comprises the steps of firstly establishing a linear state space model of a power grid system for electric vehicle charging and voltage regulation, then calculating according to a preset voltage target value to obtain an optimal reference curve of a prediction stage, then establishing an optimization problem model of a first stage by taking the deviation between the voltage of the power grid system and a rated value and the change of a control quantity as a target function, and obtaining an optimal reachable curve which minimizes the target function of the optimization problem model according to the optimal reference curve; and finally, taking the deviation of the minimized power grid voltage, the controlled variable and a given reference value as a target function, establishing a predictive controller model in the second stage, obtaining control output according to the optimal reachable reference curve, converting the control output into a control increment of the power grid system, inputting the control increment into a state space model of the power grid system, and performing parameter control on the power grid system.
The invention has the following beneficial effects:
1) on the premise of meeting the power consumption requirement of an electric automobile user, the flexibility of the electric automobile is fully considered, the V2G technology is utilized to assist or even replace a traditional reactive power compensation device to participate in power grid voltage regulation, the operation and maintenance cost of a power grid is effectively reduced, the economic requirement of power grid operation is considered, and the method has guiding significance for solving the problem caused by grid connection of the electric automobile;
2) the invention designs a scheme of charging and participating in voltage regulation of the electric automobile based on model predictive control, achieves the aims of simultaneously meeting the power consumption requirement of the electric automobile and maintaining the voltage stability of a power grid, and can better achieve the control aim compared with the general linear/nonlinear optimization problem through the online rolling optimization of the two-stage model.
Drawings
FIG. 1 is a flowchart of an embodiment of a model predictive control method for charging and participating in voltage regulation of an electric vehicle according to the present invention;
FIG. 2 is a topology diagram of the power grid system in the present embodiment;
FIG. 3 is a load curve of a typical household user in the power grid during a periodic time assumed during simulation verification of the present embodiment;
FIG. 4 is a diagram of the weight matrix R in the first stage optimization problem in this embodiment2Not equal to 0 time-slotA plot of the end voltage of line 18 versus time;
FIG. 5 is a diagram of the weight matrix R in the first stage optimization problem in this embodiment2A plot of the terminal voltage of bus 18 versus time at 0;
FIG. 6 is a diagram of the weight matrix R in the first stage optimization problem in this embodiment2A curve graph of the terminal voltage of the bus 18 changing with the number of the electric vehicles when not equal to 0;
FIG. 7 is a diagram of the weight matrix R in the first stage optimization problem in this embodiment2A graph of the terminal voltage of the bus bar 18 as a function of the number of electric vehicles at 0;
fig. 8 is a reactive compensation curve diagram of the reactive compensation device and the electric vehicle in each case when the number of the electric vehicles is 20 in the present embodiment;
FIG. 9 is a reactive compensation curve diagram of an electric vehicle under different conditions when the number of electric vehicles is 50 in the present embodiment;
fig. 10 is a graph of the charging power of the electric vehicle EV3 and the average charging power of all electric vehicles in the present embodiment;
fig. 11 is a map of the SOC of the electric vehicle EV3 and the SOCs of all electric vehicles in the present embodiment.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
Examples
FIG. 1 is a flowchart of an embodiment of a model predictive control method for charging and participating in voltage regulation of an electric vehicle according to the present invention. As shown in fig. 1, the method for model predictive control of electric vehicle charging and participating in voltage regulation of the present invention specifically comprises the steps of:
s101: designing a linear state space model of the power grid system:
first, an electric vehicle charging linear model may be defined as follows:
e(k+1)＝e(k)+Tfc(k)
wherein k represents the operation time of the grid system, e (k), e (k +1) represents the battery SOC (State of Charge, unit KWh) of the electric vehicle at the time k and the time k +1, c (k) represents the charging power provided for the electric vehicle at the time k, and TfWhich represents the sample time, i.e., the time interval between two data samples.
The constraint conditions of the electric vehicle charging linear model are as follows:
● electric vehicle charging capacity constraint:
SOCmin≤SOCinit+Tfc(k)≤SOCmax
therein, SOCinitIndicating an initial value of the storage capacity of the battery, SOCmaxAnd SOCminRespectively representing the upper and lower limits of the battery SOC.
The writing can be simplified as:
SOCmin≤e(k)≤SOCmax
● electric vehicle charger constraint:
the active and reactive operating efficiency of an electric vehicle charger is limited by the apparent power:
Figure BDA0001728695320000061
wherein the content of the first and second substances,
Figure BDA0001728695320000062
and
Figure BDA0001728695320000063
is the active power and the reactive power of the mth electric automobile, M is 1,2, …, M, wherein M represents the total quantity of the electric automobiles connected to the power grid, S represents the apparent powermaxRepresenting the maximum apparent power. In the invention, the charging power c of the mth electric automobile is adoptedmTo replace active power
Figure BDA0001728695320000071
In the present invention, it is specified that the electric vehicle does not perform the discharging operation, so the following constraints are imposed on the active operation of the electric vehicle:
Figure BDA0001728695320000072
wherein, cmaxRepresenting the maximum charging power.
When an electric automobile injects reactive power into a power grid, the working efficiency of the electric automobile is affected by the apparent power, internal components of a charger and the like, and can be expressed as follows:
Figure BDA0001728695320000073
wherein the content of the first and second substances,
Figure BDA0001728695320000074
representing the maximum injected reactive power of the electric vehicle.
A network model of the system then needs to be built. The power flow equation in an electrical power system can be expressed as follows:
Figure BDA0001728695320000075
Figure BDA0001728695320000076
Figure BDA0001728695320000077
Figure BDA0001728695320000078
wherein, i and j represent bus serial numbers, i, j is 1,2, …, N, N represents the number of buses in the power grid, P represents the number of buses in the power gridijAnd QijRepresenting the active and reactive power, r, between the buses i and jijAnd xijRepresenting the resistance and reactance between the busbars i and j,
Figure BDA0001728695320000079
and
Figure BDA00017286953200000710
representing the real and reactive consumption of the base load at bus j,
Figure BDA00017286953200000711
and
Figure BDA00017286953200000712
the active compensation and the reactive compensation of the electric vehicle group connected at the bus j are shown,
Figure BDA00017286953200000713
is the reactive compensation quantity l of the reactive compensation device installed at the bus jijRepresenting the current between the busbars i and j, viRepresents the voltage at the bus i, ΩjThe set of bus nodes to which bus j power flows directly is shown, and b is the bus node to which bus j power flows directly.
In the present invention, some assumptions are introduced to convert the power flow equation into a linear equation capable of describing the system characteristics, which results in introducing a small relative error, but with a small magnitude and no influence on the result.
The linear model of the system network in the present invention is as follows:
Figure BDA00017286953200000714
where v denotes the voltage of the electricity network,
Figure BDA00017286953200000715
a vector of a dimension N is represented,
Figure BDA00017286953200000716
v0a constant value representing the substation voltage; r represents a N × N bus resistor matrix, elements
Figure BDA00017286953200000717
Representing the resistance between the busbars i and j, rhbRepresents the resistance between the busbars h and b, Ωi、ΩjRespectively representing the collection of bus nodes to which the power of the bus i and the power of the bus j directly flow; x denotes the bus reactance matrix of NxN, element
Figure BDA0001728695320000081
Denotes the reactance between the busbars i and j, xhbRepresents the reactance between the busbars h and b; plAn active consumption vector representing the base load in the grid system,
Figure BDA0001728695320000082
Figure BDA0001728695320000083
the active power consumption of the basic load at the bus i at the moment k is represented; pvRepresenting the active compensation vectors of the electric vehicle groups in the power grid system,
Figure BDA0001728695320000084
Minumber of electric vehicles connected by bus i, cm′Represents the charging power of the m' th electric vehicle; qlRepresenting the reactive consumption vectors of the electric vehicle groups in the grid system,
Figure BDA0001728695320000085
Qvrepresenting reactive compensation vectors of the electric vehicle groups in the grid system,
Figure BDA0001728695320000086
Figure BDA0001728695320000087
representing the reactive power consumption of the base load at the bus i at time k; qgRepresenting reactive compensation in a grid systemThe reactive compensation vector of the compensation device is,
Figure BDA0001728695320000088
Figure BDA0001728695320000089
representing the reactive power consumption of the reactive power compensation device installed at the bus i at time k.
Definition of
Figure BDA00017286953200000810
Pv＝KucThe model is further simplified:
Figure BDA00017286953200000811
wherein Q represents a reactive power compensation amount,
Figure BDA00017286953200000812
Figure BDA00017286953200000813
the reactive compensation quantity of the electric vehicle group connected at the bus i is shown,
Figure BDA00017286953200000814
is the reactive compensation quantity of the reactive compensation device arranged at the position of the bus i; k represents a connection matrix of the NxM electric automobiles and the bus, and the corresponding element K is connected to the bus i when the mth electric automobile is connected to the bus i im1, otherwise Kim＝0；uc＝(c1,…,cM)，cmThe charging power of the mth electric vehicle is shown.
As such, system network models need to be constrained. In the present invention, the constraint range for specifying the grid voltage stability is:
vmin≤vi≤vmax
wherein v ismin、vmaxRespectively represents the upper limit and the lower limit of the safe and stable range of the voltage of the power grid, viRepresenting the voltage of the bus i in the grid structure.
Meanwhile, the conventional reactive power compensation device has the following constraints:
Figure BDA0001728695320000091
wherein q isminAnd q ismaxRespectively representing the minimum value and the maximum value of the reactive power compensation quantity.
According to the established model, the summary is a linear state space model with constraint:
x(k+1)＝Ax(k)+Bu(k)
y(k)＝Cu(k)
Figure BDA0001728695320000092
where k denotes an operation time of the grid system, x (k) and x (k +1) denote SOC vectors of electric vehicles in the grid system at time k and k +1, respectively, and x (k) ═ e1(k),…,eM(k))T，x(k+1)＝(e1(k+1),…,eM(k+1))TThe superscript T denotes transposition, em(k)、em(k +1) represents the SOC of the mth electric vehicle in the grid system at time k and k +1, respectively, where M is 1,2, …, and M represents the number of electric vehicles in the grid system;
u(k)＝[Q(k)uc(k)]Tq (k) represents the reactive power compensation amount at time k,
Figure BDA0001728695320000093
Figure BDA0001728695320000094
the reactive compensation quantity of the electric vehicle group connected to the bus i at the time k is shown,
Figure BDA0001728695320000095
the reactive compensation amount of the reactive compensation device installed at the bus i at the time k is shown, i is 1,2, …, N indicates the power grid systemThe number of buses in the system; u. ofc(k)＝(c1(k),…,cM(k))，cm(k) Representing the charging power of the mth electric automobile in the power grid system at the moment k;
Figure BDA0001728695320000096
v (k) represents the voltage of the grid at time k,
Figure BDA0001728695320000097
Figure BDA0001728695320000098
a vector of a dimension N is represented,
Figure BDA0001728695320000099
v0a constant value representing the substation voltage; r represents an NxN bus resistor matrix, the element R of whichijRepresents the resistance between the busbars i and j, j being 1,2, …, N; x represents an NxN bus reactance matrix, the element X of whichijRepresents the reactance between busbars i and j; pl(k) The active consumption vector representing the base load in the grid system at time k,
Figure BDA00017286953200000910
Figure BDA00017286953200000911
the active power consumption of the basic load at the bus i at the moment k is represented; ql(k) Representing the vector of reactive consumption of the base load in the grid system at time k,
Figure BDA00017286953200000912
Figure BDA00017286953200000913
representing the reactive power consumption of the base load at the bus i at time k;
a represents an identity matrix, B ═ 0Tf]，TfRepresents a sampling time; c ═ X-RK]K represents a connection matrix of the NxM electric vehicle and the bus when the mth stationElement K corresponding to electric automobile when being connected to bus i im1, otherwise Kim＝0；
Figure BDA0001728695320000101
A constraint space representing the linear state space model, defined as follows:
Figure BDA0001728695320000102
wherein e (k) represents the SOC and SOC of the battery of the electric vehicle at the time kmaxAnd SOCminRespectively representing the upper limit and the lower limit of the battery SOC; c. Cm(k) Represents the charging power of the mth electric automobile in the power grid system at the time k,
Figure BDA0001728695320000103
represents the reactive power of the mth electric vehicle, SmaxRepresents the maximum apparent power; c. CmaxRepresents the maximum charging power;
Figure BDA0001728695320000104
representing the maximum injected reactive power of the electric vehicle; q. q.sminAnd q ismaxRespectively representing the minimum value and the maximum value of the reactive power compensation quantity of a preset reactive power compensation device; v. ofnomRepresenting a preset nominal value of the target voltage, vmaxAnd vminRespectively representing the upper limit and the lower limit of the safe and stable range of the power grid voltage.
S102: determining a control target curve:
according to the set nominal value v of the target voltagenomAnd calculating a prediction curve of the active power and the reactive power of the basic load in the power grid system to obtain an optimal reference curve r (k) in a prediction stage:
Figure BDA0001728695320000105
s103: predicting to obtain an optimal reachable curve:
and establishing an optimization problem model in a first stage by taking the minimum deviation of the voltage of the power grid system and the rated value and the change of the control quantity as objective functions:
Figure BDA0001728695320000106
Figure BDA0001728695320000107
Figure BDA0001728695320000108
Figure BDA0001728695320000109
Figure BDA00017286953200001010
Figure BDA00017286953200001011
wherein x isr(k)、ur(k)、yr(k) Respectively representing the best reachable curves of the minimized objective function obtained by solving according to the reference curve R (k), the superscript "-" represents that the corresponding parameters are the parameters obtained by solving according to the optimization problem model, | | | | represents the norm of the solution, R1And R2Representing a predetermined weight matrix for controlling the weight, T, of the corresponding two variablessThe prediction time domain in the phase model predictive control is represented. Formula (II)
Figure BDA0001728695320000111
The purpose of (a) is to force the reachability curve to be periodic.
According to the optimal reference curve r (k) obtained in step S102,solving the optimization problem model to obtain an optimal reachable curve x for minimizing the objective functionr(k)、ur(k)、yr(k)。
For the above optimization problem model, when the weight matrix R2Different values can be obtained, and different control effects are obtained: when R is2When the weight matrix is not 0, the objective function of the optimization problem model not only minimizes the deviation between the voltage and the rated voltage, but also has certain constraint on the controlled variable, and reduces the change of the controlled variable while meeting the minimized objective function; when the weight matrix R2When the control quantity is 0, the control quantity is not restrained, the control quantity is changed to enable the power grid voltage to track the rated voltage as much as possible, and a better control effect is achieved.
S104: solving for control increment:
and establishing a second-stage predictive controller model by taking the deviation of the minimized grid voltage and the controlled variable from a given reference value as an objective function:
Figure BDA0001728695320000112
Figure BDA0001728695320000113
Figure BDA0001728695320000114
Figure BDA0001728695320000115
Figure BDA0001728695320000116
Figure BDA0001728695320000117
Figure BDA0001728695320000118
wherein R is3And R4Representing a preset weight matrix for controlling the weight of the corresponding two variables, the superscript "-" representing that the corresponding parameter is a parameter solved according to the predictive controller model, TdRepresenting the prediction horizon for the model predictive control in this phase.
Figure BDA0001728695320000119
The effect of (a) is to make a predicted trajectory starting from the current state,
Figure BDA00017286953200001110
the function of (1) is to ensure that the terminal state of the predicted trajectory of the model reaches the optimal achievable trajectory x obtained in step S104 at the end of the prediction ranger(k) I.e. by
Figure BDA00017286953200001111
To the best achievable trajectory xr(k) Upper corresponds to the value of the time instant.
By using the predictive controller model, the optimal reachable reference curve obtained in step S103 is tracked to obtain the optimal control increment Δ U (k) satisfying the target, and then the control increment Δ U (k) (I0.. 0) is calculated]ΔU*(k)。
And realizing the online rolling optimization of model predictive control through the predictive controller model at the second stage.
S105: and (3) parameter control:
and (4) inputting the control increment delta u (k) to the state space model in the step (S101), obtaining a corresponding electric vehicle SOC vector x (k) and a voltage parameter y (k), and performing parameter control on the power grid system.
In order to better illustrate the technical effects of the invention, a specific example is adopted to carry out simulation verification on the invention. Fig. 2 is a topological diagram of the power grid system in the embodiment. As shown in FIG. 2, the number of the bus bars in the present embodiment is 18, wherein bus bar 0 is taken as a referenceThe voltage value of the examination point is 220V of the voltage output by the transformer, the rated voltage in the power grid is 220V, two electric automobile sets are set to be connected to the connection points of the power grid (namely the bus 8 and the bus 10), and the basic load is composed of a plurality of household electric loads. The battery capacity of each electric automobile is 16.8KW, and the initial SOC of the electric automobileinitIs [0.15,0.25 ]]Desired SOCdesIs 0.9.
Two control schemes are set in the simulation verification: case1 shows that only the traditional reactive power compensation device is adopted to perform reactive power compensation and further adjust the voltage; case2 indicates the incorporation of electric vehicle assistance for voltage regulation. Research shows that after a certain number of electric automobiles are achieved, the traditional reactive power compensation device can be completely replaced to meet reactive power requirements in a power grid. Fig. 3 is a load curve of a typical home user in the power grid during a periodic time assumed in the simulation verification of the present embodiment. Table 1 is a list of relevant parameters of the power grid system during simulation verification in this embodiment.
Figure BDA0001728695320000121
Next, taking the bus bar 18 in fig. 2 as an example, the terminal voltage of the bus bar 18 under the two control schemes of case1 and case2 is simulated. To illustrate the weight matrix R in the first stage optimization problem model2Influence of the value of the rating on the effect of the invention, denoted R2Not equal to 0 and R2Two cases are 0. FIG. 4 is a diagram of the weight matrix R in the first stage optimization problem in this embodiment2Graph of the voltage at the end of the bus 18 as a function of time with 0. FIG. 5 is a diagram of the weight matrix R in the first stage optimization problem in this embodiment2Graph of the voltage at the end of the bus 18 versus time at 0. The number of electric vehicles in fig. 4 and 5 is 50. Comparing fig. 4 and fig. 5, it is found that changing the weight matrix in the present invention has a significant effect on the control voltage, especially during the time period when the electric vehicle is connected to the power grid for charging. In addition, comparing the curves of the case1 and the case2 in fig. 4 and 5, it can be known that, in the model predictive control method for the electric vehicle to participate in voltage regulation, not only the installation and operation and maintenance costs of the conventional reactive power compensation device can be saved, but also the installation and operation and maintenance costs of the conventional reactive power compensation device can be savedThe power grid voltage is well tracked and regulated.
FIG. 6 is a diagram of the weight matrix R in the first stage optimization problem in this embodiment2Graph of the terminal voltage of the bus bar 18 as a function of the number of electric vehicles when not equal to 0. When R is shown in FIG. 62When not equal to 0, the more the electric automobile that inserts the electric wire netting, the influence to grid voltage is big more, especially in electric automobile's the stage of charging, voltage variation is obvious. In this case, when the number of electric vehicles reaches 70, the terminal minimum voltage is already close to the minimum value of the constraint condition. Based on this, the maximum number of connectable electric vehicles that can maintain the grid voltage within the normal range in this grid environment can be predicted. As can be seen from fig. 4, when the electric vehicle participates in voltage regulation, a better regulation effect can be obtained, and therefore, the maximum capacity of the electric vehicle which can be connected to the power grid is larger than that of the electric vehicle which can be connected to the power grid in the action environment of only the conventional reactive power compensation device.
FIG. 7 is a diagram of the weight matrix R in the first stage optimization problem in this embodiment2A plot of the end voltage of bus 18 as a function of the number of electric vehicles at 0. When R is shown in FIG. 72When the control quantity is 0, the objective function of minimizing the voltage deviation is realized in any case due to the unconstrained action of the control quantity, and the variation curve of the terminal voltage has almost no difference along with the variation of the number of the electric vehicles.
Fig. 8 is a reactive power compensation curve diagram of the reactive power compensation device and the electric vehicle in each case when the number of the electric vehicles is 20 in the present embodiment. Fig. 9 is a reactive compensation curve diagram of the electric vehicle in different situations when the number of the electric vehicles is 50 in the embodiment. Comparing fig. 8 and fig. 9, it can be seen that by changing the number of connected electric vehicles, in the environment of the base load of the power grid, when the number of electric vehicles reaches 20, the reactive power demand in the power grid can be met, and the reactive power compensation operation is performed independently. The more the number of electric vehicles is, the more reactive power is required to be provided. At the same time, the weight matrix R2When the ratio is 02When the voltage is not equal to 0, more reactive compensation is required to search for a better grid voltage tracking curve.
Taking an electric vehicle EV3 in case2 as an example, the charging power and SOC changes of the electric vehicle are simulated under different conditions. FIG. 10 isA graph of the charging power of the electric vehicle EV3 and the average charging power of all electric vehicles in the present embodiment. Fig. 11 is a map of the SOC of the electric vehicle EV3 and the SOCs of all electric vehicles in the present embodiment. As can be seen from FIGS. 10 and 11, R is compared with R2Not equal to 0, weight matrix R2When 0, the charging power of the electric vehicle is higher, the desired SOC is reached more quickly, but the control amount changes more.
In conclusion, the invention can not only meet the power consumption requirement of the electric automobile user, but also well realize the voltage regulation and control, and simultaneously reduce the installation, operation and maintenance cost of the traditional device.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims (2)

1. A model predictive control method for charging and participating in voltage regulation of an electric automobile is characterized by comprising the following steps:
s1: constructing a linear state space model of the power grid system needing model prediction control:
x(k+1)＝Ax(k)+Bu(k)
y(k)＝Cu(k)
Figure FDA0002947208700000011
where k denotes an operation time of the grid system, x (k) and x (k +1) denote SOC vectors of electric vehicles in the grid system at time k and k +1, respectively, and x (k) ═ e1(k),…,eM(k))T，x(k+1)＝(e1(k+1),…,eM(k+1))TThe superscript T denotes transposition, em(k)、em(k +1) represents the grid at time k, k +1, respectivelyThe SOC of the mth electric vehicle in the system is 1,2, …, M and M represents the number of the electric vehicles in the power grid system;
u(k)＝[Q(k)uc(k)]Tq (k) represents the reactive power compensation amount at time k,
Figure FDA0002947208700000012
Figure FDA0002947208700000013
the reactive compensation quantity of the electric vehicle group connected to the bus i at the time k is shown,
Figure FDA0002947208700000014
the reactive compensation amount of a reactive compensation device installed at a bus i at the time t is shown, i is 1,2, …, N, and N is the number of buses in the power grid system; u. ofc(k)＝(c1(k),…,cM(k))，cm(k) Representing the charging power of the mth electric automobile in the power grid system at the moment k;
Figure FDA0002947208700000015
v (k) represents the voltage of the grid at time k,
Figure FDA0002947208700000016
Figure FDA0002947208700000017
a vector of a dimension N is represented,
Figure FDA0002947208700000018
v0a constant value representing the substation voltage; r represents an NxN bus resistor matrix, the element R of whichijRepresents the resistance between the busbars i and j, j being 1,2, …, N; x represents an NxN bus reactance matrix, the element X of whichijRepresents the reactance between busbars i and j; pl(k) The active consumption vector representing the base load in the grid system at time k,
Figure FDA0002947208700000019
Figure FDA00029472087000000110
the active power consumption of the basic load at the bus i at the moment k is represented; ql(k) Representing the vector of reactive consumption of the base load in the grid system at time k,
Figure FDA00029472087000000111
Figure FDA00029472087000000112
representing the reactive power consumption of the base load at the bus i at time k;
a represents an identity matrix, B ═ 0Tf]，TfRepresents a sampling time; c ═ X-RK]K represents a connection matrix of the NxM electric vehicles and the bus, and the corresponding element K is connected to the bus i when the mth electric vehicle is connected to the bus iim1, otherwise Kim＝0；
Figure FDA00029472087000000113
A constraint space representing the linear state space model, defined as follows:
Figure FDA0002947208700000021
wherein e (k) represents the SOC and SOC of the battery of the electric vehicle at the time kmaxAnd SOCminRespectively representing the upper limit and the lower limit of the battery SOC; c. Cm(k) Represents the charging power of the mth electric automobile in the power grid system at the time k,
Figure FDA0002947208700000022
is the reactive power of the mth electric vehicle, SmaxRepresents the maximum apparent power; c. CmaxRepresents the maximum charging power;
Figure FDA0002947208700000023
representing the maximum injected reactive power of the electric vehicle; q. q.sminAnd q ismaxRespectively representing the minimum value and the maximum value of the reactive power compensation quantity; v. ofnomRepresenting a preset nominal value of the target voltage, vmaxAnd vminRespectively representing the upper limit and the lower limit of a safety and stability range of the power grid voltage;
s2: according to the set nominal value v of the target voltagenomAnd calculating a prediction curve of the active power and the reactive power of the basic load in the power grid system to obtain an optimal reference curve r (k) in a prediction stage:
Figure FDA0002947208700000024
s3: and establishing an optimization problem model in a first stage by taking the minimum deviation of the voltage of the power grid system and the rated value and the change of the control quantity as objective functions:
Figure FDA0002947208700000025
Figure FDA0002947208700000026
Figure FDA0002947208700000027
Figure FDA0002947208700000028
Figure FDA0002947208700000029
Figure FDA00029472087000000210
wherein x isr(k)、ur(k)、yr(k) Respectively representing the best reachable curves of the minimized objective function obtained by solving according to the reference curve R (k), the superscript "-" represents that the corresponding parameters are the parameters obtained by solving according to the optimization problem model, | | | | represents the norm of the solution, R1And R2Representing a predetermined weight matrix for controlling the weight, T, of the corresponding two variablessRepresenting a prediction time domain in the phase model predictive control;
according to the optimal reference curve r (k) obtained in the step S2, solving the optimization problem model to obtain the optimal reachable curve x which minimizes the objective functionr(k)、ur(k)、yr(k)；
S4: and establishing a second-stage predictive controller model by taking the deviation of the minimized grid voltage and the controlled variable from a given reference value as an objective function:
Figure FDA0002947208700000031
Figure FDA0002947208700000032
Figure FDA0002947208700000033
Figure FDA0002947208700000034
Figure FDA0002947208700000035
Figure FDA0002947208700000036
Figure FDA0002947208700000037
wherein R is3And R4Representing a preset weight matrix for controlling the weight of the corresponding two variables, the superscript "-" representing that the corresponding parameter is a parameter solved according to the predictive controller model, TdA prediction horizon representing the model predictive control in that phase;
using the predictive controller model, the best achievable reference curve x obtained in step S3 is trackedr(k)、ur(k)、yr(k) To obtain the optimal control increment delta U meeting the above target*(k) Then, Δ u (k) ═ I0 … 0 is calculated]ΔU*(k)；
S5: and (5) inputting the control increment delta u (k) into the state space model in the step S1 to obtain the corresponding SOC vector x (k) and the voltage parameter y (k) of the electric automobile, and performing parameter control on the power grid system.
2. The model predictive control method of claim 1, wherein the weight matrix R in step S32＝0。
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