CN117646978A - Intelligent air conditioner load aggregation regulation and control system and method based on model predictive control - Google Patents
Intelligent air conditioner load aggregation regulation and control system and method based on model predictive control Download PDFInfo
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Abstract
The invention provides an intelligent air conditioner load aggregation regulation system and method based on model predictive control. The central controller builds a first-order equivalent thermal parameter model of a single air conditioner load considering random interference, and adopts a least square method to solve characteristic parameters of each air conditioner; uniformly dividing a certain temperature range into a plurality of sections of temperature intervals, constructing and setting a state variable and a state space model of an opening state set, a closing state set and an air conditioner set at the current moment, and solving each matrix in the state space model; predicting state variables of a plurality of moments in the future, and solving optimal control information of the plurality of moments in the future; calculating an adjustment error at the current moment to serve as a feedback signal and serve as input of the MPC controller, and performing rolling optimization by combining the MPC controller to obtain a plurality of intelligent air conditioner state control vectors at the next moment; and the central controller controls the state of the plurality of air conditioners at the next moment according to the state control vectors of the plurality of intelligent air conditioners at the next moment. The invention realizes the overall dispatching of the air-conditioning group.
Description
Technical Field
The invention belongs to the field of demand type response, and particularly relates to an intelligent air conditioner load aggregation regulation system and method for model predictive control.
Background
The demand response is a research hotspot of current power system researchers, and can change the electricity utilization habit of a user through signals such as electricity price or excitation, adjust the load on the demand side, increase the stability of power generation, maintain the power balance to a certain extent, adjust the peak power load, lighten the pressure of a power system and save the running cost of a power grid. The air conditioner has large market volume, various types and high control flexibility, and is an important resource participating in demand type response at present.
However, how to uniformly schedule the large-scale air conditioner load is a major concern in scientific research and industry. In the aspect of the market condition of China, the regulation and control of the single air conditioner load is nonsensical, and the requirement of demand response cannot be met. Therefore, it is necessary to aggregate the air conditioning load. At present, a Monte Carlo method is mainly adopted as a polymerization method aiming at the air conditioner load. The Monte Carlo method is simple in principle, and can establish a relatively accurate aggregation model for air conditioner loads with the same parameter characteristics. When the heterogeneity of the load parameters is more pronounced, the accuracy of the monte carlo method is correspondingly reduced. Therefore, it is highly desirable to propose an air conditioning load aggregation method considering parameter heterogeneity.
Disclosure of Invention
Aiming at the technical problems, the invention provides an intelligent air conditioner load aggregation regulation system and method for model predictive control.
The technical scheme of the system is an intelligent air conditioner load aggregation regulation system controlled by model prediction, which comprises the following components:
a central controller, a plurality of intelligent air conditioners;
the central controller is sequentially and wirelessly connected with a plurality of intelligent air conditioners;
the central controller builds a first-order equivalent thermal parameter model of a single air conditioner load considering random interference, and adopts a least square method to solve characteristic parameters of each air conditioner; uniformly dividing a certain temperature range into a plurality of sections of temperature intervals, constructing and setting a state variable and a state space model of an opening state set, a closing state set and an air conditioner set at the current moment, and solving each matrix in the state space model; predicting state variables of a plurality of moments in the future, and solving optimal control information of the plurality of moments in the future; calculating an adjustment error at the current moment to serve as a feedback signal and serve as input of the MPC controller, and performing rolling optimization by combining the MPC controller to obtain a plurality of intelligent air conditioner state control vectors at the next moment; and the central controller controls the state of the plurality of air conditioners at the next moment according to the state control vectors of the plurality of intelligent air conditioners at the next moment.
The technical scheme of the method is an intelligent air conditioner load aggregation regulation method based on model predictive control, which comprises the following steps:
step 1: constructing a first-order equivalent thermal parameter model of a single air conditioner load considering random interference, and solving characteristic parameters of each air conditioner by adopting a least square method;
step 2: acquiring the on-off state and the indoor temperature of the current moment of a plurality of air conditioners, uniformly dividing a certain temperature range into a plurality of sections of temperature intervals, dividing the indoor temperature of each air conditioner to the corresponding temperature interval according to the current moment of each air conditioner, respectively constructing an on-state set and an off-state set in the same temperature interval, putting the air conditioner in the on-state into the on-state set, putting the air conditioner in the off-state into the off-state set, carrying out normalization processing on the number of the air conditioners in each air conditioner set to obtain the ratio of the number of the air conditioners in each air conditioner set to the total number of the air conditioners at the current moment, constructing a state variable and a state space model of the air conditioner set at the current moment, and solving each matrix in the state space model;
step 3: predicting state variables of a plurality of moments in the future according to the state variables of the current moment by combining the state space model, and solving optimal control information of the plurality of moments in the future according to the state variables of the plurality of moments in the future;
step 4: calculating the regulating error at the current moment to be used as a feedback signal, taking the regulating error at the current moment as the input of the MPC controller, and combining the MPC controller to perform rolling optimization to obtain a plurality of intelligent air conditioner state control vectors at the next moment;
step 5: the central controller randomly selects a plurality of intelligent air conditioners as an on state in the plurality of intelligent air conditioners according to the state control vectors of the plurality of intelligent air conditioners at the next moment, wirelessly transmits corresponding on control signals to the corresponding air conditioners, takes the rest air conditioners in the plurality of intelligent air conditioners as off states, and wirelessly transmits corresponding off control signals to the corresponding air conditioners;
preferably, the first-order equivalent thermal parameter model of the single air conditioner load considering random interference in the step 1 is specifically defined as follows:
T in,l,k+1 ＝a l *T in,l,k +(1-a l )*(T out,l -λ l *T g,l )+w l,k
wherein lambda is l The dimensionless number of the first air conditioner is 1 when the air conditioner is started and 0 when the air conditioner is closed; r is R l ,C l Is equivalent thermal resistance and equivalent heat capacity of the air conditioner, and l=1, 2,3 … and N tcls Numbering the air conditioner load, N tcls Is the total number of air conditioners. Δt is the time step, T g,l For the temperature gain of the first air conditioner, T in,l,k The indoor temperature at the kth moment of the first air conditioner is T out,l,k The outdoor temperature at the kth moment of the first air conditioner;
and step 1, solving characteristic parameters of each air conditioner by adopting a least square method, wherein the characteristic parameters are as follows:
is a characteristic parameter of the first air conditionerThe number is calculated by using temperature change data of the air conditioner in a period of time and adopting a least square method, and the method is specifically solved as follows:
preferably, in the step 2, a certain temperature range is uniformly divided into a plurality of temperature intervals, which is specifically as follows:
the temperature intervals are distinguished by the numbers m=1, 2,3, …, M; temperature interval [ T ] min ,T max ]Evenly divided into M temperature intervals, the mth temperature interval can be expressed as [ T ] m ,T m+1 ]；
The set of all closed air conditioners at the current moment and the set of all open air conditioners at the current moment are respectively defined as follows:
close k ＝{Statebin close,k,1 ,Statebin close,k,2 ,Statebin close,k,3 ,…,Statebin close,k,M }
open k ＝{Statebin open,k,1 ,Statebin open,k,2 ,Statebin open,k,3 ,…,Statebin open,k,M }
wherein, close k For all sets of closed air conditioners at the kth time, statebin close,k,m A set of all closed state air conditioners for the mth temperature interval at the kth moment; open (S) k Set for all on air conditioners at kth time, statebin open,k,m The method comprises the steps that an air conditioner set in all on states of an mth temperature interval at a kth moment is obtained, and k represents the current moment;
since there are two sets for one temperature interval, there are 2M sets for M temperature intervals;
and step 2, normalizing the number of air conditioners in each air conditioner set, wherein the normalization is specifically as follows:
defining Load k,n N=1, 2,3, …, N denotes the set of the nth air conditioning group at the kth time, where n=2m, load k,n The following relationship is satisfied:
the number of air conditioners in each air conditioner set is normalized as follows:
wherein x is k,n The method comprises the steps of representing the ratio of the number of air conditioners to the total number of air conditioners in an nth air conditioner set at a kth moment, wherein N represents the total number of air conditioners, M represents the total number of temperature intervals, and k represents the current moment;
and step 2, constructing a state variable of the air conditioner set at the current moment, wherein the state variable is specifically as follows:
will x k,n The state defined as the air conditioning set, for 2M air conditioning sets, 2M state quantities in total, are defined as follows:
X k ＝[x k,1 ,x k,2 ,x k,3 ,…,x k,N ]
wherein X is k The state variable of the air conditioner set at the kth moment is k, wherein k represents the current moment;
for state variable X k+1 The change determinants are as follows: state variable X at time k k Control signal U at kth time k Random interference W in the control process at the kth moment k ；
The state space model in the step 2 is defined as follows:
X k+1 ＝AX k +BU k +W k
y k ＝CX k
U k ＝[u k,1 ,u k,2 ,u k,3 ,…,u k,N ]
wherein U is k A control variable indicating the kth time, u k,n To be applied to Load by the controller at the kth time k,n Control signal, y of (2) k Is the sum of the constant-frequency air conditioner power of all temperature intervals at the kth moment, W k Is the kthThe random interference of moment, matrix A is the state transition matrix, matrix B is the control input matrix, matrix C is the output matrix, k represents the current moment;
and 2, solving each matrix in the state space model, wherein the method comprises the following steps of:
matrix a is an nxn matrix. The element in A is P i,j I, j e N. Wherein P is i,j For the probability of transition between two status boxes, the subscript indicates that the air conditioner in the jth status box is transited to the air conditioner in the ith status box as follows:
wherein T is j ,T j+1 Respectively represent the upper and lower boundaries, T, of the temperature interval in which the jth state box is located i ,T i+1 Respectively represent the upper and lower boundaries of the temperature interval in which the ith status box is located. a, a 1 ,a 2 Respectively denoted by T i ,T i+1 The two parameters determined are M which is the number of temperature intervals;
wherein p (a) is a uniformly distributed probability density function of the form:
wherein a is min ,a max Can be obtained by the least square fitting method in the step 1.
B is written as follows:
the form of C is as follows:
C＝P rate *N tcls *[0...0|1...1] 1×N
wherein P is rate Rated power of air conditioner, N tcls N is the total number of air conditioner sets;
preferably, the predicting state variables at a plurality of future moments in step 3 is specifically as follows:
from the state space model, the state variable at the (k+1) th moment can be predicted when the state variable at the (k) th moment is known, and N can be predicted through an iterative process p The state variables at each time are as follows:
wherein, matrix A is a state transition matrix, matrix B is a control input matrix, N p To predict step size, X k U is the state variable of the air conditioner set at the kth moment k+q A control variable representing the k+q time, k representing the current time, k+1 representing the next time, q ε [0, N p ]；
And step 3, solving optimal control information of a plurality of future moments according to state variables of the plurality of future moments, wherein the optimal control information is specifically as follows:
according to N p The state variable at each time can be obtained p The predicted outputs at each time are as follows:
wherein y is k+q Represents the sum of the constant-frequency air conditioner power of all the temperature intervals of the k+q, and q is E [0, N p ]；
And (3) making:
wherein, matrix A is a state transition matrix, matrix B is a control input matrix, matrix C is an output matrix, N p In order to predict the step size,is N p Control signals at each moment, phi is a state variable transition matrix, F is a control signal transition matrix,/>Is N p Control signals at various moments;
the following is obtained:
wherein X is k Is the state variable of the air conditioner set at the kth moment, Y is N p The prediction output of each moment, phi is a state variable transition matrix, and F is a control signal transition matrix;
defining error performance index of power tracking, concretely comprising the following steps:
wherein R is s Y is N as the input reference signal p The predicted output of the individual moments in time,is N p The control signals at each moment, G is a weight matrix with control function, can limit the frequent switching of the system, and can give different weights to the control sequence according to simulation expectations, namely, change the value of the G matrix;
according to the objective function and the constraint condition, solving the optimal N by using a quadratic programming solver in MATLAB p Control signals at various moments;
preferably, the adjustment error of the current time is calculated in step 4, specifically as follows:
e k ＝r sk -y k +w k
wherein e k For the adjustment error at the kth time, r sk For the target power at the kth time, y k The sum of the constant-frequency air conditioner power of all temperature intervals at the kth moment, w k Is the prediction error at the kth time;
and step 4, performing rolling optimization by combining the MPC controller to obtain a value of the control action at the next moment, wherein the value is specifically as follows:
the rolling optimization mode in the MPC controller is to continuously adjust U (k) according to the output power of the state space model prediction at the next moment so as to minimize the difference between the output power and the target power, specifically:
wherein R is s Y is N as the input reference signal p The predicted output of the individual moments in time,is N p The control signals at each moment, G is a weight matrix for controlling action;
the multiple intelligent air conditioner state control vectors at the next moment in the step 4 include:
the intelligent air conditioner state control vectors of the plurality of intelligent air conditioners at the next moment comprise the intelligent air conditioner number of the opening state and the intelligent air conditioner number of the closing state at the next moment;
according to the air conditioner load group regulation and control method based on the model prediction control, future power output is predicted by establishing the polymer state space model of the air conditioner load group, and under the condition that random interference exists, the idea of rolling optimization in control is controlled according to the model prediction, the random interference is used as the input of the controller at the future moment, and the control quantity at the next moment is solved through the MPC controller to carry out regulation. With the moment of propulsion, rolling optimization is carried out, and the whole negative feedback control system can continuously adjust the output of the load group to enable the load group to approach the target power, so that the whole scheduling of the air conditioner group is realized.
Drawings
Fig. 1: the method of the embodiment of the invention is a flow chart;
fig. 2: the embodiment of the invention provides an air conditioner load first-order equivalent thermal parameter model diagram;
fig. 3: the embodiment of the invention discloses a one-dimensional state box model diagram;
fig. 4: the control system flow chart of the embodiment of the invention;
fig. 5: the embodiment of the invention aims at a power change diagram;
fig. 6: the embodiment of the invention experiments a power change diagram of an air conditioner group;
fig. 7: the second embodiment of the invention shows an air conditioner group power change diagram;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In particular, the method according to the technical solution of the present invention may be implemented by those skilled in the art using computer software technology to implement an automatic operation flow, and a system apparatus for implementing the method, such as a computer readable storage medium storing a corresponding computer program according to the technical solution of the present invention, and a computer device including the operation of the corresponding computer program, should also fall within the protection scope of the present invention.
The technical scheme of the system of the embodiment of the invention is an intelligent air conditioner load aggregation regulation system controlled by model prediction, which comprises the following components:
a central controller, a plurality of intelligent air conditioners;
the central controller is sequentially and wirelessly connected with a plurality of intelligent air conditioners;
the model of the central controller is Midea KJR-37B/bp2;
the intelligent air conditioner has the model of KFR-35GW/WDAA21AU1;
the specific scene of the embodiment of the invention is to select the regulation and control capability of one thousand air conditioners; and a control test on the number of state boxes is set so as to judge the influence of different numbers of state boxes on tracking errors.
The following describes a technical scheme of the method of the embodiment of the invention with reference to fig. 1 to 7, namely an intelligent air conditioner load aggregation regulation method based on model predictive control, which comprises the following steps:
a method flow diagram of an embodiment of the invention is shown in fig. 1.
Step 1: constructing a first-order equivalent thermal parameter model of a single air conditioner load considering random interference, and solving characteristic parameters of each air conditioner by adopting a least square method;
the first-order equivalent thermal parameter model of the single air conditioner load considering random interference in the step 1 is shown in fig. 2, and is specifically defined as follows:
T in,l,k+1 ＝a l *T in,l,k +(1-a l )*(T out,l -λ l *T g,l )+w l,k
wherein lambda is l The dimensionless number of the first air conditioner is 1 when the air conditioner is started and 0 when the air conditioner is closed; r is R l ,C l Is equivalent thermal resistance and equivalent heat capacity of the air conditioner, and l=1, 2,3 … and N tcls Numbering the air conditioner load, N tcls =1000 is the total number of air conditioners; Δt=15 is the time step, T g,l For the temperature gain of the first air conditioner, T in,l,k The indoor temperature at the kth moment of the first air conditioner is T out,l,k =38 is the outdoor temperature at the kth time of the first air conditioner;
and step 1, solving characteristic parameters of each air conditioner by adopting a least square method, wherein the characteristic parameters are as follows:
is the characteristic parameter of the first air conditioner and passes through the air conditionerAnd (3) calculating the parameter by adopting a least square method according to temperature change data in a period of time, wherein the method comprises the following steps of:
step 2: acquiring the on-off state and the indoor temperature of the current moment of a plurality of air conditioners, uniformly dividing a certain temperature range into a plurality of sections of temperature intervals, dividing the indoor temperature of each air conditioner to the corresponding temperature interval according to the current moment of each air conditioner, respectively constructing an on-state set and an off-state set in the same temperature interval, putting the air conditioner in the on-state into the on-state set, putting the air conditioner in the off-state into the off-state set, carrying out normalization processing on the number of the air conditioners in each air conditioner set to obtain the ratio of the number of the air conditioners in each air conditioner set to the total number of the air conditioners at the current moment, constructing a state variable and a state space model of the air conditioner set at the current moment, and solving each matrix in the state space model;
step 2, uniformly dividing a certain temperature range into a plurality of sections of temperature intervals, wherein the specific steps are as follows:
the temperature intervals are distinguished by the numbers m=1, 2,3, …, M; temperature interval [ T ] min ,T max ]Evenly divided into M temperature intervals, the mth temperature interval can be expressed as [ T ] m ,T m+1 ]；
The set of all off air conditioners at the current moment and the set of all on air conditioners at the current moment are as shown in fig. 3, and are defined as follows:
close k ＝{Statebin close,k,1 ,Statebin close,k,2 ,Statebin close,k,3 ,…,Statebin close,k,M }
open k ＝{Statebin open,k,1 ,Statebin open,k,2 ,Statebin open,k,3 ,…,Statebin open,k,M }
wherein, close k For all sets of closed air conditioners at the kth time, statebin close,k,m Set of all off-state air conditioners for the kth moment mth temperature intervalCombining; open (S) k Set for all on air conditioners at kth time, statebin open,k,m The method comprises the steps that an air conditioner set in all on states of an mth temperature interval at a kth moment is obtained, and k represents the current moment;
since there are two sets for one temperature interval, there are 2M sets for M temperature intervals;
and step 2, normalizing the number of air conditioners in each air conditioner set, wherein the normalization is specifically as follows:
defining Load k,n N=1, 2,3, …, N denotes the set of the nth air conditioning group at the kth time, where n=2m, load k,n The following relationship is satisfied:
the number of air conditioners in each air conditioner set is normalized as follows:
wherein x is k,n The method comprises the steps of representing the ratio of the number of air conditioners to the total number of air conditioners in an nth air conditioner set at a kth moment, wherein N represents the total number of air conditioners, M represents the total number of temperature intervals, and k represents the current moment;
and step 2, constructing a state variable of the air conditioner set at the current moment, wherein the state variable is specifically as follows:
will x k,n The state defined as the air conditioning set, for 2M air conditioning sets, 2M state quantities in total, are defined as follows:
X k ＝[x k,1 ,x k,2 ,x k,3 ,…,x k,N ]
wherein X is k The state variable of the air conditioner set at the kth moment is k, wherein k represents the current moment;
for state variable X k+1 The change determinants are as follows: state variable X at time k k Control signal U at kth time k And random in the regulatory process at the kth timeInterference W k ；
The state space model in the step 2 is defined as follows:
X k+1 ＝AX k +BU k +W k
y k ＝CX k
U k ＝[u k,1 ,u k,2 ,u k,3 ,…,u k,N ]
wherein U is k A control variable indicating the kth time, u k,n To be applied to Load by the controller at the kth time k,n Control signal, y of (2) k Is the sum of the constant-frequency air conditioner power of all temperature intervals at the kth moment, W k The random interference at the kth moment is represented by a matrix A which is a state transition matrix, a matrix B which is a control input matrix, a matrix C which is an output matrix, and k which represents the current moment;
and 2, solving each matrix in the state space model, wherein the method comprises the following steps of:
matrix a is an nxn matrix. The element in A is P i,j I, j e N. Wherein P is i,j For the probability of transition between two status boxes, the subscript indicates that the air conditioner in the jth status box is transited to the air conditioner in the ith status box as follows:
wherein T is j ,T j+1 Respectively represent the upper and lower boundaries, T, of the temperature interval in which the jth state box is located i ,T i+1 Respectively represent the upper and lower boundaries of the temperature interval in which the ith status box is located. a, a 1 ,a 2 Respectively denoted by T i ,T i+1 The two parameters determined are M which is the number of temperature intervals;
wherein p (a) is a uniformly distributed probability density function of the form:
wherein a is min ,a max Can be obtained by the least square fitting method in the step 1.
B is written as follows:
the form of C is as follows:
C＝P rate *N tcls *[0...0|1...1] 1×N
wherein P is rate Rated power of air conditioner, N tcls N is the total number of air conditioner sets;
step 3: predicting state variables of a plurality of future moments according to the state variables of the current moment by combining the state space model, solving optimal control information of the plurality of future moments according to the state variables of the plurality of future moments,
and 3, predicting state variables at a plurality of future moments, wherein the state variables are specifically as follows:
from the state space model, the state variable at the (k+1) th moment can be predicted when the state variable at the (k) th moment is known, and N can be predicted through an iterative process p The state variables at each time are as follows:
wherein, matrix A is a state transition matrix, matrix B is a control input matrix, N p To predict step size, X k U is the state variable of the air conditioner set at the kth moment k+q A control variable representing the k+q time, k representing the current time, k+1 representing the next time, q ε [0, N p ]；
And step 3, solving optimal control information of a plurality of future moments according to state variables of the plurality of future moments, wherein the optimal control information is specifically as follows:
according to N p The state variable at each time can be obtained p The predicted outputs at each time are as follows:
wherein y is k+q Represents the sum of the constant-frequency air conditioner power of all the temperature intervals of the k+q, and q is E [0, N p ]；
And (3) making:
wherein, matrix A is a state transition matrix, matrix B is a control input matrix, matrix C is an output matrix, N p In order to predict the step size,is N p Control signals at each moment, phi is a state variable transition matrix, F is a control signal transition matrix,/>Is N p Control signals at various moments;
the following is obtained:
wherein X is k Is the state variable of the air conditioner set at the kth moment, Y is N p The prediction output of each moment, phi is a state variable transition matrix, and F is a control signal transition matrix;
defining error performance index of power tracking, concretely comprising the following steps:
wherein R is s Y is N as the input reference signal p The predicted output of the individual moments in time,is N p The control signals at each moment, G is a weight matrix with control function, can limit the frequent switching of the system, and can give different weights to the control sequence according to simulation expectations, namely, change the value of the G matrix;
according to the objective function and the constraint condition, solving the optimal N by using a quadratic programming solver in MATLAB p Control signals at various moments;
step 4: calculating the regulating error at the current moment to be used as a feedback signal, taking the regulating error at the current moment as the input of the MPC controller, and combining the MPC controller to perform rolling optimization to obtain a plurality of intelligent air conditioner state control vectors at the next moment;
and 4, calculating an adjustment error of the current moment, wherein the adjustment error is specifically as follows:
e k ＝r sk -y k +w k
wherein e k For the adjustment error at the kth time, r sk For the target power at the kth time, y k The sum of the constant-frequency air conditioner power of all temperature intervals at the kth moment, w k Is the prediction error at the kth time;
and step 4, performing rolling optimization by combining the MPC controller to obtain a value of the control action at the next moment, wherein the value is specifically as follows:
the rolling optimization mode in the MPC controller is to continuously adjust U (k) according to the output power of the state space model prediction at the next moment so as to minimize the difference between the output power and the target power, specifically:
wherein R is s Y is N as the input reference signal p The predicted output of the individual moments in time,is N p The control signals at each moment, G is a weight matrix for controlling action;
the multiple intelligent air conditioner state control vectors at the next moment in the step 4 include:
the intelligent air conditioner state control vectors of the plurality of intelligent air conditioners at the next moment comprise the intelligent air conditioner number of the opening state and the intelligent air conditioner number of the closing state at the next moment;
step 5: the central controller randomly selects a plurality of intelligent air conditioners as an on state in the plurality of intelligent air conditioners according to the state control vectors of the plurality of intelligent air conditioners at the next moment, wirelessly transmits corresponding on control signals to the corresponding air conditioners, takes the rest air conditioners in the plurality of intelligent air conditioners as off states, and wirelessly transmits corresponding off control signals to the corresponding air conditioners;
in a detailed embodiment, we build a simulation platform with a thousand air conditioners, as shown in fig. 4, and take a random target power as a reference signal as shown in fig. 5.
The following two groups of control experiments were performed:
experiment one: the number of status boxes was set to 20, and the status box temperatures were uniformly differentiated.
Experiment II: the number of status boxes was set to 40 and the status box temperatures were uniformly differentiated.
For the above experiments, scheduling was performed using the scheme of the present invention. From the experimental results shown in fig. 6 and fig. 7, the experimental scheme can schedule the air conditioning group within a certain time step, and can greatly reduce the influence of random interference on the whole regulation and control process, so that the target power can be tracked.
And from the comparison of the two experiments, the increase of the number of the state boxes can reduce tracking errors, but the communication lines can be increased, so that the control cost is further increased. The user can select the number of the status boxes according to the self regulation and control requirements.
Compared with other schemes, the air conditioner load group scheduling scheme controlled by the model prediction is provided with the polymer model, the polymer model can be modeled only by the equivalent capacitance and conductance parameters of the air conditioner load, and the polymer model is not influenced by the distribution condition of the air conditioner. In the control process, the scheme realizes the target power tracking of the air conditioning group by considering random interference in model prediction.
It should be understood that parts of the specification not specifically set forth herein are all prior art.
Although the terms central controller, mobile user terminal, network access point, etc. are used more herein, the possibility of using other terms is not precluded. These terms are only used to facilitate a more complete description of the nature of the invention and should be construed as requiring no additional limitations whatsoever.
It should be understood that the foregoing description of the preferred embodiments is not intended to limit the scope of the invention, but rather to limit the scope of the claims, and that those skilled in the art can make substitutions or modifications without departing from the scope of the invention as set forth in the appended claims.
Claims (9)
1. An intelligent air conditioner load aggregation regulation and control system controlled by model prediction, which is characterized by comprising:
a central controller, a plurality of intelligent air conditioners;
the central controller is sequentially and wirelessly connected with a plurality of intelligent air conditioners;
the central controller builds a first-order equivalent thermal parameter model of a single air conditioner load considering random interference, and adopts a least square method to solve characteristic parameters of each air conditioner; uniformly dividing a certain temperature range into a plurality of sections of temperature intervals, constructing and setting a state variable and a state space model of an opening state set, a closing state set and an air conditioner set at the current moment, and solving each matrix in the state space model; predicting state variables of a plurality of moments in the future, and solving optimal control information of the plurality of moments in the future; calculating an adjustment error at the current moment to serve as a feedback signal and serve as input of the MPC controller, and performing rolling optimization by combining the MPC controller to obtain a plurality of intelligent air conditioner state control vectors at the next moment; and the central controller controls the state of the plurality of air conditioners at the next moment according to the state control vectors of the plurality of intelligent air conditioners at the next moment.
2. An intelligent air conditioner load aggregation control method for model predictive control applied to the intelligent air conditioner load aggregation control system for model predictive control according to claim 1, characterized in that: the method comprises the following steps:
step 1: constructing a first-order equivalent thermal parameter model of a single air conditioner load considering random interference, and solving characteristic parameters of each air conditioner by adopting a least square method;
step 2: acquiring the on-off state and the indoor temperature of the current moment of a plurality of air conditioners, uniformly dividing a certain temperature range into a plurality of sections of temperature intervals, dividing the indoor temperature of each air conditioner to the corresponding temperature interval according to the current moment of each air conditioner, respectively constructing an on-state set and an off-state set in the same temperature interval, putting the air conditioner in the on-state into the on-state set, putting the air conditioner in the off-state into the off-state set, carrying out normalization processing on the number of the air conditioners in each air conditioner set to obtain the ratio of the number of the air conditioners in each air conditioner set to the total number of the air conditioners at the current moment, constructing a state variable and a state space model of the air conditioner set at the current moment, and solving each matrix in the state space model;
step 3: predicting state variables of a plurality of moments in the future according to the state variables of the current moment by combining the state space model, and solving optimal control information of the plurality of moments in the future according to the state variables of the plurality of moments in the future;
step 4: calculating the regulating error at the current moment to be used as a feedback signal, taking the regulating error at the current moment as the input of the MPC controller, and combining the MPC controller to perform rolling optimization to obtain a plurality of intelligent air conditioner state control vectors at the next moment;
step 5: and the central controller randomly selects a plurality of intelligent air conditioners from the plurality of intelligent air conditioners as an on state according to the state control vectors of the plurality of intelligent air conditioners at the next moment, wirelessly transmits corresponding on control signals to the corresponding air conditioners, takes the rest air conditioners from the plurality of intelligent air conditioners as off states, and wirelessly transmits corresponding off control signals to the corresponding air conditioners.
3. The intelligent air conditioner load aggregation regulation method based on model predictive control according to claim 2, wherein: the first-order equivalent thermal parameter model of the single air conditioner load considering random interference in the step 1 is specifically defined as follows:
T in,l,k+1 ＝a l *T in,l,k +(1-a l )*(T out,l -λ l *T g,l )+w l,k
wherein lambda is l The dimensionless number of the first air conditioner is 1 when the air conditioner is started and 0 when the air conditioner is closed; r is R l ,C l Is equivalent thermal resistance and equivalent heat capacity of the air conditioner, and l=1, 2,3 … and N tcls Numbering the air conditioner load, N tcls Is the total number of air conditioners; Δt is the time step, T g,l For the temperature gain of the first air conditioner, T in,l,k The indoor temperature at the kth moment of the first air conditioner is T out,l,k The outdoor temperature at the kth moment of the first air conditioner;
and step 1, solving characteristic parameters of each air conditioner by adopting a least square method, wherein the characteristic parameters are as follows:
for the characteristic parameters of the first air conditioner, the parameters are obtained by a least square method according to temperature change data of the air conditioner within a period of time, and the specific solutions are as follows:
4. the intelligent air conditioner load aggregation regulation method of model predictive control according to claim 3, wherein:
step 2, uniformly dividing a certain temperature range into a plurality of sections of temperature intervals, wherein the specific steps are as follows:
the temperature intervals are distinguished by the numbers m=1, 2,3, …, M; temperature interval [ T ] min ,T max ]Evenly divided into M temperature intervals, the mth temperature interval can be expressed as [ T ] m ,T m+1 ]；
The set of all closed air conditioners at the current moment and the set of all open air conditioners at the current moment are respectively defined as follows:
close k ＝{Statebin close,k,1 ,Statebin close,k,2 ,Statebin close,k,3 ,…,Statebin close,k,M }
open k ＝{Statebin open,k,1 ,Statebin open,k,2 ,Statebin open,k,3 ,…,Statebin open,k,M }
wherein, close k For all sets of closed air conditioners at the kth time, statebin close,k,m A set of all closed state air conditioners for the mth temperature interval at the kth moment; open (S) k Set for all on air conditioners at kth time, statebin open,k,m The method comprises the steps that an air conditioner set in all on states of an mth temperature interval at a kth moment is obtained, and k represents the current moment;
since there are two sets for one temperature interval, there are 2M sets for M temperature intervals;
and step 2, normalizing the number of air conditioners in each air conditioner set, wherein the normalization is specifically as follows:
defining Load k,n N=1, 2,3, …, N denotes the set of the nth air conditioning group at the kth time, where n=2m, load k,n The following relationship is satisfied:
the number of air conditioners in each air conditioner set is normalized as follows:
wherein x is k,n The ratio of the number of air conditioners to the total number of air conditioners in the nth air conditioner set at the kth time is represented, N represents the total number of air conditioners, M represents the total number of temperature intervals, and k represents the current time.
5. The intelligent air conditioner load aggregation regulation method based on model predictive control according to claim 4, wherein the intelligent air conditioner load aggregation regulation method is characterized in that:
and step 2, constructing a state variable of the air conditioner set at the current moment, wherein the state variable is specifically as follows:
will x k,n The state defined as the air conditioning set, for 2M air conditioning sets, 2M state quantities in total, are defined as follows:
X k ＝[x k,1 ,x k,2 ,x k,3 ,…,x k,N ]
wherein X is k The state variable of the air conditioner set at the kth moment is k, wherein k represents the current moment;
for state variable X k+1 The change determinants are as follows: state variable X at time k k Control signal U at kth time k Random interference W in the control process at the kth moment k ；
The state space model in the step 2 is defined as follows:
X k+1 ＝AX k +BU k +W k
y k ＝CX k
U k ＝[u k,1 ,u k,2 ,u k,3 ,…,u k,N ]
wherein U is k A control variable indicating the kth time, u k,n To be applied to Load by the controller at the kth time k,n Control signal, y of (2) k Is the sum of the constant-frequency air conditioner power of all temperature intervals at the kth moment, W k For the random interference at the kth moment, a matrix A is a state transition matrix, a matrix B is a control input matrix, a matrix C is an output matrix, and k represents the current moment.
6. The intelligent air conditioner load aggregation regulation method based on model predictive control according to claim 5, wherein the intelligent air conditioner load aggregation regulation method is characterized in that:
and 2, solving each matrix in the state space model, wherein the method comprises the following steps of:
matrix A is an N matrix; the element in A is P i,j I, j, N; wherein P is i,j For the probability of transition between two status boxes, the subscript indicates that the air conditioner in the jth status box is transited to the air conditioner in the ith status box as follows:
wherein T is j ,T j+1 Respectively represent the upper and lower boundaries, T, of the temperature interval in which the jth state box is located i ,T i+1 Respectively representing the upper and lower boundaries of the temperature interval in which the ith state box is positioned; a, a 1 ,a 2 Respectively denoted by T i ,T i+1 The two parameters determined are M which is the number of temperature intervals;
wherein p (a) is a uniformly distributed probability density function of the form:
wherein a is min ,a max The method can be obtained by the least square fitting in the step 1;
b is written as follows:
the form of C is as follows:
C＝P rate *N tcls *[0 ... 0|1 ... 1] 1×N
wherein P is rate Rated power of air conditioner, N tcls For the total number of air conditioning loads, N is the total number of air conditioning sets.
7. The intelligent air conditioner load aggregation regulation method based on model predictive control according to claim 6, wherein:
and 3, predicting state variables at a plurality of future moments, wherein the state variables are specifically as follows:
from the state space model, the state variable at the (k+1) th moment can be predicted when the state variable at the (k) th moment is known, and N can be predicted through an iterative process p The state variables at each time are as follows:
wherein, matrix A is a state transition matrix, matrix B is a control input matrix, N p To predict step size, X k U is the state variable of the air conditioner set at the kth moment k+q A control variable representing the k+q time, k representing the current time, k+1 representing the next time, q ε [0, N p ]。
8. The intelligent air conditioner load aggregation regulation method based on model predictive control according to claim 7, wherein:
and step 3, solving optimal control information of a plurality of future moments according to state variables of the plurality of future moments, wherein the optimal control information is specifically as follows:
according to N p The state variable at each time can be obtained p The predicted outputs at each time are as follows:
wherein y is k+q Represents the sum of the constant-frequency air conditioner power of all the temperature intervals of the k+q, and q is E [0, N p ]；
And (3) making:
wherein, matrix A is a state transition matrix, matrix B is a control input matrix, matrix C is an output matrix, N p In order to predict the step size,is N p Control signals at each moment, phi is a state variable transition matrix, F is a control signal transition matrix,/>Is N p Control signals at various moments;
the following is obtained:
wherein X is k Is the state variable of the air conditioner set at the kth moment, Y is N p The prediction output of each moment, phi is a state variable transition matrix, and F is a control signal transition matrix;
defining error performance index of power tracking, concretely comprising the following steps:
wherein R is s Y is N as the input reference signal p The predicted output of the individual moments in time,is N p The control signals at each moment, G is a weight matrix with control function, can limit the frequent switching of the system, and can give different weights to the control sequence according to simulation expectations, namely, change the value of the G matrix;
according to the objective function and the constraint condition, solving the optimal N by using a quadratic programming solver in MATLAB p Control signals at each instant.
9. The intelligent air conditioner load aggregation regulation method based on model predictive control according to claim 8, wherein the intelligent air conditioner load aggregation regulation method is characterized in that:
and 4, calculating an adjustment error of the current moment, wherein the adjustment error is specifically as follows:
e k ＝r sk -y k +w k
wherein e k For the adjustment error at the kth time, r sk For the target power at the kth time, y k The sum of the constant-frequency air conditioner power of all temperature intervals at the kth moment, w k Is the prediction error at the kth time;
and step 4, performing rolling optimization by combining the MPC controller to obtain a value of the control action at the next moment, wherein the value is specifically as follows:
the rolling optimization mode in the MPC controller is to continuously adjust U (k) according to the output power of the state space model prediction at the next moment so as to minimize the difference between the output power and the target power, specifically:
wherein the method comprises the steps of，R s Y is N as the input reference signal p The predicted output of the individual moments in time,is N p The control signals at each moment, G is a weight matrix for controlling action;
the multiple intelligent air conditioner state control vectors at the next moment in the step 4 include:
the intelligent air conditioner state control vectors of the plurality of intelligent air conditioners at the next moment comprise the intelligent air conditioner number of the opening state and the intelligent air conditioner number of the closing state at the next moment.
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