CN104298191B - Heat prediction management based energy consumption control method in intelligent building - Google Patents
Heat prediction management based energy consumption control method in intelligent building Download PDFInfo
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Abstract
The invention provides a heat prediction management based energy consumption control method in an intelligent building, which comprises the steps of 1, establishing a heat model of the building; 2, establishing a thermodynamic model in a state space; 3, carrying out a schedulability test and judging whether a heat load of the building has schedulability or not under given energy consumption load budget; and 4, operating a model predictive control (MPC) based heat control strategy to solve a corresponding control output variable. According to the invention, an HVAC (heating, ventilating and air conditioning) system in the building is adjusted, and the peak power is enabled to be reduced under various constraints such that the room temperature is small in variation, a task processing constraint is met, and the like. In addition, the system can carry out schedulability analysis very well, and carries out good judgment and correction on whether the current energy consumption budget can meet requirements or not.
Description
Technical field
The present invention relates to consume management, in particular it relates to the energy consumption controlling party based on heat prediction management in intelligent building
Method.
Background technology
These years recently, what the energy resource consumption in building increased is very fast.The correlational study of 2009 shows, the U.S. builds
Build energy resource consumption in thing and account for U.S.'s total energy consumption nearly 40%.Recently, the energy information portion (Energy of the U.S.
Information Administration) prediction, between 2012 to the year two thousand thirty, the total energy consumption in building will reach
4.74QBtu.Energy resource consumption in building has salient feature, and that is, electricity consumption peak demand is more apparent.For ensureing to user's
Power supply service quality, during building construction, needs to make it meet the ability of user power utilization peak value, and this will improve the construction of building
Cost.On the other hand, for reducing the operation risk of electrical network, electrical network is often applied with more severe price for peak load and punishes
Penalize, that is, improve electrical network peak value electricity price.Therefore, gentle building electricity consumption curve, for electricity consumption user and supplier, is all beneficial
's.
Under construction, consumption a large portion of energy is derived from HVAC (Heating, Ventilating and in building
Air Conditioning, heating ventilation and air adjustment) system.Meanwhile, in big building, typically it is also required to be related to complexity
HVAC system.Therefore, for the HVAC system in building, carry out electric energy management so as to obtain more stable electricity consumption curve,
User can be made to obtain larger benefit.HVAC system is mainly used in eliminating the heat that in building, energy dissipation applications produce.Therefore,
We use MPC (Model Predictive Control, Model Predictive Control) method to consider thermal load prediction management, and
Comprehensive energy consumption control method, the electricity consumption load peak problem in building HVAC system is modeled controlling.
Currently, the dissipative system aggregative model Forecasting Methodology in building and consumption control method are combined and controlled
The mode of system is less.Existing method, concentrates under a multi-level structure, carries out energy dissipation applications operational control and optimum
Energy expenditure manages.This structure comprises a multilamellar scheduling system, and bottom is to be carried out based on Mixed integer linear programming
Linear load scheduling strategy.This strategy is used for minimizing total operating cost, and has carried out energy consumption energy power restriction.Load system and electricity
Interaction between net is by demand response manager processes.But, this strategy does not quantitatively study schedulability problem, that is, in limit
Under surely consuming, HVAC system can be made to adjust building temperature and to reach specific range of temperatures.
Content of the invention
For defect of the prior art, it is an object of the invention to provide being based on heat prediction management in a kind of intelligent building
Consumption control method.
According to the consumption control method based on heat prediction management in a kind of intelligent building that the present invention provides, including as follows
Step：
Step 1：Set up heat model in building；
Step 2：Set up the thermodynamical model under state space；
Step 3：Carry out schedulability test, judge under given load budget budget, the thermal load of this building
Whether there is schedulability；
Step 4：Run the heat control strategy based on Model Predictive Control MPC, formula (10) is carried out minimize with solution, solution
Go out corresponding control output variable；
Wherein, described step 4 is specific as follows：
The object function that formula (11) is represented, under the constraint in meeting formula (10), using solve standard integer
A young waiter in a wineshop or an inn takes advantage of the mode of optimization problem to be solved：
Wherein,Represent temperature reference vector,Represent F zone heater reference power input rate square
Battle array,Represent budget reference vector, WcK () represents perturbation vector, L represents heater number, and N represents prediction length,
Represent status predication vector,Represent state model parameter,Represent state model parameter,Represent i-th of j-th region
The ON/OFF state of heater, UpThe new system input variable using after (k) expression (10) deformation；
As follows for the implication of formula in formula (10)：
Wherein, M represents prediction length, Yr(k+M | k) status predication in m-th sampling interval after k-th time of expression；X
(k+M | k) represents the status predication in m-th sampling interval after k-th time, and M represents prediction length, and N represents prediction length, U (k
+ N-1 | k) represent the status predication in N-1 sampling interval after k-th time,Represent the extraneous air temperature in F region
Degree, X (k) represents k moment system mode, and R represents two interregional equivalent resistances, and Ψ represents F zone heater power input speed
Rate matrix, G represents separate manufacturing firms model parameter, and H represents separate manufacturing firms model parameter, and Φ represents separate manufacturing firms
Model parameter,Represent the total power budget number in building,Represent the power input speed of F zone heater to
Amount；
Wherein,SQRepresent On Square-Rooting Matrices,Represent SQTransposed matrix, SRTable
Show On Square-Rooting Matrices,Represent SRTransposed matrix, UpThe new system input variable using after (k) expression (10) deformation；Q
It is used to the compensation matrix to time error and input power with R respectively.
Preferably, described heat model, specific as follows：
For j-th region having n heater, by law of conservation of energy, obtaining heat balance equation is
T express time, F represents areal, NjRepresent heater number in the j of region, σjRepresent wiener noise variance, Tj
Represent j-th intra-zone temperature,Represent j-th region with outside thermal resistance, CjRepresent the thermal capacitance in j-th region,
Represent j-th region exterior temperature,Represent the thermal resistance in ith zone and j-th region, TiRepresent inside ith zone
Temperature,Represent solar radiation quantity ΦsThe effective window areas that can enter,Represent the energy variation that solar radiation produces,Represent the power of n-th heater in j-th region, ωjExpression standard wiener noise；
In the system model in j-th region, system mode is indoor air temperature, and system input is heater power；
Disturbance factor comprises three aspects, ambient temperature, solar radiation heat, standard wiener noise.
Preferably, described thermodynamical model, specific as follows：
X (k)=Φ X (k-1)+GU (k-1)+HW (k-1),
Y (k)=X (k), (4)
X (k) represents k moment system mode, and Φ represents separate manufacturing firms model parameter, and X (k-1) represents that the k-1 moment is
System state, G represents separate manufacturing firms model parameter, etching system input quantity when U (k-1) represents k-1, and H represents that discrete state is empty
Between model parameter, W (k-1) represent k-1 moment system disturbance amount, Y (k) represent k when etching system control output；
Wherein,It is kth time sampling interval TsLower system mode, discrete-time system model matrix table
It is shown asWith
Wherein, TsExpress time is spaced, and e represents that constant e, s represent integral term, and A represents state-space model parameter, B table
Show state-space model parameter, D represents state-space model parameter, G represents separate manufacturing firms model parameter, H represents discrete
State-space model parameter.
Preferably, described step 3 comprises the steps：
Step 3.1：By schedulability testing algorithm, obtain the load budget budget of the minimum of needs satisfactionmin；
Step 3.2：If currently available load budget budget ＜ budgetmin, then execution step 3.3, otherwise, hold
Row step 3.4；
Step 3.3：Request increases energy consumption load budget, obtains a new load budget budget ', re-executes step
3.1, or loosening heat limits, and obtains the load budget ' of a new minimummin, re-execute step 3.2；
Step 3.4：Execution step 4.
Preferably, described schedulability testing algorithm, specially：
First, schedulability problem is designed to following optimization problem：
s.t.-CA-1(DW+Bη)∈int(Safe). (7)
Wherein, s.t. represent constrained in；
There is η=[η in definitionj]∈[0,1]FSo that
ηjRepresent that one of [0,1] interval existing counts, []FRepresent F dimensional vector, F represents F- schedulability, NjTable
Show j-th zone heater number,Represent the η value of the presence of i-th heater in j-th region,Represent power input speed
Rate；
A represents state-space model parameter, and B represents state-space model parameter, and D represents state-space model parameter, W table
Show system disturbance, C represents state-space model parameter；
Set Safe is defined as
Wherein, Yl FRepresent the lower bound vector of F zone heater,Represent the upper bound of F zone heater to
Amount；
Minimum load budget budgetminObtained by this optimization problem；budgetminShould be not less than target letter
The load of numerical value counts budget in advance, setting
Restriction in optimization problem is linear, is represented as：
Yl+CA-1DW ＜-CA-1BP η ＜ Yu+CA-1DW.⑻
Wherein, YlRepresent the lower bound controlling output variable, YuRepresent control output variable the upper bound, P represent power input to
Amount；
Wherein
Yl=[Yl 1, Yl 2..., Yl F]T,
This optimization problem passes through linear programming for solution.
Compared with prior art, the present invention has following beneficial effect：
Consumption control method based on heat prediction management in a kind of intelligent building of major design of the present invention, for building
In thing HVAC system be adjusted so as to the less variation of room temperature, meet task process constraint etc. each item constraint while,
Reduce peak power.And, whether system can carry out Schedulability Analysis well, can expire for current energy consumption budget
Foot requires, and is judged well and revises.
By this inventive point, the effect that we can obtain is：
1st, when making each regional temperature of building be maintained near certain certain value, temperature change is steady.
2nd, effectively load balancing can be done to heat load so that the load peak of heat load reduces.
3rd, well the schedulability of thermal load in building is analyzed, is judged well and revise.
Brief description
The detailed description with reference to the following drawings, non-limiting example made by reading, the further feature of the present invention,
Objects and advantages will become more apparent upon：
Fig. 1 is room layout.
Fig. 2 is ambient temperature and intensity of solar radiation disturbance.
Fig. 3 is on/off control algolithm lower heater 1-4 power input.
Fig. 4 is on/off control algolithm lower heater 5-8 power input.
Fig. 5 is MPC control algorithm lower heater 1-4 power input.
Fig. 6 is MPC control algorithm lower heater 5-8 power input.
Fig. 7 controls lower room 1-4 range of temperature for MPC.
Fig. 8 controls lower room 5-8 range of temperature for MPC.
Fig. 9 is the power peak comparing ON/OFF and MPC control.
Figure 10 is system structure.
Specific embodiment
With reference to specific embodiment, the present invention is described in detail.Following examples will be helpful to the technology of this area
Personnel further understand the present invention, but the invention is not limited in any way.It should be pointed out that the ordinary skill to this area
For personnel, without departing from the inventive concept of the premise, some deformation can also be made and improve.These broadly fall into the present invention
Protection domain.
In view of this, we devise the consumption control method in intelligent building based on heat prediction management.For heating power
Power dispatching problem in system, defines the optimization problem of a belt restraining.Using MPC technology, heat is predicted.Phase
The energy peak problem answered, becomes the constraint of one of optimization problem, is solved.And schedulability problem is carried out with test solution
Certainly.
In the method, our basic structure is as shown in Figure 10.
In structurized system design shown in Figure 10, mainly comprise several levels：(1)PAC(Predictive
Admission Controller, predictability admission controller) (2) LB (Load Balance, load balancing) (3) D/R
(Demand Response, demand response) manager and load predicter.
PAC is partially in the bottom, for interacting with physical equipment, carries out real-time power access control.On top
Layer, D/R manager is the inlet point of system, as the interface of same electrical network interaction.LF layer is supplied information to based on the load of prediction
D/R manager and LB layer.LB layer, based on the load information predicted and current pricing information, provides energy consumption budget to PAC layer.
In the present invention, it is primarily upon PAC layer, the operation of the entrance for management energy and control electricity consumption application.All
Electricity consumption request submit to PAC layer.PAC layer, based on total loading condition, determines that this request accepts or refuses.PAC layer is main
Including two modules：Schedulability test and heat control.Schedulability test module is used for checking total energy consumption upon execution
Whether budget is observed.Heat control module, based on Current Temperatures, total energy consumption budget, the temperature range setting, management building
Each heater (refrigerator) in thing HVAC system.
Afterwards, problem is modeled processing.Parameter list is as follows：
1. first, set up heat model in building
Between floors, heating and refrigeration system are used for controlling between building zones of different (for example, not chummery) or build
Build the energy transmission with external environment for the thing zones of different.Simplification is processed, and only considers heating system (refrigeration system situation in the present invention
Similar).The target that heating system controls is to ensure that in room, each regional temperature maintains comfortable scope.In each region
Thermokineticss, all affected by three below factor：(for example, solar radiation is to external surface of buildings or with outer for outdoor energy
The heat exchange of boundary's environment), indoor energy (for example, the heat of heater or miscellaneous equipment generation, activity of people etc.), interregional energy
Amount (for example, heat passes to another room from a room by wall).
For j-th region having n heater, by law of conservation of energy, we can obtain thermal balance side
Formula is
T express time, F represents areal, NjRepresent heater number in the j of region, σjRepresent wiener noise variance；
In the system model in j-th region, system mode is indoor air temperature, and system input is heater power.
Disturbance factor comprises three aspects, ambient temperature, solar radiation heat, standard wiener noise.Relevant parameter implication refers to parameter
Table.
The control input of heater follows different operational circumstancesIt is expressed asWhereinRepresent and add
Hot device OFF (closing),Represent heater ON (opening).WithRepresent the power of respective heater, then actual heater powerFor
Therefore, heater can be controlled by ON-OFF.Wherein, under ON state, the power of heater is constant, constant.
2. the thermodynamical model under state space
SetFor the number of regions in building, and there is N in each j-th regionjIndividual heater.
Therefore, a total ofIndividual heater.With X=[T1, T2..., TF]TRepresent state vector, useRepresent NjThe control input vector of individual heater, with U=[U1,U2,…,UF]TExpression system
Control input vector.WithRepresent the disturbance in j-th region.System disturbance vector representation is W=
[W1,W2,…,WF]T.
State-space model is expressed as
Y=X, (3)
Wherein,Represent system mode derivative, X represents system mode vector, and A represents state-space model parameter, and B represents
State-space model parameter, D represents state-space model parameter, and W represents system disturbance, and Y represents that system controls output vector, U
Expression system input vector；
Sytem matrix is defined as：
B=diag (B1B2… BF),
D=diag (D1D2… DF)
AFRepresent variable parameter name in A matrix, be defined by the formula, CFThe corresponding thermal capacitance in F region of expression, BFRepresent in B matrix
Variable parameter name, is defined by the formula, DFRepresent variable parameter name in D matrix, be defined by the formula；
For j=1,2 ..., F
Wherein,Represent power input speed,Represent wiener noise variance, R represents two interregional equivalent resistances；
In order to simplify design and analyze, continuous time model is converted into discrete time model by us.
X (k)=Φ X (k-1)+GU (k-1)+HW (k-1),
Y (k)=X (k), (4)
X (k) represents k moment system mode, and Φ represents separate manufacturing firms model parameter, and X (k-1) represents that the k-1 moment is
System state, G represents separate manufacturing firms model parameter, etching system input quantity when U (k-1) represents k-1, and H represents that discrete state is empty
Between model parameter, W (k-1) represent k-1 moment system disturbance amount, Y (k) represent k when etching system control output；
Wherein,It is kth time sampling interval TsLower system mode, discrete-time system model matrix table
It is shown asWith
Wherein, TsExpress time is spaced, and e represents that constant e, s represent integral term.
3. schedulability test
The target controlling is to make the indoor air temperature Y of building zones of different be maintained at a comfortable interval.By area
Between bound be expressed as YlAnd Yu
Yl≤Y≤Yu. ⑸
Control system submits to load, by total energy consumption, the energy consumption load restriction that budget determines, is represented by：
It is that building heats
Total energy consumption budget in system, Ψ represents F zone heater power input rate matrix, ΨFRepresent F zone heater
Power input velocity vectors,Represent the real vector of 1*L, L represents total heater number, ΨjRepresent that j-th region adds
The power input velocity vectors of hot device,Represent power input speed, NjRepresent that there is N in j-th regionjIndividual heater；
In addition, the input of heater must be ON/OFF state：
Wherein, U represents heater ON/OFF state vector,Represent the ON/OFF shape of i-th heater in j-th region
State, L represents total heater number；
Therefore, schedulability problem is, while automatically carrying out thermal management, meet the constraint (5) and (6).
We carry out solve problem using k- schedulability, that is, be schedulable in the case of ensureing k heater.System
Control input U is considered the ON/OFF state to single heater and is controlled.If in the case of U input, Neng Gouman
All of regional temperature is adjusted to comfortable interval by foot, and meets energy consumption budget limit, then we this input and be called
“safe”.Further, we define system " Safe " and are：Existence time τ >=0 so that as t >=τ Y (t) ∈ Safe, its
In, t represents t, and Y (t) represents system output.Our systems are called that k is schedulable, work as satisfaction：There is sacurity dispatching U,
So thatWherein, ‖ ‖1Represent 1- norm, U (t) represents the control input of system,Represent j-th
The ON/OFF state of i-th heater in region, k represents schedulability tolerance k.
Further, when there is η=[η in our definitioni]∈[0,1]FSo thatAnd meet-CA-1(D+B
η)∈int(Safe).Then system is obviously also that k is schedulable.
Wherein, int (Safe) represents set of integers Safe；
Further, when there is η=[ηi]∈[0,1]FSo thatAnd-CA-1(D+
B η) ∈ int (Safe), system is that budget is schedulable.Wherein, budget is load budget,Represent i-th of j-th region
The η value of the presence of heater,Represent power input speed.
Based on this, we design the schedulability testing algorithm in the present invention.
First, schedulability problem is designed to following optimization problem.
s.t.-CA-1(DW+Bη)∈int(Safe). (7)
Wherein, s.t. represent constrained in；
Set Safe is defined as
Wherein, Yl FRepresent the lower bound vector of F zone heater,Represent the upper bound of F zone heater to
Amount；
Minimum peak load budget budgetminCan be obtained by this optimization problem.budgetminShould be not little
Load in target function value counts in advance, can arrange
Being limited in optimization problem is linear, can be represented as：
Yl+CA-1DW ＜-CA-1BP η ＜ Yu+CA-1DW.⑻
Wherein, YlRepresent the lower bound controlling output variable, YuRepresent the upper bound controlling output variable；
Wherein
Yl=[Yl 1, Yl 2..., Yl F]T,
Therefore, this optimization problem can pass through linear programming for solution.
Therefore, the schedulability problem method of testing of the present invention is, within each time cycle, executes following algorithm：
1) solve the optimization problem in formula (7), obtain the load budget budget of the minimum of needs satisfactionmin
2) if currently available load budget budget ＜ budgetmin, then execution step 3), otherwise, execution step 4)
3) system asks to increase energy consumption budget to upper strata (LB layer), obtains a new budget ', re-executes step
1), or loosen heat limit, obtain a new budget 'min, re-execute step 2
4) the heat control strategy based on MPC for the system operation.
4. the heat control strategy based on MPC
In MPC controls and calculates, controller is controlled to input U (k), for minimizing following object function J (K)：
M and N represents prediction length scope respectively, and M is the prediction length scope controlling output vector, and N is perturbation vector
Prediction length scope, Y (k+s | k) is the status predication in s-th sampling interval after k-th time, YrWhen (k |+s | k) is k-th
Between after s-th sampling interval status predication, U (k+s | the k) status predication in s-th sampling interval after k-th time.Q and R divides
It is not used to the compensation matrix to time error and input power.
This cost function J (K) is mainly and minimizes temperature tracking error and power demand.Even if power demand is less same
When so that the temperature change of different time compartment is less.
For the system model in formula (3), again become and turn to following form,
Yp(k)=Xp(k),
Wherein,Represent status predication vector,Represent state model parameter,Represent state model parameter；
Therefore, in this strategy, need, in each time interval, to solve minima to this cost function, and meet phase
Answer constraints.Optimization problem arranges as follows：
Wherein,Represent temperature reference vector,Represent F zone heater reference power input rate square
Battle array,Represent budget reference vector, WcRepresent perturbation vector,Represent output reference vector lower bound,Represent output with reference to
The amount upper bound, L represents heater number, and N represents prediction length；
Corresponding matrix implication is as follows：
Wherein, M represents prediction length, Yr(k+M | k) status predication in m-th sampling interval after k-th time of expression；
Wherein, X (k+M | k) represents the status predication in m-th sampling interval after k-th time, and M represents prediction length, N table
Show prediction length, U (k+N-1 | k) represents the status predication in N-1 sampling interval after k-th time,Represent the F area
The external air temperature in domain；
The matrix of express time error compensation matrix,Represent the matrix of the compensation matrix of input power,Represent
The matrix of output reference vector lower bound,Represent the matrix in the output reference vector upper bound,Represent the general power in building
Count in advance,Represent separate manufacturing firms model parameter matrix；
Represent output reference vector lower bound,Represent the output reference vector upper bound, Q and R is used to the time by mistake respectively
Difference and the compensation matrix of input power；
Object function can be deformed into：
Wherein, SQ, SRRepresent
Under this object function, meet the corresponding constraint in formula (10), this problem is deformed into the integer least square of standard
Optimization problem, it is possible to use in Matlab, YALMIP storehouse is solved.
In described intelligent building, the consumption control method based on heat prediction management, specifically includes following steps：
Step 1：Set up heat model in building；
Step 2：Set up the thermodynamical model under state space；
Step 3：Carry out schedulability test, judge under given load budget budget, the thermal load of this building
Whether there is schedulability；
Step 4：Run the heat based on MPC (Model Predictive Control, Model Predictive Control) and control plan
Slightly, formula (10) is carried out minimizing solving, solve corresponding control output variable.
For making the purpose of the present invention, technical scheme and a little clearer, below in conjunction with the accompanying drawings with one specific embodiment
The present invention is done and describes in detail progressively.
In this embodiment, we have selected one 120m2Single story building, each room is equipped with one or two
Heater.Shown in its room layout accompanying drawing 1.Partial heat parameter is shown in Fig. 2 and following allocation list：
Heat parameter configuration table
The non-null variable of thermal resistance is arranged
Heater power consumes
For MPC controller, arrange Q (1)=...=Q (M)=diag (10,10,10,10,10,10,10,10), M=5,
R (1)=...=R (N)=diag (1,1,1,1,1,1,1,1), N=5, the sampling interval is 2 minutes, i.e. Ts=120s.Setting room
Between temperature be 22-24 DEG C between,
The method that the present invention is introduced, with not using MPC PREDICTIVE CONTROL, is only compared using ON-OFF mode, each room
Between middle power input result see Fig. 3-Fig. 6.Its result is collected for Fig. 9 the peak load it is found that under MPC control method
Little compared with common ON-OFF strategy peak load.Energy consumption budget can preferably be met, show that the schedulability of system is preferable.With
When, accompanying drawing 8- Fig. 9 shows, MPC controls lower heater, can well by each room temperature control setting 22-24
Between degree Celsius, and the amplitude that temperature changes at any time and changes is less.
Above the specific embodiment of the present invention is described.It is to be appreciated that the invention is not limited in above-mentioned
Particular implementation, those skilled in the art can make various modifications or modification within the scope of the claims, this not shadow
Ring the flesh and blood of the present invention.
Claims (1)
1. in a kind of intelligent building the consumption control method based on heat prediction management it is characterised in that comprising the steps：
Step 1：Set up heat model in building；
Step 2：Set up the thermodynamical model under state space；
Step 3：Carry out schedulability test, judge that the thermal load of this building whether under given load budget budget
There is schedulability；
Step 4：Run the heat control strategy based on Model Predictive Control MPC, formula (10) is carried out minimize solution, solve phase
The control output variable answered；
Wherein, described step 4 is specific as follows：
The object function representing for formula (11), under the constraint in meeting formula (10), using an integer young waiter in a wineshop or an inn for the standard of solution
The mode taking advantage of optimization problem is solved：
Wherein,Represent temperature reference vector,Represent F zone heater reference power input rate matrix,Represent budget reference vector, WcK () represents perturbation vector, L represents heater number, and N represents prediction length,Table
Show status predication vector,Represent state model parameter,Represent state model parameter,Represent that j-th region adds for i-th
The ON/OFF state of hot device, UpThe new system input variable using after (k) expression (10) deformation；
As follows for the implication of formula in formula (10)：
Wherein, M represents prediction length, Yr(k+M | k) status predication in m-th sampling interval after k-th time of expression；X(k+M|
K) represent the status predication in m-th sampling interval after k-th time, M represents prediction length, N represents prediction length, U (k+N-1 |
K) represent k-th time after N-1 sampling interval status predication,Represent the external air temperature in F region, X
K () represents k moment system mode, R represents two interregional equivalent resistances, and Ψ represents F zone heater power input speed square
Battle array, G represents separate manufacturing firms model parameter, and H represents separate manufacturing firms model parameter, and Φ represents separate manufacturing firms model
Parameter,Represent the total power budget number in building,Represent the power input velocity vectors of F zone heater；
Wherein,SQRepresent On Square-Rooting Matrices,Represent SQTransposed matrix, SRTable
Show On Square-Rooting Matrices,Represent SRTransposed matrix, UpThe new system input variable using after (k) expression (10) deformation；Q
It is used to the compensation matrix to time error and input power with R respectively；
Described heat model, specific as follows：
For j-th region having n heater, by law of conservation of energy, obtaining heat balance equation is
T express time, F represents areal, NjRepresent heater number in the j of region, σjRepresent wiener noise variance, TjRepresent
J-th intra-zone temperature,Represent j-th region with outside thermal resistance, CjRepresent the thermal capacitance in j-th region,Represent
J-th region exterior temperature,Represent the thermal resistance in ith zone and j-th region, TiRepresent ith zone internal temperature,Represent solar radiation quantity ΦsThe effective window areas that can enter,Represent the energy variation that solar radiation produces,Table
Show the power of n-th heater in j-th region, ωjExpression standard wiener noise；
In the system model in j-th region, system mode is indoor air temperature, and system input is heater power；Disturbance
Factor comprises three aspects, ambient temperature, solar radiation heat, standard wiener noise；
The control input of heater follows different operational circumstancesIt is expressed asWhereinRepresent heater
Close,Represent that heater leaves；WithRepresent the power of respective heater, thenFor
Wherein, under open state, the power of heater is constant；Described thermodynamical model, specific as follows：
X (k)=Φ X (k-1)+GU (K-1)+HW (k-1),
Y (k)=X (k),（4）
X (k) represents k moment system mode, and Φ represents separate manufacturing firms model parameter, etching system shape when X (k-1) represents k-1
State, G represents separate manufacturing firms model parameter, etching system input quantity when U (k-1) represents k-1, and H represents separate manufacturing firms mould
Shape parameter, W (k-1) represents k-1 moment system disturbance amount, and when Y (k) represents k, etching system controls output；
Wherein,It is kth time sampling interval TsLower system mode, discrete-time system model matrix is expressed as
Φ=eATs With
Wherein, TsExpress time is spaced, and e represents that constant e, s represent integral term, and A represents state-space model parameter, and B represents state
Spatial model parameter, D represents state-space model parameter, and G represents separate manufacturing firms model parameter, and H represents that discrete state is empty
Between model parameter；
Described step 3 comprises the steps：
Step 3.1：By schedulability testing algorithm, obtain the load budget budget of the minimum of needs satisfactionmin；
Step 3.2：If currently available load budget budget ＜ budgetmin, then execution step 3.3, otherwise, execute step
Rapid 3.4；
Step 3.3：Request increases energy consumption load budget, obtains a new load budget budget ', re-executes step 3.1,
Or loosening heat limits, obtain the load budget ' of a new minimummin, re-execute step 3.2；
Step 3.4：Execution step 4；
Described schedulability testing algorithm, specially：
First, schedulability problem is designed to following optimization problem：
s.t. -CA-1(DW+Bη)∈int(Safe). ⑺
Wherein, s.t. represent constrained in；
There is η=[η in definitionj]∈[0,1]FSo that
ηjRepresent that one of [0,1] interval existing counts, []FRepresent F dimensional vector, F represents F- schedulability, NjRepresent jth
Individual zone heater number,Represent the η value of the presence of i-th heater in j-th region,Represent power input speed；
A represents state-space model parameter, and B represents state-space model parameter, and D represents state-space model parameter, and W represents system
System disturbance, C represents state-space model parameter；
Set Safe is defined as
Wherein,Represent the lower bound vector of F zone heater,Represent the upper bound vector of F zone heater；
Minimum load budget budgetminObtained by this optimization problem；budgetminShould be not less than target function value
Load count in advance budget, setting
Restriction in optimization problem is linear, is represented as：
Yl+CA-1DW ＜-CA-1BPη＜ Yu+CA-1DW. (8)
Wherein, YlRepresent the lower bound controlling output variable, YuRepresent the upper bound controlling output variable, P represents power input vector；
Wherein
This optimization problem passes through linear programming for solution；
B=diag (B1B2…BF),
D=diag (D1D2… DF)
AFRepresent variable parameter name in A matrix, be defined by the formula, CFThe corresponding thermal capacitance in F region of expression, BFRepresent variable in B matrix
Parameter name, is defined by the formula, DFRepresent variable parameter name in D matrix, be defined by the formula；
For j=1,2 ..., F
Wherein,Represent power input speed,Represent wiener noise variance, R represents two interregional equivalent resistances.
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