CN104898422B - The hierarchical optimal algorithm of United system - Google Patents
The hierarchical optimal algorithm of United system Download PDFInfo
- Publication number
- CN104898422B CN104898422B CN201510225593.6A CN201510225593A CN104898422B CN 104898422 B CN104898422 B CN 104898422B CN 201510225593 A CN201510225593 A CN 201510225593A CN 104898422 B CN104898422 B CN 104898422B
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- mtd
- refrigerating machine
- mtr
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Pharmaceuticals Containing Other Organic And Inorganic Compounds (AREA)
- Air Conditioning Control Device (AREA)
- Feedback Control In General (AREA)
Abstract
The present invention provides a kind of hierarchical optimal algorithm for the United system being made of conventional electric refrigerating machine and ice-storage system, first according to each low-temperature receiver economic model and tou power price policy, using Integer programming, optimize the opening and optimal power setting value of each low-temperature receiver, at the same time in view of the dynamic adjustment performance and physical constraint of each low-temperature receiver, in order to preferably improve dynamic performance, the coordination Distributed Predictive Control method of design object coupling, the setting value of each low-temperature receiver of re-optimization under Distributed Architecture, so that each low-temperature receiver ensures to track optimal refrigeration work consumption setting value while total load as far as possible in dynamic process.Mixed integer programming is respectively adopted in the present invention and Distributed Predictive Control method quantitatively solves the stable state and dynamic scheduling problem of United system, and rational suggestion is provided for cold supply system energy management and Optimized Operation.
Description
Technical field
The present invention relates to energy net scheduling field, particularly a kind of connection being made of conventional electric refrigerating machine and ice-storage system
Close the hierarchical optimal algorithm of cold supply system.
Background technology
Due to large size city generally existing land used it is nervous the problem of, in order to increase city as far as possible using limited land area
City's capacity, skyscraper become urban planners and preferably select.Skyscraper often has independent cooling network, there is provided
The various cold demands of entire building.With the appearance of various emerging technologies and the popularization of tou power price policy, only make
It is unsatisfactory with the cold supply system economic benefit of conventional electrical chillers, in order to improve the economy of skyscraper cold supply system
Benefit, more cooling unit complementation cooperative systems are applied more and more common.Typical more cooling unit complementation cooperative systems are general
Including：Ice cold-storage, conventional electric refrigerating machine, cold, heat and electricity triple supply and earth source heat pump etc., wherein conventional electric refrigerating machine and ice cold-storage one
As shared more than 90% refrigeration duty.Due to can autonomous control energy storage with can the period, ice-storage system increasingly by
To the favor of people, coordination control effectively is carried out with conventional electric refrigerating machine to it and is to ensure that the cooperative system is energy-saving and carries
The key of high economic benefit.
Ice-storage system rests on qualitative aspect more with jointly controlling for conventional electrical chillers at present, i.e., in electricity price paddy
Cold storage of ice making, the cool thermal discharge at electricity price peak, lacks the analysis and control quantitative to whole system, this results in its controlling party
Method is not superior, and since the cooling power that electric refrigerating machine reaches setting from startup has a dynamic process, so only examining
The control method for considering stable state is difficult to ensure that the actual cooling power of whole system keeps up with rapidly prediction load.Therefore, it is necessary to study
A kind of optimal control method causes system to ensure high-efficiency and economic operation on the premise of total load in dynamic process.
For such a distributed system of skyscraper cold supply system, general steady state optimization method can be usually used to it
It is scheduled, yet with electric refrigerating machine dynamic response problem, is further supplied using Distributed Predictive Control optimization electric refrigerating machine
Cold power dynamic property.For the dynamic control of distributed system, converted via centralization to distributed control structure at present,
Because centerized fusion can cause, method is complicated and calculation amount is bigger than normal.In practical application in industry, Model Predictive Control (MPC) can
By the pulse of controlled device or step response sequence prediction object change in future and process time lag can be naturally introduced by, for
Multi-variable system can obtain the control effect more outstanding than conventional control methods.In large scale system, traditional model is pre-
Observing and controlling system derives Distributed Predictive Control (DMPC) this form, and large scale system resolved into multiple subsystems, subsystem with
Data mutually exchange between subsystem, and each subsystem completes the MPC of itself using the data of itself and its sub-systems
The solution of problem, significantly reduces calculation amount and can reach the optimum state of whole system.
Therefore, the present invention intend using for reference more than to the coordination approach of coupled system, not coupling between goal in research coupling but subsystem
The dynamic optimization of the distributed system of conjunction so that whole system high-efficiency and economic on the premise of ensuring to provide total prediction refrigeration duty is transported
OK.
The content of the invention
For deficiencies of the prior art, the present invention proposes a kind of hierarchical optimal side of United system
Method, this method carry out electric refrigerating machine and Ice Storage Tank economy modeling and obtain economy with the method for mixed integer programming and refer to
Mark the cooling power during steady-state operation of each electric refrigerating machine and Ice Storage Tank under optimal conditions；Lower floor uses Distributed Predictive Control side
Method ensures each electric refrigerating machine and Ice Storage Tank tracks stable state setting value that upper strata economic optimization is drawn simultaneously as far as possible in dynamic process
And total cooling power tracking prediction load, solve the problems, such as target coupling but the Distributed Predictive Control that subsystem does not couple.
To realize above-mentioned purpose, the present invention provides a kind of hierarchical optimal algorithm of United system, and the joint supplies
Cooling system is the United system being made of conventional electric refrigerating machine and ice-storage system, and the method specifically includes following step
Suddenly：
Step S1：Its power consumption is fitted on refrigeration work consumption and cooling water inlet temperature using electric refrigerating machine operation data
Function；
Step S2：Using the electric refrigerating machine power consumption obtained in previous step on its refrigeration work consumption and cooling water inlet
The function of temperature establishes the economic model of electric refrigerating machine cooling and and then establishes the economic model of ice-storage system cooling, most end form
Into steady-state economy optimization problem object function and constraints and solved, obtain each electric refrigerating machine and Ice Storage Tank in economy
Start and stop state and cooling power under benefit optimal conditions；
Step S3：On the basis of previous step, the coordination Distributed Predictive Control method of design object coupling is again excellent
Change whole cold supply system in the real-time cooling set value of the power of each electric refrigerating machine of each sampling instant to improve whole system response
The dynamic property of load；
Step S4：Above-mentioned Distributed Predictive Control problem is solved using alternative manner, obtains each sampling of each electric refrigerating machine
The cooling set value of the power at moment.
It is preferred that in the step S1：The centrifugal most important performance parameter Energy Efficiency Ratio of electric refrigerating machine is refrigeration work consumption
With the ratio of power consumption.In engineering practice, it is necessary to according to actual cooling water inlet temperature, power consumption and refrigeration work consumption etc.
Data by regression analysis by power consumption represent as cooling water inlet temperature and refrigeration work consumption Binary quadratic functions form with
Just the solution of subsequent step optimization problem is carried out using Mathematical Planning instrument.
It is preferred that in the step S2：Every electric refrigerating machine have open and close two working statuses, therefore use 0-1 from
Scattered variable Y describes the working status of electric refrigerating machine.Differentiation for Double-working-condition electric refrigerating machine air conditioning condition and ice making operating mode is then
It need to be described respectively using two 0-1 discrete variables.The refrigeration work consumption of electric refrigerating machine is then described with continuous variable.According to step
The regression analysis carried out in S1, for conventional electric refrigerating machine, its power consumption can be expressed as on refrigeration work consumption and cooling water
The Binary quadratic functions of inlet temperature：
Then the electricity charge cost C of electric refrigerating machine cooling generation is：
Wherein p is cooling period electricity price, and t is cooling period of time length, and Y is electric refrigerating machine start and stop mark,
It is the electric refrigerating machine power consumption that is fitted in step S1 on refrigeration work consumption and the function of cooling water inlet temperature.
On the premise of ignoring Ice Storage Tank storage cold and being lost with storage time, in order to consider the cost of Ice Storage Tank cooling, draw
Enter Ice Storage Tank cold average price psConcept, describes the price of unit cold, this price is with filling cold, filling cold period electricity price and duplex
Condition electric refrigerating machine ice making operating mode input/output relation is related, is defined by the formula：
Wherein pinitFor cold average price initial value, WinitFor Ice Storage Tank cold initial value, tinTo rush cool time, toutDuring to let cool
Between, PmFor the total ice making power of Double-working-condition electric refrigerating machine, PsFor Ice Storage Tank cooling power, YsFor Ice Storage Tank cooling start and stop mark.By storing
The definition of ice bank cold average price can be seen that without considering storage cold with time loss on the premise of, whether average price and Ice Storage Tank
Let cool it is unrelated, only may change when Ice Storage Tank rushes cold.
Ice Storage Tank stores up cold increment：
Δ W=tinP-toutPsYs (4)
Object function is various electric refrigerating machine air conditioning condition coolings and the sum of electricity charge needed for Ice Storage Tank cooling：
P is cooling period electricity price, and t is cooling period of time length, YiFor i-th electric refrigerant start and stop mark,It is i-th electric refrigerating machine power consumption on refrigeration work consumption and the function of cooling water inlet temperature, wherein Pout,i
For i-th electric refrigerating machine cooling power, Tc,i inFor i-th electric refrigerating machine cooling water inlet temperature, YsFor Ice Storage Tank cooling start and stop
Mark, psFor Ice Storage Tank cold average price, PsFor Ice Storage Tank cooling power, YsFor Ice Storage Tank cooling start and stop mark.
Constraints includes cooling power and prediction balancing the load, the limitation of each electric refrigerating machine rated power, Ice Storage Tank storage are cold
Measure bound and ice-storage system operating mode is unique：
Wherein YiFor i-th electric refrigerant start and stop mark, Pout,iFor i-th electric refrigerating machine cooling power, PsFor Ice Storage Tank
Cooling power, YsFor Ice Storage Tank cooling start and stop mark, PpTo predict load, αi∈ (0,1) is the minimum output of i-th electric refrigerating machine
Power proportions, Pi,ratedFor i-th electric refrigerating machine rated output power, W stores up cold, W for Ice Storage TankmaxAnd WminStored up for Ice Storage Tank
Cold bound；YiceFor refrigerating unit with dual duty ice making operating mode start and stop mark, when giving tacit consent to ice making operating mode, all Double-working-condition cold are at the same time
Open, accelerate ice making speed；Yair,iFor i-th refrigerating unit with dual duty air conditioning condition start and stop mark, common N platforms refrigerating unit with dual duty；
Finally, to above-mentioned optimization problem on the premise of the object function of steady-state economy optimization problem and constraints is formed
Solved, obtain the start and stop state and cooling power of each electric refrigerating machine and Ice Storage Tank under the conditions of optimal economic benefit.
It is preferred that in step S3, the coordination Distributed Predictive Control method of the target coupling is specific as follows：
Dcs is divided into N number of subsystem according to air conditioning condition electric refrigerating machine quantity, and each subsystem is by model
PREDICTIVE CONTROL MPC and the electric refrigerating machine composition accordingly controlled, the data of different sub-systems MPC carry out real-time exchange using communicator
Calculating task is completed, so that the economic benefit obtained in each air conditioning condition electric refrigerating machine cooling power tracking step S2 is most
Under the conditions of each air conditioning condition electric refrigerating machine optimal economic benefit of cooling power and total cooling power tracking under the conditions of excellent
The sum of cooling power；
Electric refrigerating machine refrigeration work consumption dynamic process model transmission function is as follows：
τ is first order inertial loop time constant, τdFor delay time, then s-th of subsystem separate manufacturing firms model shape
Formula is：
Wherein us(k) it is k moment electric refrigerating machine output power setting values, is the optimization change of optimization problem in step S3
Amount, ps(k) it is k moment electric refrigerating machine real outputs, xs(k)=[ps(k)ps(k+1)…ps(k+nd)]TFor state variable,
ndMultiple for electric refrigerating machine delay time relative to the discrete system sampling time；As、Bs、CsTo be in state space equation
Matrix number, whereinDuring by discrete system sampling time and electric refrigerating machine dynamic process
Between constant determine, R represent set of real numbers, if the sampling time is Δ t, then have：
For s-th of subsystem, s=1,2, N；MPC coordination strategies include electric refrigerating machine in following P sampling
The prediction time domain interior prediction output power of moment composition is passed through as close possible to the obtained single air conditioner operating mode electric refrigerating machines of step S2
The cooling power r to help under benefit optimal conditionss, and general MPC strategies are different from, in order to control all electric refrigerating machines to predict
Output power keeps up with the sum of the cooling power under the conditions of each air conditioning condition electric refrigerating machine optimal economic benefit r as far as possible, in mesh
Each air conditioning condition electric refrigerating machine always pre- power scale and the confession under the conditions of optimal economic benefit in prediction time domain is introduced in scalar functions
The quadratic sum of the sum of cold power gap, therefore different sub-systems MPC object functions couple；To sum up, s-th of subsystem MPC
Object function is as follows：
Wherein, Us=[us(k)us(k+1)…us(k+P-1)]TFor electric refrigerating machine output power setting value sequence in prediction time domain
Row, us(k) it is k moment electric refrigerating machine output power setting values, is the optimized variable of optimization problem；And Q, R ∈ [0,1] are power
Weight coefficient, is respectively intended to weigh separate unit electric refrigerating machine prediction Steam Generator in Load Follow degree and all electric refrigerating machine total loads tracking degree
Importance；rs(k+i) it is the cooling power under the conditions of (k+i) moment s platforms air conditioning condition electric refrigerating machine optimal economic benefit, r
(k+i) it is the sum of the cooling power of each air conditioning condition electric refrigerating machine of (k+i) moment under the conditions of optimal economic benefit；Predicted at the k moment by prediction model for s platform air conditioning condition electric refrigerating machines its (k+i) moment output
Power,Lead to for remaining air conditioning condition electric refrigerating machine in addition to s platform air conditioning condition electric refrigerating machines at the k moment
Cross prediction model prediction its in the sum of the output power at (k+i) moment；
MPC prediction models are：
For (k+i) the moment s platforms air conditioning condition electric refrigerating machine calculated at the k moment by prediction model
State variable in state space equation,It is pre- by prediction model at the k moment for s platform air conditioning condition electric refrigerating machines
Survey its (k+i) moment output power；As i-jAnd As iFor the corresponding power of coefficient matrix in state space equation.
For s-th of subsystem MPC controller, whole optimization problem form is：
Wherein, { us,min,us,max},{ps,min,ps,maxVariable in order to control boundary constraint.
It is preferred that the step S4 is specifically included：
Step S41：At the k moment, according to the every air conditioning condition electric refrigerating machine optimal economic benefit bar obtained in step S3
Cooling power r under parts, initialize electric refrigerating machine output power setting value sequence U of each subsystem in prediction time domains, order
Iterations l=0：
It is above-mentionedFor electric refrigerating machine output power setting value of the l times iteration at (k+1) moment, P is prediction time domain
Length；
Step S42：According to MPC prediction models, output quantity predicted value in each subsystem MPC controls time domain is calculatedAnd notify to give other N-1 subsystem MPC；
Step S43：For each subsystem, the optimization problem in solution procedure S3, obtains optimal solution, i.e., optimal refrigeration machine
Output power sets value sequence
Step S44：Check whether the condition of convergence of all subsystem MPC meets, i.e., to given precisionWhether haveExpire if all of the subsystem condition of convergence
Foot, makes each subsystem optimum control amountValue sequence is set with optimal refrigeration machine output powerIt is equal, i.e.,
Go to step S45；Otherwise, makeα is the constant between 0 and 1, and introducing should
Parameter chooses numerical values recited, l=l+1, goes to step S42 on demand for the not convergent situation of avoiding method of trying one's best；
Step S45：Controlled quentity controlled variable is chosen at the k momentAct on corresponding son
System.
Step S46：To next sampling instant, i.e. k+1 → k, return to step S41, repeats the above steps barrel shift.
Compared with prior art, the invention has the advantages that：
Hierarchical optimal algorithm of the present invention for the United system being made of conventional electric refrigerating machine and ice-storage system
In by establishing the economic model of electric refrigerating machine and Ice Storage Tank, and use mixed integer programming and Distributed Predictive Control respectively
Solve the problems, such as steady-state optimization and optimization problems, quantitatively determine the dispatching method of United system so that whole system
High-efficiency and economic is run on the premise of ensuring to provide total prediction refrigeration duty, and conjunction is provided for cold supply system energy management and Optimized Operation
The suggestion of reason.
Brief description of the drawings
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, further feature of the invention,
Objects and advantages will become more apparent upon：
Fig. 1 is the United system structure chart being made of conventional electric refrigerating machine and ice-storage system；
Fig. 2 is the flow chart of one embodiment of the invention；
Fig. 3 is the method schematic diagram of one embodiment of the invention；
Fig. 4 is the Distributed Predictive Control iterative solution method flow diagram of one embodiment of the invention；
Fig. 5 is 3900kW cold regression analysis figures in one embodiment of the invention；
Fig. 6 is 2150kW cold regression analysis figures in one embodiment of the invention；
Fig. 7 is 6329kW cold regression analysis figures in one embodiment of the invention；
Fig. 8 is load prediction figure in one embodiment of the invention；
Fig. 9 is electricity charge comparison diagram of two kinds of strategies per half an hour in one embodiment of the invention；
Figure 10 is the Dynamic performance Optimization simulation result of one embodiment of the invention, wherein：(a) to be overall, (b) is first
Platform 3900kW electric refrigerating machines, (c) are second 3900kW electric refrigerating machine, and (d) is First 2150kW electric refrigerating machines.
Embodiment
With reference to specific embodiment, the present invention is described in detail.Following embodiments 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, various modifications and improvements can be made.These belong to the present invention
Protection domain.Below in conjunction with the accompanying drawings come the air conditioning system being made of conventional electric refrigerating machine and ice-storage system to the present invention
System hierarchical optimal algorithm is described in further detail.
As shown in Figure 1, it is the United system structure being made of conventional electric refrigerating machine and ice-storage system.
Fig. 2 is that the joint being made of conventional electric refrigerating machine and ice-storage system of the specific embodiment of the present invention supplies
The flow chart of cooling system hierarchical optimal algorithm.First according to each low-temperature receiver economic model and tou power price policy, using MIXED INTEGER
Planing method, optimizes the opening and optimal power setting value of each low-temperature receiver, while in view of the dynamic adjustment performance of each low-temperature receiver
And physical constraint, in order to preferably improve dynamic performance, devise a kind of coordination Distributed Predictive control of target coupling
Method processed, the setting value of each low-temperature receiver of re-optimization under Distributed Architecture so that each low-temperature receiver ensures total load in dynamic process
While track optimal refrigeration work consumption setting value as far as possible.
Specifically, the as shown in Fig. 2, United system being made of conventional electric refrigerating machine and ice-storage system of the present invention
Hierarchical optimal algorithm, comprises the following steps：
Step S1：Its power consumption is fitted on refrigeration work consumption and cooling water inlet temperature using electric refrigerating machine operation data
Function；
The centrifugal most important performance parameter Energy Efficiency Ratio (COP) of electric refrigerating machine is the ratio of refrigeration work consumption and power consumption.
, it is necessary to pass through regression analysis according to data such as actual cooling water inlet temperature, power consumption and refrigeration work consumptions in engineering practice
Power consumption is represented to become cooling water inlet temperature and refrigeration work consumption Binary quadratic functions form to utilize Mathematical Planning work
Tool carries out the solution of subsequent step optimization problem.
Step S2：The economic model of electric refrigerating machine and ice-storage system cooling is established, forms steady-state economy optimization problem
Object function and constraints are simultaneously solved；
Every electric refrigerating machine, which has, opens and closes two working statuses, therefore describes electric refrigerating machine using 0-1 discrete variables Y
Working status.Differentiation for Double-working-condition electric refrigerating machine air conditioning condition and ice making operating mode then needs to use two 0-1 discrete variables
To describe respectively.The refrigeration work consumption of electric refrigerating machine is then described with continuous variable.It is right according to the regression analysis carried out in step S1
In conventional electric refrigerating machine, its power consumption can be expressed as the dihydric phenol letter on refrigeration work consumption and cooling water inlet temperature
Number：
Then the electricity charge cost C of electric refrigerating machine cooling generation is：
Wherein p is cooling period electricity price, and t is cooling period of time length, and Y is electric refrigerating machine start and stop mark,
It is the electric refrigerating machine power consumption that is fitted in step S1 on refrigeration work consumption and the function of cooling water inlet temperature.
On the premise of ignoring Ice Storage Tank storage cold and being lost with storage time, in order to consider the cost of Ice Storage Tank cooling, draw
Enter Ice Storage Tank cold average price psConcept, describes the price of unit cold, this price is with filling cold, filling cold period electricity price and duplex
Condition electric refrigerating machine ice making operating mode input/output relation is related, is defined by the formula：
Wherein pinitFor cold average price initial value, WinitFor Ice Storage Tank cold initial value, tinTo rush cool time, toutDuring to let cool
Between, PmFor the total ice making power of Double-working-condition electric refrigerating machine, PsFor Ice Storage Tank cooling power, YsFor Ice Storage Tank cooling start and stop mark.By storing
The definition of ice bank cold average price can be seen that without considering storage cold with time loss on the premise of, whether average price and Ice Storage Tank
Let cool it is unrelated, only may change when Ice Storage Tank rushes cold.
Ice Storage Tank stores up cold increment：
Δ W=tinP-toutPsYs (4)
Object function is various electric refrigerating machine air conditioning condition coolings and the sum of electricity charge needed for Ice Storage Tank cooling：
Pout,iFor i-th electric refrigerating machine cooling power, Tc,i inFor i-th electric refrigerating machine cooling water inlet temperature.
Main constraints includes cooling power and prediction balancing the load, the limitation of each electric refrigerating machine rated power, ice-reserving
Groove stores up cold bound and ice-storage system operating mode is unique：
Wherein PpTo predict load, αi∈ (0,1) is i-th electric refrigerating machine minimum output power ratio, Pi,ratedFor i-th
Platform electric refrigerating machine rated output power, WmaxAnd WminCold bound is stored up for Ice Storage Tank.YiceFor refrigerating unit with dual duty ice making operating mode
Start and stop mark, all Double-working-condition cold are opened at the same time when giving tacit consent to ice making operating mode, accelerate ice making speed.Yair,iFor i-th Double-working-condition
Refrigeration machine air conditioning condition start and stop mark, common N platforms refrigerating unit with dual duty.
Step S3：One is designed on the basis of the optimal refrigeration work consumption of each electric refrigerating machine that previous step steady-state economy optimizes
The coordination Distributed Predictive Control method of kind target coupling optimizes the dynamic property of whole cold supply system response load；
Dcs is divided into N number of subsystem according to air conditioning condition electric refrigerating machine quantity, each subsystem by MPC and
The electric refrigerating machine composition accordingly controlled, the data of different sub-systems MPC carry out real-time exchange using communicator and complete calculating task
So that the desired value calculated in each air conditioning condition electric refrigerating machine cooling power tracking step S2 and total cooling power
Total expected value is tracked, specific distributed control system structure is as shown in Figure 3.
Electric refrigerating machine refrigeration work consumption dynamic process model transmission function is as follows：
τ is first order inertial loop time constant, τdFor delay time, then s-th of subsystem separate manufacturing firms model shape
Formula is：
Wherein u (k) is k moment electric refrigerating machine output power setting values, as the optimized variable of optimization problem, ps(k) it is k
Moment electric refrigerating machine real output, ndMultiple for electric refrigerating machine delay time relative to the discrete system sampling time.xs
(k)=[ps(k)ps(k+1)…ps(k+nd)T] it is state variable.By the sampling time and
Electric refrigerating machine time constant determines, if the sampling time is Δ t, then has：
For s-th subsystem (s=1,2, N), MPC coordination strategies include electric refrigerating machine and are sampled at following P
The expectation power r that the prediction time domain interior prediction output power of moment composition is calculated as close possible to step S2s, and be different from
General MPC strategies, in order to control all electric refrigerating machine prediction output powers to keep up with total expectation power r as far as possible, this strategy exists
Introduced in object function total pre- power scale in prediction time domain with it is total it is expected difference power away from quadratic sum, therefore different sub-systems MPC
Object function couples.To sum up, s-th of subsystem MPC object function is as follows：
Wherein, Us=[u (k) u (k+1) ... u (k+P-1)]TFor electric refrigerating machine output power setting value sequence in prediction time domain
Row, and Q, R ∈ [0,1] are weight coefficient, are respectively intended to weigh separate unit electric refrigerating machine prediction Steam Generator in Load Follow degree and all electricity are made
Cold total load tracks the importance of degree.
MPC prediction models are：
For s-th of subsystem MPC controller, whole optimization problem form is：
Wherein, { us,min,us,max},{ps,min,ps,maxVariable in order to control boundary constraint.
Step S4：Above-mentioned Distributed Predictive Control problem is solved using alternative manner, obtains each sampling of each electric refrigerating machine
The cooling set value of the power at moment；
Alternative manner flow chart is as shown in Figure 4.
Step S41：At the k moment, according to the every electric refrigerating machine ideal refrigeration work consumption r solved in step S3sInitialization
U of each subsystem in prediction time domains, iterations l=0 is made,
Step S42：According to MPC prediction models, output quantity predicted value in each subsystem MPC controls time domain is calculatedAnd notify to give other N-1 subsystem MPC.
Step S43：Optimization problem in each subsystem step S3, obtains optimal solution
Step S44：Check whether the condition of convergence of all subsystem MPC meets, i.e., to given precisionWhether haveMeet if all of the subsystem condition of convergence,
Make each subsystem optimum control amountGo to step S45；Otherwise, make
α is the constant between 0 and 1, introduces the parameter for the not convergent situation of avoiding method of trying one's best, it is big to choose numerical value on demand
Small, l=l+1, goes to step S42.
Step S45：Controlled quentity controlled variable is chosen at the k momentAct on corresponding son
System.
Step S46：To next sampling instant, i.e. k+1 → k, return to step S41, repeats the above steps barrel shift.
Based on above-mentioned implementation process, the present invention is emulated by taking the low area's cold supply system of Shanghai high-rise building as an example
Verification.In the cold supply system, conventional electric refrigerating machine totally three, two specified refrigeration work consumption 3900kW, a specified refrigeration work(
Rate 2150kW, Double-working-condition electric refrigerating machine totally three, the specified refrigeration work consumption 6329kW of air conditioning condition, the specified refrigeration work consumption of ice making operating mode
3868kW。
First, the fitting of measured data progress input/output relation function is run according to electric refrigerating machine by step S1.
Fig. 5, Fig. 6, Fig. 7 are respectively the centrifugal electric refrigerating machine Regression Analysis Result figure of 3900kW, 2150kW and 6329kW.Respectively
X1 is cold cooling power perunit value in figure, and X2 is cold cooling water inlet temperature, and Y1 is cold power consumption perunit value.In figure
Confidential interval of the region that red curve is surrounded corresponding to confidence level 99%
From Fig. 5-7 it can be seen from the figure thats, dualistic and quadric regression analysis can relatively accurately establish electric refrigerating machine input electricity
Power and the functional relation for exporting cold power and cooling water inlet temperature, specific function expression is for example following various, PinFor electricity
Refrigeration machine power consumption, PoutFor electric refrigerating machine refrigeration work consumption,For electric refrigerating machine cooling water inlet temperature.
Have for the centrifugal electric refrigerating machines of 3900kW：
Have for the centrifugal electric refrigerating machines of 2150kW：
Have for Double-working-condition electric refrigerating machine 6329kW air conditioning conditions：
Ice making operating mode for Double-working-condition electric refrigerating machine, in order to accelerate ice making speed, the electricity system in the case where distributing ice making
Cold total power ice making, so there is no the operating mode of Partial Power ice making, ice making power cold and power consumption elec divide at this time
Jin Wei not be on cooling water inlet temperature Tc inOne- place 2-th Order function：
Then, steady-state economy optimization Simulation is carried out by step S2, predicts that load and electrovalence policy are as follows.As shown in Figure 8.
1 tou power price policy of table
In order to which economic optimization strategy section takes effect, a kind of conventional stable state scheduling strategy is chosen as a comparison, which exists
Preferential Ice Storage Tank is rushed cold during electricity price valley, and the preferential electric refrigerating machine cooling in electricity price level values and peak value, each electric refrigerating machine is by specified
The ascending order of power enables.Two kinds of strategies are per the half an hour electricity charge to such as Fig. 9.
By calculating, under given prediction loading condiction, 6 interior total electricity bill costs when small are steady-state economy optimisation strategy
3970 yuan, and Comparing method total electricity bill cost is 4053 yuan, 6 interior present invention strategy opposite save when small take 2%.
Then, the design of Distributed Predictive Control optimisation strategy and problem solving are carried out according to step S3 and S4.Dynamic optimization
Sampling interval is 2.5 minutes, i.e., the stable state scheduling strategy of step S2 calculates once each electric refrigerating machine and Ice Storage Tank most in every 30 minutes
After excellent cooling power, and once each electric refrigerating machine cooling set value of the power is each to ensure for Dynamic performance Optimization renewal in every 2.5 minutes
Electric refrigerating machine and the Ice Storage Tank stable state setting value that tracking step S2 economic optimizations are drawn as far as possible in dynamic process and always supply
Cold power tracking predicts load.Ice bank cooling first order modeling time constant is set to 2 minutes, and each electric refrigerating machine air conditioning condition dynamic
Model time constant and delay time setting are as shown in the table：
2 air conditioning condition electric refrigerating machine dynamic parameter of table
The expectation that overall He Getai opens used air conditioning condition electric refrigerating machine undertakes load with application respectively with not applying
Real-time cooling power curve caused by DMPC strategies is as shown in group picture 10, due to the 6329kW electricity system under current predictive load condition
Cold is not opened, so only including 3900kW and 2150kW electric refrigerating machines in group picture.
Whole system and every open used air conditioning condition electricity when being used with or without DMPC strategies for quantitative description
Area of the real-time cooling power of refrigeration machine with the optimal cooling power degree of closeness under the conditions of the economic optimum that is calculated in step S2
Not, the quadratic sum of each sampling instant deviation in 6 hours is calculated, numerical value is as shown in table 3, it can be seen that using dynamic property
Sum of square of deviations is obviously reduced after optimisation strategy, realizes responding rapidly to for overall system and each subsystem, embodies good
Control effect.
3 dynamic optimization effect of table
| Load object | Using DMPC strategies | Do not use DMPC tactful |
| It is overall | 3.40×107 | 1.03×108 |
| First 3900kW electric refrigerating machine air conditioning conditions | 4.91×107 | 1.11×108 |
| Second 3900kW electric refrigerating machine air conditioning condition | 8.71×106 | 3.82×107 |
| First 2150kW electric refrigerating machine air conditioning conditions | 8.71×106 | 3.82×107 |
Comprehensive simulating is as a result, the United system proposed by the present invention being made of conventional electric refrigerating machine and ice-storage system
Hierarchical optimal algorithm have preferable economic value and dynamic control effect concurrently, for actual cold supply system economic optimization operation have
Certain directive significance.
In conclusion the passing for the United system being made of conventional electric refrigerating machine and ice-storage system of the present invention
By establishing the economic model of electric refrigerating machine and Ice Storage Tank in rank optimization method, and mixed integer programming and distribution are used respectively
Formula PREDICTIVE CONTROL solves the problems, such as steady-state optimization and optimization problems, quantitatively determines the dispatching method of United system.
The specific embodiment of the present invention is described above.It is to be appreciated that the invention is not limited in above-mentioned
Particular implementation, those skilled in the art can make various deformations or amendments within the scope of the claims, this not shadow
Ring the substantive content of the present invention.
Claims (3)
1. a kind of hierarchical optimal algorithm of United system, the United system is by conventional electric refrigerating machine and ice cold-storage
The United system that system is formed, it is characterised in that described method includes following steps：
Step S1：Its power consumption is fitted on refrigeration work consumption and the letter of cooling water inlet temperature using electric refrigerating machine operation data
Number；
Step S2：Using the electric refrigerating machine power consumption obtained in previous step on its refrigeration work consumption and cooling water inlet temperature
Function establish electric refrigerating machine cooling economic model and and then establish the economic model of ice-storage system cooling, ultimately form steady
The object function and constraints of state economic optimization problem are simultaneously solved, and obtain each electric refrigerating machine and Ice Storage Tank in economic benefit
Start and stop state and cooling power under optimal conditions；
Step S3：On the basis of previous step, the coordination Distributed Predictive Control method re-optimization of design object coupling is whole
A cold supply system responds load in the real-time cooling set value of the power of each electric refrigerating machine of each sampling instant to improve whole system
Dynamic property；
Step S4：Above-mentioned Distributed Predictive Control problem is solved using alternative manner, obtains each sampling instant of each electric refrigerating machine
Cooling set value of the power；
In the step S1：The centrifugal most important performance parameter Energy Efficiency Ratio of electric refrigerating machine is refrigeration work consumption and power consumption
Ratio, according to actual cooling water inlet temperature, power consumption and refrigeration work consumption data by regression analysis by power consumption table
It is shown as cooling water inlet temperature and refrigeration work consumption Binary quadratic functions form, subsequently to be walked using Mathematical Planning instrument
The solution of rapid optimization problem；
In the step S2：Every electric refrigerating machine, which has, opens and closes two working statuses, is described using 0-1 discrete variables Y
The working status of electric refrigerating machine；Differentiation for Double-working-condition electric refrigerating machine air conditioning condition and ice making operating mode then using two 0-1 from
Scattered variable describes respectively；The refrigeration work consumption of electric refrigerating machine is then described with continuous variable；According to the recurrence carried out in step S1
Analysis, for conventional electric refrigerating machine, its power consumption PinIt is expressed as on refrigeration work consumption PoutWith cooling water inlet temperature Tc in's
Binary quadratic functions：
<mrow>
<msub>
<mi>P</mi>
<mrow>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
<mo>=</mo>
<mi>g</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>,</mo>
<msubsup>
<mi>T</mi>
<mi>c</mi>
<mrow>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
Then the electricity charge cost C of electric refrigerating machine cooling generation is：
<mrow>
<mi>C</mi>
<mo>=</mo>
<mi>Y</mi>
<mo>&times;</mo>
<mi>g</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>,</mo>
<msubsup>
<mi>T</mi>
<mi>c</mi>
<mrow>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>&times;</mo>
<mi>p</mi>
<mo>&times;</mo>
<mi>t</mi>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein p is cooling period electricity price, and t is cooling period of time length, and Y is electric refrigerating machine start and stop mark,For step
The electric refrigerating machine power consumption fitted in rapid S1 is on refrigeration work consumption and the function of cooling water inlet temperature；
On the premise of ignoring Ice Storage Tank storage cold and being lost with storage time, in order to consider the cost of Ice Storage Tank cooling, introduce and store
Ice bank cold average price psConcept, describe unit cold price, this price and fill cold, fill cold period electricity price and Double-working-condition electricity
Refrigeration machine ice making operating mode input/output relation is related, is defined by the formula：
<mrow>
<msub>
<mi>p</mi>
<mi>s</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>p</mi>
<mi>s</mi>
</msub>
<msub>
<mi>W</mi>
<mrow>
<mi>i</mi>
<mi>n</mi>
<mi>i</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>+</mo>
<msup>
<mi>pt</mi>
<mrow>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msup>
<msub>
<mi>P</mi>
<mi>m</mi>
</msub>
<mo>-</mo>
<msub>
<mi>p</mi>
<mrow>
<mi>i</mi>
<mi>n</mi>
<mi>i</mi>
<mi>t</mi>
</mrow>
</msub>
<msup>
<mi>t</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
</mrow>
</msup>
<msub>
<mi>P</mi>
<mi>s</mi>
</msub>
<msub>
<mi>Y</mi>
<mi>s</mi>
</msub>
</mrow>
<mrow>
<msub>
<mi>W</mi>
<mrow>
<mi>i</mi>
<mi>n</mi>
<mi>i</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>+</mo>
<msup>
<mi>t</mi>
<mrow>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msup>
<msub>
<mi>P</mi>
<mi>m</mi>
</msub>
<mo>-</mo>
<msup>
<mi>t</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
</mrow>
</msup>
<msub>
<mi>P</mi>
<mi>s</mi>
</msub>
<msub>
<mi>Y</mi>
<mi>s</mi>
</msub>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein pinitFor cold average price initial value, WinitFor Ice Storage Tank cold initial value, tinTo rush cool time, toutFor Discharging time, Pm
For the total ice making power of Double-working-condition electric refrigerating machine, PsFor Ice Storage Tank cooling power, YsFor Ice Storage Tank cooling start and stop mark；By Ice Storage Tank
The definition of cold average price finds out on the premise of without considering storage cold with time loss, it is unrelated whether average price lets cool with Ice Storage Tank,
It may only change when Ice Storage Tank rushes cold；
Ice Storage Tank stores up cold increment Delta W：
Δ W=tinP-toutPsYs (4)
Object function is various electric refrigerating machine air conditioning condition coolings and the sum of electricity charge needed for Ice Storage Tank cooling：
<mrow>
<munder>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
<mrow>
<msub>
<mi>Y</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msup>
<mi>Y</mi>
<mi>s</mi>
</msup>
<mo>,</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>,</mo>
<msup>
<mi>P</mi>
<mi>s</mi>
</msup>
</mrow>
</munder>
<mi>J</mi>
<mo>=</mo>
<msub>
<mi>pt&Sigma;Y</mi>
<mi>i</mi>
</msub>
<msub>
<mi>g</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>,</mo>
<msubsup>
<mi>T</mi>
<mrow>
<mi>c</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mrow>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>p</mi>
<mi>s</mi>
</msub>
<msub>
<mi>tP</mi>
<mi>s</mi>
</msub>
<msub>
<mi>Y</mi>
<mi>s</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
P is cooling period electricity price, and t is cooling period of time length, YiFor i-th electric refrigerant start and stop mark,For
I-th electric refrigerating machine power consumption is on refrigeration work consumption and the function of cooling water inlet temperature, wherein Pout,iFor i-th electricity refrigeration
Machine cooling power, Tc,i inFor i-th electric refrigerating machine cooling water inlet temperature, YsFor Ice Storage Tank cooling start and stop mark, psFor ice-reserving
Groove cold average price, PsFor Ice Storage Tank cooling power, YsFor Ice Storage Tank cooling start and stop mark；
Constraints includes cooling power and is stored up with prediction balancing the load, the limitation of each electric refrigerating machine rated power, Ice Storage Tank on cold
Lower limit and ice-storage system operating mode are unique：
<mrow>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>Y</mi>
<mi>i</mi>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>P</mi>
<mi>s</mi>
</msub>
<msub>
<mi>Y</mi>
<mi>s</mi>
</msub>
<mo>=</mo>
<msub>
<mi>P</mi>
<mi>p</mi>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>&alpha;</mi>
<mi>i</mi>
</msub>
<msub>
<mi>P</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>r</mi>
<mi>a</mi>
<mi>t</mi>
<mi>e</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>o</mi>
<mi>u</mi>
<mi>t</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>P</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>r</mi>
<mi>a</mi>
<mi>t</mi>
<mi>e</mi>
<mi>d</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>W</mi>
<mi>min</mi>
</msub>
<mo>&le;</mo>
<mi>W</mi>
<mo>&le;</mo>
<msub>
<mi>W</mi>
<mrow>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>Y</mi>
<mi>s</mi>
</msub>
<mo>&times;</mo>
<msub>
<mi>Y</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mi>e</mi>
</mrow>
</msub>
<mo>=</mo>
<mn>0</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>Y</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mi>e</mi>
</mrow>
</msub>
<mo>&times;</mo>
<msub>
<mi>Y</mi>
<mrow>
<mi>a</mi>
<mi>i</mi>
<mi>r</mi>
<mo>,</mo>
<mi>i</mi>
</mrow>
</msub>
<mo>=</mo>
<mn>0</mn>
<mo>,</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>N</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein YiFor i-th electric refrigerant start and stop mark, Pout,iFor i-th electric refrigerating machine cooling power, PsFor Ice Storage Tank cooling work(
Rate, YsFor Ice Storage Tank cooling start and stop mark, PpTo predict load, αi∈ (0,1) is i-th electric refrigerating machine minimum output power ratio
Example, Pi,ratedFor i-th electric refrigerating machine rated output power, W stores up cold, W for Ice Storage TankmaxAnd WminStored up for Ice Storage Tank on cold
Lower limit；YiceFor refrigerating unit with dual duty ice making operating mode start and stop mark, all Double-working-condition cold are opened at the same time when giving tacit consent to ice making operating mode,
Accelerate ice making speed；Yair,iFor i-th refrigerating unit with dual duty air conditioning condition start and stop mark, common N platforms refrigerating unit with dual duty；
Finally, above-mentioned optimization problem is carried out on the premise of the object function of steady-state economy optimization problem and constraints is formed
Solve, obtain the start and stop state and cooling power of each electric refrigerating machine and Ice Storage Tank under the conditions of optimal economic benefit.
2. the hierarchical optimal algorithm of United system as claimed in claim 1, it is characterised in that in step S3, the mesh
The coordination Distributed Predictive Control method of coupling is marked, it is specific as follows：
Dcs is divided into N number of subsystem according to air conditioning condition electric refrigerating machine quantity, and each subsystem is by model prediction
Control MPC and the electric refrigerating machine composition accordingly controlled, the data of different sub-systems MPC carry out real-time exchange completion using communicator
Calculating task, so that the optimal economic benefit bar obtained in each air conditioning condition electric refrigerating machine cooling power tracking step S2
The cooling under the conditions of each air conditioning condition electric refrigerating machine optimal economic benefit of cooling power and total cooling power tracking under part
The sum of power；
Electric refrigerating machine refrigeration work consumption dynamic process model transmission function is as follows：
<mrow>
<mi>H</mi>
<mrow>
<mo>(</mo>
<mi>s</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mn>1</mn>
<mo>+</mo>
<mi>&tau;</mi>
<mi>s</mi>
</mrow>
</mfrac>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<msub>
<mi>&tau;</mi>
<mi>d</mi>
</msub>
<mi>s</mi>
</mrow>
</msup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
τ is first order inertial loop time constant, τdFor delay time, then s-th of subsystem separate manufacturing firms model form is：
<mrow>
<mtable>
<mtr>
<mtd>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>x</mi>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>A</mi>
<mi>s</mi>
</msub>
<msub>
<mi>x</mi>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>B</mi>
<mi>s</mi>
</msub>
<msub>
<mi>u</mi>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>p</mi>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>C</mi>
<mi>s</mi>
</msub>
<msub>
<mi>x</mi>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>s</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>N</mi>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>8</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein us(k) it is k moment electric refrigerating machine output power setting values, the optimized variable of optimization problem, p in as step S3s
(k) it is k moment electric refrigerating machine real outputs, xs(k)=[ps(k)ps(k+1)…ps(k+nd)]TFor state variable, ndFor
Electric refrigerating machine delay time relative to the discrete system sampling time multiple；As、Bs、CsFor the coefficient square in state space equation
Battle array, wherein By discrete system sampling time and electric refrigerating machine dynamic process time constant
Determine,Represent set of real numbers, if the sampling time is Δ t, then have：
<mrow>
<msub>
<mi>B</mi>
<mi>s</mi>
</msub>
<mo>=</mo>
<msup>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<mn>1</mn>
<mo>-</mo>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mi>&Delta;</mi>
<mi>t</mi>
<mo>/</mo>
<mi>&tau;</mi>
</mrow>
</msup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
Cs=[1 0 ... 0]
For s-th of subsystem, s=1,2 ..., N；MPC coordination strategies are formed including electric refrigerating machine in following P sampling instant
The single air conditioner operating mode electric refrigerating machine economic benefit that obtains as close possible to step S2 of prediction time domain interior prediction output power most
Cooling power r under the conditions of excellents, and in order to control all electric refrigerating machine prediction output powers to keep up with each air conditioning condition electricity system
The sum of cooling power under the conditions of cold optimal economic benefit r, introduces each air conditioning condition in prediction time domain in object function
The quadratic sum of total the sum of cooling power under the conditions of the pre- power scale and optimal economic benefit gap of electric refrigerating machine, therefore different subsystems
System MPC object functions couple；To sum up, s-th of subsystem MPC object function is as follows：
<mrow>
<mtable>
<mtr>
<mtd>
<munder>
<mi>min</mi>
<msub>
<mi>U</mi>
<mi>s</mi>
</msub>
</munder>
</mtd>
<mtd>
<mrow>
<msub>
<mi>J</mi>
<mi>s</mi>
</msub>
<mo>=</mo>
<mi>Q</mi>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>P</mi>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>r</mi>
<mi>s</mi>
</msub>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
</mrow>
<mo>)</mo>
<mo>-</mo>
<msub>
<mover>
<mi>p</mi>
<mo>^</mo>
</mover>
<mi>s</mi>
</msub>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>|</mo>
<mi>k</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow>
<mo>+</mo>
<mi>R</mi>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>P</mi>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<mi>r</mi>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
</mrow>
<mo>)</mo>
<mo>-</mo>
<msub>
<mover>
<mi>p</mi>
<mo>^</mo>
</mover>
<mi>s</mi>
</msub>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>|</mo>
<mi>k</mi>
</mrow>
<mo>)</mo>
<mo>-</mo>
<munderover>
<munder>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
</munder>
<mrow>
<mi>j</mi>
<mo>&NotEqual;</mo>
<mi>s</mi>
</mrow>
<mi>N</mi>
</munderover>
<msub>
<mover>
<mi>p</mi>
<mo>^</mo>
</mover>
<mi>j</mi>
</msub>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>|</mo>
<mi>k</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, Us=[us(k)us(k+1)…us(k+P-1)]TValue sequence is set for electric refrigerating machine output power in prediction time domain,
us(k) it is k moment electric refrigerating machine output power setting values, is the optimized variable of optimization problem；And Q, R ∈ [0,1] are weight
Coefficient, is respectively intended to weigh the weight of separate unit electric refrigerating machine prediction Steam Generator in Load Follow degree and all electric refrigerating machine total loads tracking degree
The property wanted；rs(k+i) it is the cooling power under the conditions of (k+i) moment s platforms air conditioning condition electric refrigerating machine optimal economic benefit, r (k+
I) it is the sum of the cooling power of each air conditioning condition electric refrigerating machine of (k+i) moment under the conditions of optimal economic benefit；
Its output power at (k+i) moment predicted for s platform air conditioning condition electric refrigerating machines at the k moment by prediction model,Pass through for remaining air conditioning condition electric refrigerating machine in addition to s platform air conditioning condition electric refrigerating machines at the k moment pre-
Its of survey model prediction is in the sum of the output power at (k+i) moment；
MPC prediction models are：
<mrow>
<mtable>
<mtr>
<mtd>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msup>
<msub>
<mi>A</mi>
<mi>s</mi>
</msub>
<mi>i</mi>
</msup>
<msub>
<mi>x</mi>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>i</mi>
</munderover>
<mrow>
<mo>(</mo>
<msup>
<msub>
<mi>A</mi>
<mi>s</mi>
</msub>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mi>j</mi>
</mrow>
</msup>
<msub>
<mi>B</mi>
<mi>s</mi>
</msub>
<msub>
<mi>u</mi>
<mi>s</mi>
</msub>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>p</mi>
<mo>^</mo>
</mover>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>C</mi>
<mi>s</mi>
</msub>
<msub>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>P</mi>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
For (k+i) the moment s platforms air conditioning condition electric refrigerating machine state calculated at the k moment by prediction model
State variable in space equation,Predicted for s platform air conditioning condition electric refrigerating machines at the k moment by prediction model
Its output power at (k+i) moment；As i-jAnd As iFor the corresponding power of coefficient matrix in state space equation；
For s-th of subsystem MPC controller, whole optimization problem form is：
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<munder>
<mi>min</mi>
<msub>
<mi>U</mi>
<mi>s</mi>
</msub>
</munder>
</mtd>
<mtd>
<mrow>
<msub>
<mi>J</mi>
<mi>s</mi>
</msub>
<mo>=</mo>
<mi>Q</mi>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>P</mi>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>r</mi>
<mi>s</mi>
</msub>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
</mrow>
<mo>)</mo>
<mo>-</mo>
<msub>
<mover>
<mi>p</mi>
<mo>^</mo>
</mover>
<mi>s</mi>
</msub>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>|</mo>
<mi>k</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow>
<mo>+</mo>
<mi>R</mi>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>P</mi>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<mi>r</mi>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
</mrow>
<mo>)</mo>
<mo>-</mo>
<msub>
<mover>
<mi>p</mi>
<mo>^</mo>
</mover>
<mi>s</mi>
</msub>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>|</mo>
<mi>k</mi>
</mrow>
<mo>)</mo>
<mo>-</mo>
<munderover>
<munder>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
</munder>
<mrow>
<mi>j</mi>
<mo>&NotEqual;</mo>
<mi>s</mi>
</mrow>
<mi>N</mi>
</munderover>
<msub>
<mover>
<mi>p</mi>
<mo>^</mo>
</mover>
<mi>j</mi>
</msub>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>|</mo>
<mi>k</mi>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<mi>s</mi>
<mo>.</mo>
<mi>t</mi>
<mo>.</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msup>
<msub>
<mi>A</mi>
<mi>s</mi>
</msub>
<mi>i</mi>
</msup>
<msub>
<mi>x</mi>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>i</mi>
</munderover>
<mrow>
<mo>(</mo>
<msup>
<msub>
<mi>A</mi>
<mi>s</mi>
</msub>
<mrow>
<mi>i</mi>
<mo>-</mo>
<mi>j</mi>
</mrow>
</msup>
<msub>
<mi>B</mi>
<mi>s</mi>
</msub>
<msub>
<mi>u</mi>
<mi>s</mi>
</msub>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mover>
<mi>p</mi>
<mo>^</mo>
</mover>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>C</mi>
<mi>s</mi>
</msub>
<msub>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>u</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mi>min</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>u</mi>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msub>
<mi>u</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mi>max</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>p</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mi>min</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mover>
<mi>p</mi>
<mo>^</mo>
</mover>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>|</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msub>
<mi>p</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mi>max</mi>
</mrow>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow></mrow>
</mtd>
<mtd>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>P</mi>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>12</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, { us,min,us,max},{ps,min,ps,maxVariable in order to control boundary constraint.
3. such as the hierarchical optimal algorithm of claim 1-2 any one of them United systems, it is characterised in that the step
In S4, the Distributed Predictive Control problem in alternative manner solution procedure S3 is taken, is comprised the following steps that：
Step S41：At the k moment, under the conditions of the every air conditioning condition electric refrigerating machine optimal economic benefit obtained in step S3
Cooling power rs, initialize electric refrigerating machine output power setting value sequence U of each subsystem in prediction time domains, make iteration
Number l=0：
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>U</mi>
<mi>s</mi>
<mi>l</mi>
</msubsup>
<mo>=</mo>
<msup>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>u</mi>
<mi>s</mi>
<mi>l</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mrow>
<msubsup>
<mi>u</mi>
<mi>s</mi>
<mi>l</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<mrow>
<msubsup>
<mi>u</mi>
<mi>s</mi>
<mi>l</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>P</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>=</mo>
<msup>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>r</mi>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>r</mi>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
<mtd>
<mn>...</mn>
</mtd>
<mtd>
<mrow>
<msub>
<mi>r</mi>
<mi>s</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>P</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mi>T</mi>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>s</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<mi>N</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
It is above-mentionedFor electric refrigerating machine output power setting value of the l times iteration at (k+1) moment, P is prediction time domain length；
Step S42：According to MPC prediction models, output quantity predicted value in each subsystem MPC controls time domain is calculatedAnd notify to give other N-1 subsystem MPC；
Step S43：For each subsystem, the optimization problem in solution procedure S3, obtains optimal solution, i.e., optimal refrigeration machine output
Power setting value sequence
Step S44：Check whether the condition of convergence of all subsystem MPC meets, i.e., to given precision εs∈ R (s=1,2 ...,
N), if haveMeet if all of the subsystem condition of convergence, make each subsystem
System optimum control amountValue sequence is set with optimal refrigeration machine output powerIt is equal, i.e.,Go to step S45；It is no
Then, makeα is the constant between 0 and 1, and l=l+1, goes to step S42；
Step S45：Controlled quentity controlled variable is chosen at the k momentAct on corresponding subsystem；
Step S46：To next sampling instant, i.e. k+1 → k, return to step S41, repeats the above steps barrel shift.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510225593.6A CN104898422B (en) | 2015-05-05 | 2015-05-05 | The hierarchical optimal algorithm of United system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510225593.6A CN104898422B (en) | 2015-05-05 | 2015-05-05 | The hierarchical optimal algorithm of United system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104898422A CN104898422A (en) | 2015-09-09 |
| CN104898422B true CN104898422B (en) | 2018-05-04 |
Family
ID=54031141
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510225593.6A Active CN104898422B (en) | 2015-05-05 | 2015-05-05 | The hierarchical optimal algorithm of United system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104898422B (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105353611B (en) * | 2015-10-27 | 2018-03-27 | 广州中国科学院沈阳自动化研究所分所 | Towards the refrigeration work consumption balance control method and its control system of refrigerated container ship |
| US10564612B2 (en) * | 2016-06-30 | 2020-02-18 | Johnson Controls Technology Company | Variable refrigerant flow system with multi-level model predictive control |
| US11789415B2 (en) | 2016-06-30 | 2023-10-17 | Johnson Controls Tyco IP Holdings LLP | Building HVAC system with multi-level model predictive control |
| US11067955B2 (en) | 2016-06-30 | 2021-07-20 | Johnson Controls Technology Company | HVAC system using model predictive control with distributed low-level airside optimization |
| US20180004171A1 (en) | 2016-06-30 | 2018-01-04 | Johnson Controls Technology Company | Hvac system using model predictive control with distributed low-level airside optimization and airside power consumption model |
| CN109416191B (en) | 2016-06-30 | 2021-08-17 | 江森自控科技公司 | Variable refrigerant flow system with predictive control |
| WO2018005180A1 (en) * | 2016-06-30 | 2018-01-04 | Johnson Controls Technology Company | Hvac system using model predictive control with distributed low-level airside optimization |
| WO2018200854A1 (en) | 2017-04-27 | 2018-11-01 | Johnson Controls Technology Company | Building energy system with predictive control |
| CN107703745B (en) * | 2017-09-21 | 2020-11-03 | 东南大学 | MGT-CCHP control system based on economic predictive control |
| CN108154309B (en) * | 2017-12-30 | 2022-03-18 | 国网天津市电力公司电力科学研究院 | Energy internet economic dispatching method considering multi-load dynamic response of cold, heat and electricity |
| CN110794671B (en) * | 2019-11-22 | 2022-12-16 | 广东电网有限责任公司广州供电局 | Household energy management method and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101667013A (en) * | 2009-09-04 | 2010-03-10 | 天津大学 | Control method of optimized running of combined cooling and power distributed energy supply system of micro gas turbine |
| CN102073272A (en) * | 2011-02-24 | 2011-05-25 | 北京恩耐特分布能源技术有限公司 | System and method for optimizing overall planning of distributed energy |
| CN102420428A (en) * | 2011-12-19 | 2012-04-18 | 天津市电力公司 | Method and system for managing microgrid energy |
| CN102968111A (en) * | 2012-12-14 | 2013-03-13 | 新奥科技发展有限公司 | Method and system for controlling distributive energy system |
| CN103093017A (en) * | 2011-11-04 | 2013-05-08 | 新奥科技发展有限公司 | Distributed energy system design method |
| CN103544655A (en) * | 2013-10-18 | 2014-01-29 | 国家电网公司 | Layered optimization method of regional distribution network comprising micro-grid |
-
2015
- 2015-05-05 CN CN201510225593.6A patent/CN104898422B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101667013A (en) * | 2009-09-04 | 2010-03-10 | 天津大学 | Control method of optimized running of combined cooling and power distributed energy supply system of micro gas turbine |
| CN102073272A (en) * | 2011-02-24 | 2011-05-25 | 北京恩耐特分布能源技术有限公司 | System and method for optimizing overall planning of distributed energy |
| CN103093017A (en) * | 2011-11-04 | 2013-05-08 | 新奥科技发展有限公司 | Distributed energy system design method |
| CN102420428A (en) * | 2011-12-19 | 2012-04-18 | 天津市电力公司 | Method and system for managing microgrid energy |
| CN102968111A (en) * | 2012-12-14 | 2013-03-13 | 新奥科技发展有限公司 | Method and system for controlling distributive energy system |
| CN103544655A (en) * | 2013-10-18 | 2014-01-29 | 国家电网公司 | Layered optimization method of regional distribution network comprising micro-grid |
Non-Patent Citations (2)
| Title |
|---|
| 基于递阶预测控制的多微电网系统能量管理;徐俊等;《系统仿真学报》;20140430;第26卷(第4期);论文第910-913页 * |
| 大型楼宇多类型供冷系统的经济优化调度;刘楚晖等;《现代建筑电气》;20140831;第5卷(第8期);论文第5-10页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104898422A (en) | 2015-09-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104898422B (en) | The hierarchical optimal algorithm of United system | |
| Pan et al. | Feasible region method based integrated heat and electricity dispatch considering building thermal inertia | |
| Li et al. | A hybrid optimization-based scheduling strategy for combined cooling, heating, and power system with thermal energy storage | |
| Niu et al. | Implementation of a price-driven demand response in a distributed energy system with multi-energy flexibility measures | |
| Tushar et al. | Distributed real-time electricity allocation mechanism for large residential microgrid | |
| CN106230028B (en) | A kind of Multipurpose Optimal Method of wind-powered electricity generation-water-storage association system | |
| Tarragona et al. | Model predictive control applied to a heating system with PV panels and thermal energy storage | |
| CN108537409A (en) | A kind of industrial park power distribution network collaborative planning method considering multiple-energy-source coupled characteristic | |
| Chen et al. | A water mass method and its application to integrated heat and electricity dispatch considering thermal inertias | |
| JP4347602B2 (en) | Heat source operation support control method, system and program | |
| CN108053069A (en) | A kind of integrated energy system traffic control method suitable for multiple-objection optimization scene | |
| CN105183991A (en) | Planning and design method of regional integrated energy system | |
| CN104698843A (en) | Model prediction control based energy saving control method of data center | |
| She et al. | Life cycle cost and life cycle energy in zero-energy building by multi-objective optimization | |
| CN110410942A (en) | A kind of Cooling and Heat Source machine room energy-saving optimal control method and system | |
| CN104866702B (en) | A kind of method of utilization pivot decoupling computation hot-summer and cold-winter area building load | |
| CN107292766A (en) | Towards the power system peak regulation means economic evaluation method and system of wind electricity digestion | |
| CN105759773B (en) | Petroleum chemical enterprise's plant model extended method based on object-oriented analysis | |
| Lingmin et al. | A Q-learning based optimization method of energy management for peak load control of residential areas with CCHP systems | |
| Zhang et al. | Hybrid data-driven method for low-carbon economic energy management strategy in electricity-gas coupled energy systems based on transformer network and deep reinforcement learning | |
| Liu et al. | Multi-objective optimal scheduling of community integrated energy system considering comprehensive customer dissatisfaction model | |
| Liu et al. | Two-stage scheduling strategy for integrated energy systems considering renewable energy consumption | |
| CN106507640A (en) | A kind of server admin strategy of green data center temperature sensing | |
| CN114358473A (en) | Multi-station integrated energy supply system | |
| Wu et al. | Distributed energy trading on networked energy hubs under network constraints |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |