CN108287477A - Cluster temperature control duty control method based on model prediction and multiple dimensioned priority - Google Patents
Cluster temperature control duty control method based on model prediction and multiple dimensioned priority Download PDFInfo
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Abstract
The present invention relates to a kind of cluster temperature control duty control method based on model prediction and multiple dimensioned priority.（1）The 2D states storehouse of cluster temperature control load models；（2）Solve control load time-varying state spatial model；（3）The cluster temperature control spatial load forecasting model at current time is obtained based on Model Predictive Control Algorithm；（4）Multiple dimensioned priority ranking index carries out load object and selects；（5）Execute Model Predictive Control optimum control signal.The present invention proposes a kind of cluster temperature control duty control method controlled based on model prediction rolling optimization, increases the load selection process of the multiple dimensioned priority ranking based on normalized temperature distance, power similarity and accumulative control number.Advantage is to improve the accuracy and speed of load responding optimum control signal vector, and compared to traditional control method, the comprehensive performance that the carried control method of the present invention participates in demand response fairness etc. in control accuracy, response speed and load is more excellent.
Description
Technical field
The invention belongs to flexible interaction intelligent power and demand response field, more particularly to it is a kind of based on model prediction and more
The cluster temperature control duty control method of scale priority.
Background technology
In recent years, China's Renewable Energy Development is swift and violent, and it is new that wind-powered electricity generation in 2016 increases 19,300,000 kilowatts of grid connection capacity, photovoltaic newly
Increase grid connection capacity up to 34,240,000 kilowatts.However, the regenerative resources such as wind-powered electricity generation, photovoltaic have " unfriendly " such as randomness, intermittences
Feature, large-scale grid connection can bring adverse effect to power system security reliability service.It recent studies have shown that both at home and abroad, dynamic is whole
The effective way for improving new energy digestion capability will be increasingly becoming by closing Demand-side resource.Temperature control based on air-conditioning, water heater etc.
Load has the characteristics such as thermmal storage and quick-break, smaller on users'comfort influence, and aggregated its participates in system call and have a high potential,
Demand response is participated in be of great significance.
Effective modeling of cluster temperature control load is that it participates in the prerequisite of demand response spatial load forecasting.Current cluster temperature control
The modeling method of load includes mainly a large amount of temperature control load equivalent heat parametric techniques, state storehouse group technology and virtual energy storage etc.
Imitate thermal cell method.State storehouse group technology realizes the grouping of load condition according to users'comfort temperature bound, compares
The dimension of calculating matrix is greatly reduced in a large amount of temperature control load equivalent heat parametric techniques.Battery eliminator model distinguishes other two kinds
Modeling method is that cluster temperature control load is angularly equivalent to battery energy storage model from general charge-discharge electric power, battery capacity.
The control method of existing cluster temperature control load includes mainly Model Predictive Control and sliding moding structure control from top level control model
System；It includes mainly two kinds of control methods of state queue and temperature distance that it is upper, which to select control, from local layer load.Model prediction can carry
The variation of preceding perception DR echo signals, and then temperature control load switching is adjusted in advance, realization rolling optimization, and sliding formwork control
System response time can then be improved.State queue is lined up according to the state transfer characteristic of different temperature control loads, and warm
Degree distance method is then to carry out temperature prioritised grade sequence according to the current temperature status of temperature control load.
The key problem of spatial load forecasting is to obtain optimum control signal and select control object in demand response.Currently, existing
There is control method to be primarily present two aspect problems：On the one hand, the quality of output performance is special dependent on the time-varying of given tracking signal
Property, it can not ensure superior response effect；On the other hand, it does not consider how optimum control signal and Optimal Load control pair
As being closely connected together, to realize better control effect.
Invention content
The purpose of the present invention is to provide a kind of cluster temperature control spatial load forecasting based on model prediction and multiple dimensioned priority
Method relies on time-varying characteristics and the control of given tracking signal for solving existing cluster temperature control duty control method response effect
The problems such as precision processed is low.
To achieve the above object, the technical scheme is that：A kind of collection based on model prediction and multiple dimensioned priority
Group's temperature control duty control method, includes the following steps,
Step S1, the 2D states storehouse modeling of cluster temperature control load, i.e. the heat operation equation based on air conditioner load are negative to air-conditioning
Lotus carries out stochastic simulation, divides storehouse according to the positions 2D, carry out state residing for load, obtains 2D states storehouse model；
Step S2, control load time-varying state spatial model is solved, i.e., the 2D states storehouse model obtained in step S1 is used
Transition probability between Markov chain solving state storehouse, obtains time-varying state space equation；
Step S3, the cluster temperature control spatial load forecasting model at current time is obtained based on Model Predictive Control Algorithm, to step
The time-varying state space equation obtained in S2 solves the predictive control model at current time using Model Predictive Control Algorithm；
Step S4, multiple dimensioned priority ranking index carries out load object and selects, i.e., to Model Predictive Control in step S3
The optimum control signal that algorithm solves, using more rulers based on normalized temperature distance, power similarity and accumulative control number
Degree priority ranking index carries out load object and selects；
Step S5, Model Predictive Control optimum control signal is executed, the load object of selecting in step S4 is allowed to execute model
The optimum control signal of PREDICTIVE CONTROL.
In an embodiment of the present invention, in the step S1, the 2D states storehouse of cluster temperature control load models, detailed process
For：
Step S11, it is classified as closing group according to cluster temperature control load current switch states and opens group；
Step S12, it is directed to and closes group in two dimensional surface, respectively according to users'comfort indoor air temperature upper lower limit valueWith the upper lower limit value of indoor mass temperatureTemperature range equal length is divided into Ni/ 2 indoor air temperatures are small
Section and Nm/ 2 indoor mass temperature minizones, form Na*Nm/ 4 state storehouses；Similarly, for opening group, using same sample prescription
Formula.
In an embodiment of the present invention, general using the transfer between Markov chain solving state storehouse in the step S2
Rate detailed process is：
Step S21,24 hours hot operation curves of air conditioner load group's Time-temperature of whole day are based on, according to each air conditioner load
In the 2D temperature of different moments, state storehouse number is carried out successively；
Step S22, for statistics between adjacent emulation moment k to k+1, the air conditioner load of state storehouse i is transferred to of state storehouse j
Number, wherein i, j=1,2 ..., N；
Step S23, the air conditioner load for calculating first order Markov chain kth time period state storehouse i is transferred to the state of state storehouse j
Transition probability：
In formula, ni,j(k) indicate that the air conditioner load of kth time period state storehouse i is transferred to the number of state storehouse j；ni(k) it indicates
The total load number that generating state shifts in kth time period state storehouse i；N indicates state storehouse sum.
In an embodiment of the present invention, the detailed process of the step S3 is：
Step S31, the cluster temperature control load time-varying state space equation obtained in step S2 is taken, is write as model prediction
The lower status predication equation of control, and write as matrix form；
Step S32, since the polymerization output tracking target that the target of model predictive control system is cluster air conditioner load is born
Therefore lotus curve of output minimizes tracking error as object function to use；
Step S33, by with the minimum target of cluster output tracking error, by the optimizing control models of cluster air conditioner load
It is converted into quadratic programming problem.
In an embodiment of the present invention, the detailed process of the step S4 is：
Step S41, the model prediction optimum control signal at the current time obtained from step S3；
Step S42, according to echo signal, power index of similarity, normalized temperature distance, accumulative control time are based respectively on
Several carries out load sequence to cluster temperature control load group successively；
Step S42, the weighting point of three index parameters in combining step S42, each index parameter is variant, after normalization
All it is the dimensionless factor of value range between zero and one, value size indicates corresponding priority index；Therefore, according to pre-
Three indexs are weighted summation, integrated ordered reference value can be obtained by fixed weight coefficient.
Compared to the prior art, the invention has the advantages that：
One, it proposes a kind of temperature control load modeling method shifting time-varying Markov chain based on 2D states storehouse, fully considers
Load isomerism and diversity, constant markov chain modelling method when compared to tradition, have higher temperature control load modeling essence
Degree；
Two, it proposes a kind of cluster temperature control duty control method based on the control of model prediction rolling optimization, increases and be based on
The load selection process of the multiple dimensioned priority ranking of normalized temperature distance, power similarity and accumulative control number, is improved
The accuracy and speed of load responding optimum control signal vector；
Even if three, in the case of the variation acutely of target requirement response signal graph, control method of the present invention can be felt in advance
Know the variation of DR echo signals, and then temperature control load switching is adjusted in advance, realize rolling optimization, has reached preferable dynamic
State control performance.
Description of the drawings
Fig. 1 is the control flow chart of the method for the present invention.
Fig. 2 is 2D states storehouse metastasis model figure.
Specific implementation mode
Below in conjunction with the accompanying drawings, technical scheme of the present invention is specifically described.
The present invention is based on flow chart such as Fig. 1 institutes of model prediction and the cluster temperature control duty control method of multiple dimensioned priority
Show, detailed process is as follows：
1) the 2D states storehouse modeling of cluster temperature control load, detailed process are：
11) as shown in Fig. 2, being classified as closing group according to cluster temperature control load current switch states and opening group；
12) it is directed to and closes group in two dimensional surface, respectively according to users'comfort indoor air temperature and indoor substance temperature
Degree upper lower limit value (With) temperature range equal length is divided into Ni/ 2 indoor air temperature minizones and Nm/
2 indoor mass temperature minizones, form Na*Nm/ 4 state storehouses；For opening group, using same way.
2) temperature control load time-varying state spatial model is solved, it can using Ma Er to the 2D states storehouse model obtained in step 1)
Transition probability between husband's chain solving state storehouse, obtains time-varying state space equation.Detailed process is：
21) 24 hours hot operation curves of air conditioner load group's Time-temperature of whole day are based on, according to each air conditioner load in difference
The 2D temperature at moment carries out state storehouse number successively；
22) statistics is between adjacent emulation moment k to k+1, the air conditioner load of state storehouse i be transferred to the number of state storehouse j (i,
J=1,2 ..., N)；
23) air conditioner load for calculating first order Markov chain kth time period state storehouse i is transferred to the state transfer of state storehouse j
Probability：
In formula, ni,j(k) indicate that the air conditioner load of kth time period state storehouse i is transferred to the number of state storehouse j；ni(k) it indicates
The total load number that generating state shifts in kth time period state storehouse i；N indicates state storehouse sum.
3) the cluster temperature control spatial load forecasting model at current time is obtained based on Model Predictive Control Algorithm, to the step 2)
In obtained time-varying state space equation the predictive control model at current time is solved using Model Predictive Control Algorithm.Specific stream
Cheng Wei：
31) acquired cluster temperature control load condition space equation in the step 2) is taken, is write as model prediction control
The lower status predication equation of system, and write as matrix form, it indicates as follows：
X (k)=AP(k)x(k|k)+BP(k)U(k) (2)
Wherein,
X (k)=[x (k+1 | k) x (k+2 | k) ... x (k+p | k)]T (3)
U (k)=[u (k | k) u (k+1 | k) ... u (k+p-1 | k)]T (4)
AP(k)=[A (k) A (k+1) A (k) ... A (k+p-1) ... A (k)]T
APInterior matrix in block form Ap=[A (k+p-1) ... A (k+1) A (k)] indicates to inscribe when current k, system kth+p moment shapes
The predicted value of state transfer matrix, elements Ap(i, j) expression only each state storehouse load number vector x of known current time k (k |
K), for system in+p time steps of kth, the air conditioner load of state storehouse j is transferred to the transition probability predicted value of state storehouse i.BP
All fours repeats no more.X (k) indicates current k moment each storehouse internal loading number percentage；U (k) indicates the kth moment
Signal is controlled, i.e. air conditioner load in each state storehouses current time k needs the percentage switched；It is indicated when the signal is positive value
Breakdown action indicates closing motion when being negative value.
32) since the polymerization output tracking target that the main target of the model predictive control system is cluster air conditioner load is born
Lotus curve of output, so, tracking error is minimized as object function to use：
Wherein, WerrThe tracking error weight coefficient matrix for indicating model output and realistic objective value, is set as unit square herein
Battle array；D (k)=diag { C (k+1) C (k+2) ... C (k+p) }, R (k)=[r (k+1) r (k+2) ... r (k+p)]T, and r (k+ ζ) table
Show and exports target trajectory value at the k+ ζ moment.
33) by the way that with the minimum target of cluster output tracking error, the optimizing control models of cluster air conditioner load are converted
For quadratic programming problem：
0 and 1 in formula (7) constraints is vector form, meanwhile, which can pass through calling
The quadratic programming function that MATLAB Optimization Toolboxes provide is solved.State storehouse in time domain p* Δs t just must be controlled after solving
Air conditioner load switchs the optimal control sequence that number is constituted, and the one-component of the optimization is only issued at the current scheduling moment
u*(k|k).It waits for arrive next dispatching cycle, repeats above-mentioned rolling optimization process.
4) multiple dimensioned priority ranking index progress load object is selected, to Model Predictive Control in step 3) described above
The optimum control signal that algorithm solves, using more rulers based on normalized temperature distance, power similarity and accumulative control number
Degree priority ranking index carries out object and selects.Detailed process is：
41) in order to improve the accuracy and rapidity of air conditioner load response, a kind of load sequence of power similarity is introduced
Index.Define SIMi,(p,q)For the index similarity of air-conditioning load power in state storehouse (p, q) and required adjustment power, calculate public
Formula is as follows：
Wherein, PiFor the rated power of i-th of air conditioner load, Paim,(p,q)The target work(for needing to respond for state storehouse (p, q)
Rate.N(p,q)Air conditioner load number in expression state storehouse (p, q).As the rated power P of i-th of air conditioner loadiCloser to required
When adjusting power, the power index similarity SIM of the air conditioner loadi,(p,q)Smaller, then the response response of the air conditioner load is preferential
Grade is higher.By formula (14) it is found that SIMi,(p,q)It is also the dimensionless factor of value range between zero and one.
42) temperature of a certain air conditioner load at a time and the difference of its boundary temperature can be described as the load at this
The temperature distance at quarter.Since indoor mass temperature is small compared to influence of the indoor air temperature to users'comfort, therefore only consider room
Interior air themperature distance.Meanwhile being lined up conveniently with combination priority grade to calculate, normalized temperature distance, table are used herein
It is up to formula：
Wherein, NTDi,kNormalized temperature distance of i-th of air conditioner load at the current k moment, be value range in 0 and 1
Between dimensionless factor.δ indicates temperature dead zone, i.e. users'comfort temperature upper limit value and lower limit value θhighAnd θlowDifference.θi,tIt indicates
I-th of air conditioner load is in the temperature at current k moment, OkAnd CkIt is illustrated respectively in the unlatching group at current k moment and closes group, m is empty
Adjust load total number.When cluster air conditioner load responds optimum control signal, choosing can be ranked up according to normalized temperature distance
Take regulation and control object, the priority of the bigger response of NTD values higher.
43) accumulative control numbers of the air conditioner load i at the k moment is denoted as：Ci,k.In order to can be by accumulative control number and its
His index is weighted to obtain an overall target, needs to normalize between 0 and 1 first.It is as follows to normalize formula：
NCi,k=(Ci,k-Ck,min)/(Ck,max-Ck,max) (10)
Ck,minAnd Ck,maxIndicate that current time k load has been controlled the minimum value and maximum value of number.
In summary the weighting point of three index parameters, each index parameter is variant, is all value range after normalization
Dimensionless factor between zero and one, value size indicate corresponding priority index.Therefore, according to certain weight system
Three indexs are weighted summation by number[6][21], integrated ordered reference value can be obtained.By taking the load of controllable open state group as an example,
Integrated ordered reference value Γs of its air conditioner load i in moment kopenFor：
KT, KSAnd KCRespectively corresponding weight coefficient, according to multiple dimensioned priority composite index ΓopenValue size
Air conditioner load is ranked up in each 2D states storehouse corresponding to open state group, ΓopenIt is worth smaller expression air conditioner load in the shape
Priority in state storehouse is higher.
5) Model Predictive Control optimum control signal is executed, the load object of selecting in step 4) described above is allowed to execute mould
The optimum control signal of type PREDICTIVE CONTROL.
The above are preferred embodiments of the present invention, all any changes made according to the technical solution of the present invention, and generated function is made
When with range without departing from technical solution of the present invention, all belong to the scope of protection of the present invention.
Claims (5)
1. a kind of cluster temperature control duty control method based on model prediction and multiple dimensioned priority, it is characterised in that：Including such as
Lower step,
Step S1, cluster temperature control load 2D states storehouse modeling, i.e., based on air conditioner load heat operation equation to air conditioner load into
Row stochastic simulation divides storehouse, obtains 2D states storehouse model according to the positions 2D, carry out state residing for load；
Step S2, control load time-varying state spatial model is solved, i.e., Ma Er is used to the 2D states storehouse model obtained in step S1
Transition probability that can be between husband's chain solving state storehouse, obtains time-varying state space equation；
Step S3, the cluster temperature control spatial load forecasting model at current time is obtained based on Model Predictive Control Algorithm, in step S2
Obtained time-varying state space equation solves the predictive control model at current time using Model Predictive Control Algorithm；
Step S4, multiple dimensioned priority ranking index carries out load object and selects, i.e., to Model Predictive Control Algorithm in step S3
The optimum control signal of solution, using based on the multiple dimensioned excellent of normalized temperature distance, power similarity and accumulative control number
First grade sequence index carries out load object and selects；
Step S5, Model Predictive Control optimum control signal is executed, the load object of selecting in step S4 is allowed to execute model prediction
The optimum control signal of control.
2. the cluster temperature control duty control method according to claim 1 based on model prediction and multiple dimensioned priority,
It is characterized in that：In the step S1, the 2D states storehouse of cluster temperature control load models, and detailed process is：
Step S11, it is classified as closing group according to cluster temperature control load current switch states and opens group；
Step S12, it is directed to and closes group in two dimensional surface, respectively according to users'comfort indoor air temperature upper lower limit valueWith the upper lower limit value of indoor mass temperatureTemperature range equal length is divided into Ni/ 2 indoor air temperatures are small
Section and Nm/ 2 indoor mass temperature minizones, form Na*Nm/ 4 state storehouses；Similarly, for opening group, using same sample prescription
Formula.
3. the cluster temperature control duty control method according to claim 1 based on model prediction and multiple dimensioned priority,
It is characterized in that：In the step S2, use the transition probability detailed process between Markov chain solving state storehouse for：
Step S21,24 hours hot operation curves of air conditioner load group's Time-temperature of whole day are based on, according to each air conditioner load not
2D temperature in the same time carries out state storehouse number successively；
Step S22, for statistics between adjacent emulation moment k to k+1, the air conditioner load of state storehouse i is transferred to the number of state storehouse j,
Wherein, i, j=1,2 ..., N；
Step S23, the air conditioner load for calculating first order Markov chain kth time period state storehouse i is transferred to the state transfer of state storehouse j
Probability：
In formula, ni,j(k) indicate that the air conditioner load of kth time period state storehouse i is transferred to the number of state storehouse j；ni(k) when indicating kth
The total load number that generating state shifts in section state storehouse i；N indicates state storehouse sum.
4. the cluster temperature control duty control method according to claim 1 based on model prediction and multiple dimensioned priority,
It is characterized in that：The detailed process of the step S3 is：
Step S31, the cluster temperature control load time-varying state space equation obtained in step S2 is taken, is write as Model Predictive Control
Lower status predication equation, and write as matrix form；
Step S32, due to the target of model predictive control system be cluster air conditioner load polymerization output tracking target load it is defeated
Go out curve, therefore, tracking error is minimized as object function to use；
Step S33, by the way that with the minimum target of cluster output tracking error, the optimizing control models of cluster air conditioner load are converted
For quadratic programming problem.
5. the cluster temperature control duty control method according to claim 1 based on model prediction and multiple dimensioned priority,
It is characterized in that：The detailed process of the step S4 is：
Step S41, the model prediction optimum control signal at the current time obtained from step S3；
Step S42, it according to echo signal, is based respectively on power index of similarity, normalized temperature distance, adds up control number
Load sequence is carried out to cluster temperature control load group successively；
Step S42, the weighting point of three index parameters in combining step S42, each index parameter is variant, is all after normalization
The dimensionless factor of value range between zero and one, value size indicate corresponding priority index；Therefore, according to scheduled
Three indexs are weighted summation, integrated ordered reference value can be obtained by weight coefficient.
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| CN110190614B (en) * | 2019-04-03 | 2023-02-14 | 国网江西省电力有限公司电力科学研究院 | Electric heating load control method for reducing power shortage of power grid |
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| CN111141003A (en) * | 2020-01-04 | 2020-05-12 | 厦门山川净化科技有限公司 | Air conditioner control method and system |
| CN113067340A (en) * | 2021-03-25 | 2021-07-02 | 山东大学 | Dynamic state estimation method and system for constant temperature control load system |
| CN113162054A (en) * | 2021-04-26 | 2021-07-23 | 国网山东省电力公司经济技术研究院 | Step aggregation method and system of comprehensive service station based on large-scale controllable load |
| CN113162054B (en) * | 2021-04-26 | 2022-09-30 | 国网山东省电力公司经济技术研究院 | Step aggregation method and system of comprehensive service station based on large-scale controllable load |
| CN113311713A (en) * | 2021-05-31 | 2021-08-27 | 燕山大学 | Temperature control load demand response control method based on cluster state statistics |
| WO2023160110A1 (en) * | 2022-02-25 | 2023-08-31 | 中国电力科学研究院有限公司 | System frequency modulation method and system for thermostatically controlled load cluster, and electronic device and storage medium |
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