CN109598454A - A kind of schedulable capacity analysis method of temperature control load, device and equipment - Google Patents
A kind of schedulable capacity analysis method of temperature control load, device and equipment Download PDFInfo
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- CN109598454A CN109598454A CN201910059005.4A CN201910059005A CN109598454A CN 109598454 A CN109598454 A CN 109598454A CN 201910059005 A CN201910059005 A CN 201910059005A CN 109598454 A CN109598454 A CN 109598454A
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
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- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
This application discloses a kind of schedulable capacity analysis methods of temperature control load, device and equipment, user-responsiveness is set as to meet the stochastic variable of normal distribution, consider temperature control load user time and uncertainty spatially, stratified sampling analysis is carried out to the uncertain of user-responsiveness using Latin Hypercube Sampling method, the sample data that sampling can be made the to obtain range that more comprehensively covering variable is distributed, response capacity distribution is enabled to more realistically to reflect actual response condition, carry out response estimation with avoiding excessively optimistic or pessimistic estimation, solve time and spatial location laws that the existing schedulable capacity analysis method of temperature control load does not account for user response, the technical issues of leading to the excessively optimistic or pessimistic dispatch situation estimated to demand response.
Description
Technical field
This application involves electric load technical field more particularly to a kind of schedulable capacity analysis methods of temperature control load, dress
It sets and equipment.
Background technique
With the high speed development of power industry, power grid scale is significantly expanded, and power grid user side becomes to become increasingly complex, power grid
User side load carrys out the scheduling of responsive electricity grid as a kind of resource, even the automatically demand of responsive electricity grid, so that load sheet
Body becomes a kind of resource for power grid close friend.
Power scheduling is to guarantee that power network safety operation, external reliable power supply, the work of all kinds of power generations orderly carry out
And a kind of effective management means used.Maximal workload and comfort level, which come, mainly to be considered to the power scheduling of temperature control load at present
Temperature control load is effectively dispatched, but there is no the uncertainties for considering temperature control load responding, actually due to logical
Interrogate the influence of the uncertain factors such as delay, element fault, next day weather condition and emergency event, each power scheduling user response
There is uncertainty over time and space, especially as air conditioner user, the psychology of user is also to cause to respond probabilistic one
A key factor will lead to excessively optimistic or pessimistic estimation if not accounting for these times and uncertainty spatially
To the dispatch situation of demand response.
Summary of the invention
The embodiment of the present application provides a kind of schedulable capacity analysis method of temperature control load, device and equipment, solves existing
Some schedulable capacity analysis methods of temperature control load do not account for time and the spatial location laws of user response, cause excessively to find pleasure in
The technical issues of sight or the pessimistic dispatch situation estimated to demand response.
In view of this, the application first aspect provides a kind of schedulable capacity analysis method of temperature control load, comprising:
According to the thermal power of the temperature control load of each grid nodes, single start-up and shut-down control period and each start-up and shut-down control week
Shut-in time in phase calculates total schedulable capacity of each grid nodes according to preset schedulable capacity formula；
User-responsiveness is obtained, the user-responsiveness is to meet the stochastic variable of normal distribution；
Stratified sampling is carried out to the user-responsiveness according to Latin Hypercube Sampling method, it is uncertain to generate reflection space-time
Responsiveness sample；
The responsiveness sample and total schedulable capacity are subjected to product calculating, obtain practical schedulable capacity.
Preferably, the thermal power of the temperature control load according to each grid nodes, single start-up and shut-down control period and described every
Shut-in time in a start-up and shut-down control period calculates total schedulable appearance of each grid nodes according to preset schedulable capacity formula
Amount, before further include:
The equivalent heat parameter model for establishing temperature control load calculates pass of the temperature control load within the single start-up and shut-down control period
Close time and opening time.
Preferably, the thermal power of the temperature control load according to each grid nodes, single start-up and shut-down control period and described every
Shut-in time in a start-up and shut-down control period calculates total schedulable capacity of each grid nodes according to preset schedulable capacity formula
Later, the acquisition user-responsiveness, the user-responsiveness are before meeting the stochastic variable of normal distribution, further includes:
Turn-off time clustering can be carried out according to current to the temperature control load under each grid nodes, obtained schedulable
Multiple Clusterings.
Preferably, described that stratified sampling is carried out to the user-responsiveness according to Latin Hypercube Sampling method, generate reflection
The probabilistic responsiveness sample of space-time, specifically includes:
Stratified sampling is carried out to the user-responsiveness according to Latin Hypercube Sampling method, it is entire described random to obtain covering
The sample data of the distribution of variable；
By the sample data according to Spearman rank correlation method carry out correlation processing, and to the correlation processing after
The sample data be ranked up, generate reflection the probabilistic responsiveness sample of space-time.
Preferably, the preset schedulable capacity formula are as follows:
Wherein, C is total schedulable capacity of n platform temperature control load, PiFor the thermal power of i-th temperature control load, hiIt is i-th
The start-up and shut-down control period of temperature control load, hoff,iFor the shut-in time in the start-up and shut-down control period of i-th temperature control load.
Preferably, the equivalent heat parameter model are as follows:
Wherein, s is switch state, and s=1 is in off state, and s=0 is open state,For the room temperature of t moment,For the indoor and outdoor temperature at t+1 moment, η is temperature control load Energy Efficiency Ratio, and P is the thermal power of temperature control load, and ε is scattered
Hot coefficient: ε=e-Δh/RC, R is the equivalent thermal resistance in house.
Preferably, the algorithm of the clustering is K-means algorithm.
The application second aspect provides a kind of schedulable capacity analysis device of temperature control load, comprising:
Computing module, for according to the thermal power of the temperature control loads of each grid nodes, single start-up and shut-down control period and described
Shut-in time in each start-up and shut-down control period calculates total schedulable appearance of each grid nodes according to preset schedulable capacity formula
Amount；
Module is obtained, for obtaining user-responsiveness, the user-responsiveness is to meet the stochastic variable of normal distribution；
Sampling module generates anti-for carrying out stratified sampling to the user-responsiveness according to Latin Hypercube Sampling method
Reflect the probabilistic responsiveness sample of space-time；
Generation module obtains reality for the responsiveness sample and total schedulable capacity to be carried out product calculating
Schedulable capacity.
Preferably, further includes:
Modeling module calculates the temperature control load in single start and stop for establishing the equivalent heat parameter model of temperature control load
Control the shut-in time and opening time in the period；
Cluster module, for cluster point can be carried out the turn-off time according to current to the temperature control load under each grid nodes
Analysis, obtains schedulable multiple Clusterings.
The application third aspect provides a kind of schedulable capacity analysis equipment of temperature control load, and the equipment includes processor
And memory；
Said program code is transferred to the processor for storing program code by the memory；
The processor is adjustable for the temperature control load according to the instruction execution first aspect in said program code
Spend capacity analysis method.
As can be seen from the above technical solutions, the embodiment of the present application has the advantage that
In the application, a kind of schedulable capacity analysis method of temperature control load is provided, comprising: according to the temperature of each grid nodes
The shut-in time in thermal power, single start-up and shut-down control period and each start-up and shut-down control period of load is controlled, according to preset schedulable
Capacity formula calculates total schedulable capacity of each grid nodes；User-responsiveness is obtained, user-responsiveness is to meet normal distribution
Stochastic variable；Stratified sampling is carried out to user-responsiveness according to Latin Hypercube Sampling method, it is uncertain to generate reflection space-time
Responsiveness sample；Responsiveness sample and total schedulable capacity are subjected to product calculating, obtain practical schedulable capacity.This
The method for applying providing, user-responsiveness is set as to meet the stochastic variable of normal distribution, it is contemplated that temperature control load user time
Uncertainty spatially carries out stratified sampling point to the uncertain of user-responsiveness using Latin Hypercube Sampling method
Analysis, the sample data that sampling can be made the to obtain range that more comprehensively covering variable is distributed, enables to response capacity
Distribution more realistically reflects actual response condition, carries out response estimation with avoiding excessively optimistic or pessimistic estimation, solves existing
Some schedulable capacity analysis methods of temperature control load do not account for time and the spatial location laws of user response, cause excessively to find pleasure in
The technical issues of sight or the pessimistic dispatch situation estimated to demand response.
Detailed description of the invention
Fig. 1 is the flow diagram of one of the embodiment of the present application schedulable capacity analysis method of temperature control load；
Fig. 2 is one of the embodiment of the present application another flow diagram of the schedulable capacity analysis method of temperature control load；
Fig. 3 is the structural schematic diagram of one of the embodiment of the present application schedulable capacity analysis device of temperature control load；
Fig. 4 is a kind of Latin Hypercube Sampling signal of the schedulable capacity analysis method of temperature control load in the embodiment of the present application
Figure.
Specific embodiment
In order to make those skilled in the art more fully understand application scheme, below in conjunction in the embodiment of the present application
Attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only this
Apply for a part of the embodiment, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art exist
Every other embodiment obtained under the premise of creative work is not made, shall fall in the protection scope of this application.
In order to make it easy to understand, referring to Fig. 1, a kind of schedulable capacity analysis of temperature control load provided in the embodiment of the present application
Method, comprising:
Step 101, thermal power, single start-up and shut-down control period and each start and stop control according to the temperature control loads of each grid nodes
Shut-in time in period processed calculates total schedulable capacity of each grid nodes according to preset schedulable capacity formula.
It should be noted that being calculated first total schedulable capacity of each grid nodes in the embodiment of the present application, temperature
Control load can be the controllable burdens such as air-conditioning, refrigerator and micro-wave oven, the schedulable capacity of temperature control load and the hot merit of temperature control load
Rate, start-up and shut-down control period are related with the shut-in time, can according to the thermal power of temperature control load, start-up and shut-down control period and shut-in time
Total schedulable capacity of respective nodes is calculated.
Step 102 obtains user-responsiveness, and user-responsiveness is to meet the stochastic variable of normal distribution.
It should be noted that due to uncertain factors such as communication delay, element fault, next day weather condition and time bursts
Influence, each user response of dispatching has uncertainty over time and space, and especially as air conditioner user, the psychology of user is also
It causes to respond a probabilistic key factor.In order to reflect the uncertainty of response, by user's in the embodiment of the present application
Responsiveness is set as meeting the stochastic variable of normal distribution.
Step 103 carries out stratified sampling to user-responsiveness according to Latin Hypercube Sampling method, and it is not true to generate reflection space-time
Qualitative responsiveness sample.
It should be noted that Latin Hypercube Sampling method sampling process schematic diagram is as shown in figure 4, Latin Hypercube Sampling method
Sampled point is set to cover the distribution of entire stochastic variable by stratified sampling.Assuming that X1,X2,…,XNIt is probabilistic loadflow
The N number of input variable calculated.XkCumulative probability distribution be:
Zk=Fk(Xk), k=1,2 ... N,
Taking sampling scale is A, sampling step are as follows:
1) by ZkValue range [0,1] be uniformly divided into A equal portions, i.e.,
2) value is successively extracted out of all sections and is used as a sampled value, and the extraction in section is random；
3) Z is distributed by cumulative probabilitykInverse function transformation after, just can obtain input variable XkSample data.
A-th of section ZkSampled value and XkN-th of sampled value it is as follows:
A total of N number of input variable, each stochastic variable sampling scale is A, it is assumed that by the data of stochastic variable with behavior
Unit is arranged successively, then the sample matrix of N*A rank may finally be obtained.
Responsiveness sample and total schedulable capacity are carried out product calculating by step 104, obtain practical schedulable capacity.
It should be noted that total schedulable appearance obtained in the sample matrix and step 101 of the responsiveness that sampling is obtained
Amount is multiplied, and can access the schedulable capacity of real response.
User-responsiveness is set as meeting just by the schedulable capacity analysis method of temperature control load provided in the embodiment of the present application
The stochastic variable of state distribution, it is contemplated that temperature control load user time and uncertainty spatially, using Latin Hypercube Sampling
Method carries out stratified sampling analysis to the uncertain of user-responsiveness, and the sample data that sampling can be made to obtain is more comprehensively
The range that covering variable is distributed enables to response capacity distribution more realistically to reflect actual response condition, avoids excessively
Response estimation is carried out to optimistic or pessimistic estimation, the existing schedulable capacity analysis method of temperature control load is solved and does not account for using
The time of family response and spatial location laws cause excessively optimistic or pessimistic estimation to ask the technology of the dispatch situation of demand response
Topic.
In order to make it easy to understand, referring to Fig. 2, another schedulable capacity analysis side of temperature control load in the embodiment of the present application
Method, comprising:
Step 201, the equivalent heat parameter model for establishing temperature control load calculate temperature control load within the single start-up and shut-down control period
Shut-in time and opening time.
Further, equivalent heat parameter model are as follows:
Wherein, s is switch state, and s=1 is in off state, and s=0 is open state,For the room temperature of t moment,For the indoor and outdoor temperature at t+1 moment, η is temperature control load Energy Efficiency Ratio, and P is the thermal power of temperature control load, and ε is scattered
Hot coefficient: ε=e- Δ hRC, R are the equivalent thermal resistance in house.
It should be noted that in the embodiment of the present application, using air-conditioning as controllable burden, the equivalent thermal parameter mould of air conditioner load
Type are as follows:
Room temperature Tout variation is set in a section, when temperature reaches TmaxWhen, air-conditioning is opened, until temperature declines
To Tmin, air-conditioning closes, and temperature increases again later, until air-conditioning reopens, such continuous circulation.Assuming that Tout, P, R are
Definite value, the shut-in time is h in a start-up and shut-down control period hoff, opening time hon.It can obtain:
It may further obtain:
Step 202, thermal power, single start-up and shut-down control period and each start and stop control according to the temperature control loads of each grid nodes
Shut-in time in period processed calculates total schedulable capacity of each grid nodes according to preset schedulable capacity formula.
It should be noted that the step 202 in the embodiment of the present application is consistent with the step 101 in a upper embodiment, herein
No longer it is described in detail.
Further, preset schedulable capacity formula are as follows:
Wherein, C is total schedulable capacity of n platform temperature control load, PiFor the thermal power of i-th temperature control load, hiIt is i-th
The start-up and shut-down control period of temperature control load, hoff,iFor the shut-in time in the start-up and shut-down control period of i-th temperature control load.
Step 203 can carry out clustering according to current to the temperature control load under each grid nodes the turn-off time, obtain
Schedulable multiple Clusterings.
Further, the algorithm of clustering is K-means algorithm.
It should be noted that a start-up and shut-down control period of air-conditioning includes shut-in time hoffWith opening time hon.When closing
Between length then can reflect the schedulable amount of capacity of air-conditioning.Every air-conditioning can turn-off time H and corresponding equivalent thermal resistance R, equivalent
The original state of thermal capacitance C and air-conditioning has very big relationship.The original state that air-conditioning is indicated with A, has:
A (t)=0 indicates that air-conditioning is located at temperature upper limit Tmax, A (t)=1 indicate air-conditioning be located at lowest temperature Tmin。
Assuming that dispatching cycle is T, air-conditioning can the turn-off time are as follows:
H=A (0) hoff+q1·hoff+q3；
Wherein:
A(0)·hoffThe turn-off time when temperature range upper limit is risen to by original state for air-conditioning.In the remaining time
In, air-conditioning carries out repeatedly wheel and controls, but scheduling time T-A (0) hoffIt tends not to be controlled period h and divide exactly, so q1For wheel
The number of control, q2For the time in a remaining discontented control period.Work as q2Greater than honWhen, it can still need to the turn-off time add q3.Air-conditioning
Real-time running state change also with the closing and unlatching of air-conditioning:
Can turn-off time H be the overall target for reflecting schedulable capacity, and the purpose of clustering is exactly by schedulable appearance
Measuring close air-conditioning cluster is one group, to give full play to the schedulable capacity of air-conditioning, therefore choose while scheduling facilitates
H is as the clustering target for judging the similitude between each object.
Clustering is carried out respectively to the air-conditioning under controllable node, it is assumed that have n platform air-conditioning, S under a nodeHFor n platform
Air-conditioning can the turn-off time sample, for H according to sorting from small to large, H is bigger, expression can the turn-off time it is longer.
SH={ H1,H2,…Hn|H1≤H2≤…≤Hn}；
Select operability high in common clustering algorithm, the most classical K-means algorithm.K-means algorithm
Principle is exactly that (K is phylogenetic group number) is clustered centered on the K point chosen in space, by near the object of pericenter successively
Sort out.Therefore select suitable initial center most important to Clustering Effect.In order to reasonably select initial cluster center, so that
The capacity of each phylogenetic group is substantially close to initial center is chosen in the following way:
For the cluster centre of jth group,For first element number of phylogenetic group jth group,For phylogenetic group jth group
The last one element number.The process of cluster is as follows:
Due to can turn-off time sample SHIt is to cluster since the sample of number i=1, successively exist by sorting from small to large
It is added in phylogenetic group j=1 and numbers bigger sample until meeting formulaIt is repeated in, obtains j=2,
3 ... the phylogenetic group of K.Air-conditioning can be divided into K group according to above-mentioned rule, every group of power summation is essentiallyNumber in every group
It is neighbouring, can the turn-off time it is close, while the bigger phylogenetic group of j can the turn-off time it is longer.
The objective function of cluster:
In order to enable the error Z of cluster is minimum, indicated with the mean square deviation entirely clustered:
Since K-means algorithm is at the beginning it needs to be determined that K value, the selection of K value also directly affect the precision of regulation, so
It is most important to choose suitable K value.K value is too small to will affect Clustering Effect, causes biggish scheduling deviation, and K value can also surpass greatly very much
The dispatching of Load aggregation quotient out.In order to choose suitable K value, maximum K is determined based on the dispatching of Load aggregation quotient
It is worth, then reselection optimum k value.
Step 204 obtains user-responsiveness, and user-responsiveness is to meet the stochastic variable of normal distribution.
It should be noted that the step 204 in the embodiment of the present application is consistent with the step 102 in a upper embodiment, herein
No longer it is described in detail.
Step 205 carries out stratified sampling to user-responsiveness according to Latin Hypercube Sampling method, and it is entire random to obtain covering
The sample data of the distribution of variable.
Step 206, by sample data according to Spearman rank correlation method carry out correlation processing, and to correlation processing after
Sample data be ranked up, generate reflection the probabilistic responsiveness sample of space-time.
It should be noted that Latin Hypercube Sampling method enables sampled point to cover entire random change by stratified sampling
The distribution of amount.Assuming that X1,X2,…,XNIt is N number of input variable that probabilistic loadflow calculates.
XkCumulative probability distribution be:
Zk=Fk(Xk), k=1,2 ... N,
Taking sampling scale is A, sampling step are as follows:
1) by ZkValue range [0,1] be uniformly divided into A equal portions, i.e.,
2) value is successively extracted out of all sections and is used as a sampled value, and the extraction in section is random；
3) Z is distributed by cumulative probabilitykInverse function transformation after, just can obtain input variable XkSample data.
A-th of section ZkSampled value and XkN-th of sampled value it is as follows:
A total of N number of input variable, each stochastic variable sampling scale is A, it is assumed that by the data of stochastic variable with behavior
Unit is arranged successively, then the sample matrix of N*A rank may finally be obtained.
Since the sample of each variable sampling at this time has certain correlation, so also needing the processing of correlation so that becoming
It is mutually indepedent between amount.Correlation between user can handle using Spearman rank correlation method, and no longer need to worry
Input the distribution pattern of stochastic variable, the related question between input variable suitable for solving various distributions.At the method
When managing the correlation between input variable, Cholesky decomposition is first passed through, then phase can be obtained by sample matrix rearrangement
Closing property very little sample.It comprises the concrete steps that:
1) to input variable X1,X2,…,XNIt samples according to distribution situation, scale 1*K, the N*K rank most started
Sample matrix F；
2) a N*K rank random order matrix A is generated, obtains the Spearman of random sequence matrix A by above content
Correlation matrix PA, according to formula PA=QQTTo PAIt is decomposed, obtains a lower triangular matrix Q；
3) according to formula D=Q-1X converts sequential matrix, transforms into D matrix.By each row member in initial sample F
Element is completed to sort again referring to each row element order corresponding in D matrix, at this time sample FDCorrelation degree greatly reduce, visually
It is independent between variable.
Responsiveness sample and total schedulable capacity are carried out product calculating by step 207, obtain practical schedulable capacity.
It should be noted that the step 104 in step 207 and a upper embodiment is consistent, herein not in the embodiment of the present application
It is described in detail again.
The embodiment of the present application enables to response to hold by analysis air conditioner user time and the uncertainty spatially responded
Amount distribution more realistically reflects actual response condition, it may be considered that the various situations of response.Using Latin Hypercube Sampling
Method is sampled analysis to the uncertainty of user-responsiveness, and the data that sampling can be made to obtain more comprehensively cover variable institute
The range of distribution can reduce sampling scale compared to Monte Carlo simulation approach in the case where same precision, and Latin is super
Cube sampling robustness it is more preferable.
In order to make it easy to understand, referring to Fig. 3, the embodiment of the present application provides a kind of electric load model construction device, packet
It includes:
Computing module 301, for according to the thermal power of the temperature control loads of each grid nodes, single start-up and shut-down control period and every
Shut-in time in a start-up and shut-down control period calculates total schedulable appearance of each grid nodes according to preset schedulable capacity formula
Amount.
Module 302 is obtained, for obtaining user-responsiveness, user-responsiveness is to meet the stochastic variable of normal distribution.
Sampling module 303 generates reflection for carrying out stratified sampling to user-responsiveness according to Latin Hypercube Sampling method
The probabilistic responsiveness sample of space-time.
Generation module 304 obtains practical schedulable for responsiveness sample and total schedulable capacity to be carried out product calculating
Capacity.
Further, further includes:
Modeling module 300 calculates temperature control load in single start and stop control for establishing the equivalent heat parameter model of temperature control load
Shut-in time and opening time in period processed.
Cluster module 305, for that can be clustered turn-off time to the temperature control load under each grid nodes according to current
Analysis, obtains schedulable multiple Clusterings.
Further, equivalent heat parameter model are as follows:
Wherein, s is switch state, and s=1 is in off state, and s=0 is open state,For the room temperature of t moment,For the indoor and outdoor temperature at t+1 moment, η is temperature control load Energy Efficiency Ratio, and P is the thermal power of temperature control load, and ε is scattered
Hot coefficient: ε=e-Δh/RC, R is the equivalent thermal resistance in house.
Further, preset schedulable capacity formula are as follows:
Wherein, C is total schedulable capacity of n platform temperature control load, PiFor the thermal power of i-th temperature control load, hiIt is i-th
The start-up and shut-down control period of temperature control load, hoff,iFor the shut-in time in the start-up and shut-down control period of i-th temperature control load.
Further, the algorithm of clustering is K-means algorithm.
Provide a kind of schedulable capacity analysis equipment of temperature control load in the embodiment of the present application, equipment include processor and
Memory:
Program code is transferred to processor for storing program code by memory；
Processor is used for according to the schedulable capacity of temperature control load in the instruction execution embodiment above-mentioned in program code
Analysis method.
The description of the present application and term " first " in above-mentioned attached drawing, " second ", " third ", " the 4th " etc. are (if deposited
) it is to be used to distinguish similar objects, without being used to describe a particular order or precedence order.It should be understood that use in this way
Data are interchangeable under appropriate circumstances, so that embodiments herein described herein for example can be in addition to illustrating herein
Or the sequence other than those of description is implemented.In addition, term " includes " and " having " and their any deformation, it is intended that
Covering non-exclusive includes to be not necessarily limited to clearly for example, containing the process, method of a series of steps or units, product or equipment
Those of list to Chu step or unit, but may include be not clearly listed or for these process, methods, product or
The intrinsic other step or units of equipment.
In several embodiments provided herein, it should be understood that disclosed system, device and method can be with
It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unit
It divides, only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components
It can be combined or can be integrated into another system, or some features can be ignored or not executed.Another point, it is shown or
The mutual coupling, direct-coupling or communication connection discussed can be through some interfaces, the indirect coupling of device or unit
It closes or communicates to connect, can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product
When, it can store in a computer readable storage medium.Based on this understanding, the technical solution of the application is substantially
The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words
It embodies, which is stored in a storage medium, including some instructions are used so that a computer
Equipment (can be personal computer, server or the network equipment etc.) executes the complete of each embodiment the method for the application
Portion or part steps.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (full name in English: Read-Only
Memory, english abbreviation: ROM), random access memory (full name in English: Random Access Memory, english abbreviation:
RAM), the various media that can store program code such as magnetic or disk.
The above, above embodiments are only to illustrate the technical solution of the application, rather than its limitations；Although referring to before
Embodiment is stated the application is described in detail, those skilled in the art should understand that: it still can be to preceding
Technical solution documented by each embodiment is stated to modify or equivalent replacement of some of the technical features；And these
It modifies or replaces, the spirit and scope of each embodiment technical solution of the application that it does not separate the essence of the corresponding technical solution.
Claims (10)
1. a kind of schedulable capacity analysis method of temperature control load characterized by comprising
According in the thermal power of the temperature control load of each grid nodes, single start-up and shut-down control period and each start-up and shut-down control period
Shut-in time, total schedulable capacity of each grid nodes is calculated according to preset schedulable capacity formula；
User-responsiveness is obtained, the user-responsiveness is to meet the stochastic variable of normal distribution；
Stratified sampling is carried out to the user-responsiveness according to Latin Hypercube Sampling method, generates the reflection probabilistic sound of space-time
Response sample；
The responsiveness sample and total schedulable capacity are subjected to product calculating, obtain practical schedulable capacity.
2. the schedulable capacity analysis method of temperature control load according to claim 1, which is characterized in that described according to each power grid
Shut-in time in the thermal power of the temperature control load of node, single start-up and shut-down control period and each start-up and shut-down control period, root
Total schedulable capacity of each grid nodes is calculated according to preset schedulable capacity formula, before further include:
The equivalent heat parameter model for establishing temperature control load, when calculating closing of the temperature control load within the single start-up and shut-down control period
Between and the opening time.
3. the schedulable capacity analysis method of temperature control load according to claim 1, which is characterized in that described according to each power grid
Shut-in time in the thermal power of the temperature control load of node, single start-up and shut-down control period and each start-up and shut-down control period, root
After the total schedulable capacity for calculating each grid nodes according to preset schedulable capacity formula, the acquisition user-responsiveness is described
User-responsiveness is before meeting the stochastic variable of normal distribution, further includes:
Turn-off time clustering can be carried out according to current to the temperature control load under each grid nodes, obtained schedulable multiple
Clustering.
4. the schedulable capacity analysis method of temperature control load according to claim 1, which is characterized in that described super according to Latin
Cube sampling method carries out stratified sampling to the user-responsiveness, generates the reflection probabilistic responsiveness sample of space-time, specifically
Include:
Stratified sampling is carried out to the user-responsiveness according to Latin Hypercube Sampling method, obtains covering the entire stochastic variable
Distribution sample data；
The sample data is subjected to correlation processing according to Spearman rank correlation method, and to the correlation treated institute
It states sample data to be ranked up, generates the reflection probabilistic responsiveness sample of space-time.
5. the schedulable capacity analysis method of temperature control load according to claim 1, which is characterized in that described preset schedulable
Capacity formula are as follows:
Wherein, C is total schedulable capacity of n platform temperature control load, PiFor the thermal power of i-th temperature control load, hiFor i-th temperature control
The start-up and shut-down control period of load, hoff,iFor the shut-in time in the start-up and shut-down control period of i-th temperature control load.
6. the schedulable capacity analysis method of temperature control load according to claim 2, which is characterized in that the equivalent thermal parameter
Model are as follows:
Wherein, s is switch state, and s=1 is in off state, and s=0 is open state,For the room temperature of t moment,For the indoor and outdoor temperature at t+1 moment, η is temperature control load Energy Efficiency Ratio, and P is the thermal power of temperature control load, and ε is scattered
Hot coefficient: ε=e-Δh/RC, R is the equivalent thermal resistance in house.
7. the schedulable capacity analysis method of temperature control load according to claim 3, which is characterized in that the clustering
Algorithm is K-means algorithm.
8. a kind of schedulable capacity analysis device of temperature control load characterized by comprising
Computing module, for according to the thermal power of the temperature control loads of each grid nodes, single start-up and shut-down control period and described each
Shut-in time in the start-up and shut-down control period calculates total schedulable capacity of each grid nodes according to preset schedulable capacity formula；
Module is obtained, for obtaining user-responsiveness, the user-responsiveness is to meet the stochastic variable of normal distribution；
Sampling module, for carrying out stratified sampling to the user-responsiveness according to Latin Hypercube Sampling method, when generating reflection
Empty probabilistic responsiveness sample；
Generation module obtains practical adjustable for the responsiveness sample and total schedulable capacity to be carried out product calculating
Spend capacity.
9. the schedulable capacity analysis device of a kind of temperature control load according to claim 8, which is characterized in that further include:
Modeling module calculates the temperature control load in single start-up and shut-down control for establishing the equivalent heat parameter model of temperature control load
Shut-in time and opening time in period；
Cluster module is obtained for that turn-off time can carry out clustering according to current to the temperature control load under each grid nodes
To schedulable multiple Clusterings.
10. a kind of schedulable capacity analysis equipment of temperature control load, which is characterized in that the equipment includes processor and storage
Device:
Said program code is transferred to the processor for storing program code by the memory；
The processor is used for according to the described in any item temperature control loads of instruction execution claim 1-7 in said program code
Schedulable capacity analysis method.
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