CN113902291A - Clustering feature-based temperature control equipment cluster control method and system - Google Patents

Clustering feature-based temperature control equipment cluster control method and system Download PDF

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CN113902291A
CN113902291A CN202111172523.0A CN202111172523A CN113902291A CN 113902291 A CN113902291 A CN 113902291A CN 202111172523 A CN202111172523 A CN 202111172523A CN 113902291 A CN113902291 A CN 113902291A
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刘思嘉
刘凤鸣
陈珂
王隗东
陈麒宇
陈晓光
王镇林
王浩扬
杨秀媛
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China Electric Power Research Institute Co Ltd CEPRI
Beijing Information Science and Technology University
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Beijing Information Science and Technology University
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Abstract

The invention discloses a cluster control method and system for temperature control equipment based on clustering characteristics. The method comprises the steps of firstly carrying out statistical analysis on local temperature information acquired by single temperature control equipment, identifying first-order thermal equivalent parameters of the equipment by adopting a recursive least square method and reporting the first-order thermal equivalent parameters to a control center. And the control center clusters similar individuals in the cluster into a plurality of clusters by using a mean shift method based on the real-time identified thermal equivalent parameters. And then, taking the parameter of the cluster center point of each population as the population characteristic, and adopting the tendency index to judge the control method of the population. And finally, respectively adopting a Markov chain-based probability control method and a priority distribution-based direct load control method to control different clusters, wherein the probability control method can ensure low control cost and low rebound effect of the clusters, and the direct load control is used as an error correction method of the total power of the clusters to improve the response speed and the control precision of the clusters, and the two methods jointly realize the control of the temperature control clusters.

Description

Clustering feature-based temperature control equipment cluster control method and system
Technical Field
The invention relates to the technical field of power demand side management, in particular to a cluster control method of temperature control equipment based on clustering characteristics.
Background
With the depletion of fossil energy, countries and economic entities in the world are exploring new energy patterns. Among them, new energy power generation technologies such as wind power generation and solar power generation are adopted by more and more countries due to their environmental protection and economic characteristics. However, although the advantages of the new energy power generation technology are outstanding, there are still many problems. Firstly, due to the random fluctuation and intermittence of new energy and the fact that a large number of power electronic devices are connected into a power grid, the security investment cost of the power grid is continuously increased; secondly, the power consumption of new energy on the load side is insufficient, so that the phenomenon of 'wind and light abandoning' is caused, and the recovery of investment cost is hindered. In order to realize the double increase of the generated energy and the new energy ratio, the double decrease of the electricity abandonment and the electricity abandonment rate and relieve the contradiction of the consumption of clean energy, a new power grid supply and demand regulation mode, namely demand response, is adopted. By using the demand response, the power grid can realize peak clipping and valley filling, stabilize load fluctuation, enhance the capability of the power grid for maintaining supply and demand balance, promote more renewable energy sources to be accessed and consumed, and simultaneously promote the interaction degree of the power grid and users, thereby providing the supply and demand value-added service for the users. Therefore, demand response will be an indispensable support technology for smart grids. The consumption of new energy by using the demand response is the most feasible means for realizing the balance of the power supply and demand of the power grid.
With the development of energy internet and communication technology, the popularization of a wide area measurement system and the application of a 5G slicing technology in a power system, information can be efficiently interacted. With the intellectualization of the electric appliances, the load resources of residents become high-quality resources for participating in demand response on the demand side. How to reasonably regulate and control demand side resources and match with new energy consumption to realize supply and demand balance is a key technology for the construction of the existing smart grid and is an important way for improving the utilization rate of new energy. The intelligent optimization configuration of the demand response resources is one of key technologies for solving the problems, and a reasonable method is urgently needed to achieve reasonable optimization of the demand response resources under the background of large-scale new energy grid connection, control and scheduling of demand side resources are performed to the maximum extent, consumption of new energy is achieved, and safe and stable operation of a power grid is guaranteed.
Disclosure of Invention
The invention aims to provide a cluster control method of temperature control equipment based on clustering characteristics, which is used for controlling and scheduling demand side resources, realizing new energy consumption and ensuring safe and stable operation of a power grid.
In order to achieve the purpose, the invention provides the following scheme:
a cluster control method of temperature control equipment based on clustering characteristics comprises the following steps:
collecting local temperature information of each single temperature control device;
based on the local temperature information, adopting a recursive least square method to identify a first-order thermal equivalent parameter of each single temperature control device;
clustering a plurality of single temperature control devices into a plurality of populations by adopting a mean shift method based on the first-order thermal equivalent parameters; the cluster center point of each population is a population cluster characteristic;
calculating tendency indexes of each population according to the population clustering characteristics;
and determining a control method of each population according to the tendency index, and controlling the corresponding population according to the determined control method.
Optionally, the model of the first order thermal equivalent parameter is as follows:
Figure BDA0003293851400000021
wherein t is the internal temperature, theta is the external temperature, Q is the cooling capacity, Q0Is an internal heat generation amount, PNThe rated power is R, the equivalent thermal resistance is C, the equivalent thermal capacity is C, and the coefficient of heat dissipation is tau; eta is the efficiency of converting electric energy into internal energy, and k is the sampling time; Δ k is the sampling time interval and s is the switching function.
Optionally, the calculation formula of the tendency index is as follows:
Figure BDA0003293851400000022
wherein lωThe number of devices in the population # omega,
Figure BDA0003293851400000023
representing the average power characteristics of all populations
Figure BDA0003293851400000024
The maximum value of the number of the first and second,
Figure BDA0003293851400000025
representing the taking of equivalent heat capacity characteristics in all populations
Figure BDA0003293851400000026
A maximum value; f. ofiThe weight of the characteristic quantity i is represented,
Figure BDA0003293851400000027
average of population number omegaThe characteristics of the power of the electric motor,
Figure BDA0003293851400000028
and U represents the total number of the devices as equivalent heat capacity characteristics of the omega population.
Optionally, the method for determining a control method of each population according to the tendency index specifically includes:
the population with the tendency index larger than the set threshold is controlled by adopting a direct load control method;
and the rest populations are controlled by a probability control method based on a Markov chain.
Optionally, a probability-controlled population is adopted, a de-aggregation operation is performed according to the total scheduling requirement of the control center to obtain de-aggregation information of the population, the de-aggregation information is transmitted to individuals in the population, each individual solves a Markov transfer matrix of the individual through the de-aggregation information, and starting and stopping of the equipment are performed according to the iteration result of the Markov transfer matrix.
Optionally, inside the population adopting direct load control, the single temperature control device divides a temperature interval in which the user allows to work, the priority of the device is defined according to the temperature interval in which the real-time temperature of the device is located, and the device inside the population responds to the control center instruction according to the priority.
Optionally, according to the total output requirement of the control center, issuing inverse aggregation information to the probability-controlled population as a control signal, and issuing a prediction error to the direct load-controlled population as a control signal; the prediction error is the difference value between the actual total output and the expected total output of the temperature control equipment cluster.
The invention also provides a cluster control system of temperature control equipment based on clustering characteristics, which comprises:
the acquisition module is used for acquiring local temperature information of each single temperature control device;
the identification module is used for identifying the first-order thermal equivalent parameters of each single temperature control device by adopting a recursive least square method based on the local temperature information;
the clustering module is used for clustering a plurality of single temperature control devices into a plurality of populations by adopting a mean shift method based on the first-order thermal equivalent parameters; the cluster center point of each population is a population cluster characteristic;
the tendency index control module is used for calculating tendency indexes of all populations according to the population clustering characteristics;
and the control module is used for determining the control method of each population according to the tendency index and controlling the corresponding population according to the determined control method.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a cluster control method of temperature control equipment based on clustering characteristics. And the control center clusters similar individuals in the cluster into a plurality of clusters by using a mean shift method based on the real-time identified thermal equivalent parameters. And then, taking the parameter of the cluster center point of each population as the population characteristic, and adopting the tendency index to judge the control method of the population. And finally, controlling different clusters by respectively adopting a Markov chain-based probability control method and a priority distribution-based direct load control method, wherein the probability control method can ensure low control cost and low rebound effect of the clusters, the direct load control is used as an error correction method of the total power of the clusters, the response speed and the control precision of the clusters are improved, the probability control method and the direct load control jointly realize the control of the temperature control clusters, the control and the scheduling of demand side resources are realized, the consumption of new energy is realized, and the safe and stable operation of a power grid is ensured.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a cluster control method for a temperature control device based on clustering characteristics according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a cluster control method for temperature control devices based on clustering characteristics according to an embodiment of the present invention;
FIG. 3 illustrates an identification process of equivalent thermal parameters of a single temperature control device;
FIG. 4 is a diagram of a cluster clustering process and clustering results; a) a cluster data probability density convergence process; b) clustering results by a mean shift method;
FIG. 5 is a reverse polymerization process based on a demand curve;
figure 6 is a process of probability control based on a markov chain;
FIG. 7 is a priority packet of a queue ordering method;
FIG. 8 is a comparison of peak clipping effects of a cluster control method of temperature control devices based on clustering characteristics and other control methods; a) cluster power peak clipping effect; b) and comparing the cluster communication quantity.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a cluster control method of temperature control equipment based on clustering characteristics, which is used for controlling and scheduling demand side resources, realizing new energy consumption and ensuring safe and stable operation of a power grid.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1-2, the cluster control method for temperature control devices based on clustering features provided in the present invention includes the following steps:
step 101: and collecting local temperature information of each single temperature control device.
Step 102: and identifying the first-order thermal equivalent parameters of each single temperature control device by adopting a recursive least square method based on the local temperature information.
Step 103: clustering a plurality of single temperature control devices into a plurality of populations by adopting a mean shift method based on the first-order thermal equivalent parameters; the cluster center point of each cluster is a cluster characteristic of the clusters.
Step 104: and calculating the tendency index of each population according to the cluster characteristics of the populations.
Step 105: and determining a control method of each population according to the tendency index, and controlling the corresponding population according to the determined control method.
The specific process of step 102 is as follows:
(1) a first-order thermal equivalent parameter model of the single temperature control equipment: the spatial energy content is described in relation to the conversion of electrical energy.
Figure BDA0003293851400000051
In the formula, t is the internal temperature (the air conditioner and the heat pump correspond to the indoor temperature, and the water heater corresponds to the internal temperature of the water tank) and DEG C; θ is the external temperature, deg.C; q is refrigerating capacity, kW; q0Internal heating power, kW; pNRated power, kW; r is equivalent thermal resistance, DEG C/kW; c is equivalent heat capacity, kJ/DEG C; tau is a heat dissipation coefficient; eta is the efficiency of converting electric energy into internal energy; k is the sampling time; Δ k is the sampling time interval, min; s is a switching function, and when the z-th air conditioner does not participate in the demand response, the switching function of the z-th air conditioner at the moment k is as follows:
Figure BDA0003293851400000052
in the formula, ttpMaximum temperature allowed within the comfort range set for the user, tbtIs the lowest temperature.
(2) Identification value of parameter vector
Figure BDA0003293851400000061
A process vector of recursive least squares is described.
Figure BDA0003293851400000062
In the formula:
Figure BDA0003293851400000063
the thermal equivalent parameter models are derived from a least square recursion formula.
(3) A first-order thermal equivalent parameter standard recurrence formula:
Figure BDA0003293851400000064
in the formula: kk+1Is a gain matrix; lambda is a forgetting factor, and lambda is more than or equal to 0 and less than or equal to 1; wkIs a weighting matrix of which the initial value W0＝α2E, alpha is a constant with a larger value; e is an identity matrix; ΨkIs an observation vector; gamma raykIs a vector of parameters to be identified,
Figure BDA0003293851400000065
is its identification value; y isk+1And outputting the value for the system. Let yk＝θk-tk，uk＝QkThe first order thermal equivalent parametric model can be rewritten as:
Figure BDA0003293851400000066
in the formula: a is1,k、b1,kThe parameters to be identified when the first-order thermal equivalent parameters are rewritten into the standard form of the discrete state equation. Substituting the equivalent parameter into a first-order thermal equivalent parameter standard recurrence formula to obtain an identification parameter vector corresponding to an equivalent thermal parameter model equation
Figure BDA0003293851400000067
And the observation vector Ψk-1Comprises the following steps:
Figure BDA0003293851400000068
substituting the recursion result into a first-order thermal equivalent parameter standard recursion formula to obtain a first-order thermal equivalent parameter identification value:
Figure BDA0003293851400000069
in the formula:
Figure BDA00032938514000000610
the more recursion times are, the closer the identification result can be to the true value. FIG. 3 illustrates an identification process of equivalent thermal parameters.
Wherein, the specific process of step 103 is as follows:
(1) in d-dimensional space RdThe existence of U points xz Z 1, …, U, optionally at a point x in space0As an initial point, at the same time, define the Mean-Shift vector mhComprises the following steps:
Figure BDA0003293851400000071
in the formula: slIs a sphere with a center of x0A high-dimensional sphere region with a radius h (generally, h is the kernel size or bandwidth), which can be regarded as a window under the two-dimensional condition; l indicates that there are l points falling into S in U sampleslWithin the region of (a).
Obtain Mean-Shift vector mhThen, m is puthAs an initial point x0Can obtain a new sphere center x1＝x0+mhRepeating the iteration in this way, the sphere center will finally converge to the position with the highest probability density.
(2) Selecting z-number monomer temperature control equipment
Figure BDA0003293851400000072
And average power
Figure BDA00032938514000000710
As a point xzI.e.:
Figure BDA0003293851400000073
(3) multivariate kernel density estimation
Figure BDA0003293851400000074
Defined at point x as:
Figure BDA0003293851400000075
in the formula: normal nucleus
Figure BDA0003293851400000076
And the kernel profile c (x) is:
Figure BDA0003293851400000077
in the formula: mu is a normalization constant such that
Figure BDA0003293851400000078
The integral is 1.
(4) The Mean-Shift algorithm is actually a gradient in probability density, so the multivariate kernel density estimate can be modified as:
Figure BDA0003293851400000079
let g (x) be equal to c' (x) to determine the nuclear contour g (x) be equal to μgg(||x||2) The recursion formula of the kernel G sphere center position χ can be derived as follows:
Figure BDA0003293851400000081
(5) selecting arbitrary xzAs seed point chi1，εmIn order to tolerate errors, the Mean-Shift clustering temperature control device is realized according to the following steps:
step 1: x is to bezIs substituted into the initial point χ1。
Step 2: will be ChijSubstituting formula (3.12) to obtain new core G sphere center position χj+1。
Step 3: if | |% χj+1-χj||＜εmThen, it is measured as xj+1As the coordinate of (a) as xzAnd proceeds to Step 4; otherwise, go to Step2 after j + 1.
Step 4: if all points are marked, then go to Step 5; otherwise, there will be no marked xzSubstituted into the initial point χ1And then to Step 2.
Step 5: x having the same labelzAnd merging the temperature control devices into the same class to realize clustering of the temperature control devices.
The clustering process of the clusters is shown in fig. 4; a) a cluster data probability density convergence process; b) the clustering result is obtained by a mean shift method.
Wherein, the specific process of step 104 is as follows:
the average electric power, equivalent heat capacity and the number of the clusters are subjected to per unit value processing, then different weights are distributed according to requirements, and the tendency index of each cluster can be obtained through calculation.
(1) Cluster centers of n populations resulting from step 103
Figure BDA0003293851400000082
χωAnd the cluster center coordinates are of the omega population, and the cluster center represents the cluster characteristics of the omega population.
(2) According to the cluster characteristics of the population, the control tendency index F (χ) can be obtainedω,lω)：
Figure BDA0003293851400000083
In the formula: lωThe number of devices in the population # omega,
Figure BDA0003293851400000084
representing the average power characteristics of all populations
Figure BDA0003293851400000085
The maximum value of the number of the first and second,
Figure BDA0003293851400000086
representing the taking of equivalent heat capacity characteristics in all populations
Figure BDA0003293851400000087
A maximum value; f. ofiThe weight of the characteristic quantity i is represented,
Figure BDA0003293851400000088
is the average power characteristic of the population # omega,
Figure BDA0003293851400000089
and U represents the total number of the devices as equivalent heat capacity characteristics of the omega population.
Wherein, the specific process of step 105 is as follows: and according to the control method for distributing the population of the population tendency index, the population with larger tendency index adopts direct load control, and the rest of the population adopts probability control based on a Markov chain.
1) The probability-controlled population needs to perform inverse aggregation operation according to the total scheduling requirement of the control center, inverse aggregation information beta of the population can be obtained, then the beta is transmitted to individuals in the population, each individual solves a Markov transfer matrix of the individual through the beta, and starting and stopping of the equipment are operated according to the iteration result of the Markov transfer matrix.
The probability control process based on the Markov chain is as follows:
(1) obtaining cluster base average power
Figure BDA0003293851400000091
Maximum steady state power of cluster
Figure BDA0003293851400000092
Cluster minimum steady state power
Figure BDA0003293851400000093
(2) As shown in FIG. 5, from
Figure BDA0003293851400000094
Figure BDA0003293851400000095
The three points form a total demand curve, and the same method is adopted to form
Figure BDA0003293851400000096
And a U-shaped single air conditioner demand curve can be formed. Cluster total expected power Pd,sumAfter the point is substituted into the total demand curve, the abscissa value of the point is beta, the dispatching center only needs to issue the beta to each individual air conditioner load, the air conditioner load substitutes the beta into the demand curve of the dispatching center by taking the beta as the abscissa, and the ordinate value of the point is the expected power of a single air conditioner
Figure BDA0003293851400000097
(3) Markov matrix M: the transition probability of the temperature control device in each state is represented. When the temperature control equipment is iterated through the same Markov matrix all the time, the average power of the equipment is converged to a stable value. Figure 6 shows a markov chain based probabilistic control process with a controller of the air conditioning load locally generating a random number variable r ═ rand (1, T)n) The function rand may be in the closed interval [1, Tn]To produce uniformly distributed random integers. Is and only r ═ TnWhen the state transition matrix M is in the original state, the state transition matrix T of the air-conditioning load is obtained according to the relationship between the state transition matrix M and the state transition matrix T of the air-conditioning load
Figure BDA0003293851400000098
In the formula, T1To T4The time of the air conditioner staying in the four states of starting, shutting down locking and starting locking is respectively represented, and the shutting down locking and the starting locking are generally constant.
(4) Let x1＝T1,z，x2＝T2,zAnd is and
Figure BDA0003293851400000099
the desired power can be built
Figure BDA00032938514000000910
The relation with the Markov matrix M is
Figure BDA00032938514000000911
s.t.x1≤T1,max,z
x2≤T2,max,z
The above formula is a typical discrete optimization solving problem, and the solving of M can be completed by adopting a genetic algorithm.
2) In the population adopting direct load control, the single temperature control equipment divides the temperature interval which is allowed by a user to work, and then the priority of the equipment is defined according to the temperature interval in which the real-time temperature of the equipment is located. And the devices in the population respond to the command of the control center according to the priority.
The direct load control is implemented as follows:
(1) priority grouping. In order to improve the utilization rate of data transmission, the air conditioner load is divided into 2 in a binary complement modenAnd a priority level, wherein a temperature dead zone sigma is set for preventing the shutdown (startup) time of the air conditioner from being too short. When t is more than or equal to ttpσ or t ≧ tbtAt + σ, the priority of the air conditioning load is 0, and the air conditioning load does not participate in scheduling. If tk+1-tkMore than or equal to 0, the priority is positive, otherwise, the priority is negative. Temperature averaging division of a controlled area into 2n-1The higher the indoor temperature t, the larger the absolute value of the load priority. Fig. 7 is a priority packet of the queue ordering method.
(2) A method of operating based on priority logic. When the system needs to reduce the load, the load with the negative priority can be cut off, and the load with the high priority is cut off firstly; when the system needs to increase the load, the load with positive priority can be started, and the load with high priority is started first. And when the load with the high priority acts, the system load reaches an expected value, and the load with the low priority still operates according to the logic of the uncontrolled load.
3) And comparing the actual total output of the temperature control equipment cluster with the expected total output, and calculating a prediction error. And according to the total output requirement of the control center, issuing beta to the probability control population as a control signal, and issuing the difference value between the expected total output and the actual total output to the direct load control population as the control signal.
Probability control is open-loop edge calculation, the control center can make the total power of the population converge to the vicinity of an expected value only by sending beta to the probability control population, and the rebound effect is small after the control is quitted; the direct load control is closed-loop control, each individual in a population needs to be monitored in real time and individual information needs to be fed back, the response speed is high, the control precision is high, the cost is high, and the calculation amount is exponentially increased along with the increase of the control amount.
As shown in fig. 8, the clustering control method based on clustering characteristics of the temperature control device cluster of the present invention and the existing single direct load control and probability control method are respectively adopted to perform peak clipping operations on chinese characters from 12:00 to 13:00 in a certain area, and the results of a) cluster power peak clipping effect and b) cluster communication quantity comparison are displayed: the control effect obtained by the method of the invention is obviously superior to that of the existing method, and the communication cost of the control is also lower.
The invention also provides a cluster control system of temperature control equipment based on clustering characteristics, which comprises:
the acquisition module is used for acquiring local temperature information of each single temperature control device;
the identification module is used for identifying the first-order thermal equivalent parameters of each single temperature control device by adopting a recursive least square method based on the local temperature information;
the clustering module is used for clustering a plurality of single temperature control devices into a plurality of populations by adopting a mean shift method based on the first-order thermal equivalent parameters; the cluster center point of each population is a population cluster characteristic;
the tendency index control module is used for calculating tendency indexes of all populations according to the population clustering characteristics;
and the control module is used for determining the control method of each population according to the tendency index and controlling the corresponding population according to the determined control method.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (8)

1. A cluster control method of temperature control equipment based on cluster characteristics is characterized by comprising the following steps:
collecting local temperature information of each single temperature control device;
based on the local temperature information, adopting a recursive least square method to identify a first-order thermal equivalent parameter of each single temperature control device;
clustering a plurality of single temperature control devices into a plurality of populations by adopting a mean shift method based on the first-order thermal equivalent parameters; the cluster center point of each population is a population cluster characteristic;
calculating tendency indexes of each population according to the population clustering characteristics;
and determining a control method of each population according to the tendency index, and controlling the corresponding population according to the determined control method.
2. The cluster control method for temperature control devices based on clustering characteristics as claimed in claim 1, wherein the model of the first order thermal equivalent parameters is as follows:
Figure FDA0003293851390000011
wherein t is the internal temperature, theta is the external temperature, Q is the cooling capacity, Q0Is an internal heat generation amount, PNThe rated power is R, the equivalent thermal resistance is C, the equivalent thermal capacity is C, and the coefficient of heat dissipation is tau; eta is the efficiency of converting electric energy into internal energy, and k is the sampling time; Δ k is the sampling time interval and s is the switching function.
3. The cluster control method for temperature control devices based on cluster characteristics as claimed in claim 1, wherein the calculation formula of the tendency index is as follows:
Figure RE-FDA0003346185430000012
wherein, F (χ)ω,lω) As a tendency index,. lωThe number of devices in the population # omega,
Figure RE-FDA0003346185430000013
representing the average power characteristics of all populations
Figure RE-FDA0003346185430000014
The maximum value of the number of the first and second,
Figure RE-FDA0003346185430000015
representing the taking of equivalent heat capacity characteristics in all populations
Figure RE-FDA0003346185430000016
A maximum value; f. ofiThe weight of the characteristic quantity i is represented,
Figure RE-FDA0003346185430000017
is the average power characteristic of the population # omega,
Figure RE-FDA0003346185430000018
and U represents the total number of the devices as equivalent heat capacity characteristics of the omega population.
4. The cluster control method for temperature control devices based on cluster characteristics according to claim 1, wherein the determining the control method for each cluster according to the tendency index specifically comprises:
the population with the tendency index larger than the set threshold is controlled by adopting a direct load control method;
and the rest populations are controlled by a probability control method based on a Markov chain.
5. The cluster control method for temperature control devices based on clustering characteristics as claimed in claim 4, wherein a probabilistic controlled population is adopted, a de-aggregation operation is performed according to the total scheduling requirement of the control center to obtain de-aggregation information of the population, the de-aggregation information is transmitted to individuals in the population, each individual solves its own Markov transition matrix through the de-aggregation information, and the start and stop of the devices are performed according to the iteration result of the Markov transition matrix.
6. The cluster control method for temperature control equipment based on cluster characteristics as claimed in claim 4, wherein, within a cluster controlled by direct load, the single temperature control equipment divides the temperature interval in which the user is allowed to work, the priority of the equipment is defined according to the temperature interval in which the real-time temperature of the equipment is located, and the equipment within the cluster responds to the command of the control center according to the priority.
7. The cluster control method for temperature control devices based on cluster features as claimed in claim 5, wherein according to the total output requirement of the control center, inverse aggregation information is issued to the probability controlled population as a control signal, and a prediction error is issued to the direct load controlled population as a control signal; the prediction error is the difference value between the actual total output and the expected total output of the temperature control equipment cluster.
8. The utility model provides a control by temperature change equipment cluster control system based on clustering characteristic which characterized in that includes:
the acquisition module is used for acquiring local temperature information of each single temperature control device;
the identification module is used for identifying the first-order thermal equivalent parameters of each single temperature control device by adopting a recursive least square method based on the local temperature information;
the clustering module is used for clustering a plurality of single temperature control devices into a plurality of populations by adopting a mean shift method based on the first-order thermal equivalent parameters; the cluster center point of each population is a population cluster characteristic;
the tendency index control module is used for calculating tendency indexes of all populations according to the population clustering characteristics;
and the control module is used for determining the control method of each population according to the tendency index and controlling the corresponding population according to the determined control method.
CN202111172523.0A 2021-10-08 2021-10-08 Clustering feature-based temperature control equipment cluster control method and system Pending CN113902291A (en)

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