CN113067340B - Dynamic state estimation method and system for constant temperature control load system - Google Patents

Dynamic state estimation method and system for constant temperature control load system Download PDF

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CN113067340B
CN113067340B CN202110320280.4A CN202110320280A CN113067340B CN 113067340 B CN113067340 B CN 113067340B CN 202110320280 A CN202110320280 A CN 202110320280A CN 113067340 B CN113067340 B CN 113067340B
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temperature control
state
constant
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aggregation group
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CN113067340A (en
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张文
张婷婷
赵琪
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Shandong University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/26—Arrangements for eliminating or reducing asymmetry in polyphase networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/50—Arrangements for eliminating or reducing asymmetry in polyphase networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Abstract

The invention discloses a dynamic state estimation method and a system for a constant temperature control load system, wherein the method comprises the following steps: clustering the monomer constant-temperature control load into K constant-temperature control load aggregation groups to obtain average parameters of the load aggregation groups; establishing a bilinear state space model for each constant-temperature control load aggregation group, and predicting the state and aggregation power of each constant-temperature control load aggregation group at the moment k; and performing state estimation on each constant-temperature control load aggregation group based on a singular value decomposition method and a weighted least square method with constraint to obtain a constant-temperature control load state estimation value. The invention adopts the K-means clustering method to aggregate large-scale single constant temperature control loads into load aggregation groups with similar parameters, thereby reducing the scale of the state estimation problem, reducing the communication traffic, effectively utilizing the limited measurement information to realize the state perception of an underdetermined constant temperature control load system and obtaining better estimation precision.

Description

Dynamic state estimation method and system for constant temperature control load system
Technical Field
The invention relates to the technical field of constant temperature control load, in particular to a dynamic state estimation method and system for a constant temperature control load system.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
In recent years, the technology of the smart grid is continuously developed, and conditions are provided for the load side resources to participate in the regulation and control of the power system. The smart power grid integrates a communication technology, an automatic control technology and the like, covers all links of a power system, is a future development direction of the power grid, and provides an open platform for power users to participate in the system in a rapid development mode.
In numerous household electric appliances, constant temperature control loads such as air conditioners, refrigerators, water heaters and the like have certain heat storage performance, and the user experience cannot be obviously influenced by the interruption of operation in a short time. With the development of society and economy, the air-conditioning load capacity accounts for a large proportion in large and medium-sized cities in China, and particularly the air-conditioning load proportion is as high as 30-50% at peak load in summer; in some commercially developed cities, the air conditioning load is over 50%. Through reasonable control design, the constant temperature control load represented by the air conditioner load can quickly respond to the control signal of the power grid, the load requirement is reduced, and meanwhile, the control cost is lower and is easier to realize.
If the control of the constant temperature control load is to be realized, the running state of the load is firstly acquired, and the controllable capacity of the load is further estimated according to the running state. The air conditioner load is the load type most sensitive to meteorological factors, the active power and the reactive power consumed by the air conditioner load are easily influenced by the external environment temperature and the like, and particularly when the temperature rises quickly in summer, the load capacity of the air conditioner load can greatly rise. Meanwhile, because the air conditioner has a large duty ratio, if the operation state of the air conditioner cannot be accurately grasped, the regulation and control effect of the air conditioner can be seriously influenced. Therefore, accurate estimation of the air conditioning load operating state is critical to achieving participation thereof in demand-side management.
At present, the state estimation method of the constant temperature control load system has the following problems:
(1) The number of the thermostatic control loads is large, if the running state of the single thermostatic control load is to be obtained, the state estimation dimensionality is large, the required communication traffic is large, and the calculation cost and the communication cost are high.
(2) Due to traffic limitation, the real-time measurement quantity of each load aggregation group is limited, the load aggregation group state estimation problem is an underdetermined problem, and the traditional state estimation method is difficult to solve.
Disclosure of Invention
In order to solve the problems, the invention provides a dynamic state estimation method and a dynamic state estimation system for a constant temperature control load system, which are used for realizing large-scale monomer constant temperature control load aggregation based on a K-means clustering method, establishing a bilinear state space model by taking the average parameters of the monomer constant temperature control load aggregation to realize constant temperature control load aggregation group modeling, realizing constant temperature control load state prediction based on the model, and performing state estimation on each constant temperature control load aggregation group based on a singular value decomposition method and a weighted least square method with constraint to obtain an estimated value of the running state of the constant temperature control load.
In some embodiments, the following technical scheme is adopted:
a dynamic state estimation method for a constant temperature control load system comprises the following steps:
clustering the monomer constant-temperature control loads into K constant-temperature control load aggregation groups, and acquiring average parameters of the load aggregation groups, namely average values of thermal resistance, thermal capacity, rated thermal power, thermoelectric conversion efficiency, set temperature and temperature control dead zones of the monomer constant-temperature control loads;
establishing a bilinear state space model for each constant-temperature control load aggregation group, and predicting the state and aggregation power of each constant-temperature control load aggregation group at the moment k;
and performing state estimation of each constant temperature control load aggregation group based on a singular value decomposition method and a weighted least square method with constraint to obtain a constant temperature control load state estimation value.
In other embodiments, the following technical solutions are adopted:
a thermostat-controlled load system oriented dynamic state estimation system comprising:
the single constant-temperature control load clustering module is used for clustering the single constant-temperature control loads into K constant-temperature control load aggregation groups and acquiring average parameters of the load aggregation groups, namely the average values of the thermal resistance, the thermal capacity, the rated thermal power, the thermoelectric conversion efficiency, the set temperature and the temperature control dead zone of the single constant-temperature control loads;
the constant-temperature control load aggregation group state prediction module is used for establishing a bilinear state space model for each constant-temperature control load aggregation group and predicting the state and aggregation power of each constant-temperature control load aggregation group at the moment k;
and the constant temperature control load state estimation module is used for carrying out state estimation on each constant temperature control load aggregation group based on a singular value decomposition method and a weighted least square method with constraint to obtain a constant temperature control load state estimation value.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is configured to store a plurality of instructions, wherein the instructions are adapted to be loaded by the processor and to perform the above-described method for estimating a dynamic state of a climate controlled load system.
In other embodiments, the following technical solutions are adopted:
a computer-readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to execute the above-described method for dynamic state estimation for a thermostatically-controlled load system.
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention adopts a K-means clustering method to aggregate large-scale monomer constant-temperature control loads into load aggregation groups with similar parameters, thereby reducing the scale of the state estimation problem and reducing the communication traffic.
(2) The method carries out state estimation of the constant temperature control load aggregation group based on singular value decomposition and a weighted least square method with constraint, can effectively utilize limited measurement information to realize state perception of an underdetermined constant temperature control load system, reduces the influence caused by measurement errors, and obtains better estimation precision.
Additional features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flowchart of a dynamic state estimation method for a thermostatic control load system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a constant temperature control load aggregation group bilinear state space model according to an embodiment of the present invention;
FIG. 3 is an example of an elbow normal line diagram of an embodiment of the present invention;
FIG. 4 is a diagram illustrating the variation of ambient temperature according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating an estimation accuracy result of a dynamic state estimation of a thermostatic control load according to an embodiment of the present invention;
FIG. 6 is a diagram of a variation of the mean value of the root mean square error of the state variables estimated for the dynamic state of the thermostatically controlled load under different measurement accuracies according to an embodiment of the present invention;
fig. 7 is a graph showing the variation of the relative error of the total power aggregated externally according to the dynamic state estimation of the thermostatic control load under different measurement accuracy conditions according to the embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
According to the embodiment of the invention, a dynamic state estimation method for a constant temperature control load system is disclosed, and referring to fig. 1, the method comprises the following steps:
(1) Clustering the single constant-temperature control loads into K constant-temperature control load aggregation groups, and acquiring average parameters of the load aggregation groups, namely the average values of the thermal resistance, the thermal capacity, the rated thermal power, the thermoelectric conversion efficiency, the set temperature and the temperature control dead zone of the single constant-temperature control loads;
specifically, a K-means clustering method is adopted to cluster the single constant temperature control load into K constant temperature control load aggregation groups, and load aggregation group clustering parameters are obtained.
It is assumed that the thermostatically controlled loads connected to the nodes of the distribution network are managed by different load operators. For the single constant temperature control load managed by each load operator, respectively extracting the parameters of the single constant temperature control load to form a parameter set:
Figure BDA0002992783040000051
wherein R and C are respectively thermal resistance and thermal capacity, P r The rated thermal power is shown, eta is the thermoelectric conversion efficiency,
Figure BDA0002992783040000052
And δ represent the set temperature and the temperature control dead zone thereof, respectively.
And based on the data set P, clustering by adopting a K-means method, wherein the clustering number K is determined by adopting an elbow method. The algorithm idea is as follows: with increasing K and increasing cluster centers, the data set P will be segmented in more detail, and the corresponding Sum of Squared Errors (SSE) will gradually decrease. When the value of K is less than the true cluster number, the value of SSE varies greatly as the value of K increases. When K is equal to the true cluster number, the change in the SSE value is smaller as the value of K increases. Therefore, the SSE value versus K value graph is presented as a "elbow" type line graph, where the "elbow" is the optimal K value.
The elbow method comprises the following steps:
(1) a K value range is determined.
(2) And based on a K-means method, clustering the data set P by using all K values.
(3) The SSE corresponding to each K value is calculated according to the following formula
Figure BDA0002992783040000061
Wherein p is the ith class group L i Data object of (1), q i Represents the ith class group L i Average of all data objects in (a).
(4) SSE values are plotted against K values.
(5) And selecting the elbow of the relation graph as an optimal K value.
(2) Establishing a bi-linear state space model for each constant temperature control load aggregation group;
and (2) for the K constant-temperature control load aggregation groups obtained in the step (1), establishing a bilinear state space model of the K constant-temperature control load aggregation groups by taking the quantity of the switch states between different temperature sections as state variables and the temperature set point of the load group as a control variable and taking the temperature of the external time-varying environment into account based on the clustered average parameters of the load aggregation groups, so as to describe the temperature evolution process of the load aggregation groups. Fig. 2 is a schematic diagram of the bilinear state space model.
Figure BDA0002992783040000062
Wherein the content of the first and second substances,
Figure BDA0002992783040000063
and representing the quantity of the single constant-temperature control load in the on or off state in different temperature intervals for the state variable of the load aggregation group. The output variable y (t) represents an outward aggregate power estimate for the load aggregation group. Control variable u TCL (t) by u TCL ＝(T base -T a (T))/CR given, where T a (T) and T base Respectively, the outside real-time environment temperature and the reference value thereof. N is a radical of hydrogen bin Is the number of state bins; set temperature for a given load cluster
Figure BDA0002992783040000064
And its control dead zone delta, the upper and lower temperature limits are respectively
Figure BDA0002992783040000065
And
Figure BDA0002992783040000066
if the temperature interval length is Delta T, then N bin Can be expressed as N bin ＝2(T max -T min )/ΔT。
Figure BDA0002992783040000067
Respectively, the state transition matrix, the input matrix and the output matrix are constant, and respectively satisfy the following conditions:
Figure BDA0002992783040000068
Figure BDA0002992783040000071
Figure BDA0002992783040000072
Figure BDA0002992783040000073
Figure BDA0002992783040000074
Figure BDA0002992783040000075
Figure BDA0002992783040000076
B＝A(-1,-1)
Figure BDA0002992783040000077
wherein the content of the first and second substances,
Figure BDA0002992783040000078
and
Figure BDA0002992783040000079
the average rates of temperature decrease and increase of the load are thermostatically controlled, respectively. A. The 11 、A 12 、A 21 And A 22 Are all N bin /2×N bin A matrix of dimension/2.
(3) Predicting the state and the aggregation power of each constant-temperature control load aggregation group at the moment k based on a bilinear state space model;
discretizing the continuous-time bilinear state space model in the step (2), and predicting the k-time state of each constant-temperature control load aggregation group by taking the k-1 time state as a reference based on the discrete-time bilinear state space model
Figure BDA0002992783040000081
And the polymerization power thereof. The discrete dual-linearity state space model is
Figure BDA0002992783040000082
Wherein k represents time, X k And y k Respectively, the state variable of the load aggregation group at the time k and the estimated value of the external aggregation power. h is the discretized time step. And I is an identity matrix. u. u TCL,k-1 The ambient temperature at time k, which is predicted at time k-1.
(4) And performing state estimation of each constant temperature control load aggregation group based on a singular value decomposition method and a weighted least square method with constraint to obtain a constant temperature control load state estimation value.
And performing state estimation based on a singular value decomposition method and a weighted least square method with constraint to obtain a constant temperature control load state estimation value. Due to the traffic limitation, the real-time measurement of each load aggregation group is that the active power and the reactive power P are aggregated to the outside TCL,meas And Q TCL,meas Meanwhile, the monomer load total balance relationship is used as a virtual measurement. The measurement equation for the dynamic state estimation filtering process for each load aggregation group is as follows:
Figure BDA0002992783040000083
wherein the content of the first and second substances,
Figure BDA0002992783040000084
measurement of quantities for the load aggregation groupAnd (4) a matrix.
Figure BDA0002992783040000085
Is a jacobian matrix of the measurement equation. N is a radical of hydrogen TCL The amount of the load is thermostatically controlled for the monomers contained in the load cluster. Generally, the thermostatic load is mostly a motor load, and in a normal operation condition, a terminal voltage thereof generally operates near a rated voltage thereof, and a power factor θ thereof TCL The variation is small. To simplify the calculation, it is assumed that the load aggregation group operates in a constant power factor manner, i.e., Q is satisfied r ＝P r θ TCL . From the measurement equation, H TCL Is less than the number of state variables N bin The state estimation problem is a solution problem of an underdetermined linear equation set. The method adopts a weighted least square method with constraint based on a singular value decomposition method to solve.
Solving a special solution X of the measurement equation based on a singular value decomposition method:
X * ＝SΣ -1 U T z TCL
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002992783040000091
is an orthogonal matrix;
Figure BDA0002992783040000092
is a semi-positive definite diagonal matrix with diagonal elements of H TCL R non-zero singular values. The general solution of the equation can be represented by
X＝X * +X null λ
Wherein the content of the first and second substances,
Figure BDA0002992783040000093
a free variable matrix is to be solved.
Figure BDA0002992783040000094
Is H TCL A set of bases of null space. Thus, solving for X k Will translate into a parameter estimation problem that solves λ.
Based on a weighted least square method with constraint, the predicted value of the state variable at the k moment obtained in the step (3)
Figure BDA0002992783040000095
For auxiliary measurement, the parameter lambda to be solved is taken as a state variable, the general solution expression is taken as a measurement equation, and the optimal value of the lambda is solved by solving the following quadratic programming problem.
Figure BDA0002992783040000096
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002992783040000097
measuring for auxiliary quantities
Figure BDA0002992783040000098
The measured covariance matrix. Substituting the optimal lambda value obtained by solving into the general solution expression to obtain the estimated value of the load aggregation group state
Figure BDA0002992783040000099
TABLE 1 average parameters of load aggregation groups after K-means clustering
Figure BDA00029927830400000910
The average parameters of 14 groups of load aggregation groups after being subjected to K-means clustering are given in the table 1 and are used for simulating and verifying the state estimation method. Taking node 42 as an example, the elbow method line graph for determining the clustering number K is shown in FIG. 3. The ambient temperature variation is shown in fig. 4. Selecting 11 parts of the material with violent change of the external environment temperature: 00-15: 00 simulation was performed, with an estimation every five minutes, and the load aggregation group measurement error was taken to be 5%.
Fig. 5 shows the estimation accuracy result of the dynamic state estimation of the thermostatic control load according to the present invention. Fig. 6 and fig. 7 are respectively a variation of the estimation accuracy of the dynamic state estimation under different measurement accuracy conditions, where fig. 6 is a variation of the mean root mean square error of the state variables, and fig. 7 is a variation of the relative error of the external aggregation power.
As can be seen from fig. 5, the method for estimating the dynamic state of the thermostatic control load according to the present invention can effectively use the limited measurement information to realize the state sensing of the underdetermined thermostatic control load system, reduce the influence caused by the measurement error, and obtain better estimation accuracy. As can be seen from fig. 6 and 7, although the estimation error will slightly increase with the increase of the measurement error, the method for estimating the dynamic state of the thermostatic control load according to the present invention can still effectively reduce the adverse effect of the measurement error and has better performance of error resistance. The relative error of the equivalent measurement is increased from 0 to 20 percent, the mean value of the root-mean-square error of the state variables can be kept near 13.5, and the relative error of the external polymerization power is increased from 0 to 2.5 percent.
Example two
In one or more embodiments, a dynamic state estimation system for a thermostatically controlled load system is disclosed, comprising:
the single constant-temperature control load clustering module is used for clustering the single constant-temperature control loads into K constant-temperature control load aggregation groups to obtain average parameters of the load aggregation groups, namely the average values of the thermal resistance, the thermal capacity, the rated thermal power, the thermoelectric conversion efficiency, the set temperature and the temperature control dead zone of the single constant-temperature control loads;
the constant-temperature control load aggregation group state prediction module is used for establishing a bilinear state space model for each constant-temperature control load aggregation group and predicting the state and aggregation power of each constant-temperature control load aggregation group at the moment k;
and the constant temperature control load state estimation module is used for performing state estimation on each constant temperature control load aggregation group based on a singular value decomposition method and a weighted least square method with constraint to obtain a constant temperature control load state estimation value.
It should be noted that specific implementation manners of the modules have been described in the first embodiment, and are not described herein again.
EXAMPLE III
In one or more embodiments, a terminal device is disclosed, which includes a server including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the dynamic state estimation method for a thermostatic control load system in the first embodiment when executing the computer program. For brevity, further description is omitted herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software.
The dynamic state estimation method for the thermostatic control load system in the first embodiment may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and combines hardware thereof to complete the steps of the method. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Example four
In one or more implementations, a computer-readable storage medium is disclosed having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform the method for dynamic state estimation for a thermostatically controlled load system described in example one.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (6)

1. A dynamic state estimation method for a constant temperature control load system is characterized by comprising the following steps:
clustering the monomer constant-temperature control load into K constant-temperature control load aggregation groups to obtain average parameters of the load aggregation groups;
establishing a bilinear state space model for each constant-temperature control load aggregation group, and predicting the state and aggregation power of each constant-temperature control load aggregation group at the moment k; based on the clustered average parameters of each load aggregation group, taking the quantity of the on-off state loads between different temperature segments as state variables, taking the temperature set point of the load group as a control variable, taking the temperature of the external time-varying environment into consideration, and establishing a double linear state space model of the load aggregation group; the dual-linearity state space model specifically comprises:
Figure FDA0003940938140000011
wherein X (t) is a state variable of the load aggregation group and is characterizedControlling the load quantity at the constant temperature of the single bodies in the on or off state in different temperature intervals; the output variable y (t) represents the estimated value of the outward aggregation power of the load aggregation group; control variable u TCL (t) from u TCL ＝(T base -T a (T))/CR given, where T a (T) and T base Respectively representing the external real-time environment temperature and a reference value thereof, and R and C respectively representing thermal resistance and thermal capacity; A. b and C are respectively a constant state transition matrix, an input matrix and an output matrix;
performing state estimation on each constant-temperature control load aggregation group based on a singular value decomposition method and a weighted least square method with constraints to obtain a constant-temperature control load state estimation value; the method for estimating the state of each constant-temperature control load aggregation group based on a singular value decomposition method and a weighted least square method with constraints specifically comprises the following steps:
constructing an underdetermined measurement equation for the dynamic state estimation filtering process of each load aggregation group; the under-quantification measurement equation is constructed on the basis of the quantity of the monomer constant-temperature control loads contained in the load aggregation group, and the external polymerization active power and reactive power of the load aggregation group;
solving the constructed under-quantitative measurement equation based on a singular value decomposition method and a weighted least square method with constraint to obtain a constant temperature control load state estimation value;
solving the constructed under-quantitative measurement equation based on a singular value decomposition method and a weighted least square method with constraint to obtain a constant temperature control load state estimation value, and specifically comprising the following steps of:
solving a special solution of the under-quantitative measurement equation based on a singular value decomposition method, and obtaining a general solution equation based on the special solution and a free variable matrix;
at the moment, converting the state estimation problem of solving the state variables of the load aggregation group at the moment k into a parameter estimation problem of solving a free variable matrix;
based on a weighted least square method with constraints, the predicted value of the state variable at the moment k is taken as an auxiliary quantity for measurement, the free variable matrix to be solved is taken as a state variable, the general solution equation is taken as a measurement equation, and the optimal value of the free variable matrix is solved by solving a quadratic programming problem;
and obtaining a load aggregation group state estimation value based on the optimal value of the free variable matrix.
2. The dynamic state estimation method for the thermostatic control load system according to claim 1, wherein a K-means clustering method is adopted to cluster the single thermostatic control load into K thermostatic control load aggregation groups.
3. The method for estimating the dynamic state of the thermostatically controlled load system as claimed in claim 1, characterized in that the continuous-time bilinear state space model is discretized, and the state of each thermostatically controlled load aggregation group at time k is predicted based on the discrete-time bilinear state space model from the state at time k-1
Figure FDA0003940938140000021
And the polymerization power thereof.
4. A dynamic state estimation system for a thermostatically controlled load system, comprising:
the single constant temperature control load clustering module is used for clustering the single constant temperature control loads into K constant temperature control load clustering groups to obtain average parameters of the load clustering groups;
the constant-temperature control load aggregation group state prediction module is used for establishing a bilinear state space model for each constant-temperature control load aggregation group and predicting the state and aggregation power of each constant-temperature control load aggregation group at the moment k; based on the clustered average parameters of each load aggregation group, taking the quantity of the on-off state loads between different temperature segments as state variables, taking the temperature set point of the load group as a control variable, taking the temperature of the external time-varying environment into consideration, and establishing a double linear state space model of the load aggregation group; the dual-linearity state space model specifically comprises:
Figure FDA0003940938140000031
wherein X (t) is a state variable of the load aggregation group and represents the quantity of the single constant-temperature control loads in the on or off states in different temperature intervals; the output variable y (t) represents the estimated value of the outward aggregation power of the load aggregation group; control variable u TCL (t) by u TCL ＝(T base -T a (T))/CR given, where T a (T) and T base Respectively representing the external real-time environment temperature and a reference value thereof, and R and C respectively representing thermal resistance and thermal capacity; A. b and C are respectively a constant state transition matrix, an input matrix and an output matrix;
the constant temperature control load state estimation module is used for carrying out state estimation on each constant temperature control load aggregation group based on a singular value decomposition method and a weighted least square method with constraint to obtain a constant temperature control load state estimation value; the method for estimating the state of each constant-temperature control load aggregation group based on the singular value decomposition method and the weighted least square method with the constraint specifically comprises the following steps:
constructing an underdetermined measurement equation for the dynamic state estimation filtering process of each load aggregation group; the under-quantification measurement equation is constructed on the basis of the quantity of the monomer constant-temperature control loads contained in the load aggregation group, and the external polymerization active power and reactive power of the load aggregation group;
solving the constructed under-quantitative measurement equation based on a singular value decomposition method and a weighted least square method with constraint to obtain a constant temperature control load state estimation value;
solving the constructed under-quantitative measurement equation based on a singular value decomposition method and a weighted least square method with constraint to obtain a constant temperature control load state estimation value, and specifically comprising the following steps of:
solving a special solution of the under-quantitative measurement equation based on a singular value decomposition method, and obtaining a general solution equation based on the special solution and a free variable matrix;
at the moment, converting the state estimation problem of solving the state variables of the load aggregation group at the moment k into a parameter estimation problem of solving a free variable matrix;
based on a weighted least square method with constraints, measuring by taking a predicted value of a state variable at the moment k as an auxiliary quantity, taking a free variable matrix to be solved as a state variable, taking the general solution equation as a measurement equation, and solving an optimal value of the free variable matrix by solving a quadratic programming problem;
and obtaining a load aggregation group state estimation value based on the optimal value of the free variable matrix.
5. A terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is configured to store a plurality of instructions adapted to be loaded by the processor and to perform the method for dynamic state estimation for a thermostatically controlled load system as claimed in any one of claims 1 to 3.
6. A computer-readable storage medium having stored therein a plurality of instructions, characterized in that the instructions are adapted to be loaded by a processor of a terminal device and to perform the method for dynamic state estimation for a thermostatically controlled load system as claimed in any one of claims 1 to 3.
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