A dynamic estimation method, system and model for MIL of heating network under quality regulation

Abstract

The present invention discloses a method, system, and model for dynamic estimation of the MIL (Minimum Information Loss) of a heating network under mass regulation. The method comprises the following steps: establishing a heating network thermal condition model; establishing a heating network thermal condition state space model based on the heating network thermal condition model; and establishing a heating network thermal condition minimum information loss state estimation model based on the heating network thermal condition state space model. This invention provides a theoretical foundation for existing research on MIL state estimation models.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy systems, and in particular relates to a method, system and model for dynamic estimation of MIL of a heating network under quality regulation. Background Art

[0002] Integrated energy systems, as a future development trend, can effectively contribute to the achievement of my country's "dual carbon" goals. State estimation, as the foundational core of energy management systems, is becoming increasingly important in this field. Whether it's a heat or gas grid, its dynamic processes can ultimately be transformed into a linear discrete system.

[0003] State estimation is the foundation and core of energy management systems. In the field of power systems, state estimation plays a fundamental role in the safe operation of power grids. Methods such as weighted least squares (WLS) and weighted least absolute value (WLAV) have been extensively studied and applied. State estimation research has also garnered significant attention in the field of integrated energy systems. The aforementioned models all assume steady-state operation of heating and natural gas networks and use a single cross-section for estimation.

[0004] However, real-world heating and gas grids involve dynamic regulation processes, and their dynamic characteristics cannot be ignored. Current literature assumes that measurement noise is independent Gaussian white noise, but lacks the applicable scenarios and theoretical basis of the used formats. As for the multi-snapshot weighted least absolute value (MWLAV) format, there are currently no relevant applications.

[0005] Information theory was first proposed by Shannon. After decades of development, the scientific measurement of information has gradually replaced the minimum mean square error criterion for power representation in the field of information technology as the general theoretical foundation for information processing. Against this backdrop, in the field of power systems, in-depth research has been conducted on the fundamental theoretical issues of static state estimation in power systems from an information science perspective. A minimum information loss (MIL) state estimation model has been established, which considers both digital and analog measurements. Theoretically, it has been proven that WLS is a special case of MIL state estimation, thus endowing traditional power system state estimation methods with informatics connotations.

[0006] In the field of integrated energy systems, static state estimation methods can be modeled after power system MIL state estimation, giving it informatics significance. However, for dynamic multi-section state estimation, existing MIL state estimation model research has yet to reveal its theoretical basis. Summary of the Invention

[0007] The object of the present invention is to provide a method, system and model for dynamic estimation of MIL of a heating network under quality regulation, so as to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: a method for dynamically estimating the MIL of a heating network under quality regulation, comprising the following steps:

[0009] Establish thermal operating model of heating network;

[0010] Establish a state space model of the thermal condition of the heating network based on the thermal condition model of the heating network;

[0011] Based on the state space model of the thermal conditions of the heating network, a state estimation model with minimum information loss of the thermal conditions of the heating network is established.

[0012] Preferably, the thermal network thermal condition model is:

[0013]

[0014] Where ρ is the density; C p is the specific heat capacity of hot water; S K is the cross-sectional area of ​​the pipe section; T K is the temperature of the hot water element; m K is the flow rate; t is the time coordinate; x is the space coordinate; T a is the ambient temperature.

[0015] Preferably, the thermal network thermal condition model is established by using a discrete difference method, dividing the pipeline into N sections, and establishing the following discrete model of the internal temperature of the pipeline.

[0016] Preferably, the discrete model of the internal temperature of the pipeline is as follows:

[0017]

[0018] Where R k is the thermal resistance per unit length of the pipe.

[0019] Preferably, the thermal network thermal condition model further includes a temperature hybrid model of each node, a head-end temperature model, a heat source power model, and a heat load temperature model, which are respectively expressed as follows:

[0020]

[0021] m i (t) = m j (t),i + =j + (0.11)

[0022] Φ s (t) = C p m s [T s,t (t)-T s,f (t)], (0.12)

[0023] Φ l (t) = C p m l [T l,f (t)-T l,t (t)] (0.13).

[0024] Preferably, the establishment of the state space model of the thermal working condition of the heating network includes: defining the state vector X(t) of the thermal working condition of the heating network,

[0025] The state vector X(t) is composed of the internal state variables of each pipeline, including the temperature vector considered after each pipeline segmentation Respectively expressed as:

[0026]

[0027]

[0028] Preferably, the establishment of the state space model of the thermal condition of the heating network further comprises: defining an input vector V(t) of the thermal condition of the heating network,

[0029] The input vector V(t) includes the heat output vector Φ of the heat source specified in the heating plan s (t), load

[0030]

[0031] Where, is a random variable about the planned value and the predicted value, and the ambient temperature T a (t) can be obtained in real time for a given variable.

[0032] Preferably, the establishment of the state space model of the thermal condition of the heating network further includes: defining a measurement vector Z(t) of the thermal condition of the heating network, wherein:

[0033] The measurement vector Z(t) includes the head end temperature vector T of each pipeline p,f (t), the terminal temperature vector T of each pipeline p,t(t), the inlet temperature vector T of the heat source s,f (t), the outlet temperature vector T of the heat source s,t (t), the inlet temperature vector T of the heat load l,f (t), outlet temperature vector T of heat load l,t (t), thermal power measurement vector Φ of the heat source s (t), thermal power measurement vector Φ of heat load l (t), outlet temperature vector T of heat load l,t (t), specifically expressed as follows:

[0034]

[0035] When the obtained measurement vector Z(t) is interfered by noise, the measurement noise vector is defined as η(t).

[0036] Preferably, the state space model of the thermal network thermal condition is expressed in the form of a state space equation:

[0037]

[0038] Among them, A, F, and C are the forms after the heat network model is rewritten as a matrix, which is a given constant matrix.

[0039] A dynamic estimation system for MIL of a heating network under quality regulation, based on the above-mentioned dynamic estimation method for MIL of a heating network under quality regulation, the dynamic estimation system includes

[0040] The first establishment module is used to establish a thermal network thermal condition model;

[0041] The second establishment module is used to establish a state space model of the thermal condition of the heating network based on the thermal condition model of the heating network;

[0042] The third establishment module is used to establish a minimum information loss state estimation model for the thermal conditions of the heating network based on the state space model of the thermal conditions of the heating network.

[0043] A heating network MIL dynamic estimation model under quality regulation, which is applied to the heating network MIL dynamic estimation method under quality regulation described above, and includes a heating network thermal operating condition state space model and a heating network thermal operating condition minimum information loss state estimation model.

[0044] Preferably, the state space model of the thermal network thermal condition is

[0045]

[0046] Among them, A, F, and C are the forms after the heat network model is rewritten as a matrix, which are given constant matrices.

[0047] Preferably, the minimum information loss state estimation model for the thermal network thermal conditions is

[0048]

[0049]

[0050] Among them, V consists of the input vector at all times, f V Its probability density function; X0 is the initial state vector, Its probability density function; W is composed of all measurement noise vectors, f W Its probability density function.

[0051] The technical effects and advantages of the present invention are as follows:

[0052] This paper reexamines and studies the fundamental theoretical issues of slow-dynamic system state estimation in integrated energy systems from an information science perspective and verifies this approach using a heating network as an example. First, a source-channel model for a linear discrete system is established, along with a MIL dynamic state estimation model. Then, based on various commonly used assumptions, a practical model is derived. It is theoretically demonstrated that common methods such as MWLS and MWLAV are special cases of the MIL dynamic state estimation model, imbuing it with informatics. Next, a heating network state space model and a MIL dynamic estimation model are established. Finally, a numerical example verifies the universality of the proposed theory, considering measurement noise probability distributions and noise correlations.

[0053] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 A flow chart of the steps of the thermal network MIL dynamic estimation method under quality regulation is shown. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] The present invention provides a method for dynamic estimation of MIL of a heat network under quality regulation, such as Figure 1 As shown, the thermal network MIL dynamic model includes the following steps:

[0058] Establish thermal operating model of heating network;

[0059] Establish a state space model of the thermal condition of the heating network based on the thermal condition model of the heating network;

[0060] Based on the state space model of the thermal conditions of the heating network, a state estimation model with minimum information loss of the thermal conditions of the heating network is established.

[0061] Furthermore, the thermal network thermal condition model is established by using a discrete difference method, dividing the pipeline into N sections, and establishing the following discrete model of the internal temperature of the pipeline.

[0062] Furthermore, the establishment of the thermal network thermal condition state space model includes the following steps:

[0063] Define the state vector X(t) of the thermal network thermal condition,

[0064] Define the input vector V(t) of the thermal network thermal condition,

[0065] When the obtained measurement vector Z(t) is interfered by noise, the measurement noise vector is defined as η(t).

[0066] It should be noted that, based on the minimum information loss (MIL) decision principle, the present invention proposes a new general MIL dynamic state estimation principle for linear discrete systems. The proposed model is verified using the dynamic state estimation problem under mass regulation of a centralized heating system as an example. In this embodiment, the hydraulic conditions in the pipe network are assumed to be known.

[0067] This paper further takes the heating network as an example to establish its state space model and minimum information loss estimation model, and verifies its universality in considering different measurement noise probability distributions, noise correlations, etc. The specific steps are as follows:

[0068] S1: Establishing a thermal network thermal condition model;

[0069] For the thermal dynamics in the pipeline, ignoring the heat conduction process between the infinitesimal elements, it can be expressed as a partial differential equation:

[0070]

[0071] Where, ρ is the density; Cp is the specific heat capacity of hot water; S is the cross-sectional area of ​​the pipe section; T is the temperature of the hot water element; m is the flow rate; t is the time coordinate; x is the space coordinate; R' is the thermal resistance per unit length of the pipeline; Ta is the ambient temperature.

[0072] Using the discrete difference method, the pipeline is divided into N sections, and the following discrete equation for the internal temperature of the pipeline can be established:

[0073]

[0074] In addition, the heat network model also includes the temperature mixing model of each node, the head end temperature model, the heat source power model and the heat load temperature model. Specifically,

[0075] The temperature mixing model is expressed as:

[0076] The head end temperature model is expressed as: m i (t) = m j (t),i + =j + (0.22)

[0077] The heat source power model is expressed as: Φ s (t) = C p m s [T s,t (t)-T s,f (t)], (0.23)

[0078] The heat load temperature model is expressed as: Φ l (t) = C p m l [T l,f (t)-T l,t (t)] (0.24)

[0079] S2: Establishing a state space model of the thermal condition of the heating network based on the thermal condition model of the heating network;

[0080] The establishment of the heat network thermal condition state space model comprises the following steps:

[0081] Define the state vector X(t) of the thermal network thermal condition,

[0082] Define the input vector V(t) of the thermal network thermal conditions.

[0083] Specifically, the state vector X(t) of the thermal network thermal condition is defined, which consists of the internal state variables of each pipeline, including the temperature vector considered after each pipeline segmentation Respectively expressed as:

[0084]

[0085]

[0086] Then, define the input vector V(t) of the thermal network thermal condition. This includes the heat output vector Φ of the heat source specified in the heating plan. s (t), load heat consumption power vector Φ l (t), and ambient temperature T a (t), is expressed as follows:

[0087]

[0088] During the operation of the heating network, the heat source output can use the planned value in the output plan as a reference, and the load consumption heat power can use the load forecast power as a reference. It is a random variable related to the planned value and the predicted value, while the ambient temperature Ta(t) can be obtained in real time and is a given variable.

[0089] During the operation of the heating network, the following steps are included:

[0090] Define the measurement vector Z(t) of the thermal network thermal conditions,

[0091] Define the measurement noise vector as η(t).

[0092] Specifically, the measurement vector Z(t) of the thermal network thermal condition is defined, including the head end temperature vector T of each pipeline p,f (t), the terminal temperature vector T of each pipeline p,t (t), the inlet temperature vector T of the heat source s,f (t), the outlet temperature vector T of the heat source s,t (t), the inlet temperature vector T of the heat load l,f (t), outlet temperature vector T of heat load l,t (t), thermal power measurement vector Φ of the heat source s (t), thermal power measurement vector Φ of heat load l (t), outlet temperature vector T of heat load l,t (t) is expressed as follows:

[0093]

[0094] The obtained measurement is interfered by noise, and the measurement noise vector is defined as η(t).

[0095] In summary, the heat network model can be rewritten in the form of the following state space equation:

[0096]

[0097] Among them, A, F, and C are the forms after the heating network model is rewritten as a matrix. Since the present invention considers the operating conditions of mass regulation, they are given constant matrices.

[0098] S3: Establish a state estimation model for the minimum information loss of the thermal conditions of the heating network based on the state space model of the thermal conditions of the heating network.

[0099] The minimum information loss state estimation model for the thermal network thermal conditions is established as follows:

[0100]

[0101]

[0102] Among them, V consists of the input vector at all times, f V Its probability density function; X0 is the initial state vector, Its probability density function; W is composed of all measurement noise vectors, f W Its probability density function.

[0103] The above-mentioned model 1.62 is a general model that does not limit the specific form of the relevant probability density function. At the same time, the model can be simplified under various assumptions to obtain a practical state estimation model.

[0104] The present invention further provides a heating network MIL dynamic estimation system under quality regulation, the dynamic estimation system is based on the above-mentioned heating network MIL dynamic estimation method under quality regulation, and the dynamic estimation system includes:

[0105] The first establishment module is used to establish a thermal network thermal condition model;

[0106] The second establishment module is used to establish a state space model of the thermal condition of the heating network based on the thermal condition model of the heating network;

[0107] The third establishment module is used to establish a minimum information loss state estimation model for the thermal conditions of the heating network based on the state space model of the thermal conditions of the heating network.

[0108] A heating network MIL dynamic estimation model under quality regulation, the model applies the heating network MIL dynamic estimation method under quality regulation described above, the heating network MIL dynamic estimation model includes a heating network thermal condition state space model and a heating network thermal condition minimum information loss state estimation model.

[0109] Specifically, the state space model of the thermal network thermal condition is:

[0110]

[0111] Among them, A, F, and C are the forms after the heat network model is rewritten as a matrix, which are given constant matrices.

[0112] Specifically, the minimum information loss state estimation model for the thermal network thermal conditions is:

[0113]

[0114]

[0115] Among them, V consists of the input vector at all times, f V Its probability density function; X0 is the initial state vector, Its probability density function; W is composed of all measurement noise vectors, f W Its probability density function.

[0116] The present invention verifies the minimum information loss state estimation model by taking the dynamic state estimation problem under quality regulation of a centralized heating system as an example, and provides a theoretical basis for the existing MIL state estimation model research considering dynamic multi-section state estimation.

[0117] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for dynamic estimation of MIL of a heating network under quality control, characterized by: The following steps are involved: Establish a thermal network thermal condition model; the thermal network thermal condition model is: Where, is the density; Cp is the specific heat capacity of hot water; SK is the cross-sectional area of ​​the pipe section; TK is the temperature of the hot water element; m K is the flow rate; t is the time coordinate; x is the space coordinate; T a is the ambient temperature; Establishing a heat network thermal condition state space model based on the heat network thermal condition model; said establishing the heat network thermal condition state space model comprises the following steps: Define the state vector of the thermal network thermal conditions , Define the input vector of the thermal network thermal conditions , When the obtained measurement vector In the presence of noisy interference, the measurement noise vector is defined as ; The state space model of the thermal network thermal condition is expressed in the form of a state space equation: in, 、 、 It is the form after rewriting the heat network model into a matrix, which is a given constant matrix; A state estimation model for the minimum information loss of the thermal conditions of the heating network is established based on the state space model of the thermal conditions of the heating network. The state estimation model for the minimum information loss of the thermal conditions of the heating network is expressed as follows: in, It consists of the input vector at all times, is its probability density function; is the initial state vector, is its probability density function; is composed of all measurement noise vectors, Its probability density function.

2. The method for dynamic estimation of MIL of a heating network under quality control according to claim 1, characterized in that: The thermal network thermal condition model is established by using a discrete difference method, dividing the pipeline into N sections, and establishing the following discrete model of the internal temperature of the pipeline.

3. The method for dynamic estimation of MIL of a heating network under quality control according to claim 2, characterized in that: The discrete model of the internal temperature of the pipeline is as follows: Where R k is the thermal resistance per unit length of the pipe.

4. The method for dynamic estimation of MIL of a heating network under quality control according to any one of claims 1 to 3, characterized in that: The thermal network thermal condition model also includes a temperature mixing model of each node, a head end temperature model, a heat source power model, and a heat load temperature model, which are respectively expressed as follows: ， ， 。 5. The method for dynamic estimation of MIL of a heating network under quality control according to claim 1, characterized in that: The establishment of the state space model of the thermal working condition of the heating network includes: defining the state vector of the thermal working condition of the heating network , The state vector It consists of the internal state variables of each pipeline, including the temperature vector considered after each pipeline segment , respectively expressed as: 。 6. The method for dynamic estimation of MIL of a heating network under quality control according to claim 5, characterized in that: The said establishing the state space model of the thermal condition of the heating network also includes: defining the input vector of the thermal condition of the heating network , The input vector Including the heat output vector of the heat source specified in the heating plan Thermal power consumption vector of load , and ambient temperature , respectively expressed as: Where, 、 is a random variable about the planned value and the predicted value, and the ambient temperature Can be obtained in real time for a given variable.

7. The method for dynamic estimation of MIL of a heating network under quality control according to claim 6, characterized in that: The said establishing the state space model of the thermal working condition of the heating network also includes: defining the measurement vector of the thermal working condition of the heating network ,in, The measurement vector Including the temperature vector of the head end of each pipe , the terminal temperature vector of each pipeline , the inlet temperature vector of the heat source , the outlet temperature vector of the heat source , the inlet temperature vector of the heat load , outlet temperature vector of heat load , the heat power measurement vector of the heat source , thermal power measurement vector of heat load , outlet temperature vector of heat load , specifically expressed as follows: When the obtained measurement vector In the presence of noisy interference, the measurement noise vector is defined as .

8. A heat network MIL dynamic estimation system under quality regulation, characterized by: Based on the method for dynamic estimation of MIL of a heating network under quality control according to any one of claims 5 to 7, the dynamic estimation system includes: The first establishment module is used to establish a thermal network thermal condition model; The second establishment module is used to establish a heat network thermal condition state space model based on the heat network thermal condition model; The third establishment module is used to establish a minimum information loss state estimation model for the thermal conditions of the heating network based on the state space model of the thermal conditions of the heating network.

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