Dynamic state estimation method for minimum information loss of heat supply network and gas network of multi-energy system

Abstract

The invention provides a dynamic state estimation method for minimum information loss of a heat network and a gas network of a multi-energy system, and the method comprises the steps: building all subsystems of a comprehensive energy system and a multi-energy system coupling equipment model based on different physical characteristics and different time scales of an electric system, a heat system and a gas system; establishing a heat supply network state estimation model and a pseudo-dynamic state estimation model based on each subsystem and the multi-energy system coupling equipment model; substituting the parameters of the pre-estimated moment into a heat supply network state estimation model to obtain a heat supply network state estimation value; and substituting the parameters of the pre-estimated moment into the quasi-dynamic state estimation model, and solving the quasi-dynamic state estimation model by adopting an alternating direction multiplier method to obtain a gas network state estimation value. According to the method, a nonlinear problem in state estimation is converted into a linear problem, the accuracy of state estimation is greatly improved, the contradiction between the calculation complexity and the solution accuracy in the dynamic calculation of the multi-energy system network is well balanced, the numerical stability is good, and the calculation efficiency is high.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-energy system state estimation, and in particular to a method for dynamic state estimation of a multi-energy system heat network and a gas network with minimum information loss. Background Art

[0002] In order to reduce environmental pollution, improve energy efficiency, and achieve dual carbon goals, the integrated energy system has become an important direction for adjusting the energy structure. The integrated energy system has great advantages in improving energy efficiency, promoting new energy consumption, and reducing user energy consumption costs through the deep coupling of multiple energy forms such as electricity, heat, cold, and gas. It is the development trend of the future energy system. In order to ensure the safe, economical, green and efficient operation of the integrated energy system, it is necessary to establish a multi-energy flow integrated energy management system to realize the intelligent regulation of its energy.

[0003] State estimation is the foundation and core of energy management systems. For power systems, state estimation plays a key role in their safe operation. The research on power grid state estimation is already very mature, and steady-state power flow models are generally used for modeling. However, for the dynamic state estimation of multi-energy systems, although the existing technology can represent the conversion and coupling relationship between multiple energies, the established model is a highly abstract simplified model that cannot accurately characterize the complex characteristics of the conversion and coupling between multiple energies. At the same time, the existing research on multi-energy system state estimation needs to be further improved in terms of numerical stability, robustness, estimation accuracy and computational efficiency. Summary of the invention

[0004] In order to solve the problem that the existing technology cannot accurately characterize the complex characteristics of conversion and coupling between multiple energies, and the state estimation of multi-energy systems still cannot meet the requirements in terms of numerical stability, robustness, estimation accuracy and computational efficiency, the present invention proposes a method for dynamic state estimation of a multi-energy system heat network and gas network with minimum information loss, including:

[0005] Based on the different physical characteristics and time scales of electricity, heat and gas systems, establish models of subsystems of the integrated energy system and coupled equipment of multi-energy systems;

[0006] Based on the models of each subsystem and multi-energy system coupling equipment, a dynamic state estimation model of the heat network and a pseudo-dynamic model of the gas network are established;

[0007] Substitute the parameters at the estimated time into the heating network state estimation model to obtain the heating network state estimation value;

[0008] The parameters at the estimated time are substituted into the quasi-dynamic state estimation model, and the quasi-dynamic state estimation model is solved by using the alternating direction multiplier method to obtain the gas network state estimation value.

[0009] Optionally, the establishment of a heat network dynamic state estimation model and a gas network pseudo-dynamic model based on each subsystem and multi-energy system coupling device model includes:

[0010] Establish a multi-energy system coupling equipment model based on the different physical characteristics and time scales of electricity, heat and gas systems;

[0011] The multi-energy system coupling device model is used to describe the bidirectional boundary conditions and bidirectional constraint conditions of the electric, thermal and gas multi-energy system;

[0012] Construct the objective function with the goal of minimizing information loss;

[0013] Constructing a heating network state estimation model based on the objective function, the bidirectional boundary conditions and the bidirectional constraint conditions;

[0014] The residual objective function is constructed with the goal of minimizing the measurement residual of each subsystem;

[0015] A heating network state estimation model is constructed by the residual objective function, bidirectional boundary conditions and bidirectional constraint conditions.

[0016] Optionally, the objective function is as shown below:

[0017]

[0018] In the formula, I loss (ν * ,Z) is the final information loss, ν * is the final prediction result, Z is the prediction result vector of various single prediction algorithms at this prediction point, I loss (ν,Z) is the amount of information loss in the decision-making process, and ν is the measurement error vector.

[0019] Optionally, the subsystems of the integrated energy system and the multi-energy system coupling equipment models are established based on the different physical characteristics and different time scales of the electricity, heat and gas systems, including:

[0020] Based on the multi-time scale characteristics of the integrated energy system, the network information flow of the multi-energy system is analyzed, and the data model of each subsystem of the integrated energy system is constructed;

[0021] Associate the data models of each subsystem to build a multi-energy system coupling equipment model;

[0022] The data models of the subsystems include: an electrical system mathematical model, a natural gas system mathematical model and a heating system mathematical model.

[0023] Optionally, the electrical system mathematical model is as shown below:

[0024]

[0025] In the formula, x e is the state of the electrical system, y e is an algebraic variable, F e is the dynamic equation of the power system, G e is the algebraic constraint equation, is the state change of the electrical system.

[0026] Optionally, the natural gas system mathematical model is as follows:

[0027]

[0028] Where ρ, p and ω represent gas density, pressure and velocity respectively; λ, D, g and θ G They represent pipeline friction coefficient, pipeline diameter, gravity acceleration and pipeline inclination angle respectively; t is unit time, and x is unit length of the pipeline network.

[0029] Optionally, the mathematical model of the heating system is as follows:

[0030]

[0031] In the formula, and are respectively the gas consumption and power generation of gas turbine i; α GT , β GT and γ GT are the corresponding gas consumption calculation coefficients respectively; Represents the set of gas turbine numbers in the integrated energy system, and i is the gas turbine number in the integrated energy system.

[0032] Optionally, substituting the parameters at the estimated time into a heating network state estimation model to obtain a heating network state estimation value includes:

[0033] A variety of dynamic state estimation algorithms are used to respectively predict and estimate the state quantities of the multi-energy system at the estimation time, and the state quantity estimation results at the estimation time are obtained;

[0034] Select multiple reference moments in history, use multiple prediction algorithms to perform virtual predictions on the dynamic states of the multi-energy system at the multiple reference moments, and obtain virtual prediction values;

[0035] Estimate the probability density function of the channel and the information source according to the virtual prediction value and the actual state quantity at multiple reference moments in history;

[0036] According to the probability density function of the channel and the information source, solving the objective function in the heating network state estimation model to obtain the minimum value of information loss;

[0037] The heating network state value corresponding to the minimum value of the information loss is used as the heating network state estimation value.

[0038] Optionally, the adopting of the alternating direction multiplier method to solve the quasi-dynamic state estimation model to obtain the gas network state estimation value includes:

[0039] The bidirectional boundary conditions and bidirectional constraints in the quasi-dynamic state estimation model are relaxed to establish the augmented Lagrangian formula.

[0040] The augmented Lagrangian formula is solved alternately using a cyclic algorithm to obtain the dynamic state estimation value of the minimum information loss of the heat network and gas network of the multi-energy system.

[0041] Optionally, the method of using a cyclic algorithm to alternately solve the augmented Lagrangian formula to obtain the dynamic state estimation value of the minimum information loss of the heat network and the gas network of the multi-energy system includes:

[0042] S31 regards z and λ as constants and calculates the value of x when the augmented Lagrangian equation is minimized;

[0043] S32 performs data exchange at the boundary to update the value of z;

[0044] S33 updates the Lagrangian multiplier λ in the augmented Lagrangian equation based on the updated values ​​of x and z;

[0045] S34 determines whether it converges, and ends if it converges, otherwise returns to S31.

[0046] On the other hand, the present application also provides a multi-energy system heat network, gas network minimum information loss dynamic state estimation system, characterized in that it includes:

[0047] The equipment model building module is used to build equipment models for the subsystems of the integrated energy system and the coupling of multiple energy systems based on the different physical characteristics and time scales of the electricity, heat and gas systems;

[0048] A computational model building module is used to establish a dynamic state estimation model and a quasi-dynamic state estimation model for the heating network based on the models of each subsystem and multi-energy system coupling equipment;

[0049] A heating network state estimation module, used for substituting the parameters at the estimated time into a heating network state estimation model to obtain a heating network state estimation value;

[0050] The gas network state estimation module is used to substitute the parameters of the estimated time into the quasi-dynamic state estimation model, and solve the quasi-dynamic state estimation model by using the alternating direction multiplier method to obtain the gas network state estimation value.

[0051] Optionally, the device model building module is specifically used to:

[0052] Based on the multi-time scale characteristics of the integrated energy system, the network information flow of the multi-energy system is analyzed, and the data model of each subsystem of the integrated energy system is constructed;

[0053] Associate the data models of each subsystem to build a multi-energy system coupling equipment model;

[0054] The data models of the subsystems include: an electrical system mathematical model, a natural gas system mathematical model and a heating system mathematical model.

[0055] Optionally, the electrical system mathematical model is as shown below:

[0056]

[0057] In the formula, x e is the state of the electrical system, y e is an algebraic variable, F e is the dynamic equation of the power system, G e is the algebraic constraint equation, is the state change of the electrical system.

[0058] Optionally, the natural gas system mathematical model is as follows:

[0059]

[0060] Where ρ, p and ω represent gas density, pressure and velocity respectively; λ, D, g and θ G They represent pipeline friction coefficient, pipeline diameter, gravity acceleration and pipeline inclination angle respectively; t is unit time, and x is unit length of the pipeline network.

[0061] Optionally, the mathematical model of the heating system is as follows:

[0062]

[0063] In the formula, and are respectively the gas consumption and power generation of gas turbine i; α GT , β GT and γ GT are the corresponding gas consumption calculation coefficients respectively; Represents the set of gas turbine numbers in the integrated energy system, and i is the gas turbine number in the integrated energy system.

[0064] Optionally, the computational model building module is specifically used for:

[0065] Establish a multi-energy system coupling equipment model based on the different physical characteristics and time scales of electricity, heat and gas systems;

[0066] The multi-energy system coupling device model is used to describe the bidirectional boundary conditions and bidirectional constraint conditions of the electric, thermal and gas multi-energy system;

[0067] Construct the objective function with the goal of minimizing information loss;

[0068] Constructing a heating network state estimation model based on the objective function, the bidirectional boundary conditions and the bidirectional constraint conditions;

[0069] The residual objective function is constructed with the goal of minimizing the measurement residual of each subsystem;

[0070] A heating network state estimation model is constructed by the residual objective function, bidirectional boundary conditions and bidirectional constraint conditions.

[0071] Optionally, the objective function is as shown below:

[0072]

[0073] In the formula, I loss (ν * ,Z) is the final information loss, ν * is the final prediction result, Z is the prediction result vector of various single prediction algorithms at this prediction point, I loss (ν,Z) is the amount of information loss in the decision-making process, and ν is the measurement error vector.

[0074] Optionally, the residual objective function is as follows:

[0075]

[0076] In the formula, J EPS , J NGN , J DHN Represents the weighted sum of the measurement residuals of the power grid, heat grid, and gas grid systems, respectively, and x EPS represents the decision variable in the grid state estimation, x NGN represents the decision variable in the natural gas system state estimation, x DHN Represents the decision variables in the state estimation of the heating system.

[0077] Optionally, the heating network state estimation module is specifically used for:

[0078] A variety of dynamic state estimation algorithms are used to respectively predict and estimate the state quantities of the multi-energy system at the estimation time, and the state quantity estimation results at the estimation time are obtained;

[0079] Select multiple reference moments in history, use multiple prediction algorithms to perform virtual predictions on the dynamic states of the multi-energy system at the multiple reference moments, and obtain virtual prediction values;

[0080] Estimate the probability density function of the channel and the information source according to the virtual prediction value and the actual state quantity at multiple reference moments in history;

[0081] According to the probability density function of the channel and the information source, solving the objective function in the heating network state estimation model to obtain the minimum value of information loss;

[0082] The heating network state value corresponding to the minimum value of the information loss is used as the heating network state estimation value.

[0083] Optionally, the gas network state estimation module is specifically used for:

[0084] Substituting the parameters of the estimated time into a pre-built quasi-dynamic state estimation model, relaxing the bidirectional boundary conditions and bidirectional constraint conditions in the quasi-dynamic state estimation model, and establishing an augmented Lagrangian formula;

[0085] The augmented Lagrangian formula is solved alternately using a cyclic algorithm to obtain the dynamic state estimation value of the minimum information loss of the heat network and gas network of the multi-energy system.

[0086] In another aspect, the present application further provides a computing device, comprising: at least one processor and a memory;

[0087] The memory is used to store one or more programs;

[0088] When the one or more programs are executed by the at least one processor, the above-mentioned method for dynamic state estimation of the minimum information loss of the heat network and gas network of a multi-energy system is implemented.

[0089] On the other hand, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements a method for dynamic state estimation of a multi-energy system heat network and gas network with minimum information loss as described above.

[0090] Compared with the prior art, the present invention has the following beneficial effects:

[0091] The present invention provides a method for dynamic state estimation of a multi-energy system heat network and gas network with minimum information loss, including: establishing each subsystem of the comprehensive energy system and a multi-energy system coupling device model based on different physical characteristics and different time scales of the electric, thermal and gas systems; establishing a heat network state estimation model and a quasi-dynamic state estimation model based on each subsystem and the multi-energy system coupling device model; substituting the parameters at the estimated time into the heat network state estimation model to obtain a heat network state estimation value; substituting the parameters at the estimated time into the quasi-dynamic state estimation model, solving the quasi-dynamic state estimation model using the alternating direction multiplier method, and obtaining a gas network state estimation value. The present invention converts nonlinear problems in state estimation into linear problems, greatly improves the accuracy of state estimation, and better balances the contradiction between computational complexity and solution accuracy in the dynamic calculation of the multi-energy system network, and has good numerical stability and error resistance, and high computational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a flow chart of a method for estimating the minimum information loss of a multi-energy system heat network and gas network dynamic state of the present invention;

[0093] Figure 2 It is a schematic diagram of the flow of the heating network state estimation method of the present invention;

[0094] Figure 3 It is a diagram for realizing the algorithm of the heat network state estimation method of the present invention;

[0095] Figure 4 It is a flow chart of the gas network state estimation method of the present invention;

[0096] Figure 5 The figure is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0097] The present invention proposes a method for dynamic state estimation of the heat network and gas network of a multi-energy system with minimum information loss, which refines the energy interaction and dynamic process of the coupling elements of the multi-energy system, establishes unified two-way boundary conditions and two-way constraints from both the system under study and other systems associated with it, and avoids the one-way effect of the connected system on the system under study. At the same time, the nonlinear problem in the network model of the multi-energy system is converted into a linear problem, and efficient distributed computing is realized based on the asynchronous computing framework, which can improve the computing efficiency and better balance the contradiction between the computational complexity and the solution accuracy in the dynamic computing of the multi-energy system network.

[0098] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0099] Embodiment 1:

[0100] A dynamic state estimation method for the minimum information loss of the heat and gas networks of a multi-energy system, such as Figure 1 As shown, including:

[0101] Step 1: Based on the different physical characteristics and time scales of electricity, heat and gas systems, establish the subsystems of the integrated energy system and the coupled equipment models of the multi-energy system;

[0102] Step 2: Establish a dynamic state estimation model and a quasi-dynamic state estimation model for the heating network based on the models of each subsystem and multi-energy system coupling equipment;

[0103] Step 3: Substitute the parameters at the estimated time into the heating network state estimation model to obtain the heating network state estimation value;

[0104] Step 4: Substitute the parameters at the estimated time into the quasi-dynamic state estimation model, and use the alternating direction multiplier method to solve the quasi-dynamic state estimation model to obtain the gas network state estimation value.

[0105] The following is a description of each step of the present invention.

[0106] Step 1: Based on the different physical characteristics and time scales of electricity, heat and gas systems, establish the subsystems of the integrated energy system and the multi-energy system coupling equipment model, including:

[0107] Based on the multi-time scale characteristics of the integrated energy system, the network information flow of the multi-energy system is analyzed, and the data model of each subsystem of the integrated energy system is constructed;

[0108] Associate the data models of each subsystem to build a multi-energy system coupling equipment model;

[0109] The data models of the subsystems include: an electrical system mathematical model, a natural gas system mathematical model and a heating system mathematical model.

[0110] Furthermore, the electrical system mathematical model is shown in the following formula:

[0111]

[0112] In the formula, x e is the state of the electrical system, y e is an algebraic variable, F e is the dynamic equation of the power system, G e is the algebraic constraint equation, is the state change of the electrical system.

[0113] Furthermore, the natural gas system mathematical model is shown as follows:

[0114]

[0115] Where ρ, p and ω represent gas density, pressure and velocity respectively; λ, D, g and θ G They represent pipeline friction coefficient, pipeline diameter, gravity acceleration and pipeline inclination angle respectively; t is unit time, and x is unit length of the pipeline network.

[0116] Furthermore, the mathematical model of the heating system is shown in the following formula:

[0117]

[0118] In the formula, and are respectively the gas consumption and power generation of gas turbine i; α GT , β GT and γ GT are the corresponding gas consumption calculation coefficients respectively; Represents the set of gas turbine numbers in the integrated energy system, and i is the gas turbine number in the integrated energy system.

[0119] Step 1 specifically includes:

[0120] S1: Establish mathematical models of each subsystem of the integrated energy system and the corresponding coupling model;

[0121] S101: Based on the multi-time scale characteristics of the integrated energy system, the network information flow of the multi-energy system is analyzed, and the power network, thermal network and natural gas network are refined and modeled, and the network state variables and quantity measurements of the multi-energy system subsystems are clarified. The power system state variables include the dynamic variables of the generator and the complex phase of the node voltage. The quantity measurements are the dynamic variables of the generator, the node voltage amplitude and phase angle, the node injection power (including active and reactive power), and the branch power; the thermal system state variables include the node pressure, the node heating temperature and the node reheating temperature. The quantity measurements are the node pressure, the node injection flow, the node heating temperature, the node reheating temperature, the branch flow and the node injection heat power; the natural gas system state quantity is the node pressure, and the quantity measurements are the node pressure, the node load flow and the branch flow.

[0122] S102: Without considering the coupling equipment, centralized estimation is mainly used to establish mathematical models of the electrical system, the natural gas system and the heating system respectively.

[0123] 1) Electrical system mathematical model: The micro gas turbine can be regarded as an independent component in the electrical system, and its dynamic process can be considered uniformly in the electrical system model. Therefore, if the state of the electrical system is x e , the algebraic variable is y e , then the dynamic process of the electrical system with a micro gas turbine can be described by the following differential algebraic equation:

[0124]

[0125] In the formula, the dynamic equation and algebraic constraint equation of the power system are respectively represented by F e and G e Given.

[0126] 2) Mathematical model of natural gas system: Natural gas system usually consists of gas source, gas load, and gas transmission and distribution network, among which the gas transmission and distribution network includes natural gas pipelines and non-pipe components (compressors, pressure regulating valves, etc.). The dynamic mathematical model of gas in the pipeline can be represented by a group of partial differential equations. The partial differential equations of the pipeline can be represented by the continuity equation and momentum equation, which are written as:

[0127]

[0128] Where ρ, p and ω represent gas density, pressure and velocity respectively; λ, D, g and θ G They represent the pipeline friction coefficient, pipeline diameter, gravity acceleration and pipeline inclination angle respectively.

[0129] 3) Mathematical model of heating system: For the electrical coupling network, the gas turbine is both a natural gas load and an electric power generation equipment, and its gas consumption calculation formula is:

[0130]

[0131] In the formula, and are respectively the gas consumption and power generation of gas turbine i; α GT , β GT and γ GT are the corresponding gas consumption calculation coefficients respectively; Represents a collection of gas turbine numbers in an integrated energy system.

[0132] S103: Establish a multi-energy system coupling device model to associate multiple types of heterogeneous energy flow models in different subsystems of the multi-energy system. The coupling model includes: electrical system mathematical model, electric heating system model, and joint model of electrical energy and thermal energy:

[0133] 1) Electric heating system model: Cogeneration is the most important coupling element in the combined heat and power network. As an energy source, cogeneration can generate electricity and heat at the same time. According to the characteristics of cogeneration itself, its heating and power generation are related to each other, and a circulating pump is needed to drive the flow of liquid in the heat network pipeline. The circulating pump consumes electricity from the power grid and generates a pressure difference between the nodes in the water network, thereby promoting the circulation of liquid in the water network. The electricity required for the circulating pump to drive the liquid circulation is:

[0134]

[0135] Where P p is the power consumption of the circulation pump, in MW; m p Refers to the amount of water pumped by the circulating pump, in kg / s; η p is the circulation pump efficiency, unit %; g is the acceleration due to gravity; H p is the pump pressure head, in m.

[0136] 2) Joint model of electrical energy and thermal energy: In summary, for the combined thermal power network, its power flow equation can be written as:

[0137]

[0138] In the formula, h h is the actual value of the heating network; f h is the flow equation of the heating network; h e is the actual value of the power grid; f e is the power flow equation of the power grid; h gas is the actual value of the natural gas pipeline network; f gas is the power flow equation of the natural gas grid; g(h i ,T si ,T ri ,U j ,θ j ) is the coupling unit constraint condition; U j and θ j is the voltage amplitude and phase angle of the corresponding state quantity in the power grid; h i is the current load node heat value; T si is the temperature before the hot water load node is injected; T ri It is the temperature of the hot water flowing out of the load node after mixing with the water in other pipes at the pipe joint; is the upstream pressure of the natural gas pipeline; is the downstream pressure of the natural gas pipeline.

[0139] Table 1 Comparative analysis of state estimation between multi-energy system and other systems

[0140]

[0141] The present invention dynamically estimates the operating status of the heat network and gas network of the comprehensive energy system, introduces a discretized natural gas pipeline equation to describe its transient change process, establishes a partial differential equation of the heat network to describe the change process of the heat network, and realizes real-time tracking of the system operating status.

[0142] Step 2: Based on the subsystems and multi-energy system coupling equipment models, a dynamic state estimation model and a quasi-dynamic state estimation model for the heating network are established, including:

[0143] Establish a multi-energy system coupling equipment model based on the different physical characteristics and time scales of electricity, heat and gas systems;

[0144] The multi-energy system coupling device model is used to describe the bidirectional boundary conditions and bidirectional constraint conditions of the electric, thermal and gas multi-energy system;

[0145] Construct the objective function with the goal of minimizing information loss;

[0146] Constructing a heating network state estimation model based on the objective function, the bidirectional boundary conditions and the bidirectional constraint conditions;

[0147] The residual objective function is constructed with the goal of minimizing the measurement residual of each subsystem;

[0148] A heating network state estimation model is constructed by the residual objective function, bidirectional boundary conditions and bidirectional constraint conditions.

[0149] Furthermore, the objective function is shown in the following formula:

[0150]

[0151] In the formula, I loss (ν * ,Z) is the final information loss, ν * is the final prediction result, Z is the prediction result vector of various single prediction algorithms at this prediction point, I loss (ν,Z) is the amount of information loss in the decision-making process, and ν is the measurement error vector.

[0152] Furthermore, the residual objective function is as follows:

[0153]

[0154] In the formula, J EPS , J NGN , J DHN Represents the weighted sum of the measurement residuals of the power grid, heat grid, and gas grid systems, respectively, and x EPS represents the decision variable in the grid state estimation, x NGN represents the decision variable in the natural gas system state estimation, x DHN Represents the decision variables in the state estimation of the heating system.

[0155] Step 2 specifically includes:

[0156] S201: Establish a measurement model for the electric-heat interconnected integrated energy system:

[0157] Common measurement types in heating systems include pipeline mass flow and pipeline temperature, and real-time thermal power measurement is also configured at key nodes. Ordinary heat load nodes generally lack real-time thermal power measurement, or only have the total thermal energy consumption of the network within a day. The measurement of ordinary heating network system can be expressed as:

[0158]

[0159] In the formula, x e 、h e (x e ) are the state variables and measurement equations in the power grid, respectively. h 、h h (x h ) are the state variables and measurement equations in the heating network respectively; e and z h are the quantity measurements in the power grid and the heat network respectively; ε e and ε h are the error vectors of power grid and heat network measurements respectively.

[0160] S202: Select reasonable state variables to fully describe the state of the system. There are three ways to represent state variables in the heating network, namely:

[0161] 1)[m,T s ,T r ]: Its advantages are intuitive expression, simple calculation and high accuracy, but its overall description of the heating network model is poor.

[0162] 2)[h f ,T s ,T r ]: The calculation is simple, but the accuracy is low.

[0163] 3)[H n ,T n ,T bt ]: The overall description of the heating network model is good, but the measurement redundancy is low.

[0164] Among them, T s is the water supply temperature vector; T r H is the return water temperature vector after node mixing; n and T n are the pressure and temperature vectors of node n respectively; T bt is the temperature vector at the end of each branch, that is, the mixed water temperature before the node; m is the pipeline flow vector; h f is the head loss vector in the pipe.

[0165] Taking into account the large time constant of the heating network, when the operating conditions change, the system reaches a new steady-state operating state slowly, so the third representation method is selected.

[0166] S203: Considering the influence of source information loss, a heating network state estimation model with minimum information loss of a linear discrete system is established. This model is a general model. In actual use, the formula can be simplified by combining the properties of linear discrete systems and some common assumptions about measurement noise and external interference.

[0167] S204: Establishing the gas network measurement equation and covariance matrix. The measurement is usually the sum of the measurement function and the measurement noise. The corresponding measurement equation is:

[0168] z=h(x)+v (8)

[0169] Wherein, z is the measurement vector; x is the state variable vector; v is the measurement error vector; h(x) is the measurement function vector, which can be obtained through the existing mathematical model of the system.

[0170] It is usually assumed that the measurement residual v = {v1,v2,…,v n} are independent random variables, v n is the nth measurement error vector and has a normal distribution V i (0,σ i ), expected E(v i ) is 0, the variance σ 2 i The covariance matrix R of the measurement error can be written as:

[0171]

[0172] In the formula, E(V T V) is the covariance matrix of the measurement error; V is the measurement error vector; σ 2 i is the i-th variance; is the nth variance;

[0173] For the integrated energy system, each term in equation (16) comes from the power grid, gas grid, heat grid and coupling elements. The measurements include: node pressure in the gas grid, pipeline flow, gas load, etc. Due to the popularity of various measurement devices, the measurement range of gas grid and heat grid is wide and multi-objective. In addition to obtaining the power angle of the power system, the main purpose of state estimation is to eliminate the random error of system measurement, rather than obtaining the estimated value of the system operation state based on incomplete measurement.

[0174] S205: Establish a quasi-dynamic state estimation model. The integrated energy system consists of a power system, a natural gas system, a heating system, and coupling elements. Corresponding to each subsystem in the network (grid, gas network, and heating network), the optimization problem can be written as:

[0175]

[0176] stg EPS (x EPS )≤0 (11)

[0177] g NGN (x NGN )≤0 (12)

[0178] g DHN (x DHN )≤0 (13)

[0179] A EPS x EPS +B EPS z+C EPS =0 (14)

[0180] A NGN x NGN +B NGN z+C NGN =0 (15)

[0181] A DHN x DHN +B DHN z+C DHN =0 (16)

[0182] In the formula, x EPS represents the decision variables in the grid state estimation, including voltage amplitude and phase angle, active / reactive node power injection, active / reactive line power transmission and natural gas generator power generation; x NGN represents the decision variables in the natural gas system state estimation, including node gas pressure, pipeline natural gas flow, natural gas flow at the load, and compressor power consumption; x DHN represents the decision variables in the state estimation of the heating system, including the node water pressure, water pump power consumption, pipeline hot water flow, heating supply / heat consumption of each heat source / heat load, and water temperature of each node; where J EPS , J NGN , J DHN Represents the weighted sum of measurement residuals of power grid, heat grid and gas grid systems; g EPS , g NGN , g DHN Respectively represent the state variables in the system network; A EPS , B EPS , C EPS A is the power grid measurement optimization coefficient; NGN , B NGN , C NGN A is the heat network measurement optimization coefficient; DHN , B DHN , C DHNIt is the gas network measurement optimization coefficient.

[0183] Step 3: Substitute the parameters at the estimated time into the heating network state estimation model to obtain the heating network state estimation value, such as Figure 2 and Figure 3 As shown, specifically including:

[0184] S2: Based on the dynamic characteristics of the heating network temperature, a dynamic state estimation algorithm for the heating network of the integrated energy system is proposed.

[0185] The S2 comprises the following steps:

[0186] The algorithm implementation is given as follows:

[0187] For the multi-energy system quantity measurements and state quantities at different time points in a day, the minimum information loss heating network state estimation model analyzes and processes the quantity measurements and state quantities at each time point separately. Taking the state quantity at a certain point to be predicted as an example, the steps are as follows:

[0188] 1) Single algorithm estimation: First, use M kinds of dynamic state estimation algorithms to estimate the state quantity of the multi-energy system at a certain time on the prediction day, and obtain their respective results, Z = [z1 z2 … z M ], where z m It is the prediction result of the mth algorithm at this time point on the prediction day. The range of m is (1,M), and m represents any number in (1,2…M).

[0189] 2) Historical virtual prediction: Select N reference moments in history, use M prediction algorithms to perform virtual predictions on the dynamic states of the multi-energy system at these N reference moments, and obtain the results

[0190]

[0191] where y nm It represents the virtual prediction value of the mth prediction method at the nth historical moment, m represents any number in (1,2…M), n represents any number in (1,2…N). The real value of the system state at N reference moments is Y N =[y1y2…y N ],y N is the true value of the system state at the Nth reference moment.

[0192] 3) Probability density estimation: Based on the results of historical virtual predictions and the actual state quantity at the historical reference time, estimate the probability density function of the channel and the source: F C (Zv) and f s (v).

[0193] 4) Solve the optimization problem: Based on the estimated channel and source probability density functions, solve the optimization problem with the goal of minimizing information loss. Get to make of ν * , as the final comprehensive model prediction result.

[0194] The measurement equation cannot fully describe the coupling constraint relationship of the variables in the system, so it is necessary to supplement the constraint equation to improve the model accuracy. The constraint equation is as follows:

[0195] 1) Zero injection node active power constraint:

[0196] P i =real{V(YV) *}=0 (18)

[0197] Where, Y is the grid node admittance matrix;

[0198] 2) Zero injection node reactive power constraint:

[0199] Q i =imag{V(YV) *}=0 (19)

[0200] 3) Constraints of water supply pipe temperature drop equation:

[0201] A s T s ′-b s =0 (20)

[0202] 4) Return pipe temperature drop equation constraints:

[0203] A r T r ′-b r =0 (21)

[0204] 5) Loop pressure drop equation constraints:

[0205] BKm|m|=0 (22)

[0206] Where m is the pipeline flow vector;

[0207] 6) Zero injection heat power / injection flow constraint:

[0208] A p m=0 (23)

[0209] 7) Coupling element constraints:

[0210] P source -ζΦ=0 (24)

[0211] Where: Pi and Q i are the zero-injection node active and reactive power vectors, respectively; Y is the grid node admittance matrix; the superscript * indicates the conjugate of a complex number; T s ′ and T r ′ is the difference between the node supply water temperature and the return water temperature and the ambient temperature; A p is the node-branch correlation matrix of the zero injected power node; P source is the coupling element power; A s , b s , A r , b r is the temperature coefficient; B is the branch-loop association matrix; ζ is the coupling coefficient of the coupling elements such as cogeneration and heat pump; K is the pipeline resistance coefficient vector; Φ is the node heat load power vector; real{·} and imag{·} represent the real part and imaginary part, respectively.

[0212] In the bad data identification phase, the Lagrange equation is constructed and solved, and based on this, the LNR method bad data identification is performed to achieve bad data identification of temperature measurement. The bad data identification steps are:

[0213] 1) Obtain the status information of power grid and heating network;

[0214] 2) Input the multi-energy system heat network state estimator to obtain an estimated value;

[0215] 3) Calculate the residual sensitivity matrix S and the regularized residual;

[0216] 4) Determine whether the regularized residual is greater than the threshold and perform quantitative elimination.

[0217] Step 4: Substitute the parameters at the estimated time into the quasi-dynamic state estimation model, and use the alternating direction multiplier method to solve the quasi-dynamic state estimation model to obtain the gas network state estimation value, such as Figure 4 As shown, specifically including:

[0218] S3: Based on the quasi-dynamic state estimation model, efficient distributed computing is achieved based on the asynchronous computing framework.

[0219] The S3 is a quasi-dynamic state estimation model that implements efficient distributed computing based on an asynchronous computing framework, including the following steps:

[0220] Alternating solving algorithm for complex constraints:

[0221] The alternating direction multiplier method aims to perform distributed solution to the original optimization problem. Each sub-problem will be solved independently in the algorithm, with limited data interaction only at the boundary nodes. This method can ensure the computational independence of each subsystem and effectively protect the information of each subsystem from being disclosed. It is suitable for the optimization of integrated energy systems with multiple operation and dispatch centers or different stakeholders.

[0222] For natural gas generators, if the power system node n1 is equipped with a gas turbine, the natural gas consumed by the unit The node m1 from the natural gas system, then the coupling constraint it constitutes can be written as: - With an auxiliary variable z g1 Constructing coupling constraints, the specific form of the optimization problem for natural gas generator sets is:

[0223]

[0224] In the formula, The natural gas consumed by the gas turbine unit, The natural gas consumed for the node m1 load, Supplying natural gas to the natural gas system;

[0225] For an electrically driven compressor, if the natural gas pipeline b is equipped with a compressor and the power consumed by the compressor is supplied by the power system node n2, the coupling constraint it constitutes can be written as: With an auxiliary variable z g2 Constructing coupling constraints, the specific form of the optimization problem for the electric drive compressor is:

[0226]

[0227] In the formula, The power consumed by the compressor, is the output power of the compressor unit, Supply power to power system node n2, is the remaining power of the pipeline;

[0228] By relaxing the constraints, we can establish the augmented Lagrangian formula for the state estimation problem:

[0229]

[0230] Where L(x,z,λ) is the Lagrangian factor; λ NGN and β are the parameters corresponding to the natural gas system state.

[0231] In order to minimize the state estimation equation, a loop algorithm is used to solve the augmented Lagrangian equation. In each loop, the values ​​of x, z and λ are calculated in turn. The specific steps are as follows:

[0232] 1) Consider z and λ as constants and calculate the value of x that minimizes equation (29). Since L(x, z, λ) and constraints (22), (23), and (24) are independent of each subsystem, x can be divided into subproblems and solved independently within each subproblem. The subproblem corresponding to the gas network is:

[0233]

[0234] Where J represents the weighted sum of measurement residuals.

[0235] 2) Solve the above sub-problems separately and exchange data at the boundary (gas consumption of natural gas units, power consumption of compressors, etc.) to update the value of z:

[0236] z in formulas (25) and (26) g1 The calculation formula is:

[0237]

[0238] z in formulas (27) and (28) g2 The calculation formula is:

[0239]

[0240] In the formula, is the natural gas consumed by the gas turbine at node m1, The natural gas supplied to the system node n1, is the natural gas consumed by the n1 node load, The power consumed by the compressor, is the output power of the compressor unit;

[0241] 3) Based on the updated values ​​of x and z, the Lagrange multiplier λ in the augmented Lagrangian equation can be updated. For the nth cycle, the calculation formula of the multiplier λ is:

[0242]

[0243] In the formula, is the fixed parameter value after the n+1th cycle, is the fixed parameter value for the nth cycle, x n is the decision value, z n+1 is the variable value after the n+1th cycle;

[0244] The above three steps are performed alternately until the algorithm converges.

[0245] The present invention fully considers the different physical characteristics and different time scales of electricity, heat and gas systems, and establishes mathematical models of each subsystem and corresponding coupling elements of the integrated energy system; based on the above mathematical model, a heat network state estimation model is established to describe the temperature propagation characteristics in the heating system, and a state estimation algorithm for the integrated energy system considering the dynamic characteristics of the heat network temperature is proposed to achieve dynamic estimation with minimum information loss; a quasi-dynamic state estimation model is constructed, and based on the alternating direction multiplier method, an asynchronous computing framework is proposed to achieve efficient distributed computing.

[0246] The minimum information dynamic estimation method of the present invention can be fully matched and combined according to different noise types to achieve accurate estimation, effectively reduce the calculation scale, and provide accurate and reliable real-time operation data for the multi-energy system, which is the guarantee for the normal operation of various advanced applications of the multi-energy system. The proposed state estimation method retains the coupling relationship between the electrical and thermal subsystems, divides the state estimation problem into sub-problems and realizes independent solution of each sub-problem, ensuring the decision independence and information privacy between subsystems; at the same time, the estimation method adopts an asynchronous computing framework, which improves the computing efficiency and can quickly provide data for the operation, control, and analysis of the integrated energy system.

[0247] The present invention combines information theory to reduce information loss when the system operating state changes, so that the state estimation result can reach the real state.

[0248] Embodiment 2:

[0249] The present invention based on the same inventive concept also provides a multi-energy system heat network, gas network minimum information loss dynamic state estimation system, comprising:

[0250] The equipment model building module is used to build equipment models for the subsystems of the integrated energy system and the coupling of multiple energy systems based on the different physical characteristics and time scales of the electricity, heat and gas systems;

[0251] A computational model building module is used to establish a dynamic state estimation model and a quasi-dynamic state estimation model for the heating network based on the models of each subsystem and multi-energy system coupling equipment;

[0252] The heating network state estimation module is used to substitute the parameters at the estimated time into the heating network state estimation model to obtain the heating network state estimation value;

[0253] The gas network state estimation module is used to substitute the parameters of the estimated time into the quasi-dynamic state estimation model, and solve the quasi-dynamic state estimation model by using the alternating direction multiplier method to obtain the gas network state estimation value.

[0254] Optionally, the device model building module is specifically used to:

[0255] Based on the multi-time scale characteristics of the integrated energy system, the network information flow of the multi-energy system is analyzed, and the data model of each subsystem of the integrated energy system is constructed;

[0256] Associate the data models of each subsystem to build a multi-energy system coupling equipment model;

[0257] The data models of the subsystems include: an electrical system mathematical model, a natural gas system mathematical model and a heating system mathematical model.

[0258] Optionally, the electrical system mathematical model is as shown below:

[0259]

[0260] In the formula, x e is the state of the electrical system, y e is an algebraic variable, F e is the dynamic equation of the power system, G e is the algebraic constraint equation, is the state change of the electrical system.

[0261] Optionally, the natural gas system mathematical model is as follows:

[0262]

[0263] Where ρ, p and ω represent gas density, pressure and velocity respectively; λ, D, g and θ G They represent pipeline friction coefficient, pipeline diameter, gravity acceleration and pipeline inclination angle respectively; t is unit time, and x is unit length of the pipeline network.

[0264] Optionally, the mathematical model of the heating system is as follows:

[0265]

[0266] In the formula, and are respectively the gas consumption and power generation of gas turbine i; α GT , β GT and γ GT are the corresponding gas consumption calculation coefficients respectively; Represents the set of gas turbine numbers in the integrated energy system, and i is the gas turbine number in the integrated energy system.

[0267] Optionally, the computational model building module is specifically used for:

[0268] Establish a multi-energy system coupling equipment model based on the different physical characteristics and time scales of electricity, heat and gas systems;

[0269] The multi-energy system coupling device model is used to describe the bidirectional boundary conditions and bidirectional constraint conditions of the electric, thermal and gas multi-energy system;

[0270] Construct the objective function with the goal of minimizing information loss;

[0271] Constructing a heating network state estimation model based on the objective function, the bidirectional boundary conditions and the bidirectional constraint conditions;

[0272] The residual objective function is constructed with the goal of minimizing the measurement residual of each subsystem;

[0273] A heating network state estimation model is constructed by the residual objective function, bidirectional boundary conditions and bidirectional constraint conditions.

[0274] Optionally, the objective function is as shown below:

[0275]

[0276] In the formula, I loss (ν * ,Z) is the final information loss, ν * is the final prediction result, Z is the prediction result vector of various single prediction algorithms at this prediction point, I loss (ν,Z) is the amount of information loss in the decision-making process, and ν is the measurement error vector.

[0277] Optionally, the residual objective function is as follows:

[0278]

[0279] In the formula, J EPS , J NGN , J DHN Represents the weighted sum of the measurement residuals of the power grid, heat grid, and gas grid systems, respectively, and x EPS represents the decision variable in the grid state estimation, x NGN represents the decision variable in the natural gas system state estimation, x DHN Represents the decision variables in the state estimation of the heating system.

[0280] Optionally, the heating network state estimation module is specifically used for:

[0281] A variety of dynamic state estimation algorithms are used to respectively predict and estimate the state quantities of the multi-energy system at the estimation time, and the state quantity estimation results at the estimation time are obtained;

[0282] Select multiple reference moments in history, use multiple prediction algorithms to perform virtual predictions on the dynamic states of the multi-energy system at the multiple reference moments, and obtain virtual prediction values;

[0283] Estimate the probability density function of the channel and the information source according to the virtual prediction value and the actual state quantity at multiple reference moments in history;

[0284] According to the probability density function of the channel and the information source, solving the objective function in the heating network state estimation model to obtain the minimum value of information loss;

[0285] The heating network state value corresponding to the minimum value of the information loss is used as the heating network state estimation value.

[0286] Optionally, the gas network state estimation module is specifically used for:

[0287] Substituting the parameters of the estimated time into a pre-built quasi-dynamic state estimation model, relaxing the bidirectional boundary conditions and bidirectional constraint conditions in the quasi-dynamic state estimation model, and establishing an augmented Lagrangian formula;

[0288] The augmented Lagrangian formula is solved alternately using a cyclic algorithm to obtain the dynamic state estimation value of the minimum information loss of the heat network and gas network of the multi-energy system.

[0289] Example 3

[0290] like Figure 5 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0291] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of a method for dynamic state estimation of a multi-energy system heat network and gas network with minimum information loss in the above-mentioned embodiment.

[0292] Example 4

[0293] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in the electronic device for storing programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and the extended storage medium supported by the electronic device. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a method for dynamic state estimation of minimum information loss of a multi-energy system heat network and gas network in the above embodiment.

[0294] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0295] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0296] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0297] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0298] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for dynamic state estimation of minimum information loss of heat network and gas network in a multi-energy system, characterized in that: include: Based on the different physical characteristics and time scales of electricity, heat and gas systems, establish models of subsystems of the integrated energy system and coupled equipment of multi-energy systems; Establish a heating network state estimation model and a pseudo-dynamic state estimation model based on each subsystem and multi-energy system coupling equipment model; Substitute the parameters at the estimated time into the heating network state estimation model to obtain the heating network state estimation value; The parameters at the estimated time are substituted into the quasi-dynamic state estimation model, and the quasi-dynamic state estimation model is solved by using the alternating direction multiplier method to obtain the gas network state estimation value.

2. The method according to claim 1, characterized in that The heat network state estimation model and the quasi-dynamic state estimation model are established based on each subsystem and multi-energy system coupling device model, including: Establish a multi-energy system coupling equipment model based on the different physical characteristics and time scales of electricity, heat and gas systems; The multi-energy system coupling device model is used to describe the bidirectional boundary conditions and bidirectional constraint conditions of the electric, thermal and gas multi-energy system; Construct the objective function with the goal of minimizing information loss; Constructing a heating network state estimation model based on the objective function, the bidirectional boundary conditions and the bidirectional constraint conditions; The residual objective function is constructed with the goal of minimizing the measurement residual of each subsystem; A heating network state estimation model is constructed by the residual objective function, bidirectional boundary conditions and bidirectional constraint conditions.

3. The method according to claim 2, characterized in that The objective function is shown in the following formula: AND loss (ν * ,Z)=minI loss (ν,Z) ν In the formula, I loss (ν * ,Z) is the final information loss, ν * is the final prediction result, Z is the prediction result vector of various single prediction algorithms at this prediction point, I loss (ν,Z) is the amount of information loss in the decision-making process, and ν is the measurement error vector.

4. The method according to claim 1, characterized in that Based on the different physical characteristics and time scales of the electricity, heat and gas systems, the subsystems of the integrated energy system and the multi-energy system coupling equipment models are established, including: Based on the multi-time scale characteristics of the integrated energy system, the network information flow of the multi-energy system is analyzed, and the data model of each subsystem of the integrated energy system is constructed; Associate the data models of each subsystem to build a multi-energy system coupling equipment model; The data models of the subsystems include: an electrical system mathematical model, a natural gas system mathematical model and a heating system mathematical model.

5. The method according to claim 4, characterized in that The electrical system mathematical model is shown as follows: In the formula, x e is the state of the electrical system, y e is an algebraic variable, F e is the dynamic equation of the power system, G e is the algebraic constraint equation, is the state change of the electrical system.

6. The method according to claim 4, characterized in that The mathematical model of the natural gas system is shown as follows: Where ρ, p and ω represent gas density, pressure and velocity respectively; λ, D, g and θ G They represent pipeline friction coefficient, pipeline diameter, gravity acceleration and pipeline inclination angle respectively; t is unit time, and x is unit length of the pipeline network.

7. The method according to claim 4, characterized in that The mathematical model of the heating system is shown in the following formula: In the formula, and are respectively the gas consumption and power generation of gas turbine i; α GT , β GT and γ GT are the corresponding gas consumption calculation coefficients respectively; Represents the set of gas turbine numbers in the integrated energy system, and i is the gas turbine number in the integrated energy system.

8. The method according to claim 1, characterized in that Substituting the parameters at the estimated time into the heating network state estimation model to obtain the heating network state estimation value includes: A variety of dynamic state estimation algorithms are used to respectively predict and estimate the state quantities of the multi-energy system at the estimation time, and the state quantity estimation results at the estimation time are obtained; Select multiple reference moments in history, use multiple prediction algorithms to perform virtual predictions on the dynamic states of the multi-energy system at the multiple reference moments, and obtain virtual prediction values; Estimate the probability density function of the channel and the information source according to the virtual prediction value and the actual state quantity at multiple reference moments in history; According to the probability density function of the channel and the information source, solving the objective function in the heating network state estimation model to obtain the minimum value of information loss; The heating network state value corresponding to the minimum value of the information loss is used as the heating network state estimation value.

9. The method according to claim 1, characterized in that The method of using the alternating direction multiplier method to solve the quasi-dynamic state estimation model to obtain the gas network state estimation value includes: The bidirectional boundary conditions and bidirectional constraints in the quasi-dynamic state estimation model are relaxed to establish the augmented Lagrangian formula. The augmented Lagrangian formula is solved alternately using a cyclic algorithm to obtain the dynamic state estimation value of the minimum information loss of the heat network and gas network of the multi-energy system.

10. The method according to claim 9, characterized in that The loop algorithm is used to alternately solve the augmented Lagrangian formula to obtain the dynamic state estimation value of the minimum information loss of the heat network and gas network of the multi-energy system, including: S31 regards z and λ as constants and calculates the value of x when the augmented Lagrangian equation is minimized; S32 performs data exchange at the boundary to update the value of z; S33 updates the Lagrangian multiplier λ in the augmented Lagrangian equation based on the updated values ​​of x and z; S34 determines whether it converges, and ends if it converges, otherwise returns to S31.

11. A multi-energy system heat network and gas network minimum information loss dynamic state estimation system, characterized in that: include: The equipment model building module is used to build equipment models for the subsystems of the integrated energy system and the coupling of multiple energy systems based on the different physical characteristics and time scales of the electricity, heat and gas systems; A computational model building module is used to establish a heating network state estimation model and a pseudo-dynamic state estimation model based on each subsystem and multi-energy system coupling device model; The heating network state estimation module is used to substitute the parameters at the estimated time into the heating network state estimation model to obtain the heating network state estimation value; The gas network state estimation module is used to substitute the parameters of the estimated time into the quasi-dynamic state estimation model, and solve the quasi-dynamic state estimation model by using the alternating direction multiplier method to obtain the gas network state estimation value.

12. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a method for dynamic state estimation of a heat network and a gas network of a multi-energy system with minimum information loss as described in any one of claims 1 to 10 is implemented.

13. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, a dynamic state estimation method for minimum information loss of a multi-energy system heat network and gas network as described in any one of claims 1 to 10 is implemented.

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