Chemical industry park distributed state estimation method and system considering multiple energy mass flow dynamics

Abstract

The invention belongs to the technical field of distributed state estimation, and provides a chemical industry park distributed state estimation method and system considering multiple energy mass flow dynamics. State space models of a power distribution network, a heat supply network, a chemical material flow network and the like and a coupling element model of hydrogen / ammonia / methanol chemical production equipment are constructed, so that a real-time state estimation model of the green power chemical park considering material-energy coupling and dynamic characteristics is constructed; a particle filter algorithm is used as a framework, and distributed state estimation is constructed based on an asynchronous parallel architecture, so that the problem of data privacy between power and chemical subsystems is solved; and constructing a multi-time-scale distributed state estimation architecture of the green power chemical industrial park in combination with an innovation triggering mechanism. According to the invention, the problems of measurement uploading period difference and computing resource waste caused by time constant difference between different networks are solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of distributed state estimation, and particularly relates to a chemical industrial park distributed state estimation method and system considering the dynamics of multi-energy substance flow, in particular, a green electricity chemical industrial park distributed state estimation method and system considering the dynamic characteristics of multi-element substance flow and heterogeneous energy flow. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] The green electricity chemical industrial park refers to a wind-solar-hydrogen-ammonia-alcohol integrated park that replaces fossil fuels with green electricity and green hydrogen to supply energy for heavy chemical systems. The park integrates controllable units such as distributed power generation, energy storage, and complete chemical production equipment to realize load-following power supply for chemical production, which is of great significance.

[0004] In the power and chemical subsystems of the green electricity chemical industrial park, there are spatio-temporal cross-coupled electric and thermal heterogeneous energy flows and hydrogen / ammonia / alcohol / ester multi-element substance flows. Different types of energy flows and substance flows have significant differences in dynamic characteristics and mathematical descriptions. To optimize the operating state of the green electricity chemical industrial park while ensuring the safety of chemical production, the real-time operating state of the power and chemical subsystems needs to be obtained as a data basis for the optimization and scheduling of the green electricity chemical industrial park. Therefore, real-time state estimation is needed. However, the current real-time state estimation method for the green electricity chemical industrial park has the following problems: Electric and thermal heterogeneous energy flows and hydrogen / ammonia / alcohol / ester multi-element substance flows have significant differences in dynamic characteristics and mathematical descriptions. The propagation time constant of power flow is usually in the order of microseconds or milliseconds, while the propagation time constant of thermal and material flows is usually in the order of minutes or hours. If only the steady-state or quasi-steady-state time scale commonly used in power subsystem state estimation is used for state estimation of the chemical subsystem heating network and material network, the inherent dynamic characteristics of the system cannot be captured, resulting in poor state estimation accuracy, which affects the accuracy of subsequent scheduling and other advanced application decisions, and thus affects the safe operation of the system.

[0005] The power and chemical subsystems of the green electricity chemical industrial park have data privacy issues due to different management subjects. Due to the differences in dynamic characteristics and estimation model properties of the power grid, heating network, and material network, their state estimation execution cycles are different, and their estimation efficiencies also differ. In addition, the power and chemical subsystems have different sensitivities to changes in their coupled power. Therefore, traditional centralized state estimation cannot guarantee data privacy. The single time scale synchronous parallel distributed state estimation strategy has problems such as difficulty in fully utilizing computational resources, wasting waiting time, and affecting estimation efficiency. If state estimation is performed when any system coupled power measurement value changes, it may also lead to waste of computational resources in scenarios with small overall state changes. SUMMARY

[0006] The present application proposes a chemical industrial park distributed state estimation method and system considering energy and mass flow dynamics to solve the above problems, which fully considers the time sequence coupling of heterogeneous energy flow and hydrogen / ammonia / alcohol multi-element substance flow and their differentiated dynamic characteristics, and realizes efficient and accurate state estimation of electric, heat and substance flow networks.

[0007] According to some embodiments, the present application adopts the following technical scheme: A chemical industrial park distributed state estimation method considering multi-energy and mass flow dynamics, comprising the following steps: Divide the green electricity chemical industrial park into an electric power subsystem and a chemical subsystem, wherein the electric power subsystem manages the operation of the power distribution network, and the chemical subsystem manages the operation of the heat supply network, the chemical material flow network and the chemical production equipment; Respectively acquire network information and equipment information in each subsystem, and perform parameter unitization processing on the coupling elements of each branch in the power distribution network, the heat supply network and the chemical material flow network, and the hydrogen / ammonia / methanol chemical production equipment; For the electric power subsystem, according to the power distribution network information, the state transition equation of the power distribution network under quasi-steady state is constructed by using the backflow calculation method, and the power distribution network state space model is constructed in combination with the power distribution network measurement model; For the chemical subsystem, according to the material flow network pipe pressure equation, material flow momentum equation and material flow pipe mass balance equation, the partial differential model of the material flow network is established and discretized, the state transition equation of the material flow network is constructed, and the material flow network state space model is constructed in combination with the measurement model of the material flow network; according to the dynamic thermodynamic model of the heat supply network considering pipe transmission loss, the partial differential model of the heat supply network is established and discretized, and the heat supply network state space model considering temperature transmission dynamic characteristics is constructed in combination with the measurement model of the hydraulic network and the heat network in the heat supply network; For the hydrogen / ammonia / methanol chemical production equipment and the coupling elements, the equation constraints describing the power consumption, raw material input and chemical production heat release are constructed, and the coupling element state space model is constructed; Based on the constructed power distribution network state space model, material flow network state space model and heat supply network state space model, the green electricity chemical industrial park real-time state estimation model considering the dynamic characteristics of multi-element substance flow and heterogeneous energy flow is constructed, and the green electricity chemical industrial park distributed state estimation model is constructed based on the distributed state estimation strategy of asynchronous parallel computing architecture; An innovation triggering mechanism is introduced. Based on the execution cycle of the state estimation of the electricity, heat and material flow network and the innovation triggering mechanism, it is determined whether to perform real-time state estimation of the electricity, heat and material flow coordination at the current moment. If so, the distributed state estimation of the corresponding network is performed using the distributed state estimation model of the green chemical industrial park to obtain the state estimation value. Determine if the current time is the predetermined end time. If so, use the obtained state estimate as the result; otherwise, proceed to the next time step and return to the previous step.

[0008] As an optional implementation, the distribution network information includes distribution network topology information, impedance and admittance of each branch, capacitance to ground of each node, distribution network measurement information, and distribution network state estimation execution cycle. The information on the chemical heating network includes heating network topology information, length of each heating pipe, cross-sectional area of ​​the pipe, mass flow rate of hot water in the pipe, pipe friction coefficient, pipe heat transfer coefficient, ambient temperature, heating network measurement information, and the execution cycle of heating network status estimation. Material flow networks are represented by hydrogen supply networks. Information includes the hydrogen supply network topology, pipeline length, pipeline cross-sectional area, average hydrogen flow velocity and hydrogen sound velocity in the pipeline, pipeline friction coefficient, hydrogen temperature, hydrogen compressibility coefficient, hydrogen gas constant, hydrogen supply network measurement information, and hydrogen supply network status estimation execution cycle.

[0009] As an alternative implementation method, the process of constructing the quasi-steady-state distribution network state transition equations using the reverse power flow calculation method and combining them with the distribution network measurement model to construct the distribution network state-space model includes: setting the distribution network state quantity matrix. x e =[ V , θ ] T Including the voltage amplitude of each node V i and voltage phase angle θ i Measurement information matrix z e =[ P ij,meas , Q ij,mes , I ij,meas , V meas , θ meas ,P i,meas ,Q i,meas ] T, including partial branch active, reactive or current measurements uploaded by remote terminal units, partial node voltage amplitude and phase angle measurements uploaded by synchrophasor units, and node injected active and reactive power pseudo measurements generated according to historical load data; The power distribution network state space model is constructed as follows: ; ; Wherein, the subscript k represents k time; u e,k is the input variable matrix of the power distribution network, including the active and reactive power injected at each node, Δ u e,k is the change of node injected power between adjacent time instants, which is obtained by load prediction; f e (·) is the state transition equation of the power distribution network, which is nonlinear; J e,k-1 is the Jacobian matrix of the power distribution network calculated according to the state at time k -1; ξ e,k is the non-Gaussian process noise of the power distribution network, and its error covariance matrix is ; z e,k is the measurement matrix of the power distribution network; h e (·) are the nonlinear measurement equations of the power distribution network, respectively; ω e,k is the non-Gaussian measurement noise matrix of the power distribution network, and its error covariance matrix is R e,k .

[0010] As an optional implementation, the process of constructing the state transition equation of the material flow network, combined with the measurement model of the material flow network, to construct the state space model of the material flow network includes: setting the state quantity matrix of the material flow network x M [ ρ , M ] T , including the hydrogen density ρ i and the hydrogen mass flow M i of each gas network node, and the measurement information matrix z M [ ρ i,meas , M i,meas , p i,meas ,0] Tincluding partial node hydrogen density, hydrogen mass flow rate and node gas pressure, 0 corresponds to virtual measurement in material flow network; Assuming that hydrogen temperature in material flow network is constant and hydrogen supply pipeline is horizontal, according to material flow network information, material flow momentum equation, material flow pipeline mass balance equation and pipeline pressure equation constructed based on fluid mechanics analysis, partial differential-algebraic simplified model of material flow network is established, namely: ; ; ; wherein, t and s represent time and space distance; v is gas flow rate in pipeline; p is pipeline pressure; λ M is friction coefficient of hydrogen supply pipeline, d is pipeline diameter; c is sound speed in hydrogen; Discrete processing is carried out on the above formula based on Lax-Wendroff difference format, and state transition equation of material flow network is constructed, namely: ; wherein, ρ i,k is hydrogen density of node k at time i ; M i,k and M j,k are hydrogen mass flow rate of outflow node k and inflow node ij of pipeline i at time j ; L ij is length of pipeline ij , S ij is cross-sectional area thereof, and corresponding pipeline radius is d ij ; p i,k is gas pressure of node k at time i ; is average flow rate of hydrogen in pipeline ij ; State space model of material flow network is constructed in combination with measurement model of material flow network: ; ; wherein, uM,k The input variable matrix of the material flow network includes the hydrogen density of the gas source node and the hydrogen load node; f M The state transition equation of the material flow network, i.e., the differential equation mentioned above; ξ M,k The non-Gaussian process noise of the material flow network, and the error covariance matrix is ; z M,k The measurement matrix of the material flow network; h M The measurement equation of the material flow network, respectively; ω M,k The non-Gaussian measurement noise matrix of the material flow network, and the error covariance matrix is R M,k The gas continuity equation of the material flow network and the gas source pressure set value will be used as virtual measurements.

[0011] As an optional implementation, the state quantity matrix of the heating network x H [ T s , T r ] T , including the heating temperature of each node T s,i and the regenerative temperature T r,i , the measurement information matrix z M [ H , T s , T r ,0] T , including the node heat power, part of the node heating temperature and regenerative temperature, and 0 corresponds to the virtual measurement in the heating network; according to the dynamic thermodynamic model of the heating network considering the pipeline transmission loss, the partial differential model of the heating network in the heating network is established and discretized, and combined with the measurement model of the hydraulic network and the heating network in the heating network, the state space model of the heating network considering the temperature transmission dynamic characteristics is constructed; Specifically, it is assumed that the heating network adopts the constant flow variable temperature working medium regulation mode, and according to the law of conservation of heat, the partial differential model of the heating network in the heating network is established: ; Wherein, T is the node temperature of the heating network; m is the mass flow of hot water in the pipeline, T a is the ambient temperature;C p The specific heat capacity of water, ρ water The density of water; λ M The heat dissipation coefficient of the hydraulic network; Discretize the above equation using the Lax-Wendroff difference scheme to construct the state transition equation for the heating network, namely: ; Construct a state-space model of the heating network by combining the measurement model of the heating network: ; ; in, u H,k Input variable matrix for heating network; f H (·) represents the state transition equation of the heating network, i.e., the difference equation mentioned above; ξ H,k The non-Gaussian process noise of the heating network has the following error covariance matrix: ; z H,k For heating network quantity measurement matrix; h H (·) represent the measurement equations for the heating network; ω H,k The non-Gaussian measurement noise matrix of the heating network has the following error covariance matrix: R H,k ; The values ​​of the water flow continuity equation, nodal temperature mixing equation, and loop water pressure equation of the heating network will be used as virtual measurements to improve the measurement redundancy of the heating network.

[0012] As an alternative implementation, the process of constructing the state-space model of the coupling elements includes: for the electrolyzer, based on the chemical reaction equation for hydrogen production from water electrolysis, constructing an electrolyzer model that considers the coupling of power consumption-hydrogen production and hydrogen production-water consumption, i.e.: ; in, for k Molar flow rate of hydrogen produced in the electrolyzer at any given time; for k The electrical power consumed by the electrolytic cell at any given time; for k The amount of water used for hydrogen production in the electrolyzer at any given time; η EL Hydrogen production efficiency of the electrolyzer; α 0 represents the water consumption efficiency of the electrolyzer; qH2 is the low-grade heat energy of hydrogen gas; For green electricity synthesis ammonia equipment, according to the chemical reaction equation of ammonia synthesis, a green electricity synthesis ammonia equipment model is constructed, which takes into account the coupling of power consumption-hydrogen consumption, ammonia production-hydrogen consumption, and heat production-ammonia production, i.e.: ; wherein, , , and are the molar flow rates of air, nitrogen, hydrogen and ammonia generated by the ammonia synthesis equipment at k ; is the total electric power consumed by the ammonia synthesis reactor at k ; is the heat release power of the ammonia synthesis equipment at k ; is the ammonia production at k ; M air is the molar mass of air, R H2 is the hydrogen gas constant, T AS is the air separation device inlet temperature, p AS in is the air separation device compressor inlet gas pressure, p AS out is the air separation device compressor outlet gas pressure, η C is the mechanical efficiency of the air separation device compressor; P P2A N is the fixed electric power of the ammonia synthesis reactor, l is the electric power consumed to produce unit molar flow rate of ammonia; η h,P2A is the heat release rate of the ammonia synthesis equipment; q NH3 is the heat release of producing unit mass of ammonia; For methanol synthesis equipment, according to the chemical reaction equation of ammonia synthesis, a methanol synthesis equipment model is constructed, which takes into account the coupling of power consumption-hydrogen consumption, alcohol production-hydrogen consumption, and heat production-hydrogen consumption: ; wherein, is the electric power consumed by the methanol synthesis equipment at k ; , and are the molar flow rates of carbon dioxide and hydrogen consumed by the methanol synthesis equipment at k ; is the methanol synthesis at kThe exothermic power of the methanol synthesis equipment at any given time; η P2M The methanol synthesis efficiency; Δ H P2M The heat of reaction released for the synthesis of one unit mass of methanol; For hydrogen storage tanks, their state transition equations are constructed based on their energy balance constraints and production scheduling plan. Taking into account the coupling constraints of power consumption and hydrogen intake, the hydrogen storage tank model is completed, i.e.: ; in, for k The constant pressure level of the hydrogen storage tank; and for k Real-time inlet and outlet mass flow rates of hydrogen storage tank; for k The electrical power consumed by the compressor of the hydrogen storage tank at all times; V HSS This refers to the capacity of the hydrogen storage tank. η ch HSS and η dis HSS represents the hydrogen storage tank filling and discharging efficiency, respectively. T HSS This refers to the inlet temperature of the hydrogen storage tank. p HSS in and p HSS and out represent the inlet and outlet pressures of the hydrogen storage tank compressor, respectively. η HC The mechanical efficiency of the hydrogen storage tank; M H2 This refers to the molar mass of hydrogen gas. For electric heating equipment, a model is constructed based on its power consumption minus heat generation constraint, namely: ; in, for k The electrical power consumed by the electric heating equipment at all times; for k The heat output of the electric heating equipment at all times.

[0013] As an alternative implementation method, the process of constructing a real-time state estimation model for a green electric chemical industrial park that takes into account the dynamic characteristics of multiple material flows and heterogeneous energy flows, based on the constructed state space model of the power distribution network, the state space model of the material flow network, and the state space model of the heating network, includes: setting independent estimators for the power distribution network of the power subsystem and the heating network and material flow network of the chemical subsystem respectively; each network estimator performs its own state estimation based on the particle filter algorithm according to the corresponding state space model constructed. Each network estimator obtains the power values of the coupling elements in the other two types of networks according to the coupling element model based on the state variable estimation values obtained by performing state estimation of itself, and transmits the power values as limited interaction information to the other two types of network estimators for asynchronous parallel iteration until the equivalent power estimation values obtained by the coupling elements from the adjacent networks deviate from the power estimation values of the network by less than a threshold value.

[0014] As an alternative embodiment, the process of constructing a distributed state estimation model of a green chemical industrial park based on a distributed state estimation strategy based on an asynchronous parallel computing architecture includes: obtaining the interaction quantities of the other two types of networks as new pseudo-measurements before performing state estimation on the current network, and if the adjacent area information is not updated, the last measurement value is used for estimation; When a network completes estimation, the state estimation value of the network is corrected using the obtained adjacent network information; The equivalent power values of the other two types of networks calculated using the state estimation values of the nodes where the coupling elements of the network are located are transmitted to the other two networks; For a sub-network, the state estimation of the network converges only when the power estimation values of all coupling elements in the network deviate from the equivalent power estimation values obtained from the adjacent networks by less than the corresponding threshold value, otherwise the asynchronous iteration is continued.

[0015] As an alternative embodiment, the process of introducing a new information trigger mechanism includes: the trigger value of the new information trigger mechanism is selected according to the state estimation error requirements of the green chemical industrial park through sensitivity analysis of the power change value of the power distribution network-state variable error of the heating network, the power change value of the power distribution network-state variable error of the material network, the power change value of the heating network-state variable error of the power distribution network, and the power change value of the material network-state variable error of the power distribution network. If any threshold value is met, the new information trigger mechanism is triggered.

[0016] As an alternative embodiment, the process of determining whether to perform real-time state estimation of the electric, thermal, and material flow at the current time according to the state estimation execution period of the electric, thermal, and material flow networks and the new information trigger mechanism includes: if the current time is the estimation time of the electric, thermal, and material flow networks and the new information trigger mechanism is met, distributed state estimation of the three is performed and the state estimation value is output; if the current time is the estimation time of the electric and material flow networks and the new information trigger mechanism is met, distributed state estimation of the two is performed and the state estimation value is output; if it is only the estimation time of the power distribution network or the new information trigger mechanism is not met, only the power distribution network state estimation is performed and the state estimation value is output.

[0017] A distributed state estimation system for a chemical industrial park considering the dynamics of multi-energy flow, comprising: The green electric chemical industrial park parameter preprocessing module is used for dividing the green electric chemical industrial park into an electric power subsystem and a chemical subsystem, wherein the electric power subsystem manages power distribution network operation, and the chemical subsystem manages heat supply network, chemical material flow network and chemical production equipment operation; network information and equipment information in each subsystem are respectively acquired, and each branch in the power distribution network, the heat supply network and the chemical material flow network and coupling elements of hydrogen / ammonia / methanol chemical production equipment are subjected to parameter dimensionless processing; The electric power subsystem state estimation model construction module is used for constructing, for the electric power subsystem, a power distribution network state transition equation under quasi-steady state according to power distribution network information by using a back / forward sweep method, and constructing a power distribution network state space model in combination with a power distribution network measurement model; The chemical subsystem state estimation model construction module is used for establishing a partial differential model of the material flow network and performing discretization processing according to a material flow network pipe pressure equation, a material flow momentum equation and a material flow pipe mass balance equation, constructing a material flow network state transition equation, and constructing a material flow network state space model in combination with a material flow network measurement model; a partial differential model of the heat supply network is established and discretized according to a dynamic thermodynamic model of the heat supply network considering pipe transmission loss, and a heat supply network state space model considering temperature transmission dynamic characteristics is constructed in combination with measurement models of a hydraulic network and a thermal network in the heat supply network; The coupling element state space model construction module is used for constructing an equation constraint among power consumption, raw material inflow and chemical production heat release for hydrogen / ammonia / methanol chemical production equipment and coupling elements, and constructing a coupling element state space model; The distributed state estimation modeling module is used for constructing a green electric chemical industrial park real-time state estimation model considering dynamic characteristics of multiple substance flows and heterogeneous energy flows based on the constructed power distribution network state space model, the material flow network state space model and the heat supply network state space model, and constructing a green electric chemical industrial park distributed state estimation model based on a distributed state estimation strategy of an asynchronous parallel computing architecture; The distributed state estimation execution module is used for introducing a innovation trigger mechanism, judging whether real-time state estimation of electricity, heat and material flow is performed at the current time according to a state estimation execution cycle of electricity, heat and material flow and the innovation trigger mechanism, and if so, performing distributed state estimation of the corresponding network by using the green electric chemical industrial park distributed state estimation model to obtain a state estimation value; The state estimation end condition judgment module is used for judging whether the current time is a predetermined end time, and if so, taking the obtained state estimation value as a result, and otherwise, entering the next time, and calling the distributed state estimation execution module again.

[0018] Compared with the prior art, the green electric chemical industrial park real-time state estimation method has the following beneficial effects: The application provides a green electricity chemical industrial park distributed state estimation method considering dynamic characteristics of material flow and energy flow, can fully consider time sequence coupling of electric, thermal heterogeneous energy flow and hydrogen / ammonia / alcohol multi-element material flow and differentiated dynamic characteristics thereof, realizes efficient and accurate state estimation of electric, thermal and material flow networks, and provides a data basis for subsequent advanced application functions. The application combines an asynchronous parallel computing architecture and a innovation trigger mechanism, and constructs a multi-time scale distributed state estimation architecture, which solves the data privacy problem between the power and chemical subsystems, and solves the measurement uploading period difference problem and the computing resource waste problem caused by the time constant difference between different networks.

[0019] According to the time sequence coupling of electric, thermal heterogeneous energy flow and hydrogen / ammonia / alcohol multi-element material flow and differentiated dynamic characteristics thereof, the application constructs state space models of distribution network, heating network and chemical material flow network and coupling element models of hydrogen / ammonia / methanol chemical production equipment, so as to construct a green electricity chemical industrial park real-time state estimation model considering material-energy coupling and dynamic characteristics; the distributed state estimation is constructed based on the asynchronous parallel architecture with the particle filter algorithm as the framework, so as to solve the data privacy problem between the power and chemical subsystems; the multi-time scale distributed state estimation architecture of the green electricity chemical industrial park combined with the innovation trigger mechanism is constructed, and the measurement uploading period difference problem and the computing resource waste problem caused by the time constant difference between different networks are solved.

[0020] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the following preferred embodiments are described in detail below, and the accompanying drawings are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0021] The drawings accompanying the specification of the application form part of the application and serve to further understand the application. The schematic embodiments of the application and the description thereof serve to explain the application and do not constitute an improper limitation of the application.

[0022] Figure 1 The design scheme flowchart provided for an embodiment of the application is provided. Figure 2 The multi-time scale distributed state estimation architecture of the green electricity chemical industrial park combined with the innovation trigger mechanism provided for an embodiment of the application is provided. DETAILED DESCRIPTION

[0023] The application is further described below in combination with the drawings and embodiments.

[0024] It should be pointed out that the following detailed description is exemplary and is intended to provide further description of the application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which the application belongs.

[0025] It is to be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments consistent with the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0026] The embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0027] Embodiment one According to Figure 1 , the embodiment provides a chemical industrial park distributed state estimation method considering multi-energy flow dynamics, and specifically includes the following steps: Step 1: The green electricity chemical industrial park is divided into an electric power subsystem and a chemical subsystem according to the differences in management subjects, wherein the electric power subsystem manages the operation of the power distribution network, and the chemical subsystem manages the operation of the heat supply network, the chemical material flow network and the chemical production equipment; the network and equipment information in different subsystems is obtained respectively, and the parameter unitization processing is performed on the branches in the power distribution network, the heat supply network and the chemical material flow network and the coupling elements such as hydrogen / ammonia / methanol chemical production equipment.

[0028] Specifically, the power distribution network information includes power distribution network topology information, branch impedance and ground admittance information, node ground capacitance information, power distribution network measurement information, power distribution network state estimation execution period Δ t e ; the chemical heat supply network information includes heat supply network topology information, heat supply pipeline length, pipeline cross-sectional area, pipeline water mass flow, pipeline friction coefficient, pipeline heat transfer coefficient, environment temperature, heat supply network measurement information and heat supply network state estimation execution period Δ t h ; the material flow network takes the hydrogen supply network as a representative, and the information thereof includes hydrogen supply network topology, pipeline length, pipeline cross-sectional area, pipeline average hydrogen flow rate and hydrogen sound speed, pipeline friction coefficient, hydrogen temperature, hydrogen compression coefficient, hydrogen gas constant, hydrogen supply network measurement information and hydrogen supply network state estimation execution period Δ t M ; the coupling elements such as hydrogen / ammonia / methanol chemical production equipment, hydrogen storage tank and electric heating equipment have the electric-hydrogen conversion coefficient, hydrogen-heat conversion coefficient, raw material-hydrogen / hydrogen-product conversion coefficient and electric-heat conversion coefficient.

[0029] Step 2: For the electric power subsystem, according to the power distribution network information, the power distribution network state transition equation under quasi-steady state is constructed based on the back / forward sweep method, and the power distribution network state space model is constructed in combination with the power distribution network measurement model.

[0030] Specifically, the power distribution network state quantity matrix is set x e =[ V , θ ] T , including the voltage amplitude of each node V i and the voltage phase angle θ i , the measurement information matrix z e =[ P ij,meas , Q ij,mes , I ij,meas , V meas , θ meas ,P i,meas ,Q i,meas ] T , including part of the branch active, reactive or current measurement uploaded by the remote terminal unit (RTU), part of the node voltage amplitude and phase angle measurement uploaded by the synchronous phasor unit (PMU), and the node injected active and reactive power pseudo measurement generated according to the historical load data. The power distribution network state space model is constructed as follows ; ; Wherein, the subscript k represents the time k ; u e,k is the input variable matrix of the power distribution network, including the active and reactive power injection of each node, Δ u e,k is the change of node injection power between adjacent time, which is obtained by load prediction. f e (·) is the state transition equation of the power distribution network; J e,k-1 is the Jacobian matrix of the power distribution network calculated according to the state at time k -1; ξ e,k is the non-Gaussian process noise of the power distribution network, and its error covariance matrix is ; z e,k is the measurement quantity matrix of the power distribution network; h e (·) are the nonlinear measurement equations of the power distribution network; ω e,k is the non-Gaussian measurement noise matrix of the power distribution network, and its error covariance matrix isR e,k .

[0031] Step 3: For the chemical subsystem, according to the material flow network, heat supply network and coupling element information, a state space model is constructed considering dynamic characteristics.

[0032] Step 3.1: Set the state quantity matrix of the material flow network (i.e. the hydrogen supply network) x M =[ ρ , M ] T , including the hydrogen density of each gas network node ρ i and the hydrogen mass flow M i , the measurement information matrix z M =[ ρ i,meas , M i,meas , p i,meas ,0] T , including the hydrogen density, hydrogen mass flow and node gas pressure of some nodes, and 0 corresponds to the virtual measurement in the material flow network. Assuming that the hydrogen temperature in the material flow network is constant and the hydrogen supply pipeline is horizontal, the partial differential-algebraic simplified model of the material flow network is established based on the material flow momentum equation, material flow pipeline mass balance equation and pipeline pressure equation constructed according to the material flow network information, that is ; ; ; wherein t and s represent time and spatial distance; v is the gas flow rate in the pipeline; p is the pipeline pressure; λ M is the friction coefficient of the hydrogen supply network pipeline, d is the pipeline diameter; c is the internal sound speed of hydrogen.

[0033] The above formula is discretized based on the Lax-Wendroff difference format, and the state transition equation of the material flow network is constructed, that is: ; wherein, ρ i,k is the hydrogen density of node i at time k ; M i,k and Mj,k for k Time Pipeline ij Outflow node i With the inflow node j hydrogen mass flow rate; L ij For pipelines ij Length, S ij Its cross-sectional area, and the corresponding pipe radius is d ij ; p i,k for k Time Node i air pressure; For pipelines ij Average flow rate of hydrogen gas inside the vessel.

[0034] The state-space model of the material flow network is constructed by combining the measurement model of the material flow network, as shown in the following equation. ; ; in, u M,k Input variable matrix to the material flow network, including hydrogen density of gas source nodes and hydrogen load nodes. f M (·) is the state transition equation of the material flow network, i.e., the difference equation mentioned above; ξ M,k The noise of the material flow network is a non-Gaussian process, and its error covariance matrix is: ; z M,k For material flow network quantity measurement matrix; h M (·) represent the material flow network measurement equations; ω M,k Here is the non-Gaussian measurement noise matrix of the material flow network, and its error covariance matrix is: R M,k .

[0035] Furthermore, the gas continuity equation of the material flow network and the gas source pressure setpoint will be used as virtual measurements, i.e.: ; ; Step 3.2: Set up the heating network state variable matrix x H =[ T s , T r ] T, including the node heat supply temperature T s,i and the heat recovery temperature T r,i , the measurement information matrix z M [ H , T s , T r ,0] T , including the node heat power, part of the node heat supply temperature and the heat recovery temperature, and 0 corresponds to the virtual measurement in the heat supply network. According to the dynamic thermodynamic model of the heat supply network considering the pipeline transmission loss, the partial differential model of the heat network in the heat supply network is established and discretized, and combined with the measurement model of the hydraulic network and the heat network in the heat supply network, the state space model of the heat supply network considering the dynamic characteristics of temperature transmission is constructed.

[0036] Specifically, assuming that the heat supply network adopts the constant flow variable temperature working medium regulation mode, according to the law of conservation of heat, the partial differential model of the heat network in the heat supply network is established as follows, ; wherein, T is the node temperature of the heat supply network; m is the mass flow of hot water in the pipeline, T a is the ambient temperature; C p is the specific heat capacity of water, ρ water is the density of water; λ M is the heat dissipation coefficient of the hydraulic network.

[0037] Based on the Lax-Wendroff difference format, the above formula is discretized to construct the state transition equation of the heat supply network, that is, ; Combined with the measurement model of the heat supply network, the state space model of the heat supply network is constructed as shown in the following formula ; ; wherein, u H,k is the input variable matrix of the heat supply network. f H (·) is the state transition equation of the heat supply network, that is, the above difference equation; ξ H,k is the non-Gaussian process noise of the heat supply network, and the error covariance matrix is ; zH,k For heating network quantity measurement matrix; h H (·) represent the measurement equations for the heating network; ω H,k The non-Gaussian measurement noise matrix of the heating network has the following error covariance matrix: R H,k .

[0038] Furthermore, the values ​​of the water flow continuity equation, node temperature mixing equation, and loop water pressure equation of the heating network will be used as virtual measurements to improve the measurement redundancy of the heating network.

[0039] Step 3.3: For the coupled components such as hydrogen / ammonia / methanol chemical production equipment, hydrogen storage tanks and electric heating equipment, construct equation constraints describing the power consumption, raw material feed rate and heat release of chemical production, and the heat generation power of electric heating equipment, and construct the state space model of the coupled components.

[0040] Specifically, for the electrolyzer, according to the chemical reaction equation for producing hydrogen through water electrolysis, i.e. An electrolyzer model was constructed that considers the coupling of power consumption and hydrogen production, as well as the coupling of hydrogen production and water consumption. ; in, for k Molar flow rate of hydrogen produced in the electrolyzer at any given time; for k The electrical power consumed by the electrolytic cell at any given time; for k The amount of water used for hydrogen production in the electrolyzer at any given time; η EL Hydrogen production efficiency of the electrolyzer; α 0 represents the water consumption efficiency of the electrolyzer; q H2 It is the lower heat energy of hydrogen.

[0041] For green electricity ammonia synthesis equipment, according to the chemical reaction equation for ammonia synthesis, i.e. A green electricity ammonia synthesis equipment model was constructed, taking into account the coupling of power consumption-hydrogen consumption, ammonia production-hydrogen consumption, and heat production-ammonia production. ; in, , , and They are respectively k The molar flow rates of air, nitrogen, hydrogen, and ammonia produced by the ammonia synthesis equipment at all times; yes k The total electrical power consumed by the ammonia synthesis reactor at any given time; fork exothermic power of the ammonia synthesis plant at time t; for k ammonia production at time t. M air molar mass of air, R H2 hydrogen gas constant, T AS air separation unit inlet temperature, p AS in air separation unit compressor inlet pressure, p AS out air separation unit compressor outlet pressure, η C air separation unit compressor mechanical efficiency; P P2A N fixed electrical power of the ammonia synthesis reactor, l electrical power consumed to produce one mole of ammonia flow rate; η h,P2A exothermic rate of the ammonia synthesis plant; q NH3 exothermic power of the ammonia synthesis plant at time t;

[0042] For the methanol synthesis plant, according to the chemical reaction equation of ammonia synthesis, i.e. , a methanol synthesis plant model is constructed, which takes into account the coupling of electrical power consumption-hydrogen consumption, alcohol production-hydrogen consumption, and heat production-hydrogen consumption.

[0043] ; wherein, is k electrical power consumed by the methanol synthesis plant at time t; , and are k carbon dioxide and hydrogen consumed by the methanol synthesis plant at time t, and the mole flow rate of synthesized methanol; exothermic power of the methanol synthesis plant at time t; k P2M methanol synthesis efficiency; η P2M reaction heat released for synthesizing one unit mass of methanol. H P2M reaction heat released for synthesizing one unit mass of methanol.

[0044] For the hydrogen storage tank, according to its hydrogen storage tank energy balance constraint and production scheduling plan, its state transition equation is constructed, and then the hydrogen storage tank model is completed by taking into account the coupling constraint of electrical power consumption-hydrogen consumption, i.e. ; wherein, is k charging state of the hydrogen storage tank at time t; and is k the mass flow rate of the hydrogen storage tank at time t; is k the electrical power consumed by the hydrogen storage tank compressor at time t; V HSS is the capacity of the hydrogen storage tank; η ch HSS and η dis HSS are the charging and discharging efficiencies of the hydrogen storage tank, respectively; T HSS is the temperature of the hydrogen storage tank at time t, p HSS in and p HSS out are the inlet and outlet pressures of the hydrogen storage tank compressor, respectively; η HC is the mechanical efficiency of the hydrogen storage tank; M H2 is the molar mass of hydrogen.

[0045] For the electric heating equipment, an electric heating equipment model is constructed based on its power consumption-heat production constraints, i.e., ; where, is k the electrical power consumed by the electric heating equipment at time t; is k the heat production power of the electric heating equipment at time t.

[0046] Step 4: Based on the above power subsystem and chemical subsystem state space models, a real-time state estimation model of the green electricity chemical industrial park considering the differentiated dynamic characteristics of multi-substance / energy flow is constructed, and then based on the particle filtering algorithm, a distributed state estimation model of the green electricity chemical industrial park is constructed based on the distributed state estimation strategy of asynchronous parallel computing architecture.

[0047] Step 4.1: Set up independent estimators for the power subsystem distribution network and the chemical subsystem heat supply network and material flow network, respectively. Each network estimator performs its own state estimation based on the particle filtering algorithm according to the state space model constructed in steps 2 and 3.

[0048] Specifically, each network collects N particles according to the prior distribution of its state variables, and based on the Bayesian theory, recursively calculates the weight wjk of each particle. Correspondingly, the posterior distribution and its mean value and the calculation method of the weight of each particle are as follows ; ; ; where,X ( i ) k , i =1,2,…, N} is from the reference distribution q ( x k | x k-1 , z 1:k A set of random samples (particles) collected in ) δ (·) represents the Dirac-Delta function; for wjk Normalized importance weights. X 0:k As of the end k The system's state variable sequence at time t, and it is assumed that they are independent and identically distributed. z 1:k As of the end k The measurement sequence of the time system. p ( X k | z 1:k-1 Let be the probability density function of the state variables during the prediction phase. p ( X k | X k-1 Let be the prior density of the state variable. p ( z k | X k To measure the likelihood density, p ( X k | z 1:k )for k The posterior density of the state variables at time 1. When using... p ( X k | X k-1 As a reference distribution, the weights of each particle can be determined solely based on its weights at its previous time step. w k With measurement likelihood probability p ( z k | X k Recursive calculation, i.e. ; Furthermore, to address the particle degradation problem encountered in particle filtering, a sampling-importance-resampling method is employed, resampling based on the normalized weights of each particle. After resampling, the normalized weights of each particle are... 1 / N .

[0049] State variable estimates for each network state estimation based on particle filtering With error It can be given by its expectation and covariance, i.e. ; ; Step 4.2: Based on the state variable estimates obtained in Step 4.1, each network estimator calculates the power values ​​of the coupled elements in the other two types of networks according to the coupled element model in Step 3.3. This power value is then transmitted to the other two types of network estimators as limited interaction information. Specifically, taking the distribution network in the power subsystem as an example, based on the estimated values ​​of the node voltage amplitude and phase angle of the nodes where the coupled elements are located in the distribution network, the hydrogen production / consumption power and heat production / consumption power of the hydrogen / ammonia / methanol chemical production equipment, hydrogen storage tanks, and electric heating equipment are calculated. This power value is used as limited interaction information for information exchange with the material flow network and heating network of the chemical subsystem. Asynchronous parallel iteration is performed until the deviation between the equivalent power estimates obtained by all coupled elements from adjacent networks and the power estimates of this network is less than a certain threshold. ; ; ; in, P couple,e i , k , P couple,H i , k and P couple,M i , k They respectively represent the use of k The power consumed by the coupling elements calculated based on the estimated state values ​​of the power distribution network, heating network, and material network at any given time; H couple,e i , k , H couple,H i , k and H couple,M i , k They respectively represent the use of kThe heat power generated / used by the coupling elements calculated based on the estimated state values ​​of the power distribution network, heating network, and material network at all times; M couple,e i , k , M couple,H i , k and M couple,M i , k They respectively represent the use of k The hydrogen power generated / used by the coupled components is calculated based on the estimated states of the distribution network, heating network, and material network at all times. This ensures both global consistency across multiple network states and data privacy for different management entities.

[0050] Furthermore, an asynchronous parallel computing architecture is constructed to achieve joint estimation of the electricity-heat-material flow network in the Green Power Chemical Industrial Park. This avoids the problem of wasted estimator waiting time caused by differences in the computational efficiency of different networks under synchronous iterative methods. The steps for asynchronous state estimation in the Green Power Chemical Industrial Park are as follows: (1) Before performing state estimation in this network, obtain the interaction quantities of the other two types of networks as new pseudo-measures. If the information of the adjacent regions is not updated, continue to use the previous measurement value for estimation. (2) After a network has been estimated, the state estimate of the current network is corrected using the neighboring network information obtained in step (1); (3) The equivalent power values ​​of the other two types of networks are calculated using the state estimates of the nodes where the coupling elements of this network are located, and then transferred to the other two networks; (4) For a certain sub-network, the network state estimation converges if and only if the deviation between the local network power estimate of all nodes where its coupled elements are located and the equivalent power estimate obtained from the adjacent network is less than a certain threshold. Otherwise, asynchronous iteration continues.

[0051] Step 5: Construct a multi-time scale distributed state estimation architecture of a green electricity chemical industrial park combined with a new information trigger mechanism. According to the state estimation execution period of the electric, thermal, and material flow networks, that is, whether the new information trigger mechanism is met, to determine whether to perform real-time state estimation of the electric, thermal, and material flow at the current time. Generally, the time constants of the electric flow, hydrogen flow, and thermal flow increase in turn, that is, the state estimation execution period of the power distribution network is the smallest, and the state estimation execution period of the heating network is the longest. If the current time is the estimation time of the electric, thermal, and material flow networks and the new information trigger mechanism is met, then perform distributed state estimation of the three and output the state estimation value. If the current time is the estimation time of the electric and material flow networks and the new information trigger mechanism is met, then perform distributed state estimation of the two and output the state estimation value. If it is only the state estimation time of the power distribution network or the new information trigger mechanism is not met, then only perform state estimation of the power distribution network and output the state estimation value.

[0052] Further, the trigger value of the new information trigger mechanism is selected according to the state estimation error requirement of the green electricity chemical industrial park by performing sensitivity analysis of the electric power change value of the power distribution network-state variable error of the heating network, the electric power change value of the power distribution network-state variable error of the material network, the thermal power change value of the heating network-state variable error of the power distribution network, and the hydrogen power change value of the material network-state variable error of the power distribution network. If any threshold value is met, the new information trigger mechanism is triggered.

[0053] Step 6: Determine whether the current time is the predetermined end time. If yes, output the state estimation value of step 5 as the result. Otherwise, go to the next time and return to execute step 5.

[0054] Embodiment Two The embodiment provides a chemical industrial park distributed state estimation system considering multi-energy substance flow dynamics, comprising: A green electricity chemical industrial park parameter preprocessing module is configured to acquire information of a power distribution network, a heating network, a chemical material flow network, and hydrogen / ammonia / methanol chemical production equipment of the park, and perform unitization processing on the parameters. An electric power subsystem state estimation model construction module is configured to construct a power distribution network state space model as an electric power subsystem state estimation model. A chemical subsystem state estimation model construction module is configured to construct a heating network and material flow network state space model considering dynamic characteristics, and construct a hydrogen / ammonia / methanol chemical production equipment and electric heating equipment, hydrogen tank model as a chemical subsystem state estimation model. A green electricity chemical industrial park distributed state estimation modeling module is configured to construct a green electricity chemical industrial park distributed state estimation model considering multi-element substance flow and heterogeneous energy flow dynamics based on a distributed state estimation strategy of an asynchronous parallel computing architecture. The green electric chemical industrial park distributed state estimation execution module is used for combining the new information trigger mechanism to perform the park multi-time scale distributed state estimation according to the electric, heat, and material flow network state estimation execution period difference and park measurement new information. The green electric chemical industrial park state estimation end condition judgment module is used for judging whether the above distributed state estimation reaches the estimation end condition.

[0055] Those skilled in the art will understand that embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can 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-ROMs, optical storage devices, etc.) containing computer-usable program code. CD - ROM

[0056] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams 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 apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus generate an apparatus that implements the functions specified in the flowcharts and / or block diagrams of the flows and / or blocks. Figure 1 The functions specified in the flowcharts and / or block diagrams of the flows and / or blocks. Figure 1 The functions specified in the flowcharts and / or block diagrams of the flows and / or blocks.

[0057] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams of the flows and / or blocks. Figure 1 The functions specified in the flowcharts and / or block diagrams of the flows and / or blocks. Figure 1 The functions specified in the flowcharts and / or block diagrams of the flows and / or blocks.

[0058] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable data processing apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable data processing apparatus provide steps for implementing the functions specified in the flowcharts and / or block diagrams of the flows and / or blocks. Figure 1 The functions specified in the flowcharts and / or block diagrams of the flows and / or blocks. Figure 1 The functions specified in the flowcharts and / or block diagrams of the flows and / or blocks.

[0059] ​The above merely describes the preferred embodiments of the present application and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made by those skilled in the art without departing from the spirit and principle of the present application shall fall within the protection scope of the present application.

Claims

1. A chemical industrial park distributed state estimation method considering multi-energy flow dynamics, characterized in that, The method comprises the following steps: The green electricity chemical industry park is divided into an electricity subsystem and a chemical subsystem, wherein the electricity subsystem manages the operation of the power distribution network, and the chemical subsystem manages the operation of the heat supply network, the chemical material flow network and the chemical production equipment; Network information and equipment information in each subsystem are obtained respectively, and the branches in the power distribution network, the heat supply network and the chemical material flow network and the coupling elements of the hydrogen / ammonia / methanol chemical production equipment are subjected to parameter unitization processing; For the electricity subsystem, according to the power distribution network information, the state transition equation of the power distribution network under quasi-steady state is constructed by using the back / forward sweep method, and the power distribution network state space model is constructed in combination with the power distribution network measurement model; For the chemical subsystem, according to the material flow network pipe pressure equation, the material flow momentum equation and the material flow pipe mass balance equation, the partial differential model of the material flow network is established and is subjected to discretization processing, the state transition equation of the material flow network is constructed, and the material flow network state space model is constructed in combination with the measurement model of the material flow network; according to the dynamic thermodynamic model of the heat supply network considering the pipe transmission loss, the partial differential model of the heat supply network is established and is subjected to discretization processing, and the heat supply network state space model considering the temperature transmission dynamic characteristics is constructed in combination with the measurement model of the hydraulic network and the heat network in the heat supply network; For the hydrogen / ammonia / methanol chemical production equipment and the coupling elements, equation constraints describing the power consumption, the raw material input and the chemical production heat release are constructed, and the coupling element state space model is constructed; Based on the constructed power distribution network state space model, the material flow network state space model and the heat supply network state space model, the green electricity chemical industry park real-time state estimation model considering the dynamic characteristics of the multi-element material flow and the heterogeneous energy flow is constructed, and the green electricity chemical industry park distributed state estimation model is constructed based on the distributed state estimation strategy of the asynchronous parallel computing architecture; The innovation news trigger mechanism is introduced, whether the real-time state estimation of electricity, heat and material flow is performed at the current time is judged according to the state estimation execution period of the electricity, heat and material flow network and the innovation news trigger mechanism, if yes, the distributed state estimation of the corresponding network is performed by using the green electricity chemical industry park distributed state estimation model, and the state estimation value is obtained; Whether the current time is the predetermined end time is judged, if yes, the obtained state estimation value is taken as the result, otherwise, the next time is entered, and the step is returned.

2. The method according to claim 1, wherein the method is characterized by, The power distribution network information comprises power distribution network topology information, branch impedance and ground admittance information, node-to-ground capacitance information, power distribution network measurement information and power distribution network state estimation execution period; The chemical heat supply network information comprises heat supply network topology information, heat supply pipe length, pipe cross-sectional area, pipe friction coefficient, pipe heat transfer coefficient, environment temperature, heat supply network measurement information and heat supply network state estimation execution period; The material flow network takes the hydrogen supply network as a representative, and the information comprises hydrogen supply network topology, pipe length, pipe cross-sectional area, pipe friction coefficient, hydrogen temperature, hydrogen compression coefficient, hydrogen gas constant, hydrogen supply network measurement information and hydrogen supply network state estimation execution period.

3. The method according to claim 1, wherein the method is characterized by, The process of constructing the state space model of the distribution network by using the reverse power flow calculation method to construct the state transition equation of the distribution network under quasi-steady state and combining the measurement model of the distribution network includes the following steps x e =[ V , θ ] T , including the voltage amplitude V i and the voltage phase angle θ i of each node z e =[ P ij,meas , Q ij,mes , I ij,meas , V meas , θ meas ,P i,meas ,Q i,meas ] T , including the partial branch active power, reactive power or current measurement uploaded by the remote terminal unit, the partial node voltage amplitude and phase angle measurement uploaded by the synchronous phasor unit, and the node injected active power and reactive power pseudo measurement generated according to historical load data; The power distribution network state space model is constructed as follows: ； ； Among them, subscript k express k time; u e,k The input variable matrix for the distribution network includes the active and reactive power injection at each node, Δ. u e,k The power changes injected into nodes between adjacent time points are obtained from load forecasting. f e (·) represents the nonlinear state transition equation of the distribution network; J e,k-1 According to k Jacobian matrix of distribution network calculated at time -1; ξ e,k The non-Gaussian process noise of the distribution network has the following error covariance matrix: ; z e,k For distribution network quantity measurement matrix; h e (·) represent the nonlinear measurement equations for the distribution network; ω e,k The non-Gaussian measurement noise matrix of the distribution network has the following error covariance matrix: R e,k .

4. The method according to claim 1, wherein the method is characterized by, The process of constructing the state space model of the material flow network by constructing the state transition equation of the material flow network and combining the measurement model of the material flow network comprises: setting a state quantity matrix of the material flow network x M =[ ρ , M ] T , including hydrogen density of each gas network node ρ i and hydrogen mass flow M i , a measurement information matrix z M =[ ρ i,meas , M i,meas , p i,meas , 0] T , including hydrogen density, hydrogen mass flow and node pressure of part of nodes, and 0 corresponds to virtual measurement in the material flow network; Assuming that the hydrogen temperature in the material flow network is constant and the hydrogen supply pipeline is horizontal, based on the material flow network information, the material flow momentum equation, the material flow pipeline mass balance equation and the pipeline pressure equation constructed based on fluid mechanics analysis, the partial differential-algebraic simplified model of the material flow network is established, that is: ； ； ； wherein, t and s denote time and spatial distance; v is the gas flow rate in the pipe; p is the pipe pressure; λ M is the hydrogen supply network pipe friction coefficient, d is the pipe diameter; c is the hydrogen internal sound speed; Based on the Lax-Wendroff difference format, the above formula is discretized to construct the state transition equation of the material flow network, that is: ； in, ρ i,k for k Time Node i The density of hydrogen gas; M i,k With M j,k for k Time Pipeline ij Outflow node i With inflow node j hydrogen mass flow rate; L ij For pipelines ij Length, S ij Its cross-sectional area, and the corresponding pipe radius is d ij ; p i,k for k Time Node i air pressure; For pipelines ij Average internal hydrogen flow rate; The state space model of the material flow network is constructed in combination with the measurement model of the material flow network: ； ； where, u M,k is the input variable matrix of the material flow network, including the hydrogen density of the gas source node and the hydrogen load node; f M is the state transition equation of the material flow network, i.e., the above differentiation equation; ξ M,k is the non-Gaussian process noise of the material flow network, and the error covariance matrix is ; z M,k is the measurement matrix of the material flow network; h M is the measurement equation of the material flow network, respectively; ω M,k is the non-Gaussian measurement noise matrix of the material flow network, and the error covariance matrix is R M,k The gas continuity equation and the gas source pressure set value of the material flow network will be used as virtual measurements.

5. The method according to claim 1, wherein the method is characterized by, Setting a heat supply network state quantity matrix x H [ T s , T r ] T , including each node heat supply temperature T s,i and heat return temperature T r,i , a measurement information matrix z M [ H , T s , T r , 0] T , including node heat power, partial node heat supply temperature and heat return temperature, and 0 corresponding to a virtual measurement in the heat supply network; according to a dynamic thermodynamic model of the heat supply network considering pipeline transmission loss, a partial differential model of the heat supply network is established and discretized, and combined with a measurement model of the hydraulic network and the thermodynamic network in the heat supply network, a heat supply network state space model considering temperature transmission dynamic characteristics is constructed; Specifically, assuming that the heat supply network adopts a constant flow variable temperature working medium regulation mode, according to the law of conservation of heat, the partial differential model of the heat network in the heat supply network is established: ； wherein, T is the temperature of the heat supply network node; m is the mass flow of hot water in the pipe, T a is the ambient temperature; C p is the specific heat capacity of water, ρ water is the density of water; λ M is the heat loss coefficient of the hydraulic network; Based on the Lax-Wendroff difference format, the above formula is discretized to construct the state transition equation of the heat supply network, that is: ； In combination with the measurement model of the heat supply network, the state space model of the heat supply network is constructed: ； ； wherein, u H,k is the input variable matrix of the heating network; f H (·) is the state transition equation of the heating network, i.e. the above differentiation equation; ξ H,k is the non-Gaussian process noise of the heating network, and the error covariance matrix thereof is ; z H,k is the measurement matrix of the heating network; h H (·) are the measurement equations of the heating network, respectively; ω H,k is the non-Gaussian measurement noise matrix of the heating network, and the error covariance matrix thereof is R H,k ; The water flow continuity equation, the node temperature mixing equation and the loop water pressure equation of the heat supply network will be used as virtual measurements to improve the measurement redundancy of the heat supply network.

6. The method of claim 1, wherein the method is characterized by, The process of constructing the state space model of the coupling element includes: for the electrolytic cell, according to the chemical reaction equation of electrolytic water to produce hydrogen, the electrolytic cell model considering the coupling of power consumption-hydrogen production and hydrogen production-water consumption is constructed, that is: ； wherein, is the molar flow rate of hydrogen produced by the electrolyzer at the time instant t; k is the molar flow rate of hydrogen produced by the electrolyzer at the time instant t; is the electrical power consumed by the electrolyzer at the time instant t; k is the electrical power consumed by the electrolyzer at the time instant t; is the water consumption of the electrolyzer at the time instant t; k is the water consumption of the electrolyzer at the time instant t; η EL is the hydrogen production efficiency of the electrolyzer; α 0 is the water consumption efficiency of the electrolyzer; q H2 is the low-grade thermal energy of hydrogen; For green electricity ammonia synthesis equipment, according to the chemical reaction equation of ammonia synthesis, the green electricity ammonia synthesis equipment model considering the coupling of power consumption-hydrogen consumption, ammonia production-hydrogen consumption and heat production-ammonia production is constructed, that is: ； wherein , , and are respectively k the molar flow rates of air, nitrogen, hydrogen and ammonia produced at the moment of consumption by the ammonia synthesis plant; is k the total electric power consumed at the moment by the ammonia synthesis reactor; is k the exothermic power of the ammonia synthesis plant at the moment; is k the ammonia production at the moment; M air is the molar mass of air, R H2 is the hydrogen gas constant, T AS is the air separation plant inlet gas temperature, p AS in is the air separation plant compressor inlet gas pressure, p AS out is the air separation plant compressor outlet gas pressure, η C is the mechanical efficiency of the air separation plant compressor; P P2A N is the fixed electric power of the ammonia synthesis reactor, l is the electric power consumed to produce a unit molar flow rate of ammonia; η h,P2A is the exothermic rate of the ammonia synthesis plant; q NH3 is the exothermic quantity to produce a unit mass of ammonia; For methanol synthesis equipment, according to the chemical reaction equation of ammonia synthesis, the methanol synthesis equipment model considering the coupling of power consumption-hydrogen consumption, alcohol production-hydrogen consumption and heat production-hydrogen consumption is constructed: ； wherein, is k the electric power consumed by the methanol synthesis plant at the instant t; , and are respectively k the molar flow rates of carbon dioxide and hydrogen consumed by the methanol synthesis plant at the instant t and the synthesis of methanol; is k the exothermic power of the methanol synthesis plant at the instant t; η P2M is the methanol synthesis efficiency; Δ H P2M is the heat released by the synthesis of a unit mass of methanol; For hydrogen storage tank, according to its energy balance constraint and production scheduling plan, its state transition equation is constructed, and the hydrogen storage tank model is completed considering the coupling constraint of power consumption-hydrogen import, that is: ； wherein, is k the state of charge of the hydrogen storage tank at time t; and is k the mass flow rate of the hydrogen storage tank at time t; is k the electrical power consumed by the hydrogen storage tank compressor at time t; V HSS is the capacity of the hydrogen storage tank; η ch HSS and η dis HSS are the charging and discharging efficiencies of the hydrogen storage tank, respectively; T HSS is the temperature of the hydrogen storage tank at time t, p HSS in and p HSS out are the inlet and outlet pressures of the hydrogen storage tank compressor, respectively; η HC is the mechanical efficiency of the hydrogen storage tank; M H2 is the molar mass of hydrogen; For electric heating equipment, according to its power consumption-heat production constraint, the electric heating equipment model is constructed, that is: ； wherein, is k the electric power consumed by the electric heating equipment at the moment; is k the heat production power of the electric heating equipment at the moment.

7. The method according to claim 1, wherein the method is characterized by, Based on the constructed state space model of the power distribution network, the state space model of the material flow network and the state space model of the heat supply network, the process of constructing the real-time state estimation model of the green chemical industrial park considering the dynamic characteristics of multi-substance flow and heterogeneous energy flow includes: setting independent estimators for the power subsystem power distribution network and the chemical subsystem heat supply network and material flow network respectively, and each network estimator performs its own state estimation based on the particle filtering algorithm according to the corresponding state space model constructed; Based on the state variable estimation value obtained by each network estimator performing its own state estimation, the power value of the coupling element in the other two types of networks is calculated according to the coupling element model, which is transmitted to the other two types of network estimators as limited interaction information, and asynchronous parallel iteration is performed until the deviation between the equivalent power estimation value of the coupling element obtained from the adjacent network and the network power estimation value is less than a threshold value.

8. The method according to claim 1, wherein the method is characterized by, The process of constructing a distributed state estimation model of a green chemical industrial park based on an asynchronous parallel computing architecture includes: before state estimation of a current network, obtaining interaction quantities of two other types of networks as new pseudo-measurements, and if adjacent area information is not updated, continuing to use last measurement values for estimation; After a network completes estimation, using obtained adjacent network information to correct state estimation values of the network; Using state estimation values of nodes where coupling elements of the network are located to calculate equivalent power values of the other two types of networks, and transmitting the equivalent power values to the other two networks; For a sub-network, the state estimation of the network converges only when the deviation between the power estimation values of the network at nodes where all coupling elements are located and the equivalent power estimation values obtained from adjacent networks is less than a corresponding threshold value, otherwise, asynchronous iteration is continued.

9. A distributed state estimation method for chemical industrial parks considering multi-energy mass flow dynamics as described in claim 1, characterized in that it introduces... The process of the innovation trigger mechanism includes: a trigger value of the innovation trigger mechanism is selected according to state estimation error requirements of the green chemical industrial park by performing sensitivity analysis of power change value of the power distribution network-state variable error of the heat supply network, power change value of the power distribution network-state variable error of the material network, heat power change value of the heat supply network-state variable error of the power distribution network and hydrogen power change value of the material network-state variable error of the power distribution network, and the innovation trigger mechanism is triggered if any threshold value is met; The process of determining whether to perform real-time state estimation of the electric, heat and material flow networks in coordination according to the state estimation execution period of the electric, heat and material flow networks and the innovation trigger mechanism includes: if the current time is the estimation time of the electric, heat and material flow networks and the innovation trigger mechanism is met, performing distributed state estimation of the three and outputting state estimation values; if the current time is the estimation time of the electric and material flow networks and the innovation trigger mechanism is met, performing distributed state estimation of the two and outputting state estimation values; and if the current time is only the estimation time of the power distribution network or the innovation trigger mechanism is not met, performing state estimation of only the power distribution network and outputting state estimation values.

10. A distributed state estimation system for a chemical industrial park that takes into account the dynamics of multiple properties, characterized in that, The process includes: A green chemical industrial park parameter preprocessing module is configured to divide the green chemical industrial park into an electric power subsystem and a chemical subsystem, the electric power subsystem manages operation of a power distribution network, and the chemical subsystem manages operation of a heat supply network, a chemical material flow network and chemical production equipment; network information and equipment information in each subsystem are obtained respectively, and coupling elements of branches in the power distribution network, the heat supply network and the chemical material flow network and hydrogen / ammonia / methanol chemical production equipment are subjected to parameter normalization processing; An electric power subsystem state estimation model construction module is configured to, for the electric power subsystem, construct a power distribution network state transition equation under quasi-steady state according to power distribution network information by using a back / forward sweep method, and construct a power distribution network state space model in combination with a power distribution network measurement model; The chemical subsystem state estimation model construction module is configured to, for the chemical subsystem, establish a partial differential model of the material flow network and perform discretization processing according to a material flow network pipe pressure equation, a material flow momentum equation and a material flow pipe mass balance equation, construct a state transition equation of the material flow network, and construct a material flow network state space model in combination with a measurement model of the material flow network; establish a partial differential model of the heat supply network and perform discretization processing according to a dynamic thermodynamic model of the heat supply network considering pipe transmission loss, and construct a heat supply network state space model considering temperature transmission dynamic characteristics in combination with measurement models of a hydraulic network and a thermal network in the heat supply network; The coupling element state space model construction module is configured to, for the hydrogen / ammonia / methanol chemical production equipment and the coupling element, construct an equation constraint describing the relationship among power consumption, raw material input and chemical production heat release, and construct a coupling element state space model; The distributed state estimation modeling module is configured to, based on the constructed power distribution network state space model, the material flow network state space model and the heat supply network state space model, construct a green electricity chemical industrial park real-time state estimation model considering dynamic characteristics of multi-element material flow and heterogeneous energy flow, and construct a green electricity chemical industrial park distributed state estimation model based on a distributed state estimation strategy of an asynchronous parallel computing architecture; The distributed state estimation execution module is configured to introduce a new information trigger mechanism, determine whether to perform real-time state estimation of electricity, heat and material flow at the current time according to a state estimation execution cycle of the electricity, heat and material flow network and the new information trigger mechanism, if so, perform distributed state estimation of the corresponding network by using the green electricity chemical industrial park distributed state estimation model to obtain state estimation values; The state estimation end condition judgment module is configured to determine whether the current time is a predetermined end time, if so, take the obtained state estimation values as a result, and if not, enter the next time and call the distributed state estimation execution module again.

Images