An electric-thermal-gas-hydrogen multi-energy microgrid probability energy flow calculation method

Abstract

The present application relates to the field of new energy microgrid technology, and specifically provides a kind of electricity-heat-gas-hydrogen multi-energy microgrid probability energy flow calculation method, comprising: based on the uncertainty source of the double side of multi-energy microgrid source and load, generate random control variable sample set with correlation;According to the physical characteristics of hydrogen blending operation of natural gas system, the linearization energy flow model of multi-energy microgrid is established;Using random control variable sample set and linearization energy flow model, generate operating variable sample set, and obtain the basic operating point set of power grid, heat network and gas network subsystem;Multiple subsystem basic operating point set is executed respectively Multi-point linearization Monte Carlo probability energy flow calculation, obtain electricity-heat-gas-hydrogen multi-energy microgrid probability energy flow calculation result.The present application method has both calculation precision and operation efficiency, can effectively reflect the operating characteristics of hydrogen blending multi-energy microgrid, suitable for high proportion new energy access scene under electricity-heat-gas-hydrogen multi-energy microgrid probability energy flow calculation.

Description

Technical Field

[0001] This invention relates to the field of new energy microgrid technology, and in particular to a probabilistic energy flow calculation method for an electric-heat-gas-hydrogen multi-energy microgrid. Background Technology

[0002] With the depletion of fossil fuels and the continued aggravation of environmental pollution, the transformation of the energy structure towards a low-carbon and diversified direction has become an urgent task. The volatility and intermittency of renewable energy output place high demands on the flexible backup and regulation capabilities of microgrid systems. Meanwhile, constrained by factors such as energy storage capacity, transmission bottlenecks, and source-load interaction mechanisms, the current renewable energy curtailment rate in microgrids remains high. Among numerous solutions to improve the absorption of new energy, multi-energy microgrids, with their heterogeneous energy interconnection and complementarity and flexible and efficient operation characteristics, have become a key support for promoting the large-scale utilization of renewable energy and improving system flexibility. However, as the installed capacity of new energy continues to increase, the uncertainties within multi-energy microgrids have significantly increased. The strong coupling between subsystems can also lead to the propagation of random disturbances between different energy networks, which brings new challenges to the energy flow modeling and operational analysis of multi-energy microgrids.

[0003] On the one hand, in traditional multi-energy microgrids (E-Gather-Gas), the introduction of technologies such as Power to Gas (P2G) and Power to Heat (P2H) has improved the absorption of new energy sources, but it also transmits the fluctuations in wind and solar power output to the source side of the gas and heat networks. However, existing probabilistic energy flow studies for multi-energy microgrids rarely consider the stochasticity of P2G and P2H outputs simultaneously. Furthermore, existing studies generally assume that P2G products have the same properties as natural gas, failing to account for the gas quality uncertainty caused by hydrogen blending. Therefore, it is necessary to propose a probabilistic energy flow calculation method suitable for E-Gather-Gas-Hydrogen multi-energy microgrids.

[0004] On the other hand, probabilistic energy flow (PEF) analysis can characterize the distribution characteristics of system state variables under random disturbances and has been widely used in the research on the operational safety of multi-energy systems. Among the existing PEF calculation methods, simulation methods, represented by Monte Carlo simulation (MS), have high fidelity given a sufficient sample size, but are inefficient. While approximation methods and simulation methods, represented by point estimation and semi-invariant methods, which fit the probability distribution of random variables based on their numerical characteristics, can effectively reduce computational costs, in high-penetration new energy scenarios, the state variables of multi-energy microgrids exhibit complex distribution characteristics such as non-Gaussian and multimodal distributions, which will significantly increase the fitting error of their higher-order moments. In response, some scholars have proposed PEF methods based on Single-point Linear MS (SLMS) and Multi-point Linear MS (MLMS). These methods select one or more benchmark operating points within the range of random variable fluctuations and linearly expand the energy flow equation at each point. The actual effect largely depends on the selection of the linear expansion points and the division of the random samples. To achieve efficient sample partitioning, some studies have proposed the traditional clustering-based multilinear probabilistic energy flow calculation method (TCMS). However, this method directly uses random samples of control variables as the reference for selecting basic operating points, which is unreasonable. This is because the input control variables need to be mapped through the energy flow equation to obtain the output state variables, and this mapping process obviously changes the numerical characteristics of the output state variables. In addition, since the subsystems in a multi-energy microgrid have significant differences in operating characteristics and uncertainties, and TCMS treats all random variables as a whole for centralized clustering, the obtained basic operating points are the result of weighing the probability distribution of the operating states of each subsystem. It is difficult to cover the probability distribution space of different subsystems at the same time, and it is easy to find that the linearization accuracy of a certain subsystem in some clusters meets the standard while that of other subsystems does not. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a probabilistic energy flow calculation method for an electric-thermal-gas-hydrogen multi-energy microgrid, which can take into account the influence of hydrogen mixing on the operating state of the multi-energy microgrid and effectively quantify the influence of multiple uncertain disturbances of source and load on the energy flow distribution of the multi-energy microgrid.

[0006] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0007] This invention provides a method for calculating the probabilistic energy flow of an electric-thermal-gas-hydrogen multi-energy microgrid, comprising:

[0008] Based on the uncertainties on both the source and load sides of a multi-energy microgrid, a stochastic control variable generation model for the multi-energy microgrid is constructed; and using the stochastic control variable generation model, a set of stochastic control variables with correlation is generated; wherein, the uncertainties include the power of the electricity-to-hydrogen injection and the power of the electricity-to-heat heat source;

[0009] Based on the physical characteristics of hydrogen-blended operation in natural gas systems, an energy flow model for the multi-energy microgrid is established, and the energy flow model is linearized using a first-order Taylor expansion to obtain a linearized energy flow model.

[0010] Based on the linearized energy flow model, a single-point linearized Monte Carlo probability energy flow calculation is performed on the random control variable sample set to generate a running variable sample set;

[0011] Distributed clustering is performed on the sample set of operating variables to obtain the basic operating point set of the power grid subsystem, the basic operating point set of the heating network subsystem, and the basic operating point set of the gas network subsystem.

[0012] Multi-point linearized Monte Carlo probabilistic energy flow calculations were performed on the basic operating point sets of the power grid subsystem, the heating network subsystem, and the gas network subsystem, respectively, to obtain the probabilistic energy flow calculation results of the electricity-heat-gas-hydrogen multi-energy microgrid.

[0013] In some embodiments of the present invention, the sources of uncertainty include source-side uncertainty sources and load-side uncertainty sources;

[0014] The sources of uncertainty on the source side include: photovoltaic power plant output, wind turbine output, power of electricity-to-hydrogen injection, and power of electricity-to-heat heat source.

[0015] The sources of uncertainty on the load side include: electrical load, thermal load, and gas load.

[0016] In some embodiments of the present invention, a stochastic control variable generation model for a multi-energy microgrid is constructed based on the uncertainties on both the source and load sides of the multi-energy microgrid, including:

[0017] The uncertainty of solar irradiance received by a photovoltaic power station is characterized by the Beta distribution, so as to obtain the active power output probability model and the reactive power output probability model of the photovoltaic power station.

[0018] The uncertainty of wind speed of wind turbines is characterized by a two-parameter Weibull distribution, so as to obtain the active power output probability model and the reactive power output probability model of wind turbines.

[0019] Based on the active power output probability model of the wind turbine and / or the active power output probability model of the photovoltaic power station, a power probability model for electricity-to-hydrogen conversion and hydrogen injection is obtained.

[0020] Based on the active power output probability model of the wind turbine and / or the active power output probability model of the photovoltaic power station, the power probability model of the electricity-to-heat source is obtained.

[0021] The uncertainty of electricity load, heat load and gas load is characterized by the normal distribution, so as to establish a probabilistic model for electricity load, heat load and gas load.

[0022] By integrating the active power output probability model of the photovoltaic power station, the reactive power output probability model of the photovoltaic power station, the active power output probability model of the wind turbine, the reactive power output probability model of the wind turbine, the power probability model of the electricity-to-hydrogen injection, the power probability model of the electricity-to-heat heat source, the power load probability model, the heat load probability model, and the gas load probability model, a stochastic control variable generation model for multi-energy microgrids is constructed.

[0023] In some embodiments of the present invention, an energy flow model of the multi-energy microgrid is established based on the physical characteristics of hydrogen-blended operation of a natural gas system, including:

[0024] A distributed balancing node mechanism is introduced to construct a power grid energy flow model that includes multiple gas turbines participating in power balancing.

[0025] Construct a heat network energy flow model that includes hydraulic and thermal models;

[0026] Based on the physical characteristics of hydrogen-blended operation in natural gas systems, an energy flow model for hydrogen-blended gas networks is constructed.

[0027] A gas turbine model, a gas boiler model, and an electric-to-hydrogen device model are constructed to obtain a multi-energy microgrid coupling device model.

[0028] The energy flow model of the multi-energy microgrid is established by integrating the power grid energy flow model, the heat network energy flow model, the hydrogen-mixed gas network energy flow model, and the coupling equipment model.

[0029] In some embodiments of the present invention, a hydrogen-blended gas network energy flow model is constructed based on the physical characteristics of hydrogen-blended operation in a natural gas system, including:

[0030] The gas energy flow deviation equations of the hydrogen-mixed gas network energy flow model It is expressed as follows:

[0031] ;

[0032] ;

[0033] ;

[0034] in, This represents the volumetric flow rate vector of the gas network pipeline. This is the gas network node-pipeline correlation matrix; This is the direction factor for the gas network pipeline; This is the squared pressure vector at the starting point of the pipeline; This is the squared pressure vector at the end of the pipeline; This is the gas network pipeline coefficient vector; For matrix operators; These are the characteristic parameters of the pipeline; The temperature of the gas inside the pipe; The diameter of the pipe; The coefficient of friction of the pipeline; This refers to the length of the pipe. This is the vector of gas compressibility factors inside the pipeline; This is a directional gas network node-pipeline correlation matrix; This is the density vector of natural gas; This is a column vector representing the density difference between hydrogen and natural gas. This is the vector of the mole fraction of hydrogen gas. This refers to the flow rate index of the gas pipeline network. is the diameter index of the gas network pipeline; μ is the specific gravity index of the gas network pipeline. =[ , This represents the gas network node-equipment association matrix. The correlation matrix between gas network nodes and balanced gas sources; This is the correlation matrix between gas network nodes and compressors; This is the correlation matrix between gas network nodes and unbalanced gas sources. =[ ; [ ] represents the volumetric flow rate vector of the gas network equipment; The volumetric flow rate vector of the gas source for gas network balance; This is the volumetric flow rate vector of the gas network compressor; This is the energy flow vector of the unbalanced gas source in the gas network. This is the calorific value vector of the unbalanced gas source in the gas network. This represents the load energy flow vector of the gas network node; The correlation matrix between nodes in the hydrogen mixing network and compressor intake points; The volumetric flow rate vector of the gas-driven compressor; This is the equivalent energy flow vector of the gas volume flow rate consumed by the gas-driven compressor under the standard gas calorific value. This is the gas calorific value vector at the extraction node; , and All are energy conversion coefficient vectors of air-driven compressors; For the horsepower vector of the compressor; This is the compressor power coefficient vector; These are characteristic parameters of the compressor; The gas temperature under standard conditions; Gas pressure under standard conditions; This refers to the gas temperature at the compressor inlet. This is the gas compressibility factor vector at the compressor inlet; The compression index vector of the compressor; This is the compressor inlet pressure vector; This is the compressor outlet pressure vector; For the compressor's polytropic exponential vector; This is a vector representing the calorific value of natural gas. This is a column vector representing the calorific value difference between hydrogen and natural gas. This is the hydrogen mole fraction vector at the extraction node; This is the coefficient matrix of the squared pressure at the nodes of the gas network; The squared pressure vector of the gas network node; d is the coefficient matrix of the gas network equipment flow rate; d is the compressor control parameter vector. The hydrogen mole fraction vector of the gas injected at the equilibrium gas source; The hydrogen mole fraction vector is the gas injected at the non-equilibrium gas source. This is the vector of volumetric flow rate deviation at the nodes of the hydrogen mixing network. This represents the deviation vector of the equipment control equations; This is the vector of hydrogen flow rate deviation at the nodes of the hydrogen mixing network.

[0035] The unknown state variable vector of the hydrogen-mixed gas network energy flow model .

[0036] In some embodiments of the present invention, the energy flow model is linearized using a first-order Taylor expansion to obtain a linearized energy flow model, including:

[0037] The energy flow model is expressed as follows: ,in, This is the output state variable vector of the energy flow model. This represents the input control variable vector for the energy flow model;

[0038] Assume the multi-energy microgrid is currently operating at point ( ). , ),exist( , A first-order Taylor expansion is performed on the energy flow model at point () to obtain the operating point (). , ) and Linearized relationship between them;

[0039] The expression for the first-order Taylor expansion is as follows:

[0040] ;

[0041] in, For multi-energy microgrids at the operating point ( , The Jacobian matrix at () For multi-energy microgrids at the operating point ( , The sensitivity matrix of the state variables at position ( ). This is a vector of changes in the input control variables; This outputs a vector of changes in the state variables.

[0042] The dependency variable vector in a multi-energy micronet is represented as follows ,exist( , Performing a first-order Taylor expansion on the dependent variable vector at point () yields the run point () , ) and Linearized relationship between them;

[0043] The expression for the first-order Taylor expansion is as follows:

[0044] ;

[0045] in, For multi-energy microgrids at the operating point ( , The sensitivity matrix of the dependent variables at position (). This is a vector of changes in the dependent variables;

[0046] definition , , ;in, The water supply temperature vector for each node in the heating network; The return water temperature vector of the heating network node; For the pressure vector of the gas network node; This represents the active power vector of the net load of the power grid bus. The reactive power vector of the net load of the power grid bus; This represents the net load power vector of the heating network nodes. This represents the active power vector of the power grid branch; For the Wobbe number vector of the gas flowing out of the gas network node; For the combustion potential vector of the gas flowing out of the gas network node;

[0047] Multi-energy microgrid at the operating point ( , The linearized energy flow model of ) is expressed as:

[0048] ;

[0049] ;

[0050] in, For multi-energy microgrids at the operating point ( , The linearized matrix of the state variables at position (). For multi-energy microgrids at the operating point ( , Linearization of the dependency variables at position () for In and variables Corresponding row sums and variables The submatrix formed by the corresponding columns; for In and variables Corresponding row sums and variables The submatrix formed by the corresponding columns.

[0051] In some embodiments of the present invention, distributed clustering is performed on the sample set of operating variables to obtain a set of basic operating points for the power grid subsystem, a set of basic operating points for the heating network subsystem, and a set of basic operating points for the gas network subsystem, including:

[0052] The maximum value of the sample set of running variables is standardized.

[0053] The standardized sample set of operating variables is divided into the sample set of operating variables of the power grid subsystem, the sample set of operating variables of the heating network subsystem, and the sample set of operating variables of the gas network subsystem.

[0054] The K-means++ clustering algorithm was used to perform independent clustering on the sample sets of operating variables of the power grid subsystem, the heating network subsystem, and the gas network subsystem, respectively.

[0055] Using the cluster centers obtained from clustering as the basic operating points of the corresponding subsystems, we obtain the basic operating point sets of the power grid subsystem, the heating network subsystem, and the gas network subsystem.

[0056] In some embodiments of the present invention, multi-point linearized Monte Carlo probabilistic energy flow calculations are performed on the basic operating point sets of the power grid subsystem, the heating network subsystem, and the gas network subsystem, respectively, to obtain the probabilistic energy flow calculation results of the electricity-heat-gas-hydrogen multi-energy microgrid, including:

[0057] Perform multi-point linearized Monte Carlo probabilistic energy flow calculations on the set of basic operating points of the power grid subsystem to obtain multi-linear results for the power grid operating variables;

[0058] Perform multi-point linearized Monte Carlo probabilistic energy flow calculations on the set of basic operating points of the heating network subsystem to obtain multi-linear results of heating network operating variables;

[0059] Perform multi-point linearized Monte Carlo probabilistic energy flow calculations on the set of basic operating points of the gas network subsystem to obtain multilinear results of gas network operating variables;

[0060] Correct the node temperatures in the multilinear results of the heating network operation variables;

[0061] The multilinear results of the temperature-corrected heating network operation variables are integrated with the multilinear results of the power grid operation variables and the gas network operation variables to obtain the probabilistic energy flow calculation results of the electricity-heat-gas-hydrogen multi-energy microgrid.

[0062] In some embodiments of the present invention, multi-point linearized Monte Carlo probabilistic energy flow calculations are performed on the set of basic operating points of the power grid subsystem to obtain multilinear results of power grid operating variables, including:

[0063] The formula for performing multi-point linearized Monte Carlo probabilistic energy flow calculation on the basic operating point set of the power grid subsystem is as follows:

[0064] ;

[0065] ;

[0066] in, For the control variable sample of the multi-energy microgrid corresponding to the cth basic operating point in the set of basic operating points of the power grid subsystem; is the sample mean vector of control variables of the multi-energy microgrid corresponding to the c-th basic operating point in the set of basic operating points of the power grid subsystem; for The corresponding mean vector of state variables; for The corresponding mean vector of the compliance variables; for The corresponding power grid state variables; for The corresponding compliance variables; for The corresponding sample of running variables is a single linear result; For a multi-energy microgrid, the linearized matrix of the grid state variables at the c-th basic operating point in the set of basic operating points of the grid subsystem is used; For a multi-energy microgrid, the linearized matrix of the grid dependency variables at the c-th basic operating point in the set of basic operating points of the grid subsystem is used; The results are multilinear results for the power grid operation variable samples.

[0067] In some embodiments of the present invention, the nodal temperatures in the multilinear results of the heating network operation variables are corrected, including:

[0068] The thermal energy flow deviation equations of the heating network energy flow model It is expressed as follows:

[0069] ;

[0070] ;

[0071] ;

[0072] in, Specific heat capacity of the heat transfer medium; The node-pipeline correlation matrix of the heating network; For the reason A matrix formed by rows of heat source nodes in Central Africa; For the reason A matrix formed by rows of nodes of the non-equilibrium heat source in China; This represents the mass flow rate of hot water in the pipe. ' is the equivalent outflow temperature vector of non-source nodes in the water supply network; ' is the equivalent outflow temperature vector of the source node in the water supply network; ' is the equivalent outflow temperature vector of the source node in the recirculation network; ' is the equivalent outflow temperature vector of non-source nodes in the return water network; ' is the equivalent inflow temperature vector of a given source node in the water supply network; ' is the equivalent inflow temperature vector of a non-source node in the return water network; This is the vector of heat power consumed at non-source nodes; This is the vector of heat power consumed at the source node; the superscript S represents the water supply network; the superscript R represents the return water network; the subscript s indicates that the matrix is ​​composed of the rows containing the source nodes in the original matrix; the subscript nn indicates that the matrix is ​​composed of the rows and columns containing the non-source nodes in the original matrix. The relationship matrix between the heating network loop and the pipeline; This represents the vector of resistance coefficients for the heating network pipeline. This represents the Hadamard product operation of matrices; ~ , ~ and All are matrix operators; This is a directional heat network node-pipeline correlation matrix; This refers to the direction factor of the heating network pipeline; This is a matrix operator that represents the mean of the absolute values ​​of the target object and itself. The mass flow rate of hot water consumed at the nodes of the water supply network; The vector of heat transfer coefficients in the pipeline; This is the pipe length vector; This is the deviation vector of thermal power at non-heat source nodes; This is the deviation vector of thermal power at non-heat source nodes; This is the deviation vector of the cumulative voltage drop in the loop; ' is the vector of deviations in the equivalent water supply temperature of non-heat source nodes; ' is the vector of deviations in the equivalent water supply temperature of the heat source node; ' is the deviation vector of the equivalent return water temperature of non-heat source nodes; ' is the deviation vector of the equivalent return water temperature of the heat source node; Let [the vector be] the nodal thermal power deviation, given by matrix [ ; The result is obtained by performing elementary row operations based on the original node index of the heating network. ' is the vector of deviations in the equivalent water supply temperature at the nodes, defined by matrix [ '; The result is obtained by performing elementary row operations based on the original node index of the heating network. Let [the vector be] the deviation of the equivalent return water temperature at the node, defined by matrix [ '; The result is obtained by performing elementary row operations based on the original node index of the heating network.

[0073] The unknown state variable vector of the heat network energy flow model ;

[0074] Based on the aforementioned heat network energy flow model, the mass flow rate of hot water in the pipes is... Multilinear results and given supply and return water temperatures The formula for inversely calculating the node temperature is as follows:

[0075] ;

[0076] in, ' is the degree vector of the equivalent water supply temperature of the node; ' is the equivalent return water temperature vector of the node.

[0077] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0078] This invention, by taking into account not only the power fluctuations of wind power, photovoltaic power, and multiple loads, but also the uncertainties of the power of hydrogen injection from electricity to hydrogen and the power of heat source from electricity to heat, comprehensively characterizes various random factors on both the source and load sides of a multi-energy microgrid (electricity-heat-gas-hydrogen) under a high proportion of new energy access scenarios. Simultaneously, considering the impact of hydrogen blending on the system's operating state, the resulting probabilistic energy flow calculation results not only reflect the distribution characteristics of conventional multi-energy microgrid operating variables, but also accurately describe the gas network's gas quality uncertainty caused by hydrogen blending through parameters such as the Wobbe number and combustion potential. Furthermore, the probabilistic energy flow calculation method of this invention can effectively adapt to the differences in operating characteristics and random factors among the various subsystems of the multi-energy microgrid. The obtained basic operating points can cover the probability distribution space of different subsystems to the greatest extent, exhibiting excellent fitting accuracy when dealing with highly non-normal, multi-peaked random variables caused by a high proportion of new energy access, and ensuring high computational efficiency based on the core idea of ​​multi-point linearization. Attached Figure Description

[0079] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other embodiments based on these drawings without creative effort.

[0080] Figure 1 This is a flowchart illustrating a probabilistic energy flow calculation method for an electric-thermal-gas-hydrogen multi-energy microgrid provided in an embodiment of the present invention;

[0081] Figure 2 This is a flowchart illustrating another probabilistic energy flow calculation method for an electric-thermal-gas-hydrogen multi-energy microgrid provided in an embodiment of the present invention;

[0082] Figure 3 This is a schematic diagram of the multi-energy microgrid testing system structure in an embodiment of the present invention;

[0083] Figure 4 This is a graph showing the relationship between the number of clusters and the SSE index in an embodiment of the present invention;

[0084] Figure 5 The voltage amplitude of each method in the embodiments of the present invention. A diagram showing the comparison of the root mean of the calculation results;

[0085] Figure 6 The active power of the branches in each method of the embodiments of the present invention A diagram showing the comparison of the root mean of the calculation results;

[0086] Figure 7 The pipeline flow rate of each method in the embodiments of the present invention. A diagram showing the comparison of the root mean of the calculation results;

[0087] Figure 8 The water supply temperature in each method of the embodiments of the present invention is A diagram showing the comparison of the root mean of the calculation results;

[0088] Figure 9 The return water temperature of each method in the embodiments of the present invention. A diagram showing the comparison of the root mean of the calculation results;

[0089] Figure 10 The node air pressure of each method in the embodiments of the present invention. A diagram showing the comparison of the root mean of the calculation results;

[0090] Figure 11 The device flow rate of each method in the embodiments of the present invention. A diagram showing the comparison of the root mean of the calculation results;

[0091] Figure 12 The pipeline flow rate of each method in the embodiments of the present invention. A diagram showing the comparison of the root mean of the calculation results;

[0092] Figure 13 These are the nodal combustion potentials of the methods in the embodiments of the present invention. A diagram showing the comparison of the root mean of the calculation results;

[0093] Figure 14 The number of Wah-Bai nodes in each method of the embodiments of the present invention. A diagram showing the comparison of the root mean of the calculation results;

[0094] Figure 15 This is a schematic diagram comparing the probability density function of the voltage amplitude of the power grid bus E9 in an embodiment of the present invention;

[0095] Figure 16 This is a comparative schematic diagram of the probability density function of the power flow active power of power grid branches 23-36 in this embodiment of the invention;

[0096] Figure 17 This is a comparative schematic diagram of the probability density function of the flow rate in heating network pipes 25-26 in an embodiment of the present invention;

[0097] Figure 18 This is a comparative schematic diagram of the probability density function of the water supply temperature at heating network node H25 in an embodiment of the present invention;

[0098] Figure 19 This is a comparative schematic diagram of the probability density function of the air pressure at the gas network node G16 in this embodiment of the invention;

[0099] Figure 20This is a comparative schematic diagram of the probability density function of the combustion potential of the gas network node G20 in an embodiment of the present invention;

[0100] Figure 21 This is a schematic diagram comparing the expected average relative error of different methods when the number of clusters is 50 in an embodiment of the present invention.

[0101] Figure 22 This is a schematic diagram comparing the average relative error of the standard deviation of the calculation results of different methods when the number of clusters is 50 in an embodiment of the present invention.

[0102] Figure 23 This is a schematic diagram comparing the expected average relative error of different methods when the number of clusters is 100 in an embodiment of the present invention.

[0103] Figure 24 This is a schematic diagram comparing the average relative error of the standard deviation of the results calculated by different methods when the number of clusters is 100 in this embodiment of the invention.

[0104] Figure 25 This is a schematic diagram comparing the expected average relative error of different methods when the number of clusters is 150 in an embodiment of the present invention.

[0105] Figure 26 This is a schematic diagram comparing the average relative error of the standard deviation of the results calculated by different methods when the number of clusters is 150 in this embodiment of the invention.

[0106] Figure 27 In the embodiments of the present invention = A diagram comparing the expected average relative error of different calculation methods when the result is 25%;

[0107] Figure 28 In the embodiments of the present invention = A diagram comparing the average relative error of the standard deviation of the results calculated by different methods when the value is 25%.

[0108] Figure 29 In the embodiments of the present invention = A diagram comparing the expected average relative error of different calculation methods when the result is 50%;

[0109] Figure 30 In the embodiments of the present invention = A diagram comparing the average relative error of the standard deviation of the results calculated by different methods when the result is 50%;

[0110] Figure 31 In the embodiments of the present invention = A diagram comparing the expected average relative error of different calculation methods when the result is 75%;

[0111] Figure 32 In the embodiments of the present invention = A diagram comparing the average relative error of the standard deviation of the results calculated by different methods when the result is 75%;

[0112] Figure 33 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0113] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0114] like Figure 1 As shown, this embodiment of the invention provides a probabilistic energy flow calculation method for an electric-thermal-gas-hydrogen multi-energy microgrid. Figure 1 This is a flowchart illustrating the probabilistic energy flow calculation method for an electric-thermal-gas-hydrogen multi-energy microgrid. This flowchart only shows the logical sequence of the method in this embodiment. In other possible embodiments of the invention, different methods may be used, provided they do not conflict. Figure 1 Complete the steps shown or described in the order indicated.

[0115] See Figure 1 and Figure 2 The method of this invention specifically includes the following steps:

[0116] Step S101: Based on the sources of uncertainty on both the source and load sides of the multi-energy microgrid, construct a random control variable generation model for the multi-energy microgrid; and use the random control variable generation model to generate a sample set of correlated random control variables.

[0117] The uncertainties on both the source and load sides of a multi-energy microgrid include uncertainties on the source side and uncertainties on the load side. Uncertainties on the source side include: power output of photovoltaic power plants, power output of wind turbines, power of electricity-to-hydrogen injection, and power of electricity-to-heat heat source. Uncertainties on the load side include: electricity load, heat load, and gas load.

[0118] In this embodiment of the invention, based on the uncertain sources on both the source and load sides of the multi-energy microgrid, a random control variable generation model for the multi-energy microgrid is constructed, which may include steps S1011 to S1016.

[0119] Step S1011: Use the Beta distribution to characterize the uncertainty of solar irradiance received by the photovoltaic power station, so as to obtain the active power output probability model and the reactive power output probability model of the photovoltaic power station.

[0120] The uncertainty of the irradiance received by the photovoltaic panel is described by a Beta distribution, and its probability density function is calculated as follows: A photovoltaic power station consists of several photovoltaic panels, further based on... Building photovoltaic power stations is a meritorious act. probability model Photovoltaic power plants can typically be considered to operate in constant power factor mode, and the reactive power output of the photovoltaic power plant can be obtained based on a given power factor. probability model .

[0121] Step S1012: Use the two-parameter Weibull distribution to characterize the uncertainty of wind speed of wind turbines, so as to obtain the active power output probability model and the reactive power output probability model of wind turbines.

[0122] The uncertainty of wind turbine wind speed is described by a two-parameter Weibull distribution, and its probability density function is calculated as follows: A four-segment linear function is used to describe wind speed. Power output of wind turbine The relationship between them is further based on Establish probability model Wind turbines can also be considered to operate in constant power factor mode. They can be equated to PQ nodes in the power grid, absorbing reactive power from the grid. The reactive power output of the wind turbine can be obtained based on a given power factor. probability model .

[0123] Step S1013: Based on the active power output probability model of wind turbine and / or the active power output probability model of photovoltaic power station, obtain the power probability model of electricity-to-hydrogen injection.

[0124] Step S1014: Based on the active power output probability model of wind turbine and / or the active power output probability model of photovoltaic power station, obtain the power probability model of the electricity-to-heat heat source.

[0125] To improve the high-level absorption of renewable energy, both the power-to-hydrogen and power-to-heat conversion equipment are directly driven by the power output of new energy sources. This allows surplus power output after grid connection to be converted into hydrogen and heat energy, which then serve as the gas and heat sources to power the gas and heat networks respectively, thereby significantly reducing the curtailment rate of new energy. The conversion ratios of active power output from new energy sources to the electrical power of the power-to-hydrogen and power-to-heat conversion equipment are respectively... and The remaining active power output is fed into the grid, and the power generation from electricity to hydrogen and electricity to heat is connected to the gas grid and heat grid, respectively. At this point, the probabilistic models for the hydrogen injection power from electricity to hydrogen, the heat source power from electricity to heat, and the active power output from renewable energy can all be obtained based on the probabilistic model for the active power output of renewable energy.

[0126] In this embodiment of the invention, a power probability model for electricity-to-hydrogen injection is obtained based on the active power output probability model of wind turbine generators; and a power probability model for electricity-to-heat source is obtained based on the active power output probability model of photovoltaic power plants.

[0127] Specifically, the power of electro-hydrogen conversion and hydrogen injection. And wind power grid connection The probability model can be expressed as:

[0128] ;

[0129] In the formula, for probability density function for The probability density function; The hydrogen production efficiency of the electro-hydrogen conversion equipment.

[0130] Similar to the probabilistic model of hydrogen injection power from electro-to-hydrogen conversion, the power of the heat source from electro-to-heat conversion... Contributing to grid connection of photovoltaic power The probability model can be expressed as:

[0131] ;

[0132] In the formula, for The probability density function; for The probability density function; The heat generation efficiency of the electric-to-heat conversion equipment.

[0133] Step S1015: Use the normal distribution to characterize the uncertainty of electricity load, heat load and gas load, so as to establish the probability model of electricity load, heat load and gas load.

[0134] Electricity active power load is generally considered to follow a normal distribution, and the load power factor remains constant. Therefore, electricity active power load and reactive power load The probability model can be expressed as:

[0135] ;

[0136] In the formula, for The probability density function; for The probability density function; for Expectations; for Standard deviation; for and The power factor angle between them.

[0137] For thermal load and gas load Similarly, assuming it follows a normal distribution, a probabilistic model of the heat load can be obtained. Probabilistic model of gas load .

[0138] Step S1016: Integrate the active power output probability model of photovoltaic power station, the reactive power output probability model of photovoltaic power station, the active power output probability model of wind turbine generator, the reactive power output probability model of wind turbine generator, the power probability model of electricity-to-hydrogen injection, the power probability model of electricity-to-heat heat source, the power load probability model, the heat load probability model, and the gas load probability model to construct a stochastic control variable generation model for multi-energy microgrid.

[0139] In this embodiment of the invention, generating a sample set of correlated random control variables using a random control variable generation model may include:

[0140] First, based on Monte Carlo simulation, random variables that follow an independent standard normal distribution are generated. samples .

[0141] Next, for a D-dimensional random variable vector in a multi-energy microgrid that follows an arbitrary probability distribution... =[ , , ..., ] Using the Pearson correlation coefficient matrix =[ ] Describing the correlation between its internal random variables, based on Nataf transform theory, according to a given correlation coefficient matrix. Find the correlation coefficient matrix of the random variable B that follows a standard normal distribution. .

[0142] Subsequently, By performing Cholesky triangular decomposition, its lower triangular matrix can be obtained. ,Depend on Sample Transform into a standard normally distributed random variable sample with correlation .

[0143] Finally, the correlation coefficient matrix is ​​generated based on the equal probability marginal transformation. And follow a specified distribution random variables samples .

[0144] Step S102: Based on the physical characteristics of hydrogen-blended operation of natural gas system, establish the energy flow model of multi-energy microgrid, and use first-order Taylor expansion to linearize the energy flow model to obtain a linearized energy flow model.

[0145] In this embodiment of the invention, establishing an energy flow model for a multi-energy microgrid based on the physical characteristics of hydrogen-blended operation in a natural gas system may include steps S1021 to S1025.

[0146] Step S1021: Introduce a distributed balance node mechanism to construct a power grid energy flow model that includes multiple gas turbines participating in power balance.

[0147] Introducing a distributed balancing node power grid model to better recreate the real-world scenario where multiple balancing units simultaneously track system power fluctuations under large-scale renewable energy integration, the power flow deviation equations in this case... It can be represented as:

[0148] ;

[0149] in, The active power deviation at bus i of the power grid; The reactive power deviation at bus i of the power grid; This is the active power deviation vector at the power grid bus. This is the reactive power deviation vector at the power grid bus. The set of all busbars in the power grid; The voltage amplitude of bus i; = - The voltage phase difference between bus i and bus j; This represents the total number of busbars in the power grid. Indicates the conductance of branch ij; Indicates the susceptance of branch ij; This represents the set of PQ buses in the power grid; 'This refers to the injected active power at bus i, excluding the balancing gas turbine; Let i be the active power of the load at bus i; This represents the injected reactive power at bus i; This represents the reactive power of the load at bus i; The actual active power output of the balancing gas turbine at bus i is expressed as follows:

[0150] ;

[0151] in, To balance the preset value of the active power output of gas turbine i; This represents the total active power imbalance in the power grid. It is a collection of busbars containing multiple gas turbines that participate in power balancing; The proportional gain for balancing the participation of gas turbine i in unbalanced power regulation. The unknown state variable vector of the grid energy flow model. , For the unknown PQ and PV bus phase angle vectors, The unknown PQ bus voltage vector.

[0152] Step S1022: Construct a heat network energy flow model that includes a hydraulic model and a thermal model.

[0153] The operating characteristics of the heating network are characterized by both hydraulic and thermal models, and the thermal energy flow deviation equations of the heating network energy flow model are as follows. It is expressed as follows:

[0154] ;

[0155] ;

[0156] ;

[0157] in, Specific heat capacity of the heat transfer medium; The node-pipeline correlation matrix of the heating network; For the reason A matrix formed by rows of heat source nodes in Central Africa; For the reason A matrix formed by rows of nodes of the non-equilibrium heat source in China; This represents the mass flow rate of hot water in the pipe. ' is the equivalent outflow temperature vector of non-source nodes in the water supply network; ' is the equivalent outflow temperature vector of the source node in the water supply network; ' is the equivalent outflow temperature vector of the source node in the recirculation network; ' is the equivalent outflow temperature vector of non-source nodes in the return water network; ' is the equivalent inflow temperature vector of a given source node in the water supply network; ' is the equivalent inflow temperature vector of a non-source node in the return water network; This is the vector of heat power consumed at non-source nodes; This is the vector of heat power consumed at the source node; the superscript S represents the water supply network; the superscript R represents the return water network; the subscript s indicates that the matrix is ​​composed of the rows containing the source nodes in the original matrix; the subscript nn indicates that the matrix is ​​composed of the rows and columns containing the non-source nodes in the original matrix. The relationship matrix between the heating network loop and the pipeline; This represents the vector of resistance coefficients for the heating network pipeline. This represents the Hadamard product operation of matrices; ~ , ~ and All are matrix operators; This is a directional heat network node-pipeline correlation matrix; This refers to the direction factor of the heating network pipeline; This is a matrix operator that represents the mean of the absolute values ​​of the target object and itself. The mass flow rate of hot water consumed at the nodes of the water supply network; The vector of heat transfer coefficients in the pipeline; This is the pipe length vector; This is the deviation vector of thermal power at non-heat source nodes; This is the deviation vector of thermal power at non-heat source nodes; This is the deviation vector of the cumulative voltage drop in the loop; ' is the vector of deviations in the equivalent water supply temperature of non-heat source nodes; ' is the vector of deviations in the equivalent water supply temperature of the heat source node; ' is the deviation vector of the equivalent return water temperature of non-heat source nodes; ' is the deviation vector of the equivalent return water temperature of the heat source node; Let [the vector of deviations in nodal thermal power] be defined by matrix [ ; The result is obtained by performing elementary row operations based on the original node index of the heating network. ' is the vector of deviations in the equivalent water supply temperature at the nodes, defined by matrix [ '; The result is obtained by performing elementary row operations based on the original node index of the heating network. ' is the deviation vector of the equivalent return water temperature at the node, represented by the matrix [ '; The result is obtained by performing elementary row operations based on the original node index of the heating network.

[0158] Unknown state variable vector of the heat network energy flow model .

[0159] Step S1023: Construct a hydrogen-blended gas network energy flow model based on the physical characteristics of the natural gas system operating with hydrogen blending.

[0160] The hydrogen-mixed gas network model consists of a hydraulic component and a gas composition tracking component. The gas-energy flow deviation equations of the hydrogen-mixed gas network energy flow model are as follows: It is expressed as follows:

[0161] ;

[0162] ;

[0163] ;

[0164] in, This represents the volumetric flow rate vector of the gas network pipeline. This is the gas network node-pipeline correlation matrix; This is the direction factor for the gas network pipeline; This is the squared pressure vector at the starting point of the pipeline; This is the squared pressure vector at the end of the pipeline; This is the gas network pipeline coefficient vector; For matrix operators; These are the characteristic parameters of the pipeline; The temperature of the gas inside the pipe; The diameter of the pipe; The coefficient of friction of the pipeline; This refers to the length of the pipe. This is the vector of gas compressibility factors within the pipeline; This is a directional gas network node-pipeline correlation matrix; This is the density vector of natural gas; This is a column vector representing the density difference between hydrogen and natural gas. This is the vector of the mole fraction of hydrogen gas. This refers to the flow rate index of the gas pipeline network. is the diameter index of the gas network pipeline; μ is the specific gravity index of the gas network pipeline. =[ , This represents the gas network node-equipment association matrix. The correlation matrix between gas network nodes and balanced gas sources; This is the correlation matrix between gas network nodes and compressors; This is the correlation matrix between gas network nodes and unbalanced gas sources. =[ ; [ ] represents the volumetric flow rate vector of the gas network equipment; The volumetric flow rate vector of the gas source for gas network balance; This is the volumetric flow rate vector of the gas network compressor; This is the energy flow vector of the unbalanced gas source in the gas network. This is the calorific value vector of the unbalanced gas source in the gas network. This represents the load energy flow vector of the gas network node; The correlation matrix between nodes in the hydrogen mixing network and compressor intake points; The volumetric flow rate vector of the gas-driven compressor; This is the equivalent energy flow vector of the gas volume flow rate consumed by the gas-driven compressor under the standard gas calorific value. This is the gas calorific value vector at the extraction node; , and All are energy conversion coefficient vectors of air-driven compressors; For the horsepower vector of the compressor; This is the compressor power coefficient vector; These are characteristic parameters of the compressor; The gas temperature under standard conditions; Gas pressure under standard conditions; This refers to the gas temperature at the compressor inlet. This is the gas compressibility factor vector at the compressor inlet; The compression index vector of the compressor; This is the compressor inlet pressure vector; This is the compressor outlet pressure vector; For the compressor's polytropic exponential vector; This is a vector representing the calorific value of natural gas. This is a column vector representing the calorific value difference between hydrogen and natural gas. This is the hydrogen mole fraction vector at the extraction node; This is the coefficient matrix of the squared pressure at the nodes of the gas network; The squared pressure vector of the gas network node; This is a coefficient matrix representing the flow rate of the gas network equipment. This is the compressor control parameter vector; The hydrogen mole fraction vector of the gas injected at the equilibrium gas source; The hydrogen mole fraction vector is the gas injected at the non-equilibrium gas source. This is the vector of volumetric flow rate deviation at the nodes of the hydrogen mixing network. This represents the deviation vector of the equipment control equations; This is the vector of hydrogen flow rate deviation at the nodes of the hydrogen mixing network.

[0165] Unknown state variable vector of hydrogen-mixed gas network energy flow model .

[0166] Step S1024: Construct a gas turbine model, a gas boiler model, and an electric-to-hydrogen device model to obtain a multi-energy microgrid coupling equipment model.

[0167] Typical coupling devices considered include gas turbines that can achieve gas-to-electricity energy conversion, gas boilers that can achieve gas-to-thermal energy conversion, and electro-hydrogen converters that can achieve electro-hydrogen energy conversion. Their models can be represented as follows:

[0168] ;

[0169] ;

[0170] ;

[0171] in, This refers to the output electrical power of the gas turbine. This refers to the volumetric flow rate of the gas consumed by the gas turbine. The electrical efficiency of the gas turbine; The calorific value of the gas consumed by the gas turbine; This refers to the output thermal power of the gas-fired boiler. This refers to the volumetric flow rate of the gas consumed by the gas-fired boiler. Thermal efficiency of gas-fired boilers; The calorific value of the gas consumed by the gas boiler; The volumetric flow rate of the gas generated by the electro-hydrogen conversion unit; The electrical power consumed by the electro-hydrogen conversion unit; Energy conversion efficiency of the electro-hydrogen conversion device; The calorific value of the gas generated by the electro-hydrogen conversion device.

[0172] Step S1025: Integrate the power grid energy flow model, the heating network energy flow model, the mixed hydrogen network energy flow model, and the coupled equipment model to establish the energy flow model of the multi-energy microgrid.

[0173] In this embodiment of the invention, the energy flow model is linearized using a first-order Taylor expansion to obtain a linearized energy flow model, including:

[0174] The energy flow model is expressed as ,in, This is the output state variable vector of the energy flow model. This represents the input control variable vector for the energy flow model;

[0175] Assume the multi-energy microgrid is currently operating at point ( ). , ),exist( , A first-order Taylor expansion of the energy flow model is performed at point ( ), yielding the operating point ( ). , ) and Linearized relationship between them;

[0176] The expression for the first-order Taylor expansion is as follows:

[0177] ;

[0178] in, For multi-energy microgrids at the operating point ( , The Jacobian matrix at () For multi-energy microgrids at the operating point ( , The sensitivity matrix of the state variables at position ( ). This is a vector of changes in the input control variables; This outputs a vector of changes in the state variables.

[0179] The dependency variable vector in a multi-energy micronet is represented as: ,exist( , Performing a first-order Taylor expansion on the dependency variable vector at point () yields the run point () , ) and Linearized relationship between them;

[0180] The expression for the first-order Taylor expansion is as follows:

[0181] ;

[0182] in, For multi-energy microgrids at the operating point ( , The sensitivity matrix of the dependent variables at position (). This is a vector of changes in the dependent variables;

[0183] definition , , ;in, The water supply temperature vector for each node in the heating network; The return water temperature vector of the heating network node; For the pressure vector of the gas network node; This represents the active power vector of the net load of the power grid bus. The reactive power vector of the net load of the power grid bus; This represents the net load power vector of the heating network nodes. This represents the active power vector of the power grid branch; The Wobbe number vector of the gas outflowing from the gas network node; The combustion potential vector of the gas flowing out of the gas network node;

[0184] Multi-energy microgrid at the operating point ( , The linearized energy flow model of ) is expressed as:

[0185] ;

[0186] ;

[0187] in, For multi-energy microgrids at the operating point ( , The linearized matrix of the state variables at position (). For multi-energy microgrids at the operating point ( , Linearization of the dependency variables at position () for In and variables Corresponding row sums and variables The submatrix formed by the corresponding columns; for In and variables Corresponding row sums and variables The submatrix formed by the corresponding columns.

[0188] Step S103: Based on the linearized energy flow model, perform single-point linearized Monte Carlo probability energy flow calculation on the random control variable sample set to generate the running variable sample set.

[0189] Generate a sample set of random control variables using a two-sided random control variable generation model for multi-energy microgrid sources and loads. Based on a sample set of random control variables and a linearized energy flow model, a single-point linearized Monte Carlo probabilistic energy flow calculation is performed to generate the basis for selecting the basic operating point, i.e., the operating variable sample. .remember Sample of random control variables The mean vector is obtained by determining the performance flow calculation. The corresponding mean vector of state variables and the mean vector of the compliance variables At the running point ( , A linearized model of the multi-energy microgrid is established at point ), and the solution is obtained based on this model. The corresponding single-point linearization result of the state variable sample Single-point linearization results of compliance variable samples , and Collectively referred to as the single-point linearization result of the running variable sample. The above process can be described as follows:

[0190] ;

[0191] in, For multi-energy microgrids at the operating point ( , The linearized matrix of the state variables at position (). For multi-energy microgrids at the operating point ( , Linearization of the dependency variables at ().

[0192] Step S104: Perform distributed clustering on the sample set of operating variables to obtain the basic operating point set of the power grid subsystem, the basic operating point set of the heating network subsystem, and the basic operating point set of the gas network subsystem.

[0193] In this embodiment of the invention, step S104 may include steps S1041 to S1044.

[0194] Step S1041: Standardize the maximum value of the sample set of running variables.

[0195] Using the maximum value standardization method Preprocessing is performed to eliminate numerical scale differences between different variables while preserving the sample's fluctuation characteristics:

[0196] ;

[0197] in, The element in the nth row and jth column of the unstandardized running variable sample; ' is the element in the nth row and jth column of the standardized running variable sample.

[0198] Step S1042: The standardized operating variable sample set is split into the power grid subsystem operating variable sample set, the heating network subsystem operating variable sample set, and the gas network subsystem operating variable sample set.

[0199] Step S1043: Using the K-means++ clustering algorithm, perform independent clustering on the sample sets of operating variables of the power grid subsystem, the sample sets of operating variables of the heating network subsystem, and the sample sets of operating variables of the gas network subsystem.

[0200] Step S1044: Use the cluster centers obtained from clustering as the basic operating points of the corresponding subsystems to obtain the basic operating point sets of the power grid subsystem, the heating network subsystem, and the gas network subsystem.

[0201] Taking into account the differences in operating characteristics and uncertainties among the subsystems in a multi-energy microgrid, the standardized power grid operating variable samples were analyzed separately. Sample of operating variables of heating network 'Sample of operational variables of Heqi.com Perform clustering to obtain the set of basic operating points of the power grid subsystem. Set of basic operating points of the heating network subsystem Set of basic operating points of the Harmony Network subsystem This allows for the coverage of the probability distribution space of the operating states of each subsystem as much as possible.

[0202] The clustering algorithm used in this embodiment of the invention is the K-means++ algorithm, which performs distributed clustering based on the Euclidean distance between samples. To avoid an excessive number of clusters leading to increased computational costs for the clustering algorithm and subsequent multilinear expansion, an intra-cluster error sum of squares index is introduced to assist in determining the number of basic operating points for each subsystem. This index is used to determine the number of basic operating points obtained from clustering. The calculation method for this indicator is as follows:

[0203] ;

[0204] in, For set The number of basic running points (number of clusters); For set The c-th basic run point (c-th class cluster) in the process; 'To be assigned to the basic operating point' The i-th running variable sample (the i-th element of the c-th cluster); 'To be assigned to the basic operating point' The mean of all running variable samples (the cluster center of the c-th cluster).

[0205] Step S105: Perform multi-point linearized Monte Carlo probabilistic energy flow calculations on the basic operating point sets of the power grid subsystem, the heating network subsystem, and the gas network subsystem, respectively, to obtain the probabilistic energy flow calculation results of the electricity-heat-gas-hydrogen multi-energy microgrid.

[0206] In this embodiment of the invention, step S105 specifically includes:

[0207] Step S1051: Perform multi-point linearized Monte Carlo probabilistic energy flow calculations on the basic operating point sets of the power grid subsystem, the heating network subsystem, and the gas network subsystem, respectively, to obtain multi-linear results of power grid operating variables, heating network operating variables, and gas network operating variables.

[0208] The basic set of running points obtained based on distributed clustering , and The corresponding subsystems can be considered precise, respectively in For the power grid subsystem, For heating network subsystem and Multi-point linearization expansion is performed on the gas grid subsystem to obtain the multi-point linearization results of the power grid operation variable samples. Multi-point linearization results of heating network operation variable samples Multi-point linearization results of the gas network operating variable samples .

[0209] Taking the power grid subsystem as an example, the process of obtaining multilinear results for power grid operating variables can be described as follows:

[0210] ;

[0211] ;

[0212] in, To be divided into The sample vector of control variables of the multi-energy microgrid at the c-th basic operating point; To be divided into The sample mean vector of the control variables of the multi-energy microgrid at the c-th basic operating point; The result is obtained from the deterministic performance flow calculation. The corresponding mean vector of state variables; The result is obtained from the deterministic performance flow calculation. The corresponding mean vector of the compliance variables; for A subvector consisting of the elements corresponding to the parameters of the power grid; for A subvector consisting of the elements corresponding to the parameters of the power grid; for The single-point linearization result of the corresponding power grid state variable sample; for The single-point linearization results of the corresponding power grid compliance variable samples; for The single-point linearization results of the corresponding power grid operation variable samples; For multi-energy microgrids in The linearized matrix of the power grid state variables at the c-th basic operating point; For multi-energy microgrids in The linearized matrix of the power grid dependency variables at the c-th basic operating point.

[0213] The process of obtaining the multilinear results of the operating variables of the heating network and the gas network is the same, and will not be elaborated here.

[0214] Step S1052: Correct the nodal temperatures in the multi-point linearization results of the heating network operation variables. The heating network model shows... and and The existence of high-order nonlinear functional relationships between them leads to problems obtained based on the linear expansion approach. and The probabilistic energy flow calculation results contain errors. Therefore, this embodiment of the invention corrects the node temperature in the multilinear results of the heating network operation variables.

[0215] Based on the energy flow model of the heating network, the mass flow rate of hot water in the pipes is... Multilinear results and given supply and return water temperatures The formula for inversely calculating the node temperature is as follows:

[0216] ;

[0217] in, ' is the degree vector of the equivalent water supply temperature of the node; ' is the equivalent return water temperature vector of the node.

[0218] Step S1053: Integrate the multilinear results of the temperature-corrected heating network operation variables with the multilinear results of the power grid operation variables and the gas network operation variables to obtain the probabilistic energy flow calculation results of the electricity-heat-gas-hydrogen multi-energy microgrid.

[0219] The multi-point linearization results of the operational variable samples of each subsystem are integrated as the probabilistic energy flow calculation results for the entire multi-energy microgrid. =[ , , According to statistical methods, The probability distribution information of each operating variable of the multi-energy microgrid is generated, and the probabilistic energy flow calculation results of the electric-thermal-gas-hydrogen multi-energy microgrid are obtained.

[0220] To verify the effectiveness of the probabilistic energy flow calculation method for the electric-thermal-gas-hydrogen multi-energy microgrid provided in this embodiment of the invention, such as... Figure 3As shown, this embodiment of the invention constructs a 39-32-20 node multi-energy microgrid test system. The power grid operates in a multi-balance node mode, with four gas turbines jointly serving as the balancing unit. The imbalance power adjustment ratio of each gas turbine is the ratio of its respective rated power, and bus E30 is the voltage phase angle reference bus. The balancing units of the heating network and the gas network are respectively undertaken by the gas boiler at node H1 and the natural gas source at node G1. In addition, four wind turbine units and two photovoltaic power stations are connected to the power grid. When operating independently, the renewable energy penetration level of the power grid is approximately 42%. Detailed parameters of each renewable energy unit are shown in Tables 1 and 2. Among them, wind power and P2G (electricity-to-gas) units together form an energy hub, acting as the power source for the power grid and the hydrogen source for the gas network; photovoltaic power and P2H (electric boiler) units together form an energy hub, acting as the power source for the power grid and the auxiliary heat source for the heating network. To fully reflect the impact of renewable energy output fluctuations on the operating status of the multi-energy microgrid, it is assumed that the P2G and P2H capacities are sufficient. and The values ​​were set to 100°C and 30°C respectively, the gas temperature of the gas network was set to 288.15K, and the standard pressure and standard temperature of the gas measurement were defined as 1.01325 bar and 293.15K respectively.

[0221] Regarding uncertainties, all electrical loads in the power grid except those at nodes E4, E8, E31, and E38, all heat loads in the heating network, and all gas loads in the gas network are random factors. Their mean is the predicted system load value at the current moment, and the standard deviation is 10% of the mean. The probability distribution parameters for wind power and photovoltaic output are shown in Tables 1 and 2, and the conversion ratio... and The default values ​​are all 50%. The default autocorrelation coefficients for electrical load, heat load, gas load, wind power, and photovoltaic output are all 0.5.

[0222] Table 1 Wind turbine parameters

[0223]

[0224] Table 2 Photovoltaic Power Plant Parameters

[0225]

[0226] First, 20,000 random control variable samples were generated under default parameters. Using the SLMS results of the operational variables to be evaluated for each subsystem as the clustering objects, the number of clusters for each subsystem in the proposed DCMS probabilistic energy flow calculation method was tuned. The relationship between the number of clusters and the sum of squared errors (SSE) index was plotted as follows: Figure 4As shown, the clustering effect of each subsystem continuously improves with the increase of the number of clusters, but the improvement becomes insignificant when the number of clusters exceeds 60. Therefore, considering both algorithm efficiency and computational accuracy, the number of clusters in each subsystem is set to 50. In fact, in practical applications, the number of clusters in each subsystem can be set independently according to the strength of uncertainty in each subsystem, thereby further balancing the timeliness and accuracy of the algorithm.

[0227] Next, under default operating parameters, the calculation results of the proposed DCMS were compared with those of SLMS, MLMS, and TCMS to verify the superiority of the proposed method in terms of calculation accuracy. MLMS used the equivalent total gas load as the reference for sample division, setting the number of segment intervals to 51, similar to the number of clusters in DCMS; the number of clusters in TCMS was the same as that in DCMS, set to 50. Using the probabilistic energy flow obtained from 20,000 MS calculations as a benchmark, the relative error (RE) and average root mean square (ARMS) were used to describe the accuracy of the results of each method.

[0228] ;

[0229] ;

[0230] Where RE represents the relative error value; and These represent the numerical characteristics and type of the runtime variable, respectively; and These represent runtime variables obtained without MS and with MS respectively. Digital characteristics ARMS stands for Root Mean. and Let represent the value of the i-th point in the Cumulative Distribution Function (CDF) curves obtained by non-MS and MS methods, respectively; This represents the number of sampling points for the CDF curve.

[0231] The expected value and standard deviation of the results calculated by each method were obtained by analyzing 20,000 identical samples. The relative errors are shown in Table 3. and Table 3 shows the average and maximum values ​​of the relative errors of the corresponding operating variables, respectively. As can be seen from Table 3, the relative errors of the operating variables obtained by SLMS are the largest among all methods, clearly making it difficult to cope with the multiple uncertainties brought about by the high proportion of new energy integration. Due to the adoption of a multi-point linearization strategy, the relative errors of most operating variables obtained by MLMS are lower than those obtained by SLMS, especially in the gas network hydraulic operating variables, where the effect is most significant, particularly in the nodal gas pressure... The average relative error is even lower than that of DCMS, but for other operating variables, such as the return water temperature of the heating network, the maximum relative error of the standard deviation reaches 69.8311%. This is mainly because MLMS uses equivalent air load as the basis for sample division, which means that the selected linear expansion points can only cover the operating space of the gas network hydraulic parameters and cannot take into account the probability distribution characteristics of other operating variables. In contrast, the accuracy of most results is further improved in TCMS, but due to its concentrated clustering strategy and weak overall sensitivity index characterization ability, the fitting accuracy of some operating variables has regressed, such as nodal air pressure. Furthermore, the relative errors of some operating variables remain relatively large, such as voltage amplitude |V| and pipeline flow rate. The expected error, the standard deviation error of the supply and return water temperatures and the gas network operating variables; excluding node gas pressure Apart from the expected relative error, the relative errors of all operating variables obtained by DCMS are the smallest, which shows that the proposed method has an accuracy advantage over the existing multilinear probabilistic energy flow calculation methods. At the same time, except for the supply and return water temperatures, the average value of all relative errors is less than 0.2%, proving that the proposed method is suitable for multi-energy microgrid operation scenarios with high renewable energy penetration.

[0232] Table 3 Comparison of relative errors of calculation results by different methods

[0233]

[0234] Furthermore, the fitting accuracy of the CDF curves obtained by different methods was further analyzed. Box plots were created using different methods to analyze the ARMS index of the same operating variable under different methods. The results are as follows: Figures 5 to 14 As shown. From Figures 5 to 14 It can be seen that, except for the nodal combustion potential Furthermore, the CDF curves for the remaining operating variables obtained by DCMS were the most accurate among all methods, with the ARMS index value not exceeding 2%. In contrast, SLMS performed the worst in calculating all operating variables, while MLMS and TCMS showed different fitting accuracies for different subsystems. MLMS fitted the CDF of the gas network operating variables more accurately, while TCMS generated the CDF of the heat network operating variables more accurately. These findings indicate that the aforementioned methods failed to fully account for the differences in operating characteristics and uncertainties among the various subsystems of the multi-energy microgrid. Overall, the analysis results based on ARMS generally agree with the results of the relative error analysis.

[0235] Finally, combining Figures 15 to 20 The probability density functions (PDFs) of some typical operating variables are given to qualitatively explain the above error indices. Figure 15 and Figure 16 It is evident that only DCMS can accurately fit the probability distribution of power grid operation variables, while other methods all have some degree of bias. Furthermore, as... Figure 17 and Figure 18 As shown, the power fluctuations of the heat source caused by P2H result in a highly irregular probability distribution of the heating network operation variables. The water supply temperature at node H25 exhibits a typical multi-peak probability density function. For this variable, neither SLMS nor MLMS can achieve a multi-peak fit, and TCMS only performs an imprecise fit on the bimodal probability density function with the larger probability density. In contrast, DCMS accurately fits all the peaks of the probability density function. Regarding the gas network operation variables, Figure 19 This confirms that MLMS and DCMS fit the probability distribution of air pressure variables better than TCMS. Figure 20 This indicates that only DCMS can accurately calculate the gas quality parameters of the gas network. It is worth noting that, although... Figure 13 The data shows that DCMS is inferior to MLMS in terms of ARMS metric, but its relative error metric and... Figure 20 The probability density function curves, represented by the curves, all indicate that DCMS has higher accuracy in calculating atmospheric and gaseous variables than MLMS.

[0236] To verify the rationality of the distributed clustering method adopted by DCMS, the average values ​​of the expected value and standard deviation relative errors of the calculation results of TCMS, UCMS (Unified Clustering-based MLMS, which performs unified clustering and unified linear expansion on the running variables obtained by SLMS), and DCMS were statistically analyzed under different numbers of clusters. The results are as follows: Figures 21 to 26 As shown. From Figures 21 to 26It can be seen that the accuracy of different clustering methods improves with the increase of the number of clusters, but the improvement is limited. For all different numbers of clusters, TCMS has lower overall computational accuracy than UCMS, verifying the rationality of the clustering object selection in the proposed clustering strategy. Furthermore, the mean relative error of the expected value and standard deviation of all operating variables obtained by DCMS is the lowest among the three clustering methods, thus proving the effectiveness of the distributed clustering strategy proposed in this paper.

[0237] To verify the applicability of the proposed method to multi-energy microgrids with a high proportion of renewable energy access, the accuracy of the calculation results of SLMS, MLMS, TCMS, and DCMS was compared and analyzed at different renewable energy penetration rates. The number of segment intervals for MLMS and the number of clusters for the clustering method were set to 51 and 50, respectively. The renewable energy conversion ratio was adjusted accordingly. and To realize the operation scenarios of each subsystem under different new energy penetration rates, respectively in = =25%, = =50% and = The average of the relative errors of the expected value and standard deviation of the results calculated by different methods was obtained under the condition of 75%. The results are as follows: Figures 27 to 32 As shown.

[0238] from Figures 27 to 32 It can be seen that the accuracy of each method is more or less affected by the penetration rate of new energy sources. For the power grid, an increase in the conversion ratio can be seen as a decrease in the capacity of the renewable energy units connected to it, resulting in a reduction in the uncertainty of its operating state, and the accuracy of each method in solving the probability distribution of the power grid operating variables also improves. For heating networks and gas networks, an increase in the conversion ratio can be seen as an increase in the capacity of P2H and P2G connected to it, so its operating state is more significantly affected by the randomness of new energy output, causing the accuracy of the heating network and gas network operating variables obtained by each method to fluctuate with the change in the conversion ratio. However, compared with other methods, DCMS can maintain good accuracy for all operating variables under different new energy penetration rate operating scenarios, with the average values ​​of expected relative error and standard deviation relative error not exceeding 0.05% and 3.5%, respectively. This indicates that the proposed method has a good suppression effect on the problem of improper selection of basic operating points in the multi-point linearization process caused by the differences in operating characteristics and random factors among the subsystems in multi-energy microgrids.

[0239] Finally, the computational efficiency of SLMS, MLMS, TCMS, and DCMS is analyzed. The computation time of each method under different sample sizes is shown in Table 4. It can be seen that although the computational efficiency of DCMS is not as high as the other methods, it is significantly improved compared to MS, and the improvement becomes more significant with the increase in sample size. The main reason for the increased time consumption of DCMS compared to TCMS is that the distributed computing structure of DCMS results in three times the number of executions of its clustering algorithm and energy flow calculation compared to TCMS. Apart from this, the computational costs of the two methods are the same. As mentioned earlier, since the computation of each subsystem in DCMS is independent, it can be accelerated using a parallel computing structure. Table 4 shows that the computation time of DCMS under the parallel implementation is similar to that of TCMS, indicating that this strategy can effectively improve the computational efficiency of the proposed method.

[0240] Table 4 Comparison of computational efficiency of various methods

[0241]

[0242] An embodiment of the present invention also provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a computer processor, is used to cause the computer to perform the probabilistic energy flow calculation method for an electric-thermal-gas-hydrogen multi-energy microgrid according to an embodiment of the present invention.

[0243] Embodiments of the present invention also provide a computer program product, including a computer program, wherein the computer program, when executed by a computer processor, is used to cause the computer to perform the probabilistic energy flow calculation method for an electric-thermal-gas-hydrogen multi-energy microgrid according to embodiments of the present invention.

[0244] Embodiments of this invention also provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the probabilistic energy flow calculation method for an electric-thermal-gas-hydrogen multi-energy microgrid according to embodiments of this invention.

[0245] refer to Figure 33The present invention will now describe a structural block diagram of an electronic device that can serve as an embodiment of the present invention, serving as an example of a hardware device applicable to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0246] like Figure 33 As shown, the electronic device includes a computing unit 101, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 102 or a computer program loaded from a storage unit 108 into a random access memory (RAM) 103. The RAM 103 may also store various programs and data required for the operation of the electronic device. The computing unit 101, ROM 102, and RAM 103 are interconnected via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0247] Multiple components in the electronic device are connected to I / O interface 105, including: input unit 106, output unit 107, storage unit 108, and communication unit 109. Input unit 106 can be any type of device capable of inputting information into the electronic device. Input unit 106 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 107 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 108 may include, but is not limited to, disks and optical discs. Communication unit 109 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and / or wireless communication transceivers, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0248] The computing unit 101 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 101 include, but are not limited to, CPUs, graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 101 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as computer programs tangibly contained in a machine-readable medium, such as storage unit 108. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 102 and / or communication unit 109. In some embodiments, the computing unit 101 can be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0249] Computer programs for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0250] In the context of embodiments of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0251] It should be noted that the term "comprising" and its variations used in the embodiments of this invention are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of this invention are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated otherwise in the context, they should be understood as "one or more".

[0252] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this invention are subject to strict compliance with relevant laws, regulations, and regulatory requirements in their collection, storage, use, processing, transmission, provision, and disclosure, and adhere to the principles of legality, legitimacy, necessity, and good faith. The acquisition of relevant information and data is premised on the user's explicit consent or other legitimate reasons, and a clear and convenient authorization management approach is provided to the user, allowing the user to independently choose to consent, withdraw consent, or refuse to provide relevant information. For functions that rely on user information, if the user does not authorize or withdraws authorization, the corresponding technical function cannot be implemented, and the technical solution of this invention is not applicable in this scenario.

[0253] The steps described in the method embodiments provided by the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0254] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily imply the same embodiment, nor does it imply independence or alternativeity from other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0255] The above embodiments merely illustrate several implementation methods of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for calculating probabilistic energy flow in an electric-thermal-gas-hydrogen multi-energy microgrid, characterized in that, Includes the following steps: Based on the uncertainties on both the source and load sides of a multi-energy microgrid, a stochastic control variable generation model for the multi-energy microgrid is constructed; and using the stochastic control variable generation model, a set of stochastic control variables with correlation is generated; wherein, the uncertainties include the power of the electricity-to-hydrogen injection and the power of the electricity-to-heat heat source; Based on the physical characteristics of hydrogen-blended operation in natural gas systems, an energy flow model for the multi-energy microgrid is established, and the energy flow model is linearized using a first-order Taylor expansion to obtain a linearized energy flow model. Based on the linearized energy flow model, a single-point linearized Monte Carlo probability energy flow calculation is performed on the random control variable sample set to generate a running variable sample set; Distributed clustering is performed on the sample set of operating variables to obtain the basic operating point set of the power grid subsystem, the basic operating point set of the heating network subsystem, and the basic operating point set of the gas network subsystem. Multi-point linearized Monte Carlo probabilistic energy flow calculations were performed on the basic operating point sets of the power grid subsystem, the heating network subsystem, and the gas network subsystem, respectively, to obtain the probabilistic energy flow calculation results of the electricity-heat-gas-hydrogen multi-energy microgrid. The sources of uncertainty include source-side uncertainties and load-side uncertainties; The sources of uncertainty on the source side include: photovoltaic power plant output, wind turbine output, power of electricity-to-hydrogen injection, and power of electricity-to-heat heat source. The sources of load-side uncertainty include: electrical load, thermal load, and gas load; Based on the uncertainties on both the source and load sides of a multi-energy microgrid, a stochastic control variable generation model for the multi-energy microgrid is constructed, including: The uncertainty of solar irradiance received by a photovoltaic power station is characterized by the Beta distribution, so as to obtain the active power output probability model and the reactive power output probability model of the photovoltaic power station. The uncertainty of wind speed of wind turbines is characterized by a two-parameter Weibull distribution, so as to obtain the active power output probability model and the reactive power output probability model of wind turbines. Based on the active power output probability model of the wind turbine and / or the active power output probability model of the photovoltaic power station, a power probability model for electricity-to-hydrogen conversion and hydrogen injection is obtained. Based on the active power output probability model of the wind turbine and / or the active power output probability model of the photovoltaic power station, the power probability model of the electricity-to-heat source is obtained. The uncertainty of electricity load, heat load and gas load is characterized by the normal distribution, so as to establish a probabilistic model for electricity load, heat load and gas load. By integrating the active power output probability model of the photovoltaic power station, the reactive power output probability model of the photovoltaic power station, the active power output probability model of the wind turbine, the reactive power output probability model of the wind turbine, the power probability model of the electricity-to-hydrogen injection, the power probability model of the electricity-to-heat heat source, the power load probability model, the heat load probability model, and the gas load probability model, a stochastic control variable generation model for multi-energy microgrids is constructed.

2. The method for calculating the probabilistic energy flow of an electric-thermal-gas-hydrogen multi-energy microgrid according to claim 1, characterized in that, Based on the physical characteristics of hydrogen-blended operation in natural gas systems, an energy flow model for the multi-energy microgrid is established, including: A distributed balancing node mechanism is introduced to construct a power grid energy flow model that includes multiple gas turbines participating in power balancing. Construct a heat network energy flow model that includes hydraulic and thermal models; Based on the physical characteristics of hydrogen-blended operation in natural gas systems, an energy flow model for hydrogen-blended gas networks is constructed. A gas turbine model, a gas boiler model, and an electric-to-hydrogen device model are constructed to obtain a multi-energy microgrid coupling device model. The energy flow model of the multi-energy microgrid is established by integrating the power grid energy flow model, the heat network energy flow model, the hydrogen-mixed gas network energy flow model, and the coupling equipment model.

3. The method for calculating the probabilistic energy flow of an electric-thermal-gas-hydrogen multi-energy microgrid according to claim 2, characterized in that, Based on the physical characteristics of hydrogen-blended natural gas systems, a hydrogen-blended gas network energy flow model is constructed, including: The gas energy flow deviation equations of the hydrogen-mixed gas network energy flow model It is expressed as follows: ； ； ； in, This represents the volumetric flow rate vector of the gas network pipeline. This is the gas network node-pipeline correlation matrix; This is the direction factor for the gas network pipeline; This is the squared pressure vector at the starting point of the pipeline; This is the squared pressure vector at the end of the pipeline; This is the gas network pipeline coefficient vector; For matrix operators; These are the characteristic parameters of the pipeline; The temperature of the gas inside the pipe; The diameter of the pipe; The coefficient of friction of the pipeline; This refers to the length of the pipe. This is the vector of gas compressibility factors inside the pipeline; This is a directional gas network node-pipeline correlation matrix; This is the density vector of natural gas; This is a column vector representing the density difference between hydrogen and natural gas. This is the vector of the mole fraction of hydrogen gas. This refers to the flow rate index of the gas pipeline network. is the diameter index of the gas network pipeline; μ is the specific gravity index of the gas network pipeline. =[ , This represents the gas network node-equipment association matrix. The correlation matrix between gas network nodes and balanced gas sources; This is the correlation matrix between gas network nodes and compressors; This is the correlation matrix between gas network nodes and unbalanced gas sources. =[ ; [ ] represents the volumetric flow rate vector of the gas network equipment; The volumetric flow rate vector of the gas source for gas network balance; This is the volumetric flow rate vector of the gas network compressor; This is the energy flow vector of the unbalanced gas source in the gas network. This is the calorific value vector of the unbalanced gas source in the gas network. This represents the load energy flow vector of the gas network node; The correlation matrix between nodes in the hydrogen mixing network and compressor intake points; The volumetric flow rate vector of the gas-driven compressor; This is the equivalent energy flow vector of the gas volume flow rate consumed by the gas-driven compressor under the standard gas calorific value. This is the gas calorific value vector at the extraction node; , and All are energy conversion coefficient vectors of air-driven compressors; For the horsepower vector of the compressor; This is the compressor power coefficient vector; These are characteristic parameters of the compressor; The gas temperature under standard conditions; Gas pressure under standard conditions; This refers to the gas temperature at the compressor inlet. This is the gas compressibility factor vector at the compressor inlet; The compression index vector of the compressor; This is the compressor inlet pressure vector; This is the compressor outlet pressure vector; For the compressor's polytropic exponential vector; This is a vector representing the calorific value of natural gas. This is a column vector representing the calorific value difference between hydrogen and natural gas. This is the hydrogen mole fraction vector at the extraction node; This is the coefficient matrix of the squared pressure at the nodes of the gas network; The squared pressure vector of the gas network node; d is the coefficient matrix of the gas network equipment flow rate; d is the compressor control parameter vector. The hydrogen mole fraction vector of the gas injected at the equilibrium gas source; The hydrogen mole fraction vector is the gas injected at the non-equilibrium gas source. This is the vector of volumetric flow rate deviation at the nodes of the hydrogen mixing network. This represents the deviation vector of the equipment control equations; This is the vector of hydrogen flow rate deviation at the nodes of the hydrogen mixing network. The unknown state variable vector of the hydrogen-mixed gas network energy flow model .

4. The probabilistic energy flow calculation method for an electric-thermal-gas-hydrogen multi-energy microgrid according to claim 3, characterized in that, The energy flow model is linearized using a first-order Taylor expansion to obtain a linearized energy flow model, including: The energy flow model is expressed as follows: ,in, This is the output state variable vector of the energy flow model. This is the input control variable vector for the energy flow model; Assume the multi-energy microgrid is currently operating at point ( ). , ),exist( , A first-order Taylor expansion is performed on the energy flow model at point () to obtain the operating point (). , ) and Linearized relationship between them; The expression for the first-order Taylor expansion is as follows: ； in, For multi-energy microgrids at the operating point ( , The Jacobian matrix at () For multi-energy microgrids at the operating point ( , The sensitivity matrix of the state variables at position ( ). This is a vector of changes in the input control variables; This outputs a vector of changes in the state variables. The dependency variable vector in a multi-energy micronet is represented as: ,exist( , Performing a first-order Taylor expansion on the dependent variable vector at point () yields the run point () , ) and Linearized relationship between them; The expression for the first-order Taylor expansion is as follows: ； in, For multi-energy microgrids at the operating point ( , The sensitivity matrix of the dependent variables at position (). This is a vector of changes in the dependent variables; definition , , ;in, The unknown PQ bus voltage vector; This represents the mass flow rate of hot water in the pipe. The water supply temperature vector for each node in the heating network; The return water temperature vector of the heating network node; For the pressure vector of the gas network node; This represents the active power vector of the net load of the power grid bus. The reactive power vector of the net load of the power grid bus; This represents the net load power vector of the heating network nodes. This represents the active power vector of the power grid branch; For the Wobbe number vector of the gas flowing out of the gas network node; For the combustion potential vector of the gas flowing out of the gas network node; Multi-energy microgrid at the operating point ( , The linearized energy flow model of ) is expressed as: ； ； in, For multi-energy microgrids at the operating point ( , The linearized matrix of the state variables at position (). For multi-energy microgrids at the operating point ( , Linearization of the dependency variables at position () for In and variables Corresponding row sums and variables The submatrix formed by the corresponding columns; for In and variables Corresponding row sums and variables The submatrix formed by the corresponding columns.

5. The method for calculating the probabilistic energy flow of an electro-thermal-gas-hydrogen multi-energy microgrid according to claim 4, characterized in that, Distributed clustering is performed on the sample set of operating variables to obtain the basic operating point sets of the power grid subsystem, the heating network subsystem, and the gas network subsystem, including: The maximum value of the sample set of running variables is standardized. The standardized sample set of operating variables is divided into the sample set of operating variables of the power grid subsystem, the sample set of operating variables of the heating network subsystem, and the sample set of operating variables of the gas network subsystem. The K-means++ clustering algorithm was used to perform independent clustering on the sample sets of operating variables of the power grid subsystem, the heating network subsystem, and the gas network subsystem, respectively. Using the cluster centers obtained from clustering as the basic operating points of the corresponding subsystems, we obtain the basic operating point sets of the power grid subsystem, the heating network subsystem, and the gas network subsystem.

6. The method for calculating the probabilistic energy flow of an electric-thermal-gas-hydrogen multi-energy microgrid according to claim 5, characterized in that, Multi-point linearized Monte Carlo probabilistic energy flow calculations were performed on the basic operating point sets of the power grid subsystem, the heating network subsystem, and the gas network subsystem, respectively, to obtain the probabilistic energy flow calculation results for the electricity-heat-gas-hydrogen multi-energy microgrid, including: Perform multi-point linearized Monte Carlo probabilistic energy flow calculations on the set of basic operating points of the power grid subsystem to obtain multi-linear results for the power grid operating variables; Perform multi-point linearized Monte Carlo probabilistic energy flow calculations on the set of basic operating points of the heating network subsystem to obtain multi-linear results of heating network operating variables; Perform multi-point linearized Monte Carlo probabilistic energy flow calculations on the set of basic operating points of the gas network subsystem to obtain multilinear results of gas network operating variables; Correct the node temperatures in the multilinear results of the heating network operation variables; The multilinear results of the temperature-corrected heating network operation variables are integrated with the multilinear results of the power grid operation variables and the gas network operation variables to obtain the probabilistic energy flow calculation results of the electricity-heat-gas-hydrogen multi-energy microgrid.

7. The method for calculating the probabilistic energy flow of an electric-thermal-gas-hydrogen multi-energy microgrid according to claim 6, characterized in that, Perform multi-point linearized Monte Carlo probabilistic energy flow calculations on the set of basic operating points of the power grid subsystem to obtain multilinear results for power grid operating variables, including: The formula for performing multi-point linearized Monte Carlo probabilistic energy flow calculation on the basic operating point set of the power grid subsystem is as follows: ； ； in, This is the set of basic operating points of the power grid subsystem; For the control variable sample of the multi-energy microgrid corresponding to the cth basic operating point in the set of basic operating points of the power grid subsystem; is the sample mean vector of control variables of the multi-energy microgrid corresponding to the c-th basic operating point in the set of basic operating points of the power grid subsystem; for The corresponding mean vector of state variables; for The corresponding mean vector of the compliance variables; for A subvector consisting of the elements corresponding to the parameters of the power grid; for A subvector consisting of the elements corresponding to the parameters of the power grid; for The corresponding power grid state variables; for The corresponding compliance variables; for The corresponding sample of running variables is a single linear result; For a multi-energy microgrid, the linearized matrix of the grid state variables at the c-th basic operating point in the set of basic operating points of the grid subsystem is used; For a multi-energy microgrid, the linearized matrix of the grid dependency variables at the c-th basic operating point in the set of basic operating points of the grid subsystem is used; The results are multilinear results for the power grid operation variable samples.

8. The method for calculating the probabilistic energy flow of an electric-thermal-gas-hydrogen multi-energy microgrid according to claim 6, characterized in that, Correcting the nodal temperatures in the multilinear results of the heating network operation variables includes: The thermal energy flow deviation equations of the heating network energy flow model It is expressed as follows: ； ； ； in, Specific heat capacity of the heat transfer medium; The node-pipeline correlation matrix of the heating network; For the reason A matrix formed by rows of heat source nodes in Central Africa; For the reason A matrix formed by rows of nodes of the non-equilibrium heat source in China; This represents the mass flow rate of hot water in the pipe. ' is the equivalent outflow temperature vector of non-source nodes in the water supply network; ' is the equivalent outflow temperature vector of the source node in the water supply network; ' is the equivalent outflow temperature vector of the source node in the recirculation network; ' is the equivalent outflow temperature vector of non-source nodes in the return water network; ' is the equivalent inflow temperature vector of a given source node in the water supply network; ' is the equivalent inflow temperature vector of a non-source node in the return water network; This is the vector of heat power consumed at non-source nodes; This is the vector of heat power consumed at the source node; the superscript S represents the water supply network; the superscript R represents the return water network; the subscript s indicates that the matrix is ​​composed of the rows containing the source nodes in the original matrix; the subscript nn indicates that the matrix is ​​composed of the rows and columns containing the non-source nodes in the original matrix. The relationship matrix between the heating network loop and the pipeline; This represents the vector of resistance coefficients for the heating network pipeline. This represents the Hadamard product operation of matrices; ~ , ~ and All are matrix operators; This is a directional heat network node-pipeline correlation matrix; This refers to the direction factor of the heating network pipeline; This is a matrix operator that represents the mean of the absolute values ​​of the target object and itself. The mass flow rate of hot water consumed at the nodes of the water supply network; The vector of heat transfer coefficients in the pipeline; This is the pipe length vector; This is the deviation vector of thermal power at non-heat source nodes; This is the deviation vector of thermal power at non-heat source nodes; This is the deviation vector of the cumulative voltage drop in the loop; ' is the vector of deviations in the equivalent water supply temperature of non-heat source nodes; ' is the vector of deviations in the equivalent water supply temperature of the heat source node; ' is the deviation vector of the equivalent return water temperature of non-heat source nodes; ' is the deviation vector of the equivalent return water temperature of the heat source node; Let [the vector be] the nodal thermal power deviation, given by matrix [ ; The result is obtained by performing elementary row operations based on the original node index of the heating network. ' is the vector of deviations in the equivalent water supply temperature at the nodes, defined by matrix [ '; The result is obtained by performing elementary row operations based on the original node index of the heating network. Let [the vector be] the deviation of the equivalent return water temperature at the node, defined by matrix [ '; The result is obtained by performing elementary row operations based on the original node index of the heating network. The unknown state variable vector of the heat network energy flow model ; Based on the aforementioned heat network energy flow model, the mass flow rate of hot water in the pipes is... Multilinear results and given supply and return water temperatures The formula for inversely calculating the node temperature is as follows: ； in, ' is the degree vector of the equivalent water supply temperature of the node; ' is the equivalent return water temperature vector of the node.

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