Sensitivity analysis method for integrated energy system oriented to hydrogen blending operation of natural gas network

Abstract

The application provides a kind of natural gas network hydrogen-oriented operation comprehensive energy system sensitivity analysis method.The method comprises: according to the topological structure and equipment parameters of comprehensive energy system, generating power network energy flow model, heat network energy flow model, hydrogen mixed gas network energy flow model and respective Jacobian matrix, coupling device model;Based on each energy flow model and its Jacobian matrix, generate hydrogen mixed comprehensive energy system sensitivity matrix;Obtain the operating state data of comprehensive energy system at the operating point to be analyzed, and substitute the operating state data into each sensitivity matrix for operation, to obtain the numerical calculation result of each sensitivity matrix;According to the absolute value of the data calculation result of each sensitivity matrix, identify the weak link of comprehensive energy system operation.The technical scheme provided by the application can improve the accuracy of hydrogen mixed operation comprehensive energy system sensitivity analysis, to accurately identify the weak link of the system.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system technology, and in particular to a sensitivity analysis method for integrated energy systems operating with hydrogen in natural gas networks. Background Technology

[0002] With the advancement of energy structure transformation, integrated energy systems (IES) have become an important vehicle for achieving multi-energy synergistic optimization and improving energy utilization efficiency. IES typically comprises multiple sub-networks, including electricity, heat, and natural gas, with energy interaction achieved through coupling devices. In recent years, hydrogen blending into natural gas networks has become an important pathway to reduce carbon emissions and promote the large-scale application of hydrogen energy. The blending of hydrogen alters the physicochemical properties of natural gas (such as calorific value and specific gravity), thereby affecting the energy flow characteristics of the natural gas network. Simultaneously, this influence is transmitted to the electricity and heat networks through coupling devices, leading to more complex operating characteristics of the integrated energy system.

[0003] Sensitivity analysis is a core technology for comprehensive energy system operation assessment, fault diagnosis, and optimized control. Its core is to quantify the impact of changes in control variables (such as load and gas injection rate) on system operating state variables (such as voltage, pressure, and temperature) and dependent variables (such as branch power and gas Wobbe number), thereby identifying weak links in system operation. In existing technologies, sensitivity analysis is mostly applied to single energy networks (such as pure power networks or pure natural gas networks), or it does not consider the impact of hydrogen blending in natural gas networks, and therefore cannot adapt to the complex characteristics of multi-energy network coupling in hydrogen blending scenarios. Summary of the Invention

[0004] The present invention provides a sensitivity analysis method, apparatus, electronic device, storage medium, and computer program product for integrated energy systems operating with hydrogen blending in natural gas networks. This method is adapted to the hydrogen blending operation scenario of natural gas networks and takes into account the coupling characteristics of multiple energy networks. It improves the accuracy of sensitivity calculation to a certain extent, thereby accurately identifying weak links in the system and meeting the requirements for safe and stable operation of hydrogen blended integrated energy systems.

[0005] In a first aspect, the present invention provides a sensitivity analysis method for an integrated energy system operating with hydrogen blending in a natural gas network. The integrated energy system includes multiple sub-networks, including an electricity network, a heating network, and a natural gas network operating with hydrogen blending. The method includes:

[0006] Based on the topology and equipment parameters of the integrated energy system, an energy flow model for the power network and its Jacobian matrix, an energy flow model for the thermal network and its Jacobian matrix, an energy flow model for the mixed hydrogen network and its Jacobian matrix, and a coupled equipment model are generated; wherein, the coupled equipment model includes the coupling between the power network and the mixed hydrogen network, and the coupling between the thermal network and the mixed hydrogen network;

[0007] Based on the energy flow models of each sub-network and their Jacobian matrices, a sensitivity matrix for the mixed hydrogen integrated energy system is generated.

[0008] The operating status data of the integrated energy system at the operating point to be analyzed is obtained, and the operating status data is substituted into each sensitivity matrix for calculation to obtain the numerical calculation results of each sensitivity matrix.

[0009] Based on the absolute values ​​of the data calculation results of each of the aforementioned sensitivity matrices, the weak links in the operation of the integrated energy system are identified.

[0010] Secondly, the present invention provides a sensitivity analysis device for an integrated energy system operating with hydrogen blending in a natural gas network. The integrated energy system includes multiple sub-networks, including an electricity network, a heating network, and a natural gas network operating with hydrogen blending. The sensitivity analysis device for the integrated energy system operating with hydrogen blending in a natural gas network includes:

[0011] The energy flow model generation module is used to generate, based on the topology and equipment parameters of the integrated energy system, an energy flow model of the power network and its Jacobian matrix, an energy flow model of the thermal network and its Jacobian matrix, an energy flow model of the mixed hydrogen network and its Jacobian matrix, and a coupled equipment model; wherein, the coupled equipment model includes the coupling between the power network and the mixed hydrogen network, and the coupling between the thermal network and the mixed hydrogen network;

[0012] The sensitivity matrix generation module is used to generate the sensitivity matrix of the mixed hydrogen integrated energy system based on the energy flow model of each sub-network and its Jacobian matrix.

[0013] The sensitivity matrix calculation module is used to acquire the operating status data of the integrated energy system at the operating point to be analyzed, and substitute the operating status data into each sensitivity matrix for calculation to obtain the numerical calculation results of each sensitivity matrix.

[0014] The weak link identification module is used to identify the weak links in the operation of the integrated energy system based on the absolute value of the data calculation results of each of the sensitivity matrices.

[0015] Thirdly, the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the sensitivity analysis method for integrated energy systems for hydrogen-blended operation of natural gas networks as described in any of the above claims.

[0016] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the sensitivity analysis method for integrated energy systems for hydrogen-blended operation of natural gas networks as described in any of the preceding claims.

[0017] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, causes the computer to perform the sensitivity analysis method for integrated energy systems operating with hydrogen blending in natural gas networks as described in any of the preceding claims.

[0018] This invention provides a sensitivity analysis method for integrated energy systems operating with hydrogen blended natural gas networks. It combines hydrogen-blended natural gas networks with power and heat networks, constructs energy flow models for each sub-network and coupling equipment models, generates a sensitivity matrix, and calculates the system's operational weaknesses based on the calculated results. This enables precise sensitivity analysis of integrated energy systems in hydrogen blending scenarios, thereby improving the accuracy of sensitivity calculations and accurately identifying system weaknesses, thus meeting the requirements for safe and stable operation of hydrogen-blended integrated energy systems. Attached Figure Description

[0019] 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.

[0020] Figure 1 This is a flowchart illustrating a sensitivity analysis method for integrated energy systems operating with hydrogen in natural gas networks, provided in an embodiment of the present invention.

[0021] Figure 2 This is a structural diagram of the HRIES testing system provided in an embodiment of the present invention.

[0022] Figure 3 This is a sensitivity matrix provided in an embodiment of the present invention. A diagram illustrating the calculation results.

[0023] Figure 4 This is a sensitivity matrix provided in an embodiment of the present invention. A diagram illustrating the calculation results.

[0024] Figure 5 This is a sensitivity matrix provided in an embodiment of the present invention. A diagram illustrating the calculation results.

[0025] Figure 6This is a sensitivity matrix provided in an embodiment of the present invention. A diagram illustrating the calculation results.

[0026] Figure 7 This is a sensitivity matrix provided in an embodiment of the present invention. A diagram illustrating the calculation results.

[0027] Figure 8 This is a sensitivity matrix provided in an embodiment of the present invention. A diagram illustrating the calculation results.

[0028] Figure 9 This is a sensitivity matrix provided in an embodiment of the present invention. A diagram illustrating the calculation results.

[0029] Figure 10 This is a sensitivity matrix provided in an embodiment of the present invention. A diagram illustrating the calculation results.

[0030] Figure 11(a) is a sensitivity matrix provided in an embodiment of the present invention. A diagram illustrating the calculation results.

[0031] Figure 11(b) is a sensitivity matrix provided in an embodiment of the present invention. A diagram illustrating the calculation results.

[0032] Figure 11(c) is a sensitivity matrix provided in an embodiment of the present invention. A diagram illustrating the calculation results.

[0033] Figure 12 It is a sensitivity matrix A diagram illustrating the calculation results.

[0034] Figure 13 This is a schematic diagram of the structure of a sensitivity analysis device for integrated energy systems operating with hydrogen in natural gas networks, provided in an embodiment of the present invention.

[0035] Figure 14 This is a schematic diagram of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0036] 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.

[0037] Hydrogen energy is an ideal renewable secondary energy source with water as its combustion product and high energy density. Currently, the market application of hydrogen energy still faces many bottlenecks, one of which is the high construction cost of dedicated hydrogen transmission pipeline networks. Hydrogen blending technology into natural gas pipelines is one of the mainstream solutions to this problem at present. Against this backdrop, the Hydrogen-blended Integrated Energy System (HIES), which integrates the power grid, heating network, and hydrogen blending network, has attracted much attention, providing a realistic path to achieve efficient hydrogen utilization and a green and low-carbon transformation of the energy system. However, considering that hydrogen is a heterogeneous gas source, its injection will profoundly change the operating status and safety margin of existing integrated energy systems, and trigger a series of operational safety hazards. Therefore, it is necessary to fully consider the differences in the physical properties of hydrogen itself, clarify the interaction and coupling mechanism between the hydrogen blending network and other energy subsystems, and achieve accurate perception of the operating status and weak points of the HIES, thereby avoiding potential operational risks caused by hydrogen blending.

[0038] On the one hand, current research on energy flow modeling for hydrogen-mixed gas networks in HIES is still incomplete. While some related technologies have constructed gas network models that consider the injection of multiple heterogeneous gas sources, these are essentially geared towards generalized application scenarios involving the simultaneous mixing of multiple gases. The coupling relationships between state variables within these models under hydrogen-mixed operation scenarios still need clarification. Furthermore, existing energy flow models often default to energy control of the hydrogen-mixed gas network load, neglecting the more common volume control methods used in engineering practice.

[0039] On the other hand, sensitivity analysis methods, by constructing differential mapping relationships between different operating variables, can quantify the degree of response variable shift caused by control variable disturbances in the current system state, enabling accurate perception of the system's potential future operating state. Considering that hydrogen blending will alter the gas network's operating state, and this state difference will propagate to other subsystems through coupling devices, thereby changing the multi-energy coupling characteristics of the entire HIES, existing sensitivity analysis methods that do not consider the impact of hydrogen blending are clearly no longer applicable. Therefore, in-depth research into the interaction mechanism between the gas system and other energy subsystems after hydrogen blending, and thus achieving HIES operating state perception and weak point location, is of great significance for the safe and stable operation of the HIES.

[0040] Based on this, the present invention provides a sensitivity analysis method for integrated energy systems operating with hydrogen blended in natural gas networks. This method combines hydrogen-blended natural gas networks with power and heat networks, generates a sensitivity matrix by constructing energy flow models for each sub-network and coupling equipment models, and calculates the sensitivity matrix by substituting operational status data. Based on the calculation results, it identifies weak links in the system operation, achieving accurate sensitivity analysis of integrated energy systems under hydrogen blending scenarios. This improves the accuracy of sensitivity calculation to a certain extent and accurately identifies weak links in the system, meeting the requirements for safe and stable operation of hydrogen-blended integrated energy systems.

[0041] Please see Figure 1 The present invention provides a sensitivity analysis method for an integrated energy system operating with hydrogen blending in a natural gas network. This integrated energy system includes multiple sub-networks, including an electricity network, a heating network, and a natural gas network operating with hydrogen blending. The method may include the following steps.

[0042] Step S110: Based on the topology and equipment parameters of the integrated energy system, generate the power network energy flow model and its Jacobian matrix, the heat network energy flow model and its Jacobian matrix, the hydrogen-gas mixed network energy flow model and its Jacobian matrix, and the coupled equipment model; wherein, the coupled equipment model includes the coupling between the power network and the natural gas network, and the coupling between the heat network and the natural gas network.

[0043] In this embodiment, the integrated energy system comprises three mutually coupled sub-networks: an electric power network (responsible for the transmission, distribution, and conversion of electrical energy), a thermal power network (responsible for the transmission, distribution, and utilization of thermal energy), and a hydrogen-blended natural gas network (natural gas mixed with a certain proportion of hydrogen for energy transmission and energy conversion with other sub-networks). The three networks interact with each other through coupling devices, forming a complete multi-energy collaborative system.

[0044] In this embodiment, the topology refers to the connection methods and layouts of devices (such as buses, pipes, and coupling devices) in each sub-network. For example, the connection relationship between buses and lines in a power network, and the connection relationship between nodes and pipes in a natural gas network, forms the basis for constructing the energy flow model. Device parameters are the inherent attribute parameters of devices in each sub-network, such as the conductivity and susceptance of lines in a power network, the heat transfer coefficient and length of pipes in a thermal network, the diameter and friction coefficient of pipes in a natural gas network, and the efficiency parameters of coupling devices.

[0045] In this embodiment, the power network energy flow model is a mathematical model describing the relationship between active power, reactive power, voltage phase angle, and voltage amplitude in the power grid; the power network Jacobian matrix is ​​a matrix obtained by taking the partial derivative of the energy flow deviation vector with respect to the power grid state variables, used to quantify the correlation between changes in state variables and deviations, and is a core tool for calculating power network sensitivity. The thermal network energy flow model is a mathematical model describing the relationship between hot water mass flow rate, node temperature, and thermal power in a heating network; the thermal network Jacobian matrix is ​​a matrix obtained by taking the partial derivative of the thermal energy flow deviation vector with respect to the heating network state variables, used to quantify the correlation between changes in heating network state variables and deviations. The hydrogen-blended gas network energy flow model is a mathematical model describing the relationship between volumetric flow rate, node pressure, hydrogen mole fraction, and energy transmission in a natural gas network after considering hydrogen blending; the hydrogen-blended gas network Jacobian matrix is ​​a matrix obtained by taking the partial derivative of the hydrogen-blended gas energy flow deviation vector with respect to the hydrogen-blended gas network state variables, adapted to the energy flow characteristics of natural gas networks under hydrogen blending scenarios.

[0046] In this embodiment, the coupling device model is a mathematical model that describes the energy conversion relationship between the power network, the heat network, and the hydrogen-blended natural gas network. Its core is to quantify the laws governing the conversion of one energy form to another, including three conversion types: gas-to-electricity, gas-to-heat, and electricity-to-gas.

[0047] In this embodiment, generating the energy flow model, Jacobian matrix, and coupled device model of each sub-network is a mathematical model that quantifies the operating rules of each sub-network by using topology and device parameters.

[0048] Step S120: Based on the energy flow model of each sub-network and its Jacobian matrix, generate the sensitivity matrix of the mixed hydrogen integrated energy system.

[0049] In this embodiment, the sensitivity matrix of the hydrogen-mixed integrated energy system is a matrix constructed based on the Jacobian matrix of each subsystem and the correlation between each sub-network and coupling device. It describes the relationship between changes in system control variables, state variables, and dependent variables, and is the core output of sensitivity analysis.

[0050] Step S130: Obtain the operating status data of the integrated energy system at the operating point to be analyzed, and substitute the operating status data into each sensitivity matrix for calculation to obtain the numerical calculation results of each sensitivity matrix.

[0051] In this embodiment, the operating point to be analyzed is a specific operating state of the integrated energy system, such as the load level, gas injection amount, and system operating state under the hydrogen doping ratio at a certain moment, which is the specific object of sensitivity analysis.

[0052] In this embodiment, the operating status data of the hydrogen-mixed integrated energy system includes the active / reactive power injected by the power source at each bus of the power network, the active / reactive power consumed by the load, the voltage amplitude, the voltage phase angle, and the active / reactive power of the power flow in each branch; the heat source injection power, the load consumption power, the supply water temperature, the return water temperature, and the flow rate of each pipeline at each node of the thermal network; the gas source injection volume / energy flow rate, the load consumption volume / energy flow rate, the pressure, the hydrogen mole fraction of the outflowing gas, the Wobbe number of the outflowing gas, the combustion potential of the outflowing gas, and the flow rate of each pipeline / equipment at each node of the hydrogen-mixed gas network; and the energy consumption and energy production of each coupled device.

[0053] When the aforementioned operational status data can be directly obtained, it can be substituted into the various sensitivity matrices for calculation. When the operational status data cannot be directly obtained, it is necessary to solve the energy flow model of the hydrogen-mixed integrated energy system based on the boundary conditions at the current operating point of the system, thereby obtaining all operational status data of the system. The boundary conditions of the hydrogen-mixed integrated energy system at a certain operating point include the voltage amplitude and voltage phase angle of the power network balance bus. Bus power injection active / reactive power and load consumption active / reactive power Bus power injection / load consumption active power and voltage amplitude, water supply temperature of balanced heat source nodes, water supply temperature and heat source injection power of unbalanced heat source nodes, return water temperature and load consumption power of unbalanced heat source nodes, pressure of balanced nodes in the mixed hydrogen network, gas source injection volume / energy flow rate and load consumption volume / energy flow rate of unbalanced nodes, hydrogen mole fraction of injected gas at nodes, energy consumption and energy production of coupling equipment that does not serve as a balancing unit, and necessary parameters of power grid branches, heat network pipelines, gas network pipelines / equipment, and coupling equipment.

[0054] Step S140: Identify the weak links in the operation of the integrated energy system based on the absolute values ​​of the data calculation results of each of the sensitivity matrices.

[0055] In this embodiment, the weak link is the vulnerable operating variable (such as a certain bus voltage or a certain pipeline flow) or key control variable (such as a certain load or a certain gas source) in the system that is most sensitive to changes in control variables. Even a small disturbance in these links can cause significant changes in the system's operating state, and they are the key focus of attention for the safe operation of the system.

[0056] In the above embodiments, a sensitivity analysis framework for integrated energy systems adapted to hydrogen-blended scenarios is constructed by combining hydrogen-blended natural gas networks with power and heat networks. This solves the problems of existing technologies that do not consider the impact of hydrogen blending and cannot adapt to multi-energy coupling scenarios. Secondly, by constructing Jacobian matrices for each sub-network and coupling device models, the influence relationship between control variables and operating variables is accurately quantified, improving the accuracy of sensitivity analysis. Furthermore, by identifying weak links based on the absolute values ​​of sensitivity values, the most vulnerable operating variables to disturbances and their corresponding key control variables are clearly identified, providing a precise basis for the operation and control of integrated energy systems and fault prevention, ensuring the safe and stable operation of the system under hydrogen-blended scenarios. This sensitivity analysis method is highly versatile and adaptable to integrated energy systems with different hydrogen blending ratios and network sizes, making it widely applicable.

[0057] In some embodiments, in step S110, generating the power network energy flow model and its Jacobian matrix, the thermal network energy flow model and its Jacobian matrix, the hydrogen-mixed gas network energy flow model and its Jacobian matrix, and the coupled device model based on the topology and equipment parameters of the integrated energy system may include the following steps.

[0058] Step S111: Construct an energy flow deviation vector based on the active power deviation vector and reactive power deviation vector of the power network, and obtain the Jacobian matrix of the power network by taking the partial derivative of the energy flow deviation with respect to the power grid state variables.

[0059] Step S112: Construct a heat flow deviation vector based on the node heat power deviation vector, loop cumulative pressure drop deviation vector, node equivalent supply water temperature deviation vector, and node equivalent return water temperature deviation vector of the heat network, and obtain the Jacobian matrix of the heat network by taking the partial derivative of the heat flow deviation vector with respect to the heat network state variables.

[0060] Step S113: Construct a hydrogen-gas energy flow deviation vector based on the node volume flow deviation vector, equipment control equation deviation vector, and node hydrogen volume flow deviation vector of the hydrogen-gas network, and take the partial derivative of the hydrogen-gas energy flow deviation vector with respect to the state variables of the hydrogen-gas network to obtain the Jacobian matrix of the hydrogen-gas network.

[0061] Step S114: Based on the energy conversion between the power network, the heat network, and the mixed hydrogen network, construct energy conversion models respectively; wherein, the energy conversion models include a gas-to-electricity energy conversion model, a gas-to-heat energy conversion model, and an electricity-to-gas energy conversion model.

[0062] In this embodiment, the active power deviation vector is a vector composed of the difference between the actual active power flowing into and out of each bus in the power network, reflecting the degree of active power imbalance in the power grid. The reactive power deviation vector is a vector composed of the difference between the actual reactive power flowing into and out of each bus in the power network, reflecting the degree of reactive power imbalance in the power grid. The energy flow deviation vector is a deviation vector constructed based on the active power deviation vector and the reactive power deviation vector, used to describe the energy flow imbalance state of the power network, and is the core of constructing the power network energy flow model. The power grid state variables are the core variables describing the operating state of the power network, including the bus voltage phase angle. and voltage amplitude .

[0063] In this embodiment, the node heat power deviation vector is a vector formed by the difference between the actual heat power consumed and the rated heat power consumed by each node in the thermal network. The loop cumulative pressure drop deviation vector is a vector formed by the sum of the actual cumulative pressure drops of the loops in the thermal network, reflecting the unbalanced state of the thermal network pipeline resistance. The node equivalent supply water temperature deviation vector is a vector formed by the difference between the actual inflow and outflow mass temperatures of the supply water at each node in the thermal network. The node equivalent return water temperature deviation vector is a vector formed by the difference between the actual inflow and outflow mass temperatures of the return water at each node in the thermal network. The heat energy flow deviation vector is a deviation vector constructed based on the deviation vectors of node heat power, loop cumulative pressure drop, and node equivalent supply and return water temperatures. It is used to describe the unbalanced state of energy flow in the thermal network and is the core of constructing the thermal network energy flow model. The thermal network state variables are the core variables describing the operating state of the thermal network, including hot water mass flow rate. Node equivalent water supply temperature Node equivalent return water temperature .

[0064] In this embodiment, the node volumetric flow rate deviation vector of the hydrogen-mixed gas network is a vector formed by the difference between the actual inflow volumetric flow rate and the outflow volumetric flow rate of each node in the hydrogen-mixed gas network. The equipment control equation deviation vector is a vector formed by the difference between the actual operating state and the rated operating state of each piece of equipment (such as compressors and gas sources) in the hydrogen-mixed gas network. The node hydrogen volumetric flow rate deviation vector is a vector formed by the difference between the actual inflow hydrogen volumetric flow rate and the outflow hydrogen volumetric flow rate of each node in the hydrogen-mixed gas network. The hydrogen-mixed gas energy flow deviation vector is a deviation vector constructed based on the node volumetric flow rate, equipment control equation, and node hydrogen volumetric flow rate deviation vector of the hydrogen-mixed gas network. It is used to describe the unbalanced state of the energy flow in the natural gas network under the hydrogen blending scenario and is the core of constructing the hydrogen-mixed gas network energy flow model. The hydrogen-mixed gas network state variables are the core variables describing the operating state of the hydrogen-mixed gas network, including the node square pressure. Equipment volumetric flow rate hydrogen mole fraction .

[0065] In this embodiment, the energy conversion model is a mathematical model for the coupling device to realize the conversion of different energy forms, including three types: gas-electricity (gas gas → electricity), gas-heat (gas gas → heat energy), and electricity-gas (electricity energy → gas gas), which quantifies the energy loss and conversion relationship in the energy conversion process.

[0066] The above embodiments clarify the construction methods of the energy flow models and Jacobian matrices of each sub-network, providing a solid mathematical foundation for the generation of the sensitivity matrix and ensuring the accuracy of sensitivity calculations. Furthermore, considering the unique characteristics of hydrogen-blended natural gas networks, an energy flow deviation vector incorporating hydrogen mole fraction and mixed hydrogen volumetric flow rate is constructed to adapt to the energy flow characteristics of natural gas networks in hydrogen-blended scenarios, solving the problem that traditional gas network models cannot adapt to hydrogen-blended scenarios. Secondly, through three types of coupled device models, the energy conversion laws between each sub-network are clarified, realizing deep coupling analysis of multi-energy networks and avoiding the limitations of single-network analysis.

[0067] In some embodiments, the state variable vector of the power network is assumed. In step S111, an energy flow deviation vector is constructed based on the active power deviation vector and reactive power deviation vector of the power network, and the partial derivative of the energy flow deviation with respect to the power grid state variables is obtained to obtain the Jacobian matrix of the power network. This may include the following steps.

[0068] Step S1111: Based on the topology and equipment parameters of the integrated energy system, generate the grid node admittance matrix Y;

[0069] Step S1112: Based on the power flow deviation vector Constructing an energy flow model for a power network:

[0070]

[0071] In the formula, V is the bus voltage vector. and These represent operations on the real and imaginary parts, respectively. These are the active power and reactive power deviation vectors at the power grid bus, respectively. These are vector representations of the active power injected by the power source at the grid bus, the active power consumed by the load, the reactive power injected by the power source, and the reactive power consumed by the load; * indicates conjugate operation;

[0072] Step S1113: [The text appears to be incomplete and contains several grammatical errors. A more accurate translation would require the full context.] right Taking the partial derivative, we obtain the Jacobian matrix of the power network. ;in, The compact expression is:

[0073]

[0074]

[0075]

[0076] In the formula, The apparent power deviation vector at the power grid bus; The imaginary unit is the complex number; the subscripts PQ and PV represent the PQ and PV bus indices in the power grid, respectively. Indicates by A matrix composed of the row indices (PQ and PV) and column indices (PQ and PV). Indicates by The matrix consisting of the PQ row index and the PQ and PV column index elements; Indicates by A matrix composed of the row indices of PQ and PV and the column indices of PQ; Indicates by A matrix composed of the PQ row index and PQ column index elements; This represents diagonal matrix operations.

[0077] In this embodiment, the node admittance matrix It is a matrix describing the electrical connections between buses in a power network, with dimensions n×n (where n is the number of buses), and its elements... Indicates busbar With busbar The equivalent admittance between buses reflects the power transfer capacity between them and is a core parameter for constructing a power grid energy flow model. (Nodal admittance matrix) The generation method can be obtained by using the power flow calculation tool MATPOWER.

[0078] The above embodiments clarify the definitions of power network state variables and the specific expressions for the energy flow deviation vector, making the construction of the power grid energy flow model more targeted and operable, and avoiding ambiguity in model construction. A compact expression for the Jacobian matrix and the definitions of each submatrix are given, simplifying the calculation process of the Jacobian matrix, improving computational efficiency, and ensuring the accuracy of the matrix. Simultaneously, it provides a precise Jacobian matrix foundation for the subsequent generation of the power network sensitivity matrix, ensuring the accuracy of power network sensitivity calculation and providing reliable support for identifying system weaknesses.

[0079] In some embodiments, in step S120, generating a sensitivity matrix for the hybrid hydrogen integrated energy system based on the energy flow models of each sub-network and their Jacobian matrices may include the following steps.

[0080] Step S1211: The state variable vector of the power network is The control variable vector is , These are the active power and reactive power of the busbar net load, respectively. Compared to Sensitivity matrix Represented as:

[0081]

[0082] In the formula, It is the identity matrix; This is the active power sensitivity matrix of bus voltage phase angle and bus net load. This is the bus voltage phase angle - bus net load reactive power sensitivity matrix; This is the active power sensitivity matrix of bus voltage amplitude minus bus net load. This is the bus voltage amplitude minus the bus net load reactive power sensitivity matrix.

[0083] Step S1212: The dependent variable of the power network is the active power vector of the branch power flow. , Compared to Sensitivity matrix Represented as:

[0084]

[0085]

[0086]

[0087] In the formula, The active power of branch l is a vector. The first in One element; and Branch roads The starting and ending busbars; and These represent the voltage magnitude and phase angle of bus x, respectively; and They are respectively and The first in line, number Column elements; Indicates busbar and busbar Voltage phase difference between them; and Let represent the conductance and susceptance of branch l, respectively; branch road The transformer turns ratio.

[0088] In this embodiment, the control variable is the core variable affecting the power grid operation state, and the core of sensitivity analysis is to quantify its impact on the state variable and the dependent variable. Compared to The sensitivity matrix is ​​used to describe the power grid state variable vector. (Voltage phase angle, amplitude) varies with the control variable vector Sensitivity to changes in net active and reactive power load, i.e., the change in state variable for every unit change in control variable.

[0089] In this embodiment, the dependency variable In other words, the active power of a branch is a variable determined by state variables, and its change depends on the change of state variables. It is used to reflect the operating status of the power grid branch. The calculation formula can be expressed as: Electricity network dependency variables Relative to control variables Sensitivity matrix describe Changes The degree of influence, the core expression is based on For state variables The partial derivative, combined with right The sensitivity matrix was derived.

[0090] The above embodiments clarify the correlation between power network control variables, state variables, and dependent variables, generating a complete power network sensitivity matrix and achieving precise quantification of power network sensitivity. The specific derivation process and expression of the sensitivity matrix are given, making sensitivity calculation more operable and avoiding the subjectivity and ambiguity of traditional sensitivity calculations. Simultaneously considering the sensitivity of state variables (voltage phase angle, amplitude) and dependent variables (branch active power), the embodiments comprehensively reflect the impact of control variables on the power grid operating state, providing a more comprehensive basis for identifying weak links in the power grid.

[0091] In some embodiments, the state variable vector of the thermal network is assumed. In step S112, a heat flow deviation vector is constructed based on the deviation vectors of node heat power, loop cumulative pressure drop, node equivalent supply water temperature, and node equivalent return water temperature of the thermal network. The partial derivative of the heat flow deviation vector with respect to the thermal network state variables is then obtained to obtain the Jacobian matrix of the thermal network. This may include the following steps.

[0092] Step S1121: Based on the topology and equipment parameters of the integrated energy system, generate a heat network node-pipeline correlation matrix. Heating network loop-pipeline correlation matrix ;

[0093] Step S1122: Based on the thermal energy flow deviation vector Constructing a thermal network energy flow model:

[0094]

[0095]

[0096]

[0097] In the formula, Specific heat capacity of the heat transfer medium; For the reason A matrix formed by rows of heat source nodes in Central Africa; For the reason The matrix formed by the nodes of the non-equilibrium heat source in the middle; m is the mass flow rate of hot water in the pipe; This is the equivalent outflow temperature vector of the non-source nodes in the water supply network. This is the equivalent outflow temperature vector of the source node in the water supply network. This is the equivalent outflow temperature vector of the source node in the recirculation network. This is the equivalent outflow temperature vector of the non-source nodes in the return water network; For a given source node in the water supply network, the equivalent inflow temperature vector is used. The equivalent inflow temperature vector for a given non-source node in the return water network; This is the vector of heat power consumed at non-source nodes; This is the heat power vector consumed at the source node; the superscripts S and R represent the water supply and return networks, respectively; the subscripts s and n indicate that the matrix is ​​composed of the rows containing the source nodes and the non-source nodes in the original matrix, respectively; the double subscript nn indicates that the matrix is ​​composed of the rows and columns containing the non-source nodes in the original matrix; the double subscript ns indicates that the matrix is ​​composed of the rows containing the source nodes and the columns containing the source nodes in the original matrix; the double subscript ss indicates that the matrix is ​​composed of the rows and columns containing the source nodes in the original matrix; the double subscript sn indicates that the matrix is ​​composed of the rows containing the source nodes and the columns containing the non-source nodes in the original matrix. This represents the resistance coefficient vector of 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; This is the deviation vector of the equivalent water supply temperature at non-heat source nodes; This is the vector of deviations in the equivalent water supply temperature of the heat source node. This is the deviation vector of the equivalent return water temperature of non-heat source nodes; This is the deviation vector of the equivalent return water temperature at the heat source node; The vector of nodal thermal power deviation is given by the matrix. 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 water supply temperature at the node, given by the matrix. Obtained by performing elementary row operations based on the original node index of the heating network; Let be the vector of deviations in the equivalent return water temperature at the nodes, derived from the matrix. Obtained by performing elementary row operations based on the original node index of the heating network;

[0098] Step S1123: ... right Taking the partial derivative, we obtain the Jacobian matrix of the thermal network. ;in, The expression for a compact matrix is:

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] In the formula, This indicates that elementary matrix transformations are performed based on the original node index of the heating network; It is a matrix operator.

[0105] In this embodiment, the operating characteristics of the heating network are jointly characterized by a hydraulic model and a thermal model. Based on the classic loose heating network model, a thermal network energy flow model can be constructed. State variable vector This is a unified representation of the state variables of a thermal network, where m is the hot water mass flow vector (unit: kg / s) in the water supply network pipeline. This is the equivalent water supply temperature vector for each node in the heating network (unit: °C). The equivalent return water temperature vector (unit: °C) of the heating network node is concatenated with the other three to form a state variable vector, which comprehensively describes the operating state of the heating network.

[0106] In this embodiment, the heat network node-pipeline association matrix is ​​a matrix describing the connection relationships between nodes and pipes in the heat network, with dimensions of [missing information]. ( This represents the number of nodes in the heating network. (Number of heating network pipes). Heating network node-pipeline correlation matrix. The matrix elements can be represented as:

[0107] .

[0108] In this embodiment, the heat network loop-pipeline correlation matrix A matrix used to describe the connection relationships between loops and pipes in a thermal network, with dimensions of 1. ( This refers to the number of loops in the heating network. (Number of heating network pipes), heating network loop-pipeline correlation matrix The matrix elements can be represented as:

[0109] .

[0110] In this embodiment, the Jacobian matrix of the thermal network Thermal energy flow deviation For state variables The matrix obtained by taking the partial derivative is used to describe and The relationship between the rates of change, whose compact expression contains multiple sub-matrices, is a core tool for calculating the sensitivity of thermal networks.

[0111] In the above embodiments, a complete thermal energy flow deviation vector including heat power, loop pressure drop, and node supply and return water temperatures was constructed, comprehensively quantifying the energy flow imbalance state of the thermal network and solving the problem that traditional thermal network models only consider a single parameter and are not comprehensive. The introduction of node-pipe correlation matrices and loop-pipe correlation matrices clarifies the relationship between the thermal network topology and equipment parameters, making the model construction more logical and operable, and adaptable to thermal networks of different topological scales. Key factors such as pipe heat transfer loss and loop pressure drop were considered, and combined with operations such as Hadamard product and elementary matrix row operations, a compact expression for the thermal network Jacobian matrix and the calculation methods for each submatrix were accurately derived, ensuring the accuracy and practicality of the Jacobian matrix. Source nodes and non-source nodes were distinguished, and a targeted temperature deviation calculation method was set, closely matching the actual operating characteristics of the thermal network. This provides a precise mathematical foundation for the subsequent generation of the thermal network sensitivity matrix, ensuring the accuracy of thermal network sensitivity analysis.

[0112] In some embodiments, in step S120, generating a sensitivity matrix for the hybrid hydrogen integrated energy system based on the energy flow models of each sub-network and their Jacobian matrices may include the following steps.

[0113] Step S1221: The state variable vector of the thermal network is The control variable vector is , This is the node net load power vector. Compared to Sensitivity matrix Represented as:

[0114]

[0115] In the formula, This is the pipeline flow-node net load power sensitivity matrix; This is the node temperature-node net load power sensitivity matrix.

[0116] In this embodiment, the state variable included in the sensitivity analysis of the thermal network is defined as the pipe flow rate. and node temperature The control variable is the node net load power. The sensitivity matrix that can be constructed in the thermal network includes the pipeline flow rate-node net load power sensitivity matrix. and node temperature-node net load power sensitivity matrix Control variables of a thermal network It is a core variable affecting the operating status of the heating network, and the core of sensitivity analysis is to quantify its impact on state variables. Compared to The sensitivity matrix is ​​used to describe the state variables of the heating network. With control variable Sensitivity to change refers to the change in the state variable for every unit change in the control variable. The sensitivity matrix to be determined. and It can be obtained directly from the inverse matrix of the Jacobian matrix of the thermal network.

[0117] In some embodiments, the hydrogen mixing network operates in a volumetric flow rate control mode, and the state variable vector of the hydrogen mixing network is defined. In step S113, a mixed hydrogen gas energy flow deviation vector is constructed based on the mixed hydrogen network node volume flow deviation vector, the equipment control equation deviation vector, and the node hydrogen volume flow deviation vector. The partial derivative of the mixed hydrogen gas energy flow deviation vector with respect to the mixed hydrogen network state variables is then obtained to obtain the Jacobian matrix of the mixed hydrogen network. This may include the following steps.

[0118] Step S1131: Based on the topology and equipment parameters of the integrated energy system, generate a hydrogen-mixed gas network node-pipeline correlation matrix. Hydrogen-mixed gas network node-balanced gas source correlation matrix Hydrogen-mixed gas network node-compressor correlation matrix Hydrogen mixing network node-equipment association matrix =[ , ] , Hydrogen-mixed gas network node-compressor intake point correlation matrix Hydrogen-mixed gas network node-nonequilibrium gas source correlation matrix ;

[0119] Step S1132: Based on the hydrogen-mixed gas energy flow deviation vector when the hydrogen-mixed gas network is operating in volumetric flow control mode. Constructing an energy flow model for a hydrogen-mixed gas network:

[0120]

[0121]

[0122]

[0123] In the formula, This represents the volumetric flow rate vector of the gas network pipeline. 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 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 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 represents the volumetric flow rate vector of the gas network node load. This is the volumetric flow rate vector of the unbalanced gas source in the gas network. 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; 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.

[0124] Step S1133: ... right Taking the partial derivative, we obtain the Jacobian matrix of the hydrogen-mixed gas network operating in volumetric flow control mode. ;in, The expression for a compact matrix is:

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133] .

[0134] In this embodiment, the mathematical model of the hydrogen-mixing network consists of a hydraulic component and a gas composition tracking component. When the hydrogen-mixing network operates in volumetric flow control mode, its energy flow model can be adopted. To represent. State variable vector The core state variables of the hydrogen-mixed gas network, This is the nodal squared pressure vector (unit: bar²). ε is the volumetric flow rate vector of the equipment (compressor, gas source) (unit: m³ / h), and ε is the hydrogen mole fraction vector of the node (unitless, value 0~1). The three are combined to comprehensively describe the operating status of the hydrogen-blended natural gas network.

[0135] In this embodiment, the node-pipeline association matrix The elements can be represented as:

[0136] ;

[0137] Node-Balanced Gas Source Correlation Matrix The elements can be represented as:

[0138] ;

[0139] Node-compressor association matrix It can be represented as:

[0140] ;

[0141] Node-compressor intake point correlation matrix It can be represented as:

[0142] ;

[0143] Node-Nonequilibrium Gas Source Correlation Matrix It can be represented as:

[0144] .

[0145] In this embodiment, the specific values ​​of the control parameters for the gas network equipment can be found in Table 1 below, where, , , , and respectively equipment Control parameters under different operating modes; , and They are respectively The Line number Column elements; ,Right now for The diagonal element of the k-th row; Let be the k-th element in d.

[0146] Table 1. Control Parameter Values ​​for Gas Network Equipment

[0147]

[0148] In this embodiment, the Jacobian matrix of the hydrogen-mixed gas network The deviation of the energy flow of the mixed hydrogen fuel gas For state variables The matrix obtained by taking the partial derivative is used to describe and The relationship between the rates of change, whose compact expression contains multiple sub-matrices, is a core tool for calculating the sensitivity of a hydrogen-mixed network.

[0149] In the above embodiments, for hydrogen-blended scenarios, an energy flow deviation vector incorporating the hydrogen mole fraction was constructed to accurately adapt to the physicochemical characteristics of the hydrogen-blended gas network, solving the problem that traditional gas network models cannot account for the effects of hydrogen blending. The specific construction methods for various correlation matrices and equipment control equations were clarified, and the sub-matrix composition of the Jacobian matrix was refined to ensure the accuracy and operability of the energy flow model. Key factors such as the hydrogen blending ratio, gas specific gravity, and pipeline parameters were integrated, enabling the model to reflect the impact of changes in the hydrogen blending ratio on the energy flow of the gas network, laying the foundation for subsequent sensitivity analysis.

[0150] In some embodiments, in step S120, generating a sensitivity matrix for the hybrid hydrogen integrated energy system based on the energy flow models of each sub-network and their Jacobian matrices may include the following steps.

[0151] Step S1231: The state variable vector of the hydrogen-mixed gas network when operating in volumetric flow control mode is... The control variable vector is , Compared to Sensitivity matrix Represented as:

[0152]

[0153] In the formula, This is the node pressure-node unbalanced gas source volumetric flow rate sensitivity matrix; This is the node pressure-node load volumetric flow rate sensitivity matrix; The sensitivity matrix for equipment flow rate versus node unbalanced gas source volumetric flow rate; This is the sensitivity matrix for equipment flow rate versus node load volumetric flow rate. The sensitivity matrix of hydrogen mole fraction in node effluent gas versus non-equilibrium gas source volumetric flow rate at the node. The sensitivity matrix is ​​defined as the hydrogen mole fraction of the node effluent versus the volumetric flow rate of the node load.

[0154] Step S1232: In volumetric flow rate control mode, the dependent variable of the hydrogen-mixed network is the Wobbe number vector of the effluent gas from the nodes. Node outflow gas combustion potential vector and pipeline flow vector , , and Compared to The sensitivity matrices are expressed as follows:

[0155]

[0156]

[0157]

[0158] In the formula, The sensitivity matrix of node effluent gas Wobbe number - node non-equilibrium gas source volumetric flow rate; The sensitivity matrix for the Wobbe number of the outflowing gas from the node versus the volumetric flow rate of the node load. The sensitivity matrix of node outflow gas combustion potential versus node non-equilibrium gas source volumetric flow rate; The sensitivity matrix of nodal outflow gas combustion potential versus nodal load volumetric flow rate; The sensitivity matrix for pipeline flow rate versus node unbalanced gas source volumetric flow rate; This is the pipeline flow rate-node load volumetric flow rate sensitivity matrix; The node pressure vector; The density vector of the gas flowing out of the node; This represents the difference in specific gravity between hydrogen and natural gas. - Let be the derivative coefficients of the Wobbe number equation and the combustion potential equation for the nodal outflow gas with respect to ε.

[0159] In this embodiment, the hydrogen-mixed network node The number of Wobbe gas flowing out of the site and combustion potential The calculation formulas are as follows:

[0160]

[0161]

[0162] In the formula, and These are the calorific value and specific gravity of natural gas, respectively. and These are the differences in calorific value and specific gravity between hydrogen and natural gas, respectively. For nodes The correction factor for the oxygen content of the outflowing gas; , , and They are nodes The molar percentages of hydrocarbons (excluding methane), carbon monoxide, methane, and oxygen in the outflowing gas; , and They are nodes The calorific value, specific gravity, and hydrogen mole fraction of the gas flowing out.

[0163] In this embodiment, the control variable is the core variable affecting the operating state of the hydrogen-mixed gas network, and the core of the sensitivity analysis is to quantify its impact on the state variable and the compliance variable. Compared to The sensitivity matrix is ​​used to describe the state variable vector of the hydrogen-mixed gas network. With control variable vector Sensitivity to change refers to the change in the state variable when the control variable changes by one unit.

[0164] In the above embodiments, a complete sensitivity matrix system was constructed for hydrogen-blended gas networks, covering state variables and dependent variables. This system quantifies the impact of control variables such as hydrogen blending ratio and gas source flow rate on the operation of the gas network, filling the gap in sensitivity analysis of gas networks under hydrogen blending scenarios. The derivation process of the sensitivity matrix and the meaning of its sub-matrices were clarified, and combined with the characteristics of hydrogen blending, a quantitative basis for controlling the hydrogen blending ratio was provided.

[0165] In some embodiments, the hydrogen-mixing network operates in an energy flow control mode, and the state variable vector of the hydrogen-mixing network is defined. In step S113, a mixed hydrogen gas energy flow deviation vector is constructed based on the deviation vector of the node volume flow rate of the mixed hydrogen gas network, the deviation vector of the equipment control equation, and the deviation vector of the node hydrogen flow rate. The partial derivative of the mixed hydrogen gas energy flow deviation with respect to the state variables of the mixed hydrogen gas network is then obtained to obtain the Jacobian matrix of the mixed hydrogen gas network. This may include the following steps.

[0166] Step S1136: Based on the topology and equipment parameters of the integrated energy system, generate a hydrogen-mixed gas network node-pipeline correlation matrix. Hydrogen-mixed gas network node-balanced gas source correlation matrix Hydrogen-mixed gas network node-compressor correlation matrix Hydrogen mixing network node-equipment association matrix Hydrogen mixing network node-compressor gas intake point correlation matrix Hydrogen-mixed gas network node-nonequilibrium gas source correlation matrix ;

[0167] Based on the hydrogen gas flow deviation vector when the hydrogen-mixed gas network is operating in energy flow control mode Constructing an energy flow model for a hydrogen-mixed gas network:

[0168]

[0169] In the formula, This is the energy calorific value vector of the non-equilibrium gas source in the gas network. This represents the load energy flow vector of the gas network node; This is the energy flow vector of the unbalanced gas source in the gas network.

[0170] Step S1137: ... right Taking the partial derivative, we obtain the Jacobian matrix of the hydrogen-mixed gas network operating in energy flow control mode. ;in, The expression for a compact matrix is:

[0171]

[0172]

[0173] .

[0174] In this embodiment, for the Jacobian matrix For the remaining submatrix blocks, please refer to The expression format.

[0175] In some embodiments, in step S120, generating a sensitivity matrix for the hybrid hydrogen integrated energy system based on the energy flow models of each sub-network and their Jacobian matrices may include the following steps.

[0176] Step S1236: The state variable vector of the hydrogen-mixed network when operating in energy flow control mode is... The control variable vector is , Compared to Sensitivity matrix Represented as:

[0177]

[0178] In the formula, The vector of calorific value of the gas flowing out of the node; The node pressure-node unbalanced gas source energy flow sensitivity matrix; This is the node pressure-node load energy flow sensitivity matrix; The equipment flow rate-node unbalanced gas source energy flow rate sensitivity matrix; This is the equipment flow rate - node load energy flow rate sensitivity matrix; The node effluent hydrogen mole fraction-node nonequilibrium gas source energy flow sensitivity matrix; The node effluent hydrogen mole fraction-node load energy flow sensitivity matrix;

[0179] Step S1237: In the energy flow control mode, the dependent variable vector of the hydrogen-mixed network is the Wobbe number vector of the effluent gas from the nodes. Node outflow gas combustion potential vector and pipeline flow vector , , and Compared to The sensitivity matrices are expressed as follows:

[0180]

[0181]

[0182]

[0183] In the formula, The node effluent gas Wobbe number - node non-equilibrium gas source energy flow sensitivity matrix; The node effluent gas Wobbe number - node load energy flow sensitivity matrix; The node outflow gas combustion potential-node non-equilibrium gas source energy flow sensitivity matrix; The node outflow gas combustion potential-node load energy flow sensitivity matrix; The pipeline flow rate-node unbalanced gas source energy flow rate sensitivity matrix; This is the pipeline flow-node load energy flow sensitivity matrix.

[0184] In some embodiments, the coupling device includes a gas turbine for realizing gas-to-electric energy conversion, a gas boiler for gas-to-thermal energy conversion, and an electric-to-gas device for electric-to-gas energy conversion; in step S114, energy conversion models are constructed according to the energy conversion between the power network, the thermal network, and the mixed hydrogen network, which may include the following steps.

[0185] Step S1141: Based on the output electrical power of the gas turbine Electrical efficiency and the calorific value of the gas consumed by the gas turbine Determine the volumetric flow rate of the gas consumed by the gas turbine. ;in, ;

[0186] Step S1142: Based on the output thermal power of the gas boiler Thermal efficiency and the calorific value of the gas consumed by the gas boiler Determine the volumetric flow rate of the gas consumed by the gas boiler. ;in, ;

[0187] Step S1143: Based on the electrical power consumed by the electro-gas conversion device Energy conversion efficiency The calorific value of the gas generated by the electro-gas converter Determine the volumetric flow rate of the gas generated by the electro-gas converter. ;in, .

[0188] In this embodiment, the circulating pump in the thermal network is driven by an independent power source and is not coupled to the power network; the compressor in the hydrogen-mixing network is driven by gas and can operate in multiple modes such as constant compression ratio, constant pressure difference, constant flow rate, constant inlet pressure, and constant outlet pressure; the hydrogen-mixing network contains both hydrogen and natural gas sources, and the gas produced by the electric-to-gas device is hydrogen.

[0189] In some embodiments, in step S120, generating a sensitivity matrix for the hybrid hydrogen integrated energy system based on the energy flow models of each sub-network and their Jacobian matrices may include the following steps.

[0190] Step S1241: When the hydrogen-mixed gas network is operating in energy flow control mode, and there is a situation where the gas turbine is operating in electric balance unit mode or the gas boiler is operating in heat balance unit mode, construct the state variables of the hydrogen-mixed gas network. Relative to grid control variables and heating network control variables Sensitivity matrix:

[0191]

[0192]

[0193]

[0194]

[0195] In the formula, This is the active power sensitivity matrix for node pressure versus node net load. The active power sensitivity matrix is ​​defined as the ratio of equipment flow to node net load. The mole fraction of hydrogen in the outflow gas from the node is the active power sensitivity matrix of the node net load. This is the node pressure-node net load power sensitivity matrix; This is the equipment flow-node net load power sensitivity matrix; The node effluent hydrogen mole fraction - node net load power sensitivity matrix; and These represent the flow rates of gas energy consumed by the gas turbine and the gas boiler, respectively. and These refer to the power output of the gas turbine and the gas boiler, respectively. and These are the energy conversion efficiencies of gas turbines and gas boilers, respectively. , and They are respectively , and The column vectors related to the coupling device x; Total grid loss; This represents the total number of busbars in the power network. and They are respectively and The i-th element in; and They are respectively and The first in One element; for The first in One element; , and These are the total network loss of the heating network, the network loss of the water supply network, and the network loss of the return water network; and These are the equivalent temperature vectors at the inlet of the water supply pipe and the return pipe, respectively. and They are respectively and The first in One element; This is the equivalent temperature vector at the inlet of the heating network pipeline. , , and m, respectively , and The l-th element in;

[0196] Step S1242: Construct the hydrogen-mixed network dependency variables , and Relative to grid control variables and heating network control variables Sensitivity matrix:

[0197]

[0198]

[0199] In the formula, The active power sensitivity matrix is ​​defined as the number of gas escaping from the node minus the net load of the node. The active power sensitivity matrix is ​​defined as the combustion potential of the outflowing gas at the node versus the net load at the node. This is the active power sensitivity matrix for pipeline flow rate versus node net load. The node effluent gas Wobbe number - node net load power sensitivity matrix; The node outflow gas combustion potential - node net load power sensitivity matrix; This is the pipeline flow-node net load power sensitivity matrix; , and They are respectively , and In-line coupling device The relevant column vectors;

[0200] Step S1243: When the hydrogen-mixed gas network is operating in energy flow control mode and there is an electric-to-gas device operating in gas balance unit mode, construct the grid state variables. Relative to the control variables of the hydrogen-mixed network and Sensitivity matrix:

[0201]

[0202]

[0203] In the formula, Node voltage amplitude - node unbalanced gas source energy flow sensitivity matrix; This is a sensitivity matrix for node voltage amplitude versus node load energy flow. for The column vectors related to the electro-gas conversion process; and They are respectively and The row vectors related to the conversion of electricity to gas; The volumetric flow rate of the gas produced by the electro-gas converter; The power consumed by the electro-gas conversion device; The energy conversion efficiency of the electro-gas conversion device; This refers to the calorific value of hydrogen.

[0204] Step S1244: Construct power grid dependency variables Relative to the control variables of the hydrogen-mixed network and Sensitivity matrix:

[0205]

[0206]

[0207] In the formula, The sensitivity matrix of branch power flow active power - node unbalanced gas source energy flow rate; For the branch power flow active power-node load energy flow sensitivity matrix; for The column vectors related to the electro-gas conversion process;

[0208] In this embodiment, due to the lack of corresponding disturbance transmission equipment, when the hydrogen-mixed network operates in energy flow control mode, there is no sensitivity matrix of thermal network operating variables (state variables and dependent variables are collectively referred to as operating variables) relative to the hydrogen-mixed network control variables.

[0209] Step S1245: When the mixed hydrogen gas network is operating in volumetric flow control mode, and there is an electric-to-gas conversion device operating in gas balance unit mode or a gas turbine or gas boiler operating in non-balance unit mode, construct the state variables of the power grid and heating network. Relative to the control variables of the hydrogen-mixed network and Sensitivity matrix:

[0210]

[0211]

[0212]

[0213]

[0214]

[0215]

[0216] In the formula, This is the node voltage amplitude-node unbalanced gas source volumetric flow rate sensitivity matrix; This is the node voltage amplitude - node load volumetric flow rate sensitivity matrix; The sensitivity matrix for pipeline flow rate versus node unbalanced gas source volumetric flow rate; This is the pipeline flow rate-node load volumetric flow rate sensitivity matrix; This is the node temperature-node non-equilibrium gas source volumetric flow rate sensitivity matrix. This is the node temperature-node load volumetric flow rate sensitivity matrix; and These are the node voltage amplitude and node unbalanced gas source volume flow sensitivity matrices formed by the coupling of the electro-gas conversion device and the gas turbine, respectively. and These are the node voltage amplitude-node load volume flow sensitivity matrices formed by the coupling of the electric-to-gas device and the gas turbine; for The column vectors related to gas turbines; and They are respectively and The row vectors related to the conversion of electricity to gas; and They are respectively and The row vectors related to gas turbines; and They are respectively and The column vectors related to gas-fired boilers; and They are respectively and The row vectors related to gas-fired boilers; This represents the difference in calorific value between hydrogen and natural gas. , and These are the calorific value of the gas consumed by the gas-fired boiler, the mole fraction of hydrogen in the gas consumed, and the volumetric flow rate of the gas consumed. , and These are the calorific value of the gas consumed by the gas turbine, the mole fraction of hydrogen in the gas consumed, and the volumetric flow rate of the gas consumed; This represents the temperature vector of the heating network node;

[0217] Step S1246: Construct power grid dependency variables Relative to the control variables of the hydrogen-mixed network and Sensitivity matrix:

[0218]

[0219]

[0220] In the formula, The sensitivity matrix of active power flow of branch circuits and non-equilibrium gas source volumetric flow rate at nodes; For the branch power flow active power-node load volumetric flow sensitivity matrix; and These are the active power flow-node unbalanced gas source volumetric flow sensitivity matrices formed by the coupling of the electric-to-gas conversion device and the gas turbine in the branch power flow; and These are the active power-nodal load volumetric flow sensitivity matrices of the branch power flow formed by the coupling of the electric-to-gas conversion device and the gas turbine; for The column vectors related to gas turbines.

[0221] In some embodiments, in step S140, identifying the weak links in the operation of the integrated energy system based on the absolute values ​​of the data calculation results of each of the sensitivity matrices may include the following steps.

[0222] Step S141: Sort the matrix elements corresponding to the specified control variables in each sensitivity matrix in descending order of absolute value, and determine the operating variable corresponding to the matrix element with the largest absolute value as the vulnerable operating variable corresponding to the specified control variable at the operating point of the integrated energy system.

[0223] Step S142: Sort the matrix elements corresponding to the specified operating variables in each sensitivity matrix from largest to smallest absolute value, and determine the control variable corresponding to the matrix element with the largest absolute value as the key control variable corresponding to the specified operating variable at the operating point of the integrated energy system.

[0224] The general matrix energy flow model for hydrogen-mixed gas networks established in this invention can effectively reflect the impact of hydrogen blending on the operating state of the gas network. Based on the sorting out of the redundant relationships between various state variables in the gas network under the hydrogen blending operation scenario, the solution efficiency of the model is improved while reducing the scale of variables. In addition, it also takes into account the energy flow control mode commonly used in the prior art and the volume flow control mode more commonly used in engineering practice, which can realize the efficient characterization of the operating state of hydrogen-mixed gas networks under different control modes.

[0225] Furthermore, the energy flow model of the hydrogen-mixed integrated energy system established in this invention is constructed using a general matrix format, which significantly improves the characterization capability and implementation efficiency compared to existing technologies. All types of Jacobian matrices are derived along the gradient descent direction and are expressed using a compact matrix format, which has advantages in construction accuracy and computational efficiency, ensuring the accuracy of subsequent sensitivity analysis results.

[0226] Secondly, the sensitivity analysis method for the mixed hydrogen integrated energy system designed in this invention takes into account key state parameters affecting the operational safety of the mixed hydrogen network, such as the Wobbe number and combustion potential. Based on the differential-algebraic relationship between the variables, a sensitivity matrix is ​​constructed that can characterize the operating characteristics of each subsystem and be used for cross-system coupling and interaction analysis. This reveals the interaction coupling mechanism between the mixed hydrogen network and other energy networks under different control modes, and can provide a reference for system operation status perception and weak link identification.

[0227] In one specific embodiment, to verify the effectiveness of the above sensitivity analysis method, a HIES test system was constructed, consisting of a modified IEEE-39-node power network, a 32-node Barry Island thermal network, and a 20-node Belgian gas network. Its structure is as follows: Figure 2As shown. In the 39-node power grid, the generator at bus E30 is replaced with a gas turbine and connected to the gas network node G16; a 150MW wind turbine is connected to bus E26, which also supplies power to the power-to-gas conversion unit in the gas network. In the 32-node heating network, heat source node H1 is heated by a gas-fired boiler, which draws gas from the gas network node G20; otherwise, the remaining heat sources in the heating network are all upstream heat exchange stations. , The ambient temperatures were set to 100°C, 30°C, and 10°C, respectively. In the 20-node gas network, in addition to the original natural gas source, hydrogen sources were connected at nodes G1, G4, G11, and G15, and an electric-to-gas conversion device was installed at node G6 (the output of the electric-to-gas conversion device is hydrogen), thereby realizing the hydrogen blending transformation of the gas network; compressors 1 and 2 in the gas network are both gas-driven compressors that draw gas from the inlet node, and both operate in constant compression ratio mode, with pressure boost ratios of 1.2 and 1.1, respectively; the gas temperature of the gas network was set to 288.15K, and the standard pressure and standard temperature for gas measurement were defined as 1.01325 bar and 293.15K, respectively.

[0228] Case Result 1: Analysis of Electric / Heat-Based Constant Gas Operation Mode

[0229] In the aforementioned HIES test system, the gas turbine (GTG) at node E30 and the gas boiler (GB) at node H1 were set as balancing units for their respective power grids and heating networks, respectively. The natural gas source at node G1 was set as the balancing unit for the mixed hydrogen network. The node pressure was kept constant at 56 bar, and the hydrogen production of the power-to-gas (P2G) converter at node G6 was set to 0.7 MSCMD. At this time, the mixed hydrogen network was in energy flow control mode, and HIES operated in a power / heat-driven gas supply mode. The potential control variable of the system was the net active power of the grid nodes. and net load power of heating network nodes The operating variables of the hydrogen-mixed gas network that need to be examined include node pressure. Equipment flow Node outflow gas Wobbe number Combustion potential of gas flowing out of the node and pipeline flow The following will be based on , and For example, a sensitivity analysis was conducted under the electric / thermal constant gas operation mode.

[0230] Sensitivity matrix The calculation results are as follows Figure 3 As shown in the figure, the sensitivity calculation results are all negative, indicating that... will follow Decrease as increases, or decrease with . The sensitivity of the pressure at node G20 relative to the net active power of bus E8 is -14.2 × 10⁻⁶. −4 It has the largest absolute value among all sensitivity calculation results. Because right The impact is transmitted through the GTG, therefore the above calculation results can also be understood as follows: the net load active power fluctuation at bus E8 has the largest impact on the GTG output power among all grid buses, and the GTG gas load fluctuation has the largest impact on the pressure at node G20 among all gas grid nodes. At this time, the pressure at node G20 will be considered as the pressure under the current operating conditions. For any busbar The fragile operating variables, the net active power of bus E8 is correspondingly considered as For any node The key control variables.

[0231] Sensitivity matrix The calculation results are as follows Figure 4 As shown. Similarly, at this time... Figure 4 The sensitivity values ​​are all negative, which means will follow Decrease as increases, or decrease with . The decrease in load increases the value of the gas network. Since the incremental rates of network losses corresponding to the net load power of each node in the heating network are very close, the sensitivity of the pressure at any gas network node relative to the net load power of all heating network nodes can be considered equal, with the largest absolute value being the sensitivity of the pressure at node G20. Because... right The impact is transmitted through GB, therefore the above results can also be interpreted as the impact of GB gas load fluctuations on the pressure of node G20 being the largest among all gas network node pressures. The above sensitivity analysis results indicate that in the current HIES... For any node The vulnerable operating variable is the stress on node G20, and the net load power of any node can be considered as... For any node The key control variables.

[0232] Sensitivity matrix The calculation results are as follows Figure 5 As shown. With As the hydrogen supply increases, the load demand of the hydrogen-mixed gas network will also increase. Since the hydrogen supply injection rate is constant, the natural gas supply injection rate at the equilibrium node of the hydrogen-mixed gas network will increase, causing the hydrogen mole fraction in the outflowing gas to decrease, ultimately leading to... Decrease, or vice versa Then it will rise. Figure 5The sensitivity results shown are all negative, confirming the above analysis. It can also be observed that the sensitivity of the combustion potential at node G1 relative to the net active power of bus E8 is −0.01, which is the largest absolute value among all sensitivity calculations. Considering... right The impact is also transmitted through GTG, and the above results also show that the influence of GTG gas load fluctuations on the combustion potential of node G1 is the largest among all gas network node combustion potentials. Therefore, the combustion potential of node G1 will be regarded as the current system operating state. For any busbar The fragile operating variables, the net active power of bus E8 is correspondingly considered as For any node The key control variables.

[0233] Sensitivity matrix The calculation results are as follows Figure 6 As shown. With The calculation results are the same. Figure 6 The sensitivity values ​​in the data are all negative, which means that... will follow It decreases as it increases, with The decrease leads to an increase. Among all sensitivity results, the combustion potential at node G1 relative to... The absolute value of the sensitivity is the largest, which also indicates that the impact of GB gas load fluctuations on the combustion potential of node G1 is the largest among all gas network node combustion potentials. Therefore, the combustion potential of node G1 will be regarded as the current system operating state. For any node The fragile operating variables, the net load power of any node can be regarded as For any node The key control variables.

[0234] Sensitivity matrix The calculation results are as follows Figure 7 As shown. By Figure 7 As can be seen, all sensitivity values ​​are positive, which means that the flow rate of the hydrogen-mixed gas network pipeline is positive. The changing trend and The fluctuations are positively correlated, with the largest absolute value representing the sensitivity of the flow rate in pipe 18 relative to the net active power of bus E8. At this point, the flow rate in pipe 18 represents the sensitivity analysis under the current system operating conditions. For any busbar The fragile operating variables, the net active power of bus E8 is correspondingly considered as For any pipe The key control variables.

[0235] Sensitivity matrix The calculation results are as follows Figure 8 As shown. The sensitivity calculation results show that, except for the flow rates in pipes 14-18... Apart from showing a negative correlation, the remaining pipeline flow rates are all related to They show a positive correlation. Specifically, the absolute values ​​of the sensitivity for the flow rates in pipes 14, 15, 16, and 19 are equal, and they have the largest absolute values ​​among all pipe flow rates. Therefore, the flow rates of these pipes represent the current system operating state in the sensitivity analysis. For any node The fragile operating variables, the net load power of any node can be regarded as For any pipe The key control variables.

[0236] Case Result 2: Analysis of Gas-Driven Electricity / Heat Operation Mode

[0237] Maintaining constant output from the gas turbine and gas boiler, the balancing units of the power grid and heating network are switched to the generator at bus E37 and the heat source at node H32, respectively. Simultaneously, the power-to-gas conversion unit at node G6 is added as a balancing unit for the mixed hydrogen network, with the pressure kept constant at 54 bar. At this point, HIES operates in a gas-driven power / heat mode, and the system's potential control variable is the energy flow rate of the node's unbalanced gas source under energy flow control mode. Load energy flow The volumetric flow rate of the unbalanced gas source at the node under the volumetric flow control mode Load volumetric flow rate The cross-system operating variables that need to be examined include node voltage amplitude. Branch circuit power flow active power The following will use the following as a reference: flow rate m in the heating network pipeline and temperature T at the heating network node. , Sensitivity analysis was conducted using m as an example under the gas-controlled electric / thermal operation mode.

[0238] When the hydrogen-mixed gas network operates in energy flow control mode, the sensitivity matrix is ​​obtained. The calculation results are as follows Figure 9 As shown, when the gas load energy demand increases or decreases, the power consumption of the P2G will increase or decrease accordingly, which in turn leads to a decrease or increase in the grid voltage amplitude. This is consistent with... Figure 9 The sensitivity results are consistent with those in the original text. Specifically, the voltage amplitude sensitivity of bus E26 ranges from −2.47 × 10⁻⁶. −4 to −2.97×10 −6 It is the most sensitive of all busbars. This is mainly because... right The impact is transmitted through the P2G, and bus E26 is directly connected to the P2G; therefore, its voltage amplitude is most significantly affected by P2G power fluctuations. The sensitivity of bus E26's voltage amplitude relative to the load at node G6 is the largest in absolute value among all gas loads. This also means that the impact of load fluctuations at node G6 on the hydrogen injection amount of the balancing unit P2G is the largest among all gas loads. Therefore, the above sensitivity analysis indicates that under the current system operating conditions, the voltage amplitude of bus E26 is... As for the air network any The fragile runtime variables, the load of node G6 is For any busbar The key control variables.

[0239] When the hydrogen-mixed gas network operates in energy flow control mode, the sensitivity matrix is ​​obtained. The calculation results are as follows Figure 10 As shown. By Figure 10 It can be seen that, apart from the hydrogen gas source at node G1, the power of the remaining gas sources is... There is a positive correlation. This is because the hydrogen source at node G1 is directly connected to the natural gas balancing source. A decrease in its power will lead to a decrease in the proportion of hydrogen in the gas network and an increase in the node's calorific value. When the load energy demand remains constant, a higher calorific value results in a lower flow rate, thus reducing the P2G's power consumption. When the remaining gas source power decreases, the P2G, as a balancing unit, will increase its injected power to fill the power gap in the gas network. Among all gas sources, the absolute sensitivity of the bus E26 voltage amplitude relative to the node G5 gas source is the largest. Therefore, under the current system operating conditions, the bus E26 voltage amplitude remains... As for the air network any The fragile operational variables, and the gas source of node G5 is... The key control variable for any bus |V|.

[0240] When the hydrogen-mixed gas network operates in volumetric flow control mode, the sensitivity matrix is ​​obtained. The calculation results are shown in Figure 11(a). Under volumetric flow control mode, right The effects can be transmitted not only through P2G but also through GTG. The sensitivity matrices corresponding to these two coupling transmission methods... and The calculation results are shown in Figures 11(b) and (c), respectively. When the gas source flow rate changes, the output of P2G also fluctuates, thus affecting the active power distribution of the power flow in the branches of the power grid. The impact of the gas source at node G5 is the most significant. Furthermore, changes in the gas source flow rate also cause fluctuations in the calorific value of the gas in the gas network. At this time, the output of GTG, which has a constant gas consumption volume flow rate, will also change, thereby altering the active power flow in the branches of the power grid. Except for the gas source at node G5, fluctuations in the flow rate of the remaining gas sources will have varying degrees of impact on the active power flow in the branches. As shown in Figure 11(a), and The sum of them is Compared to Sensitivity matrix This shows that under the same gas source, the active power of the tidal flow in each branch exhibits both increasing and decreasing trends. Branches with more sensitive active power are mainly concentrated around branches 3, 30, and 41. This is because these branches are located around the P2G, GTG, and equilibrium nodes. Further analysis of specific sensitivity values ​​leads to the conclusion that, under the current system operating conditions, the active power of branch 41 is... As for the air network any The fragile operating variables, node G5 gas source is For the power grid any The key control variables.

[0241] When the hydrogen-mixed gas network operates in volumetric flow control mode, the sensitivity matrix is ​​obtained. The calculation results are as follows Figure 12 As shown. From this, it can be seen that, except for the gas load at node G10 (corresponding to...) Figure 12 The intermediate gas load (4) and the residual gas load have almost no impact on the flow rate of the heating network pipelines. This is because the gas intake node G20 of GB is located at the end of the mixed hydrogen gas network, and the only node upstream of node G20 that can affect the downstream gas composition is the load node G10. Different heating network pipeline flow rates exhibit different trends under the influence of the gas load at node G10. Pipelines 1, 4, 10, and 32 are more sensitive because they are located between the GB access node H1 and the heating network balance node H32. Based on the specific sensitivity values, it can be determined that, under the current system operating conditions, the mass flow rate of pipeline 4 is... As for the air network any The fragile operating variable, the gas load of node G10 is Regarding the hot network any The key control variables.

[0242] Please see Figure 13One embodiment of the present invention provides a sensitivity analysis device for an integrated energy system operating with hydrogen blending in a natural gas network. The integrated energy system includes multiple sub-networks, including an electricity network, a heating network, and a natural gas network operating with hydrogen blending. The sensitivity analysis device for the integrated energy system operating with hydrogen blending in a natural gas network may include: an energy flow model generation module, a sensitivity matrix generation module, a sensitivity matrix calculation module, and a weak link identification module.

[0243] The energy flow model generation module is used to generate, based on the topology and equipment parameters of the integrated energy system, an energy flow model of the power network and its Jacobian matrix, an energy flow model of the thermal network and its Jacobian matrix, an energy flow model of the mixed hydrogen network and its Jacobian matrix, and a coupled equipment model; wherein, the coupled equipment model includes the coupling between the power network and the mixed hydrogen network, and the coupling between the thermal network and the mixed hydrogen network;

[0244] The sensitivity matrix generation module is used to generate the sensitivity matrix of the mixed hydrogen integrated energy system based on the energy flow model of each sub-network and its Jacobian matrix.

[0245] The sensitivity matrix calculation module is used to acquire the operating status data of the integrated energy system at the operating point to be analyzed, and substitute the operating status data into each sensitivity matrix for calculation to obtain the numerical calculation results of each sensitivity matrix.

[0246] The weak link identification module is used to identify the weak links in the operation of the integrated energy system based on the absolute value of the data calculation results of each of the sensitivity matrices.

[0247] The specific functions and effects of the sensitivity analysis device for integrated energy systems operating with hydrogen blending in natural gas networks can be explained by referring to other embodiments in this specification, and will not be repeated here. Each module in the sensitivity analysis device for integrated energy systems operating with hydrogen blending in natural gas networks can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0248] Please see Figure 14 One embodiment of the present invention can provide an electronic device, the electronic device comprising:

[0249] A memory, and one or more processors communicatively connected to the memory;

[0250] The memory stores instructions that can be executed by the one or more processors, which, when executed by the one or more processors, enable the one or more processors to implement the sensitivity analysis method for integrated energy systems operating with hydrogen in natural gas networks as described in any of the above embodiments.

[0251] One embodiment of the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the sensitivity analysis method for integrated energy systems operating with hydrogen blending in natural gas networks as described in any of the above embodiments.

[0252] This specification also provides a computer program product containing instructions that, when executed by a computer, cause the computer to perform the sensitivity analysis method for integrated energy systems operating with hydrogen blending in natural gas networks as described in any of the above embodiments.

[0253] It is understood that the specific examples in this document are only intended to help those skilled in the art better understand the embodiments described herein, and are not intended to limit the scope of the invention.

[0254] It is understood that in the various embodiments described in this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments described in this specification.

[0255] It is understood that the various implementation methods described in this specification can be implemented individually or in combination, and the implementation methods in this specification are not limited in this respect.

[0256] Unless otherwise stated, all technical and scientific terms used in the embodiments of this specification have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this specification. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items. The singular forms "a," "the," and "the" as used in the embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0257] It is understood that the processor in this invention can be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method implementation can be completed by the integrated logic circuitry in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this specification. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this specification can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0258] It is understood that the memory in this invention can be volatile memory or non-volatile memory, or may include both. Specifically, the non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM). It should be noted that the memory in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0259] 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, data stored, data displayed, etc.) involved in the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0260] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this specification.

[0261] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the aforementioned method implementations, and will not be repeated here.

[0262] In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0263] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0264] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0265] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of this specification, in essence, or the parts that contribute to the prior art, or parts of the technical solutions, can be embodied in the form of software products. These computer software products are stored in a storage medium and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0266] The above description is merely a specific embodiment of this specification, but the scope of protection of this invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this specification should be included within the scope of protection of this specification. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. A sensitivity analysis method for integrated energy systems operating with hydrogen blending in natural gas networks, characterized in that, The integrated energy system comprises multiple sub-networks, including an electricity network, a heating network, and a hydrogen-blended natural gas network. The method includes: Based on the topology and equipment parameters of the integrated energy system, an energy flow model and its Jacobian matrix for the power network, an energy flow model and its Jacobian matrix for the thermal network, an energy flow model and its Jacobian matrix for the mixed hydrogen network, and a coupled equipment model are generated; wherein, the coupled equipment model includes the coupling between the power network and the natural gas network, and the coupling between the thermal network and the natural gas network; Based on the energy flow models of each sub-network and their Jacobian matrices, a sensitivity matrix for the mixed hydrogen integrated energy system is generated. The operating status data of the integrated energy system at the operating point to be analyzed is obtained, and the operating status data is substituted into each sensitivity matrix for calculation to obtain the numerical calculation results of each sensitivity matrix. Based on the absolute value of the data calculation results of each of the aforementioned sensitivity matrices, the weak links in the operation of the integrated energy system are identified; Specifically, based on the topology and equipment parameters of the integrated energy system, the following models are generated: power network energy flow model and its Jacobian matrix, heat network energy flow model and its Jacobian matrix, hydrogen-mixed gas network energy flow model and its Jacobian matrix, and coupled equipment model: Based on the active power deviation vector and reactive power deviation vector of the power network, construct the power flow deviation vector, and take the partial derivative of the power flow deviation with respect to the power grid state variables to obtain the Jacobian matrix of the power network. Based on the node heat power deviation vector, loop cumulative pressure drop deviation vector, node equivalent supply water temperature deviation vector, and node equivalent return water temperature deviation vector of the thermal network, a heat flow deviation vector is constructed, and the partial derivative of the heat flow deviation vector with respect to the thermal network state variables is obtained to obtain the Jacobian matrix of the thermal network. Based on the node gas volume flow deviation vector, equipment control equation deviation vector, and node hydrogen volume flow deviation vector of the hydrogen-mixed gas network, a hydrogen-mixed gas energy flow deviation vector is constructed, and the partial derivative of the hydrogen-mixed gas energy flow deviation vector with respect to the state variables of the hydrogen-mixed gas network is obtained to obtain the Jacobian matrix of the hydrogen-mixed gas network. Based on the energy conversion between the power network, the thermal network, and the mixed hydrogen network, energy conversion models for the coupling devices are constructed respectively; wherein, the energy conversion models include a gas-to-electricity energy conversion model, a gas-to-thermal energy conversion model, and an electricity-to-gas energy conversion model; When the hydrogen-mixing network operates in volumetric flow control mode, let the state variable vector of the hydrogen-mixing network be... =[ Π ; ; ε ],in, Π The squared pressure vector at the node. =[ ; [ ] represents the device volumetric flow rate vector. To balance the gas source volumetric flow rate vector, This is the compressor volumetric flow rate vector. ε The vector represents the mole fraction of gaseous hydrogen. Based on the node gas volumetric flow rate deviation vector, equipment control equation deviation vector, and node hydrogen volumetric flow rate deviation vector of the hydrogen-mixed gas network, a hydrogen-mixed gas energy flow deviation vector is constructed. The partial derivative of the hydrogen-mixed gas energy flow deviation vector with respect to the state variables of the hydrogen-mixed gas network is then calculated to obtain the Jacobian matrix of the hydrogen-mixed gas network, including: Based on the topology and equipment parameters of the integrated energy system, a hydrogen-mixed gas network node-pipeline correlation matrix is ​​generated. Hydrogen-mixed gas network node-balanced gas source correlation matrix Hydrogen-mixed gas network node-compressor correlation matrix Hydrogen mixing network node-equipment association matrix =[ , ] , Hydrogen-mixed gas network node-compressor intake point correlation matrix Hydrogen-mixed gas network node-nonequilibrium gas source correlation matrix ; Based on the hydrogen gas flow deviation vector when the hydrogen gas network is operating in volumetric flow control mode Constructing an energy flow model for a hydrogen-mixed gas network: In the formula, This represents the volumetric flow rate vector of the gas network pipeline. 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. χ This refers to the flow rate index of the gas pipeline network. κ This is the diameter index of the gas network pipeline; μ This is the weight index of the gas network pipeline; =[ ; [ ] 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 represents the volumetric flow rate vector of the gas network node load. This is the volumetric flow rate vector of the unbalanced gas source in the gas network. 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; ω 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 difference in calorific value 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; This is a coefficient matrix representing the flow rate of the gas network equipment. d 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 volumetric flow rate deviation vector of the hydrogen-mixed gas network nodes; The deviation vector of the equipment control equation; This is the hydrogen volumetric flow rate deviation vector at the nodes of the hydrogen mixing network. Will right Taking the partial derivative, we obtain the Jacobian matrix of the hydrogen-mixed gas network operating in volumetric flow control mode. ;in, The expression for a compact matrix is: ； Based on the energy flow model and Jacobian matrix of the hydrogen-mixed gas network under volumetric flow rate control mode, a sensitivity matrix for the hydrogen-mixed gas network under volumetric flow rate control mode is generated, including: The state variable vector of the hydrogen-mixed gas network when operating in volumetric flow control mode is: =[ Π ; ; ε The control variable vector is =[ ; ], Compared to Sensitivity matrix Represented as: In the formula, This is the node pressure-node unbalanced gas source volumetric flow rate sensitivity matrix; This is the node pressure-node load volumetric flow rate sensitivity matrix; The sensitivity matrix for equipment flow rate versus node unbalanced gas source volumetric flow rate; This is the sensitivity matrix for equipment flow rate versus node load volumetric flow rate. The sensitivity matrix of hydrogen mole fraction in node effluent gas versus non-equilibrium gas source volumetric flow rate at the node. The sensitivity matrix for hydrogen mole fraction in node effluent versus node load volumetric flow rate. In volumetric flow control mode, the dependent variable of the hydrogen-mixed network is the Wobbe number vector of the effluent gas from the nodes. Node outflow gas combustion potential vector and pipeline flow vector , , and Compared to The sensitivity matrices are expressed as follows: In the formula, The sensitivity matrix of node effluent gas Wobbe number - node non-equilibrium gas source volumetric flow rate; The sensitivity matrix for the Wobbe number of the outflowing gas from the node versus the volumetric flow rate of the node load. The sensitivity matrix of node outflow gas combustion potential versus node non-equilibrium gas source volumetric flow rate; The sensitivity matrix of nodal outflow gas combustion potential versus nodal load volumetric flow rate; The sensitivity matrix for pipeline flow rate versus node unbalanced gas source volumetric flow rate; This is the pipeline flow rate-node load volumetric flow rate sensitivity matrix; π The node pressure vector; ρ The density vector of the gas flowing out of the node; This represents the difference in specific gravity between hydrogen and natural gas. - The Wobbe number equation and combustion potential equation for nodal outflow gas are related to ε The derivative coefficients; When the hydrogen-mixed gas network operates in energy flow control mode, let the state variable vector of the hydrogen-mixed gas network be... =[ Π ; ; ε Based on the node volumetric flow rate deviation vector, equipment control equation deviation vector, and node hydrogen volumetric flow rate deviation vector of the hydrogen-mixed gas network, a hydrogen-mixed gas energy flow deviation vector is constructed. The partial derivative of the hydrogen-mixed gas energy flow deviation vector with respect to the state variables of the hydrogen-mixed gas network is then calculated to obtain the Jacobian matrix of the hydrogen-mixed gas network, including: Based on the topology and equipment parameters of the integrated energy system, a hydrogen-mixed gas network node-pipeline correlation matrix is ​​generated. Hydrogen-mixed gas network node-balanced gas source correlation matrix Hydrogen-mixed gas network node-compressor correlation matrix Hydrogen mixing network node-equipment association matrix =[ , ] , Hydrogen-mixed gas network node-compressor intake point correlation matrix Hydrogen-mixed gas network node-nonequilibrium gas source correlation matrix ; Based on the hydrogen gas flow deviation vector when the hydrogen-mixed gas network is operating in energy flow control mode Constructing an energy flow model for a hydrogen-mixed gas network: In the formula, This is the energy calorific value vector of the non-equilibrium gas source in the gas network. This represents the load energy flow vector of the gas network node; This is the energy flow vector of the unbalanced gas source in the gas network. Will right Taking the partial derivative, we obtain the Jacobian matrix of the hydrogen-mixed gas network operating in energy flow control mode. ;in, The expression for a compact matrix is: ； Based on the energy flow model and Jacobian matrix of the hydrogen-mixed gas network under energy flow control mode, a sensitivity matrix for the hydrogen-mixed gas network under energy flow control mode is generated, including: The state variable vector of the hydrogen-mixed network when operating in energy flow control mode is: =[ Π ; ; ε The control variable vector is =[ ; ], Compared to Sensitivity matrix Represented as: In the formula, q The vector of calorific value of the gas flowing out of the node; The node pressure-node unbalanced gas source energy flow sensitivity matrix; This is the node pressure-node load energy flow sensitivity matrix; The equipment flow rate-node unbalanced gas source energy flow rate sensitivity matrix; This is the equipment flow rate - node load energy flow rate sensitivity matrix; The node effluent hydrogen mole fraction-node nonequilibrium gas source energy flow sensitivity matrix; The node effluent hydrogen mole fraction-node load energy flow sensitivity matrix; In the energy flow control mode, the dependency variable vector of the hydrogen-mixed network is the Wobbe number vector of the effluent gas from the nodes. Node outflow gas combustion potential vector and pipeline flow vector , , and Compared to The sensitivity matrices are expressed as follows: In the formula, The node effluent gas Wobbe number - node non-equilibrium gas source energy flow sensitivity matrix; The node effluent gas Wobbe number - node load energy flow sensitivity matrix; The node outflow gas combustion potential-node non-equilibrium gas source energy flow sensitivity matrix; The node outflow gas combustion potential-node load energy flow sensitivity matrix; The pipeline flow rate-node unbalanced gas source energy flow rate sensitivity matrix; This is the pipeline flow-node load energy-flow sensitivity matrix; Identifying the weak links in the operation of the integrated energy system includes: sorting the matrix elements corresponding to the specified control variables in each sensitivity matrix from largest to smallest absolute value, and identifying the operating variable corresponding to the matrix element with the largest absolute value as the vulnerable operating variable of the specified control variable at the current operating point; sorting the matrix elements corresponding to the specified operating variables in each sensitivity matrix from largest to smallest absolute value, and identifying the control variable corresponding to the matrix element with the largest absolute value as the critical control variable of the specified operating variable at the current operating point.

2. The method according to claim 1, characterized in that, Let the state variable vector of the power network be... =[ θ ; |V| ], θ Let be the voltage phase angle vector at the power grid bus. |V| The voltage magnitude vector at the power grid bus is given; the power flow deviation vector is constructed based on the active power deviation vector and reactive power deviation vector of the power network, and the partial derivative of the power flow deviation vector with respect to the power grid state variables is obtained to obtain the Jacobian matrix of the power network, including: Based on the topology and equipment parameters of the integrated energy system, a grid node admittance matrix is ​​generated. Y ; Based on the power flow deviation vector Constructing an energy flow model for a power network: In the formula, V For the bus voltage vector, Real and Imag These represent operations on the real and imaginary parts, respectively; Δ P and Δ Q These are the active power and reactive power deviation vectors at the power grid bus, respectively. , , and These are vector representations of the active power injected by the power source at the power grid bus, the active power consumed by the load, the reactive power injected by the power source, and the reactive power consumed by the load. Indicates conjugate operation; Will right Taking the partial derivative, we obtain the Jacobian matrix of the power network. ;in, The compact expression is: In the formula, Δ S The apparent power deviation vector at the power grid bus; j The imaginary unit of a complex number; subscript PQ and PV They represent the power grid PQ and PV Bus index; / Indicates by Δ S / θ In PQ , PV row index and PQ , PV A matrix composed of column index elements; / Indicates by Δ S / θ In PQ row index and PQ , PV A matrix composed of column index elements; / Indicates by Δ S / |V| In PQ , PV row index and PQ A matrix composed of column index elements; / Indicates by Δ S / |V| In PQ row index and PQ A matrix composed of column index elements; diag( ) represents diagonal matrix operations.

3. The method according to claim 2, characterized in that, Based on the power network energy flow model and its Jacobian matrix, a power network sensitivity matrix is ​​generated, including: The state variable vector of the power network is =[ θ ; |V| The control variable vector is =[ ; ], and These are the active power and reactive power of the busbar net load, respectively. Compared to Sensitivity matrix Represented as: In the formula, E It is the identity matrix; This is the active power sensitivity matrix of bus voltage phase angle and bus net load. This is the bus voltage phase angle - bus net load reactive power sensitivity matrix; This is the active power sensitivity matrix of bus voltage amplitude minus bus net load. This is the bus voltage amplitude minus the bus net load reactive power sensitivity matrix. The dependent variable of the power network is the active power vector of the branch power flow. , Compared to Sensitivity matrix Represented as: In the formula, branch road l The active power is a vector. The first in l One element; i and j Branch roads l The starting and ending busbars; | | and They represent the busbars respectively. x Voltage amplitude and phase angle; / and / | | They are respectively / θ and / |V| The first in l line, number x Column elements; = Indicates busbar i and busbar j Voltage phase difference between them; and Representing branch roads l Conductivity and susceptance; branch road l The transformer turns ratio.

4. The method according to claim 1, characterized in that, Let the state variable vector of the thermal network be... =[ m ; ' ; ' ], m The mass flow rate of hot water in the water supply network pipeline. ' Let be the degree vector of the equivalent water supply temperature of the node. ' The equivalent return water temperature vector for each node is defined. Based on the node heat power deviation vector, loop cumulative pressure drop deviation vector, node equivalent supply water temperature deviation vector, and node equivalent return water temperature deviation vector of the thermal network, a heat flow deviation vector is constructed. The partial derivative of the heat flow deviation vector with respect to the thermal network state variables is then applied to obtain the Jacobian matrix of the thermal network, including: Based on the topology and equipment parameters of the integrated energy system, a heat network node-pipeline correlation matrix is ​​generated. Heating network loop-pipeline correlation matrix ; Based on the thermal energy flow deviation vector Constructing a thermal network energy flow model: In the formula, Specific heat capacity of the heat transfer medium; 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; m This represents the mass flow rate of hot water in the pipe. ' This is the equivalent outflow temperature vector of the non-source nodes in the water supply network. ' This is the equivalent outflow temperature vector of the source node in the water supply network. ' This is the equivalent outflow temperature vector of the source node in the recirculation network. ' This is the equivalent outflow temperature vector of the non-source nodes in the return water network; ' For a given source node in the water supply network, the equivalent inflow temperature vector is used. ' The equivalent inflow temperature vector for a given non-source node in the return water network; This is the vector of heat power consumed at non-source nodes; This is the heat power vector consumed at the source node; the superscripts S and R represent the water supply and return networks, respectively; the subscripts s and n indicate that the matrix is ​​composed of the rows containing the source nodes and the non-source nodes in the original matrix, respectively; the double subscript nn indicates that the matrix is ​​composed of the rows and columns containing the non-source nodes in the original matrix; the double subscript ns indicates that the matrix is ​​composed of the rows containing the source nodes and the columns containing the source nodes in the original matrix; the double subscript ss indicates that the matrix is ​​composed of the rows and columns containing the source nodes in the original matrix; the double subscript sn indicates that the matrix is ​​composed of the rows containing the source nodes and the columns containing the non-source nodes in the original matrix. is the vector of resistance coefficients for the heating network pipeline; · represents the Hadamard product operation of the matrix; ~ , ~ 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 thermal power deviation vector for non-heat source nodes; This is the thermal power deviation vector for non-heat source nodes; This is the cumulative voltage drop deviation vector of the loop; ' This is the equivalent water supply temperature deviation vector for non-heat source nodes; ' The equivalent water supply temperature deviation vector for the heat source node; ' This is the equivalent return water temperature deviation vector for non-heat source nodes; ' Δ is the equivalent return water temperature deviation vector 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. ' Let [the vector be] the deviation of the equivalent water supply 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. ' 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. Will right Taking the partial derivative, we obtain the Jacobian matrix of the thermal network. ;in, The expression for a compact matrix is: In the formula, This indicates that elementary matrix transformations are performed based on the original node index of the heating network; It is a matrix operator.

5. The method according to claim 4, characterized in that, Based on the thermal network energy flow model and its Jacobian matrix, a thermal network sensitivity matrix is ​​generated, including: The state variable vector of the thermal network is =[ m ; ' ; ' The control variable vector is = , This is the node net load power vector. Compared to Sensitivity matrix Represented as: In the formula, This is the pipeline flow-node net load power sensitivity matrix; This is the node temperature-node net load power sensitivity matrix.

6. The method according to claim 1, characterized in that, The coupling equipment includes a gas turbine for realizing gas-to-electric energy conversion, a gas boiler for gas-to-thermal energy conversion, and an electric-to-gas conversion device for electric-to-gas energy conversion; Based on the energy conversion between the power network, the heat network, and the hydrogen-mixed gas network, energy conversion models are constructed respectively, including: Based on the output electrical power of the gas turbine P GTG Electrical efficiency η GTG and the calorific value of the gas consumed by the gas turbine q GTGgas, determining the volumetric flow rate of the gas consumed by the gas turbine. f GTG ;in, ; According to the output thermal power of the gas boiler GB Thermal efficiency η GB and the calorific value of the gas consumed by the gas boiler q GBgas determines the volumetric flow rate of the gas consumed by the gas-fired boiler. f GB ;in, ; According to the electrical power consumed by the electro-gas conversion device P P2G Energy conversion efficiency η P2G The calorific value of the gas generated by the electro-gas converter q P2G gas, determining the volumetric flow rate of the gas generated by the electro-gas converter. f P2G ;in, .

7. The method according to claim 6, characterized in that, Based on the energy flow models of each sub-network and their Jacobian matrices, a sensitivity matrix for the mixed hydrogen integrated energy system is generated, including: When the hydrogen-mixed gas network operates in energy flow control mode, and there are instances where the gas turbine operates in electric balance unit mode or the gas boiler operates in heat balance unit mode, the state variables of the hydrogen-mixed gas network are constructed. Π , and ε Relative to grid control variables and heating network control variables Sensitivity matrix: In the formula, This is the active power sensitivity matrix for node pressure versus node net load. The active power sensitivity matrix is ​​defined as the ratio of equipment flow to node net load. The mole fraction of hydrogen in the outflow gas from the node is the active power sensitivity matrix of the node net load. This is the node pressure-node net load power sensitivity matrix; This is the equipment flow-node net load power sensitivity matrix; The node effluent hydrogen mole fraction - node net load power sensitivity matrix; and These represent the flow rates of gas energy consumed by the gas turbine and the gas boiler, respectively. and These refer to the power output of the gas turbine and the gas boiler, respectively. and These are the energy conversion efficiencies of gas turbines and gas boilers, respectively. , and They are respectively , and In-line coupling device x The relevant column vectors; Total grid loss; This represents the total number of busbars in the power network. / and / They are respectively / and / The first in i One element; / and / | | They are respectively / θ and / |V| The first in k One element; / for / The first in j One element; , and These are the total network loss of the heating network, the network loss of the water supply network, and the network loss of the return water network; ' and ' These are the equivalent temperature vectors at the inlet of the water supply pipe and the return pipe, respectively. / and / ' They are respectively / m and / ' The first in l One element; ' This is the equivalent temperature vector at the inlet of the heating network pipeline. , , and ' They are respectively m , , and ' The first in l One element; Simultaneously, constructing a hydrogen-mixed network dependency variable , and Relative to grid control variables and heating network control variables Sensitivity matrix: In the formula, The active power sensitivity matrix is ​​defined as the number of gas escaping from the node minus the net load of the node. The active power sensitivity matrix is ​​defined as the combustion potential of the outflowing gas at the node versus the net load at the node. This is the active power sensitivity matrix for pipeline flow rate versus node net load. The node effluent gas Wobbe number - node net load power sensitivity matrix; The node outflow gas combustion potential - node net load power sensitivity matrix; This is the pipeline flow-node net load power sensitivity matrix; , and They are respectively , and In-line coupling device x The relevant column vectors; When the hydrogen-to-gas network operates in energy flow control mode, and an electric-to-gas conversion device operates in gas balance unit mode, the grid state variables are constructed. |V| Relative to the control variables of the hydrogen-mixed network and Sensitivity matrix: In the formula, Node voltage amplitude - node unbalanced gas source energy flow sensitivity matrix; This is a sensitivity matrix for node voltage amplitude versus node load energy flow. for The column vectors related to the electro-gas conversion process; and They are respectively and The row vectors related to the conversion of electricity to gas; The volumetric flow rate of the gas produced by the electro-gas converter; The power consumed by the electro-gas conversion device; The energy conversion efficiency of the electro-gas conversion device; This refers to the calorific value of hydrogen. At the same time, construct power grid dependency variables Relative to the control variables of the hydrogen-mixed network and Sensitivity matrix: In the formula, The sensitivity matrix of branch power flow active power - node unbalanced gas source energy flow rate; For the branch power flow active power-node load energy flow sensitivity matrix; for The column vectors related to the electro-gas conversion process; When the mixed hydrogen gas network operates in volumetric flow control mode, and there are electric-to-gas conversion devices operating in gas balance unit mode or gas turbines or gas boilers operating in non-balance unit mode, the state variables of the power grid and heating network are constructed. |V| , m and T Relative to the control variables of the hydrogen-mixed network and Sensitivity matrix: In the formula, This is the node voltage amplitude-node unbalanced gas source volumetric flow rate sensitivity matrix; This is the node voltage amplitude - node load volumetric flow rate sensitivity matrix; The sensitivity matrix for pipeline flow rate versus node unbalanced gas source volumetric flow rate; This is the pipeline flow rate-node load volumetric flow rate sensitivity matrix; This is the node temperature-node non-equilibrium gas source volumetric flow rate sensitivity matrix. This is the node temperature-node load volumetric flow rate sensitivity matrix; and These are the node voltage amplitude-node unbalanced gas source volume flow sensitivity matrices formed by the coupling of the electro-gas conversion device and the gas turbine; and These are the node voltage amplitude-node load volume flow sensitivity matrices formed by the coupling of the electric-to-gas device and the gas turbine; for The column vectors related to gas turbines; and They are respectively and The row vectors related to the conversion of electricity to gas; and They are respectively and The row vectors related to gas turbines; and They are respectively and The column vectors related to gas-fired boilers; and They are respectively and The row vectors related to gas-fired boilers; This represents the difference in calorific value between hydrogen and natural gas. , and These are the calorific value of the gas consumed by the gas-fired boiler, the mole fraction of hydrogen in the gas consumed, and the volumetric flow rate of the gas consumed. , and These are the calorific value of the gas consumed by the gas turbine, the mole fraction of hydrogen in the gas consumed, and the volumetric flow rate of the gas consumed; T This represents the temperature vector of the heating network node; At the same time, construct power grid dependency variables Relative to the control variables of the hydrogen-mixed network and Sensitivity matrix: In the formula, The sensitivity matrix of active power flow of branch circuits and non-equilibrium gas source volumetric flow rate at nodes; For the branch power flow active power-node load volumetric flow sensitivity matrix; and These are the active power flow-node unbalanced gas source volumetric flow sensitivity matrices formed by the coupling of the electric-to-gas conversion device and the gas turbine in the branch power flow; and These are the active power-nodal load volumetric flow sensitivity matrices of the branch power flow formed by the coupling of the electric-to-gas conversion device and the gas turbine; for The column vectors related to gas turbines.

8. A sensitivity analysis device for integrated energy systems operating with hydrogen blending in natural gas networks, characterized in that, The integrated energy system comprises multiple sub-networks, including an electricity network, a heating network, and a natural gas network operating with hydrogen blending. The sensitivity analysis device for the integrated energy system operating with hydrogen blending in the natural gas network includes: The energy flow model generation module is used to generate, based on the topology and equipment parameters of the integrated energy system, an energy flow model of the power network and its Jacobian matrix, an energy flow model of the thermal network and its Jacobian matrix, an energy flow model of the mixed hydrogen network and its Jacobian matrix, and a coupled equipment model; wherein, the coupled equipment model includes the coupling between the power network and the mixed hydrogen network, and the coupling between the thermal network and the mixed hydrogen network; The sensitivity matrix generation module is used to generate the sensitivity matrix of the mixed hydrogen integrated energy system based on the energy flow model of each sub-network and its Jacobian matrix. The sensitivity matrix calculation module is used to acquire the operating status data of the integrated energy system at the operating point to be analyzed, and substitute the operating status data into each sensitivity matrix for calculation to obtain the numerical calculation results of each sensitivity matrix. The weak link identification module is used to identify the weak links in the operation of the integrated energy system based on the absolute value of the data calculation results of each of the sensitivity matrices. Specifically, based on the topology and equipment parameters of the integrated energy system, the following models are generated: power network energy flow model and its Jacobian matrix, heat network energy flow model and its Jacobian matrix, hydrogen-mixed gas network energy flow model and its Jacobian matrix, and coupled equipment model: Based on the active power deviation vector and reactive power deviation vector of the power network, construct the power flow deviation vector, and take the partial derivative of the power flow deviation with respect to the power grid state variables to obtain the Jacobian matrix of the power network. Based on the node heat power deviation vector, loop cumulative pressure drop deviation vector, node equivalent supply water temperature deviation vector, and node equivalent return water temperature deviation vector of the thermal network, a heat flow deviation vector is constructed, and the partial derivative of the heat flow deviation vector with respect to the thermal network state variables is obtained to obtain the Jacobian matrix of the thermal network. Based on the node gas volume flow deviation vector, equipment control equation deviation vector, and node hydrogen volume flow deviation vector of the hydrogen-mixed gas network, a hydrogen-mixed gas energy flow deviation vector is constructed, and the partial derivative of the hydrogen-mixed gas energy flow deviation vector with respect to the state variables of the hydrogen-mixed gas network is obtained to obtain the Jacobian matrix of the hydrogen-mixed gas network. Based on the energy conversion between the power network, the thermal network, and the mixed hydrogen network, energy conversion models for the coupling devices are constructed respectively; wherein, the energy conversion models include a gas-to-electricity energy conversion model, a gas-to-thermal energy conversion model, and an electricity-to-gas energy conversion model; When the hydrogen-mixing network operates in volumetric flow control mode, let the state variable vector of the hydrogen-mixing network be... =[ Π ; ; ε ],in, Π The squared pressure vector at the node. =[ ; [ ] represents the device volumetric flow rate vector. To balance the gas source volumetric flow rate vector, This is the compressor volumetric flow rate vector. ε The vector represents the mole fraction of gaseous hydrogen. Based on the node gas volumetric flow rate deviation vector, equipment control equation deviation vector, and node hydrogen volumetric flow rate deviation vector of the hydrogen-mixed gas network, a hydrogen-mixed gas energy flow deviation vector is constructed. The partial derivative of the hydrogen-mixed gas energy flow deviation vector with respect to the state variables of the hydrogen-mixed gas network is then calculated to obtain the Jacobian matrix of the hydrogen-mixed gas network, including: Based on the topology and equipment parameters of the integrated energy system, a hydrogen-mixed gas network node-pipeline correlation matrix is ​​generated. Hydrogen-mixed gas network node-balanced gas source correlation matrix Hydrogen-mixed gas network node-compressor correlation matrix Hydrogen mixing network node-equipment association matrix =[ , ] , Hydrogen-mixed gas network node-compressor intake point correlation matrix Hydrogen-mixed gas network node-nonequilibrium gas source correlation matrix ; Based on the hydrogen gas flow deviation vector when the hydrogen gas network is operating in volumetric flow control mode Constructing an energy flow model for a hydrogen-mixed gas network: In the formula, This represents the volumetric flow rate vector of the gas network pipeline. 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. χ This refers to the flow rate index of the gas pipeline network. κ This is the diameter index of the gas network pipeline; μ This is the weight index of the gas network pipeline; =[ ; [ ] 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 represents the volumetric flow rate vector of the gas network node load. This is the volumetric flow rate vector of the unbalanced gas source in the gas network. 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; ω 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 difference in calorific value 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; This is a coefficient matrix representing the flow rate of the gas network equipment. d 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 volumetric flow rate deviation vector of the hydrogen-mixed gas network nodes; The deviation vector of the equipment control equation; This is the hydrogen volumetric flow rate deviation vector at the nodes of the hydrogen mixing network. Will right Taking the partial derivative, we obtain the Jacobian matrix of the hydrogen-mixed gas network operating in volumetric flow control mode. ;in, The expression for a compact matrix is: ； Based on the energy flow model and Jacobian matrix of the hydrogen-mixed gas network under volumetric flow rate control mode, a sensitivity matrix for the hydrogen-mixed gas network under volumetric flow rate control mode is generated, including: The state variable vector of the hydrogen-mixed gas network when operating in volumetric flow control mode is: =[ Π ; ; ε The control variable vector is =[ ; ], Compared to Sensitivity matrix Represented as: In the formula, This is the node pressure-node unbalanced gas source volumetric flow rate sensitivity matrix; This is the node pressure-node load volumetric flow rate sensitivity matrix; The sensitivity matrix for equipment flow rate versus node unbalanced gas source volumetric flow rate; This is the sensitivity matrix for equipment flow rate versus node load volumetric flow rate. The sensitivity matrix of hydrogen mole fraction in node effluent gas versus non-equilibrium gas source volumetric flow rate at the node. The sensitivity matrix for hydrogen mole fraction in node effluent versus node load volumetric flow rate. In volumetric flow control mode, the dependent variable of the hydrogen-mixed network is the Wobbe number vector of the effluent gas from the nodes. Node outflow gas combustion potential vector and pipeline flow vector , , and Compared to The sensitivity matrices are expressed as follows: In the formula, The sensitivity matrix of node effluent gas Wobbe number - node non-equilibrium gas source volumetric flow rate; The sensitivity matrix for the Wobbe number of the outflowing gas from the node versus the volumetric flow rate of the node load. The sensitivity matrix of node outflow gas combustion potential versus node non-equilibrium gas source volumetric flow rate; The sensitivity matrix of nodal outflow gas combustion potential versus nodal load volumetric flow rate; The sensitivity matrix for pipeline flow rate versus node unbalanced gas source volumetric flow rate; This is the pipeline flow rate-node load volumetric flow rate sensitivity matrix; π The node pressure vector; ρ The density vector of the gas flowing out of the node; This represents the difference in specific gravity between hydrogen and natural gas. - The Wobbe number equation and combustion potential equation for nodal outflow gas are related to ε The derivative coefficients; When the hydrogen-mixed gas network operates in energy flow control mode, let the state variable vector of the hydrogen-mixed gas network be... =[ Π ; ; ε Based on the node volumetric flow rate deviation vector, equipment control equation deviation vector, and node hydrogen volumetric flow rate deviation vector of the hydrogen-mixed gas network, a hydrogen-mixed gas energy flow deviation vector is constructed. The partial derivative of the hydrogen-mixed gas energy flow deviation vector with respect to the state variables of the hydrogen-mixed gas network is then calculated to obtain the Jacobian matrix of the hydrogen-mixed gas network, including: Based on the topology and equipment parameters of the integrated energy system, a hydrogen-mixed gas network node-pipeline correlation matrix is ​​generated. Hydrogen-mixed gas network node-balanced gas source correlation matrix Hydrogen-mixed gas network node-compressor correlation matrix Hydrogen mixing network node-equipment association matrix =[ , ] , Hydrogen-mixed gas network node-compressor intake point correlation matrix Hydrogen-mixed gas network node-nonequilibrium gas source correlation matrix ; Based on the hydrogen gas flow deviation vector when the hydrogen-mixed gas network is operating in energy flow control mode Constructing an energy flow model for a hydrogen-mixed gas network: In the formula, This is the energy calorific value vector of the non-equilibrium gas source in the gas network. This represents the load energy flow vector of the gas network node; This is the energy flow vector of the unbalanced gas source in the gas network. Will right Taking the partial derivative, we obtain the Jacobian matrix of the hydrogen-mixed gas network operating in energy flow control mode. ;in, The expression for a compact matrix is: ； Based on the energy flow model and Jacobian matrix of the hydrogen-mixed gas network under energy flow control mode, a sensitivity matrix for the hydrogen-mixed gas network under energy flow control mode is generated, including: The state variable vector of the hydrogen-mixed network when operating in energy flow control mode is: =[ Π ; ; ε The control variable vector is =[ ; ], Compared to Sensitivity matrix Represented as: In the formula, q The vector of calorific value of the gas flowing out of the node; The node pressure-node unbalanced gas source energy flow sensitivity matrix; This is the node pressure-node load energy flow sensitivity matrix; The equipment flow rate-node unbalanced gas source energy flow rate sensitivity matrix; This is the equipment flow rate - node load energy flow rate sensitivity matrix; The node effluent hydrogen mole fraction-node nonequilibrium gas source energy flow sensitivity matrix; The node effluent hydrogen mole fraction-node load energy flow sensitivity matrix; In the energy flow control mode, the dependency variable vector of the hydrogen-mixed network is the Wobbe number vector of the effluent gas from the nodes. Node outflow gas combustion potential vector and pipeline flow vector , , and Compared to The sensitivity matrices are expressed as follows: In the formula, The node effluent gas Wobbe number - node non-equilibrium gas source energy flow sensitivity matrix; The node effluent gas Wobbe number - node load energy flow sensitivity matrix; The node outflow gas combustion potential-node non-equilibrium gas source energy flow sensitivity matrix; The node outflow gas combustion potential-node load energy flow sensitivity matrix; The pipeline flow rate-node unbalanced gas source energy flow rate sensitivity matrix; This is the pipeline flow-node load energy-flow sensitivity matrix; Identifying the weak links in the operation of the integrated energy system includes: sorting the matrix elements corresponding to the specified control variables in each sensitivity matrix from largest to smallest absolute value, and identifying the operating variable corresponding to the matrix element with the largest absolute value as the vulnerable operating variable of the specified control variable at the current operating point; sorting the matrix elements corresponding to the specified operating variables in each sensitivity matrix from largest to smallest absolute value, and identifying the control variable corresponding to the matrix element with the largest absolute value as the critical control variable of the specified operating variable at the current operating point.

9. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the sensitivity analysis method for integrated energy systems for hydrogen blending operation of natural gas networks as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the sensitivity analysis method for integrated energy systems for hydrogen-blended operation of natural gas networks as described in any one of claims 1 to 7.

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