Planning method and device for electric-hydrogen-water coupling system considering dynamic characteristics of multiple energy flows

Abstract

The present invention discloses a planning method and device for an electric, hydrogen and water coupling system considering the dynamic characteristics of multiple energy flows. The method includes: respectively characterizing the slow dynamic characteristics of gas and liquid in the coupling system pipeline by hyperbolic isothermal Euler partial differential equations and hyperbolic water hammer partial differential equations, and considering the AC flow characteristics of the distribution network to obtain the electric, hydrogen and water multiple energy flow constraints; obtaining a decision-independent uncertainty scenario set according to a preset finite scenario set; obtaining a decision-dependent uncertainty set according to the induction effect of hydrogenation demand; constructing a two-stage decision-dependent robust planning model for the coupling system with planning cost constraints as the feasible domain of the first stage and coupling system operation constraints and electric, hydrogen and water multiple energy flow constraints as the feasible domain of the second stage; solving the two-stage decision-dependent robust planning model to obtain the site selection and capacity plan of the coupling system.

Description

Technical Field

[0001] The present invention relates to the field of power system planning, and in particular to a method and device for planning an electric-hydrogen-water coupling system taking into account the dynamic characteristics of multiple energy flows. Background Art

[0002] The electric-hydrogen-water coupling system consists of a distribution network, a hydrogen-blended natural gas pipeline network, and a water distribution network. As a clean energy hub, its millisecond-level response capability and multi-network coordination mechanism can enhance the grid's dynamic regulation capabilities. At the same time, replacing fossil hydrogen with green hydrogen can reduce carbon emissions by over 90% and enable cross-seasonal energy storage to avoid the spatiotemporal mismatch of wind and solar resources. With the integration of a high proportion of renewable energy into the distribution network, grid failures caused by its strong volatility are gradually increasing. However, traditional distribution networks are limited by rigid regulation models. They neither fully tap the multi-energy synergy potential of the electric-hydrogen-water coupling system nor lack a large-scale green hydrogen consumption mechanism, which restricts the resilience of the electric-hydrogen-water coupling system.

[0003] There are three key limitations in the existing planning research of the electric-hydrogen-water coupled system: First, existing research only considers the regulatory role of the system on the distribution network in isolation, ignoring its ability to coordinate the dispatch of the gas distribution network and the water distribution network during operation. It has not established a deep coupling mechanism for the electric-hydrogen-water multi-energy network, resulting in the complementary characteristics between networks and the overall regulatory potential of the system being difficult to effectively release. Second, the current physical modeling of the pipeline network mostly uses a two-port steady-state model and simplified hypothetical algebraic equations to describe fluid dynamics, which cannot characterize transient processes such as the water hammer effect of the fluid in the pipeline and gas pressure-flow fluctuations, resulting in low accuracy of the physical model and low response speed of the system regulation. Third, traditional robust planning generally uses fixed uncertainty sets that are irrelevant to planning to model load fluctuations, which makes it difficult to capture the induced effect of the system's site selection and sizing decisions on hydrogen refueling demand, resulting in a conservative configuration plan and even instability in actual operation.

[0004] Therefore, a new technical solution is urgently needed to solve the problem of how to perform high-precision robust planning of the electric-hydrogen-water coupled system. Summary of the Invention

[0005] The present invention provides a method and device for planning an electric-hydrogen-water coupling system taking into account the dynamic characteristics of multiple energy flows, so as to solve the technical problem of how to perform high-precision robust planning on the electric-hydrogen-water coupling system.

[0006] To achieve the above objectives, the present invention provides a planning method for an electric-hydrogen-water coupling system taking into account the dynamic characteristics of multiple energy flows, comprising:

[0007] The slow dynamic characteristics of gas and liquid in the coupled system pipeline are characterized by hyperbolic isothermal Euler partial differential equations and hyperbolic water hammer partial differential equations, respectively. Taking into account the AC power flow characteristics of the distribution network, the multi-energy flow constraints of electricity, hydrogen and water are obtained. The decision-independent uncertainty scenario set is obtained based on the preset finite scenario set; and the decision-dependent uncertainty set is obtained based on the induced effect of hydrogenation demand.

[0008] A two-stage decision-dependent robust planning model for the coupled system is constructed, with planning cost constraints as the feasible domain of the first stage and coupled system operation constraints and electricity, hydrogen and water multi-energy flow constraints as the feasible domain of the second stage.

[0009] The first stage optimizes the location and capacity of the coupled system with the goal of minimizing the sum of the planning cost and the first cost; the first cost includes the sum of the operating costs of the coupled system under each scenario in the set of decision-independent uncertainty scenarios;

[0010] The second stage aims to minimize the operating cost of the coupled system under each scenario in the decision-independent uncertainty scenario set, and optimizes the coupled system operation constraints and the electricity, hydrogen and water multi-energy flow constraints based on the decision-independent uncertainty scenario set and the decision-dependent uncertainty set;

[0011] The two-stage decision-dependent robust programming model is solved to obtain the location and capacity solutions of the coupled system.

[0012] Preferably, the coupled system operation constraints include:

[0013] Modeling is performed based on the operating characteristics of each device in the coupled system to obtain the coupled system operating constraints used to describe the energy transfer and balance relationship within the coupled system, including:

[0014] Constraints describing the operational characteristics of solid oxide fuel cells and hydrogen refueling stations:

[0015] ;

[0016] ;

[0017] Characterizing constraints on water consumption in hydrogen production processes:

[0018] ;

[0019] Constraints to ensure energy conservation of the mixed gas injected into the hydrogen-blended natural gas network:

[0020] ;

[0021] Constraints to ensure power balance within each electric-hydrogen-water coupled system:

[0022] ;

[0023] Capacity constraints on electrolyzers, hydrogen storage tanks, solid oxide fuel cells, methane converters, and hydrogen refueling stations:

[0024] ;

[0025] ;

[0026] in, Indicates the site, , A collection of candidate sites for the electricity-hydrogen-water coupling system; Indicates time, , is a set of discrete operating cycles, For a preset limited time frame; and Represents the site In time input power to the electrolyzer and output power to the solid oxide fuel cell; and Represents the site In time Electricity bought and sold from the distribution grid; and Represents the site In time The amount of hydrogen consumed by solid oxide fuel cells and hydrogen refueling stations; and Represents the site In time Natural gas flow and hydrogen flow of the mixing unit; and Represents the site In time The flow rate of hydrogen-blended natural gas injected into the hydrogen-blended natural gas pipeline network by the electric-hydrogen-water coupling system and the flow rate of natural gas purchased by the fuel cell; express Node at time water flow; Representation node In time The flow of natural gas produced by the methanogen converter; and represent the energy consumption of the methane converter and the compressor respectively; 、 and Respectively represent the lower calorific value of hydrogen, natural gas and hydrogen-blended natural gas; 、 、 and They represent the fuel cell hydrogen conversion efficiency, mixed gas conversion efficiency, water consumption efficiency and electrolyzer efficiency respectively; Representation node Construction equipment The installed capacity of is the set of equipment in the electric-hydrogen-water coupling system, elz, ht, mr, fc, and hrs represent the electrolyzer, hydrogen storage tank, methane converter, solid oxide fuel cell, and hydrogen refueling station, respectively; Representation node In time The hydrogen refueling demand at the hydrogen refueling station; Indicates a site In time The amount of hydrogen stored in the hydrogen storage tank; Representation node In time The amount of hydrogen that is not met for hydrogenation demand.

[0027] Preferably, characterizing the slow dynamic characteristics of gas and liquid in the coupled system pipeline by the hyperbolic isothermal Euler partial differential equation and the hyperbolic water hammer partial differential equation respectively includes:

[0028] The dynamic characteristics of the gas in the coupled system pipeline are characterized by hyperbolic isothermal Euler partial differential equations, including:

[0029] Each pipeline The compressible gas flow in is described by the simplified one-dimensional isothermal Euler equation, which forms the hyperbolic isothermal Euler partial differential equation:

[0030] ;

[0031] ;

[0032] in, Represents a collection of hydrogen-blended natural gas pipelines; Indicates the cross-sectional area of ​​the pipe; and Represents pipelines Gas flow and pressure in the and Depends on time and spatial coordinates ,in Indicates the length of the pipeline; represents the pressure wave velocity; represents the Darcy-Weisbach friction coefficient; represents the inner diameter of the pipeline; under the basic assumption of natural gas pipeline flow modeling, the parameter 、 、 and is a constant;

[0033] At each node The gas flow balance at includes:

[0034] ;

[0035] in, represents the set of natural gas load nodes; Represents a collection of pipes in a natural gas pipeline equipped with remote control switches; Representation node With node The pipes between Representation node With node The pipes between express Time along the pipeline Gas flow at the pipe outlet; express Time along the pipeline The gas flow rate at the starting point of the pipeline; Representation node In time natural gas load; Representation node In time of natural gas reductions.

[0036] Preferably, characterizing the slow dynamic characteristics of gas and liquid in the coupled system pipeline by a hyperbolic isothermal Euler partial differential equation and a hyperbolic water hammer partial differential equation respectively further includes:

[0037] The dynamic characteristics of the liquid in the coupled system pipeline are characterized by the hyperbolic water hammer partial differential equation, including:

[0038] Each water pipe The flow dynamics in is described by the hyperbolic water hammer partial differential equation:

[0039] ;

[0040] ;

[0041] in, Represents a collection of water distribution pipes; state variables and Represents pipelines The pressure head and water flow in and are both spatiotemporally coupled variables, depending on the time and spatial coordinates ; is the acceleration due to gravity;

[0042] At each node The liquid flow balance at includes:

[0043] ;

[0044] in, represents the set of water load nodes; Represents a collection of circuits in a water distribution pipeline equipped with remote switches; express Time along the pipeline Water flow rate at the pipe outlet; express Time along the pipeline The water flow rate at the beginning of the pipeline; Representation node In time water load; Representation node In time water load reduction.

[0045] Preferably, the AC power flow characteristics of the distribution network are considered to include:

[0046] The AC power flow characteristics of the radial distribution network in the coupled system are characterized by a linearized branch power flow model, including:

[0047] Ensure closed circuit There is a parent-child hierarchy constraint between the busbars:

[0048] ;

[0049] Establish the constraints of the logical association rules between line switching status and fault outage:

[0050] ;

[0051] ;

[0052] Constraints enforcing active power balance for each bus:

[0053] ;

[0054] Gas turbine operating characteristic constraints:

[0055] ;

[0056] ;

[0057] Constraints that cap renewable energy curtailment and load reduction:

[0058] ;

[0059] ;

[0060] in, is a binary variable, if the line If closed, it is equal to 1, otherwise it is 0; is a binary variable, if the node is a node If the parent node is , it is equal to 1, otherwise it is 0; is a binary variable, if the node is a node If the parent node is , it is equal to 1, otherwise it is 0; is a binary variable, if the line If damaged, it is equal to 0, otherwise it is 1; Represents a collection of distribution network lines; Represents a collection of distribution network lines equipped with remote switches; For the line The initial state of Represented as a line The meritorious trend on For nodes In time The active power input from the substation; represents the set of water pumps in the distribution network, then Indicates the node in the distribution network Connected water pump The index of Indicates the node in the distribution network Candidate sites for connection 's index; Indicates connection to a node In time Electricity sold by the distribution network; For nodes In time The active power output of the gas turbine; and Node In time Active power generated by renewable energy and its curtailment; For in time line The meritorious trend on To connect to the node In time Active power consumption of the pump; For nodes In time Active load; Representation node In time The amount of power load reduction; Indicates connection to a node In time Electricity purchased by the distribution network; Represents a set of distribution network nodes; Representation node The efficiency of the gas engine.

[0061] Preferably, the decision-dependent uncertainty set obtained according to the hydrogenation demand induction effect includes:

[0062] Define the decision dependency uncertainty vector as , the decision dependency uncertainty set is composed of the decision dependency uncertainty vector u , include:

[0063] ;

[0064] Constraints that ensure that the basic demand is non-zero and bounded only when the electric-hydrogen-water coupled system is installed:

[0065] ;

[0066] Constraints relating induced demand to installed hydrogen refueling station capacity in the coupled electricity-hydrogen-water system:

[0067] ;

[0068] System-level deviation budget and upper and lower limits of total demand for a time period:

[0069] ;

[0070] ;

[0071] ;

[0072] Among them, x represents the planning decision variable output in the first stage, including the location of the electric-hydrogen-water coupling system Capacity of each device in the coupled system ; Is a binary variable, indicating whether Deploy the electric hydrogen water coupling system, if deployed, it is 1, otherwise it is 0; Indicates hydrogenation demand; and They represent the basic demand and induced demand for hydrogenation respectively; and They represent the basic upper and lower limits of hydrogenation demand respectively; and represents the induction coefficient; Indicates the capacity of the hydrogen refueling station; represents the deviation from the nominal induced demand; is the proportionality coefficient; and Indicates time The lower and upper bounds of the total hydrogenation demand; and Represents the scaling parameter.

[0073] Preferably, the planning cost constraints include:

[0074] Total number constraints of the electric-hydrogen-water coupled system:

[0075] ;

[0076] Capacity constraints of equipment in the electric-hydrogen-water coupling system:

[0077] ;

[0078] in, Indicates the maximum number of electric-hydrogen-water coupled systems that can be constructed; Representation node The upper limit of the capacity of each device in the electric hydrogen water coupling system.

[0079] Preferably, the two-stage decision-dependent robust programming model includes:

[0080] make represents the set of decision-making independent uncertainty scenarios, Represents the scene index, each scene The corresponding probability is ;

[0081] The two-stage decision-dependent robust programming model includes the objective function:

[0082] ;

[0083] Among them, x represents the planning decision variable output in the first stage, including the location of the electric-hydrogen-water coupling system Capacity of each device in the coupled system ; represents the feasible region of the first stage; represents planning cost; Indicates operating cost; Representation scene The decision-making under depends on the uncertainty vector; Representation scene The decision-making under the uncertainty set depends on Representation scene The second stage runs the decision variables under; Representation scene The second stage feasible domain is based on the planning decision variables x and scenarios output in the first stage. The decision-making uncertainty vector under Real-time updates;

[0084] The second stage operation decision variables include the preset parameter set in the second stage feasible domain; the preset parameter set includes 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 and ;

[0085] Planning costs include:

[0086] ;

[0087] Running costs include:

[0088] ;

[0089] in, represents the unit planning cost of the deployment of the electricity-hydrogen-water coupled system; Represents the unit planning cost of each device in the coupled system; 、 and represents the unit penalty cost of electricity, natural gas, and water load reduction; represents the unit penalty cost for not meeting hydrogen refueling demand; Representation node In time The amount of power load reduction at the location; and Represents nodes respectively In time natural gas and water load reductions; Representation node In time The hydrogen flow rate for unmet hydrogenation demand.

[0090] Preferably, solving the two-stage decision-dependent robust programming model includes:

[0091] The hyperbolic isothermal Euler partial differential equation and the hyperbolic water hammer partial differential equation are discretized in time and space using the Preissman four-point implicit finite difference scheme:

[0092] The continuous space-time domain of each pipeline is divided into discrete grids separated by space step and time step; the hyperbolic isothermal Euler partial differential equation is discretized to obtain the first set of equations:

[0093] ;

[0094] ;

[0095] ;

[0096] ;

[0097] Discretize the hyperbolic water hammer partial differential equation to obtain the second set of equations:

[0098] ;

[0099] ;

[0100] ;

[0101] in, 、 、 、 、 、 and is an auxiliary variable; represents the spatial step length, represents the time step; and Represents a pipeline exist Time step, Pressure and pressure head at spatial grid nodes; and Represents a pipeline exist Time step, Pressure and pressure head at spatial grid nodes; and Represents a pipeline exist Time step, Pressure and pressure head at spatial grid nodes; and Represents a pipeline exist Time step, Pressure and pressure head at spatial grid nodes; and Represents a pipeline exist Time step, Air flow and water flow at spatial grid nodes; and Represents a pipeline exist Time step, Air flow and water flow at spatial grid nodes; and Represents a pipeline exist Time step, Air flow and water flow at spatial grid nodes; and Represents a pipeline exist Time step, Air flow and water flow at spatial grid nodes;

[0102] The auxiliary variables are approximated by piecewise linear approximation and piecewise McCormick envelope method. and Perform linearization processing;

[0103] make and denote the operational decision variables and feasible domain of the second stage after numerical discretization and piecewise linear relaxation respectively; the objective function is reconstructed into a mixed integer linear programming problem, and the compact form includes:

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] in, and They represent the operational decision variables and feasible region of the second stage after numerical discretization and piecewise linear relaxation respectively; and is the coefficient vector; and represents the coefficient matrix; and is a binary variable dimension; 、 and is a continuous variable dimension; represents the set of positive real numbers;

[0109] The model is solved through parameterized columns and constraint generation algorithms to obtain the site selection and capacity scheme of the coupled system.

[0110] The present invention also provides an electric-hydrogen-water coupling system planning device that considers the dynamic characteristics of multiple energy flows, which is used in the method of the present invention. The device includes a first module, a second module, a third module, and a fourth module;

[0111] The first module is used to characterize the slow dynamic characteristics of gas and liquid in the coupled system pipeline through the hyperbolic isothermal Euler partial differential equation and the hyperbolic water hammer partial differential equation, respectively, and consider the AC power flow characteristics of the distribution network to obtain the multi-energy flow constraints of electricity, hydrogen and water;

[0112] The second module is used to obtain a decision-independent uncertainty scenario set based on a preset finite scenario set; and obtain a decision-dependent uncertainty set based on the hydrogenation demand induction effect;

[0113] The third module is used to construct a two-stage decision-dependent robust planning model for the coupled system, with planning cost constraints as the feasible domain for the first stage and coupled system operation constraints and electricity, hydrogen and water multi-energy flow constraints as the feasible domain for the second stage.

[0114] The first stage optimizes the location and capacity of the coupled system with the goal of minimizing the sum of the planning cost and the first cost; the first cost includes the sum of the operating costs of the coupled system under each scenario in the set of decision-independent uncertainty scenarios;

[0115] The second stage aims to minimize the operating cost of the coupled system under each scenario in the decision-independent uncertainty scenario set, and optimizes the coupled system operation constraints and the electricity, hydrogen and water multi-energy flow constraints based on the decision-independent uncertainty scenario set and the decision-dependent uncertainty set;

[0116] The fourth module is used to solve the two-stage decision-dependent robust programming model to obtain the site selection and capacity plan of the coupled system.

[0117] The present invention has the following beneficial effects:

[0118] The electric-hydrogen-water coupling system planning method of the present invention taking into account the multi-energy flow coupling characteristics takes into account the impact of the capacity configuration and spatial layout of the electric-hydrogen-water coupling system on the tidal distribution of the distribution network, water distribution network and hydrogen-blended natural gas pipeline network, and characterizes the slow dynamic characteristics of the gas and liquid in the coupling system pipeline through the hyperbolic isothermal Euler partial differential equation and the hyperbolic water hammer partial differential equation, respectively, so that the subsequently constructed model is more accurate and comprehensive. The hydrogenation demand induction effect is internalized into a decision-dependent uncertainty set that changes with the decision variables of one stage, and the strong coupling relationship between the system configuration scheme and the hydrogenation behavior is characterized. When solving the model, the integrated optimization of the system site selection and sizing and the multi-energy coordinated operation strategy can be achieved, thereby comprehensively improving the resilience support capability of the electric-hydrogen-water coupling system in the distribution network failure scenario. The method of the present invention can achieve high-precision robust planning of the electric-hydrogen-water coupling system. When extreme events such as power outages occur in the power grid, the coupled system can ensure the continuity of operation of key power users and water pumps through the hydrogen-to-electricity mechanism, while injecting hydrogen-blended natural gas into the hydrogen-blended natural gas pipeline network to alleviate local gas supply tensions caused by emergency power generation by gas turbines, thereby effectively reducing the reduction levels of the three types of loads: electricity, gas, and water.

[0119] The electric-hydrogen-water coupling system planning device taking into account the multi-energy flow coupling characteristics of the present invention is used in the method of the present invention and has the same beneficial effects as the method of the present invention.

[0120] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] The accompanying drawings, which constitute part of the present invention, are provided to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are provided to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0122] Figure 1 It is a flow chart of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0123] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered by the claims.

[0124] See also Figure 1 In a preferred embodiment of the present invention, a method for planning an electric-hydrogen-water coupling system considering the dynamic characteristics of multiple energy flows is provided, comprising:

[0125] S1. The slow dynamic characteristics of gas and liquid in the coupled system pipeline are characterized by the hyperbolic isothermal Euler partial differential equation and the hyperbolic water hammer partial differential equation respectively, and the AC power flow characteristics of the distribution network are considered to obtain the multi-energy flow constraints of electricity, hydrogen and water.

[0126] In a preferred embodiment of the present invention, the slow dynamic characteristics of the gas and liquid in the coupled system pipeline are characterized by the hyperbolic isothermal Euler partial differential equation and the hyperbolic water hammer partial differential equation, respectively, including:

[0127] The dynamic characteristics of the gas in the coupled system pipeline are characterized by hyperbolic isothermal Euler partial differential equations, including:

[0128] Each pipeline The compressible gas flow in is described by the simplified one-dimensional isothermal Euler equation, which forms the hyperbolic isothermal Euler partial differential equation:

[0129] ;

[0130] ;

[0131] in, Represents a collection of hydrogen-blended natural gas pipelines; Indicates the cross-sectional area of ​​the pipe; and Represents pipelines Gas flow and pressure in the and Depends on time and spatial coordinates ,in Indicates the length of the pipeline; represents the pressure wave velocity; represents the Darcy-Weisbach friction coefficient; represents the inner diameter of the pipeline; under the basic assumption of natural gas pipeline flow modeling, the parameter 、 、 and is a constant.

[0132] At each node The gas flow balance at includes:

[0133] ;

[0134] in, represents the set of natural gas load nodes; Represents a collection of pipes in a natural gas pipeline equipped with remote control switches; Representation node With node The pipes between Representation node With node The pipes between express Time along the pipeline Gas flow at the pipe outlet; express Time along the pipeline The gas flow rate at the starting point of the pipeline; Representation node In time natural gas load; Representation node In time of natural gas reductions.

[0135] The dynamic characteristics of the liquid in the coupled system pipeline are characterized by the hyperbolic water hammer partial differential equation, including:

[0136] Each water pipe The flow dynamics in is described by the hyperbolic water hammer partial differential equation:

[0137] ;

[0138] ;

[0139] in, Represents a collection of water distribution pipes; state variables and Represents pipelines The pressure head and water flow in and are both spatiotemporally coupled variables, depending on the time and spatial coordinates ; is the acceleration due to gravity.

[0140] At each node The liquid flow balance at includes:

[0141] ;

[0142] in, represents the set of water load nodes; Represents a collection of circuits in a water distribution pipeline equipped with remote switches; express Time along the pipeline Water flow rate at the pipe outlet; express Time along the pipeline The water flow rate at the beginning of the pipeline; Representation node In time water load; Representation node In time water load reduction.

[0143] In a preferred embodiment of the present invention, the AC power flow characteristics of the distribution network are considered to include:

[0144] The AC power flow characteristics of the radial distribution network in the coupled system are characterized by a linearized branch power flow model, including:

[0145] Ensure closed circuit There is a parent-child hierarchy constraint between the busbars:

[0146] ;

[0147] Establish the constraints of the logical association rules between line switching status and fault outage:

[0148] ;

[0149] ;

[0150] Constraints enforcing active power balance for each bus:

[0151] ;

[0152] Gas turbine operating characteristic constraints:

[0153] ;

[0154] ;

[0155] Constraints that cap renewable energy curtailment and load reduction:

[0156] ;

[0157] ;

[0158] in, is a binary variable, if the line If closed, it is equal to 1, otherwise it is 0; is a binary variable, if the node is a node If the parent node is , it is equal to 1, otherwise it is 0; is a binary variable, if the node is a node If the parent node is , it is equal to 1, otherwise it is 0; is a binary variable, if the line If damaged, it is equal to 0, otherwise it is 1; Represents a collection of distribution network lines; Represents a collection of distribution network lines equipped with remote switches; For the line The initial state of Represented as a line The meritorious trend on For nodes In time The active power input from the substation; represents the set of water pumps in the distribution network, then Indicates the node in the distribution network Connected water pump The index of Indicates the node in the distribution network Candidate sites for connection 's index; Indicates connection to a node In time Electricity sold by the distribution network; For nodes In time The active power output of the gas turbine; and Node In time Active power generated by renewable energy and its curtailment; For in time line The meritorious trend on To connect to the node In time Active power consumption of the pump; For nodes In time Active load; Representation node In time The amount of power load reduction; Indicates connection to a node In time Electricity purchased by the distribution network; Represents a set of distribution network nodes; Representation node The efficiency of the gas engine.

[0159] S2. Obtain a decision-independent uncertainty scenario set based on a preset finite scenario set; obtain a decision-dependent uncertainty set based on the hydrogenation demand induction effect.

[0160] In a preferred embodiment of the present invention, the hydrogen refueling demand of hydrogen fuel vehicles is uncertain. Therefore, the decision-dependent uncertainty set is obtained according to the hydrogen refueling demand induction effect:

[0161] Define the decision dependency uncertainty vector as , the decision dependency uncertainty set is composed of the decision dependency uncertainty vector u , include:

[0162] ;

[0163] Constraints that ensure that the basic demand is non-zero and bounded only when the electric-hydrogen-water coupled system is installed:

[0164] ;

[0165] Constraints relating induced demand to installed hydrogen refueling station capacity in the coupled electricity-hydrogen-water system:

[0166] ;

[0167] System-level deviation budget and upper and lower limits of total demand for a time period:

[0168] ;

[0169] ;

[0170] ;

[0171] Among them, x represents the planning decision variable output in the first stage, including the location of the electric-hydrogen-water coupling system Capacity of each device in the coupled system ; Is a binary variable, indicating whether Deploy the electric hydrogen water coupling system, if deployed, it is 1, otherwise it is 0; Indicates hydrogenation demand; and They represent the basic demand and induced demand for hydrogenation respectively; and They represent the basic upper and lower limits of hydrogenation demand respectively; and represents the induction coefficient; Indicates the capacity of the hydrogen refueling station; represents the deviation from the nominal induced demand; is the proportionality coefficient; and Indicates time The lower and upper bounds of the total hydrogenation demand; and Represents the scaling parameter.

[0172] S3. Taking the planning cost constraint as the feasible domain of the first stage and the coupled system operation constraint and the electricity, hydrogen and water multi-energy flow constraint as the feasible domain of the second stage, a two-stage decision-dependent robust planning model of the coupled system is constructed.

[0173] In the first stage, the location and capacity of the coupled system are optimized with the goal of minimizing the sum of the planning cost and the first cost; the first cost includes the sum of the operating costs of the coupled system under each scenario in the decision-independent uncertainty scenario set.

[0174] The second stage aims to minimize the operating cost of the coupled system under each scenario in the decision-independent uncertainty scenario set, and optimizes the operating constraints of the coupled system and the multi-energy flow constraints of electricity, hydrogen and water based on the decision-independent uncertainty scenario set and the decision-dependent uncertainty set.

[0175] In a preferred embodiment of the present invention, the planning cost constraints include:

[0176] Total number constraints of the electric-hydrogen-water coupled system:

[0177] ;

[0178] Capacity constraints of equipment in the electric-hydrogen-water coupling system:

[0179] ;

[0180] in, Indicates the maximum number of electric-hydrogen-water coupled systems that can be constructed; Representation node The upper limit of the capacity of each device in the electric hydrogen water coupling system.

[0181] In a preferred embodiment of the present invention, the coupled system operation constraints include:

[0182] Modeling is performed based on the operating characteristics of each device in the coupled system to obtain the coupled system operating constraints used to describe the energy transfer and balance relationship within the coupled system, including:

[0183] Constraints describing the operational characteristics of solid oxide fuel cells and hydrogen refueling stations:

[0184] ;

[0185] ;

[0186] Characterizing constraints on water consumption in hydrogen production processes:

[0187] ;

[0188] Constraints to ensure energy conservation of the mixed gas injected into the hydrogen-blended natural gas network:

[0189] ;

[0190] Constraints to ensure power balance within each electric-hydrogen-water coupled system:

[0191] ;

[0192] Capacity constraints on electrolyzers, hydrogen storage tanks, solid oxide fuel cells, methane converters, and hydrogen refueling stations:

[0193] ;

[0194] ;

[0195] in, Indicates the site, , A collection of candidate sites for the electricity-hydrogen-water coupling system; Indicates time, , is a set of discrete operating cycles, For a preset limited time frame; and Represents the site In time input power to the electrolyzer and output power to the solid oxide fuel cell; and Represents the site In time Electricity bought and sold from the distribution grid; and Represents the site In time The amount of hydrogen consumed by solid oxide fuel cells and hydrogen refueling stations; and Represents the site In time Natural gas flow and hydrogen flow of the mixing unit; and Represents the site In time The flow rate of hydrogen-blended natural gas injected into the hydrogen-blended natural gas pipeline network by the electric-hydrogen-water coupling system and the flow rate of natural gas purchased by the fuel cell; express Node at time water flow; Representation node In time The flow of natural gas produced by the methanogen converter; and represent the energy consumption of the methane converter and the compressor respectively; 、 and Respectively represent the lower calorific value of hydrogen, natural gas and hydrogen-blended natural gas; 、 、 and They represent the fuel cell hydrogen conversion efficiency, mixed gas conversion efficiency, water consumption efficiency and electrolyzer efficiency respectively; Representation node Construction equipment The installed capacity of is the set of equipment in the electric-hydrogen-water coupling system, elz, ht, mr, fc, and hrs represent the electrolyzer, hydrogen storage tank, methane converter, solid oxide fuel cell, and hydrogen refueling station, respectively; Representation node In time The hydrogen refueling demand at the hydrogen refueling station; Indicates a site In time The amount of hydrogen stored in the hydrogen storage tank; Representation node In time The amount of hydrogen that is not met for hydrogenation demand.

[0196] In a preferred embodiment of the present invention, the two-stage decision-dependent robust programming model includes:

[0197] Random scenarios are used to describe the independent uncertainty of decisions such as renewable energy output, multi-energy load and line failure. represents the set of decision-making independent uncertainty scenarios, Represents the scene index, each scene The corresponding probability is .

[0198] The two-stage decision-dependent robust programming model includes the objective function:

[0199] ;

[0200] Among them, x represents the planning decision variable output in the first stage, including the location of the electric-hydrogen-water coupling system Capacity of each device in the coupled system ; represents the feasible region of the first stage; represents planning cost; Indicates operating cost; Representation scene The decision-making under depends on the uncertainty vector; Representation scene The decision-making under the uncertainty set depends on Representation scene The second stage runs the decision variables under; Representation scene The second stage feasible domain is based on the planning decision variables x and scenarios output in the first stage. The decision-making uncertainty vector under Updated in real time.

[0201] The second stage operation decision variables include the preset parameter set in the second stage feasible domain; the preset parameter set includes 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 and .

[0202] Planning costs include:

[0203] ;

[0204] Running costs include:

[0205] ;

[0206] in, represents the unit planning cost of the deployment of the electricity-hydrogen-water coupled system; Represents the unit planning cost of each device in the coupled system; 、 and represents the unit penalty cost of electricity, natural gas, and water load reduction; represents the unit penalty cost for not meeting hydrogen refueling demand; Representation node In time The amount of power load reduction at the location; and Represents nodes respectively In time natural gas and water load reductions; Representation node In time The hydrogen flow rate for unmet hydrogenation demand.

[0207] S4. Solve the two-stage decision-dependent robust programming model to obtain the location and capacity solutions for the coupled system.

[0208] In a preferred embodiment of the present invention, solving the two-stage decision-dependent robust programming model includes:

[0209] The hyperbolic isothermal Euler partial differential equation and the hyperbolic water hammer partial differential equation are discretized in time and space using the Preissman four-point implicit finite difference scheme:

[0210] The continuous space-time domain of each pipeline is divided into discrete grids separated by space step and time step; the hyperbolic isothermal Euler partial differential equation is discretized to obtain the first set of equations:

[0211] ;

[0212] ;

[0213] ;

[0214] ;

[0215] Discretize the hyperbolic water hammer partial differential equation to obtain the second set of equations:

[0216] ;

[0217] ;

[0218] ;

[0219] in, 、 、 、 、 、 and is an auxiliary variable; represents the spatial step length, represents the time step; and Represents a pipeline exist Time step, Pressure and pressure head at spatial grid nodes; and Represents a pipeline exist Time step, Pressure and pressure head at spatial grid nodes; and Represents a pipeline exist Time step, Pressure and pressure head at spatial grid nodes; and Represents a pipeline exist Time step, Pressure and pressure head at spatial grid nodes; and Represents a pipeline exist Time step, Air flow and water flow at spatial grid nodes; and Represents a pipeline exist Time step, Air flow and water flow at spatial grid nodes; and Represents a pipeline exist Time step, Air flow and water flow at spatial grid nodes; and Represents a pipeline exist Time step, Air and water flows at spatial grid nodes.

[0220] The auxiliary variables are approximated by piecewise linear approximation and piecewise McCormick envelope method. and Perform linearization.

[0221] make and denote the operational decision variables and feasible domain of the second stage after numerical discretization and piecewise linear relaxation respectively; the objective function is reconstructed into a mixed integer linear programming problem, and the compact form includes:

[0222] ;

[0223] ;

[0224] ;

[0225] ;

[0226] in, and They represent the operational decision variables and feasible region of the second stage after numerical discretization and piecewise linear relaxation respectively; and is the coefficient vector; and represents the coefficient matrix; and is a binary variable dimension; 、 and is a continuous variable dimension; represents the set of positive real numbers.

[0227] In a preferred embodiment of the present invention, the Preissman four-point implicit finite difference format and piecewise linear relaxation are used to convert partial differential constraints into algebraic constraints, thereby converting the original partial differential constraint optimization model into an easy-to-solve mixed integer linear programming problem, reducing the difficulty of model calculation.

[0228] This model is a min-max-min robust optimization model. The model is solved using the Parametric C&CG algorithm to obtain the site selection and capacity plan for the coupled system.

[0229] The main problem of this model is to minimize the sum of planning cost and operating cost of the electric hydrogen water coupling system, and determine the system planning decision variables. , clarify the lower bound of the system's operating cost under the current planning scheme The sub-problems are based on the current planning decision variables , we get the polyhedron decision dependency uncertainty set , and consider the independent uncertainty scenarios of decision-making such as renewable energy output, multi-energy load output and line faults, with the goal of maximizing system operation costs, to identify the worst case of decision-making dependence uncertainty concentration , in the worst case Under this condition, the operation decision variables are solved with the minimum system operation cost as the optimization goal. , get the operating cost of the coupled system under the worst case and add cut constraints to the main problem, and summarize the scenarios by weight The worst operating cost is used to construct the expected operating cost of the system, which together with the planning cost in the main problem constitutes the objective function of the two-stage robust optimization model. Finally, the optimal system planning scheme is obtained through parameterized columns and constraint generation algorithm.

[0230] The electric-hydrogen-water coupling system planning method of the present invention taking into account the multi-energy flow coupling characteristics takes into account the impact of the capacity configuration and spatial layout of the electric-hydrogen-water coupling system on the tidal distribution of the distribution network, water distribution network and hydrogen-blended natural gas pipeline network, and characterizes the slow dynamic characteristics of the gas and liquid in the coupling system pipeline through the hyperbolic isothermal Euler partial differential equation and the hyperbolic water hammer partial differential equation, respectively, so that the subsequently constructed model is more accurate and comprehensive. The hydrogenation demand induction effect is internalized into a decision-dependent uncertainty set that changes with the decision variables of one stage, and the strong coupling relationship between the system configuration scheme and the hydrogenation behavior is characterized. When solving the model, the integrated optimization of the system site selection and sizing and the multi-energy coordinated operation strategy can be achieved, thereby comprehensively improving the resilience support capability of the electric-hydrogen-water coupling system in the distribution network failure scenario. The method of the present invention can achieve high-precision robust planning of the electric-hydrogen-water coupling system. When extreme events such as power outages occur in the power grid, the coupled system can ensure the continuity of operation of key power users and water pumps through the hydrogen-to-electricity mechanism, while injecting hydrogen-blended natural gas into the hydrogen-blended natural gas pipeline network to alleviate local gas supply tensions caused by emergency power generation by gas turbines, thereby effectively reducing the reduction levels of the three types of loads: electricity, gas, and water.

[0231] The present invention also provides an electric-hydrogen-water coupling system planning device that takes into account the dynamic characteristics of multiple energy flows, which is used in the method of the present invention. The device includes a first module, a second module, a third module and a fourth module.

[0232] The first module is used to characterize the slow dynamic characteristics of gas and liquid in the coupled system pipeline through the hyperbolic isothermal Euler partial differential equation and the hyperbolic water hammer partial differential equation, respectively, and consider the AC flow characteristics of the distribution network to obtain the multi-energy flow constraints of electricity, hydrogen and water.

[0233] The second module is used to obtain a decision-independent uncertainty scenario set based on a preset finite scenario set; and to obtain a decision-dependent uncertainty set based on the hydrogenation demand induction effect.

[0234] The third module is used to construct a two-stage decision-dependent robust planning model for the coupled system, with the planning cost constraint as the feasible domain of the first stage and the coupled system operation constraint and the electricity, hydrogen and water multi-energy flow constraint as the feasible domain of the second stage.

[0235] In the first stage, the location and capacity of the coupled system are optimized with the goal of minimizing the sum of the planning cost and the first cost; the first cost includes the sum of the operating costs of the coupled system under each scenario in the decision-independent uncertainty scenario set.

[0236] The second stage aims to minimize the operating cost of the coupled system under each scenario in the decision-independent uncertainty scenario set, and optimizes the operating constraints of the coupled system and the multi-energy flow constraints of electricity, hydrogen and water based on the decision-independent uncertainty scenario set and the decision-dependent uncertainty set.

[0237] The fourth module is used to solve the two-stage decision-dependent robust programming model to obtain the site selection and capacity plan of the coupled system.

[0238] The electric-hydrogen-water coupling system planning device taking into account the multi-energy flow coupling characteristics of the present invention is used in the method of the present invention and has the same beneficial effects as the method of the present invention.

[0239] Verification part:

[0240] To verify the energy complementarity and system resilience of the proposed method under network failure conditions, a multi-energy collaborative simulation system was constructed in a specific urban area. This system was modified and expanded based on the IEEE 33-node distribution network structure. Water electrolysis hydrogen production devices were embedded at nodes 5 and 18 to convert renewable electricity into hydrogen energy. Fuel cells were deployed at nodes 9 and 25 to provide hydrogen energy feedback to electricity, establishing a bidirectional coupling mechanism between electricity and hydrogen. The hydrogen network was repurposed from existing natural gas pipelines. Hydrogen storage tanks were installed at nodes 7 and 20 to buffer the temporal and spatial imbalances of hydrogen. A compressor was deployed at node 12 to regulate hydrogen pressure and ensure the stability of the hydrogen transmission process. The hydraulic network employed a ring-shaped water transmission structure coupled with the water electrolysis device. Node 3 was configured as the main water supply reservoir, and node 15 was equipped with a water pump station to provide a continuous and stable water source for the hydrogen production process. The system also incorporated 30 days of typical time-series load data, wind and solar power output data, and daily hydrogen demand scenarios to construct a unified optimization scheduling model. On this basis, the traditional steady-state fluid model, the traditional two-stage robust optimization model, and the proposed method were compared, and the economic efficiency and resilience of the model under typical fault disturbances were evaluated from the two dimensions of total system operation cost and multi-energy load reduction rate. This verifies the superiority of the proposed method in coordinated operation and anti-disturbance regulation in multi-energy systems. The comparison results are shown in Table 1:

[0241] Table 1 Comparative analysis of results of different models

[0242] ;

[0243] As can be seen from Table 1, under typical operating conditions, the traditional steady-state fluid model fails to accurately describe the dynamic wave propagation law during the hydrogen flow process, resulting in frequent low pressure at remote hydrogen refueling stations and a load reduction ratio of up to 17.9%, while the system operating costs remain high. Although the traditional two-stage robust optimization model has a certain adjustment capability at the steady-state level, it can partially alleviate pressure fluctuations and reduce the reduction rate to 11.2%, but it does not take into account the coupling effect between the fluid dynamics process and the site selection induced demand, and it is still difficult to adapt to the multi-energy coordinated operation requirements under complex disturbance conditions. In contrast, the method of the present invention shows significant advantages in the operation of multi-energy systems. It not only significantly reduces the total cost of system operation to 3.194 million yuan, but also compresses the multi-energy load reduction rate to 3.6%, and effectively suppresses the dynamic pressure fluctuations of the hydrogen pipeline network. At the same time, the method of the present invention absorbs wind and solar power output to the greatest extent, achieving a renewable energy utilization rate of 82.1%, while ensuring the stable operation of the system, and significantly improving the energy complementarity and overall system resilience.

[0244] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A planning method for an electric-hydrogen-water coupling system considering the dynamic characteristics of multiple energy flows, characterized in that: include: The slow dynamic characteristics of gas and liquid in the coupled system pipeline are characterized by hyperbolic isothermal Euler partial differential equations and hyperbolic water hammer partial differential equations, respectively. Taking into account the AC power flow characteristics of the distribution network, the multi-energy flow constraints of electricity, hydrogen and water are obtained. The decision-independent uncertainty scenario set is obtained based on the preset finite scenario set; and the decision-dependent uncertainty set is obtained based on the induced effect of hydrogenation demand. A two-stage decision-dependent robust planning model for the coupled system is constructed, with the planning cost constraint as the feasible domain of the first stage and the coupled system operation constraint and the electricity, hydrogen and water multi-energy flow constraint as the feasible domain of the second stage. The first stage optimizes the location and capacity of the coupled system with the goal of minimizing the sum of the planning cost and the first cost; the first cost includes the sum of the operating costs of the coupled system under each scenario in the decision-independent uncertainty scenario set; The second stage aims to minimize the operating cost of the coupled system under each scenario in the decision-independent uncertainty scenario set, and optimizes the coupled system operating constraints and the electricity, hydrogen and water multi-energy flow constraints according to the decision-independent uncertainty scenario set and the decision-dependent uncertainty set; The two-stage decision-dependent robust programming model is solved to obtain the location and capacity solutions of the coupled system.

2. The method for planning an electric-hydrogen-water coupling system considering the dynamic characteristics of multiple energy flows according to claim 1 is characterized in that: The coupled system operation constraints include: Modeling is performed based on the operating characteristics of each device in the coupled system to obtain the coupled system operating constraints used to describe the energy transfer and balance relationship within the coupled system, including: Constraints describing the operational characteristics of solid oxide fuel cells and hydrogen refueling stations: ； ； Characterizing constraints on water consumption in hydrogen production processes: ； Constraints to ensure energy conservation of the mixed gas injected into the hydrogen-blended natural gas network: ； Constraints to ensure power balance within each electric-hydrogen-water coupled system: ； Capacity constraints on electrolyzers, hydrogen storage tanks, solid oxide fuel cells, methane converters, and hydrogen refueling stations: ； ； in, Indicates the site, , A collection of candidate sites for the electricity-hydrogen-water coupling system; Indicates time, , is a set of discrete operating cycles, For a preset limited time frame; and Represents the site In time input power to the electrolyzer and output power to the solid oxide fuel cell; and Represents the site In time Electricity bought and sold from the distribution grid; and Represents the site In time The amount of hydrogen consumed by solid oxide fuel cells and hydrogen refueling stations; and Represents the site In time Natural gas flow and hydrogen flow of the mixing unit; and Represents the site In time The flow rate of hydrogen-blended natural gas injected into the hydrogen-blended natural gas pipeline network by the electric-hydrogen-water coupling system and the flow rate of natural gas purchased by the fuel cell; express Node at time water flow; Representation node In time The flow of natural gas produced by the methanogen converter; and represent the energy consumption of the methane converter and compressor respectively; 、 and Respectively represent the lower calorific value of hydrogen, natural gas and hydrogen-blended natural gas; 、 、 and They represent the fuel cell hydrogen conversion efficiency, mixed gas conversion efficiency, water consumption efficiency and electrolyzer efficiency respectively; Representation node Construction equipment The installed capacity of is the set of equipment in the electric-hydrogen-water coupling system, elz, ht, mr, fc, and hrs represent the electrolyzer, hydrogen storage tank, methane converter, solid oxide fuel cell, and hydrogen refueling station, respectively; Representation node In time The hydrogen refueling demand at the hydrogen refueling station; Indicates a site In time The amount of hydrogen stored in the hydrogen storage tank; Representation node In time The amount of hydrogen that is not met by the hydrogenation demand.

3. The method for planning an electric-hydrogen-water coupling system considering the dynamic characteristics of multiple energy flows according to claim 2 is characterized in that: The method of characterizing the slow dynamic characteristics of gas and liquid in the coupled system pipeline by using the hyperbolic isothermal Euler partial differential equation and the hyperbolic water hammer partial differential equation respectively includes: The dynamic characteristics of the gas in the coupled system pipeline are characterized by hyperbolic isothermal Euler partial differential equations, including: Each pipeline The compressible gas flow in is described by the simplified one-dimensional isothermal Euler equation, which forms the hyperbolic isothermal Euler partial differential equation: ； ； in, Represents a collection of hydrogen-blended natural gas pipelines; Indicates the cross-sectional area of ​​the pipe; and Represents pipelines Gas flow and pressure in the and Depends on time and spatial coordinates ,in Indicates the length of the pipeline; represents the pressure wave velocity; represents the Darcy-Weisbach friction coefficient; represents the inner diameter of the pipeline; under the basic assumption of natural gas pipeline flow modeling, the parameter 、 、 and is a constant; At each node The gas flow balance at includes: ； in, represents the set of natural gas load nodes; Represents a collection of pipes in a natural gas pipeline equipped with remote control switches; Representation node With node The pipes between Representation node With node The pipes between express Time along the pipeline Gas flow at the pipe outlet; express Time along the pipeline The gas flow rate at the starting point of the pipeline; Representation node In time natural gas load; Representation node In time of natural gas reductions.

4. The method for planning an electric-hydrogen-water coupling system considering the dynamic characteristics of multiple energy flows according to claim 3 is characterized in that: The method of characterizing the slow dynamic characteristics of gas and liquid in the coupled system pipeline by using a hyperbolic isothermal Euler partial differential equation and a hyperbolic water hammer partial differential equation respectively also includes: The dynamic characteristics of the liquid in the coupled system pipeline are characterized by the hyperbolic water hammer partial differential equation, including: Each water pipe The flow dynamics in is described by the hyperbolic water hammer partial differential equation: ； ； in, Represents a collection of water distribution pipes; state variables and Represents pipelines The pressure head and water flow in and are both spatiotemporally coupled variables, depending on the time and spatial coordinates ; is the acceleration due to gravity; At each node The liquid flow balance at includes: ； in, represents the set of water load nodes; Represents a collection of circuits in a water distribution pipeline equipped with remote switches; express Time along the pipeline Water flow rate at the pipe outlet; express Time along the pipeline The water flow rate at the beginning of the pipeline; Representation node In time water load; Representation node In time water load reduction.

5. The method for planning an electric-hydrogen-water coupling system considering the dynamic characteristics of multiple energy flows according to claim 4 is characterized in that: The AC power flow characteristics of the distribution network considered include: The AC power flow characteristics of the radial distribution network in the coupled system are characterized by a linearized branch power flow model, including: Ensure closed circuit There is a parent-child hierarchy constraint between the busbars: ； Establish the constraints of the logical association rules between line switching status and fault outage: ； ； Constraints enforcing active power balance for each bus: ； Gas turbine operating characteristic constraints: ； ； Constraints that cap renewable energy curtailment and load reduction: ； ； in, is a binary variable, if the line If closed, it is equal to 1, otherwise it is 0; is a binary variable, if the node is a node If the parent node is , it is equal to 1, otherwise it is 0; is a binary variable, if the node is a node If the parent node is , it is equal to 1, otherwise it is 0; is a binary variable, if the line If damaged, it is equal to 0, otherwise it is 1; Represents a collection of distribution network lines; Represents a collection of distribution network lines equipped with remote switches; For the line The initial state of Represented as a line The meritorious trend on For nodes In time The active power input from the substation; represents the set of water pumps in the distribution network, then Indicates the node in the distribution network Connected water pump The index of Indicates the node in the distribution network Candidate sites for connection 's index; Indicates connection to a node In time Electricity sold by the distribution network; For nodes In time The active power output of the gas turbine; and Node In time Active power generated by renewable energy and its curtailment; For in time line The meritorious trend on To connect to the node In time Active power consumption of the pump; For nodes In time Active load; Representation node In time The amount of power load reduction; Indicates connection to a node In time Electricity purchased by the distribution network; Represents a set of distribution network nodes; Representation node The efficiency of the gas engine.

6. The method for planning an electric-hydrogen-water coupling system considering the dynamic characteristics of multiple energy flows according to claim 5 is characterized in that: The decision-dependent uncertainty set obtained according to the hydrogenation demand induction effect includes: Define the decision dependency uncertainty vector as , the decision-dependent uncertainty set is composed of the decision-dependent uncertainty vector u , include: ； Constraints that ensure that the basic demand is non-zero and bounded only when the electric-hydrogen-water coupled system is installed: ； Constraints relating induced demand to installed hydrogen refueling station capacity in the coupled electricity-hydrogen-water system: ； System-level deviation budget and upper and lower limits of total demand for a time period: ； ； ； Among them, x represents the planning decision variable output in the first stage, including the location of the electric-hydrogen-water coupling system Capacity of each device in the coupled system ; Is a binary variable, indicating whether Deploy the electric hydrogen water coupling system, if deployed, it is 1, otherwise it is 0; Indicates hydrogenation demand; and They represent the basic demand and induced demand for hydrogenation respectively; and They represent the basic upper and lower limits of hydrogenation demand respectively; and represents the induction coefficient; Indicates the capacity of the hydrogen refueling station; represents the deviation from the nominal induced demand; is the proportionality coefficient; and Indicates time The lower and upper bounds of the total hydrogenation demand; and Represents the scaling parameter.

7. The method for planning an electric-hydrogen-water coupling system considering the dynamic characteristics of multiple energy flows according to claim 6, characterized in that: The planning cost constraints include: Total number constraints of the electric-hydrogen-water coupled system: ； Capacity constraints of equipment in the electric-hydrogen-water coupling system: ； in, Indicates the maximum number of electric-hydrogen-water coupled systems that can be constructed; Representation node The upper limit of the capacity of each device in the electric hydrogen water coupling system.

8. The method for planning an electric-hydrogen-water coupling system considering the dynamic characteristics of multiple energy flows according to claim 7, characterized in that: The two-stage decision-dependent robust programming model includes: make represents the set of independent uncertainty scenarios for the decision, Represents the scene index, each scene The corresponding probability is ; The two-stage decision-dependent robust programming model includes the objective function: ； Among them, x represents the planning decision variable output in the first stage, including the location of the electric-hydrogen-water coupling system Capacity of each device in the coupled system ; represents the feasible domain of the first stage; represents planning cost; Indicates operating cost; Representation scene The decision-making under depends on the uncertainty vector; Representation scene The decision-making under the uncertainty set depends on Representation scene The second stage runs the decision variables under; Representation scene The second stage feasible domain is based on the planning decision variables x and scenarios output in the first stage. The decision-making uncertainty vector under Real-time updates; The second stage operation decision variables include a set of preset parameters in the second stage feasible domain; the preset parameter set includes 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 、 and ; The planning costs include: ； The running costs include: ； in, represents the unit planning cost of the deployment of the electricity-hydrogen-water coupled system; Represents the unit planning cost of each device in the coupled system; 、 and represents the unit penalty cost of electricity, natural gas, and water load reduction; represents the unit penalty cost for not meeting hydrogen refueling demand; Representation node In time The amount of power load reduction at the location; and Represents nodes respectively In time natural gas and water load reductions; Representation node In time The hydrogen flow rate for unmet hydrogenation demand.

9. The method for planning an electric-hydrogen-water coupling system considering the dynamic characteristics of multiple energy flows according to claim 8, characterized in that: Solving the two-stage decision-dependent robust programming model includes: The hyperbolic isothermal Euler partial differential equation and the hyperbolic water hammer partial differential equation are discretized in time and space using the Preissman four-point implicit finite difference scheme: The continuous space-time domain of each pipeline is divided into discrete grids separated by space steps and time steps; the hyperbolic isothermal Euler partial differential equation is discretized to obtain the first set of equations: ； ； ； ； Discretize the hyperbolic water hammer partial differential equation to obtain the second set of equations: ； ； ； in, 、 、 、 、 、 and is an auxiliary variable; represents the spatial step length, represents the time step; and Represents a pipeline exist Time step, Pressure and pressure head at spatial grid nodes; and Represents a pipeline exist Time step, Pressure and pressure head at spatial grid nodes; and Represents a pipeline exist Time step, Pressure and pressure head at spatial grid nodes; and Represents a pipeline exist Time step, Pressure and pressure head at spatial grid nodes; and Represents a pipeline exist Time step, Air flow and water flow at spatial grid nodes; and Represents a pipeline exist Time step, Air flow and water flow at spatial grid nodes; and Represents a pipeline exist Time step, Air flow and water flow at spatial grid nodes; and Represents a pipeline exist Time step, Air flow and water flow at spatial grid nodes; The auxiliary variables are approximated by piecewise linear approximation and piecewise McCormick envelope method. and Perform linearization processing; make and denote the operational decision variables and feasible domain of the second stage after numerical discretization and piecewise linear relaxation respectively; the objective function is reconstructed into a mixed integer linear programming problem, and the compact form includes: ； ； ； ； in, and represent the operational decision variables and feasible region of the second stage after numerical discretization and piecewise linear relaxation respectively; and is the coefficient vector; and represents the coefficient matrix; and is a binary variable dimension; 、 and is a continuous variable dimension; represents the set of positive real numbers; The model is solved through parameterized columns and constraint generation algorithms to obtain the site selection and capacity scheme of the coupled system.

10. A device for planning an electric-hydrogen-water coupling system considering the dynamic characteristics of multiple energy flows, used in the method according to any one of claims 1 to 9, characterized in that: The device includes a first module, a second module, a third module and a fourth module; The first module is used to characterize the slow dynamic characteristics of gas and liquid in the coupled system pipeline by using the hyperbolic isothermal Euler partial differential equation and the hyperbolic water hammer partial differential equation, respectively, and consider the AC power flow characteristics of the distribution network to obtain the multi-energy flow constraints of electricity, hydrogen and water; The second module is used to obtain a decision-independent uncertainty scenario set based on a preset finite scenario set; and obtain a decision-dependent uncertainty set based on the hydrogenation demand induction effect; The third module is used to construct a two-stage decision-dependent robust planning model for the coupled system with the planning cost constraint as the feasible domain of the first stage and the coupled system operation constraint and the electricity, hydrogen and water multi-energy flow constraint as the feasible domain of the second stage; The first stage optimizes the location and capacity of the coupled system with the goal of minimizing the sum of the planning cost and the first cost; the first cost includes the sum of the operating costs of the coupled system under each scenario in the decision-independent uncertainty scenario set; The second stage aims to minimize the operating cost of the coupled system under each scenario in the decision-independent uncertainty scenario set, and optimizes the coupled system operating constraints and the electricity, hydrogen and water multi-energy flow constraints according to the decision-independent uncertainty scenario set and the decision-dependent uncertainty set; The fourth module is used to solve the two-stage decision-dependent robust programming model to obtain the site selection and capacity plan of the coupled system.

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