Safety state variable screening and dynamic optimization control method for hydrogen-electricity coupling system

Abstract

A safety state quantity screening and dynamic optimization control method for a hydrogen-electric coupling system establishes joint constraints based on a hydrogen-electric coupling system model and an accident set; screens out state quantities that do not satisfy the multi-energy flow balance constraints, the state quantities that do not satisfy the feasible domain constraints and the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition, as the state quantities to be adjusted; utilizes state quantities other than the state quantities to be adjusted, takes the optimal efficiency and safety of the feasible domain as the optimization target model and the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition to construct an optimization control model for the hydrogen-electric coupling system; based on the optimization control model, obtains the optimized value of the state quantity that simultaneously satisfies the optimization target model and the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition; controls the hydrogen-electric coupling system according to the optimized value of the state quantity, realizes the optimized control of the hydrogen-electric coupling system, and foresees the safety and stability of the system in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen-electricity coupling systems, and in particular to a safety state quantity screening and dynamic optimization control method for hydrogen-electricity coupling systems. Background Art

[0002] Hydrogen energy is an ideal clean secondary energy source. In recent years, as hydrogen energy application technology has gradually matured, electric-hydrogen coupling technology has continued to advance, enabling hydrogen to be quickly integrated into the entire micro-energy system.

[0003] Existing methods for optimizing and controlling hydrogen-electricity coupled systems include: conducting a detailed analysis of hydrogen production, storage, and transmission in a target region, using the hydrogen-electricity coupled state operation rationality index as an optimization metric to improve data analysis accuracy; establishing an objective function and constraints for the hydrogen energy conversion system that take into account operating costs and efficiency penalty coefficients, using the power of the hydrogen conversion system as the optimization variable, and ultimately operating the integrated energy conversion system with the optimized variable; optimizing the hydrogen storage capacity configuration by establishing a two-layer model. Given a given inner layer model, frequency fluctuation data is output to the outer layer optimization model. After calculating the objective function, the optimal hydrogen storage capacity configuration is obtained; obtaining internal temperature distribution data for the solid oxide electrolyzer (SOEC) stack, analyzing the relationship between temperature and electrical performance under different parameter combinations, and using a neural network predictive control algorithm to control the SOEC system temperature, thereby improving system safety and efficiency. Currently, most micro-energy grids do not consider hydrogen energy in modeling the hydrogen energy subsystem, instead considering simple wind and solar models. Currently, most safety domains are displayed as flat graphics, hyperplanes, or hyperplane slices, which lack the intuitiveness of three-dimensional visualization. No effective means were used to optimize and adjust the nodes beyond the safety domain to ensure the normal operation of the entire system. Summary of the Invention

[0004] In order to address the deficiencies in the prior art, the present invention provides a method for screening and dynamically optimizing safety state quantities of a hydrogen-electric coupling system, thereby achieving optimized control of the hydrogen-electric coupling system and predicting the safety and stability of the system in advance.

[0005] The technical solutions of the present invention are as follows.

[0006] The present invention proposes a safety state quantity screening and dynamic optimization control method for a hydrogen-electricity coupling system. The hydrogen-electricity coupling system includes: an electric energy subsystem, a thermal subsystem, and a hydrogen energy subsystem, including:

[0007] Collect the state variables of the hydrogen-electric coupling system, establish a hydrogen-electric coupling system model, and obtain fault data under various fault scenarios based on the hydrogen-electric coupling system model to construct an accident set;

[0008] Establish joint constraints based on the hydrogen-electricity coupling system model and the accident set, including: establishing multi-energy flow balance constraints based on the hydrogen-electricity coupling system model; establishing feasible domain constraints for the hydrogen-electricity coupling system under the “N-1” condition based on the hydrogen-electricity coupling system model and the multi-energy flow balance constraints, with the goal of maximizing the energy supply range of the hydrogen-electricity coupling system under the “N-1” condition; establishing safety domain constraints for the hydrogen-electricity coupling system under the “N-1” condition based on the feasible domain constraints and accident set of the hydrogen-electricity coupling system under the “N-1” condition;

[0009] Screen out the state quantities that do not meet the multi-energy flow balance constraint conditions, screen out the state quantities that do not meet the feasible domain constraint conditions of the hydrogen-electric coupling system under the "N-1" condition, and screen out the state quantities that do not meet the safety domain constraint conditions of the hydrogen-electric coupling system under the "N-1" condition as the state quantities to be adjusted;

[0010] By utilizing state quantities other than the state quantities to be adjusted, the efficiency and safety levels of the feasible domain are optimized as the optimization target model; the optimization control model of the hydrogen-electric coupling system is constructed by utilizing the optimization target model and the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition; based on the optimization control model, the state quantities to be adjusted are optimized to obtain the optimized values ​​of the state quantities that simultaneously meet the optimization target model and the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition, and the hydrogen-electric coupling system is controlled according to the optimized values ​​of the state quantities.

[0011] Preferably, the state variables of the hydrogen-electricity coupling system include: grid voltage, grid frequency and heating network temperature.

[0012] Preferably, the electric energy subsystem includes conventional thermal power units and new energy units, and the electric energy subsystem model includes:

[0013] 1) Output constraint model of conventional thermal power units:

[0014] ,

[0015] Where, for t Conventional thermal power units at all times of efforts, and Conventional thermal power units Lower and upper limits of output;

[0016] 2) New energy unit output constraint model:

[0017] ,

[0018] Where, for t New energy units are always outputting power, and They are respectively the lower and upper limits of the output of new energy units.

[0019] Preferably, the thermal subsystem includes a thermal network and a hydraulic network, and the thermal subsystem model includes:

[0020] 1) Thermal network model:

[0021] ,

[0022] Where, is the association matrix between nodes and branches in the thermal network; is the correlation matrix between loops and branches in the thermal network; is the mass flow column vector of the thermal pipeline; is the column vector of injected mass flow of source nodes and load nodes in the thermal network; is the pressure loss column vector of the thermal pipeline; is the column vector of the resistance coefficient of the thermal pipe, which depends on the pipe diameter;

[0023] 2) Hydraulic network model:

[0024] ,

[0025] Where, is the column vector of thermal power consumed or supplied to the node; is the specific heat capacity of water; is the column vector of water supply temperature; is the column vector of outlet water temperature; 、 are the initial and terminal node temperatures of the hot water pipe respectively; is the ambient temperature; is the total heat transfer coefficient per unit length of the hot water pipe; is the length of the hot water pipe; are the mass flow column vectors of each hot water pipe flowing into and out of the node respectively; is the heat medium temperature of the node; is the heat medium temperature at the end of each pipe section flowing into the node.

[0026] Preferably, the hydrogen energy subsystem includes an electrolyzer, a fuel cell, and a hydrogen storage tank, and the hydrogen energy subsystem model includes:

[0027] 1) Electrolyzer model:

[0028] ,

[0029] Where, is the actual input power of the electrolyzer; Input power to the electrolyzer; is the efficiency of the electrolyzer; is the hydrogen production power of the electrolyzer; is the hydrogen production efficiency of the electrolyzer;

[0030] The electrolyzer model includes power and ramping constraints in the operating state:

[0031] ,

[0032] Where, To indicate that the electrolytic cell is t A 0-1 variable indicating the start and stop status at all times; for t Electrolyzer input power at all times; 、 are the lower and upper limits of the electrolyzer input power, respectively;

[0033] 2) Fuel cell model:

[0034] ,

[0035] Where, is the output power of the fuel cell; is the input power of the fuel cell; for the efficiency of the fuel cell;

[0036] The fuel cell model also includes the fuel cell operating constraints:

[0037] ,

[0038] Where, To indicate that the fuel cell t A 0-1 variable indicating the start and stop status at all times; 、 are the lower and upper limits of the fuel cell input power, respectively;

[0039] 3) Hydrogen storage tank model:

[0040] ,

[0041] Where, The energy stored in the hydrogen tank at the initial moment; is the capacity of the hydrogen storage tank; for t The energy stored in the hydrogen tank at all times; 、 They are t The charging and discharging power of hydrogen storage tank at all times; 、 are the charging and discharging energy efficiencies of the hydrogen storage tank respectively; is the operating period; is the maximum capacity factor of the hydrogen storage tank; 、 They represent the hydrogen storage tanks in t A 0-1 variable indicating the state of charge and discharge at any given moment; 、 are the maximum values ​​of charging and discharging power of the hydrogen storage tank respectively.

[0042] Preferably, the failure scenarios include: failure of key pipeline outlets at energy hubs, failure of key equipment at energy hubs, and failure of imbalance between supply and demand of renewable energy.

[0043] Preferably, the fault data under each fault scenario is obtained based on the hydrogen-electricity coupling system model to construct an accident set, including:

[0044] Conduct probabilistic modeling of the output of new energy units, and construct a joint model of the output of new energy units together with the output constraint model of new energy units, thereby updating the electric energy subsystem model and obtaining an updated hydrogen-electricity coupling system model;

[0045] Among them, the probability modeling of the output of new energy units includes: establishing a probability distribution model of wind turbine output based on a two-parameter Weibull random probability model, and establishing a probability distribution model of photovoltaic generator output based on a Beta probability distribution model;

[0046] Based on the updated hydrogen-electric coupling system model, fault data under various fault scenarios are obtained to construct an accident set.

[0047] Preferably, the probability distribution model of wind turbine output is established based on the two-parameter Weibull random probability model to satisfy the following relationship:

[0048] ,

[0049] ,

[0050] Where, is the probability density function of the actual wind speed, k w is the shape coefficient of the fan unit, c w is the scale factor of the fan unit; 、 V h 、 V out 、 V in They are rated wind speed, actual wind speed, cut-out wind speed and cut-in wind speed respectively; P wmax is the maximum output power of the wind turbine, The output of wind turbines based on probability distribution;

[0051] The probability distribution model of photovoltaic generator output based on the Beta probability distribution model satisfies the following relationship:

[0052] ,

[0053] ,

[0054] Where, is the probability density function of actual illumination, is the shape factor of the photovoltaic module, β p is the scale factor of the photovoltaic module; 、 They are rated light intensity and actual light intensity respectively; is the Gamma function; Indicates the maximum output power of the photovoltaic generator set. Photovoltaic generator output based on probability distribution.

[0055] Preferably, the multi-energy flow balance constraints include:

[0056] 1) Power balance constraint, satisfying the following relationship:

[0057] ,

[0058] Where, 、 They are t Time Node i Active injection power and reactive injection power; N is the number of system nodes; 、 They are the node admittance matrices i OK j the real and imaginary parts of the columns; for t Time Node i The voltage amplitude; for t Time Node j The voltage amplitude; for t Time branch ij The phase angle difference; 、 Node i The lower and upper limits of the voltage amplitude;

[0059] 2) Hydrogen energy power balance constraint, satisfying the following relationship:

[0060] ,

[0061] Where, for t The hydrogen production power of the electrolyzer at any moment; 、 They are t The charging and discharging power of hydrogen storage tank at all times; for t Hydrogen load at the time; for t The input power of the fuel cell at any moment;

[0062] 3) Power balance constraints of the electric-hydrogen coupling system, including electric-hydrogen coupling operation constraints and electric hydrogen production power constraints. The electric-hydrogen coupling operation constraints are as follows:

[0063] ,

[0064] Where, for t Conventional thermal power units at all times contribution; is the number of conventional thermal power units; for t New energy units are always outputting power; for t The output power of the fuel cell at any moment; for t Time Node The electrical load; is the hydrogen load of the system; for t Electrolyzer input power at all times; is the number of nodes;

[0065] The power constraints for hydrogen production are as follows:

[0066] ,

[0067] Where, for Shike New Energy Station Output power; for Shike New Energy Station The load power provided; for Shike New Energy Station The abandoned power; for Shike New Energy Station Input power of the inner electrolyzer;

[0068] 4) Thermal energy balance constraint, satisfying the following relationship:

[0069] ,

[0070] Where, C CHP is the thermoelectric ratio; P CHP,h is the thermal power; P CHP,e is the electrical power;

[0071] 5) Flow constraints of hydraulic network:

[0072] ,

[0073] Where, B is the correlation matrix of the hydraulic grid; w is the branch flow vector; K is the pipe coefficient.

[0074] Preferably, the feasible region constraint of the hydrogen-electric coupling system under the “N-1” condition satisfies the following relationship:

[0075] ,

[0076] Where, For the The state quantity of each pipeline segment, The sum of the state quantities of all pipeline segments is the largest; are the equality constraints for the key pipeline segments and key equipment in the feasible domain of the hydrogen-electric coupling system under the “N-1” condition. These are the key pipeline sections and key equipment constraints of the feasible domain of the hydrogen-electric coupling system under the “N-1” condition; 、 are the lower and upper bound vectors of the inequality constraints respectively; , M is the number of pipeline segments.

[0077] Preferably, the safety domain constraint of the hydrogen-electric coupling system under the “N-1” condition satisfies the following relationship:

[0078] ,

[0079] Where, It is the security domain constraint; Equality constraints for key pipeline segments and key equipment in the safety domain of the hydrogen-electricity coupling system under the “N-1” condition; These are the inequality constraints for key pipeline segments and key equipment in the safety domain of the hydrogen-electric coupling system under the “N-1” condition.

[0080] Preferably, according to the multi-energy flow balance constraints, the feasible domain constraints and the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition, the isolation forest algorithm is used to screen the state quantities, not only to remove abnormal data in the state quantities, but also to extract the state quantities to be adjusted.

[0081] Preferably, the efficiency function of the feasible region satisfies the following relationship:

[0082] ,

[0083] Where, is the efficiency function of the feasible region, The total number of state quantities other than the state quantity to be adjusted, in the embodiment, after the state quantity screening, the hydrogen-electric coupling system The state quantity of each pipeline segment is the object, The first state quantity other than the state quantity to be adjusted The state quantity of each pipeline segment, For the The maximum value of the state quantity of each pipeline segment under the constraints of the feasible region;

[0084] The safety function of the feasible region satisfies the following relationship:

[0085] ,

[0086] Where, is the safety function of the feasible region; is the safety distance from the safe working point c in the safety domain to the upper safety boundary; The safety distance from the minimum energy supply capacity working point z in the safety domain to the upper safety boundary, where the minimum energy supply capacity refers to the minimum load carrying capacity of the system. is the coupling coefficient of the hydrogen-electric coupling system, .

[0087] Preferably, the ratio of the number of coupled devices in the total number of devices in the hydrogen-electricity coupling system is used as the coupling degree coefficient. When the ratio of the number of coupled devices in the total number of devices in the hydrogen-electricity coupling system is less than or equal to 20%, , when the number of coupled devices in the hydrogen-electricity coupling system accounts for more than 20% and less than or equal to 50% of the total number of devices .

[0088] Preferably, the optimization target model and the safety domain constraint of the hydrogen-electricity coupling system under the “N-1” condition are used to form an optimization control model of the hydrogen-electricity coupling system, satisfying the following relationship:

[0089] ,

[0090] Where, The optimization objective model that represents the optimal efficiency function and safety function of the feasible domain is represents the safety domain constraint of the hydrogen-electric coupling system under the “N-1” condition, where O is the safety domain constraint of the hydrogen-electric coupling system under the “N-1” condition, L is the state quantity set of each pipeline segment.

[0091] Preferably, based on the optimization control model, the state quantity to be adjusted is optimized, including:

[0092] Based on the three-dimensional image method, the three-dimensional safety domain corresponding to the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition is drawn;

[0093] The Manhattan distance method is used to calculate the safety distance between the state quantity to be adjusted and the boundary of the three-dimensional safety zone as the adjustment distance of the state quantity to be adjusted.

[0094] The beneficial effects of the present invention are that, compared with the prior art, at least the following are included: the hydrogen-electricity coupled micro-energy network optimization control method proposed in the present invention can optimize and control the state of the hydrogen-electricity coupled micro-energy network, build a model based on the micro-energy network, simulate the actual micro-energy network, and provide a basic framework support for subsequent steps; obtain multi-energy flow balance constraints, feasible domain constraints, and safety domain constraints through the model and accident set, providing constraints for the subsequent safety domain and feasible domain; obtain the safety domain and feasible domain through the constraints, and be able to screen the state quantity data of the micro-energy network according to the safety domain and feasible domain, so as to predict the safety and stability of the micro-energy network system in advance; and based on the analysis and optimization results of the state quantity data, be able to achieve optimized control of the micro-energy network. The present invention is helpful for studying the safe and efficient operation optimization control of the hydrogen-electricity coupled system, can improve the safety of power operation, and has good practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] Figure 1 This is a flow chart of the safety state quantity screening and dynamic optimization control method for the hydrogen-electricity coupling system proposed by the present invention;

[0096] Figure 2 In the embodiment of the present invention, the power line segment is used , thermal pipeline segment , hydrogen pipeline segment Schematic diagram of the three-dimensional safety domain composed of energy supply loads;

[0097] Figure 3 3 is a schematic diagram of a process for optimizing a state quantity to be adjusted in an embodiment of the present invention. DETAILED DESCRIPTION

[0098] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0099] The present invention proposes a safety state quantity screening and dynamic optimization control method for a hydrogen-electricity coupling system, wherein the hydrogen-electricity coupling system includes: an electric energy subsystem, a thermal subsystem, and a hydrogen energy subsystem; Figure 1 Shown, including:

[0100] Step 1: Collect the state variables of the hydrogen-electric coupling system.

[0101] In a non-limiting preferred embodiment, the state quantities of the hydrogen-electricity coupling system include but are not limited to: grid voltage, grid frequency, and heating network temperature; those skilled in the art determine the state quantities of the hydrogen-electricity coupling system to be collected based on the content of the state data.

[0102] Specifically, the corresponding state quantity data is collected through the vector measurement unit, pressure transmitter, temperature sensor and voltage transformer in the hydrogen-electricity coupling system.

[0103] In the embodiment, the hydrogen-electricity coupling system is a hydrogen-electricity coupling micro energy network.

[0104] Step 2: Establish a hydrogen-electricity coupling system model; obtain fault data under various fault scenarios based on the hydrogen-electricity coupling system model to construct an accident set.

[0105] Specifically, step 2 includes:

[0106] Step 2.1, build a hydrogen-electricity coupling system model;

[0107] Specifically, the hydrogen-electricity coupling system model includes but is not limited to: an electric energy subsystem model, a thermal subsystem model, and a hydrogen energy subsystem model.

[0108] The power subsystem includes conventional thermal power units and new energy units. The power subsystem model includes:

[0109] 1) Output constraint model of conventional thermal power units:

[0110] ,

[0111] Where, for t Conventional thermal power units at all times of efforts, and Conventional thermal power units The lower and upper limits of output.

[0112] 2) New energy unit output constraint model:

[0113] ,

[0114] Where, for t New energy units are always outputting power, and They are respectively the lower and upper limits of the output of new energy units.

[0115] The thermal subsystem includes the thermal network and the hydraulic network. The thermal subsystem model includes:

[0116] 1) Thermal network model:

[0117] ,

[0118] Where, is the association matrix between nodes and branches in the thermal network; is the correlation matrix between loops and branches in the thermal network; is the mass flow column vector of the thermal pipeline; is the column vector of injected mass flow of source nodes and load nodes in the thermal network; is the pressure loss column vector of the thermal pipeline; is the column vector of the resistance coefficient of the thermal pipe, which generally depends on the pipe diameter.

[0119] 2) Hydraulic network model:

[0120] ,

[0121] Where, is the column vector of thermal power consumed or supplied to the node; is the specific heat capacity of water; is the column vector of water supply temperature; is the column vector of outlet water temperature; 、 are the initial and terminal node temperatures of the hot water pipe respectively; is the ambient temperature; is the total heat transfer coefficient per unit length of the hot water pipe; is the length of the hot water pipe; are the mass flow column vectors of each hot water pipe flowing into and out of the node respectively; is the heat medium temperature of the node; is the heat medium temperature at the end of each pipe section flowing into the node.

[0122] It is the product of the sum of the mass flow rates of all hot water pipes out of the node and the temperature of the heat medium at the node, and is used to represent the heat provided by the heat medium; It is the sum of the products of the mass flow rate of each hot water pipe flowing into the node and the temperature of the heat medium at the end of each pipe section flowing into the node. It is used to represent the heat required by each node. When the two are balanced, the energy consumption is the lowest.

[0123] The hydrogen energy subsystem includes an electrolyzer, a fuel cell, and a hydrogen storage tank. The hydrogen energy subsystem model includes:

[0124] 1) Electrolyzer model:

[0125] ,

[0126] Where, is the actual input power of the electrolyzer; Input power to the electrolyzer; is the efficiency of the electrolyzer; is the hydrogen production power of the electrolyzer; is the hydrogen production efficiency of the electrolyzer.

[0127] The electrolyzer model includes power and ramping constraints in the operating state:

[0128] ,

[0129] Where, To indicate that the electrolytic cell is t A 0-1 variable indicating the start and stop status at all times; for t Electrolyzer input power at all times; 、 are the lower and upper limits of the electrolyzer input power respectively.

[0130] 2) Fuel cell model:

[0131] ,

[0132] Where, is the output power of the fuel cell; is the input power of the fuel cell; The efficiency of the fuel cell.

[0133] The fuel cell model includes the fuel cell operating constraints:

[0134] ,

[0135] Where, To indicate that the fuel cell t A 0-1 variable indicating the start and stop status at all times; 、 are the lower and upper limits of the fuel cell input power, respectively.

[0136] 3) Hydrogen storage tank model:

[0137] ,

[0138] Where, The energy stored in the hydrogen tank at the initial moment; is the capacity of the hydrogen storage tank; for t The energy stored in the hydrogen tank at all times; 、 They are t The charging and discharging power of hydrogen storage tank at all times; 、 are the charging and discharging energy efficiencies of the hydrogen storage tank respectively; is the operating period; is the maximum capacity factor of the hydrogen storage tank; 、 They represent the hydrogen storage tanks in t A 0-1 variable indicating the state of charge and discharge at any given moment; 、 are the maximum values ​​of charging and discharging power of the hydrogen storage tank respectively.

[0139] In step 2.2, the fault data under each fault scenario is obtained based on the hydrogen-electricity coupling system model to construct an accident set.

[0140] In a non-limiting preferred embodiment, the failure scenarios include but are not limited to: failure of key pipeline outlets at energy hubs, failure of key equipment at energy hubs, and failure of imbalance between supply and demand of renewable energy; those skilled in the art can determine the specific failure scenarios based on the probability of occurrence of each failure in the hydrogen-electricity coupling system.

[0141] When acquiring fault data for various fault scenarios based on the hydrogen-electricity coupling system model, the randomness of the output of new energy units is comprehensively considered. When analyzing the accident set, the system's energy supply capacity is increased and the upper capacity constraint limit is expanded. Therefore, considering the feasible and safety boundaries of the new energy unit integration, a probabilistic model of the new energy unit output is developed. This is combined with the new energy unit output constraint model to form a joint model of the new energy unit output, thereby updating the power subsystem model.

[0142] In a non-limiting preferred embodiment, a probability distribution model of wind turbine output is established based on a two-parameter Weibull random probability model and satisfies the following relationship:

[0143] ,

[0144] ,

[0145] Where, is the probability density function of the actual wind speed, k w is the shape coefficient of the fan unit, c w is the scale factor of the fan unit; 、 V h 、 V out 、 V in They are rated wind speed, actual wind speed, cut-out wind speed and cut-in wind speed respectively; P wmax is the maximum output power of the wind turbine, is the wind turbine output based on probability distribution.

[0146] In a non-limiting preferred embodiment, the probability distribution model of the photovoltaic generator output is established based on the Beta probability distribution model to satisfy the following relationship:

[0147] ,

[0148] ,

[0149] Where, is the probability density function of actual illumination, is the shape factor of the photovoltaic module, β p is the scale factor of the photovoltaic module; 、 They are rated light intensity and actual light intensity respectively; is the Gamma function; Indicates the maximum output power of the photovoltaic generator set. Photovoltaic generator output based on probability distribution.

[0150] This invention also considers the integration of renewable energy into micro-energy networks. Most current micro-energy networks do not consider renewable energy. By incorporating probabilistic modeling of renewable energy based on the stochastic nature of renewable energy generation, the model's sophistication is enhanced. Furthermore, this probabilistic modeling correlates the stochastic nature of renewable energy unit output with the system's energy supply capability and capacity constraints. This allows the feasible and safe domains of the hydrogen-electricity coupling system model to fully account for the stochastic nature of renewable energy unit output and provide adaptive adjustment capabilities.

[0151] Step 3: Establish joint constraints based on the hydrogen-electricity coupling system model and the accident set, including multi-energy flow balance constraints, feasible region constraints, and safety region constraints.

[0152] Specifically, step 3 includes:

[0153] Step 3.1, establish multi-energy flow balance constraints based on the hydrogen-electricity coupling system model;

[0154] In a non-limiting preferred embodiment, the multi-energy flow balance constraints include but are not limited to:

[0155] 1) Power balance constraint, satisfying the following relationship:

[0156] ,

[0157] Where, 、 They are t Time Node i Active injection power and reactive injection power; N is the number of system nodes; 、 They are the node admittance matrices i OK j the real and imaginary parts of the columns; for t Time Node i The voltage amplitude; for t Time Node j The voltage amplitude; for t Time branch ij The phase angle difference; 、 Node i The lower and upper limits of the voltage amplitude;

[0158] 2) Hydrogen energy power balance constraint, satisfying the following relationship:

[0159] ,

[0160] Where, for t The hydrogen production power of the electrolyzer at any moment; 、 They are t The charging and discharging power of hydrogen storage tank at all times; for t Hydrogen load at the time; for t The input power of the fuel cell at any moment.

[0161] 3) Power balance constraints of the electric-hydrogen coupling system:

[0162] Since the electric energy subsystem produces hydrogen through excess electricity from new energy stations to meet the energy needs of the hydrogen energy subsystem, and the hydrogen energy subsystem provides electricity to the electric energy subsystem through hydrogen fuel cells, the electric-hydrogen coupling operation constraints are as follows:

[0163] The power balance constraints of the electric-hydrogen coupling system include the electric-hydrogen coupling operation constraints and the electric-hydrogen production power constraints. The electric-hydrogen coupling operation constraints are as follows:

[0164] ,

[0165] Where, for t Conventional thermal power units at all times contribution; is the number of conventional thermal power units; for t New energy units are outputting power at all times; for t The output power of the fuel cell at any moment; for t Time Node The electrical load; is the hydrogen load of the system; for t Electrolyzer input power at all times; is the number of nodes.

[0166] When using excess electricity from new energy stations to produce hydrogen, the power constraints for hydrogen production are as follows:

[0167] ,

[0168] Where, for Shike New Energy Station Output power; for Shike New Energy Station The load power provided; for Shike New Energy Station The abandoned power for Shike New Energy Station Input power to the electrolyzer.

[0169] 4) Thermal energy balance constraints:

[0170] ,

[0171] Where, C CHP is the thermoelectric ratio; P CHP,h is the thermal power;P CHP,e For electrical power.

[0172] 5) Flow constraints of hydraulic network:

[0173] ,

[0174] Where, B is the correlation matrix of the hydraulic grid, describing the correlation between the hydraulic loop and the pipe sections; w is the branch flow vector; K is the pipe coefficient.

[0175] Step 3.2: Based on the hydrogen-electricity coupling system model and the multi-energy flow balance constraints, with the goal of maximizing the energy supply range of the hydrogen-electricity coupling system under the “N-1” condition, establish the feasible region constraints of the hydrogen-electricity coupling system under the “N-1” condition;

[0176] Specifically, there are pipeline segments, numbered 1, 2, ..., M ; Using vectors in Euclidean space Indicates the working status of the hydrogen-electric coupling system.

[0177] With the goal of maximizing the energy supply range of the hydrogen-electric coupling system under the “N-1” condition, the feasible region constraints are established to satisfy the following relationship:

[0178] ,

[0179] Where, For the The state quantity of each pipeline segment, The sum of the state quantities of all pipeline segments is maximized, thus maximizing the energy supply range of the hydrogen-electric coupling system under the "N-1" condition; are the equality constraints for the key pipeline segments and key equipment in the feasible domain of the hydrogen-electric coupling system under the “N-1” condition. These are the inequality constraints for key pipeline segments and key equipment in the feasible domain of the hydrogen-electric coupling system under the “N-1” condition; 、 are the lower bound vector and upper bound vector in the inequality constraints respectively.

[0180] Step 3.3, determine the safety domain boundary according to the feasible domain constraints and accident set of the hydrogen-electric coupling system under the "N-1" condition, and establish the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition based on the safety domain boundary.

[0181] Security domain constraints satisfy the following relationship:

[0182] ,

[0183] Where, It is the security domain constraint; Equality constraints for key pipeline segments and key equipment in the safety domain of the hydrogen-electricity coupling system under the “N-1” condition; Inequality constraints for key pipeline segments and key equipment in the safety domain of the hydrogen-electric coupling system under the “N-1” condition

[0184] Under the union of feasible region and safety region, the inequality constraint is .

[0185] In the method proposed by the present invention, the feasible domain and safety domain of the hydrogen-electric coupling system can be obtained based on the joint constraint conditions. The following principles should be observed when determining the feasible domain: the flow capacity of the key pipeline section is within the allowable operating boundary; the flow capacity of the key pipeline section of the connecting equipment is not greater than the minimum capacity of the key pipeline section; the minimum capacity of the key pipeline section should ensure the energy supply of the connected equipment. The following principles should be observed when determining the safety domain: the accident set includes accident data of key pipeline outlet failures of energy hubs, accident data of key equipment failures of energy hubs, and accident data of renewable energy supply and demand imbalance failures; the randomness of renewable energy is comprehensively considered in the calculation, and the system energy supply capacity is increased and the capacity constraint upper limit is expanded when analyzing the first accident set.

[0186] Step 4: Filter out the state quantities that do not meet the multi-energy flow balance constraint conditions, filter out the state quantities that do not meet the feasible domain constraint conditions of the hydrogen-electric coupling system under the "N-1" condition, and filter out the state quantities that do not meet the safety domain constraint conditions of the hydrogen-electric coupling system under the "N-1" condition as the state quantities to be adjusted.

[0187] Specifically, according to the multi-energy flow balance constraints, the feasible domain constraints of the hydrogen-electric coupling system under the "N-1" condition, and the safety domain constraints, the isolation forest algorithm is used to screen the state quantities, which not only removes the abnormal data in the state quantities, but also extracts the state quantities to be adjusted.

[0188] In a non-limiting preferred embodiment, the method of screening state quantities using the feasible region constraints and safety region constraints of the hydrogen-electricity coupling system includes but is not limited to: the isolation forest method.

[0189] In a non-limiting preferred embodiment, abnormal data includes but is not limited to: distorted data caused by external factors.

[0190] Specifically, the state quantity data set is screened using the isolation forest algorithm, including:

[0191] Step S1, is a state quantity data set, from X Extract samples as X Subset of Put the root node, where is the number of samples.

[0192] Step S2, specify the dimension and in the subset A random cutting point is generated in

[0193] Step S3, based on the cutting point p Subset Divide into two subspaces, including: specifying the dimension q Less than p The samples are placed in the left subspace, specifying the dimension q Not less than p into the right subspace.

[0194] Step S4, repeating steps S2 and S3 until all trees reach the specified height.

[0195] Step S5, repeating steps S1 to S4, and constructing an isolation forest using the generated r isolated trees.

[0196] For each sample , in its traversal After the isolation trees, we will calculate Average height in the forest , normalize the average height of all samples to meet the following relationship:

[0197] ,

[0198] Where, is the normalized value of the average height of all samples; To include The path length of the isolation tree in the subset of samples (the path of the isolation tree, Monotonically increasing), used to standardize the recording of external nodes x The path length satisfies the following relationship:

[0199] ,

[0200] in, , For variables The harmonic function of ξ is Euler's constant;

[0201] From the root node to the external node x Average height The average value of satisfies the following relationship:

[0202] ,

[0203] Where, is an outlier.

[0204] Furthermore, after the collected state quantities are screened using the feasible domain constraints and safety domain constraints of the hydrogen-electricity coupling system, the state quantities before and after the screening are obtained.

[0205] Step 5: Using state quantities other than the state quantities to be adjusted, the efficiency and safety levels of the feasible domain are optimized as the optimization target model; using the optimization target model and the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition to form an optimization control model of the hydrogen-electric coupling system; based on the optimization control model, the state quantities to be adjusted are optimized to obtain the optimized value of the state quantity that simultaneously satisfies the optimization target model and the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition, and the hydrogen-electric coupling system is controlled according to the optimized value of the state quantity.

[0206] Specifically, step 5 includes:

[0207] Step 5.1, using the state quantities other than the state quantities to be adjusted, the optimization target model is set with the efficiency and safety of the feasible region being the best;

[0208] In the embodiment, the hydrogen-electric coupling system has pipeline segments, numbered 1, 2, ..., M ; Using vectors in Euclidean space Indicates the working status of the hydrogen-electric coupling system, For the The state quantity of each pipeline segment, .

[0209] Among them, the efficiency function of the feasible region satisfies the following relationship:

[0210] ,

[0211] Where, is the efficiency function of the feasible region, The total number of state quantities other than the state quantity to be adjusted, in the embodiment, after the state quantity screening, the hydrogen-electric coupling system The state quantity of each pipeline segment is the object, The first state quantity other than the state quantity to be adjusted The state quantity of each pipeline segment, For the The maximum value of the state quantity of a pipeline segment under the constraints of the feasible region.

[0212] The safety function of the feasible region satisfies the following relationship:

[0213] ,

[0214] Where, is the safety function of the feasible region; is the safety distance from the safe working point c in the safety domain to the upper safety boundary; The safety distance from the minimum energy supply capacity working point z in the safety domain to the upper safety boundary, where the minimum energy supply capacity refers to the minimum load carrying capacity of the system. is the coupling coefficient of the hydrogen-electric coupling system, .

[0215] In the embodiment, the ratio of the number of coupled devices in the hydrogen-electricity coupling system to the total number of devices is used as the coupling degree coefficient. When the ratio of the number of coupled devices in the hydrogen-electricity coupling system to the total number of devices is less than or equal to 20%, , when the number of coupled devices in the hydrogen-electricity coupling system accounts for more than 20% and less than or equal to 50% of the total number of devices .

[0216] In step 5.2, the optimization target model and the safety domain constraint of the hydrogen-electric coupling system under the “N-1” condition are used to construct the optimization control model of the hydrogen-electric coupling system, which satisfies the following relationship:

[0217] ,

[0218] Where, The optimization objective model that represents the optimal efficiency function and safety function of the feasible domain is represents the safety domain constraint of the hydrogen-electric coupling system under the “N-1” condition, where O is the safety domain constraint of the hydrogen-electric coupling system under the “N-1” condition, L is the state quantity set of each pipeline segment.

[0219] In step 5.3, based on the optimization control model, the state quantity to be adjusted is optimized to obtain the optimized value of the state quantity that satisfies both the optimization target model and the safety domain constraint of the hydrogen-electric coupling system under the "N-1" condition, and the hydrogen-electric coupling system is controlled according to the optimized value of the state quantity.

[0220] In a non-limiting preferred embodiment, the state quantity to be adjusted includes but is not limited to: the load power on the pipeline segment.

[0221] Specifically, step 5.3 includes:

[0222] Step 5.3.1: Based on the three-dimensional image method, draw the three-dimensional safety domain corresponding to the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition;

[0223] Specifically, the power pipeline segment , thermal pipeline segment , hydrogen pipeline segment The three-dimensional safety domain composed of energy supply load is as follows Figure 2 In the three-dimensional safety domain, when the operating state of the micro energy grid is within the region, it means that the system is in a safe and reliable operating state. The surface BCEF is the common upper limit of safety, the surface ABCD is the common lower limit of safety, and CDF is the power pipeline segment. The upper limit of the energy supply load, AEB is the power pipeline segment The safety lower limit of the upper energy load, ADEF is the thermal pipeline segment The safe lower limit of the upper energy supply load. Figure 2 For the thermal pipeline segment Energy supply load and hydrogen pipeline segment The energy supply load is converted into numerical value and compared with the power pipeline segment The upper energy load uses the same unit, MVA.

[0224] In step 5.3.2, the Manhattan distance method is used to calculate the safety distance between the state quantity to be adjusted and the boundary of the three-dimensional safety zone, which is used as the adjustment distance of the state quantity to be adjusted, satisfying the following relationship:

[0225] ,

[0226] Where, is the adjustment amount of the state quantity, Indicates the The upper bound of the state quantity of a pipeline segment.

[0227] exist If the safety domain boundary is exceeded, it is necessary to first make safety adjustments, calculate the Mandaton distance between the operating point and the safety boundary, and then optimize the performance function. The operation adjustment process is as follows: Figure 3 As shown, the calculated data is inherently in an unsafe range. Using the Mandalton distance, we determine the distance to return it to the safe range, and then return it to the safe range. During the adjustment process, the primary consideration is the performance function, ensuring that the adjusted operating point achieves high efficiency and safety.

[0228] In a non-limiting preferred embodiment, the adjustment data is returned through the adjustment strategy; based on whether the output condition of the pipeline exceeds the safety boundary after the adjustment data is adjusted, the pipeline is returned to the safety domain and continues to participate in the electric-hydrogen coupling to operate as an energy grid.

[0229] Furthermore, the screened state quantities are used to optimize the control of the hydrogen-electric coupling system based on the optimization control model of the hydrogen-electric coupling system, and the state quantities before and after are adjusted.

[0230] In the method proposed in the present invention, the state quantities screened out are parameters that do not meet some conditions, and these parameters are often the source of predictive data for problems in the safety of system operation. Simply deleting these parameters without using these parameters to predict the safety and stability of the system will lead to the risk that the system control cannot avoid safety hazards. In the method proposed in the present invention, various constraints are gradually constructed in depth, from multi-energy flow balance constraints to feasible domain constraints of hydrogen-electric coupling systems under "N-1" conditions to safety domain constraints of hydrogen-electric coupling systems under "N-1" conditions, and the state quantities that do not meet any of the above three conditions are screened out as state quantities to be adjusted, which is the basis for realizing the prediction of system safety and stability risks.

[0231] After screening out the state quantities to be adjusted, the remaining state quantities need to achieve both efficient and safe operation of the system in the feasible domain. Therefore, the state quantities other than the state quantities to be adjusted are used to take the efficiency degree function and the safety degree function of the feasible domain as the optimal optimization target model; then the optimization target model and the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition are used to form an optimization control model of the hydrogen-electric coupling system. The optimization control model is a preventive control model that can adjust the system from an unsafe state to a safe state, and then from a safe state to a safe and efficient state. Therefore, based on the optimization control model, the state quantities to be adjusted are optimized, so that the unsafe working point can be adjusted to a safe and efficient working point according to the shortest path.

[0232] Moreover, the method for establishing the safety domain constraint conditions of the hydrogen-electric coupling system under the "N-1" condition in the present invention enables the safety domain to be intuitively displayed through three-dimensional images, which helps to adjust the unsafe working point to a safe and efficient working point along the shortest path.

Claims

1. A method for screening and dynamically optimizing safety state variables in a hydrogen-electricity coupling system, the hydrogen-electricity coupling system comprising: The electrical energy subsystem, thermal subsystem and hydrogen energy subsystem are characterized by comprising: Collect the state variables of the hydrogen-electric coupling system, establish a hydrogen-electric coupling system model, and obtain fault data under various fault scenarios based on the hydrogen-electric coupling system model to construct an accident set; Establish joint constraints based on the hydrogen-electricity coupling system model and the accident set, including: establishing multiple energy flow balance constraints based on the hydrogen-electricity coupling system model; establishing feasible domain constraints for the hydrogen-electricity coupling system under the "N-1" condition based on the hydrogen-electricity coupling system model and the multiple energy flow balance constraints, with the goal of maximizing the energy supply range of the hydrogen-electricity coupling system under the "N-1" condition; establishing safety domain constraints for the hydrogen-electricity coupling system under the "N-1" condition based on the feasible domain constraints and the accident set of the hydrogen-electricity coupling system under the "N-1" condition; Screen out the state quantities that do not satisfy the multi-energy flow balance constraint conditions, screen out the state quantities that do not satisfy the feasible domain constraint conditions of the hydrogen-electric coupling system under the "N-1" condition, and screen out the state quantities that do not satisfy the safety domain constraint conditions of the hydrogen-electric coupling system under the "N-1" condition as the state quantities to be adjusted; Using state quantities other than the state quantities to be adjusted, the efficiency and safety of the feasible domain are optimized as the optimization target model; the efficiency function of the feasible domain satisfies the following relationship: ， Where, is the efficiency function of the feasible region, The total number of state quantities other than the state quantity to be adjusted, in the embodiment, after the state quantity screening, the hydrogen-electric coupling system The state quantity of each pipeline segment is the object, The first state quantity other than the state quantity to be adjusted The state quantity of each pipeline segment, For the The maximum value of the state quantity of each pipeline segment under the constraints of the feasible region; The safety function of the feasible region satisfies the following relationship: ， Where, is the safety function of the feasible region; is the safety distance from the safe working point c in the safety domain to the upper safety boundary; The safety distance from the minimum energy supply capacity working point z in the safety domain to the upper safety boundary, where the minimum energy supply capacity refers to the minimum load carrying capacity of the system. is the coupling coefficient of the hydrogen-electric coupling system, ; An optimization control model of the hydrogen-electric coupling system is constructed using the optimization target model and the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition. Based on the optimization control model, the state quantity to be adjusted is optimized to obtain the optimized value of the state quantity that simultaneously satisfies the optimization target model and the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition. The hydrogen-electric coupling system is controlled according to the optimized value of the state quantity.

2. The method for screening and dynamically optimizing the safety state quantity of a hydrogen-electricity coupling system according to claim 1 is characterized in that: The state variables of the hydrogen-electricity coupling system include: grid voltage, grid frequency, and heating network temperature.

3. The method for screening and dynamically optimizing the safety state quantity of a hydrogen-electricity coupling system according to claim 1 is characterized in that: The power subsystem includes conventional thermal power units and new energy units. The power subsystem model includes: 1) Output constraint model of conventional thermal power units: ， Where, for t Conventional thermal power units at all times of effort, and Conventional thermal power units Lower and upper limits of output; 2) New energy unit output constraint model: ， Where, for t New energy units are always outputting power, and They are respectively the lower and upper limits of the output of new energy units.

4. The method for screening and dynamically optimizing the safety state variables of a hydrogen-electricity coupling system according to claim 1, characterized in that: The thermal subsystem includes the thermal network and the hydraulic network. The thermal subsystem model includes: 1) Thermal network model: ， Where, is the association matrix between nodes and branches in the thermal network; is the correlation matrix between loops and branches in the thermal network; is the mass flow column vector of the thermal pipeline; is the column vector of injected mass flow of source nodes and load nodes in the thermal network; is the pressure loss column vector of the thermal pipeline; is the column vector of the resistance coefficient of the thermal pipe, which depends on the pipe diameter; 2) Hydraulic network model: ， Where, is the column vector of thermal power consumed or supplied to the node; is the specific heat capacity of water; is the column vector of water supply temperature; is the column vector of outlet water temperature; 、 are the initial and terminal node temperatures of the hot water pipe respectively; is the ambient temperature; is the total heat transfer coefficient per unit length of the hot water pipe; is the length of the hot water pipe; are the mass flow column vectors of each hot water pipe flowing into and out of the node respectively; is the heat medium temperature of the node; is the heat medium temperature at the end of each pipe section flowing into the node.

5. The method for screening and dynamically optimizing the safety state variables of a hydrogen-electricity coupling system according to claim 1, characterized in that: The hydrogen energy subsystem includes an electrolyzer, a fuel cell, and a hydrogen storage tank. The hydrogen energy subsystem model includes: 1) Electrolyzer model: ， Where, is the actual input power of the electrolyzer; Input power to the electrolyzer; is the efficiency of the electrolyzer; is the hydrogen production power of the electrolyzer; is the hydrogen production efficiency of the electrolyzer; The electrolyzer model includes power and ramping constraints in the operating state: ， Where, To indicate that the electrolytic cell is t A 0-1 variable indicating the start and stop status at all times; for t Electrolyzer input power at all times; 、 are the lower and upper limits of the electrolyzer input power, respectively; 2) Fuel cell model: ， Where, is the output power of the fuel cell; is the input power of the fuel cell; for the efficiency of the fuel cell; The fuel cell model also includes the fuel cell operating constraints: ， Where, To indicate that the fuel cell t A 0-1 variable indicating the start and stop status at all times; 、 are the lower and upper limits of the fuel cell input power, respectively; 3) Hydrogen storage tank model: ， Where, The energy stored in the hydrogen tank at the initial moment; is the capacity of the hydrogen storage tank; for t The energy stored in the hydrogen tank at all times; 、 They are t The charging and discharging power of hydrogen storage tank at all times; 、 are the charging and discharging energy efficiencies of the hydrogen storage tank respectively; is the operating period; is the maximum capacity factor of the hydrogen storage tank; 、 They represent the hydrogen storage tanks in t A 0-1 variable indicating the state of charge and discharge at any given moment; 、 are the maximum values ​​of charging and discharging power of the hydrogen storage tank respectively.

6. The method for screening and dynamically optimizing the safety state variables of a hydrogen-electricity coupling system according to claim 1, characterized in that: Failure scenarios include: failure of key pipeline outlets at energy hubs, failure of key equipment at energy hubs, and imbalance between supply and demand of renewable energy.

7. The method for screening and dynamically optimizing the safety state variables of a hydrogen-electricity coupling system according to claim 6, characterized in that: Based on the hydrogen-electricity coupling system model, fault data under various fault scenarios are obtained to construct an accident set, including: Conduct probabilistic modeling of the output of new energy units, and construct a joint model of the output of new energy units together with the output constraint model of new energy units, thereby updating the electric energy subsystem model and obtaining an updated hydrogen-electricity coupling system model; Among them, the probability modeling of the output of new energy units includes: establishing a probability distribution model of wind turbine output based on a two-parameter Weibull random probability model, and establishing a probability distribution model of photovoltaic generator output based on a Beta probability distribution model; Based on the updated hydrogen-electric coupling system model, fault data under various fault scenarios are obtained to construct an accident set.

8. The method for screening and dynamically optimizing the safety state variables of a hydrogen-electricity coupling system according to claim 7, characterized in that: The probability distribution model of wind turbine output is established based on the two-parameter Weibull random probability model, which satisfies the following relationship: ， Where, is the probability density function of the actual wind speed, k w is the shape coefficient of the fan unit, c w is the scale factor of the fan unit; 、 V h 、 V out 、 V in They are rated wind speed, actual wind speed, cut-out wind speed and cut-in wind speed respectively; P wmax is the maximum output power of the wind turbine, The output of wind turbines based on probability distribution; The probability distribution model of photovoltaic generator output based on the Beta probability distribution model satisfies the following relationship: ， Where, is the probability density function of actual illumination, is the shape factor of the photovoltaic module, β p is the scale factor of the photovoltaic module; 、 They are rated light intensity and actual light intensity respectively; is the Gamma function; Indicates the maximum output power of the photovoltaic generator set. Photovoltaic generator output based on probability distribution.

9. The method for screening and dynamically optimizing safety state variables of a hydrogen-electricity coupling system according to claim 1, characterized in that: The multi-energy flow balance constraints include: 1) Power balance constraint, satisfying the following relationship: ， Where, 、 They are t Time Node i Active injection power and reactive injection power; N is the number of system nodes; 、 They are the node admittance matrices i OK j the real and imaginary parts of the columns; for t Time Node i The voltage amplitude; for t Time Node j The voltage amplitude; for t Time branch ij The phase angle difference; 、 Node i The lower and upper limits of the voltage amplitude; 2) Hydrogen energy power balance constraint, satisfying the following relationship: ， Where, for t The hydrogen production power of the electrolyzer at any moment; 、 They are t The charging and discharging power of hydrogen storage tank at all times; for t Hydrogen load at the time; for t The input power of the fuel cell at any moment; 3) Power balance constraints of the electric-hydrogen coupling system, including electric-hydrogen coupling operation constraints and electric hydrogen production power constraints. The electric-hydrogen coupling operation constraints are as follows: ， Where, for t Conventional thermal power units at all times contribution; is the number of conventional thermal power units; for t New energy units are always outputting power; for t The output power of the fuel cell at any moment; for t Time Node The electrical load; is the hydrogen load of the system; for t Electrolyzer input power at all times; is the number of nodes; The power constraints for hydrogen production are as follows: ， Where, for Shike New Energy Station Output power; for Shike New Energy Station The load power provided; for Shike New Energy Station The abandoned power; for Shike New Energy Station Input power of the inner electrolyzer; 4) Thermal energy balance constraint, satisfying the following relationship: ， Where, C CHP is the thermoelectric ratio; P CHP,h is the thermal power; P CHP,e is the electrical power; 5) Flow constraints of hydraulic network: ， Where, B is the correlation matrix of the hydraulic grid; w is the branch flow vector; K is the pipe coefficient.

10. The method for screening and dynamically optimizing the safety state variables of a hydrogen-electricity coupling system according to claim 1, characterized in that: The feasible region constraints of the hydrogen-electric coupling system under the "N-1" condition satisfy the following relationship: ， Where, For the The state quantity of each pipeline segment, The sum of the state quantities of all pipeline segments is the largest; are the equality constraints for the key pipeline segments and key equipment in the feasible domain of the hydrogen-electric coupling system under the "N-1" condition. The key pipeline sections and key equipment constraints of the feasible domain of the hydrogen-electric coupling system under "N-1" conditions; 、 are the lower and upper bound vectors of the inequality constraints respectively; , M is the number of pipeline segments.

11. The method for screening and dynamically optimizing safety state variables of a hydrogen-electricity coupling system according to claim 10, characterized in that: The safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition satisfy the following relationship: ， Where, It is the security domain constraint; Equality constraints for key pipeline segments and key equipment in the safety domain of the hydrogen-electricity coupling system under "N-1" conditions; These are the inequality constraints for key pipeline segments and key equipment in the safety domain of the hydrogen-electric coupling system under the "N-1" condition.

12. The method for screening and dynamically optimizing the safety state variables of a hydrogen-electricity coupling system according to claim 11, characterized in that: According to the multi-energy flow balance constraints, the feasible domain constraints and the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition, the isolation forest algorithm is used to screen the state quantities, which not only removes the abnormal data in the state quantities but also extracts the state quantities to be adjusted.

13. The method for screening and dynamically optimizing safety state variables of a hydrogen-electricity coupling system according to claim 1, characterized in that: The ratio of the number of coupled devices in the total number of devices in the hydrogen-electricity coupling system is used as the coupling degree coefficient. When the ratio of the number of coupled devices in the total number of devices in the hydrogen-electricity coupling system is less than or equal to 20%, , when the number of coupled devices in the hydrogen-electricity coupling system accounts for more than 20% and less than or equal to 50% of the total number of devices .

14. The method for screening and dynamically optimizing safety state variables of a hydrogen-electricity coupling system according to claim 1, wherein: The optimization target model and the safety domain constraints of the hydrogen-electricity coupling system under the "N-1" condition are used to construct the optimization control model of the hydrogen-electricity coupling system, which satisfies the following relationship: ， Where, The optimization objective model that represents the optimal efficiency function and safety function of the feasible domain is represents the safety domain constraint of the hydrogen-electric coupling system under the "N-1" condition, where O is the safety domain constraint of the hydrogen-electric coupling system under the "N-1" condition, L is the state quantity set of each pipeline segment.

15. The method for screening and dynamically optimizing the safety state variables of a hydrogen-electricity coupling system according to claim 14, characterized in that: Based on the optimization control model, the state variables to be adjusted are optimized, including: Based on the three-dimensional image method, the three-dimensional safety domain corresponding to the safety domain constraints of the hydrogen-electric coupling system under the "N-1" condition is drawn; The Manhattan distance method is used to calculate the safety distance between the state quantity to be adjusted and the boundary of the three-dimensional safety zone as the adjustment distance of the state quantity to be adjusted.

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