Method for interaction regulation and optimization of electric-hydrogen system supporting new energy consumption and power grid

Abstract

The application discloses a support new energy consumption electric hydrogen system and power grid interaction regulation optimization method, belongs to the technical field of energy dispatching;Including the following steps: step one: the parameter acquisition is carried out to electric hydrogen system and power grid equipment, and the real-time parameter of relevant equipment is obtained;Step two: the obtained real-time parameter is classified, and the input parameter is confirmed;Step three: the hierarchical control architecture is established, including daily plan layer and rolling optimization layer, the input parameter is handled by adopting hierarchical control architecture, and the daily start plan of electric hydrogen system is obtained;Step four: according to the plan of step three as implementation target, combining real-time parameter change, electric hydrogen and power grid interaction regulation optimization is carried out.The application adopts the above method, and provides a new scheme for the interaction and regulation between electric hydrogen system and power grid, and can solve the problem of electric hydrogen and power grid interaction.

Description

Technical Field

[0001] This invention relates to the technical field of energy dispatch, and in particular to a method for optimizing the interaction between an electric hydrogen system and the power grid to support the consumption of new energy sources. Background Technology

[0002] With the continuous increase in renewable energy penetration, the core value of the electro-hydrogen coupling system, as a new type of energy infrastructure, lies in achieving the dual goals of grid flexibility and efficient hydrogen production. This system converts excess electricity into green hydrogen through electrolysis equipment, solving the problem of poor electricity storage. Simultaneously, it utilizes the cross-seasonal regulation capabilities of hydrogen storage to effectively address the intermittency of renewable energy. However, in the existing interaction between electro-hydrogen and the grid, the electro-hydrogen system operates at a fixed electrolysis power level, unable to respond and regulate based on grid power shortages and surpluses, leading to power curtailment issues. Summary of the Invention

[0003] The purpose of this invention is to provide an optimization method for the interaction and control of the electric hydrogen system and the power grid to support the consumption of new energy sources. It provides a solution for the interaction between the electric hydrogen system and the power grid, which adjusts the working time and duration of the electric hydrogen according to the daily plan and the 15-minute plan, so that the electric hydrogen can better match the operation of the power grid and avoid energy waste or energy shortage.

[0004] To achieve the above objectives, this invention provides a method for optimizing the interaction between an electric hydrogen system and the power grid to support the consumption of new energy sources, comprising the following steps: Step 1: Obtain parameters from the hydrogen-electric system and power grid equipment to obtain real-time parameters of the relevant equipment; Step 2: Categorize the acquired real-time parameters and confirm the input parameters; Step 3: Establish a hierarchical control architecture, including a daily planning layer and a rolling optimization layer. The input parameters are processed using the hierarchical control architecture to obtain the daily startup plan of the hydrogen-electric system. Step 4: Based on the plan in Step 3 as the implementation goal, and combined with real-time parameter changes, optimize the interaction between hydrogen and the power grid.

[0005] Preferably, in step one, the specific process is as follows: An industrial parameter acquisition architecture is deployed, collecting all data every 60 seconds and uploading it every 15 minutes. For grid equipment parameters, parameters at the grid connection points are collected, including RMS voltage, RMS current, and power factor. The active power of the grid is calculated by multiplying the RMS voltage, RMS current, and power factor, using the following formula: In the above formula, Active power This is the effective value of the voltage. This is the effective value of the current. Power factor; The parameters for an electro-hydrogen system include DC side current, electrolyte temperature, minimum start-up power, ramp rate, start-up and shutdown costs, and electrolysis efficiency. For hydrogen storage parameters, the pressure of each hydrogen storage tank is collected to obtain the corresponding amount of hydrogen stored.

[0006] Preferably, in step two, the process of classifying parameters is as follows: The collected data is preprocessed, and the power grid parameters are preprocessed by using a sliding window mean filter with a window time of 5 minutes to eliminate instantaneous fluctuations. The harmonic components are analyzed using FFT to extract the fundamental power. For the preprocessing of parameters of electro-hydrogen: temperature data is filtered by Kalman filtering to eliminate noise, and efficiency data is dynamically corrected by temperature-current relationship model; For hydrogen storage parameters: the internal pressure of the hydrogen storage tank is collected by a pressure sensor, and the hydrogen storage capacity is calculated by the equation of state. Preprocessing method: exponential smoothing is used for the pressure data.

[0007] Preferably, in step three, the daily start-up plan of the electro-hydrogen system is obtained: The forecast points are set with a one-day time interval to obtain a monthly power grid forecast curve. The power grid curve is solved using the MILP model to predict power grid changes. When the power grid does not meet the usage requirements, the electrolysis equipment is stopped; when the power grid meets the usage requirements, the electrolysis equipment is started, thus obtaining the electrolysis equipment operation plan. The specific calculation process is as follows: Data is collected, including electricity prices and demand over different time periods. The collected data is preprocessed, including normalization and time alignment. A MILP model is constructed for calculation, and an objective function is set. ; In the above formula, This represents the electricity cost for electrolysis during time period t; This represents the electrolysis power during time period t; This represents the startup cost coefficient for electrolysis equipment; The objective function represents the operating status of the electrolysis equipment during time period t; the objective function is constrained by the parameter range and calculated using a solver.

[0008] Preferably, the constraints of the electro-hydrogen system are as follows: Electrolysis power constraints: ; In the above formula, This represents the active power of the power grid at time t. This represents the total load power of the system at time t; This represents the electrolysis power during time period t; Electrolysis equipment start-up constraints: ; ; In the above formula, This is the minimum starting power threshold for electrolysis equipment. The ramp rate of the electrolysis equipment is indicated. This indicates the minimum starting power of the electrolysis equipment; The dynamic constraints for hydrogen storage are as follows: ; In the above formula, Indicates the maximum hydrogen storage capacity. express Hydrogen storage capacity at any given time This represents the amount of hydrogen stored at time t. Indicates hydrogen production efficiency. This indicates the power output of hydrogen energy converted into electricity. This indicates the efficiency of hydrogen energy conversion into electricity. Under the above constraints, a solver is used for calculation.

[0009] Preferably, the calculation process using a solver is as follows: Gurobi was used for calculations, employing relaxed integer constraints to find initial solutions, fixed continuous variables for solution, and fine-tuning of integer solutions to obtain a one-month plan for the electrolysis equipment, as detailed below: Employing the existing mixed-integer linear programming framework, a three-layer optimization strategy is implemented using the Gurobi solver. First, an initial solution is obtained by relaxing integer constraints, converting equipment start-up and shutdown variables into continuous variables to quickly obtain an initial feasible solution for power scheduling. Second, a fixed continuous variable solution strategy is adopted, fixing the power allocation variables based on the initial feasible solution and specifically optimizing the start-up and shutdown states to reduce computational complexity. Finally, integer solution refinement optimization is performed, simultaneously optimizing all variables to obtain the global optimum. The solution strategy employs a progressively refined three-stage method: the first stage quickly obtains the initial basic solution through LP relaxation, with a time resolution that can be set to the hour level; the second stage uses a heuristic fixing strategy, fixing the continuous power variables based on the initial solution and solving for the optimal combination of start-up and shutdown variables; the third stage activates the Gurobi MIP solver to perform global integer optimization to obtain the exact solution.

[0010] Preferably, the rolling optimization layer in step three specifically uses a fixed 15-minute data upload to regulate the hydrogen production of the electro-hydrogen system. The regulation process includes adjusting the storage of the hydrogen storage tank and adjusting the temperature of the electrolysis equipment.

[0011] Preferably, the hydrogen storage tank adjustment method is as follows: ; In the above formula, This represents the conversion factor for hydrogen production via electrolysis. The conversion factor representing the conversion of hydrogen to electricity. Indicates in At what time, the amount of hydrogen stored, Indicates the adjustment amount of electrolysis power. This indicates the power of hydrogen gas to generate electricity. This indicates the power adjustment amount for hydrogen-to-electricity conversion. Conversion efficiency of hydrogen production by electrolysis.

[0012] Preferably, the temperature adjustment method for the electrolysis equipment is as follows: A piecewise function was used to establish the relationship between the temperature of the electrolysis equipment and the hydrogen production efficiency. Based on the temperature of the electrolysis equipment, low-temperature zone, constant-temperature zone, and high-temperature zone were set. In the low-temperature zone, the electrolysis power was adjusted using a linear proportional relationship, while in the high-temperature zone, the electrolysis power was adjusted using a linear inverse proportional relationship. The specific process is as follows: ; In the above formula, Rated power of the electrolysis system and For calculating parameters, This is the lowest value within the low-temperature range. This indicates the lowest value within the high-temperature zone.

[0013] Preferably, in step four, the existing conventional scheme is adopted based on the processing procedure of step three, and the particle swarm optimization algorithm is used for optimization, as follows: Particle swarm initialization: Construct a swarm containing multiple particles, each representing a possible electrolysis power scheduling scheme. The particle position vector contains the hourly electrolysis power value, hydrogen storage charging and discharging power value, and grid interaction power value within a month. The particle velocity vector represents the rate of change of each decision variable. Fitness function design: The objective function is to minimize the monthly operating cost. The fitness value is the cost value with a negative fitness value. The cost includes the cost of purchasing electricity from the grid, the revenue from selling electricity to the grid, the cost of equipment start-up and shutdown penalties, and the cost of power fluctuation penalties. It is calculated by weighted summation. Constraint handling mechanism: The penalty function method is used to handle the dynamic constraints of hydrogen storage, electrolysis power constraints and temperature zoning constraints, and the solutions that violate the constraints are mapped to the vicinity of the feasible region to ensure the feasibility of the search process; Convergence judgment and termination conditions: Set the maximum number of iterations and the fitness value convergence threshold as termination conditions. Optimization stops when the fitness value changes less than the threshold or the maximum number of iterations is reached for several consecutive generations. Real-time parameter fusion: Using the daily plan obtained in step three as the initial solution, and combining it with real-time collected grid parameters, renewable energy forecasts and load demand, the search direction and range of the particle swarm are dynamically adjusted. Multi-objective balancing: In the optimization process, economic objectives and system stability constraints are balanced, and the relative importance of different objectives is dynamically optimized through an adaptive weight adjustment mechanism.

[0014] Therefore, the above-mentioned method for optimizing the interaction between the electric hydrogen system and the power grid to support the consumption of new energy sources has the following advantages: In this invention, a preliminary 30-day plan is first formulated through a preliminary forecast combined with a daily planning layer. Then, through a rolling optimization layer, combined with data updated every 15 minutes, the daily plan is continuously updated and optimized, resulting in better plan implementation. The working status of the electrolysis equipment is measured by using electrolysis efficiency and temperature, and the power of the grid at the connection point is used to determine whether to supplement electricity or produce hydrogen.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] Figure 1 The flowchart below shows the method for optimizing the interaction between the electric hydrogen system and the power grid to support the consumption of new energy sources, as described in this invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Specific model specifications need to be selected and determined according to the actual specifications of the device, etc. The specific selection calculation method adopts existing technology in the art, and therefore will not be described in detail.

[0018] Example like Figure 1 As shown, this invention provides a method for optimizing the interaction between an electric hydrogen system and the power grid to support the consumption of new energy sources, comprising the following steps: Step 1: Acquire parameters for the hydrogen-electric system and grid equipment to obtain real-time parameters of the relevant equipment. The specific process is as follows: An industrial parameter acquisition architecture is deployed, collecting all data every 60 seconds and uploading it every 15 minutes. For grid equipment parameters, parameters at the grid connection points are collected, including RMS voltage, RMS current, and power factor. The active power of the grid is calculated by multiplying the RMS voltage, RMS current, and power factor, using the following formula: In the above formula, Active power This is the effective value of the voltage. This is the effective value of the current. Power factor; For parameters of hydrogen production in electrolytic hydrogen production: collect DC side current, electrolyte temperature, minimum start-up power, ramp rate, start-up and shutdown cost, and electrolysis efficiency; For hydrogen storage parameters, the pressure of each hydrogen storage tank is collected to obtain the corresponding amount of hydrogen stored. Step 2: Categorize the acquired real-time parameters and confirm the input parameters; the parameter categorization process is as follows: The collected data is preprocessed. The preprocessing method for relevant parameters of the power grid is as follows: a sliding window mean filter is used with a window time of 5 minutes to eliminate instantaneous fluctuations, and FFT is used to analyze harmonic components and extract fundamental power. For the preprocessing of parameters of electro-hydrogen: temperature data is filtered by Kalman filtering to eliminate noise; efficiency data is dynamically corrected using the existing temperature-current relationship model. For hydrogen storage parameters: the internal pressure of the hydrogen storage tank is collected by a pressure sensor, the hydrogen storage capacity is calculated by the equation of state, and the preprocessing method is: the pressure data is smoothed by exponential smoothing.

[0019] Step 3: Establish a hierarchical control architecture, including a daily planning layer and a rolling optimization layer. The input parameters are processed using the hierarchical control architecture to obtain the start-up plan of the hydrogen-electric system. The specific execution process of the daily planning layer is as follows: The prediction points are set with a one-day time interval to obtain a monthly power grid prediction curve (here, 30 days is used). The power grid curve is solved using the MILP model to predict power grid changes. When the power grid does not meet the usage requirements, the electrolysis equipment is stopped; when the power grid meets the usage requirements, the electrolysis equipment is started, thus obtaining the electrolysis equipment operation plan. The specific calculation process is as follows: Data is collected, including electricity prices and demand over different time periods. The collected data is preprocessed, including normalization and time alignment. A MILP model is constructed for calculation, and an objective function is set. ; In the above formula, This represents the electricity cost for electrolysis during time period t; This represents the electrolysis power during time period t; This represents the startup cost coefficient for electrolysis equipment; The objective function represents the operating status of the electrolysis equipment during time period t; the objective function is constrained by the parameter range and calculated using a solver.

[0020] The specific constraints of the hydrogen electrolysis system are as follows: Electrolysis power constraints: In the above formula, This represents the active power of the power grid at time t. This represents the total load power of the system at time t; This represents the electrolysis power during time period t; Electrolysis equipment start-up constraints: In the above formula, This is the minimum starting power threshold for electrolysis equipment. The ramp rate of the electrolysis equipment is indicated. This indicates the minimum starting power of the electrolysis equipment; The dynamic constraints for hydrogen storage are as follows: In the above formula, Indicates the maximum hydrogen storage capacity. express Hydrogen storage capacity at any given time This represents the amount of hydrogen stored at time t. Indicates hydrogen production efficiency. This indicates the power output of hydrogen energy converted into electricity. This indicates the efficiency of hydrogen energy conversion into electricity.

[0021] Under the above constraints, a solver is used for calculation. The calculation process using the solver is as follows: Gurobi was used for calculations, employing relaxed integer constraints to find initial solutions, fixed continuous variables for solution, and fine-tuning of integer solutions to obtain a one-month plan for the electrolysis equipment, as detailed below: Employing the existing mixed-integer linear programming framework, a three-layer optimization strategy is implemented using the Gurobi solver. First, an initial solution is obtained by relaxing integer constraints, converting equipment start-up and shutdown variables into continuous variables (0-1 intervals) to quickly obtain an initial feasible solution for power scheduling. Second, a fixed continuous variable solution strategy is adopted, fixing the power allocation variables based on the initial feasible solution and specifically optimizing the start-up and shutdown states to reduce computational complexity. Finally, a fine-grained integer solution optimization is performed, simultaneously optimizing all variables to obtain the global optimum. The solution strategy employs a progressively refined three-stage method: the first stage quickly obtains the initial basic solution through LP relaxation, with a time resolution that can be set to the hour level; the second stage uses a heuristic fixed strategy, fixing the continuous power variables based on the initial solution and solving for the optimal combination of start-up and shutdown variables; the third stage activates the Gurobi MIP solver to perform global integer optimization to obtain the exact solution.

[0022] The rolling optimization layer in step three uses data sent up every 15 minutes to regulate the hydrogen production of the electro-hydrogen system. The regulation process includes adjusting the storage of hydrogen in the storage tank and adjusting the temperature of the electrolysis equipment.

[0023] The hydrogen storage tank adjustment method is as follows: ; In the above formula, This represents the conversion factor for hydrogen production via electrolysis. The conversion factor representing the conversion of hydrogen to electricity. Indicates in At what time, the amount of hydrogen stored, Indicates the adjustment amount of electrolysis power. This indicates the power of hydrogen gas to generate electricity. This indicates the power adjustment amount for hydrogen-to-electricity conversion. Conversion efficiency of hydrogen production by electrolysis.

[0024] The methods for adjusting the temperature of electrolysis equipment are as follows: A piecewise function was used to establish the relationship between the temperature of the electrolysis equipment and the hydrogen production efficiency. Based on the temperature of the electrolysis equipment, low-temperature, constant-temperature, and high-temperature zones were set. In the low-temperature zone, a linear proportional relationship was used to adjust the electrolysis power, while in the high-temperature zone, a linear inverse proportional relationship was used to adjust the electrolysis power. The specific process is as follows: ; In the above formula, The system's rated power, and For calculating parameters, This is the lowest value within the low-temperature range. This indicates the lowest value within the high-temperature zone.

[0025] Step Four: Based on the plan in Step Three as the implementation objective, and considering real-time parameter changes, implement the existing conventional scheme according to the processing procedure in Step Three. Specifically, particle swarm optimization can be used for optimization. The optimization of the interaction and control between hydrogen production and the power grid is as follows: Particle swarm initialization: Construct a swarm containing multiple particles, each representing a possible electrolysis power scheduling scheme. The particle position vector contains the hourly electrolysis power value, hydrogen storage charging and discharging power value, and grid interaction power value over 30 days. The particle velocity vector represents the rate of change of each decision variable. Fitness function design: The objective function is to minimize the 30-day operating cost. The fitness value is the cost value with a negative value. The cost includes the cost of purchasing electricity from the grid, the revenue from selling electricity to the grid, the cost of equipment start-up and shutdown penalties, and the cost of power fluctuation penalties. It is calculated by weighted summation. Constraint handling mechanism: The penalty function method is used to handle the dynamic constraints of hydrogen storage, electrolysis power constraints and temperature zoning constraints, and the solutions that violate the constraints are mapped to the vicinity of the feasible region to ensure the feasibility of the search process; Convergence judgment and termination conditions: Set the maximum number of iterations and the fitness value convergence threshold as termination conditions. Optimization stops when the fitness value changes less than the threshold or the maximum number of iterations is reached for several consecutive generations. Real-time parameter fusion: Using the daily plan obtained in step three as the initial solution, and combining it with real-time collected grid parameters, renewable energy forecasts and load demand, the search direction and range of the particle swarm are dynamically adjusted. Multi-objective balancing: In the optimization process, economic objectives and system stability constraints are balanced, and the relative importance of different objectives is dynamically optimized through an adaptive weight adjustment mechanism.

[0026] Therefore, this invention adopts an interactive regulation and optimization method between the electric hydrogen system supporting the consumption of new energy and the power grid. First, a preliminary 30-day plan is formulated through preliminary forecasting combined with a daily planning layer. Through a rolling optimization layer, combined with data updated every 15 minutes, the daily plan is continuously updated and optimized, achieving better plan implementation. The working status of the electrolysis equipment is measured by using electrolysis efficiency and temperature, and the power of the grid at the connection point is used to determine whether to supplement electricity or produce electric hydrogen.

[0027] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing the interaction and control of an electric-hydrogen system with the power grid to support the consumption of new energy sources, characterized by: Includes the following steps: Step 1: Obtain parameters from the hydrogen-electric system and power grid equipment to obtain real-time parameters of the relevant equipment; Step 2: Categorize the acquired real-time parameters and confirm the input parameters; Step 3: Establish a hierarchical control architecture, including a daily planning layer and a rolling optimization layer. The input parameters are processed using the hierarchical control architecture to obtain the daily startup plan of the hydrogen-electric system. Step 4: Based on the plan in Step 3 as the implementation goal, and combined with real-time parameter changes, optimize the interaction between hydrogen and the power grid.

2. The method for optimizing the interaction between the electric hydrogen system and the power grid to support the consumption of new energy sources, as described in claim 1, is characterized in that: In step one, the specific process is as follows: An industrial parameter acquisition architecture is deployed, collecting all data every 60 seconds and uploading it every 15 minutes. For grid equipment parameters, parameters at the grid connection points are collected, including RMS voltage, RMS current, and power factor. The active power of the grid is calculated by multiplying the RMS voltage, RMS current, and power factor, using the following formula: In the above formula, Active power This is the effective value of the voltage. This is the effective value of the current. Power factor; The parameters for an electro-hydrogen system include DC side current, electrolyte temperature, minimum start-up power, ramp rate, start-up and shutdown costs, and electrolysis efficiency. For hydrogen storage parameters, the pressure of each hydrogen storage tank is collected to obtain the corresponding amount of hydrogen stored.

3. The method for optimizing the interaction between the electric hydrogen system and the power grid to support the consumption of new energy sources, as described in claim 2, is characterized in that: In step two, the process of classifying parameters is as follows: The collected data is preprocessed, and the power grid parameters are preprocessed by using a sliding window mean filter with a window time of 5 minutes to eliminate instantaneous fluctuations. The harmonic components are analyzed using FFT to extract the fundamental power. For the preprocessing of parameters of electro-hydrogen: temperature data is filtered by Kalman filtering to eliminate noise, and efficiency data is dynamically corrected by temperature-current relationship model; For hydrogen storage parameters: the internal pressure of the hydrogen storage tank is collected by a pressure sensor, and the hydrogen storage capacity is calculated by the equation of state. Preprocessing method: exponential smoothing is used for the pressure data.

4. The method for optimizing the interaction between the electric hydrogen system and the power grid to support the consumption of new energy sources, as described in claim 3, is characterized in that: In step three, the daily startup plan of the electro-hydrogen system is obtained: The forecast points are set with a one-day time interval to obtain a monthly power grid forecast curve. The power grid curve is solved using the MILP model to predict power grid changes. When the power grid does not meet the usage requirements, the electrolysis equipment is stopped; when the power grid meets the usage requirements, the electrolysis equipment is started, thus obtaining the electrolysis equipment operation plan. The specific calculation process is as follows: Data is collected, including electricity prices and demand over different time periods. The collected data is preprocessed, including normalization and time alignment. A MILP model is constructed for calculation, and an objective function is set. ； In the above formula, This represents the electricity cost for electrolysis during time period t; This represents the electrolysis power during time period t; This represents the start-up cost coefficient for electrolysis equipment; The objective function represents the operating status of the electrolysis equipment during time period t; the objective function is constrained by the parameter range and calculated using a solver.

5. The method for optimizing the interaction between the electric hydrogen system and the power grid to support the consumption of new energy sources, as described in claim 4, is characterized in that: The specific constraints of the electro-hydrogen system are as follows: Electrolysis power constraints: ； In the above formula, This represents the active power of the power grid at time t. This represents the total load power of the system at time t; This represents the electrolysis power during time period t; Electrolysis equipment start-up constraints: ； ； In the above formula, This is the minimum starting power threshold for electrolysis equipment. This indicates the ramp rate of the electrolysis equipment. This indicates the minimum starting power of the electrolysis equipment; The dynamic constraints for hydrogen storage are as follows: ； In the above formula, Indicates the maximum hydrogen storage capacity. express Hydrogen storage capacity at any given time This represents the amount of hydrogen stored at time t. Indicates hydrogen production efficiency. This indicates the power output of hydrogen energy converted into electricity. This indicates the efficiency of hydrogen energy conversion into electricity. Under the above constraints, a solver is used for calculation.

6. The method for optimizing the interaction between the electric hydrogen system and the power grid to support the consumption of new energy sources, as described in claim 5, is characterized in that: The process of using a solver to perform calculations is as follows: Gurobi was used for calculations, employing relaxed integer constraints to find initial solutions, fixed continuous variables for solution, and fine-tuning of integer solutions to obtain a one-month plan for the electrolysis equipment, as detailed below: Employing the existing mixed-integer linear programming framework, a three-layer optimization strategy is implemented using the Gurobi solver. First, an initial solution is obtained by relaxing integer constraints, converting equipment start-up and shutdown variables into continuous variables to quickly obtain an initial feasible solution for power scheduling. Second, a fixed continuous variable solution strategy is adopted, fixing the power allocation variables based on the initial feasible solution and specifically optimizing the start-up and shutdown states to reduce computational complexity. Finally, integer solution refinement optimization is performed, simultaneously optimizing all variables to obtain the global optimum. The solution strategy employs a progressively refined three-stage method: the first stage quickly obtains the initial basic solution through LP relaxation, with a time resolution that can be set to the hour level; the second stage uses a heuristic fixing strategy, fixing the continuous power variables based on the initial solution and solving for the optimal combination of start-up and shutdown variables; the third stage activates the Gurobi MIP solver to perform global integer optimization to obtain the exact solution.

7. The method for optimizing the interaction between the electric hydrogen system and the power grid to support the consumption of new energy sources, as described in claim 6, is characterized in that: The rolling optimization layer in step three specifically uses data sent up at fixed intervals of 15 minutes to regulate hydrogen production in the electro-hydrogen system. The regulation process includes adjusting the storage of hydrogen in the storage tank and adjusting the temperature of the electrolysis equipment.

8. The method for optimizing the interaction between the electric hydrogen system and the power grid to support the consumption of new energy sources, as described in claim 7, is characterized in that: The hydrogen storage tank adjustment method is as follows: ； In the above formula, This represents the conversion factor for hydrogen production via electrolysis. The conversion factor representing the conversion of hydrogen to electricity. Indicates in At what time, the amount of hydrogen stored, Indicates the adjustment amount of electrolysis power. This indicates the power of hydrogen gas to generate electricity. This indicates the power adjustment amount for hydrogen-to-electricity conversion. Conversion efficiency of hydrogen production by electrolysis.

9. The method for optimizing the interaction between the electric hydrogen system and the power grid to support the consumption of new energy sources, as described in claim 8, is characterized in that: The methods for adjusting the temperature of electrolysis equipment are as follows: A piecewise function was used to establish the relationship between the temperature of the electrolysis equipment and the hydrogen production efficiency. Based on the temperature of the electrolysis equipment, low-temperature, isothermal, and high-temperature zones were set. In the low-temperature zone, a linear proportional relationship was used to adjust the electrolysis power, while in the high-temperature zone, a linear inverse proportional relationship was used to adjust the electrolysis power. The specific process is as follows: ； In the above formula, Rated power of the electrolysis system and For calculating parameters, This is the lowest value within the low-temperature range. This indicates the lowest value within the high-temperature zone.

10. The method for optimizing the interaction between the electric hydrogen system and the power grid to support the consumption of new energy sources, as described in claim 9, is characterized in that: In step four, based on the processing procedure in step three, existing conventional solutions are adopted for implementation, specifically using the particle swarm optimization algorithm for optimization. The process is as follows: Particle swarm initialization: Construct a swarm containing multiple particles, each representing a possible electrolysis power scheduling scheme. The particle position vector contains the hourly electrolysis power value, hydrogen storage charging and discharging power value, and grid interaction power value within a month. The particle velocity vector represents the rate of change of each decision variable. Fitness function design: The objective function is to minimize the monthly operating cost. The fitness value is the cost value with a negative fitness value. The cost includes the cost of purchasing electricity from the grid, the revenue from selling electricity to the grid, the cost of equipment start-up and shutdown penalties, and the cost of power fluctuation penalties. It is calculated by weighted summation. Constraint handling mechanism: The penalty function method is used to handle the dynamic constraints of hydrogen storage, electrolysis power constraints and temperature zoning constraints, and the solutions that violate the constraints are mapped to the vicinity of the feasible region to ensure the feasibility of the search process; Convergence judgment and termination conditions: Set the maximum number of iterations and the fitness value convergence threshold as termination conditions. Optimization stops when the fitness value changes less than the threshold or the maximum number of iterations is reached for several consecutive generations. Real-time parameter fusion: Using the daily plan obtained in step three as the initial solution, and combining it with real-time collected grid parameters, renewable energy forecasts and load demand, the search direction and range of the particle swarm are dynamically adjusted; Multi-objective balancing: In the optimization process, economic objectives and system stability constraints are balanced, and the relative importance of different objectives is dynamically optimized through an adaptive weight adjustment mechanism.

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