Multi-target operation optimization method and system of green electricity hydrogen production grid-connected system considering multi-scale flexibility of power grid

Abstract

The invention relates to a multi-target operation optimization method and system for a green electricity hydrogen production grid-connected system considering multi-scale flexibility of a power grid, and the method comprises the steps: obtaining the power output of new energy at a grid side and the historical data of user load power, and obtaining typical operation days at the grid side and a load side through a clustering algorithm; establishing physical models of an electrolytic cell, a hydrogen storage tank, a fuel cell and the like in the system; considering the transferable characteristic of an electric load and the requirements of system peak regulation and new energy consumption, and formulating a day-ahead peak regulation optimal scheduling strategy according to typical operation days of a source side and a load side; an electricity-hydrogen operation optimization strategy with a primary frequency modulation capability is established based on self-adaptive droop control by considering factors such as an electrolytic bath rotation strategy, a wide-power operation characteristic and a transferable characteristic of a hydrogen load; power grid frequency deviation and electrolytic cell operation power are used as decision variables, multi-target operation optimization with the power grid frequency deviation rate and the new energy consumption rate as target functions is established, a non-dominated sorting genetic algorithm is adopted for solving, and an optimal operation scheme is obtained.

Description

Technical Field

[0001] The present invention relates to the field of new energy control technology, and in particular to a multi-objective operation optimization method and system for a green electricity hydrogen production grid-connected system taking into account the multi-scale flexibility of the power grid. Background Art

[0002] With the large-scale grid connection of new energy and the increase in the proportion of power electronic converters in new power systems, the grid's rotational inertia has decreased and its stability has deteriorated. The output of wind and photovoltaic power has typical volatility and intermittency, which has led to increasingly prominent problems in grid frequency stability, resulting in a large amount of wind and solar power abandonment, which reduces the economy of the system and also brings new difficulties to the peak load regulation of the grid. While the electricity load is supplied by wind and solar power generation systems, the use of abandoned wind and photovoltaic power generation to produce hydrogen by hydrolysis can effectively reduce the abandonment rate of electricity, promote the system's new energy consumption, and improve the system's resource utilization.

[0003] In the green electricity hydrogen production grid-connected system, the grid frequency deviation caused by the mismatch between source and load will reduce the safety and stability of the system, and ultimately cause economic losses. Existing new energy grid-connected systems mostly use load shedding and standby, and are equipped with energy storage batteries for primary frequency regulation, but load shedding and standby will cause a large amount of wind and solar abandonment, and the high price of energy storage batteries will reduce the economy of the system. The electrolyzer has the characteristics of a wide power operating range and fast dynamic response speed, and can participate in the primary frequency regulation response of the power grid; hydrogen energy storage has high energy density, can be stored and transported on a large scale, and is safe, clean and pollution-free. The development of water electrolysis to produce hydrogen is one of the important ways to store and utilize new energy. Therefore, it is necessary to carry out multi-objective operation optimization of the green electricity hydrogen production grid-connected system considering new energy consumption and grid flexibility, so as to improve the new energy consumption and grid supply guarantee capabilities of the green electricity hydrogen production grid-connected system. Summary of the invention

[0004] In order to solve the above technical problems and expand the application of hydrogen production systems in power grid flexibility, the present invention provides a multi-objective operation optimization method and system for a green electricity hydrogen production grid-connected system that considers the multi-scale flexibility of the power grid. The power of the electrolyzer has the advantages of fast, flexible and controllable operation and the characteristics of load transferability. First, the day-ahead peak-shaving scheduling strategy is formulated by considering the transferability of electricity / hydrogen load, the peak-shaving demand of the power grid and the new energy consumption demand of the system. Then, the advantages of the fast and flexible adjustment of the electrolyzer power are incorporated into the primary frequency regulation power response of the power grid, the system frequency deviation is partitioned and different response strategies are established to achieve power grid supply guarantee. Combined with the characteristics of transferable hydrogen load, the new energy consumption rate and the power grid frequency deviation rate are used as the objective function to establish a multi-objective optimization operation method for the green electricity hydrogen production grid-connected system. After solving, a multi-objective operation optimization scheme that takes into account both new energy consumption and power grid supply guarantee is determined.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: A multi-objective operation optimization method for a green electricity hydrogen production grid-connected system considering the multi-scale flexibility of the power grid includes: Obtain historical data on grid-side renewable energy output and user load power, obtain typical operating days on the source and load sides through clustering algorithms, and establish physical models of electrolyzers, hydrogen storage tanks, and fuel cells in the system to construct constraints for operation optimization; Considering the load transferability, system peak load regulation and new energy consumption requirements, a day-ahead peak load regulation scheduling strategy is established based on the typical operation day obtained by the clustering algorithm, and an objective function for the new energy consumption rate is constructed; The flexible and adjustable power characteristics of the electrolyzer are incorporated into the primary frequency regulation response of the system, and an electric-hydrogen operation optimization strategy with primary frequency regulation capability is established based on adaptive droop control. The grid frequency deviation and electrolyzer operation status are used as decision variables, and a multi-objective operation optimization strategy with new energy consumption rate and grid frequency deviation rate as the targets is established. Based on the non-dominated sorting genetic algorithm, the multi-objective operation optimization model is solved to obtain the optimal operation plan.

[0006] Furthermore, the historical data of grid-side renewable energy output and user load power are obtained, and the typical operation days of the source side and the load side are obtained through clustering algorithms. The physical models of the electrolyzer, hydrogen storage tank, and fuel cell in the system are established. The constraints used to construct the operation optimization include: Obtain historical data on grid-side renewable energy output and user load power, and obtain typical operation days of the grid and load sides through clustering algorithms; For each data point in the data, the Euclidean distance is used to calculate the distance between the data and the centroid: ; in, is a data point; It is centroid; is the dimension of the data point; They are and In the The value of the dimension.

[0007] After the data is assigned to the cluster described by its nearest centroid, the centroid position formula for each cluster is updated: ; in, It is The set of all data points in a cluster; is the number of data points in the cluster; is the new centroid position of the cluster; Calculate the real-time equivalent state of charge SOH of the hydrogen storage tank: ; in, , They are Moment and -1 The equivalent state of charge of the hydrogen storage tank at time; is the configuration capacity of the hydrogen storage tank; , Hydrogen storage tank Intake rate and exhaust rate at each moment; , is a binary variable; is the unit time interval; The input and output model of the fuel cell is calculated by the following formula: ; in, is the output power of the fuel cell; The amount of hydrogen consumed by the fuel cell; for the efficiency of the fuel cell; The amount of hydrogen consumed by a fuel cell to produce a unit of electricity.

[0008] Furthermore, the load transferability, system peak load regulation and new energy consumption requirements are considered, a day-ahead peak load regulation scheduling strategy is established according to the typical operation day obtained by the clustering algorithm, and an objective function of the new energy consumption rate is constructed, including: Consider the transferable characteristics of electrical loads: Electric loads are divided into two categories: transferable electric loads and non-transferable electric loads. Transferable electric loads can be transferred to any time period within a day, which is reflected in: ; in, The total amount of electric load transferred in and out in one day; , are the total amount of electric load transferred out and in in one day respectively; Take 24 hours; is the unit time; Considering the characteristics of transferable electric load and system peak load demand, the optimal control strategy for peak load regulation is established according to the output of renewable energy and the typical operation day of user electric load. Construct the objective function of new energy consumption rate: ; in, , are the power of wind turbine and photovoltaic respectively; , They are The input power of the electrolyzer array and the output power of the fuel cell at each moment; For system load.

[0009] Furthermore, the flexible and adjustable characteristics of the electrolyzer power are incorporated into the primary frequency regulation response of the system, and an electric-hydrogen operation optimization strategy with primary frequency regulation capability is established based on adaptive droop control; the grid frequency deviation and the electrolyzer operation status are used as decision variables, and a multi-objective operation optimization with the new energy consumption rate and the grid frequency deviation rate as the targets is established, including: Considering the electrolytic cell rotation strategy and wide power operation characteristics, the electrolytic cells in the array are numbered according to the order in which the electrolytic cells are started. The electrolytic cells that are not in operation are not included in the numbering. Under certain operation time constraints, the principle of first start first shut down is adopted for the electrolytic cells. Considering the dynamic hydrogen production efficiency, the working states of the electrolyzer are divided into four types: 1) Shutdown status: hour, ; 2) Low power working state: ; ; ; ; 3) Stable working state: All electrolytic cells are Rated power operation ; 4) Overload working state: ; ; ; ; in, is the number of electrolytic cells, Indicates The power of each electrolyzer; is the total input power of the electrolyzer array; To round down; The power of the electrolyzer has the advantage of being fast, flexible and adjustable. When the frequency deviation occurs in the regional power grid, adaptive droop control is used to take the adjustable power of the electrolyzer and the power of the fuel cell as the active power response required for primary frequency regulation; ; in, The power required for primary frequency modulation; is the frequency deviation; , are grid frequency and rated frequency respectively; is the rated droop coefficient; is the adaptive droop coefficient; Based on the peak load regulation of the power grid, a frequency regulation control is carried out, and a frequency regulation dead zone of 0.033HZ is set. When the frequency fluctuation is less than 0.033HZ, no response is given. At the same time, the frequency fluctuation of the primary frequency regulation is divided into a normal response zone and an emergency response zone. Normal response area: ; ; in, It is the electrolytic cell power fluctuation proportional coefficient during normal frequency modulation operation; is the hot standby power of the electrolyzer; is the rated operating power of a single electrolyzer; is the input power of the electrolyzer; Emergency Response Areas: ; Considering the attenuation characteristics of the electrolyzer stack, a control strategy for the green electricity hydrogen production system to participate in the grid frequency regulation response is established: when the grid frequency deviation occurs, the action response of the electrolyzer and fuel cell is selected according to the frequency deviation: The hydrogen load is divided into transferable hydrogen load and non-transferable hydrogen load. The transferable hydrogen load during the peak hydrogen consumption period is moved to the hydrogen load during the low hydrogen consumption period, and the total hydrogen consumption remains unchanged. ; in, , They are the upper and lower limits of hydrogen storage in hydrogen storage tanks in response to frequency and peak regulation planning; ; in, , are the amount of hydrogen transferred in and out in one day, respectively; It is the total amount of hydrogen transferred in and out in one day.

[0010] Furthermore, the non-dominated sorting genetic algorithm is used to solve the multi-objective operation optimization model and obtain the optimal operation plan, including: Considering the consumption of new energy and the supply of power grid, a multi-objective optimization with the consumption rate of new energy and the frequency deviation rate of power grid as the objective function is established. When optimizing the solution, the reciprocal of the consumption rate of new energy of the objective function is taken into the comprehensive evaluation index: ; in, , They are the evaluation indicators of new energy consumption rate and grid frequency deviation rate respectively; , are the weighted values ​​of the two respectively; The evaluation indicators of new energy consumption rate and grid frequency deviation rate are respectively and express: ; ; in, , are the power of wind turbine and photovoltaic respectively; , They are The input power of the electrolyzer array and the output power of the fuel cell at each moment; is the system load, , The system standard frequency and The system sampling frequency at time, is the sampling times; Establish constraints for running the optimization model: Power balance constraints: ; in, , are the power of wind turbine and photovoltaic respectively; , are the power of the fuel cell and its output ratio respectively; inputting power to the electrolyzer array; is the system load; Frequency modulation power demand constraints: ; in, is the system frequency modulation power difference; The frequency modulation response power of the electrolyzer array; represents the fuel cell power; It is a binary variable, 1 indicates the fuel cell output, and 0 indicates that the fuel cell is not started; Grid frequency deviation constraints: ; in, is the system frequency deviation; It is the maximum frequency deviation limit of the system's primary frequency modulation; Electrolyzer power ramp constraints: ; in, , The electrolytic cell is Moment and Power change value at the moment; is the power change threshold per unit time of the electrolytic cell; Electrolyzer operating state constraints: Normal FM action: ; Emergency FM action: ; in, It is the power fluctuation ratio of the electrolytic cell during normal frequency modulation operation; is the hot standby power of the electrolyzer; is the rated operating power of a single electrolyzer; is the input power of the electrolyzer; is the hot standby power of the electrolyzer; The non-dominated sorting genetic algorithm is used to solve the optimization model and obtain the optimal solution.

[0011] The present invention also provides a multi-objective operation optimization system for a green electricity hydrogen production grid-connected system considering the multi-scale flexibility of the power grid, comprising the following modules: Model acquisition module, which obtains historical data on grid-side renewable energy output and user load power forecast, obtains typical operation days on the grid and load sides through clustering algorithms, and builds physical models of electrolyzers, hydrogen storage tanks, and fuel cells in the system to construct constraints for operation optimization; The optimization module considers the transferability of electric load, the system peak load regulation and the demand for new energy consumption, establishes the optimal dispatching strategy for peak load regulation according to the output of new energy and the typical operation day of user electric load, and constructs the objective function of new energy consumption rate; The control strategy module considers factors such as the electrolyzer rotation strategy and wide power operation characteristics, establishes an operation optimization strategy for the electric-hydrogen system with primary frequency regulation capability based on adaptive droop control, takes the grid frequency deviation and the electrolyzer operation status as decision variables, and establishes a multi-objective operation optimization with the grid frequency deviation rate and the new energy consumption rate as the objective function; The solution module solves the multi-objective operation optimization of the green electricity hydrogen production grid-connected system constructed with consideration of grid flexibility to obtain the optimal operation plan.

[0012] Furthermore, the model acquisition module implements: Obtain historical data on grid-side renewable energy output and user load power, and obtain typical operation days of the grid and load sides through clustering algorithms; For each data point in the data, the Euclidean distance is used to calculate the distance between the data and the centroid: ; in, is a data point; It is centroid; is the dimension of the data point; They are and In the The value of the dimension.

[0013] After the data is assigned to the cluster described by its nearest centroid, the centroid position formula for each cluster is updated: ; in, It is The set of all data points in a cluster; is the number of data points in the cluster; is the new centroid position of the cluster; Calculate the real-time equivalent state of charge SOH of the hydrogen storage tank: ; in, , They are Moment and -1 The equivalent state of charge of the hydrogen storage tank at time; is the configuration capacity of the hydrogen storage tank; , Hydrogen storage tank Intake rate and exhaust rate at each moment; , is a binary variable; is the unit time interval; The input and output model of the fuel cell is calculated by the following formula: ; in, is the output power of the fuel cell; The amount of hydrogen consumed by the fuel cell; for the efficiency of the fuel cell; The amount of hydrogen consumed by a fuel cell to produce a unit of electricity.

[0014] Furthermore, the optimization module implements: Consider the transferable characteristics of electrical loads: Electric loads are divided into two categories: transferable electric loads and non-transferable electric loads. Transferable electric loads can be transferred to any time period within a day, which is reflected in: ; in, The total amount of electric load transferred in and out in one day; , are the total amount of electric load transferred out and in in one day respectively; Take 24 hours; is the unit time; Considering the characteristics of transferable electric load and system peak load demand, the optimal control strategy for peak load regulation is established according to the output of renewable energy and the typical operation day of user electric load. Construct the objective function of new energy consumption rate: ; in, , are the power of wind turbine and photovoltaic respectively; , They are The input power of the electrolyzer array and the output power of the fuel cell at each moment; For system load.

[0015] Furthermore, the control strategy module implements: Considering the electrolytic cell rotation strategy and wide power operation characteristics, the electrolytic cells in the array are numbered according to the order in which the electrolytic cells are started. The electrolytic cells that are not in operation are not included in the numbering. Under certain operation time constraints, the principle of first start first shut down is adopted for the electrolytic cells. Considering the dynamic hydrogen production efficiency, the working states of the electrolyzer are divided into four types: 1) Shutdown status: hour, ; 2) Low power working state: ; ; ; ; 3) Stable working state: All electrolytic cells are Rated power operation ; 4) Overload working state: ; ; ; ; in, is the number of electrolytic cells, Indicates The power of each electrolyzer; is the total input power of the electrolyzer array; To round down; The power of the electrolyzer has the advantage of being fast, flexible and adjustable. When the frequency deviation occurs in the regional power grid, adaptive droop control is used to take the adjustable power of the electrolyzer and the power of the fuel cell as the active power response required for primary frequency regulation; ; in, The power required for primary frequency modulation; is the frequency deviation; , are grid frequency and rated frequency respectively; is the rated droop coefficient; is the adaptive droop coefficient; Based on the peak load regulation of the power grid, a frequency regulation control is carried out, and a frequency regulation dead zone of 0.033HZ is set. When the frequency fluctuation is less than 0.033HZ, no response is given. At the same time, the frequency fluctuation of the primary frequency regulation is divided into a normal response zone and an emergency response zone. Normal response area: ; ; in, It is the electrolytic cell power fluctuation proportional coefficient during normal frequency modulation operation; is the hot standby power of the electrolyzer; is the rated operating power of a single electrolyzer; is the input power of the electrolyzer; Emergency Response Areas: ; Considering the attenuation characteristics of the electrolyzer stack, a control strategy for the green electricity hydrogen production system to participate in the grid frequency regulation response is established: when the grid frequency deviation occurs, the action response of the electrolyzer and fuel cell is selected according to the frequency deviation: The hydrogen load is divided into transferable hydrogen load and non-transferable hydrogen load. The transferable hydrogen load during the peak hydrogen consumption period is moved to the hydrogen load during the low hydrogen consumption period, and the total hydrogen consumption remains unchanged. ; in, , They are the upper and lower limits of hydrogen storage in hydrogen storage tanks in response to frequency and peak regulation planning; ; in, , are the amount of hydrogen transferred in and out in one day, respectively; It is the total amount of hydrogen transferred in and out in one day.

[0016] Furthermore, the solution module realizes: Considering the consumption of new energy and the supply of power grid, a multi-objective optimization with the consumption rate of new energy and the frequency deviation rate of power grid as the objective function is established. When optimizing the solution, the reciprocal of the consumption rate of new energy of the objective function is taken into the comprehensive evaluation index: ; in, , They are the evaluation indicators of new energy consumption rate and grid frequency deviation rate respectively; , are the weighted values ​​of the two respectively; The evaluation indicators of new energy consumption rate and grid frequency deviation rate are respectively and express: ; ; in, , are the power of wind turbine and photovoltaic respectively; , They are The input power of the electrolyzer array and the output power of the fuel cell at each moment; is the system load, , The system standard frequency and The system sampling frequency at time, is the sampling times; Establish constraints for running the optimization model: Power balance constraints: ; in, , are the power of wind turbine and photovoltaic respectively; , are the power of the fuel cell and its output ratio respectively; inputting power to the electrolyzer array; is the system load; Frequency modulation power demand constraints: ; in, is the system frequency modulation power difference; The frequency modulation response power of the electrolyzer array; represents the fuel cell power; It is a binary variable, 1 indicates the fuel cell output, and 0 indicates that the fuel cell is not started; Grid frequency deviation constraints: ; in, is the system frequency deviation; It is the maximum frequency deviation limit of the system's primary frequency modulation; Electrolyzer power ramp constraints: ; in, , The electrolytic cell is Moment and Power change value at the moment; is the power change threshold per unit time of the electrolytic cell; Electrolyzer operating state constraints: Normal FM action: ; Emergency FM action: ; in, It is the power fluctuation ratio of the electrolytic cell during normal frequency modulation operation; is the hot standby power of the electrolyzer; is the rated operating power of a single electrolyzer; is the input power of the electrolyzer; is the hot standby power of the electrolyzer; The non-dominated sorting genetic algorithm is used to solve the optimization model and obtain the optimal solution.

[0017] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the multi-objective operation optimization method of the green electricity hydrogen production grid-connected system considering the multi-scale flexibility of the power grid when executing the computer program.

[0018] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a multi-objective operation optimization method of a green electricity hydrogen production grid-connected system that considers the multi-scale flexibility of the power grid are implemented.

[0019] Compared with the prior art, the present invention has the following beneficial effects: The present invention takes into account factors such as new energy consumption, grid flexibility, and the transferability of electricity and hydrogen loads, and proposes a multi-objective operation optimization method for a green electricity hydrogen production grid-connected system that takes into account the multi-scale flexibility of the grid. Compared with traditional battery energy storage peak-shaving and primary frequency modulation technologies, the present invention only uses electrolyzers and fuel cells for peak-shaving and frequency modulation; by incorporating the advantages of the flexible and adjustable power of the electrolyzer into the control of the peak-shaving and frequency modulation response, the system peak-shaving is optimized and scheduled considering the transferability of the electricity and hydrogen load, and the frequency modulation response is divided into normal response and emergency response areas. In the normal response area, the influence of the electrolyzer power on the aging characteristics of the stack is considered, and corresponding restrictions are set for the power fluctuations of the electrolyzer; and the power allocation strategy for the corresponding scenario is established considering the hydrogen production efficiency of the electrolyzer and the rotation strategy. The capacity of the hydrogen energy storage configuration in the peak-shaving and frequency modulation strategy improves the system's new energy consumption, grid supply guarantee capabilities, and economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flow chart of a multi-objective operation optimization method of a green electricity hydrogen production grid-connected system considering the multi-scale flexibility of the power grid in the implementation of the present invention; Figure 2A system structure diagram of a multi-objective operation optimization method for a green electricity hydrogen production grid-connected system considering the multi-scale flexibility of the power grid implemented by the present invention; Figure 3 This is a diagram of a primary frequency modulation control strategy in the implementation of the present invention. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings.

[0022] The present invention implements a multi-objective optimization method for a green electricity hydrogen production grid-connected system considering the multi-scale flexibility of the power grid, comprising the following steps: Step S1: Obtain historical data on grid-side renewable energy output and user load power, obtain typical operation days of the grid and load sides through clustering algorithms, and establish physical models of electrolyzers, hydrogen storage tanks, fuel cells, etc. in the system; Step S2: Considering the needs of system peak load regulation and new energy consumption, the optimal control strategy for peak load regulation on the day before is established according to the new energy output on the day before and the typical day of user power load, and the objective function of new energy consumption rate is constructed; Step S3: Incorporate the flexible and adjustable power characteristics of the electrolyzer into the primary frequency regulation response of the system, and establish an electric-hydrogen operation optimization strategy with primary frequency regulation capability based on adaptive droop control; take the grid frequency deviation and the electrolyzer operation status as decision variables, and establish a multi-objective operation optimization with the power abandonment rate and the grid frequency deviation rate as targets; Step S4: Construct the constraints for operation optimization, solve the multi-objective operation optimization model based on the non-dominated sorting genetic algorithm, and obtain the optimal operation plan.

[0023] Specifically, according to Figure 1 , Figure 2 As shown, this embodiment discloses a multi-objective optimization method for a green electricity hydrogen production grid-connected system that considers the flexibility of multi-scale power grids. The input side of the grid-connected system is wind power and photovoltaic renewable energy. Wind power and photovoltaic power are connected to the grid through AC / AC converters and DC / AC converters respectively, and then power is supplied to users through the grid. Excess electricity is supplied to the electrolyzer for hydrogen production through AC / DC converters. Part of the hydrogen in the hydrogen storage tank is supplied to the hydrogen load, and part is used for fuel cell power generation to meet the peak load and frequency regulation requirements.

[0024] The step S1 comprises: Step S1.1: Obtain historical data of grid-side renewable energy output and user load power, and obtain typical operation days of the grid and load sides through the k-means clustering algorithm; For each data point in the data, the Euclidean distance is used to calculate the distance between the data and the centroid: ; in, is a data point; It is centroid; is the dimension of the data point; They are and In the The value of the dimension.

[0025] After the data is assigned to the cluster described by its nearest centroid, the centroid position formula for each cluster is updated: ; in, It is The set of all data points in a cluster; is the number of data points in the cluster; is the new centroid position of the cluster.

[0026] Step S1.2: Establish a physical model of the equivalent state of charge SOH of the hydrogen storage tank: ; Where: , They are Moment and -1 The equivalent state of charge of the hydrogen storage tank at time; is the configuration capacity of the hydrogen storage tank; , Hydrogen storage tank The air intake and exhaust rates at each moment; , is a binary variable; The unit time interval.

[0027] The input and output model of the fuel cell is calculated by the following formula: ; Where: is the output power of the fuel cell; The amount of hydrogen consumed by the fuel cell; for the efficiency of the fuel cell; The amount of hydrogen consumed by a fuel cell to produce a unit of electricity.

[0028] Furthermore, if Figure 1 As shown, step S2 includes: Step S2.1: Considering the transferability of the electric load, the transferable electric load during the peak load period is transferred to the load valley period to reduce the peak load regulation pressure of the system.

[0029] Electric loads are mainly divided into two categories: transferable electric loads and non-transferable electric loads. Transferable electric loads can be transferred to any time period within a day, which is mainly reflected in: ; Where: It is the total amount of electric load transferred in and out in one day; , are the total amount of electric load transferred out and in in one day respectively; Take 24 hours; As unit time.

[0030] Step S2.2: Considering the characteristics of transferable electric load and system peak load demand, the optimal control strategy for peak load regulation on the day before is established according to the output of renewable energy and the typical day of user electric load. This strategy mainly includes the following two aspects: 1) In order to realize the peak load shaving and valley filling of new energy, the transferable electric load during the peak load period is transferred to the load valley period, taking into account the transferability of the electric load; 2) During the load trough period, the excess power generation will be used to electrolyze water to produce hydrogen, and the hydrogen will be stored in the hydrogen storage tank. The hydrogen exceeding the requirement of the peak-shaving scheduling plan will be supplied to the hydrogen load. During the load peak period, the hydrogen will be used to generate electricity through hydrogen fuel cells to make up for the power shortage of the power grid.

[0031] Step S2.3: Construct the objective function of new energy consumption rate: ; Where: , are the power of wind turbine and photovoltaic respectively; , They are The input power of the electrolyzer array and the output power of the fuel cell at each moment; For system load.

[0032] Furthermore, if Figure 1 As shown, step S3 includes: Step S3.1: Considering the electrolytic cell rotation strategy and wide power operation characteristics, the electrolytic cells in the array are numbered and the principle of first start first shut down is adopted.

[0033] The wide power operation characteristics of the electrolytic cell are: ; Where: is the rated operating power of a single electrolyzer; is the input power of the electrolyzer.

[0034] The electrolytic cell rotation strategy is: The electrolytic cells are numbered in sequence according to the order in which they are started. The electrolytic cells that are not in operation are not included in the numbering. Under certain operating time constraints, the electrolytic cells are operated on the principle of first started first shut down. Step S3.2: Considering the dynamic hydrogen production efficiency, the working states of the electrolyzer are divided into four types: 1) Shutdown status: hour, ; 2) Low power working state: ; ; ; ; 3) Stable working state: All electrolytic cells are Rated power operation ; 4) Overload working state: ; ; ; ; Where: is the number of electrolytic cells, is the rated operating power of the electrolyzer; is the number of electrolyzers operating at low power; Indicates The power of each electrolyzer; is the residual power of the electrolyzer; is the total input power of the electrolyzer array; To round down; Step S3.3: The power of the electrolyzer has the advantage of being fast, flexible and controllable. When a frequency deviation occurs in the regional power grid, adaptive droop control is used to use the controllable power of the electrolyzer and the power of the fuel cell as the active power response required for primary frequency regulation.

[0035] ; Where: The power required for primary frequency modulation; is the frequency deviation; , are grid frequency and rated frequency respectively; is the rated droop coefficient; is the variable droop coefficient.

[0036] ; Where: is the system frequency deviation; is the derivative of the system frequency; is the coefficient.

[0037] Step S3.4: Figure 3 As shown, a frequency modulation control is performed on the basis of the peak-shaving dispatch of the power grid, and a frequency modulation dead zone of 0.033HZ is set to prevent the power grid from frequently adjusting power due to small frequency fluctuations. When the frequency fluctuation is less than 0.033HZ, no frequency modulation response is performed; When the primary frequency modulation response is divided into a normal response area and an emergency response area, When the system frequency deviation is small, it is in the normal frequency modulation response area. , then the normal response of frequency increase is adopted; if , adopt the normal response of frequency reduction. When the system is located in the FM emergency response area, if , then adopt frequency increase emergency response; if , using frequency reduction emergency response.

[0038] During the frequency modulation process, the power required to be adjusted by the electrolyzer and fuel cell is: ; Where: It is the dynamic fluctuating power of the electrolyzer; , They are the fluctuating powers required for system peak regulation and frequency regulation respectively.

[0039] ; Where: It is the dynamic fluctuating power of the fuel cell; , They are the power required for system peak regulation and frequency regulation respectively.

[0040] Normal response area: ; Where: is the frequency difference of the normal FM response range; at this time In the normal response area, when When , the electrolyzer increases the input power under the constraint condition to maintain the power balance between the source and the load; when When the load is too high, the fuel cell generates electricity to maintain the power balance between the source and the load. At the same time, considering that the power fluctuation of the electrolyzer will cause the aging of the stack, the power fluctuation of a single electrolyzer is limited to a certain range in the normal response area.

[0041] ; Where: It is the power fluctuation ratio of the electrolytic cell during normal frequency modulation operation; is the hot standby power of the electrolyzer; is the rated operating power of a single electrolyzer; is the input power of the electrolyzer.

[0042] Emergency Response Areas: ; In the system frequency emergency response area, when When , the input power of the electrolyzer increases to maintain the power balance between the source and the load, and no power constraint is set; when When , the fuel cell generates electricity to maintain the power balance between the source and the load. At this time, the power fluctuation of the electrolyzer is in the maximum range within the controllable range.

[0043] Step S3.5: Figure 3 As shown, considering the attenuation characteristics of the electrolyzer stack, a control strategy for the green electricity hydrogen production system to participate in the grid frequency regulation response is established; when the grid has a frequency deviation, the action response of the electrolyzer and fuel cell is selected according to the frequency deviation.

[0044] The electrolyzer has the characteristic of operating for a short time beyond the rated power. In the power allocation considering the system peak load regulation and new energy consumption, the upper limit of the hydrogen production power of the electrolyzer is set to Since the frequency modulation process is very short and fast, the overload power limit of the electrolyzer during frequency modulation can exceed , up to When the grid frequency deviates and is in the normal response area, the fluctuation range of the electrolyzer power is limited.

[0045] When the system is in the emergency response zone, the operating power of the electrolyzer can be flexibly adjusted to the lowest hot standby power according to the rotation strategy. , up to When the frequency drops, the fuel cell generates electricity to make up for the power difference between the source and the load to participate in the primary frequency regulation process of the power grid, ultimately achieving the effect of stabilizing the power grid frequency.

[0046] Step S3.6: In order to ensure that there is a certain amount of hydrogen left in the hydrogen storage tank to cope with peak and frequency regulation response, considering the transferability of hydrogen load, hydrogen load can be divided into transferable hydrogen load and non-transferable hydrogen load. The transferable hydrogen load during the peak hydrogen consumption period is moved to the hydrogen load during the low hydrogen consumption period, and the total hydrogen consumption remains unchanged.

[0047] ; Where: is the capacity of hydrogen in the hydrogen storage tank; , They are respectively the upper and lower limits of hydrogen storage in the hydrogen storage tank in response to frequency and peak regulation planning.

[0048] ; Where: , are the amount of hydrogen transferred in and out in one day, respectively; It is the total amount of hydrogen transferred in and out in one day.

[0049] Furthermore, if Figure 2 As shown, step S4 includes: Step S4.1: Considering the consumption of new energy and the supply of power grid, a multi-objective optimization with the consumption rate of new energy and the frequency deviation rate of power grid as the objective function is established. Since the consumption rate of new energy should be as large as possible and the frequency deviation rate of power grid should be as small as possible, the inverse of the new energy consumption rate of the objective function is taken into the comprehensive evaluation index during the optimization solution: ; Where: , They are the evaluation indicators of new energy consumption rate and grid frequency deviation rate respectively; , are the weighted values ​​of the two respectively.

[0050] The evaluation indicators of new energy consumption rate and grid frequency deviation rate are respectively and express: ; ; Where: , are the power of wind turbine and photovoltaic respectively; inputting power to the electrolyzer array; Producing power for the fuel cell; is the system load; , The system standard frequency and The system sampling frequency at time; is the unit time; is the sampling times.

[0051] Step S4.2: Establish constraints for running the optimization model: The power balance constraint is: ; Where: , are the power of wind turbine and photovoltaic respectively; , are the power of the fuel cell and its output ratio respectively; inputting power to the electrolyzer array; For system load.

[0052] Frequency modulation power demand constraints: ; Where: is the system frequency modulation power difference; The frequency modulation response power of the electrolyzer array; represents the fuel cell power; It is a binary variable, 1 indicates that the fuel cell is powered, and 0 indicates that the fuel cell is not started.

[0053] The grid frequency deviation constraint is: ; Where: is the system frequency deviation; It is the maximum frequency deviation limit of the system's primary frequency modulation.

[0054] The power ramp constraint is: ; Where: , The electrolytic cell is Moment and Power change value at the moment; is the power change threshold per unit time of the electrolytic cell.

[0055] The operating state constraints of a single electrolytic cell are: Normal FM action: ; Emergency FM action:

[0056] Where: It is the power fluctuation constraint of the electrolyzer during normal frequency modulation operation; is the hot standby power of the electrolyzer; is the rated operating power of a single electrolyzer; is the input power of the electrolyzer.

[0057] Step S4.3: Using the non-dominated sorting genetic algorithm (NSGA-II) to solve the optimization model and obtain the optimal solution, including: (1) Initialize the population: First, randomly generate an initial population containing multiple individuals. Each individual represents a potential solution.

[0058] (2) Calculate fitness: For each individual, calculate its fitness in the objective function space.

[0059] (3) Non-dominated sorting: NSGA-II uses a non-dominated sorting technique to divide individuals in the population into multiple fronts. This sorting process determines the non-dominated level of each individual to identify which individuals occupy different positions on the Pareto front. Individuals with higher levels are not dominated by individuals with lower levels.

[0060] (4) Crowding distance calculation: In order to maintain the diversity of the Pareto front, NSGA-II calculates the crowding distance to measure the density of individuals in the target space. The larger the crowding distance, the greater the distance between individuals, which helps to maintain a uniform distribution on the Pareto front.

[0061] (5) Selection operation: NSGA-II uses roulette as the selection operation. In each generation, two individuals are first randomly selected from the current population using the roulette selection operation, and then the individual with a higher non-dominated level is selected. This selection process is performed by comparing the non-dominated level and crowding distance of the individuals.

[0062] (6) Crossover / mutation operation: The selected individuals are used to generate the parent population through crossover and mutation operations. The parent and offspring are merged into a larger candidate population.

[0063] (7) Iteration: The candidate population is re-sorted by non-dominated sorting, crowding distance calculation, selection, crossover, and mutation to generate a new offspring population. The iteration is performed until the maximum number of iterations is reached or convergence to a satisfactory Pareto front solution is achieved.

[0064] The present invention also provides a multi-objective operation optimization system for a green electricity hydrogen production grid-connected system considering the multi-scale flexibility of the power grid, comprising the following modules: Model acquisition module, which obtains historical data on grid-side renewable energy output and user load power forecast, obtains typical operation days on the grid and load sides through clustering algorithms, and builds physical models of electrolyzers, hydrogen storage tanks, fuel cells, etc. in the system to construct constraints for operation optimization; The optimization module considers the transferability of electric load, system peak load regulation and the demand for new energy consumption, establishes the optimal dispatching strategy for peak load regulation based on the forecast data of new energy output and user electric load, and constructs the objective function of new energy consumption rate; The control strategy module considers factors such as the electrolyzer rotation strategy and wide power operation characteristics, establishes an operation optimization strategy for the electric-hydrogen system with primary frequency regulation capability based on adaptive droop control, takes the grid frequency deviation and the electrolyzer operation status as decision variables, and constructs a multi-objective operation optimization with the new energy consumption rate and the grid frequency deviation rate as the objective function; The solution module solves the multi-objective operation optimization of the green electricity hydrogen production grid-connected system constructed with consideration of grid flexibility to obtain the optimal operation plan.

[0065] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to execute the computer program to implement the steps of the multi-objective operation optimization method of the green electricity hydrogen production grid-connected system considering the multi-scale zero-multiple-factors of the power grid.

[0066] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a multi-objective operation optimization method of a green electricity hydrogen production grid-connected system that considers the multi-scale flexibility of the power grid are implemented.

[0067] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0068] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0069] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0071] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0072] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A multi-objective operation optimization method for a green electricity hydrogen production grid-connected system considering the multi-scale flexibility of the power grid, characterized in that: include: Obtain historical data on grid-side renewable energy output and user load power, obtain typical operating days on the source and load sides through clustering algorithms, and establish physical models of electrolyzers, hydrogen storage tanks, and fuel cells in the system to construct constraints for operation optimization; Considering the load transferability, system peak load regulation and new energy consumption requirements, a day-ahead peak load regulation scheduling strategy is established based on the typical operation day obtained by the clustering algorithm, and an objective function for the new energy consumption rate is constructed; The flexible and adjustable power characteristics of the electrolyzer are incorporated into the primary frequency regulation response of the system, and an electric-hydrogen operation optimization strategy with primary frequency regulation capability is established based on adaptive droop control; Taking the grid frequency deviation and electrolyzer operation status as decision variables, a multi-objective operation optimization with the new energy consumption rate and grid frequency deviation rate as the targets is established; Based on the non-dominated sorting genetic algorithm, the multi-objective operation optimization model is solved to obtain the optimal operation plan.

2. According to claim 1, a multi-objective optimization operation method for a green electricity hydrogen production grid-connected system considering grid flexibility is characterized in that: Obtain historical data on grid-side renewable energy output and user load power, obtain typical operating days on the source and load sides through clustering algorithms, and establish physical models of electrolyzers, hydrogen storage tanks, and fuel cells in the system. The constraints used to construct operation optimization include: Obtain historical data on grid-side renewable energy output and user load power, and obtain typical operation days of the grid and load sides through clustering algorithms; For each data point in the data, the Euclidean distance is used to calculate the distance between the data and the centroid: ； in, is a data point; It is centroid; is the dimension of the data point; They are and In the The value of the dimension; After the data is assigned to the cluster described by its nearest centroid, the centroid position formula for each cluster is updated: ； in, It is The set of all data points in a cluster; is the number of data points in the cluster; is the new centroid position of the cluster; Calculate the real-time equivalent state of charge SOH of the hydrogen storage tank: ； in, , They are Moment and -1 The equivalent state of charge of the hydrogen storage tank at time; is the configuration capacity of the hydrogen storage tank; , Hydrogen storage tank Intake rate and exhaust rate at each moment; , is a binary variable; The unit time interval.

3. The multi-objective optimization operation method of a green electricity hydrogen production grid-connected system considering grid flexibility according to claim 2 is characterized in that: The load transferability, system peak load regulation and new energy consumption requirements are considered, a day-ahead peak load regulation scheduling strategy is established based on the typical operation day obtained by the clustering algorithm, and an objective function of the new energy consumption rate is constructed, including: Consider the transferable characteristics of electrical loads: Electric loads are divided into two categories: transferable electric loads and non-transferable electric loads. Transferable electric loads can be transferred to any time period within a day, which is reflected in: ； in, The total amount of electric load transferred in and out in one day; , are the total amount of electric load transferred out and in in one day respectively; Take 24 hours; is the unit time; Considering the characteristics of transferable electric load and system peak load demand, the optimal control strategy for peak load regulation is established according to the output of renewable energy and the typical operation day of user electric load. Construct the objective function of new energy consumption rate: ； in, , are the power of wind turbine and photovoltaic respectively; , They are The input power of the electrolyzer array and the output power of the fuel cell at each moment; For system load.

4. The multi-objective optimization operation method of a green electricity hydrogen production grid-connected system considering grid flexibility according to claim 3 is characterized in that: The flexible and adjustable power characteristics of the electrolyzer are incorporated into the primary frequency modulation response of the system, and an electric-hydrogen operation optimization strategy with primary frequency modulation capability is established based on adaptive droop control; Taking the grid frequency deviation and the electrolyzer operating status as decision variables, a multi-objective operation optimization with the new energy consumption rate and the grid frequency deviation rate as the targets is established, including: Considering the electrolytic cell rotation strategy and wide power operation characteristics, the electrolytic cells in the array are numbered according to the order in which the electrolytic cells are started. The electrolytic cells that are not in operation are not included in the numbering. Under certain operation time constraints, the principle of first start first shut down is adopted for the electrolytic cells. Considering the dynamic hydrogen production efficiency, the working states of the electrolyzer are divided into four types: 1) Shutdown status: hour, ; 2) Low power working state: ; ; ; ； 3) Stable working state: All electrolytic cells are Rated power operation ； 4) Overload working state: ; ; ； in, is the number of electrolytic cells, Indicates The power of each electrolyzer; is the total input power of the electrolyzer array; To round down; The power of the electrolyzer has the advantage of being fast, flexible and adjustable. When the frequency deviation occurs in the regional power grid, adaptive droop control is used to take the adjustable power of the electrolyzer and the power of the fuel cell as the active power response required for primary frequency regulation; ； in, The power required for primary frequency modulation; is the frequency deviation; , are grid frequency and rated frequency respectively; is the rated droop coefficient; is the adaptive droop coefficient; A frequency modulation control is carried out on the basis of the peak load regulation of the power grid, and a frequency modulation dead zone of 0.033HZ is set. When the frequency fluctuation is less than 0.033HZ, no response is given. At the same time, the frequency fluctuation of the primary frequency modulation is divided into a normal response zone and an emergency response zone; Normal response area: ; ； in, It is the electrolytic cell power fluctuation proportional coefficient during normal frequency modulation operation; is the hot standby power of the electrolyzer; is the rated operating power of a single electrolyzer; is the input power of the electrolyzer; Emergency Response Areas: ; Considering the attenuation characteristics of the electrolyzer stack, a control strategy for the green electricity hydrogen production system to participate in the grid frequency regulation response is established: when the grid frequency deviates, the action response of the electrolyzer and fuel cell is selected according to the frequency deviation; The hydrogen load is divided into transferable hydrogen load and non-transferable hydrogen load. The transferable hydrogen load during the peak hydrogen consumption period is moved to the hydrogen load during the low hydrogen consumption period, and the total hydrogen consumption remains unchanged. ； in, , They are the upper and lower limits of hydrogen storage in hydrogen storage tanks in response to frequency and peak regulation planning; ； in, , are the amount of hydrogen transferred in and out in one day, respectively; It is the total amount of hydrogen transferred in and out in one day.

5. The multi-objective optimization operation method of a green electricity hydrogen production grid-connected system considering grid flexibility according to claim 4 is characterized in that: The method of solving the multi-objective operation optimization model based on the non-dominated sorting genetic algorithm to obtain the optimal operation plan includes: Considering the consumption of new energy and the supply of power grid, a multi-objective optimization with the consumption rate of new energy and the frequency deviation rate of power grid as the objective function is established. When optimizing the solution, the reciprocal of the consumption rate of new energy of the objective function is taken into the comprehensive evaluation index: ； in, , They are the evaluation indicators of new energy consumption rate and grid frequency deviation rate respectively; , are the weighted values ​​of the two respectively; The evaluation indicators of new energy consumption rate and grid frequency deviation rate are respectively and express: ； ； in, , are the power of wind turbine and photovoltaic respectively; , They are The input power of the electrolyzer array and the output power of the fuel cell at each moment; is the system load, , The system standard frequency and The system sampling frequency at time, is the sampling times; Establish constraints for running the optimization model: Power balance constraints: ； in, , are the power of wind turbine and photovoltaic respectively; , are the power of the fuel cell and its output ratio respectively; inputting power to the electrolyzer array; is the system load; Frequency modulation power demand constraints: ； in, is the system frequency modulation power difference; The frequency modulation response power of the electrolyzer array; represents the fuel cell power; It is a binary variable, 1 indicates the fuel cell output, and 0 indicates that the fuel cell is not started; Grid frequency deviation constraints: ； in, is the system frequency deviation; It is the maximum frequency deviation limit of the system's primary frequency modulation; Electrolyzer power ramp constraints: ； in, , The electrolytic cell is Moment and Power change value at the moment; is the power change threshold per unit time of the electrolytic cell; Electrolyzer operating state constraints: Normal FM action: ; Emergency FM action: ; in, It is the electrolytic cell power fluctuation proportional coefficient during normal frequency modulation operation; is the hot standby power of the electrolyzer; is the rated operating power of a single electrolyzer; is the input power of the electrolyzer; is the hot standby power of the electrolyzer; The non-dominated sorting genetic algorithm is used to solve the optimization model and obtain the optimal solution.

6. A multi-objective operation optimization system for a green electricity hydrogen production grid-connected system considering the multi-scale flexibility of the power grid, characterized in that: Includes the following modules: Model acquisition module, which obtains historical data on grid-side renewable energy output and user load power forecast, obtains typical operation days on the grid and load sides through clustering algorithms, and builds physical models of electrolyzers, hydrogen storage tanks, and fuel cells in the system to construct constraints for operation optimization; The optimization module considers the transferability of electric load, the system peak load regulation and the demand for new energy consumption, establishes the optimal dispatching strategy for peak load regulation according to the output of new energy and the typical operation day of user electric load, and constructs the objective function of new energy consumption rate; The control strategy module considers factors such as the electrolyzer rotation strategy and wide power operation characteristics, establishes an operation optimization strategy for the electric-hydrogen system with primary frequency regulation capability based on adaptive droop control, takes the grid frequency deviation and the electrolyzer operation status as decision variables, and establishes a multi-objective operation optimization with the grid frequency deviation rate and the new energy consumption rate as the objective function; The solution module solves the multi-objective operation optimization of the green electricity hydrogen production grid-connected system constructed with consideration of grid flexibility to obtain the optimal operation plan.

7. The multi-objective optimization operation system of the green electricity hydrogen production grid-connected system considering the flexibility of the power grid according to claim 6 is characterized in that: The model acquisition module implements: Obtain historical data on grid-side renewable energy output and user load power, and obtain typical operation days of the grid and load sides through clustering algorithms; For each data point in the data, the Euclidean distance is used to calculate the distance between the data and the centroid: ； in, is a data point; It is centroid; is the dimension of the data point; They are and In the The value of the dimension; After the data is assigned to the cluster described by its nearest centroid, the centroid position formula for each cluster is updated: ； in, It is The set of all data points in a cluster; is the number of data points in the cluster; is the new centroid position of the cluster; Calculate the real-time equivalent state of charge SOH of the hydrogen storage tank: ； in, , They are Moment and -1 The equivalent state of charge of the hydrogen storage tank at time; is the configuration capacity of the hydrogen storage tank; , Hydrogen storage tank Intake rate and exhaust rate at each moment; , is a binary variable; The unit time interval.

8. The multi-objective optimization operation system of a green electricity hydrogen production grid-connected system considering grid flexibility according to claim 6 is characterized in that: The optimization module implements: Consider the transferable characteristics of electrical loads: Electric loads are divided into two categories: transferable electric loads and non-transferable electric loads. Transferable electric loads can be transferred to any time period within a day, which is reflected in: ； in, The total amount of electric load transferred in and out in one day; , are the total amount of electric load transferred out and in in one day respectively; Take 24 hours; is the unit time; Considering the characteristics of transferable electric load and system peak load demand, the optimal control strategy for peak load regulation is established according to the output of renewable energy and the typical operation day of user electric load. Construct the objective function of new energy consumption rate: ； in, , are the power of wind turbine and photovoltaic respectively; , They are The input power of the electrolyzer array and the output power of the fuel cell at each moment; For system load.

9. The multi-objective optimization operation system of the green electricity hydrogen production grid-connected system considering the flexibility of the power grid according to claim 6 is characterized in that: The control strategy module implements: Considering the electrolytic cell rotation strategy and wide power operation characteristics, the electrolytic cells in the array are numbered according to the order in which the electrolytic cells are started. The electrolytic cells that are not in operation are not included in the numbering. Under certain operation time constraints, the principle of first start first shut down is adopted for the electrolytic cells. Considering the dynamic hydrogen production efficiency, the working states of the electrolyzer are divided into four types: 1) Shutdown status: hour, ; 2) Low power working state: ; ; ; ； 3) Stable working state: All electrolytic cells are Rated power operation ； 4) Overload working state: ; ; ; ； in, is the number of electrolytic cells, Indicates The power of each electrolyzer; is the total input power of the electrolyzer array; To round down; The power of the electrolyzer has the advantage of being fast, flexible and adjustable. When the frequency deviation occurs in the regional power grid, adaptive droop control is used to take the adjustable power of the electrolyzer and the power of the fuel cell as the active power response required for primary frequency regulation; ； in, The power required for primary frequency modulation; is the frequency deviation; , are grid frequency and rated frequency respectively; is the rated droop coefficient; is the adaptive droop coefficient; A frequency modulation control is carried out on the basis of the peak load regulation of the power grid, and a frequency modulation dead zone of 0.033HZ is set. When the frequency fluctuation is less than 0.033HZ, no response is given. At the same time, the frequency fluctuation of the primary frequency modulation is divided into a normal response zone and an emergency response zone; Normal response area: ; ； in, It is the power fluctuation ratio of the electrolytic cell during normal frequency modulation operation; is the hot standby power of the electrolyzer; is the rated operating power of a single electrolyzer; is the input power of the electrolyzer; Emergency Response Areas: ; Considering the attenuation characteristics of the electrolyzer stack, a control strategy for the green electricity hydrogen production system to participate in the grid frequency regulation response is established: when the grid frequency deviates, the action response of the electrolyzer and fuel cell is selected according to the frequency deviation; The hydrogen load is divided into transferable hydrogen load and non-transferable hydrogen load. The transferable hydrogen load during the peak hydrogen consumption period is moved to the hydrogen load during the low hydrogen consumption period, and the total hydrogen consumption remains unchanged. ； in, , They are the upper and lower limits of hydrogen storage in hydrogen storage tanks in response to frequency and peak regulation planning; ； in, , are the amount of hydrogen transferred in and out in one day, respectively; It is the total amount of hydrogen transferred in and out in one day.

10. The multi-objective optimization operation system of the green electricity hydrogen production grid-connected system considering the flexibility of the power grid according to claim 6, characterized in that: The solution module implements: Considering the consumption of new energy and the supply of power grid, a multi-objective optimization with the consumption rate of new energy and the frequency deviation rate of power grid as the objective function is established. When optimizing the solution, the reciprocal of the consumption rate of new energy of the objective function is taken into the comprehensive evaluation index: ； in, , They are the evaluation indicators of new energy consumption rate and grid frequency deviation rate respectively; , are the weighted values ​​of the two respectively; The evaluation indicators of new energy consumption rate and grid frequency deviation rate are respectively and express: ； ； in, , are the power of wind turbine and photovoltaic respectively; , They are The input power of the electrolyzer array and the output power of the fuel cell at each moment; is the system load, , The system standard frequency and The system sampling frequency at time, is the sampling times; Establish constraints for running the optimization model: Power balance constraints: ； in, , are the power of wind turbine and photovoltaic respectively; , are the power of the fuel cell and its output ratio respectively; inputting power to the electrolyzer array; is the system load; Frequency modulation power demand constraints: ； in, is the system frequency modulation power difference; The frequency modulation response power of the electrolyzer array; represents the fuel cell power; It is a binary variable, 1 indicates the fuel cell output, and 0 indicates that the fuel cell is not started; Grid frequency deviation constraints: ； in, is the system frequency deviation; It is the maximum frequency deviation limit of the system's primary frequency modulation; Electrolyzer power ramp constraints: ； in, , The electrolytic cell is Moment and Power change value at the moment; is the power change threshold per unit time of the electrolytic cell; Electrolyzer operating state constraints: Normal FM action: ; Emergency FM action: ; in, It is the electrolytic cell power fluctuation proportional coefficient during normal frequency modulation operation; is the hot standby power of the electrolyzer; is the rated operating power of a single electrolyzer; is the input power of the electrolyzer; is the hot standby power of the electrolyzer; The non-dominated sorting genetic algorithm is used to solve the optimization model and obtain the optimal solution.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of a multi-objective operation optimization method for a green electricity hydrogen production grid-connected system considering the multi-scale flexibility of the power grid are implemented as described in any one of claims 1-5.

12. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a multi-objective operation optimization method of a green electricity hydrogen production grid-connected system considering the multi-scale flexibility of the power grid as described in any one of claims 1-5 are implemented.

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