Operation control method and device of electrolytic cell array

Abstract

This specification provides an operation control method and device for an electrolyzer array. Based on this method, when the fluctuation amplitude of the wind and solar power generation data of the target wind and solar power station is monitored to be greater than the preset amplitude threshold, the number of electrolyzers to be scheduled in the electrolyzer array at the current time can be determined first; then a multi-objective cost function is constructed that at least includes the electrolyzer operation and maintenance cost item, the electrolyzer start and stop cost item, and the energy storage battery system operation and maintenance cost item; at the same time, according to the number of electrolyzers, a target constraint condition is constructed that at least includes the overall power balance constraint condition of the system; based on the above target constraints and multi-objective cost functions, the corresponding target scheduling strategy is obtained and utilized through optimization solution to control and adjust the operation of the electrolyzer array at the current time. Thereby, it can be better adapted to the large-scale fluctuation scenario of wind and solar power generation. When the fluctuation of wind and solar power generation is large, the operation of the system electrolyzer array can be adjusted in a timely and accurate manner.

Description

Technical Field

[0001] This specification belongs to the field of electrical data processing technology related to wind and solar energy, and in particular to an operation control method and device for an electrolytic cell array. Background Art

[0002] In new energy scenarios involving wind and solar power generation, part of the electricity generated by wind and solar power stations will be fed into the power grid, and part will be converted into hydrogen energy using an electrolyzer and stored in hydrogen storage tanks for subsequent use.

[0003] However, wind and solar power generation is easily affected by changes in natural factors, resulting in large fluctuations in the electricity generated by wind and solar power stations. When the fluctuation scale of wind and solar power generation is large, it is often difficult to accurately and effectively control and adjust the operation of the electrolyzer based on existing methods, which leads to low overall operating efficiency and poor yield of the electrolyzer.

[0004] To address the above problems, no effective solution has been proposed yet. Summary of the invention

[0005] This specification provides an operation control method and device for an electrolytic cell array, which can be well adapted to large-scale fluctuation scenarios of wind and solar power generation. When the fluctuations of wind and solar power generation are large, the operation of the system electrolytic cell array can be automatically controlled and adjusted in a timely and accurate manner, effectively improving the overall operation efficiency and overall rate of return of the electrolytic cell array.

[0006] This specification provides an operation control method of an electrolytic cell array, comprising:

[0007] When the fluctuation amplitude of the wind and solar power generation data of the target wind and solar power station is monitored to be greater than a preset amplitude threshold, obtaining wind and solar fluctuation characteristic data for the current time;

[0008] Determine the predicted value of wind power generation and the predicted value of photovoltaic power generation according to the wind-solar fluctuation characteristic data;

[0009] Determine the number of electrolytic cells to be scheduled in the electrolytic cell array at the current time according to the predicted value of wind power generation and the predicted value of photovoltaic power generation;

[0010] Acquire and construct a multi-objective cost function based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs; wherein the multi-objective cost function at least includes: an electrolyzer operation and maintenance cost item, an electrolyzer start and stop cost item, and an energy storage battery system operation and maintenance cost item;

[0011] According to the number of electrolyzers, the predicted value of wind power generation, and the predicted value of photovoltaic power generation, a target constraint condition is constructed; wherein the target constraint condition at least includes: a system overall power balance constraint condition;

[0012] Based on the multi-objective cost function and the objective constraint conditions, the operating power of the electrolytic cells to be scheduled in the electrolytic cell array at the current time is obtained through optimization and solving, and a corresponding objective scheduling strategy is generated;

[0013] According to the target scheduling strategy, the operation of the electrolyzer array at the current time is controlled and adjusted.

[0014] In one embodiment, a multi-objective cost function is constructed based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs, including:

[0015] Obtain and determine the first type of characteristic parameters, the second type of characteristic parameters, and the third type of characteristic parameters based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs; wherein the first type of characteristic parameters at least include: the unit capacity construction cost of the electrolyzer, the rated capacity of the electrolyzer, and the electrolyzer operation and maintenance cost coefficient; the second type of characteristic parameters at least include: the electrolyzer startup cost and the electrolyzer shutdown cost; the third type of characteristic parameters at least include: the unit capacity construction cost of the energy storage battery system, the rated capacity of the energy storage battery system, and the energy storage battery system operation and maintenance cost coefficient;

[0016] According to the preset construction rules, the first type of characteristic parameters are used to construct the electrolytic cell operation and maintenance cost item; the second type of characteristic parameters are used to construct the electrolytic cell start and stop cost item; the third type of characteristic parameters are used to construct the energy storage battery system operation and maintenance cost item;

[0017] The electrolyzer operation and maintenance cost items, electrolyzer start and stop cost items, and energy storage battery system operation and maintenance cost items are combined to construct the corresponding multi-objective cost function.

[0018] In one embodiment, the multi-objective cost function also includes: hydrogen storage tank operation and maintenance cost item, electric hydrogen load transfer cost item, power abandonment penalty cost item, and system and power grid interaction power cost item.

[0019] In one embodiment, a multi-objective cost function is constructed based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs, including:

[0020] According to the following formula, construct a multi-objective cost function:

[0021] min C=a 1 C 1 + a 2 C2 +a 3 C 3 + a 4 C 4 + a 5 C 5 + a 6 C 6 + a 7 C 7

[0022] Among them, C is the multi-objective cost function value, C 1 is the electrolytic cell operation and maintenance cost item, C 2 is the hydrogen storage tank operation and maintenance cost item, C 3 is the cost item of electric hydrogen load transfer, C 4 is the penalty cost for power curtailment, C 5 is the power cost item for interaction between the system and the grid, C 6 is the start-up and shutdown cost of the electrolytic cell, C 7 is the operation and maintenance cost of the energy storage battery system, a 1 is the first weight coefficient, a 2 is the second weight coefficient, a 3 is the third weight coefficient, a 4 is the fourth weight coefficient, a 5 is the fifth weight coefficient, a 6 is the sixth weight coefficient, a 7 is the seventh weight coefficient.

[0023] In one embodiment, target constraints are constructed based on the number of electrolyzers, the predicted value of wind power generation, and the predicted value of photovoltaic power generation, including:

[0024]

[0025]

[0026] Among them, P grid (t) is the interaction power between the system and the grid at time t, P batt (t) is the operating power of the energy storage battery system at time t, P pv (t) is the predicted value of photovoltaic power generation at time t, P wind (t) is the predicted value of wind power generation at time t, is the power abandoned at time t, P L (t) is the system power loss at time t, P ALK (t) is the total operating power of the electrolytic cell at time t, P ALKi (t) is the operating power of the electrolytic cell numbered i at time t, t is the current time, and n is the number of electrolytic cells to be scheduled at the current time.

[0027] In one embodiment, the target constraints further include: individual state constraints of the electrolytic cell;

[0028] The individual state constraint of the electrolytic cell is constructed based on the state values ​​of various types of states of the electrolytic cell and the operation values ​​of various types of operations;

[0029] The various types of states include: rated operation state, fluctuating operation state, standby state, and shutdown state; the various types of operations include: startup operation and shutdown operation.

[0030] In one embodiment, the individual state constraint conditions of the electrolyzer include at least one of the following: a safety operation constraint sub-condition of the electrolyzer fluctuating operation state, an electrolyzer input power constraint sub-condition, an electrolyzer hydrogen production rate constraint sub-condition, and an electrolyzer state switching constraint sub-condition.

[0031] In one embodiment, the electrolytic cell state switching constraint sub-condition for the electrolytic cell numbered n is constructed according to the following formula:

[0032] -D n(t-2) + D n(t-1) -D n(t) ≤0

[0033] R n(t) + F n(t) + S n(t) + D n(t-1) -1≤Y n(t)

[0034] R n(t-1) + F n(t-1) + S n(t-1) + D n(t) -1≤Z n(t)

[0035] R n(t) + F n(t) + S n(t) + D n(t) =1

[0036] Among them, D n(t-2) is the state value of the shutdown state at t-2, D n(t-1) is the state value of the shutdown state at t-1, D n(t) is the state value of the shutdown state at time t, R n(t) is the state value of the rated operating state at time t, F n(t) is the state value of the fluctuating running state at time t, S n(t) is the state value of the standby state at time t, Y n(t) is the operation value for starting the operation at time t, R n(t-1) is the state value of the rated operating state at t-1, Fn(t-1) is the state value of the fluctuating running state at time t-1, S n(t-1) is the state value of the standby state at t-1, Z n(t) is the operation value of the shutdown operation at time t, t is the current time, t-1 is the time of the previous unit time, and t-2 is the time of the previous two unit times.

[0037] This specification also provides an operation control device for an electrolytic cell array, comprising:

[0038] An acquisition module is used to acquire wind-solar power fluctuation characteristic data for the current time when the fluctuation amplitude of wind-solar power generation data of the target wind-solar power station is greater than a preset amplitude threshold;

[0039] A first determination module is used to determine a predicted value of wind power generation and a predicted value of photovoltaic power generation according to the wind-solar fluctuation characteristic data;

[0040] The second determination module is used to determine the number of electrolytic cells to be scheduled in the electrolytic cell array at the current time according to the predicted value of wind power generation and the predicted value of photovoltaic power generation;

[0041] The first construction module is used to obtain and construct a multi-objective cost function based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs; wherein the multi-objective cost function at least includes: an electrolyzer operation and maintenance cost item, an electrolyzer start and stop cost item, and an energy storage battery system operation and maintenance cost item;

[0042] The second building module is used to build a target constraint condition according to the number of electrolyzers, the predicted value of wind power generation, and the predicted value of photovoltaic power generation; wherein the target constraint condition at least includes: a system overall power balance constraint condition;

[0043] A solution module, used to obtain and generate a corresponding target scheduling strategy according to the operating power of the electrolytic cell to be scheduled at the current time in the electrolytic cell array through optimization solution based on the multi-objective cost function and the target constraint conditions;

[0044] The control module is used to control and adjust the operation of the electrolyzer array at the current time according to the target scheduling strategy.

[0045] The present specification also provides a computer-readable storage medium having computer instructions stored thereon, and when the instructions are executed by a processor, the relevant steps of the operation control method of the electrolytic cell array are implemented.

[0046] Based on the operation control method and device of the electrolyzer array provided in the present specification, when the fluctuation amplitude of the wind and solar power generation data of the target wind and solar power station is monitored to be greater than the preset amplitude threshold, the wind and solar fluctuation data for the current time can be first obtained and used to determine the predicted value of wind power generation and the predicted value of photovoltaic power generation; then, according to the predicted value of wind power generation and the predicted value of photovoltaic power generation, the number of electrolyzers to be scheduled in the electrolyzer array at the current time is determined; and according to the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs, a multi-objective cost function is constructed, which at least includes the electrolyzer operation and maintenance cost item, the electrolyzer start and stop cost item, the energy storage battery system operation and maintenance cost item, etc.; at the same time, according to the number of electrolyzers, the predicted value of wind power generation, and the predicted value of photovoltaic power generation, a target constraint condition including at least the overall power balance constraint condition of the system is constructed; then, based on the multi-objective cost function and the target constraint condition, the corresponding target scheduling strategy is obtained and used through optimization solution to control and adjust the operation of the electrolyzer array at the current time. This makes it better adapted to large-scale fluctuations in wind and solar power generation. When wind and solar power generation fluctuates greatly, it can automatically control and adjust the operation of the system's electrolyzer array in a timely and accurate manner, so as to effectively improve the overall operating efficiency and overall profitability of the electrolyzer array, reduce power abandonment, extend the service life of electrolyzers and other equipment, and ensure safe operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of this specification, the drawings required for use in the embodiments will be briefly introduced below. The drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 is a flow chart of an operation control method of an electrolytic cell array provided by an embodiment of the present specification;

[0049] Figure 2 It is a schematic diagram of an embodiment of an operation control method of an electrolytic cell array provided by an embodiment of this specification, in a scenario example;

[0050] Figure 3 It is a schematic diagram of an embodiment of an operation control method of an electrolytic cell array provided by an embodiment of this specification, in a scenario example;

[0051] Figure 4 It is a schematic diagram of an embodiment of an operation control method of an electrolytic cell array provided by an embodiment of this specification, in a scenario example;

[0052] Figure 5It is a schematic diagram of the structure of a server provided by an embodiment of this specification;

[0053] Figure 6 It is a schematic diagram of the structure of an operation control device of an electrolytic cell array provided by an embodiment of the present specification;

[0054] Figure 7 It is a schematic diagram of an embodiment of an operation control method of an electrolytic cell array provided by an embodiment of this specification, in a scenario example;

[0055] Figure 8 It is a schematic diagram of an embodiment of an operation control method of an electrolytic cell array provided by an embodiment of this specification, in a scenario example;

[0056] Fig. 9 It is a schematic diagram of an embodiment of an operation control method of an electrolytic cell array provided by an embodiment of this specification, in a scenario example;

[0057] Fig.10 It is a schematic diagram of an embodiment of an operation control method of an electrolytic cell array provided by an embodiment of this specification, in a scenario example. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0059] It should be noted that in the embodiments of this specification, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0060] See also Figure 1 As shown, the embodiment of this specification provides an operation control method of an electrolytic cell array, which can be specifically applied to a server side. When implemented specifically, the method may include the following contents:

[0061] S101: when it is monitored that the fluctuation amplitude of wind and solar power generation data of a target wind and solar power station is greater than a preset amplitude threshold, obtaining wind and solar fluctuation characteristic data for the current time;

[0062] S102: Determine a predicted value of wind power generation and a predicted value of photovoltaic power generation according to the wind-solar fluctuation characteristic data;

[0063] S103: Determine the number of electrolytic cells to be scheduled in the electrolytic cell array at the current time according to the predicted value of wind power generation and the predicted value of photovoltaic power generation;

[0064] S104: Acquire and construct a multi-objective cost function based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs; wherein the multi-objective cost function at least includes: an electrolyzer operation and maintenance cost item, an electrolyzer start and stop cost item, and an energy storage battery system operation and maintenance cost item;

[0065] S105: constructing target constraints according to the number of electrolytic cells, the predicted value of wind power generation, and the predicted value of photovoltaic power generation; wherein the target constraints at least include: a system overall power balance constraint;

[0066] S106: Based on the multi-objective cost function and the objective constraint conditions, through optimization and solving, obtain and generate a corresponding objective scheduling strategy according to the operating power of the electrolytic cell to be scheduled at the current time in the electrolytic cell array;

[0067] S107: According to the target scheduling strategy, control and adjust the operation of the electrolytic cell array at the current time.

[0068] The above-mentioned target wind and solar power stations can be specifically understood as new energy power stations deployed in the target area, which jointly use wind power generation and photovoltaic power generation. Based on this power generation method, through the joint action of wind turbines and solar panels, the complementarity of wind and solar energy resources can be effectively utilized to provide a relatively stable and reliable power supply.

[0069] The above-mentioned target wind and solar power stations are also connected to the power grid and energy storage battery system respectively. Accordingly, the electric energy produced by the target wind and solar power stations can be fed into the power grid or transmitted to the energy storage battery system for storage. In addition, when the target wind and solar power stations and energy storage battery systems need electric energy for operation, they can also purchase electric energy through the power grid.

[0070] The energy storage battery system may specifically include an electrolyzer array and a hydrogen storage tank. Accordingly, the electrical energy delivered to the energy storage battery system may be converted into hydrogen energy through the electrolyzer array to generate hydrogen gas, and the generated hydrogen gas may then be stored in the hydrogen storage tank for subsequent use.

[0071] The electrolytic cell array may be an alkaline electrolytic cell array. Specifically, the electrolytic cell array may include a plurality of electrolytic cells. The plurality of electrolytic cells may be the same electrolytic cells or different electrolytic cells.

[0072] In specific implementation, the server can obtain the latest wind and solar power generation data of the target wind and solar power station at the current time every unit time interval (for example, every 1 hour); at the same time, query the power generation record of the target wind and solar power station to obtain the wind and solar power generation data of the adjacent time period; then combine the latest wind and solar power generation data and the wind and solar power generation data of the adjacent time period to construct a wind and solar power generation power curve; calculate the fluctuation amplitude of the wind and solar power generation data of the target wind and solar power station according to the wind and solar power generation power curve; and detect whether the fluctuation amplitude is greater than the preset amplitude threshold. The above-mentioned preset amplitude threshold is determined by performing big data analysis and machine learning on a large amount of historical wind and solar power generation data in the target area in advance.

[0073] In the case where it is determined that the fluctuation amplitude is greater than the preset amplitude threshold, it can be judged that the fluctuation scale of wind and solar power generation is large, and the current operation mode of the electrolyzer array cannot be well adapted, and then it is determined that the control adjustment trigger conditions for the electrolyzer array are met, and the operation control-related data processing of the electrolyzer array continues. On the contrary, in the case where it is determined that the fluctuation amplitude is less than or equal to the preset amplitude threshold, it can be judged that the fluctuation scale of wind and solar power generation is small, and the current operation mode of the electrolyzer array can still be adapted, and then it is determined that the control adjustment trigger conditions for the electrolyzer array are not met, and the fluctuation of wind and solar power generation data of the target wind and solar power station continues to be monitored.

[0074] In a specific implementation, the acquisition of the wind-solar fluctuation characteristic data for the current time may include: respectively collecting the wind-solar characteristic data for the current time, the time based on the previous unit time of the current time, the previous two unit times of the current time, and the previous three unit times of the current time, and arranging them in chronological order to obtain a corresponding characteristic data sequence as the wind-solar fluctuation characteristic data for the current time. The wind-solar characteristic data may specifically include one or more of the following: wind speed, wind direction, light intensity, light area, etc.

[0075] Furthermore, historical wind and solar data records are queried to obtain wind and solar characteristic data of the previous day, the previous week, the previous unit time based on the current time, the previous two unit time based on the current time, and the previous three unit time based on the current time, and to construct reference fluctuation characteristic data for the current time. At the same time, the environmental parameters of the target wind and solar power station at the current time can also be obtained. The environmental parameters include at least one of the following: temperature, humidity, weather, season, etc.

[0076] Then, the wind and solar fluctuation characteristic data at the current time, the reference fluctuation characteristic data at the current time, and the environmental parameters at the current time are combined according to certain rules to obtain the joint characteristic data set at the current time. The pre-trained wind and solar power generation prediction model is called to process the joint characteristic data set at the current time to obtain the predicted value of wind power generation at the current time and the predicted value of photovoltaic power generation.

[0077] The wind and solar power generation prediction model is a neural network model trained by transfer learning based on a sample feature data set of a pre-joint reference area and a historical joint feature data set of a target area. The reference area is specifically an area different from the target area with rich and comprehensive sample data.

[0078] In a specific implementation, when the electrolytic cells in the electrolytic cell array are identical, the number of electrolytic cells to be scheduled in the electrolytic cell array at the current time (which can be recorded as n) can be determined based on the predicted value of wind power generation power and the predicted value of photovoltaic power generation, combined with the performance parameters of the electrolytic cells; wherein the electrolytic cell power carrying capacity based on the number of electrolytic cells to be scheduled in the electrolytic cell array at the current time is greater than the sum of the predicted value of wind power generation power, the predicted value of photovoltaic power generation power, and the preset redundant power amount.

[0079] When the electrolytic cells in the electrolytic cell array are different, the number of electrolytic cells to be scheduled in the electrolytic cell array at the current time can be determined by optimizing the predicted values ​​of wind power generation and photovoltaic power generation combined with the performance parameters of different types of electrolytic cells.

[0080] In specific implementation, based on the coupling relationship between the target wind and solar power station, the energy storage battery system, and the power grid, as well as the principle of coordinated interactive control, the multi-objective cost function can be constructed by acquiring and using the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs.

[0081] The multi-objective cost function may also include multiple cost items. Specifically, the multi-objective cost function may include at least: an electrolyzer operation and maintenance cost item, an electrolyzer start-up and shutdown cost item, and an energy storage battery system operation and maintenance cost item.

[0082] The above-mentioned electrolytic cell operation and maintenance cost item can specifically represent the cost incurred for the construction, operation and maintenance of the electrolytic cells in the electrolytic cell array. The above-mentioned electrolytic cell start-up and shutdown cost item can specifically represent the loss cost caused by the start-up and shutdown of the electrolytic cell. The above-mentioned energy storage battery system operation and maintenance cost item can specifically represent the cost incurred for the construction, operation and maintenance of the battery part of the energy storage battery system.

[0083] Furthermore, the above-mentioned multi-objective cost function can also include: hydrogen storage tank operation and maintenance cost items, electric hydrogen load transfer cost items, power abandonment penalty cost items, system and power grid interaction power cost items, etc.

[0084] Among them, the above-mentioned hydrogen storage tank operation and maintenance cost item can specifically represent the cost incurred by the construction, operation and maintenance of the hydrogen storage tank part of the energy storage battery system. The above-mentioned electric hydrogen load transfer cost item can specifically represent the cost incurred in converting electrical energy into hydrogen energy, and converting hydrogen energy into electrical energy. The above-mentioned power abandonment penalty cost item can specifically represent the penalty cost incurred when the target wind and solar power station generates too much electricity, resulting in part of the excess electricity being unable to be fed into the power grid and unable to be stored. The above-mentioned system and power grid interaction power cost item can specifically represent the cost incurred when the system feeds the generated electricity into the power grid, and the system purchases electricity from the power grid.

[0085] In specific implementation, the target constraint conditions can be obtained and constructed based on the energy balance between the entire system, the number of electrolyzers, the predicted value of wind power generation, and the predicted value of photovoltaic power generation.

[0086] The above target constraints include at least: overall system power balance constraints.

[0087] Specifically, the system power loss can be determined based on the test experimental data of the target wind and solar power station and the energy storage battery system before operation; the power of interaction between the system and the power grid, the operating power of the energy storage battery system, and the operating working rate of the electrolyzer are taken as unknown quantities to be solved; the power of interaction between the system and the power grid, the operating power of the energy storage battery system, the operating power of the electrolyzer, the predicted value of photovoltaic power generation, the predicted value of wind power generation, and the system power loss are used to construct the overall power balance constraint of the system to obtain the corresponding target constraint.

[0088] Furthermore, the above target constraint conditions may also include: individual state constraint conditions of the electrolytic cell. In this embodiment, the state of the electrolytic cell may be divided into the following four types of states: rated operation state (which may be abbreviated as R), fluctuating operation state (which may be abbreviated as F), standby state (which may be abbreviated as S), and shutdown state (which may be abbreviated as D). In addition, the operation of the electrolytic cell may be divided into the following two types of operations: startup operation (which may be abbreviated as Y) and shutdown operation (which may be abbreviated as Z).

[0089] The rated operating state may specifically refer to an optimal operating state of the electrolytic cell operating at rated power.

[0090] The above-mentioned fluctuating operating state may specifically refer to a non-optimal operating state in which the electrolyzer operates at a power value that is less than the rated power and has fluctuations. When in a fluctuating operating state, the electrolyzer can use its own rapid response capability to fully absorb the energy generated by the fluctuations; however, if it is in a fluctuating operating state for a long time, it will damage the electrode material of the electrolyzer, thereby affecting the service life of the electrolyzer. In addition, in order to prevent the explosion of hydrogen and oxygen during the operation of the electrolyzer and ensure the safe operation of the system, the operating power of the electrolyzer in a fluctuating operating state can also be safely limited (for example, the operating power of the electrolyzer in a fluctuating operating state is required to be at least greater than the safety value: 15% of the rated power, etc.).

[0091] The standby state may specifically refer to a state where the electrolytic cell stops operating. When in the standby state, a certain amount of power still needs to be input to the electrolytic cell and the corresponding cell temperature and pressure need to be maintained so that the electrolytic cell can be quickly switched to an operating state such as a rated operating state or a fluctuating operating state when needed.

[0092] The shutdown state may specifically refer to a state where the electrolyzer is disconnected from the power supply and stops running. When in the shutdown state, the electrolyzer is completely powered off. In this state, if you want to restart and enter the running state, you need to wait for a period of time (for example, 30 minutes to 60 minutes, etc.).

[0093] In a specific implementation, binary data (including only 0 and 1) can be used to set the state values ​​of various types of states and the operation values ​​of various types of operations.

[0094] Specifically, when the electrolytic cell is in the rated operating state, the state value of the corresponding rated operating state is R=1, otherwise, R=0. When the electrolytic cell is in the fluctuating operating state, the state value of the corresponding fluctuating operating state is F=1, otherwise, F=0. When the electrolytic cell is in the standby state, the state value of the corresponding standby state is S=1, otherwise, S=0. When the electrolytic cell is in the shutdown state, the state value of the corresponding shutdown state is D=1, otherwise, D=0. In addition, when a startup operation is performed on the electrolytic cell, the operation value of the startup operation is Y=1, otherwise, Y=0. When a shutdown operation is performed on the electrolytic cell, the operation value of the shutdown operation is Z=1, otherwise, Z=0. Moreover, different types of states of the electrolytic cell will switch accordingly based on the corresponding conversion relationship under different conditions (for example, performing different operations). The switching of different types of states of the electrolytic cell can be referred to. Figure 2 shown.

[0095] During specific implementation, an overload operating state may also be introduced under some special circumstances. The above-mentioned overload operating state may specifically refer to a non-optimal operating state in which the electrolyzer operates at a power value greater than the rated power. When in an overload operating state, it will cause significant damage to the electrode material of the electrolyzer, and may even cause a safety accident. Therefore, under normal circumstances, the various types of states involved here do not include an overload operating state. However, in some extreme special cases, an overload operating state may be introduced so that the operating state of a single electrolyzer can be analyzed more finely and comprehensively.

[0096] In specific implementation, under the limitation of target constraints, the function value of the multi-objective cost function can be minimized as the optimization target, and the mixed integer linear programming algorithm can be used to perform multiple rounds of global optimization iterations until convergence, thereby obtaining the operating power (which can be recorded as P) of each of the multiple (n) electrolytic cells that need to be scheduled in the electrolytic cell array. ALKi (t)). Then, according to the operating power of the electrolytic cell, a target scheduling strategy for the electrolytic cell array at the current time can be generated. Then, according to the target scheduling strategy, the relevant electrolytic cells are finely controlled to operate according to the corresponding operating power at the current time, so as to achieve precise control and adjustment of the electrolytic cell array.

[0097] Based on the above embodiment, when the fluctuation amplitude of the wind and solar power generation data of the target wind and solar power station is monitored to be greater than the preset amplitude threshold, the wind and solar fluctuation data for the current time can be obtained and used to determine the predicted value of wind power generation and the predicted value of photovoltaic power generation; then, according to the predicted value of wind power generation and the predicted value of photovoltaic power generation, the number of electrolytic cells to be scheduled in the electrolytic cell array at the current time is determined; and according to the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs, a multi-objective cost function is constructed, which at least includes the electrolytic cell operation and maintenance cost item, the electrolytic cell start and stop cost item, and the energy storage battery system operation and maintenance cost item; at the same time, according to the number of electrolytic cells, the predicted value of wind power generation, and the predicted value of photovoltaic power generation, a target constraint condition including at least the overall power balance constraint condition of the system is constructed; then, based on the multi-objective cost function and the target constraint condition, the corresponding target scheduling strategy is obtained and used through optimization solution to control and adjust the operation of the electrolytic cell array at the current time. This makes it better adapted to large-scale fluctuations in wind and solar power generation. When wind and solar power generation fluctuate greatly, it can automatically control and adjust the operation of the system's electrolyzer array in a timely and accurate manner, so as to effectively improve the overall operating efficiency and overall rate of return of the electrolyzer array, extend the service life of electrolyzers and other equipment, and ensure safe operation.

[0098] In some embodiments, see Figure 3As shown, the above acquisition and construction of a multi-objective cost function based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs include:

[0099] S1: Obtain and determine the first type of characteristic parameters, the second type of characteristic parameters, and the third type of characteristic parameters based on the equipment attribute data of the target wind and solar power station, the power grid background data of the target area to which the target wind and solar power station belongs, and the current operating parameters of the target wind and solar power station and the energy storage battery system; wherein the first type of characteristic parameters at least include: the unit capacity construction cost of the electrolyzer, the rated capacity of the electrolyzer, and the electrolyzer operation and maintenance cost coefficient; the second type of characteristic parameters at least include: the electrolyzer startup cost and the electrolyzer shutdown cost; the third type of characteristic parameters at least include: the unit capacity construction cost of the energy storage battery system, the rated capacity of the energy storage battery system, and the energy storage battery system operation and maintenance cost coefficient;

[0100] S2: According to the preset construction rules, the first type of characteristic parameters are used to construct the electrolytic cell operation and maintenance cost item; the second type of characteristic parameters are used to construct the electrolytic cell start and stop cost item; and the third type of characteristic parameters are used to construct the energy storage battery system operation and maintenance cost item;

[0101] S3: Combine the electrolyzer operation and maintenance cost items, electrolyzer start and stop cost items, and energy storage battery system operation and maintenance cost items to construct the corresponding multi-objective cost function.

[0102] Based on the above embodiment, according to the preset construction rules, the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs can be jointly used to construct and utilize the electrolytic cell operation and maintenance cost items, the electrolytic cell start and stop cost items, and the energy storage battery system operation and maintenance cost items, so as to construct a multi-objective cost function suitable for large-scale fluctuation scenarios of wind and solar power generation.

[0103] In some embodiments, the multi-objective cost function may also include: hydrogen storage tank operation and maintenance cost items, electric hydrogen load transfer cost items, power abandonment penalty cost items, system and grid interaction power cost items, etc.

[0104] Accordingly, for specific implementation, please refer to Figure 4 As shown, the method may also include the following contents:

[0105] S1: Obtain and determine the fourth type of characteristic parameters, the fifth type of characteristic parameters, the sixth type of characteristic parameters, and the seventh type of characteristic parameters based on the equipment attribute data of the target wind and solar power station, the power grid background data of the target area to which the target wind and solar power station belongs, and the current operating parameters of the target wind and solar power station and the energy storage battery system; wherein the fourth type of characteristic parameters at least include: the construction cost per unit capacity of the hydrogen storage tank, the capacity of the hydrogen storage tank, and the operation and maintenance cost coefficient of the hydrogen storage tank; the fifth type of characteristic parameters at least include: the unit transfer cost of the electric load, the conversion power of electric energy to hydrogen energy, the unit cost of the hydrogen load, and the conversion power of hydrogen energy to electric energy; the sixth type of characteristic parameters at least include: the unit cost of abandoned electricity and the amount of abandoned electricity; the seventh type of characteristic parameters at least include: the unit electricity price and the amount of purchased electricity;

[0106] S2: According to the preset construction rules, the fourth type of characteristic parameters are used to construct the hydrogen storage tank operation and maintenance cost item; the fifth type of characteristic parameters are used to construct the electric hydrogen load transfer cost item; the sixth type of characteristic parameters are used to construct the power abandonment penalty cost item; the seventh type of characteristic parameters are used to construct the system and grid interaction power cost item.

[0107] Furthermore, we can combine the electrolyzer operation and maintenance cost items, electrolyzer start and shutdown cost items, energy storage battery system operation and maintenance cost items, hydrogen storage tank operation and maintenance cost items, electric hydrogen load transfer cost items, power abandonment penalty cost items, and system and grid interaction power cost items to construct a relatively more comprehensive and effective multi-objective cost function.

[0108] In some embodiments, the above acquisition and construction of a multi-objective cost function based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs may include:

[0109] According to the following formula, construct a multi-objective cost function:

[0110] min C=a 1 C 1 + a 2 C 2 +a 3 C 3 + a 4 C 4 + a 5 C 5 + a 6 C 6 + a 7 C 7

[0111] Among them, C is the multi-objective cost function value, C 1 is the electrolytic cell operation and maintenance cost item, C 2 is the hydrogen storage tank operation and maintenance cost item, C 3 is the cost item of electric hydrogen load transfer, C4 is the penalty cost for power curtailment, C 5 is the power cost item for interaction between the system and the grid, C 6 is the start-up and shutdown cost of the electrolytic cell, C 7 is the operation and maintenance cost of the energy storage battery system, a 1 is the first weight coefficient, a 2 is the second weight coefficient, a 3 is the third weight coefficient, a 4 is the fourth weight coefficient, a 5 is the fifth weight coefficient, a 6 is the sixth weight coefficient, a 7 is the seventh weight coefficient.

[0112] Based on the above embodiments, multiple different dimensions can be integrated by constructing and combining multiple cost items based on different dimensions to construct a multi-objective cost function that integrates multiple cost factors and is suitable for large-scale fluctuation scenarios of wind and solar power generation. It is relatively comprehensive and has better effects, so that subsequently, based on the multi-objective cost function, a target scheduling strategy with relatively higher reference value, relatively stronger targeting and more refined can be obtained through optimization and solution.

[0113] In some embodiments, according to the preset construction rules, the first type of characteristic parameters are used to construct the electrolytic cell operation and maintenance cost item. In specific implementation, the electrolytic cell operation and maintenance cost item can be constructed according to the following formula:

[0114] C 1 =β ALK c ALK nP N

[0115] Among them, c ALK The unit capacity of the electrolytic cell is P N is the rated power of the electrolyzer, β ALK is the electrolytic cell operation and maintenance cost coefficient, and n is the number of electrolytic cells.

[0116] In some embodiments, according to the preset construction rules, the second type of characteristic parameters are used to construct the electrolytic cell start-stop cost item. In specific implementation, the electrolytic cell start-stop cost item can be constructed according to the following formula:

[0117] C 6 =c ON +c OFF

[0118] Among them, c ON is the electrolytic cell startup cost, c OFF The cost of electrolyzer downtime.

[0119] In some embodiments, according to the preset construction rules, the third type of characteristic parameters are used to construct the energy storage battery system operation and maintenance cost item. In specific implementation, the energy storage battery system operation and maintenance cost item can be constructed according to the following formula:

[0120] C 7 =β batt c batt P N batt

[0121] Among them, c batt is the unit capacity construction cost of the energy storage battery system, P N batt is the rated capacity of the energy storage battery system, β batt It is the operation and maintenance cost coefficient of the energy storage battery system.

[0122] In some embodiments, according to the preset construction rules, the fourth type of characteristic parameters are used to construct the hydrogen storage tank operation and maintenance cost item. In specific implementation, the hydrogen storage tank operation and maintenance cost item can be constructed according to the following formula:

[0123] C 2 =β tan c tan Q N tan

[0124] Among them, c tan is the construction cost per unit capacity of hydrogen storage tank, Q N tan is the capacity of the hydrogen storage tank, β tan is the operation and maintenance cost coefficient of the hydrogen storage tank.

[0125] In some embodiments, according to the preset construction rules, the fifth type of characteristic parameters are used to construct the electric hydrogen load transfer cost item. In specific implementation, the electric hydrogen load transfer cost item can be constructed according to the following formula:

[0126]

[0127] Among them, c Ltr is the unit transfer cost of electric load, P tr (t) is the conversion power of electric energy into hydrogen energy, c Qtr is the unit cost of hydrogen load, Q tr (t) is the conversion power of hydrogen energy into electrical energy.

[0128] In some embodiments, according to a preset construction rule, the sixth type of characteristic parameters are used to construct a power abandonment penalty cost item. In specific implementation, the power abandonment penalty cost item can be constructed according to the following formula:

[0129]

[0130] Among them, c cut is the unit power curtailment cost, P cut W (t) is the amount of power abandoned, and T is the duration of the approximate time of the current time.

[0131] In specific implementation, the electrolyzer in the electrolyzer array that is currently scheduled and in the rated operating state or the fluctuating operating state can be first determined as the currently operating electrolyzer; based on the currently operating electrolyzer, the power carrying capacity of the electrolyzer array is determined; at the same time, based on the predicted value of wind power generation and the predicted value of photovoltaic power generation, the power generation is calculated; and then based on the power generation, the power carrying capacity of the electrolyzer array and the grid interaction power at the current time, the corresponding abandoned power is determined.

[0132] In some embodiments, according to the preset construction rules, the seventh type of characteristic parameters are used to construct the system-grid interaction power cost item. In specific implementation, the system-grid interaction power cost item can be constructed according to the following formula:

[0133]

[0134] Among them, c grid is the unit electricity price, P grid (t) is the amount of electricity purchased (or the power of interaction between the system and the grid at time t), and T represents the duration of the neighborhood of the current time t.

[0135] In the specific implementation, taking into account that the power grid electricity price often fluctuates within a time range, in the specific implementation, multiple historical electricity price data and related data corresponding to the historical electricity price data (for example, time information, season information, electricity supply and demand information, etc. corresponding to the historical electricity price data) can be obtained based on the power grid background data of the target area; then, based on the multiple historical electricity price data and the related data corresponding to the historical electricity price data, an electricity price change curve of the power grid in the target area is obtained through data fitting; then, the electricity price data of the current time, the electricity price data of the adjacent time of the current time, and the corresponding related data are obtained; and by substituting the electricity price data of the current time, the electricity price data of the adjacent time of the current time, and the corresponding related data into the electricity price change curve, the unit electricity price that changes with time within the adjacent time range of the current time is calculated and determined based on the electricity price change data of the current time.

[0136] In some embodiments, when constructing a multi-objective cost function, the first weight coefficient, the second weight coefficient, the third weight coefficient, the fourth weight coefficient, the fifth weight coefficient, the sixth weight coefficient, and the seventh weight coefficient can take the same data value, for example, all 1; or they can take different data values.

[0137] In specific implementation, when the first weight coefficient, the second weight coefficient, the third weight coefficient, the fourth weight coefficient, the fifth weight coefficient, the sixth weight coefficient, and the seventh weight coefficient take different data values, a large amount of historical data can be obtained and used, and the specific values ​​of the weight coefficients corresponding to different cost items can be determined through big data analysis combined with expert experience. Specifically, the values ​​of the first weight coefficient, the sixth weight coefficient, and the seventh weight coefficient are greater than the other weight coefficients. Thus, the constructed multi-objective cost function can be made relatively more accurate.

[0138] In some embodiments, the target constraint conditions are constructed based on the number of electrolyzers, the predicted value of wind power generation, and the predicted value of photovoltaic power generation. When implemented specifically, the target constraint conditions include:

[0139]

[0140]

[0141] Among them, P grid (t) is the interaction power between the system and the grid at time t, P batt (t) is the operating power of the energy storage battery system at time t, P pv (t) is the predicted value of photovoltaic power generation at time t, P wind (t) is the predicted value of wind power generation at time t, is the power abandoned at time t, P L (t) is the system power loss at time t, P ALK (t) is the total operating power of the electrolytic cell at time t, P ALKi (t) is the operating power of the electrolytic cell numbered i at time t, t is the current time, and n is the number of electrolytic cells to be scheduled at the current time.

[0142] Specifically, the data value of the power exchanged between the system and the grid at time t can be positive or negative. When the data value is positive, it indicates that electric energy is purchased from the grid; on the contrary, when the data value is negative, it indicates that electric energy is fed into the grid.

[0143] In specific implementation, the above-mentioned system overall power balance constraint conditions are used to ensure that the overall input power and output power of the system are consistent, thereby achieving overall power balance.

[0144] Based on the above embodiment, by constructing the overall power balance constraint condition of the system based on the overall dimension of the system, the target constraint condition that meets the requirements can be obtained. Then, when performing subsequent optimization and solution, on the one hand, the target constraint condition can be used to better set boundary constraints to guide the exploration and solution in the right direction; on the other hand, the target constraint condition can also be used to ultimately determine the required operating power of each electrolytic cell scheduled in the electrolytic cell array.

[0145] In some embodiments, the above target constraints may further include: individual state constraints of the electrolytic cell;

[0146] The individual state constraint of the electrolytic cell is constructed based on the state values ​​of various types of states of the electrolytic cell and the operation values ​​of various types of operations;

[0147] The multiple types of states may specifically include: a rated operation state, a fluctuating operation state, a standby state, and a shutdown state; the multiple types of operations may specifically include: a startup operation and a shutdown operation.

[0148] In some embodiments, the electrolyzer individual state constraint condition includes at least one of the following: electrolyzer fluctuation operation state safety operation constraint sub-condition, electrolyzer input power constraint sub-condition, electrolyzer hydrogen production rate constraint sub-condition, electrolyzer state switching constraint sub-condition, etc. In addition, the above electrolyzer individual state constraint condition may also include other types of constraint sub-conditions based on the electrolyzer individual state, such as electrolyzer type state constraint sub-condition.

[0149] In specific implementation, according to specific application scenarios and processing requirements, the above-mentioned individual state constraint condition of the electrolyzer may include one of the multiple constraint sub-conditions listed above, or may include a combination of multiple of the multiple constraint conditions listed above. Accordingly, when a combination of multiple constraint sub-conditions is used as the individual state constraint condition of the electrolyzer, it is possible to comprehensively utilize the constraint characteristics of the individual electrolyzer at multiple levels such as operation safety, power output, hydrogen production rate, state switching, etc., to obtain a constraint condition with richer content and relatively better use effect for the individual electrolyzer.

[0150] Based on the above embodiments, based on the individual dimensions of a single electrolytic cell, by considering and utilizing the state data and operation data of the individual electrolytic cell, the individual state constraints of the electrolytic cell can be introduced into the target constraints, so that the target constraints can be constrained based on two different dimensions, namely the overall system dimension and the individual electrolytic cell dimension. In this way, on the one hand, the exploration scope of the optimization solution can be further narrowed, the data processing amount of the optimization solution can be effectively reduced, and the overall optimization solution efficiency can be improved; on the other hand, the target scheduling strategy finally determined can be matched not only with the system as a whole, but also with the specific conditions of the individual electrolytic cells being scheduled, so that the electrolytic cells in the electrolytic cell array can be adjusted and controlled more finely and targetedly based on the target scheduling strategy, which can improve the operating efficiency and improve the operating income while effectively ensuring the operating safety, avoiding damage to the electrolytic cell and other related equipment, and extending the service life of the electrolytic cell and other equipment.

[0151] In some embodiments, when constructing the individual state constraint conditions of the electrolytic cell, combined with the operation safety requirements of the electrolytic cell, it can be determined that the operating power of the electrolytic cell in different types of states should satisfy the following relationship, thereby establishing the electrolytic cell type state constraint sub-conditions:

[0152] P ALK =P N Rated operating status

[0153] 0.15P N ≤P ALK ≤P N Fluctuating operating state

[0154] P ALK =P ON Standby operation status

[0155] P ALK =0 Stop running state

[0156] Among them, P ALK is the operating power of the electrolyzer (in MW), P N is the rated power of the electrolyzer, P ON is the standby power of the electrolyzer.

[0157] In some embodiments, during specific implementation, the safety operation constraint sub-condition for the electrolytic cell fluctuating operation state of the electrolytic cell numbered n may be constructed according to the following formula:

[0158]

[0159] Among them, P N is the rated power of the electrolyzer, is the fluctuating power of the electrolytic cell numbered n under the fluctuating operation state, F N It is the state value of the fluctuation state of the electrolytic cell numbered n.

[0160] In some embodiments, during specific implementation, the electrolytic cell input power constraint sub-condition for the electrolytic cell numbered n may be constructed according to the following formula:

[0161]

[0162]

[0163] in, is the operating power of the electrolytic cell numbered n, P n is the rated power of the electrolyzer, R n is the state value of the rated operating state of the electrolytic cell numbered n, S n is the state value of the pending state of the electrolytic cell numbered n, P ON is the standby power of the electrolyzer, is the fluctuating power of the electrolytic cell numbered n, P ALK is the total operating power of the electrolyzer array, and n is the number of scheduled electrolyzers in the electrolyzer array.

[0164] In some embodiments, during specific implementation, the electrolyzer hydrogen production rate constraint sub-condition for the electrolyzer numbered n may be constructed according to the following formula:

[0165]

[0166]

[0167] Among them, R n is the state value of the rated operating state of the electrolytic cell numbered n, is the hydrogen production rate of the electrolyzer numbered n (in units of ), H HV is the hydrogen production efficiency of the electrolyzer (the unit can be ), Q n is the heat dissipation power of the electrolyzer, and η is the total working efficiency of the n electrolyzers scheduled in the electrolyzer array.

[0168] In some embodiments, during specific implementation, the electrolytic cell state switching constraint sub-condition for the electrolytic cell numbered n may be constructed according to the following formula:

[0169] -D n(t-2) + D n(t-1) -D n(t) ≤0

[0170] R n(t)+ F n(t) + S n(t) + D n(t-1) -1≤Y n(t)

[0171] R n(t-1) + F n(t-1) + S n(t-1) + D n(t) -1≤Z n(t)

[0172] R n(t) + F n(t) + S n(t) + D n(t) =1

[0173] Among them, D n(t-2) is the state value of the shutdown state at t-2, D n(t-1) is the state value of the shutdown state at t-1, D n(t) is the state value of the shutdown state at time t, R n(t) is the state value of the rated operating state at time t, F n(t) is the state value of the fluctuating running state at time t, S n(t) is the state value of the standby state at time t, Y n(t) is the operation value for starting the operation at time t, R n(t-1) is the state value of the rated operating state at t-1, F n(t-1) is the state value of the fluctuating running state at time t-1, S n(t-1) is the state value of the standby state at t-1, Z n(t) is the operation value of the shutdown operation at time t, t is the current time, t-1 is the time of the previous unit time, and t-2 is the time of the previous two unit times.

[0174] It should be noted that the first line of the formula above is used to limit the electrolytic cell numbered n to a state other than the shutdown state (including: standby state, rated operation state, fluctuating operation state, etc.) when it is in the shutdown state. It takes at least one unit time to switch to other states.

[0175] The second line of the above formula is used to define the constraint relationship that needs to be satisfied between the operation value when the electrolytic cell numbered n is started up and the current time and the state value of the previous unit time.

[0176] The third line of the above formula is used to define the constraint relationship that needs to be satisfied between the operation value when performing a shutdown operation on the electrolytic cell numbered n and the current time and the state value of the previous unit time.

[0177] The fourth line of the above formula is used to limit the state of the electrolytic cell numbered n to only one of the four types of states at a point in time.

[0178] Based on the above embodiments, the state values ​​of various types of states and the operation values ​​of various operations can be effectively utilized to construct the electrolytic cell state switching constraint sub-conditions for individual electrolytic cells, so that the individual electrolytic cells can be finely and effectively constrained based on the electrolytic cell state switching level.

[0179] In some embodiments, the operating power of the electrolytic cell to be scheduled at the current time in the electrolytic cell array is obtained through optimization solution based on the multi-objective cost function and the objective constraints. In specific implementation, the particle optimization algorithm and the mixed integer linear programming algorithm can be used in combination to perform multiple rounds of global optimization solution to quickly and accurately solve the operating power of the electrolytic cell to be scheduled at the current time in the electrolytic cell array that meets the requirements.

[0180] Specifically, multiple computing particles can be determined according to the number of electrolytic cells; and the initial solution of each computing particle is determined; then each computing particle is called based on a multi-objective cost function and objective constraints, and a mixed integer linear programming algorithm is used to perform multiple rounds of global optimization solutions to determine the operating power of the electrolytic cell to be scheduled in the electrolytic cell array at the current time.

[0181] In specific implementation, multiple computing particles can be called in the following manner to perform global optimization solution for the current round: obtain and screen out the retained solution of the current round that meets the retention condition based on the solution of each computing particle in the previous round; mark the computing particles holding the retained solution of the current round as first-class particles, and mark the other computing particles except the first-class particles among the multiple computing particles as second-class particles; perform preset encoding processing on the retained solution of the current round to obtain corresponding encoding data; and perform transformation processing based on the encoding data to obtain multiple transformed codes; perform decoding processing on the transformed codes to obtain multiple transformed solutions of the current round; and assign the multiple transformed solutions of the current round to the corresponding second-class particles; call the first-class particles and the second-class particles locally, and use the mixed integer linear programming algorithm to perform iterative optimization solution using the retained solution of the current round and the transformed solution of the current round respectively, to obtain the solution of the current round of each computing particle.

[0182] According to the above method, a solution that meets the requirements can be obtained through multiple rounds of global optimization. Then, the operating power of the electrolytic cell to be scheduled at the current time in the electrolytic cell array can be determined based on the solution.

[0183] In some embodiments, based on the multi-objective cost function and the objective constraints, the operating power of the electrolyzer to be scheduled in the electrolyzer array at the current time is obtained through optimization solution, and the corresponding system-grid interaction power and the operating power of the energy storage battery system can also be obtained synchronously.

[0184] Accordingly, when the method is implemented, it may also include: determining the remaining power at the current time according to the predicted value of wind power generation and the predicted value of photovoltaic power generation; and determining the processing method for the remaining power according to the power of interaction between the system and the power grid and the operating power of the energy storage battery system. The processing method includes one of the following: feeding the remaining power into the power grid; storing the remaining power into the energy storage battery system; feeding part of the remaining power into the power grid and storing the other part into the energy storage battery system.

[0185] This can provide more accurate guidance for the processing of the surplus electric energy generated by wind and solar power generation at the current time.

[0186] In some embodiments, according to the target scheduling strategy, in addition to adjusting and controlling the operating power of the electrolytic cells in the electrolytic cell array, other operating parameters such as the operating time, start and stop times, standby time and fluctuating power of the relevant electrolytic cells can also be finely adjusted, so as to obtain relatively better operating results.

[0187] As can be seen from the above, based on the operation control method of the electrolyzer array provided in the embodiment of the present specification, when the fluctuation amplitude of the wind and solar power generation data of the target wind and solar power station is monitored to be greater than the preset amplitude threshold, the wind and solar fluctuation data for the current time can be obtained and used to determine the predicted value of wind power generation and the predicted value of photovoltaic power generation; then, according to the predicted value of wind power generation and the predicted value of photovoltaic power generation, the number of electrolyzers to be scheduled in the electrolyzer array at the current time is determined; and according to the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs, a multi-objective cost function is constructed, which at least includes the electrolyzer operation and maintenance cost item, the electrolyzer start and stop cost item, and the energy storage battery system operation and maintenance cost item; at the same time, according to the number of electrolyzers, the predicted value of wind power generation, and the predicted value of photovoltaic power generation, a target constraint condition including at least the overall power balance constraint condition of the system is constructed; then, based on the multi-objective cost function and the target constraint condition, the corresponding target scheduling strategy is obtained and used through optimization solution to control and adjust the operation of the electrolyzer array at the current time. This makes it better adapted to large-scale fluctuations in wind and solar power generation. When wind and solar power generation fluctuates greatly, it can automatically control and adjust the operation of the system's electrolyzer array in a timely and accurate manner, so as to effectively improve the overall operating efficiency and overall rate of return of the electrolyzer array, reduce power abandonment, extend the service life of electrolyzers and other equipment, and ensure safe operation.

[0188] This specification embodiment provides a server, referring to Figure 5 The server includes a network communication port 501, a processor 502 and a memory 503, and the above structures are connected through internal cables so that each structure can perform specific data interaction.

[0189] The network communication port 501 can be specifically used to receive a trigger instruction.

[0190] The processor 502 can be specifically used to respond to the trigger instruction, and when it is monitored that the fluctuation amplitude of the wind and solar power generation data of the target wind and solar power station is greater than the preset amplitude threshold, obtain the wind and solar fluctuation characteristic data for the current time; determine the predicted value of wind power generation and the predicted value of photovoltaic power generation according to the wind and solar fluctuation characteristic data; determine the number of electrolytic cells to be scheduled in the electrolytic cell array at the current time according to the predicted value of wind power generation and the predicted value of photovoltaic power generation; obtain and construct a multi-objective cost according to the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs Function; wherein the multi-objective cost function includes at least: electrolytic cell operation and maintenance cost item, electrolytic cell start and stop cost item, energy storage battery system operation and maintenance cost item; construct target constraints according to the number of electrolytic cells, the predicted value of wind power generation, and the predicted value of photovoltaic power generation; wherein the target constraints include at least: the overall power balance constraint of the system; based on the multi-objective cost function and the target constraints, obtain and generate the corresponding target scheduling strategy according to the operating power of the electrolytic cells to be scheduled in the electrolytic cell array at the current time through optimization solution; according to the target scheduling strategy, control and adjust the operation of the electrolytic cell array at the current time.

[0191] The memory 503 may be specifically used to store corresponding instruction programs, as well as related data such as multi-objective cost functions and objective constraint conditions.

[0192] Based on the above method, the relevant structural performance of the server can be effectively utilized, the data processing speed of the electronic equipment can be improved, and the data processing of the operation control of the electrolytic cell array can be efficiently realized.

[0193] In this embodiment, the network communication port 501 can be a virtual port that is bound to different communication protocols so that different data can be sent or received. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. In addition, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM, CDMA, etc.; it can also be a Wifi chip; it can also be a Bluetooth chip.

[0194] In this embodiment, the processor 502 may be implemented in any appropriate manner. For example, the processor may take the form of a microprocessor or a processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) executable by the (micro)processor, a logic gate, a switch, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, etc. This specification does not limit this.

[0195] In this embodiment, the memory 503 may include multiple levels. In a digital system, anything that can store binary data can be a memory; in an integrated circuit, a circuit with a storage function but no physical form is also called a memory, such as RAM, FIFO, etc.; in a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0196] The embodiments of the present specification also provide a computer-readable storage medium based on the operation control method of the electrolyzer array mentioned above, wherein the computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed, the following are achieved: when it is monitored that the fluctuation amplitude of the wind and solar power generation data of the target wind and solar power station is greater than a preset amplitude threshold, the wind and solar fluctuation characteristic data for the current time are obtained; based on the wind and solar fluctuation characteristic data, the predicted value of the wind power generation and the predicted value of the photovoltaic power generation are determined; based on the predicted value of the wind power generation and the predicted value of the photovoltaic power generation, the number of electrolyzers to be scheduled in the electrolyzer array at the current time is determined; and the equipment attribute data of the target wind and solar power station, The multi-objective cost function is constructed based on the power grid background data of the target area to which the target wind and solar power station belongs; wherein the multi-objective cost function at least includes: electrolyzer operation and maintenance cost items, electrolyzer start and stop cost items, and energy storage battery system operation and maintenance cost items; according to the number of electrolyzers, the predicted value of wind power generation power, and the predicted value of photovoltaic power generation power, the target constraint conditions are constructed; wherein the target constraint conditions at least include: the overall power balance constraint conditions of the system; based on the multi-objective cost function and the target constraint conditions, through optimization solution, the operating power of the electrolyzer to be scheduled in the electrolyzer array at the current time is obtained and the corresponding target scheduling strategy is generated; according to the target scheduling strategy, the operation of the electrolyzer array at the current time is controlled and adjusted.

[0197] In this embodiment, the storage medium includes but is not limited to a random access memory (RAM), a read-only memory (ROM), a cache, a hard disk (HDD) or a memory card. The memory may be used to store computer program instructions. The network communication unit may be an interface for network connection communication set in accordance with the standard specified by the communication protocol.

[0198] In this embodiment, the functions and effects specifically implemented by the program instructions stored in the computer-readable storage medium can be explained in comparison with other implementations and will not be described in detail here.

[0199] The embodiments of the present specification also provide a computer program product, which at least includes a computer program. When the computer program is executed by a processor, the following method steps are implemented: when it is monitored that the fluctuation amplitude of wind and solar power generation data of a target wind and solar power station is greater than a preset amplitude threshold, wind and solar fluctuation characteristic data for the current time are obtained; based on the wind and solar fluctuation characteristic data, a predicted value of wind power generation and a predicted value of photovoltaic power generation are determined; based on the predicted value of wind power generation and the predicted value of photovoltaic power generation, the number of electrolytic cells to be scheduled in the electrolytic cell array at the current time is determined; and based on the equipment attribute data of the target wind and solar power station and the target area to which the target wind and solar power station belongs, the target area to which the target wind and solar power station belongs is obtained and the target area to which the target wind and solar power station belongs is determined; The multi-objective cost function is constructed based on the background data of the power grid in the domain; wherein the multi-objective cost function at least includes: electrolyzer operation and maintenance cost item, electrolyzer start and stop cost item, and energy storage battery system operation and maintenance cost item; according to the number of electrolyzers, the predicted value of wind power generation power, and the predicted value of photovoltaic power generation power, the target constraint condition is constructed; wherein the target constraint condition at least includes: the overall power balance constraint condition of the system; based on the multi-objective cost function and the target constraint condition, through optimization solution, the operating power of the electrolyzer to be scheduled in the electrolyzer array at the current time is obtained and a corresponding target scheduling strategy is generated; according to the target scheduling strategy, the operation of the electrolyzer array at the current time is controlled and adjusted.

[0200] See also Figure 6 As shown, the embodiment of this specification also provides an operation control device for an electrolytic cell array, which may specifically include the following structural modules:

[0201] The acquisition module 601 may be specifically used to acquire the wind-solar power fluctuation characteristic data for the current time when the fluctuation amplitude of the wind-solar power generation data of the target wind-solar power station is greater than a preset amplitude threshold;

[0202] The first determination module 602 may be specifically configured to determine a predicted value of wind power generation and a predicted value of photovoltaic power generation according to the wind-solar fluctuation characteristic data;

[0203] The second determination module 603 may be specifically used to determine the number of electrolytic cells to be scheduled in the electrolytic cell array at the current time according to the predicted value of wind power generation and the predicted value of photovoltaic power generation;

[0204] The first construction module 604 can be specifically used to obtain and construct a multi-objective cost function based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs; wherein the multi-objective cost function at least includes: an electrolyzer operation and maintenance cost item, an electrolyzer start and stop cost item, and an energy storage battery system operation and maintenance cost item;

[0205] The second construction module 605 can be specifically used to construct target constraints according to the number of electrolyzers, the predicted value of wind power generation, and the predicted value of photovoltaic power generation; wherein the target constraints at least include: system overall power balance constraints;

[0206] The solution module 606 may be specifically used to obtain and generate a corresponding target scheduling strategy according to the operating power of the electrolytic cell to be scheduled at the current time in the electrolytic cell array through optimization solution based on the multi-objective cost function and the target constraint condition;

[0207] The control module 607 may be specifically used to control and adjust the operation of the electrolytic cell array at the current time according to the target scheduling strategy.

[0208] In some embodiments, when the first construction module 604 is implemented, the multi-objective cost function can be constructed by acquiring and based on the equipment attribute data of the target wind and solar power station and the grid background data of the target area to which the target wind and solar power station belongs in the following manner: acquiring and based on the equipment attribute data of the target wind and solar power station and the grid background data of the target area to which the target wind and solar power station belongs, determining the first type of characteristic parameters, the second type of characteristic parameters, and the third type of characteristic parameters; wherein the first type of characteristic parameters at least include: the unit capacity construction cost of the electrolyzer, the rated capacity of the electrolyzer, and the electrolyzer operation and maintenance cost coefficient; the second type The characteristic parameters include at least: electrolytic cell startup cost, electrolytic cell shutdown cost, and the third type of characteristic parameters include at least: energy storage battery system unit capacity construction cost, rated capacity of energy storage battery system, energy storage battery system operation and maintenance cost coefficient; according to the preset construction rules, the first type of characteristic parameters are used to construct the electrolytic cell operation and maintenance cost item; the second type of characteristic parameters are used to construct the electrolytic cell start and stop cost item; the third type of characteristic parameters are used to construct the energy storage battery system operation and maintenance cost item; the electrolytic cell operation and maintenance cost item, the electrolytic cell start and stop cost item, and the energy storage battery system operation and maintenance cost item are combined to construct the corresponding multi-objective cost function.

[0209] In some embodiments, the multi-objective cost function may also specifically include: hydrogen storage tank operation and maintenance cost items, electric hydrogen load transfer cost items, power abandonment penalty cost items, system and grid interaction power cost items, etc.

[0210] In some embodiments, when the first construction module 604 is implemented, the multi-objective cost function can be constructed by acquiring and based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs in the following manner:

[0211] min C=a 1 C 1 + a 2 C 2 +a 3 C 3 + a 4 C 4 + a 5 C 5 + a 6 C 6 + a 7 C 7

[0212] Among them, C is the multi-objective cost function value, C 1 is the electrolytic cell operation and maintenance cost item, C 2 is the hydrogen storage tank operation and maintenance cost item, C 3 is the cost item of electric hydrogen load transfer, C 4 is the penalty cost for power curtailment, C 5 is the power cost item for interaction between the system and the grid, C 6 is the start-up and shutdown cost of the electrolytic cell, C 7 is the operation and maintenance cost of the energy storage battery system, a 1 is the first weight coefficient, a 2 is the second weight coefficient, a 3 is the third weight coefficient, a 4 is the fourth weight coefficient, a 5 is the fifth weight coefficient, a 6 is the sixth weight coefficient, a 7 is the seventh weight coefficient.

[0213] In some embodiments, when the second building module 605 is implemented, the target constraint condition may be constructed according to the number of electrolyzers, the predicted value of wind power generation, and the predicted value of photovoltaic power generation in the following manner:

[0214]

[0215]

[0216] Among them, P grid (t) is the interaction power between the system and the grid at time t, P batt (t) is the operating power of the energy storage battery system at time t, P pv (t) is the predicted value of photovoltaic power generation at time t, P wind (t) is the predicted value of wind power generation at time t, is the power abandoned at time t, P L (t) is the system power loss at time t, P ALK (t) is the total operating power of the electrolytic cell at time t, P ALKi (t) is the operating power of the electrolytic cell numbered i at time t, t is the current time, and n is the number of electrolytic cells to be scheduled at the current time.

[0217] In some embodiments, the target constraint conditions may further specifically include: individual state constraint conditions of the electrolytic cell;

[0218] The individual state constraint of the electrolytic cell is constructed based on the state values ​​of various types of states of the electrolytic cell and the operation values ​​of various types of operations;

[0219] The various types of states include: rated operation state, fluctuating operation state, standby state, and shutdown state; the various types of operations include: startup operation and shutdown operation.

[0220] In some embodiments, the individual state constraint conditions of the electrolyzer may specifically include at least one of the following: a safety operation constraint sub-condition of the electrolyzer's fluctuating operation state, an electrolyzer's input power constraint sub-condition, an electrolyzer's hydrogen production rate constraint sub-condition, an electrolyzer's state switching constraint sub-condition, etc.

[0221] In some embodiments, when the second construction module 605 is specifically implemented, the electrolytic cell state switching constraint sub-condition for the electrolytic cell numbered n may be constructed in the following manner according to the following formula:

[0222] -D n(t-2) + D n(t-1) -D n(t) ≤0

[0223] R n(t) + F n(t) + S n(t) + D n(t-1) -1≤Y n(t)

[0224] R n(t-1) + F n(t-1) + S n(t-1) + D n(t) -1≤Z n(t)

[0225] R n(t) + F n(t) + S n(t) + D n(t) =1

[0226] Among them, D n(t-2) is the state value of the shutdown state at t-2, D n(t-1) is the state value of the shutdown state at t-1, D n(t) is the state value of the shutdown state at time t, R n(t) is the state value of the rated operating state at time t, F n(t) is the state value of the fluctuating running state at time t, S n(t) is the state value of the standby state at time t, Y n(t) is the operation value for starting the operation at time t, R n(t-1) is the state value of the rated operating state at t-1, F n(t-1) is the state value of the fluctuating running state at time t-1, S n(t-1) is the state value of the standby state at t-1, Z n(t) is the operation value of the shutdown operation at time t, t is the current time, t-1 is the time of the previous unit time, and t-2 is the time of the previous two unit times.

[0227] It should be noted that the units, devices or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described separately by functions divided into various modules. Of course, when implementing this specification, the functions of each module can be implemented in the same or more software and / or hardware, or the modules that implement the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0228] It can be seen from the above that the operation control device of the electrolytic cell array provided in the embodiment of this specification can be better adapted to the large-scale fluctuation scenario of wind and solar power generation. When the fluctuation of wind and solar power generation is large, it can automatically control and adjust the operation of the system electrolytic cell array in a timely and accurate manner, so as to effectively improve the overall operation efficiency and overall yield of the electrolytic cell array, extend the service life of equipment such as the electrolytic cell, and ensure safe operation.

[0229] In a specific scenario example, the operation control method of the electrolytic cell array provided in this specification can be applied to implement the operation control of the alkaline electrolytic cell array. The specific implementation process can be referred to the following content.

[0230] In this scenario example, considering that renewable energy sources such as wind power and solar energy have significant intermittency and volatility, this poses a challenge to energy storage and grid stability. As a key device for water electrolysis to produce hydrogen, the traditional single alkaline electrolyzer plays an important role in this process. The operating status of the electrolyzer directly affects the production efficiency of hydrogen and the long-term stability of the equipment. Considering that in the application of energy storage to smooth renewable energy, the electrolyzer array needs to cope with large mutations in input power. This mutation will not only cause the electrolyzer to be unable to operate stably in the optimal state, affecting the production efficiency of hydrogen, but may also increase energy consumption due to frequent adjustments to the operating parameters of the equipment, and may even cause safety accidents due to exceeding the safe operating range of the equipment. Therefore, how to optimize the operation control strategy of the electrolyzer so that it can adapt to the volatility of renewable energy while ensuring the safety and operating efficiency of the equipment is the technical problem to be solved in this scenario example. That is, the goal that this scenario example hopes to achieve is to solve the problem in the prior art that a single electrolyzer cannot meet the requirements of high-power operation and that an alkaline electrolyzer array cannot meet the requirements of the highest efficiency operation under large-scale power fluctuations, so as to broaden the operating range of the hydrogen production system, minimize the economic cost of electricity during operation and scheduling, and make the single electrolyzer operate stably at the rated operation as much as possible to extend its service life.

[0231] Based on the above considerations, see Figure 7 As shown, this scenario example proposes the following solutions:

[0232] First, when wind power and photovoltaic power output fluctuate, relevant characteristic data (e.g., wind and photovoltaic fluctuation characteristic data) are collected according to preset time intervals; secondly, by predicting wind power (e.g., determining the predicted value of wind power generation and the predicted value of photovoltaic power generation), the number of operating units of the electrolyzer (e.g., determining the number of electrolyzers to be scheduled in the electrolyzer array) is determined.

[0233] Next, the goal is to minimize the economic cost of electricity during operation and scheduling (for example, construct a multi-objective cost function); at the same time, the coupling relationship and coordinated interactive control principle among the wind, solar, hydrogen and storage equipment, as well as constraints such as energy balance between systems, operation safety constraints, and energy state constraints are comprehensively considered (for example, construct target constraints).

[0234] Then, a multi-objective fitness function is used to perform rolling optimization of the electrolyzer to balance the electrolyzer's operating time, start-stop times, standby time, and fluctuating power.

[0235] Finally, the power and operation status of the electrolyzers are allocated in turn according to the real-time power situation. An optimal operation control model for the electrolyzer array with the goal of optimizing system economic efficiency (e.g., target scheduling strategy) is established.

[0236] In this way, the goal is to minimize the economic cost of electricity during the operation and scheduling period, comprehensively consider the coupling relationship and coordinated interactive control principle between the wind, solar, hydrogen and storage equipment, as well as the energy balance between systems, operation safety constraints, energy state constraints and other constraints, and realize the hourly power allocation of the multi-objective electrolyzer array operation optimization scheduling plan. Under the condition that the wind power, photovoltaic and load scenarios remain unchanged, the influence of the addition of the operation control method of the alkaline electrolyzer array on the economic benefits of the system and the influence of renewable energy volatility are compared and discussed, which can provide certain theoretical support for the large-scale demonstration and industrial operation of the wind, solar, hydrogen and storage microgrid system. Ensure that each energy storage system in the integrated energy system fully absorbs renewable energy electricity and minimizes the cost of purchasing electricity. A multi-objective electrolyzer array operation optimization scheduling model with the goal of optimizing system economy is established.

[0237] Furthermore, the operation and maintenance costs of the electrolyzer and hydrogen storage tank, the power cost of the system and the grid interaction, the wind abandonment cost, and the minimum transfer cost of electricity and hydrogen loads are comprehensively considered as the objective function. (e.g., a multi-objective cost function), can be expressed as:

[0238] min C=a 1 C 1 + a 2 C 2 +a 3 C 3 + a 4 C 4 + a 5 C 5 + a 6 C 6 + a 7 C 7

[0239] Where C is the total cost; C 1 is the operation and maintenance cost of the electrolyzer (for example, the electrolyzer operation and maintenance cost item); C 2 is the operation and maintenance cost of the hydrogen storage tank (for example, the hydrogen storage tank operation and maintenance cost item); C 3 is the electricity and hydrogen load transfer cost (for example, the electricity and hydrogen load transfer cost item); C 4 is the penalty cost for wind power curtailment (e.g., penalty cost item for power curtailment); C 5 is the power cost of interaction between the system and the grid (for example, the power cost item of interaction between the system and the grid); C 6 is the electrolytic cell startup and shutdown cost (for example, the electrolytic cell startup and shutdown cost item); C 7It is the operation and maintenance cost of the energy storage battery system (for example, the energy storage battery system operation and maintenance cost item).

[0240] Furthermore, the investment and operation and maintenance costs of multiple electrolyzers C 1 , expressed as:

[0241] C 1 =β ALK c ALK nP N

[0242] In the formula, c ALK is the construction cost per unit capacity of the electrolytic cell; n is the number of electrolytic cells; P N is the rated capacity of the electrolytic cell; β ALK is the operation and maintenance cost coefficient.

[0243] Furthermore, the investment and operation and maintenance costs of hydrogen storage tanks 2 , expressed as:

[0244] C 2 =β tan c tan Q N tan

[0245] In the formula, c tan is the construction cost per unit capacity of hydrogen storage tank; Q N tan is the capacity of the hydrogen storage tank, β tan is the operation and maintenance cost coefficient.

[0246] Furthermore, the electricity and hydrogen load transfer cost C 3 , expressed as:

[0247]

[0248] In the formula, c Ltr 、c Qtr are the unit transfer costs of electricity and hydrogen loads respectively.

[0249] Furthermore, the penalty cost of wind curtailment C 4 , expressed as:

[0250]

[0251] In the formula, c cut P is the penalty cost for unit wind curtailment; cut W (t) is the amount of power abandoned at time t.

[0252] Furthermore, the electricity purchase cost C 5 , expressed as:

[0253]

[0254] In the formula, c grid is the unit electricity price; P grid (t) is the electricity purchased at time t.

[0255] Furthermore, the electrolyzer start-up and shutdown cost C 6 , expressed as:

[0256] C 6 =c ON +c OFF

[0257] In the formula, c ON is the electrolytic cell startup cost; c OFF The cost of electrolyzer downtime.

[0258] Furthermore, the investment and operation and maintenance costs of energy storage batteries 7 , expressed as:

[0259] C 7 =β batt c batt P N batt

[0260] In the formula, c batt P is the unit capacity construction cost of energy storage battery; N batt is the rated capacity of the energy storage battery, β batt is the operation and maintenance coefficient and maintenance cost coefficient of the energy storage battery.

[0261] Furthermore, the power of the system interacting with the grid, wind power output, wind abandonment, electric load, power balance of the electrolyzer array, and operation constraints of the electrolyzer array (e.g., target constraints) are:

[0262]

[0263]

[0264] Where P grid (t), P batt (t), P pv (t), P wind (t), P L (t), P ALK (t) respectively represent the real-time system-grid interaction power, energy storage power station operating power, photovoltaic power field output power, wind power field output power, system power loss, and total electrolyzer operating power in the system at a certain moment. ALKi (t) represents the operating power of the i-th ALK electrolyzer at time t.

[0265] Further, in this example scenario, running constraints for an electrolyzer array follows these steps:

[0266] 1. Define four binary variables to represent the operating status of the alkaline electrolyzer: rated operating status R , Fluctuating operating state F , Standby mode S , shutdown state D Two binary variables are introduced to represent the start and stop actions of the electrolyzer: start Y (e.g., start operation) and shutdown Z (e.g., shutdown operation). The specific contents are as follows:

[0267] 1) Rated operating state and fluctuating operating state are the normal production states of alkaline electrolyzers. Among them, the rated operating state is its optimal operating state. The fluctuating operating state is the non-optimal operating state of the electrolyzer. However, at this time, the alkaline electrolyzer can fully absorb renewable energy by using its own rapid response capability, but long-term fluctuating operating state will damage the electrode materials of the electrolyzer and affect its service life. In addition, in order to prevent hydrogen and oxygen from exploding, the electrolyzer is strictly prohibited from operating below the safe power limit. The minimum safe operating power of the alkaline electrolyzer is 10%-25% of the rated power. This article selects 15% of the rated power as the minimum safe operating power of the alkaline electrolyzer.

[0268] 2) The overload operation state is the state in which the electrolytic cell is overloaded in a short period of time. In actual production, the operation of the alkaline electrolytic cell in this state will damage the electrolytic cell electrode material, reduce the operating efficiency, and even cause serious production safety accidents. Therefore, this article does not consider the overload state.

[0269] 3) The standby state is when the electrolytic cell stops running, but the alkaline electrolytic cell still needs to maintain a certain input power to keep the cell temperature and pressure, so as to quickly switch to the production state. In addition, the electrolytic cell will be completely powered off in the shutdown state, and it usually takes 30~60 minutes to restart.

[0270] 2. In this scenario example, the operating status of the alkaline electrolyzer can be divided into the following four categories:

[0271] P ALK =P N Rated operating status

[0272] 0.15P N ≤P ALK ≤P N Fluctuating operating state

[0273] P ALK =P ON Standby operation status

[0274] P ALK=0 Stop running state

[0275] Where P ALK is the input power of the electrolyzer, MW; P N and P ON They are the rated power and standby power of the electrolyzer respectively.

[0276] 3. n The power of an electrolyzer in a fluctuating state is defined as the fluctuating power and satisfies the following constraints:

[0277]

[0278] in, is the fluctuating power, P N is the rated power of the electrolyzer.

[0279] 4. No. n The input power of an electrolyzer can be expressed as:

[0280]

[0281]

[0282] in, For the n The input power of an electrolyzer, P ALK for n The total input power when the electrolyzer combination is running.

[0283] 5. The mathematical expression of the hydrogen production rate of the electrolyzer is:

[0284]

[0285]

[0286] Among them, R n is the state value of the rated operating state of the electrolytic cell numbered n, is the hydrogen production rate of the electrolyzer numbered n (in units of ), H HV is the hydrogen production efficiency of the electrolyzer (the unit can be ), Q n is the heat dissipation power of the electrolyzer, and η is the total working efficiency of the n electrolyzers scheduled in the electrolyzer array.

[0287] 6. The operation state and state change of the electrolytic cell need to meet certain logical constraints, including the restart time of 1 unit after the electrolytic cell is shut down (the specific time is adjusted according to the actual production), the coupling relationship between the operation state variable and the startup and shutdown action variables, and the electrolytic cell can only be in one operation state at a time, which can be expressed as the following formula:

[0288] -D n(t-2) + D n(t-1) -D n(t) ≤0

[0289] R n(t) + F n(t) + S n(t) + D n(t-1) -1≤Y n(t)

[0290] R n(t-1) + F n(t-1) + S n(t-1) + D n(t) -1≤Z n(t)

[0291] R n(t) + F n(t) + S n(t) + D n(t) =1.

[0292] The above formulas respectively show the restart time of 1 hour after the electrolytic cell is shut down, and the coupling relationship between the electrolytic cell state variables and the startup and shutdown action variables; the electrolytic cell can only be in one operating state at a certain moment.

[0293] The above solution is specifically verified and explained through the application of two embodiments (Embodiment 1 and Embodiment 2).

[0294] Example 1

[0295] A specific embodiment of the present invention discloses an operation control method of an alkaline electrolyzer array under an optimized scheduling strategy of a wind-solar coupled hydrogen production system, such as Figure 7 As shown, the following steps are included:

[0296] When wind power and photovoltaic power output fluctuates, wind and photovoltaic fluctuation data are collected according to preset time intervals.

[0297] Specifically, in each time period obtained by the time interval, the day-ahead forecast data of wind power and photovoltaic output is obtained through the dispatching center, including the fluctuating wind power and photovoltaic output of the wind-solar-coupled hydrogen production system.

[0298] Specifically, the time interval is set according to actual needs.

[0299] First, based on the aforementioned mathematical model, with the goal of minimizing the economic cost of electricity during operation and scheduling, the coupling relationship and coordinated interactive control principle among the wind, solar, hydrogen and storage equipment, as well as the energy balance between systems, operation safety constraints, energy state constraints and other constraints are comprehensively considered to achieve the hourly power allocation of the day-ahead economic scheduling plan of the wind, solar, hydrogen and storage microgrid system.

[0300] In the calculation example, the wind farm capacity is 200kW, the photovoltaic capacity is 200kW, the load is 200kW, the battery energy storage system is 50kW / 100kWh, and the hydrogen storage system is 100kW / 150kW. The wind and photovoltaic power generation data of a wind-solar coupling power station are collected, and the sampling interval is 15 minutes per point. The wind-solar load power forecast curve of the day-ahead is obtained. The system power difference is obtained based on the existing wind-solar load curve. The purpose of the control algorithm is to optimize the output of the energy storage system to fill the system power difference with the best economy and the minimum wind and solar abandonment rate. According to the day-ahead forecast data of wind, solar and load, the day-ahead grid dispatching instructions, and the peak-valley price difference of the grid, the day-ahead hydrogen storage system, hydrogen electrolyzer output and battery energy storage system output dispatching plan are formulated to ensure that each energy storage system in the integrated energy system fully absorbs renewable energy power and minimizes the cost of purchasing electricity. A multi-objective day-ahead optimization dispatching model with the optimal system economy as the goal is established.

[0301] Furthermore, the operation and maintenance costs of the electrolyzer and hydrogen storage tank, the power cost of the system and the grid interaction, the wind abandonment cost, and the minimum transfer cost of electricity and hydrogen loads are comprehensively considered as the objective function. , expressed as:

[0302] min C=C 1 + C 2 +C 3 + C 4 + C 5 + C 6 + C 7

[0303] Where C is the total cost; C 1 is the operation and maintenance cost of the electrolyzer; C 2 is the operation and maintenance cost of the hydrogen storage tank; C 3 is the electricity and hydrogen load transfer cost; C 4 Penalty cost for wind curtailment; C 5 is the power cost of interaction between the system and the grid; C 6 is the start-up and shutdown cost of the electrolyzer, C 7 It is the operation and maintenance cost of the energy storage battery system.

[0304] Furthermore, the investment and operation and maintenance costs of multiple electrolyzers C 1 , expressed as:

[0305] C 1 =β ALK c ALK nP N

[0306] In the formula, c ALK is the construction cost per unit capacity of the electrolytic cell; n is the number of electrolytic cells; P Nis the rated capacity of the electrolytic cell; β ALK is the operation and maintenance cost coefficient.

[0307] Furthermore, the investment and operation and maintenance costs of hydrogen storage tanks 2 , expressed as:

[0308] C 2 =β tan c tan Q N tan

[0309] In the formula, c tan Q is the construction cost per unit capacity of hydrogen storage tank; N tan is the capacity of the hydrogen storage tank, β tan is the operation and maintenance cost coefficient.

[0310] Furthermore, the electricity and hydrogen load transfer cost C 3 , expressed as:

[0311]

[0312] In the formula, c Ltr 、c Qtr are the unit transfer costs of electricity and hydrogen loads respectively.

[0313] Furthermore, the penalty cost of wind curtailment C 4 , expressed as:

[0314]

[0315] In the formula, c cut P is the penalty cost for unit wind curtailment; cut W (t) is the amount of power abandoned at time t.

[0316] Furthermore, the electricity purchase cost C 5 , expressed as:

[0317]

[0318] In the formula, c grid is the unit electricity price; P grid (t) is the electricity purchased at time t.

[0319] Furthermore, the electrolyzer start-up and shutdown cost C 6 , expressed as:

[0320] C 6 =c ON +c OFF

[0321] In the formula, c ONis the electrolytic cell startup cost; c OFF The cost of electrolyzer downtime.

[0322] Furthermore, the investment and operation and maintenance costs of energy storage batteries 7 , expressed as:

[0323] C 7 =β batt c batt P N batt

[0324] In the formula, c batt is the unit capacity construction cost of the energy storage battery, P N batt is the rated capacity of the energy storage battery, β batt is the operation and maintenance coefficient and maintenance cost coefficient of the energy storage battery.

[0325] It can be understood that, considering that the operation control method of the alkaline electrolyzer array should minimize the total system cost as much as possible, the cost structure mainly considers seven parts. The first part is the operation and maintenance cost of the electrolyzer in the wind-solar coupled hydrogen production system, which reduces the cost by reducing unnecessary losses in the hydrogen production system; the second part is the operation and maintenance cost of the hydrogen storage tank; the third part is the electricity and hydrogen load transfer cost, because the hydrogen produced by the electrolyzer has good economic benefits; the fourth part is the wind abandonment penalty cost, which maximizes the utilization rate of wind energy to reduce the unit cost; the fifth part is the power cost of the system and the power grid interaction, which reduces the cost while ensuring power supply; the sixth part is the electrolyzer start-up and shutdown cost, which reduces the cost by the number of electrolyzer start-ups and shutdowns; the seventh part is the energy storage battery system operation and maintenance cost, which reduces the cost by reducing unnecessary losses in the energy storage battery; thus, an economic cost function of the electric-hydrogen coupling system is established in this embodiment.

[0326] During implementation, in step S3, the operating constraints of the electricity-hydrogen coupling system include grid interaction power constraints, wind power output constraints, wind abandonment constraints, electric load constraints, power balance constraints of the electrolyzer array, and electrolyzer array operation constraints.

[0327] Furthermore, the power constraints of the interaction between the system and the power grid, the wind power output constraints, the wind abandonment constraints, the electric load constraints and the power balance constraints of the electrolyzer array are:

[0328]

[0329]

[0330] Where P grid (t), P batt (t), P pv (t), P wind (t), PL (t), P ALK (t) respectively represent the real-time system-grid interaction power, energy storage power station operating power, photovoltaic power field output power, wind power field output power, system power loss, and total electrolyzer operating power in the system at a certain moment. ALKi (t) represents the operating power of the i-th ALK electrolyzer at time t.

[0331] Furthermore, the n The power of an electrolyzer in a fluctuating state is defined as the fluctuating power and satisfies the following constraints:

[0332]

[0333] in, is the fluctuating power, P N is the rated power of the electrolyzer.

[0334] By optimizing the operation strategy of the electricity-hydrogen coupling system, the mixed integer linear programming algorithm is used to perform global optimization and obtain the optimal scheduling result.

[0335] Specifically, MATLAB is used for programming, and the CPLEX solver and other optimization software in the YAMIP toolbox are called to solve the model. By adjusting the power balance constraints including the interaction power between the system and the grid, wind power output, wind abandonment, electric load and electrolyzer array, the energy storage state quantity SOC before the day of departure when the economic cost function of the wind-solar coupled hydrogen production system takes the minimum value is obtained. ref (t) and charge and discharge power P batt,ref (t), ALK electrolyzer output P ALK (t), hydrogen storage system state quantity S HS,ref (t). A mixed integer linear programming algorithm is used to perform global optimization and obtain the optimal scheduling result for the day ahead.

[0336] Example 2

[0337] In order to verify the correctness of Example 1 of the present invention, this embodiment provides a specific example. In the example, the wind farm capacity is 290MW, the photovoltaic capacity is 200MW, the battery energy storage system is 75MW / 300MWh, and the hydrogen storage system is 180MW. The wind and photovoltaic power generation data of a wind-solar coupling power station are collected, and the sampling interval is one point per minute.

[0338] Solution 1: Using the method proposed in Example 1, after the wind power and photovoltaic output fluctuations occur, the electrolyzer array system is optimized and scheduled according to the wind and solar data, and then the wind-solar coupled hydrogen production system and the power grid interaction power constraints, wind power output constraints, wind abandonment constraints, electric load constraints and electrolyzer array optimization operation constraints and power balance constraints as well as the grid time-of-use electricity price are considered as proposed. Finally, the electrolyzer array is optimized through the scheduling strategy to minimize the economic cost of the system.

[0339] Solution 2: The method proposed in Example 1 is not used. After the wind power and photovoltaic output fluctuation occurs, the electrolyzer array system is not optimized and dispatched according to the wind and solar data. It only considers the power constraints of the wind-solar coupled hydrogen production system and the power grid interaction, wind power output constraints, wind abandonment constraints, power load constraints, power balance constraints of the electrolyzer array, and the grid time-of-use electricity price, etc. Finally, the wind-solar coupled hydrogen production system is optimized for the day before, so that the system's intraday power can track the day-ahead plan as much as possible and the economic cost is minimized.

[0340] Combination Figure 8 (Combined operation of electrolytic cells), Fig. 9 (Total system operation cost line), Fig.10 From the content shown in (cost curve per standard cubic meter of hydrogen), it can be seen that: compared with Scheme 2, the cost of wind-solar coupled hydrogen production Scheme 1 is less than that of Scheme 2; compared with Scheme 2, the cost per standard cubic meter of hydrogen of the wind-solar coupled hydrogen production system Scheme 1 is less than that of Scheme 2. Therefore, the method proposed in Example 1 can reduce the economic cost of the wind-solar coupled hydrogen production system when the wind and solar power fluctuate, reduce system losses, and has good economic performance.

[0341] Through the above scenario examples, it is verified that the operation control method of the electrolyzer array provided in this specification can indeed solve the problems in the prior art that a single electrolyzer cannot meet the high-power operation and the alkaline electrolyzer array cannot meet the highest efficiency operation for large-scale power fluctuations, so as to broaden the operating range of the hydrogen production system, minimize the economic cost of electricity during operation and scheduling, and make the single electrolyzer operate stably at the rated operation as much as possible to extend its service life.

[0342] Although the present specification provides method operation steps as described in the embodiments or flow charts, more or fewer operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only order of execution. When the device or client product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiments or the drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The terms "include", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements inherent to such a process, method, product or device. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. The words first, second, etc. are used to represent the name, and do not represent any particular order.

[0343] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0344] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer-readable storage media including storage devices.

[0345] Through the description of the above embodiments, it can be known that those skilled in the art can clearly understand that the present specification can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present specification can essentially be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in each embodiment of the present specification or some parts of the embodiments.

[0346] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. This specification can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0347] Although the present specification is described through embodiments, those skilled in the art will appreciate that there are many modifications and changes to the present specification without departing from the spirit of the present specification, and it is intended that the appended claims include these modifications and changes without departing from the spirit of the present specification.

Claims

1. A method for controlling the operation of an electrolytic cell array, characterized in that: include: When the fluctuation amplitude of the wind and solar power generation data of the target wind and solar power station is monitored to be greater than a preset amplitude threshold, obtaining wind and solar fluctuation characteristic data for the current time; Determine the predicted value of wind power generation and the predicted value of photovoltaic power generation according to the wind-solar fluctuation characteristic data; Determine the number of electrolytic cells to be scheduled in the electrolytic cell array at the current time according to the predicted value of wind power generation and the predicted value of photovoltaic power generation; Acquire and construct a multi-objective cost function based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs; wherein the multi-objective cost function at least includes: electrolyzer operation and maintenance cost item, electrolyzer start and stop cost item, energy storage battery system operation and maintenance cost item, hydrogen storage tank operation and maintenance cost item, electric hydrogen load transfer cost item, power abandonment penalty cost item, system and power grid interaction power cost item; the electrolyzer start and stop cost item is used to characterize the loss cost caused by starting and shutting down the electrolyzer; According to the number of electrolyzers, the predicted value of wind power generation, and the predicted value of photovoltaic power generation, target constraints are constructed; wherein the target constraints include at least: system overall power balance constraints and electrolyzer individual state constraints; the electrolyzer individual state constraints include at least: electrolyzer fluctuating operation state safety operation constraint sub-conditions, electrolyzer input power constraint sub-conditions, and electrolyzer hydrogen production rate constraint sub-conditions; Based on the multi-objective cost function and the objective constraint conditions, by using mixed integer linear programming to perform optimization and solving, the operating power of the electrolytic cell to be scheduled at the current time in the electrolytic cell array is obtained and a corresponding objective scheduling strategy is generated; Control and adjust the operation of the electrolyzer array at the current time according to the target scheduling strategy; The method also includes: obtaining the latest wind and solar power generation data of the target wind and solar power station at the current time at each unit time interval; at the same time, querying the power generation record of the target wind and solar power station to obtain the wind and solar power generation data of the adjacent time period; using the latest wind and solar power generation data and the wind and solar power generation data of the adjacent time period in combination to construct a wind and solar power generation power curve; calculating the fluctuation amplitude of the wind and solar power generation data of the target wind and solar power station according to the wind and solar power generation power curve; and detecting whether the fluctuation amplitude is greater than a preset amplitude threshold; when the fluctuation amplitude is greater than the preset amplitude threshold, determining that the control adjustment trigger condition for the electrolytic cell array is met; wherein the preset amplitude threshold is determined by performing big data analysis and machine learning on the historical wind and solar power generation data of the target area in advance.

2. The method according to claim 1, characterized in that Obtain and construct a multi-objective cost function based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs, including: Obtain and determine the first type of characteristic parameters, the second type of characteristic parameters, and the third type of characteristic parameters based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs; wherein the first type of characteristic parameters at least include: the unit capacity construction cost of the electrolyzer, the rated capacity of the electrolyzer, and the electrolyzer operation and maintenance cost coefficient; the second type of characteristic parameters at least include: the electrolyzer startup cost and the electrolyzer shutdown cost; the third type of characteristic parameters at least include: the unit capacity construction cost of the energy storage battery system, the rated capacity of the energy storage battery system, and the energy storage battery system operation and maintenance cost coefficient; According to the preset construction rules, the first type of characteristic parameters are used to construct the electrolytic cell operation and maintenance cost item; the second type of characteristic parameters are used to construct the electrolytic cell start and stop cost item; the third type of characteristic parameters are used to construct the energy storage battery system operation and maintenance cost item; The electrolyzer operation and maintenance cost items, electrolyzer start and stop cost items, and energy storage battery system operation and maintenance cost items are combined to construct the corresponding multi-objective cost function.

3. The method according to claim 1, characterized in that Obtain and construct a multi-objective cost function based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs, including: According to the following formula, construct a multi-objective cost function: min C=a1C1+ a2C2+a3C3+ a4C4+ a5C5+ a6C6+ a7C7 Among them, C is the value of the multi-objective cost function, C1 is the electrolyzer operation and maintenance cost item, C2 is the hydrogen storage tank operation and maintenance cost item, C3 is the electric hydrogen load transfer cost item, C4 is the power abandonment penalty cost item, C5 is the system and grid interaction power cost item, C6 is the electrolyzer start and stop cost item, C7 is the energy storage battery system operation and maintenance cost item, a1 is the first weight coefficient, a2 is the second weight coefficient, a3 is the third weight coefficient, a4 is the fourth weight coefficient, a5 is the fifth weight coefficient, a6 is the sixth weight coefficient, and a7 is the seventh weight coefficient.

4. The method according to claim 1, characterized in that: According to the number of electrolyzers, the predicted value of wind power generation, and the predicted value of photovoltaic power generation, the target constraints are constructed, including: Among them, P grid (t) is the interaction power between the system and the grid at time t, P batt (t) is the operating power of the energy storage battery system at time t, P pv (t) is the predicted value of photovoltaic power generation at time t, P wind (t) is the predicted value of wind power generation at time t, is the power abandoned at time t, P L (t) is the system power loss at time t, P ALK (t) is the total operating power of the electrolytic cell at time t, P ALKi (t) is the operating power of the electrolytic cell numbered i at time t, t is the current time, and n is the number of electrolytic cells to be scheduled at the current time.

5. The method according to claim 1, characterized in that The individual state constraint condition of the electrolytic cell is constructed based on state values ​​of various types of states of the electrolytic cell and operation values ​​of various types of operations; The various types of states include: rated operation state, fluctuating operation state, standby state, and shutdown state; the various types of operations include: startup operation and shutdown operation.

6. The method according to claim 5, characterized in that The electrolytic cell individual state constraint condition also includes: an electrolytic cell state switching constraint sub-condition.

7. The method according to claim 6, characterized in that The electrolytic cell state switching constraint sub-condition for the electrolytic cell numbered n is constructed according to the following formula: - D n(t-2) + D n(t-1) - D n(t) ≤0 R n(t) + F n(t) + S n(t) + D n(t-1) -1≤Y n(t) R n(t-1) + F n(t-1) + S n(t-1) + D n(t) -1≤Z n(t) R n(t) + F n(t) + S n(t) + D n(t) =1 Among them, D n(t-2) is the state value of the shutdown state at t-2, D n(t-1) is the state value of the shutdown state at t-1, D n(t) is the state value of the shutdown state at time t, R n(t) is the state value of the rated operating state at time t, F n(t) is the state value of the fluctuating running state at time t, S n(t) is the state value of the standby state at time t, Y n(t) is the operation value for starting the operation at time t, R n(t-1) is the state value of the rated operating state at t-1, F n(t-1) is the state value of the fluctuating running state at time t-1, S n(t-1) is the state value of the standby state at t-1, Z n(t) is the operation value of the shutdown operation at time t, t is the current time, t-1 is the time of the previous unit time, and t-2 is the time of the previous two unit times.

8. An operation control device for an electrolytic cell array, characterized in that: include: An acquisition module is used to acquire wind-solar power fluctuation characteristic data for the current time when the fluctuation amplitude of wind-solar power generation data of the target wind-solar power station is greater than a preset amplitude threshold; A first determination module is used to determine a predicted value of wind power generation and a predicted value of photovoltaic power generation according to the wind-solar fluctuation characteristic data; The second determination module is used to determine the number of electrolytic cells to be scheduled in the electrolytic cell array at the current time according to the predicted value of wind power generation and the predicted value of photovoltaic power generation; The first construction module is used to obtain and construct a multi-objective cost function based on the equipment attribute data of the target wind and solar power station and the power grid background data of the target area to which the target wind and solar power station belongs; wherein the multi-objective cost function at least includes: electrolyzer operation and maintenance cost item, electrolyzer start and stop cost item, energy storage battery system operation and maintenance cost item, hydrogen storage tank operation and maintenance cost item, electric hydrogen load transfer cost item, power abandonment penalty cost item, system and power grid interaction power cost item; the electrolyzer start and stop cost item is used to characterize the loss cost caused by the start and shutdown of the electrolyzer; The second construction module is used to construct target constraints according to the number of electrolyzers, the predicted value of wind power generation, and the predicted value of photovoltaic power generation; wherein the target constraints include at least: system overall power balance constraints and electrolyzer individual state constraints; the electrolyzer individual state constraints include at least: electrolyzer fluctuating operation state safety operation constraint sub-conditions, electrolyzer input power constraint sub-conditions, and electrolyzer hydrogen production rate constraint sub-conditions; A solution module, for obtaining and generating a corresponding target scheduling strategy according to the operating power of the electrolytic cell to be scheduled at the current time in the electrolytic cell array by optimizing and solving the multi-objective cost function and the target constraint conditions by using mixed integer linear programming; A control module, used to control and adjust the operation of the electrolyzer array at the current time according to the target scheduling strategy; The device is also used to: obtain the latest wind and solar power generation data of the target wind and solar power station at the current time at intervals of a unit of time; at the same time, query the power generation record of the target wind and solar power station to obtain the wind and solar power generation data of the adjacent time period; use the latest wind and solar power generation data and the wind and solar power generation data of the adjacent time period in combination to construct a wind and solar power generation power curve; calculate the fluctuation amplitude of the wind and solar power generation data of the target wind and solar power station according to the wind and solar power generation power curve; and detect whether the fluctuation amplitude is greater than a preset amplitude threshold; when the fluctuation amplitude is greater than the preset amplitude threshold, determine that the control adjustment trigger condition for the electrolytic cell array is met; wherein the preset amplitude threshold is determined by performing big data analysis and machine learning on the historical wind and solar power generation data of the target area in advance.

9. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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