A multi-electrolyzer collaborative optimization control method for adapting to wide-range power fluctuations

Abstract

The present invention discloses a collaborative optimization control method for multiple electrolyzers adaptable to wide-range power fluctuations, belonging to the field of new energy technologies. The method of the present invention uses an optimization framework to perform collaborative optimization of the power of the hydrogen production power supply, the power of the electrolyzers, the power of the electrical energy storage, and the sequential production states of the electrolyzers. In the optimization, a finite-time domain rolling solution method is adopted, the rolling time domain length is on the hour level, and the sampling time is on the minute level or the hour level. An optimization objective function and a constraint model are respectively established, and a mathematical programming or heuristic algorithm is used to solve the optimization model to obtain the operating powers of the various devices of the multiple-electrolyzer hydrogen production system and the states of the electrolyzers. The method of the present invention enables the battery energy storage to weaken the frequent start and stop of the electrolyzers under wide-range power fluctuation conditions, and enables the battery energy storage to ensure that the electrolytic hydrogen production system operates at the best efficiency under narrow-range power fluctuation conditions, ensuring the maximum utilization and efficient operation of the system energy, and providing a new method for the collaborative optimization control of the multiple-electrolyzer hydrogen production system.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy, and particularly relates to a collaborative optimization control method for multi-electrolyzer to adapt to wide-range power fluctuations. Background Art

[0002] The multi-electrolyzer hydrogen production system is an effective way to improve the consumption of new energy such as wind energy and solar energy. At present, the research on the energy management of electrolyzers at home and abroad mainly focuses on single electrolyzers, failing to take into account the hydrogen production efficiency of multiple electrolyzers and affecting the economy of the system. Therefore, it is of great significance to study the energy management of multi-electrolyzer hydrogen production systems.

[0003] The key to the efficient operation of the multi-electrolyzer hydrogen production system lies in how to balance the problems of poor production plan feasibility and low efficiency caused by the power input of the fluctuating power supply and the timing complexity of the electrolyzer state conversion. The technical difficulty lies in the differences in the timing conversion of the operating states of multiple types of electrolyzers. It is highly complex to effectively manage the cold start process and working states such as production and standby of multiple electrolyzers. In collaborative control, it is necessary to solve the problem of poor hydrogen production adaptability of the current logic rule-based method under wide-range power fluctuations.

[0004] Therefore, there is an urgent need for a new technical solution in the existing technology to solve this problem. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a collaborative optimization control method for multi-electrolyzers to adapt to wide-range power fluctuations, which is used to solve the problem that in the existing technology, under wide-range power fluctuations, there are differences in the timing conversion of the operating states of multiple types of electrolyzers and they cannot be collaboratively controlled.

[0006] The technical solution adopted by the present invention is to provide a collaborative optimization control method for multi-electrolyzers to adapt to wide-range power fluctuations. An optimization framework is used to collaboratively optimize the hydrogen production power supply, electrolyzer power, electrical energy storage power, and electrolyzer timing production state. In the optimization, a finite-time domain rolling solution method is adopted. The rolling time domain length is at the hour level, and the sampling time is at the minute level or hour level. The optimization objective function and constraint model are established respectively, and a mathematical programming or heuristic algorithm is used to solve the optimization model to obtain the operating power of each device in the multi-electrolyzer hydrogen production system and the electrolyzer state;

[0007] The objective function includes 6 sub-optimization objectives, and the normalized weighting of each sub-optimization objective is the total objective function. The sub-optimization objectives are respectively maximizing the tracking of the hydrogen production power supply, maximizing the system hydrogen production, minimizing the energy consumed by electrolyzer cold start and standby, minimizing electrolyzer attenuation, normalizing the operating time of each electrolyzer, and normalizing the power adjustment amount of each electrolyzer;

[0008] The constraint model includes power balance constraint, hydrogen production power curtailment constraint of hydrogen production power source, electrolyzer power constraint, electrolyzer state constraint, electrolyzer sequential production state constraint, electrical energy storage power constraint, electrical energy storage state of charge constraint, electrical energy storage state of charge regulation margin constraint, and electrical energy storage state of charge update equation constraint.

[0009] The specific representation of the sub-optimization objective is as follows:

[0010] 1) Maximize the tracking of hydrogen production power source power:

[0011]

[0012] In the formula: f1 is the first optimization sub-objective; P rec is the curtailment power of the hydrogen production power source, t is time; t k is the k-th moment; T is the set of discrete moments; min is minimization;

[0013] 2) Maximize the hydrogen production of the system:

[0014]

[0015] In the formula: f2 is the second optimization sub-objective; E is the set of electrolyzer numbers; Q el,i is the hydrogen production of the i-th electrolyzer; ΔT is the sampling time; max is maximization;

[0016] 3) Minimize the energy consumption of electrolyzer cold start and standby:

[0017]

[0018] In the formula: f3 is the third optimization sub-objective; is the standby state of the i-th electrolyzer; is the cold start state of the i-th electrolyzer, P sb is the standby power of the electrolyzer; P cs is the cold start power of the electrolyzer;

[0019] 4) Minimize the electrolyzer attenuation:

[0020]

[0021] In the formula: f4 is the fourth optimization sub-objective; κ hst is the hot start attenuation weight coefficient; κ cst is the cold start attenuation weight coefficient; κ sdt is the shutdown attenuation weight coefficient; κ adj is the power regulation attenuation weight coefficient; △P el,i is the power change of the i-th electrolyzer; is the rated power of the i-th electrolyzer; is the hot start time of the i-th electrolytic cell; is the cold start time of the i-th electrolytic cell; is the shutdown time of the i-th electrolytic cell;

[0022]

[0023] In the formula: is the increment of hot start voltage attenuation; is the increment of cold start voltage attenuation; is the increment of shutdown voltage attenuation; is the increment of voltage attenuation due to power change;

[0024] 5) Normalization of the operation time of each electrolytic cell:

[0025]

[0026] In the formula: f5 is the 5th optimization sub-goal; is the production status of the i-th electrolytic cell; M el,i is the total historical operation time of the i-th electrolytic cell; N el is the total number of electrolytic cells;

[0027] 6) Normalization of the power adjustment amount of each electrolytic cell:

[0028]

[0029] In the formula: f6 is the 6th optimization sub-goal; V el,i is the total historical power change of the i-th electrolytic cell.

[0030] The total objective function is expressed as:

[0031]

[0032] In the formula: G is the total optimization goal; α j is the weight coefficient of the j-th optimization sub-goal.

[0033] The specific representation of the constraint model is:

[0034] 1) Power balance constraint:

[0035]

[0036] In the formula: P re is the renewable energy power; P bc is the charging power of the battery energy storage; P bd is the discharging power of the battery energy storage;

[0037] 2) Constraint on the abandonment of the power of the hydrogen production power supply:

[0038] 0 ≤ Prec (t k ) ≤ P re (t k ) (10)

[0039] 3) Electrolyzer power constraint:

[0040]

[0041] Where: P el,i,min is the minimum operating power of the i-th electrolyzer; P el,i,max is the maximum operating power of the i-th electrolyzer;

[0042] 4) Electrolyzer status constraint:

[0043]

[0044]

[0045] Where: is the shutdown status of the i-th electrolyzer;

[0046] 5) Electrolyzer sequential production status constraint:

[0047]

[0048] Where: t k-1 is the (k - 1)-th moment; t k+1 is the (k + 1)-th moment, T cs is the number of moments for cold start;

[0049] 6) Electrical energy storage power constraint:

[0050] 0 ≤ P bc (t k ) ≤ P bat,max δ bc (t k ) (25)

[0051] 0 ≤ P bd (t k ) ≤ P bat,max δ bd (t k ) (26)

[0052] δ bc (t k ) + δ bd (t k ) ≤ 1 (27)

[0053] Where: P bat,max is the maximum charge-discharge power of the electrical energy storage; δ bc is the charging status of the electrical energy storage; δbd For the discharging state of the electrical energy storage;

[0054] 7) Constraint on the state of charge of the electrical energy storage:

[0055] SOC min ≤SOC(t k )≤SOC max (28)

[0056] Where: SOC is the state of charge of the electrical energy storage; SOC min is the minimum value of the state of charge of the electrical energy storage; SOC max is the maximum value of the state of charge of the electrical energy storage;

[0057] 8) Constraint on the regulation margin of the state of charge of the electrical energy storage:

[0058] SOC min +θ≤SOC(t end )≤SOC max -θ (29)

[0059] Where: θ is the margin of the state of charge of the electrical energy storage; t end is the last moment within the rolling time domain;

[0060] 9) Constraint on the state of charge update equation of the electrical energy storage:

[0061]

[0062] Where: η bc is the charging efficiency of the electrical energy storage; η bd is the discharging efficiency of the electrical energy storage; E bat is the rated capacity of the electrical energy storage.

[0063] The types of hydrogen production power sources of the multi - electrolyzer hydrogen production system are one or several of wind power, photovoltaic power, hydropower, and the power grid. At the same time, the operation mode of the system is selected as off - grid or on - grid according to the types of hydrogen production power sources and the electrical structure.

[0064] The types of electrolyzers of the multi - electrolyzer hydrogen production system are one or several of alkaline electrolyzers, proton exchange membrane electrolyzers, and hybrid electrolyzers composed of alkaline electrolyzers and proton exchange membrane electrolyzers.

[0065] The types of electrical energy storage of the multi - electrolyzer hydrogen production system are one or several of lithium batteries, supercapacitors, and no electrical energy storage.

[0066] Through the above - mentioned design scheme, the present invention can bring the following beneficial effects:

[0067] The present invention formulates an optimal production plan by adopting a multi-objective optimization framework, formulates a pre-startup plan for electrolyzers according to the production power curve, and optimizes the charge and discharge plan of battery energy storage. Under the condition of wide-range power fluctuations, the battery energy storage is used to weaken the frequent start-stop of electrolyzers, and under the condition of narrow-range power fluctuations, the battery energy storage is used to ensure that the electrolytic hydrogen production system operates at the best efficiency, ensuring the maximum utilization of system energy and efficient operation, and providing a new method for the collaborative optimization control of multi-electrolyzer hydrogen production systems. It has remarkable effects in aspects such as improving hydrogen production power tracking, increasing system hydrogen production, reducing the start-stop times of electrolyzers and the uniform utilization of electrolyzers, and improving system efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 FIG. is a schematic structural diagram of a multi-electrolyzer hydrogen production system for a multi-electrolyzer collaborative optimization control method adaptable to wide-range power fluctuations according to the present invention;

[0069] Figure 2 FIG. is a schematic power diagram of a multi-electrolyzer hydrogen production system for a multi-electrolyzer collaborative optimization control method adaptable to wide-range power fluctuations according to the present invention;

[0070] Figure 3 FIG. is a schematic power diagram of an electrolyzer for a multi-electrolyzer collaborative optimization control method adaptable to wide-range power fluctuations according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0071] The present invention will be further described below in conjunction with the drawings and specific embodiments:

[0072] A multi-electrolyzer collaborative optimization control method adaptable to wide-range power fluctuations specifically includes the following steps:

[0073] (1) Objective function

[0074] The total objective function includes 6 sub-optimization objectives. The first sub-objective is to optimize the hydrogen production power tracking of the hydrogen production power supply, which is expressed as:

[0075]

[0076] In the formula: f1 is the first optimization sub-objective; P rec is the discarded power of the hydrogen production power supply, t is time; t k is the kth moment; T is the set of discrete moments; min is minimization.

[0077] The second sub-objective is to maximize the hydrogen production of the system, which is expressed as:

[0078]

[0079] In the formula: f2 is the second optimization sub-objective; E is the set of electrolyzer numbers; Q el,iis the hydrogen production of the i-th electrolyzer; ΔT is the sampling time; max is to maximize.

[0080] In the operating state of the electrolyzer, the energy consumed during cold start and standby does not produce utilizable hydrogen. To minimize the energy consumed in these two states, the third sub-goal is expressed as:

[0081]

[0082] In the formula: f3 is the third optimization sub-goal; is the standby state of the i-th electrolyzer; is the cold start state of the i-th electrolyzer, P sb is the standby power of the electrolyzer; P cs is the cold start power of the electrolyzer.

[0083] To reduce the attenuation degree of each electrolyzer and improve the stability of the electrolyzer operation, minimizing the electrolyzer attenuation is taken as the fourth optimization sub-goal, which is expressed as:

[0084]

[0085] In the formula: f4 is the fourth optimization sub-goal; κ hst is the hot start attenuation weight coefficient; κ cst is the cold start attenuation weight coefficient; κ sdt is the shutdown attenuation weight coefficient; κ adj is the power regulation attenuation weight coefficient; △P el,i is the power change of the i-th electrolyzer; is the rated power of the i-th electrolyzer; is the hot start time of the i-th electrolyzer; is the cold start time of the i-th electrolyzer; is the shutdown time of the i-th electrolyzer.

[0086] The weight coefficients in the above optimization goals can be calculated through the voltage attenuation increment values caused by the electrolyzer in different operating states or during start-up and shutdown switching, which is expressed as:

[0087]

[0088] In the formula: is the hot start voltage attenuation increment; is the cold start voltage attenuation increment; is the shutdown voltage attenuation increment; is the power change voltage attenuation increment.

[0089] To ensure the utilization balance of each electrolyzer during long-term operation, the operation time and power adjustment amount of each electrolyzer are normalized and used as the 5th and 6th sub-optimization objectives respectively, expressed as:

[0090]

[0091] In the formula: f5 is the 5th optimization sub-objective; f6 is the 6th optimization sub-objective; is the production status of the i-th electrolyzer; M el,i is the total historical operation time of the i-th electrolyzer; V el,i is the total historical power change of the i-th electrolyzer; N el is the total number of electrolyzers.

[0092] The total objective function is the dynamic normalization weighting of the above 6 sub-optimization sub-objectives, expressed as:

[0093]

[0094] In the formula: G is the total optimization objective; α j is the weight coefficient of the j-th optimization sub-objective.

[0095] (2) Constraint model

[0096] All the power for system hydrogen production, standby and cold start is supplied by the hydrogen production power supply, then the power balance equation is expressed as:

[0097]

[0098] In the formula: P re is the renewable energy power; P bc is the battery energy storage charging power; P bd is the battery energy storage discharging power.

[0099] In power optimization, the amount of discarded power of the hydrogen production power supply at any time should be guaranteed to be less than its planned power value, expressed as:

[0100] 0 ≤ P rec (t k ) ≤ P re (t k ) (10)

[0101] To ensure the safe operation of the electrolyzer, the electrolyzer needs to meet the power limit constraint, expressed as:

[0102]

[0103] In the formula: P el,i,min is the minimum operating power of the i-th electrolyzer; P el,i,max is the maximum operating power of the i-th electrolyzer.

[0104] At any moment, the electrolyzer can only be in one operating state, which is expressed as:

[0105]

[0106] In the formula: is the shutdown state of the i-th electrolyzer.

[0107] The hot start moment, cold start moment, and shutdown moment of the electrolyzer can be represented by the switching of adjacent moments in the production, cold start, and standby states, as follows:

[0108]

[0109] The timing conversion logic of each operating state of the electrolyzer is represented by the conversion constraints of adjacent front and back moments of each state. It is expressed as

[0110]

[0111] In the formula: t k-1 is the (k - 1)-th moment; t k+1 is the (k + 1)-th moment.

[0112] In addition, within any rolling time domain of the electrolyzer, the time in the cold start state is not allowed to exceed the actual cold start time of the electrolyzer, which is expressed as:

[0113]

[0114] In the formula: T cs is the number of moments of the cold start time.

[0115] The electrical energy storage needs to satisfy the charge-discharge power and the constraint of non-simultaneous charge-discharge states, which is expressed as:

[0116] 0 ≤ P bc (t k ) ≤ P bat,max δ bc (t k ) (25)

[0117] 0 ≤ P bd (t k ) ≤ P bat,max δ bd (t k ) (26)

[0118] δ bc (t k ) + δ bd (t k ) ≤ 1 (27)

[0119] In the formula: P bat,max is the maximum charge-discharge power of the electrical energy storage; δbc is the charging state of the electrical energy storage; δ bd is the discharging state of the electrical energy storage.

[0120] The state of charge of the electrical energy storage needs to be guaranteed within a set range, expressed as:

[0121] SOC min ≤SOC(t k )≤SOC max (28)

[0122] In the formula: SOC is the state of charge of the electrical energy storage; SOC min is the minimum value of the state of charge of the electrical energy storage; SOC max is the maximum value of the state of charge of the electrical energy storage.

[0123] The constraint on the regulation margin of the state of charge of the electrical energy storage is expressed as:

[0124] SOC min +θ≤SOC(t end )≤SOC max -θ (29)

[0125] In the formula: θ is the margin of the state of charge of the electrical energy storage; t end is the last moment within the rolling time domain.

[0126] The update calculation of the state of charge of the electrical energy storage is expressed as:

[0127]

[0128] In the formula: η bc is the charging efficiency of the electrical energy storage; η bd is the discharging efficiency of the electrical energy storage; E bat is the rated capacity of the electrical energy storage.

[0129] The power supply types of the multi - electrolyzer hydrogen production system include but are not limited to wind power, photovoltaic power, hydropower, power grid, etc. At the same time, the system can selectively switch between off - grid and grid - connected operation modes according to the type of hydrogen production power source and the electrical structure. The electrolyzer type in the system can be either a single type of alkaline electrolyzer or proton exchange membrane electrolyzer, or a hydrogen production system with a mixture of alkaline electrolyzers and proton exchange membrane electrolyzers. Electrical energy storage can be selectively configured in the system. The types of electrical energy storage include but are not limited to lithium batteries, supercapacitors, etc., and it is also possible to choose not to connect an electrical energy storage.

[0130] To verify the effectiveness and superiority of the method of the present invention, a wind power to hydrogen system is adopted in this part, and the specific implementation and analysis of the method are carried out through a typical daily example. In the example, the installed capacity of wind power is set to 25 MW. To enable the electrolytic hydrogen production subsystem to have the ability to fully absorb wind power, 5 electrolyzers with a capacity of 5 MW are configured equivalently, and the sampling time is set to 15 minutes. The system parameters are shown in Table 1.

[0131] Table 1

[0132]

[0133] It can be seen from Figure 2 and Figure 3 that on the one hand, the electrical energy storage suppresses the fluctuation of wind power, and the operating power of the electrolytic hydrogen production subsystem is more stable. When the wind power fluctuates at the start-stop power boundary of the electrolyzer, the electrical energy storage can provide short-term power and energy support, reducing the number of unit start-stop times. In the period from 05:00 to 13:00, the wind power does not reach the minimum operating power of the electrolyzer. At this time, the electrical energy storage realizes the full absorption of wind power through two ways: discharging to make the electrolyzer operate at the lowest power or charging. In addition, when the wind power is relatively stable, the electrical energy storage adjusts the electrolyzer power through charge and discharge to enable the electrolyzer to operate at a more optimal efficiency point.

[0134] The implementation mode of the present invention is not limited by the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A multi-electrolyzer coordinated optimization control method adapting to wide range power fluctuations, characterized in that: An optimization framework is used to coordinate the optimization of hydrogen production power, electrolyzer power, electric energy storage power, and electrolyzer sequential production status. The finite time domain rolling solution method is used in the optimization. The rolling time domain length is in hours, and the sampling time is in minutes or hours. The optimization objective function and constraint model are established respectively. Mathematical programming or heuristic algorithm is used to solve the optimization model to obtain the operating power of each device and the electrolyzer status of the multi-electrolyzer hydrogen production system. The objective function includes 6 sub-optimization objectives, and the normalized weights of each sub-optimization objective are the total objective function. The sub-optimization objectives are respectively to maximize the tracking of hydrogen production power, maximize the system hydrogen production, minimize the energy consumed by the electrolyzer cold start and standby, minimize the electrolyzer attenuation, homogenize the operation time of each electrolyzer, and homogenize the power adjustment amount of each electrolyzer; The constraint model includes power balance constraints, hydrogen production power abandonment constraints, electrolyzer power constraints, electrolyzer state constraints, electrolyzer time production state constraints, energy storage power constraints, energy storage charge state constraints, energy storage charge state adjustment margin constraints, and energy storage charge state update equation constraints.

2. The method for coordinated optimization control of multiple electrolyzers adapted to wide range power fluctuations according to claim 1, characterized in that: The specific representation of the sub-optimization objective is: 1) Maximize the tracking of hydrogen production power: Where: f1 is the first optimization sub-goal; P rec is the abandoned power of hydrogen power source, t is the time; t k is the kth moment; T is the discrete moment set; min is minimization; 2) The system produces the most hydrogen: Where: f2 is the second optimization sub-goal; E is the electrolytic cell number set; Q el,i is the hydrogen production of the i-th electrolyzer; ΔT is the sampling time; max is the maximum; 3) Minimize the energy consumed by the electrolyzer during cold start and standby: Where: f3 is the third optimization sub-goal; The i-th electrolytic cell is in standby state; is the cold start state of the i-th electrolytic cell, P sb is the standby power of the electrolyzer; P cs is the cold start power of the electrolyzer; 4) Minimum electrolytic cell attenuation: Where: f4 is the fourth optimization sub-goal; κ hst is the hot start attenuation weight coefficient; κ cst is the cold start attenuation weight coefficient; κ sdt is the shutdown attenuation weight coefficient; κ adj is the power regulation attenuation weight coefficient; △P el,i is the power change of the i-th electrolytic cell; is the rated power of the i-th electrolytic cell; is the hot start time of the i-th electrolytic cell; is the cold start time of the i-th electrolyzer; is the shutdown time of the i-th electrolytic cell; Where: is the hot start voltage decay increment; is the cold start voltage decay increment; is the shutdown voltage attenuation increment; is the power change voltage attenuation increment; 5) Homogenization of the operation time of each electrolytic cell: Where: f5 is the fifth optimization sub-goal; is the production status of the i-th electrolytic cell; M el,i N is the total number of historical operating hours of the i-th electrolytic cell; el is the total number of electrolytic cells; 6) Homogenization of power regulation of each electrolytic cell: Where: f6 is the sixth optimization sub-goal; V el,i is the total historical power change of the i-th electrolyzer.

3. The method for coordinated optimization control of multiple electrolyzers adapted to wide range power fluctuations according to claim 1, characterized in that: The overall objective function is expressed as: Where: G is the overall optimization goal; α j is the weight coefficient of the jth optimization sub-goal.

4. The method for coordinated optimization control of multiple electrolyzers adapted to wide range power fluctuations according to claim 1, characterized in that: The specific representation of the constraint model is as follows: 1) Power balance constraints: Where: P re is the renewable energy power; P bc Charging power for battery energy storage; P bd Discharge power for battery energy storage; 2) Constraints on the abandonment of hydrogen production power: 0≤P rec (t k )≤P re (t k ) (10) 3) Electrolyzer power constraints: Where: P el,i,min is the minimum operating power of the i-th electrolytic cell; P el,i,max is the maximum operating power of the i-th electrolytic cell; 4) Electrolyzer state constraints: Where: The i-th electrolytic cell is in shutdown state; 5) Constraints on the timing production status of electrolytic cells: Where: t k-1 is the k-1th moment; t k+1 is the k+1th moment, T cs is the cold start time; 6) Electric energy storage power constraints: 0≤P bc (t k )≤P bat,max δ bc (t k ) (25) 0≤P bd (t k )≤P bat,max δ bd (t k ) (26) d bc (t k )+d bd (t k )≤1 (27) Where: P bat,max is the maximum charging and discharging power of the energy storage; δ bc is the charging state of the electric energy storage; bd It is the state of electric energy storage discharge; 7) Energy storage charge state constraints: SOC min ≤SOC(t k )≤SOC max (28) Where: SOC is the state of charge of the energy storage; SOC min SOC is the minimum state of charge of the energy storage; max The maximum value of the state of charge of the electric energy storage; 8) Energy storage state of charge adjustment margin constraints: SOCIETY min +θ≤SOC(t end )≤SOC max -θ (29) Where: θ is the charge state margin of the electric energy storage; t end is the last moment in the rolling time domain; 9) Energy storage charge state update equation constraint: Where: η bc Charging efficiency for electric energy storage; η bd E is the energy storage discharge efficiency; bat It is the rated capacity of electric energy storage.

5. The method for coordinated optimization control of multiple electrolyzers adapted to wide range power fluctuations according to claim 1, characterized in that: The hydrogen production power source type of the multi-electrolyzer hydrogen production system is one or more of wind power, photovoltaic power, hydropower, and power grid. At the same time, the system operation mode selects off-grid or grid-connected according to the type of hydrogen production power source and the electrical structure.

6. The method for coordinated optimization control of multiple electrolyzers adapted to wide range power fluctuations according to claim 1, characterized in that: The electrolyzer type of the multi-electrolyzer hydrogen production system is one or more of an alkaline electrolyzer, a proton exchange membrane electrolyzer, and a mixed electrolyzer consisting of an alkaline electrolyzer and a proton exchange membrane electrolyzer.

7. The method for coordinated optimization control of multiple electrolyzers adapted to wide range power fluctuations according to claim 1, characterized in that: The type of electrical energy storage of the multi-electrolyzer hydrogen production system is one or more of lithium batteries, supercapacitors, and non-electrical energy storage.

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