Multi-electrolytic cell collaborative optimization control method suitable for wide-range power fluctuation

Abstract

The invention discloses a multi-electrolytic cell collaborative optimization control method suitable for wide-range power fluctuation, and belongs to the technical field of new energy. According to the method, an optimization framework is adopted for collaborative optimization of hydrogen production power supply power, electrolytic cell power, electric energy storage power and electrolytic cell time sequence production state, a finite time domain rolling solving method is adopted in optimization, the rolling time domain length is hour level, the sampling time is minute level or hour level, an optimization objective function and a constraint model are established respectively, and the optimization objective function and the constraint model are optimized. And solving the optimization model by adopting a mathematical programming or heuristic algorithm to obtain the operation power of each device of the multi-electrolytic cell hydrogen production system and the electrolytic cell state. According to the method, under the wide-range power fluctuation working condition, battery energy storage weakening of frequent starting and stopping of the electrolytic cell is achieved, under the narrow-range power fluctuation working condition, battery energy storage is achieved, it is guaranteed that the electrolytic hydrogen production system operates at the optimal efficiency, and maximum energy utilization and efficient operation of the system are guaranteed; and a new method is provided for collaborative optimization control of the multi-electrolytic-bath hydrogen production system.

Description

Technical Field

[0001] The present invention belongs to the field of new energy technology, and in particular relates to a multi-electrolyzer collaborative optimization control method that is adaptable 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 electrolyzer energy management at home and abroad mainly focuses on single electrolyzers, and fails to take into account the hydrogen production efficiency of multiple electrolyzers, which affects the economic efficiency 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 a multi-electrolyzer hydrogen production system lies in how to balance the poor feasibility and low efficiency of production plans caused by the fluctuating power input and the timing complexity of the electrolyzer state transition. The technical difficulty lies in the differences in the timing transitions of the operating states of multiple types of electrolyzers. It is highly complex to effectively manage the cold start process and production, standby and other working states of multiple electrolyzers. In collaborative control, it is necessary to solve the problem of poor adaptability of hydrogen production under a wide range of power fluctuations based on current logic rule methods.

[0004] Therefore, a new technical solution is urgently needed in the prior art to solve this problem. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a multi-electrolyzer collaborative optimization control method that is adaptable to a wide range of power fluctuations, so as to solve the problem in the prior art that under a wide range of 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 multi-electrolyzer collaborative optimization control method that is adaptable to a wide range of power fluctuations. An optimization framework is used to coordinate the optimization of hydrogen production power, electrolyzer power, electric energy storage power, and electrolyzer sequential production status. A 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, and mathematical programming or heuristic algorithms are used to solve the optimization model to obtain the operating power of each equipment in the multi-electrolyzer hydrogen production system and the electrolyzer status.

[0007] The objective function includes 6 sub-optimization objectives, and the normalized weighted sum of each sub-optimization objective is 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 attenuation of the electrolyzer, homogenize the operating time of each electrolyzer, and homogenize the power adjustment amount of each electrolyzer.

[0008] The specific representation of the sub-optimization objective is:

[0009] 1) Maximize the tracking of hydrogen production power:

[0010]

[0011] 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;

[0012] 2) The system produces the most hydrogen:

[0013]

[0014] 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;

[0015] 3) Minimize the energy consumed by the electrolyzer during cold start and standby:

[0016]

[0017] 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;

[0018] 4) Minimum electrolytic cell attenuation:

[0019]

[0020] 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; ΔP 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 electrolyzer;

[0021]

[0022] 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;

[0023] 5) Homogenization of the operation time of each electrolytic cell:

[0024]

[0025] 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;

[0026] 6) Homogenization of power regulation of each electrolytic cell:

[0027]

[0028] Where: f6 is the sixth optimization sub-goal; V el,i is the total historical power change of the i-th electrolyzer.

[0029] The overall objective function is expressed as:

[0030]

[0031] Where: G is the overall optimization goal; α j is the weight coefficient of the jth optimization sub-goal.

[0032] 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.

[0033] The specific expression of the constraint condition is:

[0034] 1) Power balance constraints:

[0035]

[0036] Where: P re is the renewable energy power; P bc Charging power for battery energy storage; P bd Discharge power for battery energy storage;

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

[0038] 0≤P rec (tk )≤P re (t k ) (10)

[0039] 3) Electrolyzer power constraints:

[0040]

[0041] 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;

[0042] 4) Electrolyzer state constraints:

[0043]

[0044]

[0045] Where: The i-th electrolytic cell is in shutdown state;

[0046] 5) Constraints on the timing production status of electrolytic cells:

[0047]

[0048] Where: t k-1 is the k-1th moment; t k+1 is the k+1th moment, T cs is the cold start time;

[0049] 6) Electric energy storage power constraints:

[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 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;

[0054] 7) Energy storage charge state constraints:

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

[0056] 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;

[0057] 8) Energy storage charge state adjustment margin constraints:

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

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

[0060] 9) Energy storage charge state update equation constraint:

[0061]

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

[0063] 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.

[0064] 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.

[0065] 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.

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

[0067] The present invention adopts a multi-objective optimization framework to formulate an optimal production plan, formulates an electrolyzer pre-start plan based on the production power curve, optimizes the battery energy storage charge and discharge plan, and realizes battery energy storage to reduce the frequent start and stop of the electrolyzer under a wide range of power fluctuation conditions. Under a narrow range of power fluctuation conditions, battery energy storage is realized to ensure that the electrolysis 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 coordinated optimization control of multi-electrolyzer hydrogen production systems. It has significant effects in improving hydrogen production power tracking, increasing system hydrogen production, reducing the number of electrolyzer start and shutdown times and electrolyzer uniform utilization, and improving system efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A schematic diagram of the structure of a multi-electrolyzer hydrogen production system according to the present invention, which is a multi-electrolyzer coordinated optimization control method that is adaptable to wide-range power fluctuations;

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

[0070] Figure 3 A schematic diagram of electrolytic cell power according to a multi-electrolytic cell collaborative optimization control method of the present invention that is adaptable to a wide range of power fluctuations. DETAILED DESCRIPTION

[0071] The present invention is further described below with reference to the accompanying drawings and specific embodiments:

[0072] A multi-electrolyzer coordinated optimization control method adapted to wide range power fluctuations, the specific implementation of which includes the following steps:

[0073] (1) Objective function

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

[0075]

[0076] 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.

[0077] The second sub-goal is to maximize the system's hydrogen production, expressed as:

[0078]

[0079] Where: f2 is the second optimization sub-goal; Q is the number set of electrolytic cells;el,i is the hydrogen production of the i-th electrolyzer; ΔT is the sampling time; max is the maximum.

[0080] In the electrolyzer operation state, the energy consumed in cold start and standby does not produce usable hydrogen. In order to minimize the energy consumed in these two states, the third sub-goal is expressed as:

[0081]

[0082] 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.

[0083] In order to reduce the attenuation of each electrolytic cell and improve the stability of the electrolytic cell operation, the minimum attenuation of the electrolytic cell is taken as the fourth optimization sub-goal, which is expressed as:

[0084]

[0085] 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; ΔP 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.

[0086] The weight coefficient in the optimization objective of the above formula can be calculated by the voltage attenuation increment value caused by the electrolyzer in different operating states or when starting and stopping, and is expressed as:

[0087]

[0088] 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.

[0089] In order to ensure the balanced utilization of each electrolytic cell in long-term operation, the operation time and power adjustment amount of each electrolytic cell are homogenized as the fifth and sixth sub-optimization objectives, respectively, which are expressed as:

[0090]

[0091] Where: f5 is the fifth optimization sub-goal; f6 is the sixth optimization sub-goal; is the production status of the i-th electrolytic cell; M el,i V is the total historical operation time of the i-th electrolytic cell; el,i is the total historical power change of the i-th electrolyzer; N el is the total number of electrolytic cells.

[0092] The overall objective function is the dynamic normalized weighting of the above six sub-optimization sub-objectives, expressed as:

[0093]

[0094] Where: G is the overall optimization goal; α j is the weight coefficient of the jth optimization sub-goal.

[0095] (2) Constraint Model

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

[0097]

[0098] Where: P re is the renewable energy power; P bc Charging power for battery energy storage; P bd Discharge power for battery energy storage.

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

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

[0101] To ensure the safe operation of the electrolyzer, the electrolyzer must meet the power limit constraint, which is expressed as:

[0102]

[0103] 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 electrolyzer.

[0104] The electrolytic cell can only be in one operating state at any time, expressed as:

[0105]

[0106] Where: The i-th electrolytic cell is in shutdown state.

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

[0108]

[0109] The timing conversion logic of each operating state of the electrolytic cell is represented by the transition constraints of the adjacent moments before and after each state. It is represented as

[0110]

[0111] Where: t k-1 is the k-1th moment; t k+1 is the k+1th moment.

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

[0113]

[0114] Where: T cs The cold start time.

[0115] Electric energy storage needs to meet the constraints of charging and discharging power and different charging and discharging states, which can be expressed as:

[0116]

[0117] 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.

[0118] The state of charge of the energy storage must be within the set range, expressed as:

[0119] SOC min ≤SOC(t k )≤SOC max (28)

[0120] 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 It is the maximum value of the energy storage charge state.

[0121] The energy storage state of charge regulation margin constraint is expressed as:

[0122] SOC min +θ≤SOC(t end )≤SOC max -θ (29)

[0123] Where: θ is the charge state margin of the electric energy storage; t end The last moment in the rolling time domain.

[0124] The updated calculation of the energy storage state of charge is expressed as:

[0125]

[0126] Where: η bc is the energy storage charging efficiency; η bd E is the energy storage discharge efficiency; bat It is the rated capacity of electric energy storage.

[0127] The power supply types of the multi-electrolyzer hydrogen production system include but are not limited to wind power, photovoltaics, hydropower, power grid, etc. At the same time, the system can selectively switch between off-grid and grid-connected modes according to the type of hydrogen production power supply and electrical structure. The type of electrolyzer in the system can be either a single type of alkaline electrolyzer or proton exchange membrane electrolyzer, or a hydrogen production system that is a mixture of alkaline electrolyzer and proton exchange membrane electrolyzer. The system can selectively configure electrical energy storage, and the types of electrical energy storage include but are not limited to lithium batteries, supercapacitors, etc., or it can choose to access without electrical energy storage.

[0128] In order to verify the effectiveness and superiority of the method of the present invention, this section adopts a wind power hydrogen production system and implements and analyzes the method through a typical day calculation example. In the calculation example, the wind power installed capacity is set to 25MW. In order to realize that the electrolysis hydrogen production subsystem has the full wind power absorption capacity, 5 5MW electrolyzers are configured with equal capacity, and the sampling time is set to 15min. The system parameters are shown in Table 1.

[0129] Table 1

[0130]

[0131] Depend on Figure 2 and Figure 3It can be seen that on the one hand, electric energy storage can smooth the fluctuation of wind power, and the operating power of the electrolysis hydrogen production subsystem is more stable. When the wind power fluctuates at the start-stop power boundary of the electrolyzer, the electric energy storage can provide short-term power support, reducing the number of unit starts and stops. In the period of 05:00-13:00, the wind power does not reach the minimum operating power of the electrolyzer. At this time, the electric energy storage can discharge the electrolyzer to operate at the lowest power or charge it in two ways, realizing the full consumption of wind power. In addition, when wind power is relatively stable, the electric energy storage adjusts the electrolyzer power through charging and discharging to enable the electrolyzer to operate at a better efficiency point.

[0132] The implementation methods of the present invention are not limited to the above-mentioned embodiments, and any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention should be equivalent replacement methods and are 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. A 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, and mathematical programming or heuristic algorithm is used to solve the optimization model to obtain the operating power of each equipment in the multi-electrolyzer hydrogen production system and the electrolyzer status.

2. The method for coordinated optimization control of multiple electrolyzers adapted to wide range power fluctuations according to claim 1, characterized in that: The objective function includes 6 sub-optimization objectives, and the normalized weighted sum of each sub-optimization objective is 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 attenuation of the electrolyzer, homogenize the operating time of each electrolyzer, and homogenize the power adjustment amount of each electrolyzer.

3. The method for coordinated optimization control of multiple electrolyzers adapted to wide range power fluctuations according to claim 2, 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; ΔP 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 electrolyzer; 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.

4. The method for coordinated optimization control of multiple electrolyzers adapted to wide range power fluctuations according to claim 2, 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.

5. The method for coordinated optimization control of multiple electrolyzers adapted to wide range power fluctuations according to claim 1, characterized in that: 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.

6. The method for coordinated optimization control of multiple electrolyzers adapted to wide range power fluctuations according to claim 5, characterized in that: The specific expression of the constraint condition is: 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 charge state 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.

7. 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.

8. 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.

9. 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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