A hierarchical collaborative control method and device for a multi-engine hydrogen fuel cell power generation system

Abstract

This invention provides a hierarchical collaborative control method and apparatus for a multi-fuel cell power generation system, relating to the field of power generation system control. The method includes: acquiring the current output power set of the multi-fuel cell power generation system, which includes the current output power of each fuel cell and lithium battery energy storage system; calculating the total cost based on the current output power of each fuel cell and lithium battery energy storage system, using the minimum total cost as the objective function, constructing a set of constraints, and solving the objective function under the constraints of the set of constraints using a whale optimization algorithm to calculate the optimal load power of each fuel cell unit and the optimal output power of the lithium battery energy storage system that minimizes the total cost; and calculating the optimal output power of each fuel cell based on the historical health status set of each fuel cell, the historical load power set of each fuel cell unit, and the optimal load power.

Description

Technical Field

[0001] This invention relates to the field of power generation system control, and in particular to a hierarchical collaborative control method and apparatus for a multi-machine hydrogen fuel cell power generation system. Background Technology

[0002] In high-power microgrid systems, fuel cells, as the core power generation device, typically need to provide megawatt-level power. Developing high-power fuel cells is not only technically challenging but also costly. Therefore, existing solutions usually employ multiple fuel cells connected in parallel to construct a multi-fuel cell power generation system. A multi-fuel cell power generation system is inherently a complex, nonlinear, and strongly coupled system. During actual operation, internal factors (such as fuel cell manufacturing processes) and external factors (such as changes in ambient temperature and load fluctuations) can cause performance differences between individual fuel cells, thereby reducing the overall system performance. Without effective control, long-term operation may lead to decreased system efficiency and even severe economic losses. Therefore, achieving coordinated control of fuel cells within the system is crucial.

[0003] However, current research mainly focuses on the characteristic analysis of single fuel cells, and there is still a lack of in-depth exploration of the coordinated control of multi-fuel cell power generation systems. Furthermore, existing power generation systems typically only consider cost optimization without taking into account the aging factors of fuel cells, resulting in insufficient precision in the control of the power generation system, which affects the cost and lifespan of the entire power generation system. Summary of the Invention

[0004] To address the above problems, this invention provides a hierarchical cooperative control method for a multi-engine hydrogen fuel cell power generation system, comprising the following steps: Obtain the current output power set of a multi-fuel cell power generation system. The multi-fuel cell power generation system includes multiple fuel cell units and a lithium battery energy storage system. A fuel cell unit includes multiple fuel cells. The current output power set includes the current output power of each fuel cell and the lithium battery energy storage system. Entering the outer control stage, the total cost is calculated based on the current output power of each fuel cell and lithium battery energy storage system. The minimum total cost is taken as the objective function, and a set of constraints is constructed. The objective function is solved under the constraints of the set of constraints by the whale optimization algorithm. The optimal load power of each fuel cell unit and the optimal output power of the lithium battery energy storage system that minimize the total cost are calculated. Entering the inner control stage, the historical health status set of each fuel cell and the historical load power set of each fuel cell unit are obtained; based on the historical health status set of each fuel cell, the historical load power set of each fuel cell unit and the optimal load power, the optimal output power of each fuel cell is calculated. Adjust the current output power of each fuel cell to the optimal output power, and adjust the current output power of the lithium battery energy storage system to the optimal output power.

[0005] Optionally, the calculation of the total cost based on the current output power of each fuel cell and lithium battery energy storage system specifically includes: S11: Obtain the current output power of the i-th fuel cell in the j-th fuel cell unit. Based on the current output power Calculate the hydrogen consumption rate of the i-th fuel cell. ; S12: Repeat step S11 to sum the hydrogen consumption rates of each fuel cell in the j-th fuel cell unit to obtain the hydrogen consumption rate of the j-th fuel cell unit. According to the hydrogen consumption rate Calculate the hydrogen usage cost of the j-th fuel cell unit. ; S13: Obtain the change in output power of the i-th fuel cell in the j-th fuel cell unit. Based on the current output power and output power change Calculate the aging cost of the i-th fuel cell. ; S14: Repeat step S13 to sum the aging costs of each fuel cell in the j-th fuel cell unit to obtain the aging cost of the j-th fuel cell unit. ; S15: Repeat steps S11-S14 to calculate the hydrogen usage cost and aging cost of each fuel cell unit. S16: Obtain the aging unit price and rated capacity of the lithium battery energy storage system, and calculate the aging cost of the lithium battery energy storage system based on the current output power, aging unit price and rated capacity of the lithium battery energy storage system. S17: Add the hydrogen usage cost and aging cost of each fuel cell unit, as well as the aging cost of the lithium battery energy storage system, to obtain the total cost.

[0006] Optionally, the step of solving the objective function using the whale optimization algorithm under the constraints of the constraint set to calculate the optimal load power of each fuel cell unit that minimizes the total cost, and the optimal output power of the lithium battery energy storage system, specifically includes: S21: Use the current load power of each fuel cell unit as the current position of the whale, set the number of whales to N, and the maximum number of iterations to... ; S22: Perform the vth iteration; S23: Obtain the current position of whale u, and generate random numbers P and A; if P is not less than 0.5, calculate the updated position of whale u based on the current position using the bubble attack formula; if P is less than 0.5 and the absolute value of A is less than 1, calculate the updated position of whale u based on the current position using the prey encirclement formula; if P is less than 0.5 and the absolute value of A is not less than 1, calculate the updated position of whale u based on the current position using the prey search formula. S24: Input the updated load power of each fuel cell unit corresponding to the updated position of whale u into the multi-unit fuel cell unit power generation system, and calculate the updated output power and SOC value of the lithium battery energy storage system. S25: If the updated load power of each fuel cell unit, the updated output power of the lithium battery energy storage system, and the SOC value satisfy the constraint set, then update the current position of whale u+1; otherwise, update the current position of whale u again. S26: Repeat steps S23-S25 to calculate the updated position of each whale in the v-th iteration, and let v=v+1; S27: Repeat steps S22-S26 until the maximum number of iterations, input the updated load power of each fuel cell unit, the updated output power of the lithium battery energy storage system and the SOC value of each updated position into the multi-fuel cell unit power generation system to obtain the total cost of each updated position. S28: The update load power of each fuel cell unit and the update output power of the lithium battery energy storage system corresponding to the update position with the lowest total cost are taken as the optimal load power of each fuel cell unit and the optimal output power of the lithium battery energy storage system.

[0007] Optionally, the set of constraints includes: The refresh load power of each fuel cell unit is within the first preset range; The sum of the replacement load power of each fuel cell unit and the replacement output power of the lithium battery energy storage system equals the total power of the multi-unit fuel cell power generation system. The refresh output power of the lithium battery energy storage system is within a second preset range; The SOC value of the lithium battery energy storage system is within the third preset range.

[0008] Optionally, the step of calculating the optimal output power of each fuel cell based on the historical health status set of each fuel cell, the historical load power set of each fuel cell unit, and the optimal load power specifically includes: Based on the historical health status set of each fuel cell, the load power change of each fuel cell in each sampling period is calculated. Based on the historical load power set and optimal load power of each fuel cell unit, the load power change rate of each fuel cell unit in each sampling period is calculated. The initial output power of each fuel cell is obtained. Based on the load power change rate of each fuel cell unit in each sampling period, as well as the initial output power of each fuel cell and the load power change in each sampling period, the optimal output power of each fuel cell is calculated.

[0009] Optionally, the calculation process for the load power change rate specifically includes: The historical load power of the j-th fuel cell unit in the t-th sampling period is represented as: The optimal load power is expressed as ,Will The load power change rate of the j-th fuel cell unit in the t-th sampling period Where k is the current sampling period.

[0010] Optionally, the calculation process for the optimal output power of each fuel cell specifically includes: S31: Obtain the load power change rate of the j-th fuel cell unit in each sampling period, and the initial output power of the i-th fuel cell of the j-th fuel cell unit. and the change in load power in each sampling period; S32: Will The optimal output power of the i-th fuel cell in the j-th fuel cell unit ;in, Let be the change in load power of the i-th fuel cell in the j-th fuel cell unit during the t-th sampling period; S33: Repeat steps S31-S32 until the optimal output power of all fuel cells is calculated.

[0011] The present invention also provides a hierarchical cooperative control device for a multi-engine hydrogen fuel cell power generation system, used to implement the hierarchical cooperative control method for the multi-engine hydrogen fuel cell power generation system, the device comprising: The current output power acquisition module is used to acquire the current output power set of the multi-fuel cell power generation system. The multi-fuel cell power generation system includes multiple fuel cell units and a lithium battery energy storage system. A fuel cell unit includes multiple fuel cells. The current output power set includes the current output power of each fuel cell and the lithium battery energy storage system. The outer control module is used to calculate the total cost based on the current output power of each fuel cell and lithium battery energy storage system. The minimum total cost is used as the objective function. A set of constraints is constructed, and the objective function is solved under the constraints of the set of constraints by the whale optimization algorithm. The optimal load power of each fuel cell unit and the optimal output power of the lithium battery energy storage system that minimize the total cost are calculated. The inner control module is used to obtain the historical health status set of each fuel cell and the historical load power set of each fuel cell unit; based on the historical health status set of each fuel cell, the historical load power set of each fuel cell unit and the optimal load power, the optimal output power of each fuel cell is calculated. The output power adjustment module adjusts the current output power of each fuel cell to the optimal output power, and adjusts the current output power of the lithium battery energy storage system to the optimal output power.

[0012] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the hierarchical cooperative control method for the multi-machine hydrogen fuel cell power generation system.

[0013] The present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the hierarchical cooperative control method for the multi-machine hydrogen fuel cell power generation system.

[0014] The present invention has the following beneficial effects: 1. A two-layer control strategy is adopted to control the multi-fuel cell power generation system. In the outer control stage, the power distribution scheme is optimized through the whale optimization algorithm to obtain the optimal load power of each fuel cell unit and the optimal output power of the lithium battery energy storage system, thereby minimizing the system operating cost. In the inner control stage, the historical health status of each fuel cell is introduced, and the optimal output power of each fuel cell is obtained to balance the health status of each fuel cell and delay the excessive degradation of individual fuel cells. The two-layer control strategy can effectively improve the lifespan of the multi-fuel cell power generation system and reduce operating costs. 2. In the outer layer control stage, hydrogen consumption cost, fuel cell unit aging cost and lithium battery energy storage system aging cost are incorporated into a unified optimization target. Under the premise of ensuring that the load demand is met, the load power of the fuel cell unit is reasonably allocated, which can effectively reduce the operating cost of the system, extend the service life of the fuel cell unit, improve the overall operating economy, and ultimately minimize the total life cycle cost. 3. In the internal control stage, by rationally allocating the output power of each fuel cell in the unit, the output power amplitude of fuel cells with poor health is limited, thereby inhibiting their aging rate and promoting a balanced health status of each fuel cell in the unit. While ensuring normal power supply to the system, the differences in health status between fuel cells are minimized as much as possible, thereby delaying the excessive degradation of fuel cells with poor performance and improving the overall service life of the unit. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method according to an embodiment of the present invention; Figure 2 A schematic diagram of the hierarchical collaborative control strategy architecture for a multi-engine hydrogen fuel cell power generation system; Figure 3 This diagram illustrates the relationship between the output power and efficiency of a fuel cell, and between the output power and hydrogen consumption rate. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0017] Reference Figure 1 This invention provides a hierarchical cooperative control method for a multi-engine hydrogen fuel cell power generation system, comprising the following steps: Obtain the current output power set of a multi-fuel cell power generation system. The multi-fuel cell power generation system includes multiple fuel cell units and a lithium battery energy storage system. A fuel cell unit includes multiple fuel cells. The current output power set includes the current output power of each fuel cell and the lithium battery energy storage system. In some embodiments, the hierarchical collaborative control strategy architecture of a multi-machine hydrogen fuel cell power generation system is as follows: Figure 2 As shown, a multi-unit fuel cell power generation system consists of four fuel cell units and a lithium battery energy storage system. Each unit contains four fuel cells connected in parallel, and each fuel cell has a maximum net output power of 100kW. Therefore, the maximum output power of a single unit can reach 400kW.

[0018] Entering the outer control stage, the total cost is calculated based on the current output power of each fuel cell and lithium battery energy storage system. The minimum total cost is taken as the objective function, and a set of constraints is constructed. The objective function is solved under the constraints of the set of constraints by the whale optimization algorithm. The optimal load power of each fuel cell unit and the optimal output power of the lithium battery energy storage system that minimize the total cost are calculated. In some embodiments, the fuel cell unit serves as the primary power supply unit, providing continuous and stable power output, while the lithium battery energy storage system is used to smooth load fluctuations and provide short-term power support. However, during long-term operation, performance differences will emerge among the units. Without a suitable energy management strategy, some units will age excessively, thereby shortening the lifespan of the entire system. Furthermore, since fuel cell operation involves hydrogen consumption, how to ensure load requirements while simultaneously managing unit lifespan and optimizing hydrogen consumption is a key issue in improving the system's operational economy.

[0019] This invention incorporates hydrogen consumption costs, fuel cell unit aging costs, and lithium battery energy storage system aging costs into a unified optimization objective, and rationally allocates the power demand of the microgrid while ensuring that load requirements are met. This strategy can effectively reduce system operating costs, extend the service life of fuel cell units, improve overall operating economics, and ultimately minimize total lifecycle costs.

[0020] The calculation of the total cost based on the current output power of each fuel cell and lithium battery energy storage system specifically includes: S11: Obtain the current output power of the i-th fuel cell in the j-th fuel cell unit. Based on the current output power Calculate the hydrogen consumption rate of the i-th fuel cell. ; In some embodiments, the hydrogen consumption rate of a fuel cell directly affects its operating cost; therefore, accurate modeling of hydrogen consumption is crucial in the power allocation strategy. Each unit has a maximum net output power of 400 kW. Each unit consists of four fuel cells connected in parallel, each with a maximum net output power of 100 kW. The fuel cells used are from the Horizon PEMFC-100kW series. The relationships between the output power and efficiency, and between output power and hydrogen consumption rate, of this series of fuel cells are as follows: Figure 3 As shown.

[0021] hydrogen consumption rate The expression is: Where a, b, and c are fitting coefficients.

[0022] S12: Repeat step S11 to sum the hydrogen consumption rates of each fuel cell in the j-th fuel cell unit to obtain the hydrogen consumption rate of the j-th fuel cell unit. According to the hydrogen consumption rate Calculate the hydrogen usage cost of the j-th fuel cell unit. ; In some embodiments, hydrogen consumption rate The expression is: fuel cell unit per sampling period The cost of using hydrogen in the interior It can be represented as: in, The price is for hydrogen, expressed in RMB / kg.

[0023] S13: Obtain the change in output power of the i-th fuel cell in the j-th fuel cell unit. Based on the current output power and output power change Calculate the aging cost of the i-th fuel cell. ; In some embodiments, the aging cost of a fuel cell can be calculated by amortizing the amount of aging caused by load conditions. The total cost of the fuel cell is then distributed across the aging amount, and the cost corresponding to the aging amount caused by load conditions is the aging cost of the fuel cell. Therefore, the fuel cell aging cost per unit sampling period... Internal aging costs It can be represented as: in, The unit price is the aging rate, expressed in RMB / %. g1 and g2 are empirical aging coefficients based on load conditions.

[0024] S14: Repeat step S13 to sum the aging costs of each fuel cell in the j-th fuel cell unit to obtain the aging cost of the j-th fuel cell unit. ; S15: Repeat steps S11-S14 to calculate the hydrogen usage cost and aging cost of each fuel cell unit. S16: Obtain the aging unit price and rated capacity of the lithium battery energy storage system, and calculate the aging cost of the lithium battery energy storage system based on the current output power, aging unit price and rated capacity of the lithium battery energy storage system. In some embodiments, the lifespan of a lithium battery energy storage system decreases with charge-discharge cycles, therefore the lifespan of the lithium battery energy storage system per sampling period... Internal aging amount D bat The expression is: Where Q represents the maximum energy that the lithium battery energy storage system can store. This represents the current output power of the lithium battery energy storage system. This refers to the total cycle life of the lithium battery energy storage system. Aging costs of lithium battery energy storage systems The expression is: in, The unit price for aging lithium battery energy storage systems is RMB / %. This refers to the rated capacity of the lithium battery energy storage system.

[0025] S17: Add the hydrogen usage cost and aging cost of each fuel cell unit, as well as the aging cost of the lithium battery energy storage system, to obtain the total cost.

[0026] In some embodiments, the total cost C total The expression is: In some embodiments, the objective function is to minimize the total cost, and the Whale Optimization Algorithm (WOA) is used to solve this objective function. The Whale Optimization Algorithm is a metaheuristic optimization algorithm based on the social behavior of whales. This algorithm has a simple structure, few parameters, and fast convergence speed, making it suitable for fast optimization problems.

[0027] The WOA algorithm simulates the collective foraging behavior of whale pods in the ocean. A group of whales surrounds their prey by releasing a bubble screen, creating a closed space that forces the prey into the concentrated area, making it easier for the whales to feed efficiently. This foraging method is called bubble net foraging. In WOA, the position of each whale represents a potential solution. By iteratively updating the whale positions in the solution space, the globally optimal solution is eventually obtained.

[0028] The process of solving the objective function using the whale optimization algorithm under constraints of a set of conditions to calculate the optimal load power of each fuel cell unit and the optimal output power of the lithium battery energy storage system that minimizes the total cost specifically includes: S21: Use the current load power of each fuel cell unit as the current position of the whale, set the number of whales to N, and the maximum number of iterations to... ; In some embodiments, the position X of each individual whale represents a power distribution scheme for a fuel cell unit, i.e., X is a four-dimensional vector containing the load power of four fuel cell units.

[0029] In some embodiments, the set of constraints includes: The refresh load power of each fuel cell unit is within the first preset range; The sum of the replacement load power of each fuel cell unit and the replacement output power of the lithium battery energy storage system equals the total power of the multi-unit fuel cell power generation system. The refresh output power of the lithium battery energy storage system is within a second preset range; The SOC value of the lithium battery energy storage system is within the third preset range.

[0030] In some embodiments, to ensure the lithium battery energy storage system is in a safe state, the SOC of the lithium battery energy storage system needs to be limited to a safe threshold of 20% to 80%. Furthermore, to ensure the safe operation of the multi-unit fuel cell power generation system, the reference output power of each fuel cell unit needs to be limited, and to meet user-side load demands, the output power of the multi-unit fuel cell power generation system needs to be equal to the load demand power. Therefore, the expression for the set of constraints is as follows: in, For the refresh load power of the j-th fuel cell unit, For the first preset range, To update the output power of lithium battery energy storage systems The total power of a multi-engine fuel cell power generation system. This is the second preset range.

[0031] S22: Perform the vth iteration; S23: Obtain the current position of whale u, and generate random numbers P and A; if P is not less than 0.5, calculate the updated position of whale u based on the current position using the bubble attack formula; if P is less than 0.5 and the absolute value of A is less than 1, calculate the updated position of whale u based on the current position using the prey encirclement formula; if P is less than 0.5 and the absolute value of A is not less than 1, calculate the updated position of whale u based on the current position using the prey search formula. In some embodiments, the search phase of WOA includes three phases: surrounding the prey, bubble attack, and searching for the prey. Surround the prey: Whales can search for the location of prey and surround it. Assuming the current optimal solution is the target prey, other individual whales will attempt to move towards the target prey. This behavior can be represented by the prey-surrounding formula.

[0032] Where D is the distance between the individual whale and the current optimal solution, t is the current iteration number, and A and C are coefficient vectors. It updates the location. and These are the current positions for the t-th and t+1-th iterations, respectively.

[0033] The vectors A and C are calculated as follows: In the formula, a decreases linearly from 2 to 0 during the iteration process, and r1 and r2 are random numbers in [0,1].

[0034] Bubble Attack: Whales primarily employ two hunting mechanisms: encirclement and bubble attack. When using a bubble attack, the whale spirals towards its prey, gradually shrinking its bubble net while continuously releasing bubbles. The formula for a bubble attack can be expressed as: Where b is the spiral shape parameter, and l is a random number uniformly distributed between [-1, 1].

[0035] Whales randomly choose between encirclement and spiral bubble hunting methods. They randomly select to attack with a bubble or shrink to encircle their prey based on probability P. The position update formula is as follows: In the formula, P represents the probability of the predation mechanism and is a random number between [0,1]. The parameter A gradually decreases with the number of iterations t. If |A|<1, the whales gradually surround the current optimal solution.

[0036] Searching for prey: When |A|≥1, to ensure that all whales have fully searched the solution space, a random search is performed, with individual whales swimming towards random individual whales. This behavior can be described by the prey search formula, which is expressed as: in, Let t be the current position of the random whale individual in the t-th iteration.

[0037] S24: Input the updated load power of each fuel cell unit corresponding to the updated position of whale u into the multi-unit fuel cell unit power generation system, and calculate the updated output power and SOC value of the lithium battery energy storage system. S25: If the updated load power of each fuel cell unit, the updated output power of the lithium battery energy storage system, and the SOC value satisfy the constraint set, then update the current position of whale u+1; otherwise, update the current position of whale u again. S26: Repeat steps S23-S25 to calculate the updated position of each whale in the v-th iteration, and let v=v+1; S27: Repeat steps S22-S26 until the maximum number of iterations, input the updated load power of each fuel cell unit, the updated output power of the lithium battery energy storage system and the SOC value of each updated position into the multi-fuel cell unit power generation system to obtain the total cost of each updated position. S28: The update load power of each fuel cell unit and the update output power of the lithium battery energy storage system corresponding to the update position with the lowest total cost are taken as the optimal load power of each fuel cell unit and the optimal output power of the lithium battery energy storage system.

[0038] Entering the inner control stage, the historical health status set of each fuel cell and the historical load power set of each fuel cell unit are obtained; based on the historical health status set of each fuel cell, the historical load power set of each fuel cell unit and the optimal load power, the optimal output power of each fuel cell is calculated. In some embodiments, calculating the optimal output power of each fuel cell based on the historical health status set of each fuel cell, the historical load power set of each fuel cell unit, and the optimal load power specifically includes: Based on the historical health status set of each fuel cell, the load power change of each fuel cell in each sampling period is calculated. In some embodiments, the health status of the i-th fuel cell in the unit during the k-th sampling period is assumed to be... A lower health status value indicates more severe fuel cell aging. The change in load power of the fuel cell during the k-th sampling period... The expression is: Where N is a preset coefficient. The adjustment coefficient can be obtained through... To control the variance of the allocation coefficients, the preferred values ​​are... Furthermore, the allocation coefficients have been normalized, meaning the sum of all allocation coefficients is 1.

[0039] Based on the historical load power set and optimal load power of each fuel cell unit, the load power change rate of each fuel cell unit in each sampling period is calculated. In some embodiments, the calculation process for the load power change rate specifically includes: The historical load power of the j-th fuel cell unit in the t-th sampling period is represented as: The optimal load power is expressed as ,Will The load power change rate of the j-th fuel cell unit in the t-th sampling period Where k is the current sampling period.

[0040] The initial output power of each fuel cell is obtained. Based on the load power change rate of each fuel cell unit in each sampling period, as well as the initial output power of each fuel cell and the load power change in each sampling period, the optimal output power of each fuel cell is calculated.

[0041] In some embodiments, the calculation process for the optimal output power of each fuel cell specifically includes: S31: Obtain the load power change rate of the j-th fuel cell unit in each sampling period, and the initial output power of the i-th fuel cell of the j-th fuel cell unit. and the change in load power in each sampling period; S32: Will The optimal output power of the i-th fuel cell in the j-th fuel cell unit ;in, Let be the change in load power of the i-th fuel cell in the j-th fuel cell unit during the t-th sampling period; S33: Repeat steps S31-S32 until the optimal output power of all fuel cells is calculated.

[0042] In some embodiments, based on the above process, the load power distribution within the unit can be adaptively adjusted according to the health status of each fuel cell, so that fuel cells with poor health status can bear lower power fluctuations and thus reduce the load power they bear, thereby slowing down the aging rate and achieving health convergence within the unit.

[0043] Adjust the current output power of each fuel cell to the optimal output power, and adjust the current output power of the lithium battery energy storage system to the optimal output power.

[0044] In some embodiments, by adjusting the current output power of each fuel cell and lithium battery energy storage system to the optimal output power in real time, the power allocation scheme is optimized while ensuring that the power demand of the user-side load is met, thereby minimizing the system operating cost, improving the overall operating economy of the system and reducing the total life cycle cost.

[0045] The present invention also provides a hierarchical cooperative control device for a multi-engine hydrogen fuel cell power generation system, used to implement the hierarchical cooperative control method for the multi-engine hydrogen fuel cell power generation system, the device comprising: The current output power acquisition module is used to acquire the current output power set of the multi-fuel cell power generation system. The multi-fuel cell power generation system includes multiple fuel cell units and a lithium battery energy storage system. A fuel cell unit includes multiple fuel cells. The current output power set includes the current output power of each fuel cell and the lithium battery energy storage system. The outer control module is used to calculate the total cost based on the current output power of each fuel cell and lithium battery energy storage system. The minimum total cost is used as the objective function. A set of constraints is constructed, and the objective function is solved under the constraints of the set of constraints by the whale optimization algorithm. The optimal load power of each fuel cell unit and the optimal output power of the lithium battery energy storage system that minimize the total cost are calculated. The inner control module is used to obtain the historical health status set of each fuel cell and the historical load power set of each fuel cell unit; based on the historical health status set of each fuel cell, the historical load power set of each fuel cell unit and the optimal load power, the optimal output power of each fuel cell is calculated. The output power adjustment module adjusts the current output power of each fuel cell to the optimal output power, and adjusts the current output power of the lithium battery energy storage system to the optimal output power.

[0046] This application provides an electronic device, including a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements a hierarchical collaborative control method for a multi-machine hydrogen fuel cell power generation system according to any of the above schemes.

[0047] Specifically, the processor may include, for example, a general-purpose microprocessor, an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor may also include onboard memory for caching purposes. The processor may be a single processing unit or multiple processing units for performing different actions of the method flow according to embodiments of this application.

[0048] Memory can be any medium capable of containing, storing, transmitting, propagating, or transmitting instructions. For example, memory can include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, instruments, or propagation media. Specific examples of memory include: magnetic storage devices such as magnetic tape or hard disk drives (HDDs); optical storage devices such as optical discs (CD-ROMs); and also random access memory (RAM) or flash memory; and / or wired / wireless communication links.

[0049] This application also provides a computer-readable medium storing a computer program that, when executed by a processor, implements a hierarchical cooperative control method for a multi-machine hydrogen fuel cell power generation system as described above. This computer-readable medium may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The aforementioned computer-readable medium carries one or more programs, which, when executed, implement the method as described in the embodiments of this application.

[0050] According to embodiments of this application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, optical fiber, radio frequency signals, etc., or any suitable combination thereof.

[0051] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application. Therefore, the scope of this application should not be limited to the above embodiments, but should be defined not only by the appended claims, but also by their equivalents. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A hierarchical cooperative control method for a multi-machine hydrogen fuel cell power generation system, characterized in that, Including the following steps: Obtain the current output power set of a multi-fuel cell power generation system. The multi-fuel cell power generation system includes multiple fuel cell units and a lithium battery energy storage system. A fuel cell unit includes multiple fuel cells. The current output power set includes the current output power of each fuel cell and the lithium battery energy storage system. Entering the outer control stage, the total cost is calculated based on the current output power of each fuel cell and lithium battery energy storage system. The minimum total cost is taken as the objective function, and a set of constraints is constructed. The objective function is solved under the constraints of the set of constraints by the whale optimization algorithm. The optimal load power of each fuel cell unit and the optimal output power of the lithium battery energy storage system that minimize the total cost are calculated. Entering the inner control stage, the historical health status set of each fuel cell and the historical load power set of each fuel cell unit are obtained; based on the historical health status set of each fuel cell, the historical load power set of each fuel cell unit and the optimal load power, the optimal output power of each fuel cell is calculated. Adjust the current output power of each fuel cell to the optimal output power, and adjust the current output power of the lithium battery energy storage system to the optimal output power; The optimal output power of each fuel cell is calculated based on the historical health status set of each fuel cell, the historical load power set of each fuel cell unit, and the optimal load power. Specifically, this includes: Based on the historical health status set of each fuel cell, the load power change of each fuel cell in each sampling period is calculated. Based on the historical load power set and optimal load power of each fuel cell unit, the load power change rate of each fuel cell unit in each sampling period is calculated. The initial output power of each fuel cell is obtained. Based on the load power change rate of each fuel cell unit in each sampling period, as well as the initial output power of each fuel cell and the load power change in each sampling period, the optimal output power of each fuel cell is calculated. The calculation process for the load power change rate specifically includes: The historical load power of the j-th fuel cell unit in the t-th sampling period is represented as: The optimal load power is expressed as ,Will The load power change rate of the j-th fuel cell unit in the t-th sampling period Where k is the current sampling period; The calculation process for the optimal output power of each fuel cell specifically includes: S31: Obtain the load power change rate of the j-th fuel cell unit in each sampling period, and the initial output power of the i-th fuel cell of the j-th fuel cell unit. and the change in load power in each sampling period; S32: Will The optimal output power of the i-th fuel cell in the j-th fuel cell unit ;in, Let be the change in load power of the i-th fuel cell in the j-th fuel cell unit during the t-th sampling period; S33: Repeat steps S31-S32 until the optimal output power of all fuel cells is calculated.

2. The hierarchical cooperative control method for a multi-engine hydrogen fuel cell power generation system according to claim 1, characterized in that, The calculation of the total cost based on the current output power of each fuel cell and lithium battery energy storage system specifically includes: S11: Obtain the current output power of the i-th fuel cell in the j-th fuel cell unit. Based on the current output power Calculate the hydrogen consumption rate of the i-th fuel cell. ; S12: Repeat step S11 to sum the hydrogen consumption rates of each fuel cell in the j-th fuel cell unit to obtain the hydrogen consumption rate of the j-th fuel cell unit. According to the hydrogen consumption rate Calculate the hydrogen usage cost of the j-th fuel cell unit. ; S13: Obtain the change in output power of the i-th fuel cell in the j-th fuel cell unit. Based on the current output power and output power change Calculate the aging cost of the i-th fuel cell. ; S14: Repeat step S13 to sum the aging costs of each fuel cell in the j-th fuel cell unit to obtain the aging cost of the j-th fuel cell unit. ; S15: Repeat steps S11-S14 to calculate the hydrogen usage cost and aging cost of each fuel cell unit. S16: Obtain the aging unit price and rated capacity of the lithium battery energy storage system, and calculate the aging cost of the lithium battery energy storage system based on the current output power, aging unit price and rated capacity of the lithium battery energy storage system. S17: Add the hydrogen usage cost and aging cost of each fuel cell unit, as well as the aging cost of the lithium battery energy storage system, to obtain the total cost.

3. The hierarchical cooperative control method for a multi-machine hydrogen fuel cell power generation system according to claim 1, characterized in that, The process of solving the objective function using the whale optimization algorithm under constraints of a set of conditions to calculate the optimal load power of each fuel cell unit and the optimal output power of the lithium battery energy storage system that minimizes the total cost specifically includes: S21: Use the current load power of each fuel cell unit as the current position of the whale, set the number of whales to N, and the maximum number of iterations to... ; S22: Perform the vth iteration; S23: Obtain the current position of whale u, and generate random numbers P and A; if P is not less than 0.5, calculate the updated position of whale u based on the current position using the bubble attack formula; if P is less than 0.5 and the absolute value of A is less than 1, calculate the updated position of whale u based on the current position using the prey encirclement formula; if P is less than 0.5 and the absolute value of A is not less than 1, calculate the updated position of whale u based on the current position using the prey search formula. S24: Input the updated load power of each fuel cell unit corresponding to the updated position of whale u into the multi-unit fuel cell unit power generation system, and calculate the updated output power and SOC value of the lithium battery energy storage system. S25: If the updated load power of each fuel cell unit, the updated output power of the lithium battery energy storage system, and the SOC value satisfy the constraint set, then update the current position of whale u+1; otherwise, update the current position of whale u again. S26: Repeat steps S23-S25 to calculate the updated position of each whale in the v-th iteration, and let v=v+1; S27: Repeat steps S22-S26 until the maximum number of iterations, input the updated load power of each fuel cell unit, the updated output power of the lithium battery energy storage system and the SOC value of each updated position into the multi-fuel cell unit power generation system to obtain the total cost of each updated position. S28: The update load power of each fuel cell unit and the update output power of the lithium battery energy storage system corresponding to the update position with the lowest total cost are taken as the optimal load power of each fuel cell unit and the optimal output power of the lithium battery energy storage system.

4. The hierarchical cooperative control method for a multi-machine hydrogen fuel cell power generation system according to claim 3, characterized in that, The set of constraints includes: The refresh load power of each fuel cell unit is within the first preset range; The sum of the replacement load power of each fuel cell unit and the replacement output power of the lithium battery energy storage system equals the total power of the multi-unit fuel cell power generation system. The refresh output power of the lithium battery energy storage system is within a second preset range; The SOC value of the lithium battery energy storage system is within the third preset range.

5. A hierarchical collaborative control device for a multi-engine hydrogen fuel cell power generation system, used to implement the hierarchical collaborative control method for a multi-engine hydrogen fuel cell power generation system as described in any one of claims 1 to 4, characterized in that, The device includes: The current output power acquisition module is used to acquire the current output power set of the multi-fuel cell power generation system. The multi-fuel cell power generation system includes multiple fuel cell units and a lithium battery energy storage system. A fuel cell unit includes multiple fuel cells. The current output power set includes the current output power of each fuel cell and the lithium battery energy storage system. The outer control module is used to calculate the total cost based on the current output power of each fuel cell and lithium battery energy storage system. The minimum total cost is used as the objective function. A set of constraints is constructed, and the objective function is solved under the constraints of the set of constraints by the whale optimization algorithm. The optimal load power of each fuel cell unit and the optimal output power of the lithium battery energy storage system that minimize the total cost are calculated. The inner control module is used to obtain the historical health status set of each fuel cell and the historical load power set of each fuel cell unit; based on the historical health status set of each fuel cell, the historical load power set of each fuel cell unit and the optimal load power, the optimal output power of each fuel cell is calculated. The output power adjustment module adjusts the current output power of each fuel cell to the optimal output power, and adjusts the current output power of the lithium battery energy storage system to the optimal output power.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the hierarchical collaborative control method for a multi-machine hydrogen fuel cell power generation system as described in any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the hierarchical collaborative control method for a multi-machine hydrogen fuel cell power generation system as described in any one of claims 1 to 4.

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