Double-layer power distribution method of multi-machine fuel cell power generation system

Abstract

The invention discloses a double-layer power distribution method of a multi-machine fuel cell power generation system, and belongs to the field of power distribution of fuel cell power generation systems, and the method comprises the steps: carrying out the double-layer power distribution according to the system load power obtained through power prediction and the state of a multi-stack fuel cell power generation system, firstly carrying out the upper layer switching control, and then carrying out the lower layer switching control; taking the minimum first operation cost of the system as a target, solving under a first preset constraint to obtain and determine the optimal on-off state and the optimal output power of each fuel cell power generation subsystem in the switching control period; and then solving the optimal output instantaneous power of each fuel cell power generation subsystem in the instantaneous control period under a second preset constraint by taking the minimum second operation cost of the system, the minimum tracking optimal current track distance and the convergence of the health state of each fuel cell stack as targets during lower-layer instantaneous power distribution, so as to obtain the optimal output instantaneous power of each fuel cell power generation subsystem in the instantaneous control period. The total power generation income of the multi-stack fuel cell power generation system is highest, and the service life attenuation tends to be consistent.

Description

Technical Field

[0001] The present invention belongs to the field of power distribution of fuel cell power generation systems, and more specifically, relates to a two-layer power distribution method for a multi-stack fuel cell power generation system. Background Art

[0002] With the transformation of the global energy structure and the increasing requirements for environmental protection, the development of clean energy technologies has been increasingly emphasized. As an efficient and environmentally friendly energy conversion technology, fuel cells are widely regarded as having broad application prospects in fields such as rail transit, distributed power generation, and combined heat and power systems. The core advantages of fuel cell systems lie in their high energy conversion efficiency and low emission characteristics. However, single-stack fuel cell systems have problems such as low power, insufficient durability, and high costs, which limit their large-scale application. Multi-stack fuel cell power generation systems meet high-power requirements through the combination of fuel cell stacks, which can improve system efficiency and reliability. The power distribution method for multi-stack fuel cell systems is crucial for achieving optimal system operation. An effective control strategy can intelligently distribute the power output of each fuel cell stack according to different operating conditions, thereby improving the overall performance and lifespan of the system. Summary of the Invention

[0003] Aiming at the above defects or improvement requirements of the prior art, the present invention provides a two-layer power distribution method for a multi-stack fuel cell power generation system, which can improve energy utilization efficiency and reliability.

[0004] To achieve the above object, according to the first aspect of the present invention, there is provided a two-layer power distribution method for a multi-stack fuel cell power generation system. The multi-stack fuel cell power generation system includes a plurality of fuel cell stacks and DC / DC converters connected to them one-to-one. The outputs of all DC / DC converters are connected in parallel and then connected to a load through a DC bus. The method includes: S1, predicting the load power of the system for a period of time in the future; taking the minimum of the first operating cost of the system as the goal, establishing an energy scheduling model of the system, and solving it under the first preset constraints to obtain the switching states and optimal output current values of the fuel cell stacks in the system; wherein, the first operating cost includes voltage recovery benefits and a second operating cost, and the second operating cost includes power generation benefits, hydrogen consumption costs, attenuation costs, and maintenance costs; the first preset constraints include a predicted load balance equation constraint and upper and lower bounds constraints on the output currents of the fuel cell stacks, and the predicted load balance equation constraint is that the sum of the output powers of all fuel cell stacks is equal to the predicted load power; S2, aiming at minimizing the second operating cost of the system, minimizing the distance of tracking the optimal current trajectory, and making the health states of all fuel cell stacks converge, an instantaneous power distribution model of the system is established and solved under the second preset constraints to obtain the optimal instantaneous operating current of each stack; Among them, the second preset constraints include an instantaneous load balance equation constraint and upper and lower bounds constraints on the output power of each fuel cell stack. The instantaneous load balance equation constraint is that the sum of the instantaneous output powers of all fuel cell stacks is equal to the instantaneous load power.

[0005] According to the second aspect of the present invention, an electronic device is provided, including: a computer-readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method as described in the first aspect.

[0006] According to the third aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the processor to execute the method as described in the first aspect.

[0007] According to the fourth aspect of the present invention, a computer program product is provided, including a computer program or instructions. When the computer program or instructions are executed by a processor, the method as described in the first aspect is implemented.

[0008] Generally speaking, compared with the prior art by the above technical solutions conceived by the present invention, the following beneficial effects can be achieved: 1. The method provided by the present invention adopts a two-layer power distribution strategy including upper-layer switching control and lower-layer instantaneous power distribution. Among them, the upper-layer switching control has a larger optimization calculation step size. A real-time optimization method is used to establish an energy scheduling model of a multi-machine fuel cell power generation system, and the optimal switching state and optimal output power of each fuel cell stack within the switching control period are obtained by solving; T The lower-layer instantaneous power distribution establishes an instantaneous power distribution model of the multi-machine fuel cell power generation system according to the optimal switching state and optimal output power obtained by the upper-layer energy management to distribute the real-time load among each fuel cell stack, and the operating power of each stack is obtained by solving.

[0009] 2. The method provided by the present invention uses an improved hybrid particle swarm optimization algorithm to solve the energy scheduling model of a multi-machine fuel cell power generation system. According to the objective function and constraints, the hybrid particle swarm moves in the search space according to the preset movement rules, and searches for the optimal position of the hybrid particle swarm through iteration, that is, to find the switching conditions and optimal output power of each fuel cell subsystem in the multi-machine fuel cell power generation system, so as to realize the real-time allocation of the optimal output power of the working multi-machine fuel cell power generation system, reduce the attenuation of the multi-machine fuel cell power generation system, and improve the efficiency of the multi-machine fuel cell power generation system. At the same time, when the iteration termination condition is satisfied, the iteration of the particle swarm algorithm is terminated, avoiding the particle swarm algorithm falling into an infinite loop.

[0010] 3. The method provided by the present invention uses a sequential quadratic programming algorithm to solve the instantaneous power allocation model of a multi-machine fuel cell power generation system. The main idea of this algorithm is that in each iteration, the Taylor formula is used to expand and retain the first and second terms, so as to transform the nonlinear programming at this moment into a quadratic programming, which can reduce the calculation time and improve the real-time performance of instantaneous power allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic flowchart of the two-layer power allocation method for the multi-machine fuel cell power generation system provided by the embodiment of the present invention.

[0012] Figure 2 It is a schematic diagram for calculating the voltage recovery benefit of the fuel cell power generation system provided by the embodiment of the present invention.

[0013] Figure 3 It is a flowchart of the optimization solution algorithm based on the improved hybrid particle swarm provided by the embodiment of the present invention.

[0014] Figure 4 It is a flowchart of the lower-layer instantaneous power allocation based on the sequential quadratic programming provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0016] An embodiment of the present invention provides a two - layer power distribution method for a multi - machine fuel cell power generation system. The multi - machine fuel cell power generation system includes a plurality of fuel cell stacks and DC / DC converters connected to them one by one. The outputs of all DC / DC converters are connected in parallel and then connected to a load through a DC bus. The method is characterized in that it includes: S1, predicting the load power of the system in a future period of time ; taking the minimum of the first operating cost of the system as the goal, establishing an energy scheduling model of the system, and solving it under the first preset constraints to obtain the switching states and optimal output current values of each fuel cell stack in the system; where the first operating cost includes voltage recovery benefits and a second operating cost, and the second operating cost includes power generation benefits, hydrogen consumption costs, attenuation costs, and maintenance costs; the first preset constraints include a predicted load balance equation constraint and upper and lower bounds constraints on the output currents of each fuel cell stack, and the predicted load balance equation constraint is that the sum of the output powers of all fuel cell stacks is equal to ; S2, taking the minimum of the second operating cost of the system, the minimum distance of tracking the optimal current trajectory, and the convergence of the health states of each fuel cell stack as the goal, establishing an instantaneous power distribution model of the system, and solving it under the second preset constraints to distribute the real - time load among each fuel cell power generation subsystem (i.e., each fuel cell stack) to obtain the optimal instantaneous operating current of each stack; where the second preset constraints include an instantaneous load balance equation constraint and upper and lower bounds constraints on the output powers of each fuel cell stack, and the instantaneous load balance equation constraint is that the sum of the instantaneous output powers of all fuel cell stacks is equal to the real - time instantaneous power of the load.

[0017] As shown in FIG. 1, the two - layer power distribution method for a multi - machine fuel cell power generation system provided by the present invention includes power prediction 1000 and a power distribution strategy 2000. Among them, the multi - stack fuel cell power generation system 3000 is composed of a plurality of fuel cell stacks, each stack is connected to a DC / DC converter, the outputs of all DC / DC converters are connected in parallel to the DC bus, and then connected to the load. The function of power prediction 1000 is to predict the load power (i.e., the load power ) in a future period of time according to historical load data and weather factors such as temperature, humidity, wind speed, and sunshine using existing prediction methods.

[0018] The function of the power distribution strategy 2000 is to allocate the power generation of each fuel cell stack according to the load power obtained from the power prediction 1000 and the state of the multi-stack fuel cell power generation system 3000 (including current and voltage, health status, etc.), so as to maximize the power generation benefit of the multi-stack fuel cell power generation system and make the life attenuation tend to be consistent. The power distribution strategy 2000 includes two layers, the upper-layer switching control 2100 and the lower-layer instantaneous power distribution 2200. The first layer is the upper-layer switching control 2100 based on real-time optimization, including the objective function I 2110 (i.e., the objective function of the energy scheduling model), the constraint condition I 2120, and the optimization solution algorithm 2130 based on the hybrid particle swarm. The objective function I 2110 includes the power generation benefit 2111, the voltage recovery benefit 2112, the hydrogen consumption cost 2113, the attenuation cost 2114, and the maintenance cost 2115. The upper-layer switching control 2100 conducts power analysis and prediction on the load to formulate the energy scheduling plan of the multi-machine fuel cell power generation system, including the on / off state of each fuel cell stack and the optimal output current value. The lower-layer instantaneous power distribution 2200 distributes the real-time load among the fuel cell power generation subsystems according to the energy scheduling plan obtained by the upper-layer switching control 2100 to determine the optimal instantaneous working current of each stack; it includes the objective function II 2210 (i.e., the objective function of the instantaneous power distribution model), the constraint condition II 2220, and the instantaneous power distribution algorithm 2230 based on sequential quadratic programming. The objective function II 2210 includes the power generation system cost 2211, the optimal current tracking 2212, and the SOH convergence 2213. The constraint condition II 2220 includes the instantaneous load balance equality constraint 2221 and the power upper and lower bound constraints 2222.

[0019] The objective function I 2110 includes the power generation benefit 2111, the voltage recovery benefit 2112, the hydrogen consumption cost 2113, the attenuation cost 2114, and the maintenance cost 2115. In the multi-machine fuel cell power generation system There are fuel cell subsystems (i.e., fuel cell stacks, hereinafter also referred to as fuel cell power generation subsystems) in parallel, then the objective function of the multi-machine fuel cell power generation system, that is, the objective function I 2110, is: ; In the formula, ; represents the switching situation of the i-th fuel cell power generation subsystem at the operation switching period T, 1 means running, and 0 means stopping. Then the switching situations of all fuel cell power generation subsystems are ; is the power generation benefit 2111, is the voltage recovery benefit 2112, is the hydrogen consumption cost 2113, To attenuate the cost 2114 and maintenance cost 2115; They are respectively the power generation revenue, voltage recovery revenue, hydrogen consumption cost, attenuation cost and maintenance cost of the i-th fuel cell power generation subsystem.

[0020] The power generation revenue of the i-th fuel cell power generation subsystem is mainly the revenue generated by the electric energy generated by the fuel cell power generation system, that is ; In the formula, is the electricity price, in yuan per RMB; is the output voltage of the i-th fuel cell power generation subsystem, in volts (V), is the output current of the i-th fuel cell power generation subsystem at time, in amperes (A), T is the stack switching period of the upper-level energy management, is the average output current of the i-th fuel cell power generation subsystem within the stack switching period T.

[0021] The hydrogen consumption cost of the i-th fuel cell power generation subsystem is: ; In the formula, is the market price of hydrogen (in yuan per kilogram), represents the working current of the fuel cell power generation system at time, is the hydrogen flow rate consumed by the i-th fuel cell power generation system (in grams per second), is the number of single cells in the stack, is the molar mass value of hydrogen (in grams per mole), F is the Faraday constant (in coulombs per mole); is the efficiency curve of the i-th fuel cell power generation system, which can be expressed as: ; The attenuation cost of the fuel cell power generation system is defined as: the loss of the system purchase cost caused by the attenuation of the fuel cell power generation system, which is proportional to the state of health (SoH) of the fuel cell; .

[0022] In the formula, is the value at the current SoH of the fuel cell power generation system (in yuan per RMB), is the purchase price of the fuel cell power generation system (in yuan per RMB), is the market price of the single-stack system per kilowatt of fuel cell (in yuan / kW), is the rated power of a single fuel cell stack system (in kW); then the attenuation cost of the i-th fuel cell power generation system ( , in yuan) is: ; The health status of the i-th fuel cell power generation system The calculation formula is: ; Where Unew is the voltage value of the new fuel cell stack at rated power (V), and ΔUi is the voltage drop of the i-th fuel cell power generation system due to attenuation. According to the national standard GB / T38914-2020 "Service Life of Proton Exchange Membrane Fuel Cell Stacks for Vehicles", the voltage attenuation ΔU is divided into attenuation caused by four operating conditions: idling condition, rated condition, variable load condition, and start-stop condition.

[0023] The voltage drop caused by the performance degradation of the i-th fuel cell power generation system is for: ; In the formula, is the voltage decay rate of the ith fuel cell power generation system caused by the idling condition, in volts per hour (V / h); is the voltage decay rate of the ith fuel cell power generation system caused by rated operating conditions, in volts per hour (V / h); is the voltage attenuation rate of the ith fuel cell power generation system caused by the variable load condition, in volts per time (V / time); is the voltage decay rate of the ith fuel cell power generation system caused by the start-stop condition, in volts per time (V / time); is the cumulative operating time of the idling condition of the i-th fuel cell power generation system, in hours (h); is the cumulative operating time of the i-th fuel cell power generation system under rated conditions, in hours (h); is the cumulative number of operation times of the i-th fuel cell power generation system under the variable load condition, in times per hour (times / h); The cumulative number of start and stop conditions of the i-th fuel cell power generation system, in times per hour (times / h).

[0024] The maintenance cost of the fuel cell power generation system is defined as: ; In the formula, is the ratio of the fuel cell power generation system maintenance cost to the purchase cost, is the total number of seconds in a year (s); in the objective function I, the voltage recovery benefit of the i-th fuel cell power generation system under the start-stop condition can be obtained byFigure 2 Calculated; ; In the formula, is the recovery voltage at time is the irreversible decay voltage at time . After the fuel cell power generation system operates for a long time, it stops working and undergoes a certain period of rest, and its performance will recover to a certain extent. This phenomenon is called the fuel cell voltage recovery phenomenon, and this phenomenon can be expressed by the following formula: ; In the formula, is a constant that can be obtained by fitting the measured voltage data between the start-stop points.

[0025] Determination of the switching period T of the fuel cell power generation system: , is the critical time when the stop benefit and stop loss of the fuel cell power generation system are equal. The stop benefits of the fuel cell power generation system include: 1) the health benefit of stopping power generation, which is characterized by the system health status ; 2) the benefit caused by voltage recovery (i.e., voltage recovery benefit). The stop loss (i.e., decay cost) of the fuel cell power generation system includes: 1) the decay loss caused by start-stop switching; 2) the decay loss caused by the increased load of other fuel cell systems due to stopping. The specific calculation is similar to the previous decay loss and voltage recovery benefit.

[0026] The voltage drop caused by the performance decay of fuel cell stack i within the operation switching period T of the fuel cell stack is calculated as follows: ; In the formula, is the fuel cell voltage decay rate (V / h) of fuel cell stack i under rated conditions; is the average operating current (A) of fuel cell stack i within the operation switching period T of the fuel cell stack; is the rated current (A) of fuel cell stack i; is the voltage decay rate (V / h) of fuel cell stack i caused by load change; is the number of load changes per unit time (times / h) of fuel cell stack i; is the voltage decay rate (V / h) caused by one start-stop of fuel cell stack i; is the start-stop change of fuel cell stack i. If fuel cell stack i changes from operation to stop or from stop to operation, the value is 1; if the state does not change, the value is 0.

[0027] In the voltage recovery benefit formula The calculation of requires the use of which is the accumulation of

[0028] The constraint condition I 2120 of the multi-stack fuel cell power generation system is: the predicted load balance equation constraint 2121, and the sum of the output powers of each fuel cell stack should satisfy the load power , that is: ; The output current of each fuel cell power generation system has current upper and lower bound constraints 2122: ; In the formula, is the minimum output current of the fuel cell power generation system; is the maximum output current of the fuel cell power generation system.

[0029] Convert the constraint condition I 2120 of the multi-stack fuel cell power generation system into , where A is the constraint coefficient matrix; a is the constraint vector; ; Through the above analysis, the real-time optimization problem of the multi-stack fuel cell power generation system is: ; Let the optimal solution of this optimization problem be .

[0030] The real-time optimization of the multi-stack fuel cell power generation system is a mixed-integer nonlinear programming problem. This problem can be solved by methods such as the branch and bound method and the outer approximation method. Among them, the branch and bound method has a long solution time, and the outer approximation method requires calling commercial software. Based on this, preferably, the present invention uses an improved hybrid particle swarm optimization algorithm for solution. In the upper layer switching control, the specific solution process of the optimization solution algorithm 2130 based on the improved hybrid particle swarm is as Figure 3 shown, including: S301. Obtain the electrical data and operating data of the target multi-machine fuel cell system.

[0031] S302. Extract the output current of each stack of the multi-machine fuel cell from the electrical data and operating data of the running multi-machine fuel cell system, and determine the objective function I according to the output current.

[0032] S303. Determine Constraint Condition I based on the power data of the target distribution network. Among them, Constraint Condition I at least includes the following sub-constraint conditions: load balance constraint condition, upper and lower power bound constraint conditions for the output power of each fuel cell power generation system.

[0033] S304. Determine the initial positions and initial velocities of the continuous particles and discrete particles in the improved hybrid particle swarm optimization algorithm. The particles in the particle swarm move in a preset search space according to the preset movement rules. After the discrete particles are updated, in order to ensure that they are 0-1 values, a rounding operation is required. Among them, the particle can refer to the working current of a single fuel cell system, and the number of particles can refer to the number of stacks in a multi-stack fuel cell. Multiply by the dimension D of the search space of the particle. The initial positions and initial velocities of the particles can be set randomly.

[0034] Compared with the conventional particle swarm optimization algorithm, the improvement of the improved hybrid particle swarm optimization algorithm adopted in the present invention lies in that the hybrid particles in the hybrid particle swarm move in a preset search space according to the preset movement rules. Other processes are the same as those of the conventional particle swarm optimization algorithm.

[0035] The preset movement rule is: ; where is the position of the particle , is the velocity of particle k, and D is the dimension of the search space; is the optimal position of the previous search; is the optimal position of the current search; is the number of iterations; all particles start with randomly initialized velocities and positions, and ω is the inertia weight; and are the cognitive parameter and environmental parameter respectively; and are random numbers uniformly distributed in the range of [0, 1]; For the discrete particle after being updated according to the preset movement rule, in order to ensure that it is a 0-1 value, a rounding operation is required. First, define the normalized value of the particle velocity : ; Then the discrete particle takes values according to the following formula: ; In the formula, rand() is a random number.

[0036] S305. Determine the optimal position of the particle swarm in each iteration process according to the objective function I, the constraint condition I, the initial position and the initial velocity of the particles of the particle swarm algorithm. Among them, the fitness is used to characterize the quality of the particle position. The smaller the value of the fitness, the better the position of the particle.

[0037] Specifically, for each iteration process, on the basis of determining the initial position and the initial velocity of the particles of the particle swarm, make all the particles in the particle swarm satisfy the constraint condition I; then use the objective function I to calculate the fitness of each particle in the particle swarm; determine the optimal position of the particle swarm in each iteration process according to the fitness of each particle in the particle swarm. Among them, the fitness of the particles in the particle swarm can be determined by the following formula: ; Where is the fitness of the particle in the particle swarm, is the objective function I, σ is a positive penalty value, β is a constant, and generally β≥1.

[0038] S306. When the iteration process meets the iteration termination condition, use the optimal position of the particle swarm in this iteration process as the optimal switching state and the optimal output power of each fuel cell stack, that is: .

[0039] The lower-layer instantaneous power distribution 2200 has multiple objectives: 1) minimizing the cost of the multi-stack fuel cell power generation system 2211; 2) minimizing the distance to track the optimal current trajectory 2212; 3) making the health states of each fuel cell stack converge 2213. Then there is: ; In the formula, α1, α2, and α3 are weight coefficients respectively, and these three coefficients can be defined according to experience or user preferences; In the lower-layer instantaneous power distribution 2200 stage, the number of working fuel cell stacks is fixed and equal to the optimal stack switching state obtained in the upper-layer energy management stage . The expressions of each objective are as follows: 1) The cost of the multi-stack fuel cell power generation system ; ; In the formula, is the instantaneous hydrogen consumption cost of fuel cell stack i; is the instantaneous decay cost of fuel cell stack i; is the instantaneous maintenance cost of fuel cell stack i; is the instantaneous power generation income of fuel cell stack i; and The calculation method of in is changed to , is changed to , is changed to , is the instantaneous current vector allocated to the multi - machine fuel cell power generation system; 2) Tracking the distance of the optimal current trajectory : ; In the formula, is the instantaneous current allocated to fuel cell stack i; is the optimal current vector of fuel cell stack i obtained from the upper - layer energy management.

[0040] 3) The state of health of each fuel cell stack converges: 4) ; In the formula, is the state of health of fuel cell stack i; is the average state of health in the multi - stack fuel cell system. The calculation method of SoH is similar to that in the upper - layer energy management. The constraint conditions of the lower - layer instantaneous power distribution of the multi - stack fuel cell power generation system II 2220 are: the instantaneous load balance equation constraint 2221, and each fuel cell stack should satisfy the instantaneous load power , that is: ; The output power of each fuel cell power generation system has upper and lower power bounds constraints 2222; ; In the formula, is the minimum output power of the fuel cell power generation system; is the maximum output power of the fuel cell power generation system.

[0041] Similarly, constraint condition II can also be transformed into an inequality constraint form: ; In the formula, , is the constraint coefficient matrix; is the constraint vector; .

[0042] Then the lower - layer instantaneous power distribution 2200 can be transformed into a nonlinear programming problem; ; The methods for solving this non - linear programming include the Gradient Descent method, the Interior Point Method, etc. However, these methods have a long calculation time and require the invocation of commercial software. Based on this, preferably, the present invention adopts the sequential quadratic programming algorithm. The main idea of this algorithm is that at each iteration, by expanding using the Taylor formula and retaining the first - order and second - order terms, the non - linear programming at that moment is transformed into a quadratic programming, which can reduce the calculation time and improve the real - time performance of the lower - layer instantaneous power distribution 2200. The non - linear programming objective function of the lower - layer instantaneous power distribution (i.e., objective function II) can be transformed into the following form: ; Figure 4 is the flow chart of the lower - layer instantaneous power distribution based on the sequential quadratic programming. The specific algorithm steps are as follows: S401. Initialization: Receive the upper - layer handover control information , , calculate the iteration number n = T / t of the lower - layer instantaneous power distribution 2200, and receive information such as the voltage, current, and power of the multi - machine fuel cell power generation system.

[0043] S402. According to the upper - layer handover control optimal solution information, determine the sub - objective function in the objective function II and the corresponding weight coefficients of the sub - objective function.

[0044] S403. Determine the constraint condition II for the lower - layer energy management of the multi - stack fuel cell power generation system, including the load - balancing constraint and the upper and lower power bounds constraints on the output power of each fuel cell power generation system. Construct a non - linear programming problem.

[0045] S404. Initialize the optimization algorithm parameters: Select an initial point , and set the iteration number k = 0.

[0046] S405. Construct a quadratic programming sub - problem: Near the current point , use the second - order Taylor formula expansion of the objective function II to approximate the original problem.

[0047] S406. Solve the quadratic programming sub - problem: Solve the quadratic programming sub - problem constructed in the previous step to obtain the search direction .

[0048] S407. Line search: Perform a line search along the search direction to determine the step size , ensuring that the value of the objective function II decreases in the next step. The line search adopts the backtracking line search method. The specific steps are as follows: 1) Initialize the step size , and set the backtracking coefficient and the constant ; 2) If , then reduce the step size to α = ρα until the condition is satisfied.

[0049] S408. Update the solution: Update the current solution .

[0050] S409. Stopping criterion judgment: Check whether the current point satisfies the stopping criterion, such as the gradient being small enough or the number of iterations reaching the upper limit. If satisfied, stop the iteration and output the current solution as the optimal solution.

[0051] S410. Iteration: Set k = k + 1, return to step S405 to continue the iteration until the stopping criterion is satisfied. If satisfied, stop the iteration and output the current solution as the optimal solution of the instantaneous power allocation strategy.

[0052] An embodiment of the present invention provides an electronic device, including: a computer-readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; the processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method described in any of the above embodiments. An embodiment of the present invention provides a computer-readable storage medium storing computer instructions for causing a processor to execute the method described in any of the above embodiments. An embodiment of the present invention provides a computer program product including a computer program or instruction, and when the computer program or instruction is executed by a processor, the method described in any of the above embodiments is implemented. Those skilled in the art can easily understand that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A two-layer power distribution method for a multi-machine fuel cell power generation system, the multi-machine fuel cell power generation system comprising a plurality of fuel cell stacks and DC / DC converters connected to them in one-to-one correspondence, the outputs of all the DC / DC converters being connected in parallel and then connected to a load via a DC bus, characterized in that, The method includes: S1. Predict the load power of the system in a future period of time; with the goal of minimizing the first operating cost of the system, establish an energy scheduling model of the system, and solve it under the first preset constraints to obtain the switching states and optimal output current values of the fuel cell stacks in the system; wherein, the first operating cost includes voltage recovery benefits and a second operating cost, and the second operating cost includes power generation benefits, hydrogen consumption costs, attenuation costs, and maintenance costs; the first preset constraints include a predicted load balance equation constraint and upper and lower bounds constraints on the output currents of each fuel cell stack, and the predicted load balance equation constraint is that the sum of the output powers of all fuel cell stacks is equal to the predicted load power; S2. With the goals of minimizing the second operating cost of the system, minimizing the distance of tracking the optimal current trajectory, and making the health states of the working fuel cell stacks obtained in S1 converge, establish an instantaneous power distribution model of the system, and solve it under the second preset constraints to obtain the optimal instantaneous operating currents of each stack; wherein, the second preset constraints include an instantaneous load balance equation constraint and upper and lower bounds constraints on the output powers of each fuel cell stack, and the instantaneous load balance equation constraint is that the sum of the instantaneous output powers of all fuel cell stacks is equal to the instantaneous load power; use the optimal output current values of the working fuel cell stacks obtained in S1 as their instantaneous current values.

2. The method according to claim 1, characterized in that The i power generation revenue of a fuel cell stack is: ； Among them, i = 1, 2, …, N st , p ele is the electricity price, V st,i is the output voltage of the i th fuel cell stack, T is the stack switching period, I ave,i is the i th fuel cell stack at T the average output current within; The i hydrogen consumption cost of a fuel cell stack is: ； Among them, is the market price of hydrogen, is the number of single cells in the fuel cell stack, is the molar mass value of hydrogen, F is the Faraday constant, is the i efficiency of the The i attenuation cost of a fuel cell stack is: ； wherein, is the health state of the i th fuel cell stack, is the market price per kilowatt of a single fuel cell stack, is the rated power of a single fuel cell stack; The maintenance cost is: ； Among them, is the proportion coefficient of the maintenance cost of the multi-machine fuel cell power generation system to the procurement cost, is the total number of seconds in a year, is the purchase price of the multi-machine fuel cell power generation system, t is the operating time; The i voltage recovery benefit of the fuel cell stack is: ； Among them, is the recovery voltage at the moment, is the irreversible decay voltage at the moment, is the time for voltage recovery, is the output current of the i th fuel cell stack at the moment.

3. The method according to claim 2, wherein The objective function of the energy scheduling model is: ； In the formula, is the operation switching period T at the i th switching, 1 means running and 0 means stopping work. is the maintenance cost of the i th fuel cell stack. The switching situation of the multi-stack fuel cell power generation system is , is the number of fuel cell stacks.

4. The method according to any one of claims 1 to 3, characterized in that Use an improved hybrid particle swarm optimization algorithm to solve the energy scheduling model; Among them, the particles in the particle swarm move in the search space according to a preset movement rule; the preset movement rule is: ; Among them, , ,…, are the positions of the particle k at the a -th iteration, , ， , is the velocity of the particle k at the a -th iteration, D is the dimension of the search space; , ,…, are the optimal positions of the particle k at the a -th iteration in the previous search; , ,…, are the optimal positions of the particle k at the a -th iteration in this search; a is the number of iterations; all particles start with randomly initialized velocities and positions, ω is the inertia weight; c 1 and c 2 are the cognitive parameter and the environmental parameter respectively; r 11 and r 12 are random numbers uniformly distributed in the range [0, 1].

5. The method according to claim 1, wherein The objective function of the instantaneous power distribution model is: ； wherein, are all weight coefficients, , are respectively the instantaneous hydrogen consumption cost, instantaneous degradation cost, instantaneous maintenance cost, and instantaneous power generation revenue of the fuel cell stack i ; is the distance to track the optimal current trajectory, is the instantaneous current allocated to the fuel cell stack i ; is the optimal current vector of the fuel cell stack obtained by the upper-layer energy management i ; ΔSoH is the health state convergence term of each fuel cell stack.

6. The method according to claim 5, characterized in that, Use a sequential quadratic programming algorithm to solve the instantaneous power distribution model.

7. An electronic device, characterized in that, It includes: A computer-readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the processor to execute the method according to any one of claims 1-6.

9. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the processor, the method according to any one of claims 1-6 is implemented.

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