A double-layer power distribution method for a multi-machine fuel cell power generation system

Abstract

The present invention discloses a double-layer power distribution method for a multi-machine fuel cell power generation system, which belongs to the field of power distribution of fuel cell power generation systems. The method comprises: performing double-layer power distribution according to the system load power obtained by power prediction and the status of a multi-stack fuel cell power generation system, firstly performing upper-layer switching control, with the goal of minimizing the first operating cost of the system, solving under a first preset constraint to determine the optimal switching state and optimal output power of each fuel cell power generation subsystem within the switching control cycle; then, in the lower-layer instantaneous power distribution, with the goal of minimizing the second operating cost of the system, minimizing the distance of tracking the optimal current trajectory, and converging the health status of each fuel cell stack, solving under a second preset constraint to obtain the optimal output instantaneous power of each fuel cell power generation subsystem within the instantaneous control cycle, so that the total power generation income of the multi-stack fuel cell power generation system is the highest and the life decay 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 double-layer power distribution method for a multi-machine fuel cell power generation system. Background Art

[0002] With the transformation of the global energy structure and the increasing demand for environmental protection, the development of clean energy technologies is receiving increasing attention. As an efficient and environmentally friendly energy conversion technology, fuel cells are widely expected to be used in areas 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 emissions. However, single-stack fuel cell systems suffer from 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 cells, which can improve system efficiency and reliability. The power distribution method of multi-machine fuel cell systems is crucial to achieving optimized system operation. An effective control strategy can intelligently allocate the power output of each fuel cell stack according to different operating conditions, thereby improving the overall performance and life of the system. Summary of the Invention

[0003] In response to the above-mentioned defects or improvement needs of the prior art, the present invention provides a double-layer power distribution method for a multi-machine fuel cell power generation system, which can improve energy utilization efficiency and reliability.

[0004] To achieve the above objectives, according to a first aspect of the present invention, a two-layer power distribution method for a multi-fuel cell power generation system is provided. The multi-fuel cell power generation system includes multiple fuel cell stacks and DC / DC converters connected thereto in a one-to-one correspondence. The outputs of all DC / DC converters are connected in parallel and then connected to a load via a DC bus. The method includes:

[0005] S1, predicting the load power of the system in the future; establishing an energy scheduling model for the system with the goal of minimizing the first operating cost of the system, and solving the model under a first preset constraint to obtain the switching state and optimal output current value of each fuel cell stack in the system;

[0006] The first operating cost includes voltage recovery income and second operating cost, and the second operating cost includes power generation income, hydrogen consumption cost, attenuation cost and maintenance cost. The first preset constraint includes a predicted load balance equation constraint and upper and lower bound constraints on the output current of each fuel cell stack. The predicted load balance equation constraint requires that the sum of the output power of all fuel cell stacks is equal to the predicted load power.

[0007] S2, establishing an instantaneous power distribution model for the system with the objectives of minimizing the second operating cost of the system, minimizing the distance of tracking the optimal current trajectory, and achieving convergence of the health status of each fuel cell stack, and solving the model under the second preset constraint to obtain the optimal instantaneous operating current of each fuel cell stack;

[0008] Among them, the second preset constraint includes an instantaneous load balance equation constraint and upper and lower bound constraints on the output power of each fuel cell stack. The instantaneous load balance equation constraint requires that the sum of the instantaneous output powers of all fuel cell stacks is equal to the instantaneous power of the load.

[0009] According to a second aspect of the present invention, there is provided an electronic device comprising: a computer-readable storage medium and a processor;

[0010] The computer-readable storage medium is used to store executable instructions;

[0011] The processor is configured to read the executable instructions stored in the computer-readable storage medium and execute the method according to the first aspect.

[0012] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the method according to the first aspect.

[0013] According to a fourth aspect of the present invention, there is provided a computer program product comprising a computer program or instructions, which implement the method according to the first aspect when executed by a processor.

[0014] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0015] 1. The method provided by the present invention adopts a two-layer power allocation strategy including upper-layer switching control and lower-layer instantaneous power allocation; wherein, the optimization calculation step size of the upper-layer switching control is large, and a real-time optimization method is used to establish an energy scheduling model for a multi-machine fuel cell power generation system, and the power consumption of each fuel cell stack in the switching control cycle is obtained. T The optimal switching state and optimal output power within the system are obtained; the lower-level instantaneous power distribution is based on the optimal switching state and optimal output power obtained by the upper-level energy management, and an instantaneous power distribution model of the multi-machine fuel cell power generation system is established to distribute the real-time load among each fuel cell stack, and the operating power of each stack is determined by solving the problem.

[0016] 2. The method provided by the present invention adopts an improved hybrid particle swarm 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 the optimal position of the hybrid particle swarm is found through iteration, that is, the switching status and optimal output power of each fuel cell subsystem in the multi-machine fuel cell power generation system are found, so as to realize the real-time distribution 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 met, the iteration of the particle swarm algorithm is terminated, thereby avoiding the particle swarm algorithm from falling into an infinite dead loop.

[0017] 3. The method provided by the present invention uses a sequential quadratic programming algorithm to solve the instantaneous power distribution model of a multi-machine fuel cell power generation system. The main idea of ​​the algorithm is to use the Taylor formula expansion to retain the linear and quadratic terms in each iteration, and convert the nonlinear programming at that moment into a quadratic programming, which can reduce the calculation time and improve the real-time performance of the instantaneous power distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of a double-layer power distribution method for a multi-fuel cell power generation system provided in an embodiment of the present invention.

[0019] Figure 2 A schematic diagram of calculating the voltage recovery benefit of a fuel cell power generation system provided by an embodiment of the present invention.

[0020] Figure 3 A flow chart of an optimization solution algorithm based on an improved hybrid particle swarm provided in an embodiment of the present invention.

[0021] Figure 4 A flowchart of lower-layer instantaneous power allocation based on sequential quadratic programming is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0023] An embodiment of the present invention provides a two-tier power distribution method for a multi-fuel cell power generation system. The multi-fuel cell power generation system includes multiple fuel cell stacks and DC / DC converters connected thereto in a one-to-one correspondence. The outputs of all DC / DC converters are connected in parallel and then connected to a load via a DC bus. The method includes: S1, predicting the load power of the system over a period of time in the future. ; With the goal of minimizing the first operating cost of the system, an energy scheduling model of the system is established, and the solution is performed under the first preset constraint to obtain the switching state and optimal output current value of each fuel cell stack in the system; wherein, the first operating cost includes voltage recovery benefit and second operating cost, and the second operating cost includes power generation benefit, hydrogen consumption cost, attenuation cost and maintenance cost; the first preset constraint includes the predicted load balance equation constraint and the upper and lower bound constraints on the output current of each fuel cell stack, and the predicted load balance equation constraint is the sum of the output power of all fuel cell stacks and S2, with the goal of minimizing the second operating cost of the system, minimizing the distance of tracking the optimal current trajectory, and converging the health states of the fuel cell stacks, establish an instantaneous power distribution model for the system, and solve it under the second preset constraint to distribute the real-time load among the fuel cell power generation subsystems (i.e., the fuel cell stacks) to obtain the optimal instantaneous operating current of each stack;

[0024] Among them, the second preset constraint includes an instantaneous load balance equation constraint and upper and lower bound constraints on the output power of each fuel cell stack. The instantaneous load balance equation constraint requires that the sum of the instantaneous output power of all fuel cell stacks is equal to the real-time instantaneous power of the load.

[0025] As shown in FIG1 , the present invention provides a two-tier power allocation method for a multi-fuel cell power generation system, including a power forecast 1000 and a power allocation strategy 2000. The multi-stack fuel cell power generation system 3000 comprises multiple fuel cell stacks, each connected to a DC / DC converter. The outputs of all DC / DC converters are connected in parallel to a DC bus, which is then connected to the load. The function of the power forecast 1000 is to predict the load power (i.e., the load power) for a period of time in the future based on historical load data and weather factors such as temperature, humidity, wind speed, and sunshine. ) Use existing forecasting methods to make forecasts.

[0026] The power allocation strategy 2000 allocates power to each fuel cell stack based on the load power obtained from the power forecast 1000 and the status of the multi-stack fuel cell power generation system 3000 (including current, voltage, and health status), ensuring the highest power generation revenue and consistent lifetime degradation for the multi-stack fuel cell power generation system. The power allocation strategy 2000 consists of two layers: upper-layer switching control 2100 and lower-layer instantaneous power allocation 2200. The first layer, upper-layer switching control 2100, is based on real-time optimization and includes objective function I 2110 (i.e., the objective function of the energy scheduling model), constraints I 2120, and a hybrid particle swarm optimization algorithm 2130. Objective function I 2110 includes power generation revenue 2111, voltage recovery revenue 2112, hydrogen consumption cost 2113, degradation cost 2114, and maintenance cost 2115. Upper-layer switching control 2100 analyzes and predicts load power to formulate an energy scheduling plan for the multi-stack fuel cell power generation system, including the on / off status of each fuel cell stack and the optimal output current value. Lower-level instantaneous power allocation 2200 distributes the real-time load among the fuel cell power generation subsystems based on the energy scheduling plan obtained by upper-level switching control 2100, determining the optimal instantaneous operating current for each fuel cell stack. This system includes objective function II 2210 (i.e., the objective function of the instantaneous power allocation model), constraints II 2220, and an instantaneous power allocation algorithm 2230 based on sequential quadratic programming. Objective function II 2210 includes power generation system cost 2211, optimal current tracking 2212, and SOH convergence 2213. Constraints II 2220 include instantaneous load balancing equality constraints 2221 and power upper and lower bound constraints 2222.

[0027] The objective function I 2110 includes power generation income 2111, voltage recovery income 2112, hydrogen consumption cost 2113, attenuation cost 2114 and maintenance cost 2115. If multiple fuel cell subsystems (i.e., fuel cell stacks, hereinafter also referred to as fuel cell power generation subsystems) are connected in parallel, the objective function of the multi-machine fuel cell power generation system, i.e., objective function I 2110, is:

[0028] ;

[0029] Where, ; is the switching status of the ith fuel cell power generation subsystem during the operation switching cycle T, 1 means operation, 0 means stop operation. Then the switching status of all fuel cell power generation subsystems is ; For power generation income For voltage recovery benefit 2112, For hydrogen consumption cost For the decay cost 2114 and Maintenance cost 2115; are the power generation income, voltage recovery income, hydrogen consumption cost, attenuation cost and maintenance cost of the i-th fuel cell power generation subsystem respectively.

[0030] No. The income from the power generation of the fuel cell power generation system i is mainly the income from the electricity generated by the fuel cell power generation system, that is, ;

[0031] Where, is the electricity price, in RMB; is the output voltage of the electronic system of the i-th fuel cell, in volts (V), for The output current of the i-th fuel cell power generation subsystem at time, in amperes (A), T is the stack switching cycle of the upper energy management, is the average output current of the i-th fuel cell power generation subsystem during the stack switching period T.

[0032] The hydrogen consumption cost of the i-th fuel cell power generation subsystem is:

[0033] ;

[0034] Where, is the market price of hydrogen (unit: yuan per kilogram), express The working current of the fuel cell power generation system at all times, is the hydrogen flow rate consumed by the i-th fuel cell power generation system (in grams per second), is the number of cells in the stack, is the molar mass of hydrogen (in grams per mole), and F is the Faraday constant (in coulombs per mole);

[0035] is the efficiency curve of the i-th fuel cell power generation system, which can be expressed as:

[0036] ;

[0037] The fuel cell power generation system degradation cost is defined as the system purchase cost loss caused by the degradation of the fuel cell power generation system, which is proportional to the fuel cell state of health (SoH);

[0038] .

[0039] Where, is the value of the current fuel cell power generation system under SoH (in RMB), is the purchase price of the fuel cell power generation system (in RMB), is the market price of a single fuel cell stack system per kilowatt (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:

[0040] ;

[0041] Health status of the i-th fuel cell power generation system The calculation formula is:

[0042] ;

[0043] Where Unew is the voltage of a new fuel cell stack at rated power (V), and ΔUi is the voltage drop due to attenuation in the i-th fuel cell power generation system. According to the national standard GB / T38914-2020, "Service Life of Automotive Proton Exchange Membrane Fuel Cell Stacks," voltage attenuation ΔU is categorized as attenuation caused by four operating conditions: idling, rated, variable load, and start-stop.

[0044] The voltage drop caused by the performance degradation of the i-th fuel cell power generation system is for:

[0045] ;

[0046] Where, is the voltage decay rate of the i-th fuel cell power generation system caused by the idling condition, in volts per hour (V / h); is the voltage decay rate of the i-th fuel cell power generation system caused by rated operating conditions, in volts per hour (V / h); is the voltage decay rate of the i-th fuel cell power generation system caused by the variable load condition, in volts per time (V / time); is the voltage decay rate of the i-th fuel cell power generation system caused by the start-stop condition, in volts per time (V / time); is the cumulative operating time of the ith fuel cell power generation system at idle condition, 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 times the i-th fuel cell power generation system operates under variable load conditions, 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).

[0047] The maintenance cost of the fuel cell power generation system is defined as:

[0048] ;

[0049] Where, is the ratio of fuel cell power generation system maintenance cost to 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 by Figure 2 Calculated;

[0050] ;

[0051] Where, for The recovery voltage at the moment, for The irreversible decay voltage at the moment. After a fuel cell power generation system has been running for a long time, it stops working and takes a certain period of rest. Its performance will recover to a certain extent. This phenomenon is called the fuel cell voltage recovery phenomenon. This phenomenon can be expressed by the following formula:

[0052] ;

[0053] Where, is a constant and can be obtained by fitting the measured voltage data between the start and stop points.

[0054] Determination of the switching period T of the fuel cell power generation system: , The critical time when the fuel cell power generation system stops gaining and losing the same amount of time. The fuel cell power generation system stops gaining, which includes: 1) Health gain from stopping power generation, which is obtained by the health status of the system. Characterization; 2) Benefits from voltage recovery (i.e., voltage recovery benefits). Fuel cell power system shutdown losses (i.e., decay costs) include: 1) decay losses caused by start-stop switching; and 2) decay losses caused by increased load on other fuel cell systems due to shutdown. The specific calculation is similar to the decay losses and voltage recovery benefits described above.

[0055] The voltage drop caused by the performance degradation of the fuel cell stack i during the stack operation switching period T is calculated as follows:

[0056] ;

[0057] Where, is the fuel cell voltage decay rate under rated operating conditions of fuel cell stack i (V / h); is the average operating current of fuel cell stack i during the stack operation switching period T (A); is the rated current of fuel cell stack i (A); is the voltage decay rate caused by the variable load of the fuel cell stack i (V / h); is the number of load changes per unit time for fuel cell stack i (times / h); The voltage decay rate caused by one start and stop of fuel cell stack i (V / h); is the start-stop change of the fuel cell stack i. If the fuel cell stack i changes from running to stopping or from stopping to running, the value is 1; if the state does not change, the value is 0.

[0058] The voltage recovery benefit formula The calculation requires ,yes The accumulation of .

[0059] The constraint condition I 2120 of the multi-stack fuel cell power generation system is: the predicted load balance equation constraint 2121, the sum of the output power of each fuel cell stack must meet the load power ,Right now:

[0060] ;

[0061] The output current of each fuel cell power generation system has upper and lower current constraints 2122:

[0062] ;

[0063] Where, is the minimum output current of the fuel cell power generation system; is the maximum output current of the fuel cell power generation system.

[0064] The constraint condition I 2120 of the multi-stack fuel cell power generation system is transformed into; , where A is the constraint coefficient matrix; a is the constraint vector;

[0065] ;

[0066] Through the above analysis, the real-time optimization problem of the multi-stack fuel cell power generation system is:

[0067] ;

[0068] Let the optimal solution of this optimization problem be; .

[0069] The real-time optimization of a multi-stack fuel cell power generation system is a mixed integer nonlinear programming problem. This problem can be solved using methods such as branch and bound and outer approximation. The branch and bound method takes a long time to solve, and the outer approximation method requires calling commercial software. Based on this, the present invention preferably uses an improved hybrid particle swarm optimization algorithm for solution. In the upper-layer switching control, the specific solution process of the improved hybrid particle swarm optimization algorithm 2130 is as follows: Figure 3 As shown, it includes: S301, obtaining electrical data and operating data of a target multi-machine fuel cell system.

[0070] S302 : extracting the output current of each fuel cell stack of the multi-fuel cell system from the electrical data and operation data of the multi-fuel cell system in operation, and determining the objective function I according to the output current.

[0071] S303. Determine constraint condition I based on the power data of the target distribution network. Constraint condition I includes at least the following sub-constraints: load balancing constraint condition and output power upper and lower bound constraints of each fuel cell power generation system.

[0072] S304. Determine the initial positions and initial velocities of the continuous particles and discrete particles in the improved hybrid particle swarm algorithm. The particles in the particle swarm move in the preset search space according to the preset movement rules. After the discrete particles are updated, they need to be rounded to ensure that they are 0-1 values. Among them, the particle can refer to the operating current of a single fuel cell system, and the number of particles can refer to the number of stacks in a multi-machine fuel cell. Multiplying by the particle search space dimension D, the initial position and initial velocity of the particle can be set randomly.

[0073] Compared with the conventional particle swarm algorithm, the improved hybrid particle swarm algorithm adopted in the present invention has the following advantages: the hybrid particles in the hybrid particle swarm move in the preset search space according to the preset movement rules. The other processes are the same as those of the conventional particle swarm algorithm.

[0074] The preset movement rules are:

[0075] ;

[0076] in For particles location, is the velocity of particle k, D is the dimension of the search space; is the optimal position of the last search; is the optimal position for this search; is the number of iterations; all particles start with randomly initialized speeds and positions, and ω is the inertia weight; and are cognitive parameters and environmental parameters respectively; and is a random number uniformly distributed in the range [0,1];

[0077] discrete particles After updating according to the preset movement rules, in order to ensure that it is a 0-1 value, it is necessary to perform rounding operations. First, define the particle speed The normalized value of:

[0078] ;

[0079] The discrete particles take values ​​according to the following formula:

[0080] ;

[0081] Where rand() is a random number.

[0082] S305. Determine the optimal position of the particle swarm during each iteration based on the objective function I, the constraint I, and the initial position and initial velocity of the particle swarm algorithm. The fitness is used to characterize the quality of the particle position. The smaller the fitness value, the better the particle position.

[0083] Specifically, for each iteration, after determining the initial position and initial velocity of the particles in the particle swarm, all particles in the particle swarm are ensured to satisfy constraint condition I. Then, using objective function I, the fitness of each particle in the particle swarm is calculated. Based on the fitness of each particle in the particle swarm, the optimal position of the particle swarm in each iteration is determined. The fitness of the particles in the particle swarm can be determined using the following formula:

[0084] ;

[0085] in, is the fitness of the particles in the particle swarm, is the objective function I, σ is a positive penalty value, β is a constant, generally β≥1.

[0086] S306. When the iteration process meets the iteration termination condition, the optimal position of the particle swarm in the iteration process is used as the optimal switch state and optimal output power of each fuel cell stack, that is:

[0087] .

[0088] The lower layer instantaneous power allocation 2200 has multiple objectives: 1) Minimize the cost of the multi-stack fuel cell power generation system 2211; 2) Minimize the distance between tracking the optimal current trajectory 2212; 3) Ensure that the health status of each fuel cell stack is consistent 2213. Therefore:

[0089] ;

[0090] Where α1, α2, and α3 are weight coefficients, which can be defined based on experience or user preferences;

[0091] In the lower layer instantaneous power distribution 2200 stage, the number of working fuel cell stacks Fixed, equal to the optimal stack switch state obtained in the upper energy management stage The expressions of each target are as follows:

[0092] 1) Cost of multi-stack fuel cell power generation system ;

[0093] ;

[0094] Where, 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 benefit of fuel cell stack i;

[0095] and The calculation method is: take the upper energy management in Change to , Change to , Change to , is the instantaneous current vector allocated to the multi-machine fuel cell power generation system;

[0096] 2) Tracking the optimal current trajectory distance :

[0097] ;

[0098] Where, is the instantaneous current allocated to fuel cell stack i; is the optimal current vector of fuel cell stack i obtained by the upper-level energy management.

[0099] 3) The health status of each fuel cell stack is converging:

[0100] 4) ;

[0101] Where, is the health status of the 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 energy management. The constraint II 2220 of the lower instantaneous power distribution of the multi-stack fuel cell power generation system is: Instantaneous load balance equation constraint 2221, each fuel cell stack must meet the load instantaneous power ,Right now:

[0102] ;

[0103] The output power of each fuel cell power generation system has upper and lower power bounds 2222;

[0104] ;

[0105] Where, is the minimum output power of the fuel cell power generation system; is the maximum output power of the fuel cell power generation system.

[0106] Similarly, constraint II can also be transformed into inequality constraint form:

[0107] ;

[0108] Where, , is the constraint coefficient matrix; is the constraint vector;

[0109] .

[0110] Then the lower layer instantaneous power allocation 2200 can be transformed into a nonlinear programming problem;

[0111] ;

[0112] Methods for solving this nonlinear programming include gradient descent and the interior point method. However, these methods are time-consuming and require commercial software. Therefore, the present invention preferably employs a sequential quadratic programming algorithm. The key concept of this algorithm is to convert the nonlinear programming at each iteration into a quadratic programming using a Taylor formula expansion, retaining the linear and quadratic terms. This reduces computation time and improves the real-time performance of the lower-level instantaneous power allocation 2200. The nonlinear programming objective function (i.e., objective function II) for the lower-level instantaneous power allocation can be converted into the following form:

[0113] ;

[0114] Figure 4 is a flowchart of the lower-level instantaneous power allocation based on sequential quadratic programming. The specific algorithm steps are as follows:

[0115] S401, initialization: receiving upper layer switching control information , , calculate the iteration number n=T / t of the lower layer instantaneous power distribution 2200, and accept the voltage, current, power and other information of the multi-machine fuel cell power generation system.

[0116] S402: Determine the sub-objective functions in the objective function II and the weight coefficients corresponding to the sub-objective functions according to the upper-layer handover control optimal solution information.

[0117] S403: Determine the constraints II for the lower-level energy management of the multi-stack fuel cell power generation system, including the load balancing constraint and the upper and lower bound constraints on the output power of each fuel cell power generation system. Construct a nonlinear programming problem.

[0118] S404, optimization algorithm parameter initialization: select an initial point , set the number of iterations k=0.

[0119] S405, construct the quadratic programming sub-problem: at the current point Nearby, the second-order Taylor formula expansion of objective function II is used to approximate the original problem.

[0120] S406. Solve the quadratic programming subproblem: Solve the quadratic programming subproblem constructed in the previous step and obtain the search direction .

[0121] S407, Line search: along the search direction Perform line search and determine step size , ensuring that the value of objective function II decreases in the next step. Line search uses the backtracking line search method, and the specific steps are as follows:

[0122] 1) Initialization step size , and set the retrospective coefficient and constant ;

[0123] 2) If , then reduce the step size to α=ρα until the condition is met.

[0124] S408, update solution: update the current solution .

[0125] S409, stop criterion judgment: check the current point Whether the stopping criteria are met, such as the gradient is small enough or the number of iterations reaches the upper limit. If so, the iteration is stopped and the current solution is output. As the optimal solution.

[0126] S410, Iteration: Set k = k + 1, return to step S405 and continue iterating until the stopping criterion is met. If so, stop the iteration and output the current solution As the optimal solution for the instantaneous power allocation strategy.

[0127] An embodiment of the present invention provides an electronic device, comprising: a computer-readable storage medium and a processor;

[0128] 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 any of the above embodiments. An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a processor to execute the method as described in any of the above embodiments. An embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a processor, implements the method as described in any of the above embodiments. It will be readily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A two-tier power distribution method for a multi-fuel cell power generation system, wherein the multi-fuel cell power generation system comprises a plurality of fuel cell stacks and DC / DC converters connected thereto in a one-to-one correspondence, wherein the outputs of all DC / DC converters are connected in parallel and then connected to a load via a DC bus, characterized in that: The method comprises: S1, predicting the load power of the system in the future; establishing an energy scheduling model for the system with the goal of minimizing the first operating cost of the system, and solving the model under a first preset constraint to obtain the switching state and optimal output current value of each fuel cell stack in the system; The first operating cost includes voltage recovery income and second operating cost, and the second operating cost includes power generation income, hydrogen consumption cost, attenuation cost and maintenance cost. The first preset constraint includes a predicted load balance equation constraint and upper and lower bound constraints on the output current of each fuel cell stack. The predicted load balance equation constraint requires that the sum of the output power of all fuel cell stacks is equal to the predicted load power. The on / off state of the fuel cell stack is running or stopped. S2, with the objectives of minimizing the second operating cost of the system, minimizing the distance of tracking the optimal current trajectory, and ensuring convergence of the health status of each fuel cell stack in operation, establish an instantaneous power distribution model for the system, and solve the model under a second preset constraint to obtain the optimal instantaneous operating current of each fuel cell stack; Among them, the second preset constraint includes the instantaneous load balance equation constraint and the upper and lower bound constraints of the output power of each fuel cell stack. The instantaneous load balance equation constraint is that the sum of the instantaneous output power of all fuel cell stacks is equal to the instantaneous power of the load; the optimal output current value of each fuel cell stack in operation is used as its instantaneous current value; the optimal current trajectory distance is tracked. , To allocate to the fuel cell stack i The instantaneous current; Fuel cell stack for upper-level energy management i The optimal current vector of each fuel cell stack health state convergence item , Fuel cell stack i health status; is the average health status of the multi-stack fuel cell system.

2. The method according to claim 1, wherein No. i The power generation income of a fuel cell stack is: in, i =1,2,…, N st , p ele For electricity price, V st,i For the i The output voltage of the fuel cell stack, T is the stack switching cycle, I ave,i For the i A fuel cell stack T Average output current within 100mA; No. i The hydrogen consumption cost of a fuel cell stack is: in, is the market price of hydrogen, N cell is the number of cells in the stack, is the molar mass of hydrogen, F is the Faraday constant, η h,i For the i The efficiency of a fuel cell stack; No. i The degradation cost of a fuel cell stack is: in, SoH i For the i The health status of the fuel cell stack, p kw is the market price per kilowatt fuel cell stack, P n,cell is the rated power of a single fuel cell stack; The maintenance costs are: in, P main is the ratio of maintenance cost to purchase cost of multi-machine fuel cell power generation system, t year is the total number of seconds in a year, P inv The purchase price of the multi-machine fuel cell power generation system is t is the running time; No. i The voltage recovery benefit of a fuel cell stack is: in, for The recovery voltage at the moment, for The irreversible decay voltage at time t rec is the voltage recovery time, for Moment i The fuel cell stack outputs current.

3. The method according to claim 2, wherein The objective function of the energy scheduling model is: Where, k st,i Switching cycle for operation T Time i The switch status of each fuel cell stack, 1 is running, 0 is stopped, C main,i For the i The maintenance cost of a fuel cell stack and the switching situation of a multi-machine fuel cell power generation system are , N st is the number of fuel cell stacks.

4. The method according to any one of claims 1 to 3, wherein An improved hybrid particle swarm optimization algorithm is used to solve the energy scheduling model; The particles in the particle swarm move in the search space according to a preset movement rule; the preset movement rule is: in, X k ( a )=( x k1 ( a ), x k2 ( a ),…, x kD ( a )) is a particle k In the a The position at the iteration, k =1,2,…,2 N st , V k ( t )=( v k1 ( a ), v k2 ( a ),…, v kD ( a )) is a particle k In the a The speed of the iteration, D is the dimension of the search space; P k ( t )=( p k1 ( a ), p k2 ( a ),…, p kD ( a )) is the particle of the last search k In the a The optimal position at the iteration P g ( t )=( p g1 ( a ), p g2 ( a ),…, p kD ( a )) is the particle of this search k In the a The optimal position at the iteration 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 cognitive parameters and environmental parameters respectively; r 1 and r 2 is a random number uniformly distributed in the range [0,1].

5. The method according to claim 1, wherein The objective function of the instantaneous power allocation model is: Among them, α1, α2, and α3 are weight coefficients. , Fuel cell stack i Instantaneous hydrogen consumption cost, instantaneous attenuation cost, instantaneous maintenance cost, and instantaneous power generation income; is the distance to track the optimal current trajectory; ΔSoH is the convergence term of the health status of each fuel cell stack.

6. The method according to claim 5, wherein A sequential quadratic programming algorithm is used to solve the instantaneous power distribution model.

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

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

Images