Life Optimization Method of Wind-Hydrogen Microgrid System Based on MPC and Multi-electrolyzer Control

Abstract

The present invention relates to a method for optimizing the lifespan of a microgrid system. The method includes: S1, obtaining the power generation curve of the wind power subsystem; S2, constructing a state space prediction model, inputting the power generation curve into the state space prediction model, and performing rolling optimization using the MPC algorithm to obtain the optimal total power curve of the electrolyzer; S3, obtaining the power distribution value and the lifespan attenuation value of the sub-electrolyzers; S4, constructing a multi-objective optimization model, where the multi-objective optimization model includes a first objective function L1 and a second objective function L2; setting constraint conditions; inputting the power distribution value and the lifespan attenuation value of the sub-electrolyzers into the multi-objective optimization model; obtaining the optimal power distribution curve of the sub-electrolyzers; S5, using the optimal power distribution curve of the sub-electrolyzers to optimize the power of the sub-electrolyzers. This method can reduce fluctuations, increase the service life of the battery and the electrolyzer; it can also optimize the power distribution of each sub-electrolyzer, reduce the difference in different lifespan losses, and improve the overall total lifespan of the system.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a method for optimizing the life of a microgrid system. Background Art

[0002] Hydrogen energy has the characteristics of high energy density and convenient storage and transportation, and has become an important medium for the large-scale and comprehensive development of the next generation of wind power microgrid systems. In the process of hydrogen energy utilization, the user load is mainly supplied with electricity through two paths: wind turbine-electrolyzer-hydrogen storage tank-fuel cell-user load and wind turbine-battery-user load. In this process, the power input from the wind turbine to the microgrid system fluctuates.

[0003] When the wind power output is greater than the load demand, the excess power is absorbed by the battery and electrolyzer. Since the power input provided by wind power to the microgrid system is highly volatile, when the battery absorbs the excess power, it will increase its own volatility, which in turn affects the battery life.

[0004] In addition, electrolyzer types are divided into alkaline electrolyzers, PEM electrolyzers and solid oxide electrolyzers. Solid oxide electrolyzers require high temperatures above 800°C and are currently still in the laboratory stage; while alkaline electrolyzers and PEM electrolyzers have been put into use. Because of the low dynamic characteristics and low cost of alkaline electrolyzers, they can be used together with high dynamic characteristics and high cost PEM electrolyzers for mixed hydrogen production. Therefore, in actual hydrogen production systems, there are scenarios where multiple types of electrolyzers coexist and multiple electrolyzers produce hydrogen together.

[0005] In the actual hydrogen production process, the high volatility of wind power will lead to frequent start-stop switching, power fluctuations, high-frequency current ripples and other problems in multi-electrolyzer equipment, which will accelerate the degradation of electrocatalysts and membranes in the electrolyzers. In the prior art, there is a lack of a coordinated control method for power distribution of multiple electrolyzers, resulting in inconsistent degradation levels of different electrolyzers, which in turn affects the overall life of the system. Summary of the invention

[0006] The purpose of the present invention is to provide a method for optimizing the life of a wind-hydrogen microgrid system based on MPC and multi-electrolyzer control, which can reduce battery volatility, reduce the degradation of electrocatalysts and membranes in multi-electrolyzer equipment, and increase the service life of batteries and electrolyzers; it can also optimize the power distribution of each sub-electrolyzer in the multi-electrolyzer, reduce the difference in life loss of different sub-electrolyzers, and increase the overall life of the system.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] A method for optimizing the life of a wind-hydrogen microgrid system based on MPC and multi-electrolyzer control, characterized in that the method comprises:

[0009] S1. Obtaining a power generation curve of a wind electronic system;

[0010] S2, constructing a state space prediction model, inputting the power generation curve into the state space prediction model, using the MPC algorithm for rolling optimization, and obtaining the optimal total power curve of the electrolyzer;

[0011] S3, obtaining the power allocation value of the sub-electrolyzer and the life attenuation value of the sub-electrolyzer;

[0012] S4, constructing a multi-objective optimization model, wherein the multi-objective optimization model includes a first objective function L1 and a second objective function L2; setting constraints; inputting the power allocation value of the sub-electrolyzer and the life attenuation value of the sub-electrolyzer into the multi-objective optimization model; and obtaining an optimal power allocation curve for the sub-electrolyzer;

[0013] S5. Optimizing the power distribution of the sub-electrolyzers using the sub-electrolyzer optimal power distribution curve.

[0014] As a preferred embodiment of the present invention, the state space prediction model is:

[0015]

[0016] In the formula, k is the current moment; k+1 is the next moment; x is the state variable; u is the control variable; y is the output variable; A and B are the system matrix and control matrix respectively; C is the controlled output matrix;

[0017] The state variables, control variables, and output variable matrices are expressed as:

[0018]

[0019] Among them, P ELs (k) is the electrolytic cell power at the current moment; P FC (k) is the fuel cell output power at the current moment; P Bat (k) is the battery output power at the current moment; SOC(k) is the battery state of charge at the current moment; ΔP ELs (k) is the power change of the electrolytic cell; ΔP FC (k) is the change in fuel cell output power; ΔP Bat (k) is the change in battery output power; P ELs (k+1) is the electrolytic cell power at the next moment; P FC (k+1) is the output power of the fuel cell at the next moment; P Bat (k+1) is the battery output power at the next moment; SOC(k+1) is the battery state of charge at the next moment.

[0020] As a preferred embodiment of the present invention, in step S2, the power generation curve is input into the state space prediction model, and the rolling optimization is performed using the MPC algorithm. The specific steps are:

[0021] S21. Calculate the forward prediction N at time k p The predicted output of the battery state of charge SOC at the next step is:

[0022] Y p =[SOC bat (k),SOC bat (k+1),…,SOC bat (k+N p )] (2)

[0023] S22, taking the output plan of the battery state of charge SOC in the day-ahead dispatch as the expected output, specifically taking the forward prediction N at time k p The day-ahead output value of the battery SOC at the step is used as the reference vector:

[0024] Y ref =[SOC ref (k),SOC ref (k+1),…,SOC ref (k+N p )] (3)

[0025] S23, output Y based on the predicted battery state of charge SOC p and the expected output Y ref The minimum error establishes the intraday optimization objective function:

[0026] J=U T RU+(Y p -Y ref ) T Q(Y p -Y ref ) (4)

[0027] in, Q and R are weight matrices.

[0028] As a preferred embodiment of the present invention, in step S4, the first objective function L1 of the multi-objective optimization model is:

[0029] L1=Var(X),X=x1,x2,...,x n (5)

[0030] Among them, X is the life index value sequence of the electrolytic cell, n is the number of sub-electrolytic cells, and x1 is the life value of the first sub-electrolytic cell.

[0031] As a preferred embodiment of the present invention, in step S4, the second objective function L2 of the multi-objective optimization model is:

[0032]

[0033] where x i is the life attenuation value of the ith sub-electrolyzer; is the lifetime decay value per unit time caused by the alkaline sub-electrolyzer in the kth working condition; is the lifetime attenuation per unit time of the PEM sub-electrolyzer under the kth working condition; T i k is the working time of the ith sub-electrolyzer under the kth working condition.

[0034] As a preferred embodiment of the present invention, in step S4, three groups of constraint conditions are set, and the first group of constraint conditions is set as follows:

[0035] P i ≥P min

[0036] Among them, P i is the power allocated to the ith sub-electrolyzer, P min It is the minimum operating power of the sub-electrolyzer.

[0037] As a preferred embodiment of the present invention, in step S4, the second set of constraints is set as follows:

[0038] ΔP i ≤M1, ALK

[0039] ΔP i ≤M2,PEM

[0040] Among them, M1 is the upper limit of the power change rate of the alkaline sub-electrolyzer, and M2 is the upper limit of the power change rate of the PEM sub-electrolyzer.

[0041] As a preferred embodiment of the present invention, in step S4, the third set of constraints is set as:

[0042]

[0043] Among them, P i is the power of the ith sub-electrolyzer, P ELs is the optimal total power of the electrolyzer obtained.

[0044] As a preferred embodiment of the present invention, for the constructed multi-objective optimization model, the NSGA-II algorithm is used to solve the optimization problem; the steps of the NSGA-II algorithm specifically include:

[0045] Generate the initial population and perform fast non-dominated sorting;

[0046] Generate the first generation of offspring population through selection, inheritance, and mutation;

[0047] Merge the initial population and the first generation of child population, perform fast non-dominated sorting and crowding calculation, and select the new generation of parent population according to the crowding;

[0048] The new generation of parent population is selected, inherited, and mutated to generate a new generation of child population, which is then sorted and selected after merging to generate the next generation of parent population until the maximum number of iterations is reached;

[0049] On the other hand, the present invention also provides an electronic device, including a processor and a memory;

[0050] The processor is connected to the memory;

[0051] The memory is used to store executable program code;

[0052] The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute a microgrid system life optimization method based on MPC prediction and multi-electrolyzer control as described above.

[0053] In summary, the present invention has the following beneficial effects:

[0054] 1. The present invention takes into account the battery balance during operation, and achieves the goal of optimizing the energy scheduling of the energy storage system and the hydrogen energy system through the construction of the previous state space prediction model and the rolling optimization of the MPC algorithm, eliminating the deviation between the day-ahead scheduling plan and the actual output of wind power during the day; rolling corrections are performed at a specific period, and on the premise of satisfying the power regulation characteristics of the controllable power elements, the model predictive control algorithm is used to roll the output of the electrolyzer and the energy storage device during the day, thereby reducing the impact of the volatility of the wind power system on the battery and the electrolyzer, and prolonging the service life of the two.

[0055] 2. The present invention obtains the power and life values ​​allocated to each of the multiple sub-electrolyzers of different types in the electrolyzer, and uses the NSGA-II algorithm to perform multi-objective optimization under the constraints of the constraints, obtains the optimal power allocation curve of the sub-electrolyzer, and uses the curve to optimize the power allocation of the sub-electrolyzer.

[0056] This eliminates the differences in life loss among different sub-electrolyzers to the greatest extent possible, thereby increasing the overall life of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of this specification, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 A flowchart of the method is shown in FIG.

[0059] Figure 2 This is the architecture diagram of the wind-hydrogen microgrid system in this embodiment. DETAILED DESCRIPTION

[0060] The technical solutions of the embodiments of the present invention are explained and described below in conjunction with the drawings of the embodiments of the present invention, but the following embodiments are only preferred embodiments of the present invention, not all. Based on the embodiments in the implementation mode, other embodiments obtained by those skilled in the art without creative work are all within the protection scope of the present invention.

[0061] The terms "first", "second", etc. in the description and claims of this specification and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the term "comprising" and any variation thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0062] The application scenario of this method is the wind-hydrogen microgrid, whose main equipment includes power generation equipment such as wind turbines and energy storage equipment such as batteries. The wind-hydrogen microgrid management system optimizes the energy scheduling and real-time monitoring of the energy storage system and hydrogen energy system based on the demand load, weather forecast and other information, and achieves the operation goals of safety, stability and economy.

[0063] When the microgrid is in off-grid operation, the power of the hydrogen production system needs to be adjusted in real time according to the predicted output the day before and the actual value during the day, with the battery SOC fluctuation minimized as the optimization goal. In order to eliminate the deviation between the day-ahead dispatch plan and the actual output of wind power during the day, this paper performs rolling corrections with a cycle of 5 minutes. Under the premise of meeting the power regulation characteristics of the controllable power elements, the MPC model predictive control algorithm is used to roll the output of the electrolyzer and battery during the day.

[0064] In this embodiment, the overall control framework of the system is described as follows:

[0065] The system consists of wind turbines, electrolyzers, hydrogen storage tanks, fuel cells, batteries, and user loads. Wind turbines are the main energy source of this system, and the MPC controller regulates and distributes the power of electrical energy storage and hydrogen energy storage online.

[0066] When the wind power output is greater than the load demand, the control system starts the battery DC / DC converter and the electrolyzer DC / DC converter. The excess power is absorbed by the battery and the electrolyzer. The electrolyzer produces hydrogen by electrolyzing water and stores the electrical energy in the form of hydrogen energy in the hydrogen storage tank.

[0067] If the battery state of charge SOC and the hydrogen storage tank hydrogen level SOH both reach the upper limit, the electrolyzer can be operated at overload for a short period of time, but after a long time, the wind power generation level needs to be reduced and the operating point needs to be adjusted through the wind power transformer.

[0068] When the wind power output is less than the load demand, the control system starts the battery DC / DC converter and the fuel cell DC / DC converter, and the remaining power is supplied by the battery and the fuel cell. The fuel cell consumes hydrogen in the hydrogen storage tank to supply power. If the battery SOC and the hydrogen storage tank SOH both reach the lower limit, the battery and the fuel cell stop working, reducing the hydrogen production power of the electrolyzer and maintaining a low power state to ensure only the normal power load supply.

[0069] The above is the energy management strategy of the system. The specific control algorithm framework of the present invention is as follows: the control algorithm is divided into two-layer control to manage the energy of the system. The variables of the upper control layer are the power of the electrolyzer, the power of the fuel cell and the power of the battery. The energy source of the system is wind power generation. In the control algorithm, the wind power generation will be predicted before the control. The input of the control algorithm is a certain wind power generation curve. The goal of the upper control algorithm is to reduce the SOC fluctuation of the battery. Reducing the SOC fluctuation of the battery is conducive to extending the life of the battery. In the long run, the system operation life cycle cost is reduced and the economic benefits are improved, so the target is set to the SOC fluctuation of the battery. The control algorithm selects the MPC model prediction algorithm. By constructing a model for rolling optimization, the optimal total power curve of the electrolyzer can be obtained. During the optimization process, the algorithm will also correct the reference curve in real time according to the actual wind power generation.

[0070] The optimal total power curve of the electrolyzer can be obtained through the upper-level control, and the power distribution between the sub-electrolyzers is carried out through the lower-level control. The traditional power distribution strategies are the average distribution strategy and the chain distribution strategy. The average distribution strategy means that a constant number of sub-electrolyzers are put into operation, and the system input power is evenly distributed to each sub-electrolyzer. However, the average distribution strategy cannot guarantee that the process structure of each sub-electrolyzer itself and the environment it faces during work remain consistent, nor can it guarantee that the attenuation degree of the sub-electrolyzer remains consistent. In the chain distribution strategy, the sub-electrolyzers are put into operation one by one, the previous sub-electrolyzer reaches the rated power, and then the next sub-electrolyzer is started, and so on. The first sub-electrolyzer started in the chain distribution strategy will run for a long time in the high power range, resulting in large differences in life attenuation between different sub-electrolyzers.

[0071] In order to improve the overall life of the electrolyzer, a life index system was established, and a fuzzy mapping relationship between the life attenuation value and different operating conditions was established. In the lower-level control, the variance of the life attenuation values ​​between different sub-electrolyzers was taken as the first optimization target, and the total life attenuation value of the system was taken as the second optimization target. In addition, the electrolyzer was maintained in the lowest working condition to reduce the number of starts and stops of the electrolyzer. The optimization problem was solved according to the NSGA-II algorithm.

[0072] like Figure 1 and Figure 2 As shown, firstly, the power generation curve of the wind power system is obtained; in this embodiment, the power generation curve of the wind turbine generator is obtained.

[0073] Next, we build a state space prediction model.

[0074] The state space prediction model is:

[0075]

[0076] In the formula, k is the current moment; k+1 is the next moment; x is the state variable; u is the control variable; y is the output variable; A and B are the system matrix and control matrix respectively; C is the controlled output matrix.

[0077] The state variables, control variables, and output variable matrices are expressed as:

[0078]

[0079] C=[0 0 0 1]

[0080] Among them, P ELs (k) is the electrolytic cell power at the current moment; P FC (k) is the fuel cell output power at the current moment; P Bat(k) is the battery output power at the current moment; SOC(k) is the battery state of charge at the current moment; ΔP ELs (k) is the power change of the electrolytic cell; ΔP FC (k) is the change in fuel cell output power; ΔP Bat (k) is the change in battery output power; P ELs (k+1) is the electrolytic cell power at the next moment; P FC (k+1) is the output power of the fuel cell at the next moment; P Bat (k+1) is the battery output power at the next moment; SOC(k+1) is the battery state of charge at the next moment.

[0081] Next, calculate the forward prediction N at time k p The predicted output of the battery state of charge SOC at the next step is:

[0082] Y p =[SOC bat (k),SOC bat (k+1),…,SOC bat (k+N p )] (2)

[0083] Next, the output plan of the battery state of charge SOC in the day-ahead dispatch is taken as the expected output, specifically, taking the k-th time to predict N p The day-ahead output value of the battery SOC at the step is used as the reference vector:

[0084] Y ref =[SOC ref (k),SOC ref (k+1),…,SOC ref (k+N p )] (3)

[0085] The control objective of intraday rolling optimization is to make the above-mentioned forecast output track the expected output in the day-ahead scheduling.

[0086] Then, the predicted output Y of the battery state of charge SOC is used p and the expected output Y ref The minimum error establishes the intraday optimization objective function:

[0087] J=u T Ru+(Y p -Y ref ) T Q(Y p -Y ref ) (4)

[0088] in, Q and R are weight matrices; u is a control variable;

[0089] According to the above parameters and related data:

[0090]

[0091] Subject to x k+1 =Ax k +Bu k

[0092]

[0093] They are the input and output constraint sets, as follows:

[0094]

[0095] The control target here is to minimize the fluctuation of battery SOC, that is, to control J to be minimum under the constraint conditions.

[0096] The power generation curve is input into the above-mentioned state space prediction model, and the MPC algorithm is used for rolling optimization to obtain the optimal total power curve of the electrolyzer.

[0097] Then, the power distribution value of the sub-electrolyzer and the life attenuation value of the sub-electrolyzer are obtained.

[0098] In the sub-electrolyzer, the life of the sub-electrolyzer is affected: specifically, the internal resistance of the sub-electrolyzer increases. When the working current density of the sub-electrolyzer remains unchanged, the working voltage of the sub-electrolyzer will gradually increase as the degradation degree of the sub-electrolyzer increases. The rise value of the working voltage of the sub-electrolyzer is used as the life index value. The higher the working voltage, the more serious the life attenuation of the sub-electrolyzer. For different sub-electrolyzers, due to the differences in their own dynamic performance, there will be certain differences in the attenuation of the sub-electrolyzers when facing the same working conditions. However, in the mapping relationship between the specific operating parameters of the sub-electrolyzer and the life attenuation, it is difficult to establish a definite mapping relationship due to the influence of many factors. In addition, the current research lacks the actual life attenuation data of the electrolyzer, and it is difficult to build a bridge between the two through machine learning. This study is based on some existing experimental data to establish a fuzzy mapping relationship between the two, in which five electrolyzer working conditions are set up: minimum load operating condition, low load operating fluctuation condition, optimal power steady-state operating condition, high load operating fluctuation condition, and rated load operating condition. Under different working conditions, the present invention proposes the average life index change rate per unit time corresponding to each working condition based on experimental data to participate in the life control optimization of the lower electrolytic cell.

[0099] Based on this, a multi-objective optimization model is established.

[0100] Most of the existing electrolyzer control strategies only involve the overall power distribution of the electrolyzer, but lack research on the coordinated control between different sub-electrolyzers. If the coordinated control between the individual cells inside the electrolyzer is ignored, it may lead to problems such as too many starts and stops of the electrolyzer or uneven continuous operation time in an unhealthy state, which directly affects the service life of the electrolyzer. Under this strategy, the goal is to ensure that the loads between the sub-electrolyzers are relatively balanced, that is, to evenly distribute the workload. The advantage of this is to avoid individual sub-electrolyzers being in an overload or underload state for a long time, thereby reducing the degradation of individual equipment and extending the service life of the overall system.

[0101] According to this life balance control strategy, an objective function L1 is set as:

[0102] L1=Var(X),X=x1,x2,...,x n (5)

[0103] Among them, X is the life index value sequence of the electrolytic cell, n is the number of sub-electrolytic cells, and x1 is the life value of the first sub-electrolytic cell.

[0104] In another embodiment, the degradation rate of the electrolyzer is positively correlated with the operating power level, and the degradation rate of the variable power operating stack is volatile; with the accumulation of irreversible degradation such as catalyst oxidation corrosion, the performance decay rate increases, and the degradation rate is positively correlated with the operating time; after long-term operation, the electrolyzer decays more, the uncertainty of the internal physical and chemical reactions increases, and the degradation rate changes are random. The electrolyzer will have different degrees of influence on the decay process of the electrolyzer itself under different working conditions. The fluctuating state has a more verified effect on the decay of the electrolyzer than the stable input condition, and the alkaline electrolyzer and the PEM electrolyzer are also affected differently by different working conditions. The PEM electrolyzer has better dynamic performance than the alkaline electrolyzer, and the life decay under fluctuating conditions is less than that of the alkaline electrolyzer.

[0105] In order to achieve the longest life cycle of the electrolyzer, the second objective function L2 of the multi-objective optimization model is:

[0106]

[0107] where x i is the life attenuation value of the ith sub-electrolyzer; is the lifetime decay value per unit time caused by the alkaline sub-electrolyzer in the kth working condition; is the lifetime attenuation per unit time of the PEM sub-electrolyzer under the kth working condition; T i k is the working time of the ith sub-electrolyzer under the kth working condition.

[0108] Based on the two objective functions, this multi-objective optimization model also sets three constraints.

[0109] Since the electrolyzer itself will use all the starting power for the temperature rise of the electrolyzer during cold start, and will not use the power for hydrogen production, it will cause a certain amount of power waste, and the frequent start and stop of the electrolyzer will have a great impact on the life of the electrolyzer. In order to avoid frequent start and stop of the electrolyzer, all electrolyzers are maintained under the minimum working conditions. Limited by the internal material characteristics of the alkaline electrolyzer, when the electrolyzer is operated at low power, there is a risk that the hydrogen concentration in oxygen exceeds the explosion limit. In industry, when the hydrogen concentration in oxygen exceeds 2%, the electrolyzer will automatically protect and shut down, so the minimum operating power of the electrolyzer is generally 20% to 25% of the rated power. When the electrolyzer is working, the operating power can exceed the rated power for a short time, reaching 110% to 130% of the rated power.

[0110] Therefore, based on the minimum operation strategy of the electrolytic cell, this embodiment proposes the first constraint condition:

[0111] P i ≥P min

[0112] Among them, P i is the power allocated to the ith sub-electrolyzer, P min It is the minimum operating power of the sub-electrolyzer.

[0113] In another embodiment, since there is a certain upper limit to the power change rate of the electrolyzer, an excessively high power change rate may easily cause a large attenuation of components such as the electrolyzer catalyst and diaphragm. At the same time, an excessively high power change rate may lead to a decrease in gas purity during the hydrogen production process, which may easily lead to explosions and the like. Therefore, a certain upper limit needs to be set for the power change rate of the electrolyzer.

[0114] Therefore, based on the dynamic operation characteristics of the electrolytic cell, this embodiment proposes a second constraint condition:

[0115] ΔP i ≤M1, ALK

[0116] ΔP i ≤M2,PEM

[0117] Among them, M1 is the upper limit of the power change rate of the alkaline sub-electrolyzer, and M2 is the upper limit of the power change rate of the PEM sub-electrolyzer.

[0118] The optimal total power curve of the electrolytic cell can be obtained by step S4, which is a collection of the optimal total powers of multiple electrolytic cells. The optimal total power P of the electrolytic cell at the corresponding time point is extracted. ELs According to the system power balance, this embodiment proposes a third constraint:

[0119]

[0120] Among them, P i is the power of the ith sub-electrolyzer, P ELs is the optimal total power of the electrolyzer obtained.

[0121] In another embodiment, the NSGA-II algorithm is used to solve the optimization problem for the constructed multi-objective optimization model; compared with the basic genetic algorithm, stratification is performed according to the dominance relationship between individuals before performing the selection, and the crossover operator and mutation operator are not much different from the basic genetic algorithm. Through non-dominated stratification, elite individuals can have a greater chance of being inherited to the next generation. At the same time, the fitness sharing strategy can make the individuals of the final result evenly distributed, maintain the diversity of the group, and prevent premature convergence.

[0122] Input the power distribution value of the sub-electrolyzer, the life value of the sub-electrolyzer and the obtained optimal total power P of the electrolyzer ELs According to the running time, the attenuation value of the sub-electrolyzer after the running time will be obtained. The optimization goal is to minimize the variance of the attenuation value and the total attenuation value. The control variable is the power value of each sub-electrolyzer, but it is subject to the constraints. The output result is the optimal power allocation curve of the sub-electrolyzer and the attenuation value of the next optimization.

[0123] Finally, the sub-electrolyzer optimal power allocation curve is used to optimize the power allocation of the sub-electrolyzer.

[0124] In another embodiment, the present invention also provides an electronic device, including a processor and a memory; the processor is connected to the memory; the processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of DSP, FPGA, and PLA. The processor can integrate one or a combination of CPU, GPU, modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but implemented by a single chip.

[0125] The memory may include RAM or ROM. Optionally, the memory includes a non-transitory computer-readable medium. The memory may be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory may also be at least one storage device located away from the aforementioned processor. The memory as a computer storage medium may include an operating system, a network communication module, a user interface module and an application. The processor may be used to call the application stored in the memory and execute the method in one or more of the above-mentioned embodiments.

[0126] The above descriptions are only preferred embodiments disclosed in this application and descriptions of the technical principles used. Those skilled in the art should understand that the scope of protection involved in this disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this disclosure (but not limited to) to form a technical solution.

[0127] In addition, although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination.

Claims

1. A method for optimizing the life of a wind-hydrogen microgrid system based on MPC and multi-electrolyzer control, characterized in that: include: S1. Obtaining a power generation curve of a wind electronic system; S2, constructing a state space prediction model, inputting the power generation curve into the state space prediction model, using the MPC algorithm for rolling optimization, and obtaining the optimal total power curve of the electrolyzer; S3, obtaining the power allocation value of the sub-electrolyzer and the life attenuation value of the sub-electrolyzer; S4, constructing a multi-objective optimization model, wherein the multi-objective optimization model includes a first objective function L1 and a second objective function L2; setting constraints; inputting the power allocation value of the sub-electrolyzer and the life attenuation value of the sub-electrolyzer into the multi-objective optimization model; and obtaining an optimal power allocation curve for the sub-electrolyzer; S5. Optimizing the power distribution of the sub-electrolyzers using the sub-electrolyzer optimal power distribution curve.

2. According to claim 1, a method for optimizing the life of a wind-hydrogen microgrid system based on MPC and multi-electrolyzer control is characterized in that: The state space prediction model is: In the formula, k is the current moment; k+1 is the next moment; x is the state variable; u is the control variable; y is the output variable; A and B are the system matrix and control matrix respectively; C is the controlled output matrix; The state variables, control variables, and output variable matrices are expressed as: Among them, P ELs (k) is the electrolytic cell power at the current moment; P FC (k) is the fuel cell output power at the current moment; P Bat (k) is the battery output power at the current moment; SOC(k) is the battery state of charge at the current moment; ΔP ELs (k) is the power change of the electrolytic cell; ΔP FC (k) is the change in fuel cell output power; ΔP Bat (k) is the change in battery output power; P ELs (k+1) is the electrolytic cell power at the next moment; P FC (k+1) is the output power of the fuel cell at the next moment; P Bat (k+1) is the battery output power at the next moment; SOC(k+1) is the battery state of charge at the next moment.

3. The method for optimizing the life of a wind-hydrogen microgrid system based on MPC and multi-electrolyzer control according to claim 2 is characterized in that: In step S2, the power generation curve is input into the state space prediction model, and the rolling optimization is performed using the MPC algorithm. The specific steps are as follows: S21. Calculate the forward prediction N at time k p The predicted output of the battery state of charge SOC at the next step is: Y p =[SOC bat (s),SOC bat (k+1),…,SOC bat (k+N p )] (2) S22, taking the output plan of the battery state of charge SOC in the day-ahead dispatch as the expected output, specifically taking the forward prediction N at time k p The day-ahead output value of the battery SOC at the step is used as the reference vector: Y ref =[SOC ref (s),SOC ref (k+1),…,SOC ref (k+N p )] (3) S23, output Y based on the predicted battery state of charge SOC p and the expected output Y ref The minimum error establishes the intraday optimization objective function: J=u T Ru+(Y p -Y ref ) T Q(Y p -Y ref ) (4) in, Q and R are weight matrices; u is the control variable.

4. The method for optimizing the life of a wind-hydrogen microgrid system based on MPC and multi-electrolyzer control according to claim 3 is characterized in that: In step S4, the first objective function L1 of the multi-objective optimization model is: L1=Var(X),X=x1,x2,...,x n (5) Among them, X is the life index value sequence of the electrolytic cell, n is the number of sub-electrolytic cells, and x1 is the life value of the first sub-electrolytic cell.

5. The method for optimizing the life of a wind-hydrogen microgrid system based on MPC and multi-electrolyzer control according to claim 4 is characterized in that: In step S4, the second objective function L2 of the multi-objective optimization model is: where x i is the life attenuation value of the ith sub-electrolyzer; is the lifetime decay value per unit time caused by the alkaline sub-electrolyzer in the kth working condition; is the lifetime attenuation per unit time caused by the PEM sub-electrolyzer in the kth working condition; is the working time of the ith sub-electrolyzer under the kth working condition.

6. The method for optimizing the life of a wind-hydrogen microgrid system based on MPC and multi-electrolyzer control according to claim 5 is characterized in that: In step S4, three groups of constraints are set, and the first group of constraints is set as follows: P i ≥P min Among them, P i is the power allocated to the ith sub-electrolyzer, P min It is the minimum operating power of the sub-electrolyzer.

7. The method for optimizing the life of a wind-hydrogen microgrid system based on MPC and multi-electrolyzer control according to claim 6 is characterized in that: In step S4, the second set of constraints is set as: ΔP i ≤M1,ALK ΔP i ≤M2,PEM Among them, M1 is the upper limit of the power change rate of the alkaline sub-electrolyzer, and M2 is the upper limit of the power change rate of the PEM sub-electrolyzer.

8. The method for optimizing the life of a wind-hydrogen microgrid system based on MPC and multi-electrolyzer control according to claim 7 is characterized in that: In step S4, the third set of constraints is set as: Among them, P i is the power of the ith sub-electrolyzer, P ELs is the optimal total power of the electrolyzer obtained.

9. The method for optimizing the life of a wind-hydrogen microgrid system based on MPC and multi-electrolyzer control according to claim 1, characterized in that: According to the constructed multi-objective optimization model, the NSGA-II algorithm is used to solve the optimization problem.

10. An electronic device comprising a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute a wind-hydrogen microgrid system life optimization method based on MPC and multi-electrolyzer control as described in any one of claims 1 to 9.

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