Green hydrogen grid-connected optimization method based on electrolytic cell efficiency control and green hydrogen micro-grid system

Abstract

The invention relates to a green hydrogen grid-connected optimization method and a green hydrogen micro-grid system. The method comprises the following steps: S1, acquiring a generated power curve of a renewable energy source sub-module; s2, adopting an MPC control algorithm to control power generation and energy storage power in the green hydrogen micro-grid system so as to track a power generation power curve of the renewable energy source sub-module, and obtaining an optimal total power curve of the electrolytic cell; s3, constructing an electrolytic cell control objective function; obtaining an initial power distribution value of each sub-electrolytic cell; inputting the optimal total power curve of the electrolytic cell and the initial power distribution value of each sub-electrolytic cell into an electrolytic cell control objective function to obtain the optimal distribution power of each sub-electrolytic cell; and S4, adjusting the power of the sub-electrolytic cell by using the optimal distribution power of the sub-electrolytic cell. According to the method, the influence of the multi-electrolytic cell array on the overall operation efficiency of the micro-grid system combined with the green hydrogen system is comprehensively considered, the power distribution reasonability of the whole system is improved, and the energy utilization rate and the economical efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of microgrid and new energy technology, and specifically to a green hydrogen grid-connected optimization method and a green hydrogen microgrid system. Background Art

[0002] The green hydrogen system is a system that uses renewable energy (such as solar energy, wind energy, etc.) to generate electricity and produce hydrogen through water electrolysis technology. This system has the advantages of being clean, environmentally friendly, and sustainable, and is regarded as one of the important directions for future energy transformation. The green hydrogen system is mainly composed of a renewable energy power generation system (such as solar photovoltaic panels, wind turbines, etc.), a power conversion system (such as AC / DC or DC / DC converters), a water electrolysis hydrogen production system, and a hydrogen storage and fuel cell system.

[0003] Green hydrogen system grid connection refers to the introduction of green hydrogen system into the existing microgrid system to meet the power demand of user loads in the existing microgrid system under the comprehensive power supply of renewable energy power generation system, power grid system, chemical energy storage battery and fuel cell. In the above complex system, it is necessary to achieve efficient coordination of various parts and minimize the total power introduced by the grid connection system, so as to minimize the unstable interference caused by the renewable energy power generation system to the power grid system. This is the goal pursued by many technicians.

[0004] In the prior art, technicians have not conducted sufficient research on the power allocation of multi-electrolyzer arrays in water electrolysis hydrogen production systems, resulting in a lack of optimization of the overall power allocation of the microgrid system that incorporates the green hydrogen system. That is, the multi-electrolyzer array cannot always operate at the optimal allocated power, which in turn makes it possible to reduce the total interactive power of the grid system to a minimum and fail to meet the increasingly increasing efficiency and economic requirements. Summary of the invention

[0005] The purpose of the present invention is to provide a green hydrogen grid-connected optimization method based on electrolyzer efficiency control and a green hydrogen microgrid system, which comprehensively considers the impact of a multi-electrolyzer array on the overall operating efficiency of a microgrid system incorporating a green hydrogen system, improves the rationality of power allocation of the entire system, and improves energy utilization and economy.

[0006] In order to achieve the above object, the specific technical solution adopted by the present invention is as follows:

[0007] A green hydrogen grid-connected optimization method based on electrolyzer efficiency control is applied to a green hydrogen microgrid system, characterized in that the method comprises:

[0008] S1, obtaining a power generation curve of a renewable energy submodule;

[0009] S2. Using an MPC control algorithm to control the power generation and energy storage power in the green hydrogen microgrid system to track the power generation curve of the renewable energy submodule to obtain the optimal total power curve of the electrolyzer;

[0010] S3, constructing an electrolytic cell control objective function; obtaining initial power allocation values ​​for each sub-electrolytic cell; inputting the optimal total power curve of the electrolytic cell and the initial power allocation values ​​of each sub-electrolytic cell into the electrolytic cell control objective function to obtain the optimal allocated power for each sub-electrolytic cell;

[0011] S4. Use the optimal allocated power of the sub-electrolyzer to adjust the power of the sub-electrolyzer.

[0012] As a preferred embodiment of the present invention, step S2 specifically includes:

[0013] S21. Construct a linear discrete system state space model:

[0014]

[0015] 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 C is the controlled output variable; A and B are the system matrix and control matrix respectively; C C is the controlled output matrix;

[0016] SOC is the battery charge state; SOH is the hydrogen storage tank charge state; P gen is the controllable power; P mEL is the total power of the electrolytic cell; P FC is the fuel cell power; P Bat is the battery power;

[0017] S22. Construct the objective function:

[0018]

[0019] In the formula

[0020]

[0021] In the formula, (k+i|k) represents the prediction of time k for time k+1; y ref is the given reference trajectory; Δu is the increment of the control variable; N p is the prediction time domain; N c is the control time domain; the matrix Q is the weighted matrix for the output error; the matrix R is the weighted matrix for the control increment; r1, r2, and r3 represent the total power P of the electrolytic cell respectively. mEL , fuel cell power P FC and battery power P Batthe degree of importance attached to

[0022] S23, select the net power P of the difference between the power generation power of the renewable energy submodule and the user load net As the desired system output reference trajectory y ref (k+i|k), the control target is the controllable power P gen Tracking a given net power P net ;

[0023] S24. According to the linear discrete system state space model and constraint conditions, find the optimal total power curve P of an electrolytic cell that makes the objective function optimal. mEL .

[0024] As a preferred embodiment of the present invention, in step S3, the electrolytic cell optimization objective function is specifically:

[0025]

[0026] Wherein, J is the total effective power of the electrolytic cell; η i is the efficiency of the ith sub-electrolyzer, P i The power allocated to the ith electrolyzer.

[0027] As a preferred embodiment of the present invention, the efficiency of the sub-electrolyzer is the product of the Faraday efficiency and the sub-electrolyzer voltage efficiency:

[0028] η i =η f ·η v (5)

[0029] Among them, η f is the Faraday efficiency, η v is the sub-electrolyzer voltage efficiency;

[0030]

[0031] Among them I cell is the sub-electrolyzer current, T is the sub-electrolyzer temperature, and f1, f2, f3, and f4 are the parameters to be fitted;

[0032]

[0033] Among them, V cell is the sub-electrolyzer voltage, V th is the thermal neutral voltage.

[0034] As a preferred embodiment of the present invention, in S3, the electrolytic cell control objective function is optimized by a genetic algorithm, the optimal individual is output, and the optimal allocated power of each sub-electrolytic cell is obtained.

[0035] As a preferred embodiment of the present invention, step S3 further includes constructing a first constraint condition, wherein the first constraint condition is specifically:

[0036]

[0037] P+P Cs +P Eb =P mEL (9)

[0038] Where P is the total power of the electrolyzer; m is the hydrogen production rate of the electrolyzer; M H2 is the molar mass of hydrogen; LHV is the lower heating value of hydrogen; P Eb is the power of the auxiliary heating equipment; η Eb is the auxiliary heating equipment efficiency; P Cs is the cooling equipment power; η Cs is the cooling equipment efficiency; T a is the ambient temperature; R Elz is the thermal resistance of the electrolytic cell; P mEL is the optimal total power of the electrolyzer.

[0039] As a preferred embodiment of the present invention, step S3 further includes constructing a second constraint condition, wherein the second constraint condition is specifically:

[0040] 0.1P max ≤P i ≤P max

[0041] or

[0042] P i =0

[0043] Among them, P max is the maximum power value of the electrolyzer.

[0044] On the other hand, the present invention also provides a green hydrogen microgrid system, comprising a renewable energy submodule and a plurality of energy storage submodules, characterized in that the system further comprises:

[0045] A data acquisition module is used to acquire multiple power generation values ​​of renewable energy submodules and energy storage values ​​of multiple energy storage submodules within a period of time; and to calculate and acquire a power generation curve based on the multiple power generation values;

[0046] An upper control module is used to control the power generation and energy storage power in the green hydrogen microgrid system according to the power generation power curve using an MPC algorithm to obtain an optimal total power curve for the electrolyzer;

[0047] The lower control unit is used to construct an electrolytic cell control objective function; obtain the initial power allocation value of each sub-electrolytic cell; input the optimal total power curve of the electrolytic cell and the initial power allocation value of each sub-electrolytic cell into the electrolytic cell control objective function to obtain the optimal allocated power of each sub-electrolytic cell;

[0048] The adjustment module is used to adjust the power of each sub-electrolyzer according to the optimal allocated power of each sub-electrolyzer.

[0049] On the other hand, an electronic device is characterized by comprising 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 the steps of the above-mentioned green hydrogen grid-connected optimization method based on electrolyzer efficiency control.

[0053] On the other hand, a computer-readable storage medium is provided, characterized in that a computer program is stored thereon, and when the computer program is executed by a processor, the green hydrogen grid-connected optimization method based on electrolyzer efficiency control is implemented.

[0054] In summary, the present invention has the following beneficial effects:

[0055] The present invention adopts a two-layer optimization control algorithm to optimize the energy management of the multi-electrolyzer green hydrogen microgrid system. Through the upper-layer MPC control, the optimal total power curve of the optimized electrolyzer is obtained, and the interaction between the system and the power grid is minimized in response to fluctuating renewable energy and loads; the lower-layer control is aimed at the electrolyzer array, and the lower-layer power distribution is realized by optimizing the global efficiency of the electrolyzer. Through the upper and lower-layer coordinated control algorithms, the energy utilization efficiency of the multi-electrolyzer green hydrogen microgrid system is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] 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.

[0057] Figure 1 This is a flow chart of the green hydrogen grid connection optimization method;

[0058] Figure 2 This is the system block diagram of the green hydrogen microgrid system. DETAILED DESCRIPTION

[0059] 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.

[0060] 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.

[0061] Description of the overall control framework of the system: The system selected in this embodiment is a wind power grid-connected hydrogen production system, which is composed of a renewable energy sub-module, an electrolyzer, a hydrogen storage tank, a fuel cell, a battery, a user load and a power grid sub-module. In this embodiment, the renewable energy sub-module is a wind sub-module, which is the main energy source of this system. The DC bus voltage is maintained stable by the power grid, and the MPC controller regulates and distributes the power of the electric energy storage and hydrogen energy storage online. In other possible embodiments, the renewable energy sub-module can also be a photovoltaic sub-module, a hydroelectric sub-module, etc.

[0062] 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 stores electrical energy in the form of hydrogen energy by electrolyzing water to produce hydrogen. If the battery state of charge SOC and the hydrogen storage tank state of charge SOH both reach the upper limit, the battery and the electrolyzer stop working, and the remaining power of the system is absorbed by the grid sub-module.

[0063] 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.

[0064] If the battery state of charge (SOC) and the hydrogen storage tank state of charge (SOH) both reach their lower limits, the battery and fuel cell stop working and the grid provides the system with the remaining power.

[0065] like Figure 1 As shown, first obtain the power generation curve of the wind submodule. Specifically, the wind power generation power P is obtained according to the following formula:wt ,

[0066]

[0067] Where ρ is the air density, A is the contact area of ​​the wind wheel, V is the wind speed, C p (λ,β) is the wind energy utilization coefficient, where λ is the tip speed ratio and β is the pitch angle.

[0068] Get multiple wind power generation powers P within a period of time wt value, and obtain the wind electronic module power generation curve through fitting prediction in the prior art.

[0069] According to the dynamic characteristics of the power balance of the green hydrogen microgrid system and the charging and discharging status of the energy storage system, a state space model of the green hydrogen system is constructed as shown in formulas (1) and (2). The green hydrogen grid-connected operation system includes a wind submodule, an energy storage submodule, a multi-tank electrolysis hydrogen production subsystem, a hydrogen storage tank, a power conversion power electronic device and a user load; the energy storage submodule includes a battery and a fuel cell.

[0070] Next, the MPC control algorithm is used to control the power generation and energy storage power in the green hydrogen microgrid system to track the power generation curve of the renewable energy submodule and obtain the optimal total power curve of the electrolyzer; specifically:

[0071] Construct a linear discrete system state space model:

[0072]

[0073]

[0074] 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 C is the controlled output variable; A and B are the system matrix and control matrix respectively; C C is the controlled output matrix; SOC is the battery charge state; SOH is the hydrogen storage tank charge state; P gen is the controllable power; P mEL is the total power of the electrolytic cell; P FC is the fuel cell power; P Bat is the battery power.

[0075] AEL mathematical model

[0076] Temperature has a great influence on the voltage-current density characteristic curve of the electrolytic cell. A semi-empirical model based on thermodynamics and electrochemistry with temperature and electrolytic chamber area as the main parameters can be established:

[0077] V cell =V rev+r·i+s·log(t·i+1) (11)

[0078] Among them, Vrev is the reversible voltage, the second term on the right side of the equation is related to the ohmic overpotential, which is determined by the parameter r, and the third term is related to the activation overvoltage, which is determined by the parameters s and t. According to the semi-empirical model, it can be written as:

[0079]

[0080] Mathematical model of battery cell

[0081]

[0082] Where SOC is the state of charge of the battery, Δ is the self-discharge efficiency, P is the charge and discharge power, Δ cf is the charge and discharge efficiency, E h Is the maximum capacity of the battery.

[0083] Mathematical model of hydrogen storage tank

[0084]

[0085]

[0086] in, is the net hydrogen storage rate of the hydrogen storage tank, n sto is the hydrogen storage content of the hydrogen tank, R is the gas constant, T sto is the temperature of the hydrogen storage tank, V sto is the volume of the hydrogen storage tank, p sto_max It is the upper limit of hydrogen storage tank pressure.

[0087] By constructing the mathematical model of each unit, it can be known that the hydrogen production rate of the alkaline electrolyzer, the hydrogen consumption rate of the fuel cell, and the hydrogen storage tank charge state are

[0088]

[0089]

[0090]

[0091] The differential equation of SOH is obtained:

[0092]

[0093]

[0094] Discretize the differential equation to obtain SOH(k+1) and SOC(k+1). The values ​​of SOH and SOC at time k can be used to calculate P mEL , P FC , PBat Expressed as:

[0095]

[0096]

[0097] Therefore, in the state space equation:

[0098]

[0099] The elements in B are:

[0100]

[0101] In this system, the power balance equation is:

[0102] P grid (t)+P WTG (t)+P FC (t)+P Bat (t) = P mEL (t)+P load (t) (23)

[0103] Among them, P WTG is the active power of the fan, P Bat , P FC , P mEL , are the active power of the battery, fuel cell, and electrolyzer, respectively, P load P is the power demanded by the user load; grid is the interaction power between the grid submodule and the green hydrogen microgrid system. Each quantity in the formula is a data sequence of power changes over time.

[0104] In this embodiment, the reference power direction selection rule is: P FC >0 means the fuel cell generates power, P Bat (t>0 means the battery generates power, P AEL >0 means the electrolytic cell absorbs power, P grid <0 means that the grid submodule absorbs power from the coupled system.

[0105] Net power P net With controllable power P gen It can be expressed as

[0106]

[0107] Net power P net is the difference between wind power and user load demand, controllable power P gen is the difference in controllable power in the system, P net It may be >0, =0, <0. When the power generation capacity is in excess (Pnet >0), the fuel cell stops working. If the battery state of charge SOC and the hydrogen storage tank SOH are both within the constraint range, the electrolyzer and the battery absorb the remaining power.

[0108] When the power generation capacity is insufficient (P net ≤0), the electrolyzer stops working. If the battery state of charge SOC and the hydrogen storage tank SOH are both within the constraint range, the fuel cell and the battery will generate power.

[0109] In the upper-level control, it is necessary to reduce the power interaction between the grid sub-module and the green hydrogen microgrid system. This is because from the perspective of the grid, the power generated by wind power generation is highly volatile. Too much wind power connected to the grid will have a greater impact on the frequency of the grid and increase the grid load. From the perspective of the system manager, purchasing electricity from the grid will bring additional economic costs, which will cause losses to the economic benefits of the system. Therefore, the goal is set to reduce the power interaction with the grid.

[0110] Based on this, when the interaction power P between the grid submodule and the green hydrogen microgrid system grid When it is 0, the net power P net With controllable power P gen The difference is 0, and it can be considered that the difference between wind power and user load demand is entirely provided by controllable power.

[0111] In MPC, the optimal control input is found so that the predicted system output y C and the expected system output y ref The closer the better, and the control quantity changes as little as possible (that is, Δu is as small as possible), so the predicted output y C and the expected output y ref The cumulative error between and the cumulative change of the control input defines the objective function:

[0112]

[0113]

[0114] In the formula, (k+i|k) represents the prediction of time k for time k+1; y ref is the given reference trajectory; Δu is the increment of the control variable; N p is the prediction time domain; N c is the control time domain; the matrix Q is the weighted matrix for the output error; the matrix R is the weighted matrix for the control increment; r1, r2, and r3 represent the total power P of the electrolytic cell respectively. mEL , fuel cell power P FC and battery power P Bat degree of importance.

[0115] The essence of model predictive control is to solve the optimization problem online at each sampling moment, updated with the latest measured values.

[0116] Select the net power P net As the desired system reference output trajectory y ref (k+i|k), the control goal is to make the controllable power P within a certain time domain gen Tracking a given net power P net , and in the future it will be infinitely close to the given net power P net , that is, to make the predicted output as close to the reference output as possible, so that the net power P can be considered in the future net With controllable power P gen The difference is 0, that is, the interaction power between the grid sub-module and the green hydrogen microgrid system is minimized.

[0117]

[0118] Compared with traditional control algorithms, MPC algorithm can not only solve multivariable constrained optimization control problems that are difficult to solve with PID control, but also predict the output, making it a superior online optimization algorithm. Among them, prediction model, rolling optimization and feedback correction are the three major features of MPC control.

[0119] Prediction model: The prediction in model predictive control refers to the prediction of the future dynamic development of the controlled object. There is no specific requirement for the form of the prediction model. Generally, it is a state space equation in a multi-input and multi-output system.

[0120] Rolling optimization: The optimization in predictive control is a rolling optimization with a limited period. At each sampling moment, this optimization period moves forward at the same time. In predictive control, the optimization is not performed offline once, but repeatedly online. This rolling optimization effectively overcomes the interference caused by wind-solar hybrid hydrogen production system with coordinated operation of multiple types of electrolyzers, such as wind-solar volatility, intermittent harmony and load randomness.

[0121] Feedback correction: Predictive control is a closed-loop control algorithm. The introduction of feedback correction can effectively avoid the deviation of control results from the ideal state due to model mismatch and environmental interference.

[0122] Since frequent interaction with the power grid submodule will bring high costs, in the above optimization control process, the minimum interaction with the power grid submodule is taken as the goal. Further, the objective function is:

[0123]

[0124] Where Subject to x k+1 =Ax k +Bu k

[0125]

[0126]

[0127] They are the input and output constraint sets, as follows:

[0128]

[0129]

[0130] Next, based on the above linear discrete system state space model and constraint conditions, an optimal total power curve of an electrolytic cell that optimizes the objective function is obtained.

[0131] Water decomposition is an endothermic process, but due to the presence of ohmic resistance and activation resistance, the cell voltage is usually greater than the thermoneutral electrolysis voltage, so additional heat is usually generated and dissipated during the reaction. Part of this dissipated heat is exchanged between the electrolyzer and the environment at a rate that depends on the temperature difference, total heat capacity and volume of the electrolyzer, and part is carried away by the generated gas and water vapor. This heat dissipation is inevitable, so the electrolysis efficiency can be used to reflect the degree of energy utilization, system performance and the influence of each parameter. From the perspective of energy efficiency, it represents the efficiency of converting the input energy into hydrogen chemical energy. Multi-electrolyzer collaborative optimization control, the power distribution between different electrolyzers will affect the overall efficiency of the electrolyzer. In order to improve the energy utilization of the hydrogen production system, in the lower-level control, the objective function is set as follows:

[0132]

[0133] Wherein, J is the total effective power of the electrolytic cell; η i is the efficiency of the ith sub-electrolyzer, P i The power allocated to the ith electrolyzer.

[0134] The efficiency of the sub-electrolyzer is the product of the Faraday efficiency and the sub-electrolyzer voltage efficiency:

[0135] η i =η f ·η v (5)

[0136] Among them, η f is the Faraday efficiency, η vis the voltage efficiency of the sub-electrolyzer; Faraday efficiency is a key parameter because it is related to the loss caused by gas diffusion. This parameter is associated with the operating conditions (i.e., current, temperature, and pressure) and has a great influence on energy efficiency. Among them, the main influencing factors are temperature and current. The Faraday efficiency can be expressed according to the semi-empirical formula as follows:

[0137]

[0138] Among them I cell is the sub-electrolyzer current, T is the sub-electrolyzer temperature, and f1, f2, f3, and f4 are the parameters to be fitted;

[0139] The voltage efficiency is the efficiency related to the voltage used in the electrolyzer. Usually, the Faraday efficiency is higher than the voltage efficiency because it only takes into account the losses due to gas diffusion. In contrast, the voltage efficiency takes into account additional losses, such as membrane losses and heat losses in the anode and cathode. The voltage efficiency can be expressed as:

[0140]

[0141] Among them, V cell is the sub-electrolyzer voltage, V th is the thermal neutral voltage.

[0142] Temperature has a significant effect on energy efficiency and voltage loss. In addition to its direct effect on reversible voltage and activation overpotential, temperature can also indirectly affect the electrolysis voltage of each part by affecting water activity, water vapor partial pressure, bubble coverage and ion conductivity. The increase in temperature will promote the reduction of overpotential, which will then lead to a decrease in heat loss efficiency, while the energy efficiency will increase with the increase in the reduction rate. As the temperature further increases, the improvement effect on energy efficiency will gradually weaken.

[0143] From the perspective of the electrolytic cell itself, increasing the temperature is beneficial to improving the electrolysis efficiency. However, from the perspective of the energy use of the electrolysis system, too high a reaction temperature may increase the power consumption of the accessory system, which is not conducive to improving the energy efficiency of the entire system. At the same time, too high a temperature may cause the electrolyte to boil, which may easily cause safety problems. In order to control the required operating temperature of the electrolytic cell, the auxiliary heating equipment and cooling equipment in the electrolytic cell need to consume a certain amount of power, and its relationship with the temperature can be expressed as:

[0144]

[0145] P+P Cs +P Eb =P mEL (9)

[0146] Where P is the total power of the electrolyzer; m is the hydrogen production rate of the electrolyzer; M H2is the molar mass of hydrogen; LHV is the lower heating value of hydrogen; P Eb is the power of the auxiliary heating equipment; η Eb is the auxiliary heating equipment efficiency; P Cs is the cooling equipment power; η Cs is the cooling equipment efficiency; T a is the ambient temperature; T is the working temperature of the electrolytic cell; R Elz is the thermal resistance of the electrolytic cell; P mEL is the optimal total power of the electrolyzer.

[0147] When the electrolyzer operates in a lower power range, it is easy to explode due to the mixture of hydrogen and oxygen. When it operates in a higher power range, it will aggravate the irreversible attenuation of the electrolyzer and reduce its service life. According to the output constraints of the electrolyzer, its constraints include:

[0148] 0.1P max ≤P i ≤P max

[0149] or

[0150] P i =0

[0151] Among them, P max is the maximum power value of the electrolyzer.

[0152] In one embodiment, by using a genetic algorithm for optimization, the total effective utilization power J of the electrolytic cell can be maximized through the above-mentioned first constraint condition and second constraint condition, and the optimal allocation power of each sub-electrolytic cell can be obtained;

[0153] Finally, the power of the sub-electrolyzer is adjusted using the optimal allocated power of the sub-electrolyzer.

[0154] In another embodiment, if Figure 2 As shown, the present invention also provides a green hydrogen microgrid system, comprising a renewable energy submodule and a plurality of energy storage submodules, the system further comprising:

[0155] A data acquisition module is used to acquire multiple power generation values ​​of renewable energy submodules and energy storage values ​​of multiple energy storage submodules within a period of time; and to calculate and acquire a power generation curve based on the multiple power generation values;

[0156] An upper control module is used to control the power generation and energy storage power in the green hydrogen microgrid system according to the power generation power curve using an MPC algorithm to obtain an optimal total power curve for the electrolyzer;

[0157] The lower control unit is used to construct an electrolytic cell control objective function; obtain the initial power allocation value of each sub-electrolytic cell; input the optimal total power curve of the electrolytic cell and the initial power allocation value of each sub-electrolytic cell into the electrolytic cell control objective function to obtain the optimal allocated power of each sub-electrolytic cell;

[0158] The adjustment module is used to adjust the power of each sub-electrolyzer according to the optimal allocated power of each sub-electrolyzer.

[0159] In another embodiment, an electronic device is also provided, including 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 the steps of the above-mentioned green hydrogen grid-connected optimization method based on electrolyzer efficiency control.

[0160] In another embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned green hydrogen grid-connected optimization method based on electrolyzer efficiency control is implemented.

[0161] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function according to the embodiment of this specification is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted by a computer-readable storage medium. The computer instructions can be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server, a data center, etc. that contains one or more available media integrated. Available media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, digital versatile discs (DVD)), or semiconductor media (eg, solid state disks (SSD)).

[0162] 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.

[0163] 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 green hydrogen grid-connected optimization method based on electrolyzer efficiency control, applied to a green hydrogen microgrid system, characterized in that the method include: S1, obtaining a power generation curve of a renewable energy submodule; S2. Using an MPC control algorithm to control the power generation and energy storage power in the green hydrogen microgrid system to track the power generation curve of the renewable energy submodule to obtain the optimal total power curve of the electrolyzer; S3, constructing an electrolytic cell control objective function; obtaining initial power allocation values ​​for each sub-electrolytic cell; inputting the optimal total power curve of the electrolytic cell and the initial power allocation values ​​of each sub-electrolytic cell into the electrolytic cell control objective function to obtain the optimal allocated power for each sub-electrolytic cell; S4. Use the optimal allocated power of the sub-electrolyzer to adjust the power of the sub-electrolyzer.

2. The green hydrogen grid connection optimization method based on electrolyzer efficiency control according to claim 1 is characterized in that: Step S2 specifically includes: S21. Construct a linear discrete system state space model: 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 C is the controlled output variable; A and B are the system matrix and control matrix respectively; C C is the controlled output matrix; SOC is the battery charge state; SOH is the hydrogen storage tank charge state; P gen is the controllable power; P mEL is the total power of the electrolytic cell; P FC is the fuel cell power; P Bat is the battery power; S22. Construct the objective function: In the formula In the formula, (k+i|k) represents the prediction of time k for time k+1; y ref is the given reference trajectory; Δu is the increment of the control variable; N p is the prediction time domain; N c is the control time domain; the matrix Q is the weighted matrix for the output error; the matrix R is the weighted matrix for the control increment; r1, r2, and r3 represent the total power P of the electrolytic cell respectively. mEL , fuel cell power P FC and battery power P Bat the degree of importance attached to S23, select the net power P of the difference between the power generation power of the renewable energy submodule and the user load net As the desired system output reference trajectory y ref (k+i|k), the control target is the controllable power P gen Tracking a given net power P net ; S24. According to the linear discrete system state space model and constraint conditions, find the optimal total power curve P of an electrolytic cell that makes the objective function optimal. mEL .

3. The green hydrogen grid connection optimization method based on electrolyzer efficiency control according to claim 2 is characterized in that: In step S3, the electrolytic cell optimization objective function is specifically: Wherein, J is the total effective power of the electrolytic cell; η i is the efficiency of the ith sub-electrolyzer, P i The power allocated to the ith electrolyzer.

4. The green hydrogen grid connection optimization method based on electrolyzer efficiency control according to claim 3 is characterized in that: The efficiency of the sub-electrolyzer is the product of the Faraday efficiency and the sub-electrolyzer voltage efficiency: or i =the f ·or v (5) Among them, η f is the Faraday efficiency, η v is the sub-electrolyzer voltage efficiency; Among them I cell is the sub-electrolyzer current, T is the sub-electrolyzer temperature, and f1, f2, f3, and f4 are the parameters to be fitted; Among them, V cell is the sub-electrolyzer voltage, V th is the thermal neutral voltage.

5. The green hydrogen grid connection optimization method based on electrolyzer efficiency control according to claim 4 is characterized in that: In S3, the electrolytic cell control objective function is optimized by a genetic algorithm, the optimal individual is output, and the optimal distribution power of each sub-electrolytic cell is obtained.

6. The green hydrogen grid connection optimization method based on electrolyzer efficiency control according to claim 5 is characterized in that: Step S3 also includes constructing a first constraint condition, which is specifically: P+P Cs +P Eb =P mEL (9) Where P is the total power of the electrolyzer; m is the hydrogen production rate of the electrolyzer; is the molar mass of hydrogen; LHV is the lower heating value of hydrogen; P Eb is the power of the auxiliary heating equipment; η Eb is the auxiliary heating equipment efficiency; P Cs is the cooling equipment power; η Cs is the cooling equipment efficiency; T a is the ambient temperature; R Elz is the thermal resistance of the electrolytic cell; P mEL is the optimal total power of the electrolyzer.

7. The green hydrogen grid connection optimization method based on electrolyzer efficiency control according to claim 6 is characterized in that: Step S3 also includes the construction of a second constraint condition, which is specifically: 0.1P max ≤P i ≤P max or P i ＝0 Among them, P max is the maximum power value of the electrolyzer.

8. A green hydrogen microgrid system, comprising a renewable energy submodule and a plurality of energy storage submodules, characterized in that: The system also includes: A data acquisition module is used to acquire multiple power generation values ​​of renewable energy submodules and energy storage values ​​of multiple energy storage submodules within a period of time; and to calculate and acquire a power generation curve based on the multiple power generation values; An upper control module is used to control the power generation and energy storage power in the green hydrogen microgrid system according to the power generation power curve using an MPC algorithm to obtain an optimal total power curve for the electrolyzer; The lower control unit is used to construct an electrolytic cell control objective function; obtain the initial power allocation value of each sub-electrolytic cell; input the optimal total power curve of the electrolytic cell and the initial power allocation value of each sub-electrolytic cell into the electrolytic cell control objective function to obtain the optimal allocated power of each sub-electrolytic cell; The adjustment module is used to adjust the power of each sub-electrolyzer according to the optimal allocated power of each sub-electrolyzer.

9. An electronic device, characterized in that: including 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 the steps of the green hydrogen grid connection optimization method based on electrolyzer efficiency control as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the green hydrogen grid-connected optimization method based on electrolyzer efficiency control as described in any one of claims 1 to 7 above is implemented.

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