Operation control method and device for off-grid wind-solar hydrogen production ammonia synthesis system

Abstract

The invention provides an off-grid wind and light hydrogen production ammonia synthesis system operation control method and device, and relates to the technical field of renewable energy hydrogen production ammonia synthesis, and the method comprises the steps: building a system model of an off-grid wind and light hydrogen production ammonia system, the wind and light hydrogen production ammonia system satisfying a target function and a target constraint condition; wind-photovoltaic predicted output is obtained; calculating the fitness of each particle; respectively calculating hydrogen production power and ammonia synthesis power based on the hydrogen production flow and the ammonia yield; calculating the starting number of the electrolytic cells and calculating the abandoned electricity quantity; calculating energy storage output and energy storage capacity; calculating the fitness of each particle, comparing the fitness with a locally optimal solution and a globally optimal solution, and updating the locally optimal solution and the globally optimal solution according to a comparison result; and outputting the optimal solution set after the maximum number of iterations is reached. The real-time power balance of the wind-solar hydrogen production ammonia synthesis system can be met, the project profit maximization is realized, the utilization and consumption of renewable energy sources are improved, the electricity abandoning rate of new energy sources is reduced, and the green low-carbon transformation of the chemical process is facilitated.

Description

Technical Field

[0001] The present disclosure relates to the technical field of hydrogen production and ammonia synthesis from renewable energy, and in particular to an operation control method and device for an off-grid wind-solar hydrogen production and ammonia synthesis system. Background Art

[0002] In recent years, my country has actively adjusted its energy structure and rapidly promoted the construction of renewable energy. The production of these clean energy sources is greatly affected by seasons and weather, and the problem of abandoned power consumption is becoming more and more serious. Using renewable energy for hydrogen production and ammonia synthesis can not only improve the utilization and consumption of renewable energy, but also facilitate the green and low-carbon transformation of chemical processes. Most of the existing technologies are aimed at wind-solar hydrogen production systems, and some structures of wind-solar coupled hydrogen production systems are proposed. Renewable energy is used for hydrogen production, but no further consideration is given to using the prepared hydrogen for ammonia synthesis to increase the benefits. There is a lack of research on wind-solar hydrogen production and ammonia synthesis systems. In addition, for wind-solar hydrogen production and ammonia synthesis systems, existing optimization algorithms such as coyote algorithms and genetic algorithms have low search efficiency, weak operability, and poor local search capabilities, resulting in low search efficiency and slow convergence in the late evolution period. Summary of the invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0004] The first embodiment of the present disclosure provides an off-grid wind-solar hydrogen production and ammonia synthesis system operation control method, comprising:

[0005] Establishing a system model of an off-grid wind-solar-hydrogen-ammonia system, wherein the wind-solar-hydrogen-ammonia system satisfies a pre-established objective function and objective constraints;

[0006] Obtain wind power forecast output and photovoltaic forecast output;

[0007] Set the number of iterations, the number of particles, and the optimization variables contained in each particle, generate hydrogen production flow, ammonia yield, absorbed wind power and absorbed photovoltaic power, and calculate the fitness of each particle;

[0008] Based on the hydrogen production flow rate and ammonia yield, respectively calculating the hydrogen production power and the ammonia synthesis power;

[0009] The number of electrolyzers started is calculated based on the hydrogen production power, and the amount of abandoned electricity is calculated based on the predicted wind power output and the predicted photovoltaic power output, the absorbed wind power and the absorbed photovoltaic power;

[0010] Calculate the energy storage output and capacity based on the upper and lower limits of energy storage capacity, energy storage charging and discharging efficiency, energy storage initial capacity and the formulated energy storage operation strategy;

[0011] Calculate the fitness of each particle and compare it with the local optimal solution and the global optimal solution, and update the local optimal solution and the global optimal solution according to the comparison results;

[0012] After reaching the maximum number of iterations, the optimal solution set is output.

[0013] The second embodiment of the present disclosure provides an off-grid wind-solar hydrogen production and ammonia synthesis system operation control device, comprising:

[0014] A construction module is used to establish a system model of an off-grid wind-solar-hydrogen-ammonia system, wherein the wind-solar-hydrogen-ammonia system satisfies a pre-constructed objective function and objective constraints;

[0015] An acquisition module is used to obtain predicted wind power output and predicted photovoltaic power output;

[0016] The first calculation module is used to set the number of iterations, the number of particles and the optimization variables contained in each particle, generate the hydrogen production flow rate, ammonia yield, absorbed wind power and absorbed photovoltaic power, and calculate the fitness of each particle;

[0017] A second calculation module is used to calculate the hydrogen production power and the synthetic ammonia power respectively based on the hydrogen production flow rate and the ammonia yield;

[0018] A third calculation module is used to calculate the number of electrolyzer startups based on the hydrogen production power, and to calculate the amount of abandoned electricity based on the predicted wind power output and the predicted photovoltaic output, the absorbed wind power and the absorbed photovoltaic power;

[0019] A fourth calculation module is used to calculate the energy storage output and energy storage capacity based on the upper and lower limits of energy storage capacity, energy storage charging and discharging efficiency, energy storage initial capacity and the formulated energy storage operation strategy;

[0020] The update module is used to calculate the fitness of each particle and compare it with the local optimal solution and the global optimal solution, and update the local optimal solution and the global optimal solution according to the comparison results;

[0021] The output module is used to output the optimal solution set after reaching the maximum number of iterations.

[0022] The third aspect embodiment of the present disclosure proposes an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the operation control method of the off-grid wind-solar hydrogen production and ammonia synthesis system proposed in the first aspect embodiment of the present disclosure is implemented.

[0023] The fourth aspect embodiment of the present disclosure proposes a non-temporary computer-readable storage medium storing a computer program, which, when executed by a processor, implements the operation control method of an off-grid wind-solar hydrogen production and ammonia synthesis system as proposed in the first aspect embodiment of the present disclosure.

[0024] The off-grid wind-solar hydrogen production and ammonia synthesis system operation control method, device and electronic equipment provided by the present disclosure have the following beneficial effects:

[0025] In the disclosed embodiment, a system model of an off-grid wind-solar-hydrogen-ammonia system is first established, and the wind-solar-hydrogen-ammonia system satisfies the pre-constructed objective function and objective constraints. Then, the predicted wind power output and the predicted photovoltaic output are obtained. Then, the number of iterations, the number of particles, and the optimization variables contained in each particle are set to generate the hydrogen production flow, the ammonia yield, the absorbed wind power, and the absorbed photovoltaic power. The fitness of each particle is calculated. Then, based on the hydrogen production flow and the ammonia yield, the hydrogen production power and the synthetic ammonia power are calculated respectively. Then, based on the hydrogen production power, the number of electrolyzer starts is calculated, and based on the wind power predicted output and the photovoltaic predicted output, the absorbed wind power and the absorbed photovoltaic power, the amount of abandoned electricity is calculated. Then, based on the upper and lower limits of the energy storage capacity, the energy storage charging and discharging efficiency, the initial capacity of the energy storage, and the formulated energy storage operation strategy, the energy storage output and the energy storage capacity are calculated. Then, the fitness of each particle is calculated and compared with the local optimal solution and the global optimal solution. The local optimal solution and the global optimal solution are updated according to the comparison results. Then, after reaching the maximum number of iterations, the optimal solution set is output. The particle swarm algorithm can quickly find the optimal solution, has good global search capabilities, can find the optimal solution in the entire search space, and is not easy to fall into the local optimum. It shows good robustness for many types of optimization problems, and can find acceptable solutions even when the parameter settings are not appropriate. By establishing photovoltaic power station models, wind power station models, water electrolysis hydrogen production models, hydrogen synthesis ammonia section models and energy storage device models, the process of wind and solar hydrogen production and ammonia synthesis under off-grid operation is simulated. Based on typical daily wind and solar data, unit power consumption of hydrogen production and ammonia synthesis processes, related economic data, etc., under the conditions of meeting the real-time power balance of the hydrogen and ammonia production system, the abandonment rate of new energy, the characteristics of the electrolyzer, and the upper and lower limits of energy storage, the particle swarm algorithm is used to optimize the day-ahead scheduling of the wind and solar hydrogen and ammonia production system with the objective function of maximizing engineering profits, and the number of electrolyzer startups, energy storage output, water electrolysis hydrogen production power, hydrogen synthesis ammonia power and related economic data of the electric hydrogen and ammonia system in each period are obtained.

[0026] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1A schematic flow chart of an off-grid wind-solar hydrogen production and ammonia synthesis system operation control method provided in an embodiment of the present disclosure;

[0029] Figure 2 This is the particle swarm algorithm flow chart for the off-grid wind-solar hydrogen production and ammonia synthesis system;

[0030] Figure 3 Provides an operation strategy diagram for energy storage equipment;

[0031] Figure 4 A schematic diagram of the structure of an off-grid wind-solar hydrogen production and ammonia synthesis system operation control device provided in an embodiment of the present disclosure;

[0032] Figure 5 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0033] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present disclosure, and should not be construed as limitations on the present disclosure. On the contrary, the embodiments of the present disclosure include all changes, modifications and equivalents that fall within the spirit and connotation of the appended claims.

[0034] The following describes the operation control method, device, computer equipment and storage medium of the off-grid wind-solar hydrogen production and ammonia synthesis system according to the embodiments of the present disclosure with reference to the accompanying drawings.

[0035] It should be noted that the executor of the off-grid wind-solar hydrogen production and ammonia synthesis system operation control method in the embodiment of the present disclosure is the off-grid wind-solar hydrogen production and ammonia synthesis system operation control device, which can be implemented by software and / or hardware, and the device can be configured in any electronic device.

[0036] Figure 1 This is a flow chart of the operation control method of the off-grid wind-solar hydrogen production and ammonia synthesis system provided in the first embodiment of the present disclosure.

[0037] like Figure 1 As shown, the off-grid wind-solar hydrogen production and ammonia synthesis system operation control method may include the following steps:

[0038] Step 101: Establish a system model of an off-grid wind-solar-hydrogen-ammonia system, wherein the wind-solar-hydrogen-ammonia system satisfies a pre-constructed objective function and objective constraints.

[0039] Specifically, a photovoltaic power station model, a wind power station model, a water electrolysis hydrogen production model, a hydrogen synthesis ammonia model, and an energy storage device model can be established respectively, wherein the objective function includes multiple cost functions, and the multiple cost functions include at least: process electricity cost, operation and maintenance cost, electrolytic cell water consumption cost, employee wages and benefits, and equipment depreciation cost; the target constraints include at least: system power balance constraint, green electricity consumption constraint, and new energy power abandonment rate constraint.

[0040] Among them, the wind-solar hydrogen production and ammonia synthesis system in off-grid mode is composed of a photovoltaic power station model, a wind power station model, a water electrolysis hydrogen production model, a hydrogen ammonia synthesis model, and an energy storage device model.

[0041] (1) Photovoltaic power station model

[0042] The calculation formula for calculating the power generation of a photovoltaic power station based on solar irradiance data is as follows:

[0043]

[0044] in, is the power generation of the photovoltaic power station, t0 is the temperature of the photovoltaic panel at time t, in °C; I t0 is the solar radiation intensity in the area where the photovoltaic array is located, in W / m 2 , η s (t0,I t0 ) is the power generation efficiency of the photovoltaic array under the conditions of temperature and solar radiation intensity, A s is the area of ​​the photovoltaic array, in m 2 .

[0045] (2) Wind power station model

[0046] The calculation formula for calculating the power generation of a wind power station based on wind speed data is as follows:

[0047]

[0048] in, is the power generation of the wind power station, η w To consider the efficiency such as wake effect, fan loss, etc.; N w is the number of wind turbines; P w (v) is the power generation capacity of a single wind turbine, in kW; v, v ci 、v r 、v co They are regional wind speed, wind turbine cut-in wind speed, wind turbine rated design wind speed, and wind turbine cut-out wind speed, all in m / s; P wr is the rated power of the wind turbine, in kW; ρ is the air density; Aw is the swept area; C p is the wind energy conversion efficiency value.

[0049] (3) Hydrogen production model by water electrolysis

[0050] When the electrolyzer starts to start, the temperature of the electrolyzer is not high enough to meet the temperature conditions for producing hydrogen. At this time, all the power consumed is used to generate heat to increase the temperature of the electrolyzer. Due to the characteristics of the internal materials of the electrolyzer, the operating power of the electrolyzer cannot be lower than a certain limit, otherwise there is a risk of hydrogen and oxygen cross-talk exceeding the explosion limit. The limit is generally 20%-25% of the rated power of the electrolyzer. In addition, when the electrolyzer is working, its power can exceed the rated power for a short time, reaching 110%-130% of the rated power. This feature can be used to reduce the configuration capacity of the electrolyzer. The operating power constraint of the electrolyzer is expressed as follows:

[0051] 0.2PN et，n u et，n (t)≤P et，n (t)≤1.1PN et，n u et，n (t) (5)

[0052]

[0053] Where PN et，n is the rated operating power of the electrolytic cell, P et，n (t) is the operating power of the electrolytic cell at time t, u et，n (t) is the binary variable for starting and stopping the electrolytic cell.

[0054] The relationship between the power of a single electrolyzer and the amount of hydrogen produced is:

[0055] Q(t)=0.1977P et，n (t)+96.9987 (7)

[0056] The electrolyzer power ramp constraint is expressed as follows:

[0057] P et，n (t)-P et，n (t-1)≤(1-w et，n (t))r cold，n PN et，n Δt+w et，n (t)+w et，n (t)r warm，n PN et，n Δt (8)

[0058] P et，n (t-1)-P et，n (t)≤w et，n (t)rwarm PN et，n Δt (9)

[0059]

[0060] Where P et，n (t) and P et，n (t-1) are the operating power of the electrolyzer at time t and time t-1, respectively, w et，n (t) is the binary variable of electrolytic cell state, r cold，n is the ramp rate when the electrolytic cell is in a cold state, r warm，n is the ramp rate when the electrolytic cell is in a warm state.

[0061] (4) Hydrogen synthesis ammonia section model

[0062] In order to avoid exceeding the temperature and pressure limits of the synthetic ammonia reactor and various process links such as ammonia separation, circulation, and heat exchange, the load ramp rate needs to be limited:

[0063]

[0064] Among them, h is the scheduling step length; is the yield of ammonia at time t; is the synthetic ammonia load ramp rate; and The upper and lower limits of the load ramp rate are +15% and -25% of the rated output per hour.

[0065] At the same time, subject to constraints such as thermodynamic balance and catalyst activity, the synthetic ammonia yield should be maintained within a given range:

[0066]

[0067] Where: and They are the upper and lower limits of ammonia yield, respectively, taking 100% and 20% of the rated yield.

[0068] The processes of ammonia separation, circulation, and compression in the synthetic ammonia section all require a certain amount of electricity. Expressed as a function of ammonia yield:

[0069]

[0070] in, is the ground state power; is a constant coefficient.

[0071] (5) Energy storage device model

[0072] -P EN ≤P E (t)≤PEN (15)

[0073]

[0074] E E (t) = E E (0)+∑η E P E (t)Δt (17)

[0075] E E (t)≥0 (18)

[0076]

[0077] Among them, P E (t) is the energy storage output at time t, P EN is the upper and lower limits of energy storage output, E E (t) is the energy storage capacity at time t, η E is the energy storage charging and discharging efficiency, and are the upper and lower limits of energy storage capacity, η cha，E and η dis，E They are the energy storage charging efficiency and discharging efficiency respectively.

[0078] It should be noted that the objective function includes multiple cost functions, which include at least: process electricity cost, operation and maintenance cost, electrolytic cell water consumption cost, employee wages and benefits, and equipment depreciation cost.

[0079] The cost of the electric hydrogen production and ammonia synthesis system is composed of process electricity cost, operation and maintenance cost, electrolyzer water consumption cost, employee wages and benefits, and equipment depreciation cost.

[0080]

[0081] Where C is the total cost of the electric hydrogen production and ammonia synthesis system, C A is the process electricity cost, C B For operation and maintenance costs, is the water consumption cost of the electrolytic cell, C Sal For staff wages and benefits, C Dep The depreciation cost of the equipment.

[0082] (a) Process electricity cost

[0083]

[0084] Where: is the cost of hydrogen production by electrolysis of water, is the cost of hydrogen to ammonia synthesis, The cost of hydrogen storage.

[0085]

[0086] Among them, h is the scheduling step length, The price of wind power. The power of hydrogen production by electrolysis of water, Electricity for the synthetic ammonia process, Electricity used to compress hydrogen.

[0087] (b) Operation and maintenance costs

[0088]

[0089] Where: They are respectively the operation and maintenance costs of the electric hydrogen production unit, the hydrogen synthesis ammonia unit, the hydrogen compression unit, and the grid interaction unit.

[0090] (c) Water consumption cost of electrolyzer

[0091]

[0092] Among them, K Water The water consumption per unit of hydrogen produced by the electrolyzer; is the price per unit of water; It is the hydrogen production per hour of the water electrolysis hydrogen production system.

[0093] (d) Staff wages and benefits

[0094] C Sal =M×Y ave (27)

[0095] Among them, M is the number of employees in the electricity, hydrogen and ammonia system, Y ave Per capita wages and benefits.

[0096] (e) Equipment depreciation cost

[0097] C Dep =K dep ×C inv . (28)

[0098] Among them, K dep is the depreciation cost ratio coefficient, C inv For investment cost.

[0099] (2) Revenue from the electric hydrogen production and ammonia synthesis system

[0100] The revenue of the electric hydrogen ammonia system is the revenue from selling ammonia, which is expressed as follows:

[0101]

[0102] Where: is the unit selling price of ammonia, is the ammonia production rate of the P2A system during period t.

[0103] (3) Objective function of the hydrogen-to-ammonia synthesis system

[0104] The simulation model of off-grid renewable energy hydrogen production and ammonia synthesis system aims to maximize engineering profits.

[0105] Max J=SC (30)

[0106] Among them, J is the profit of the hydrogen-to-ammonia synthesis system, which consists of the income and cost of the hydrogen-to-ammonia synthesis system.

[0107] Among them, the operation and control of the electric hydrogen and ammonia system under off-grid conditions needs to meet the system power balance constraints, green electricity consumption constraints, and new energy power abandonment rate constraints.

[0108] (1) System power balance constraints

[0109] The power of the system needs to meet the following balance constraints:

[0110]

[0111] Where P t P2H , are respectively the power of hydrogen production from electricity and the power of ammonia synthesis from hydrogen, P t PV , P t WT , P t sell , P t buy are photovoltaic output and wind turbine output respectively, P t bat Contribute to energy storage.

[0112] (2) Green electricity consumption constraints

[0113] In order to improve environmental benefits and reduce costs, the P2A system gives priority to green electricity. When green electricity supply is insufficient, it purchases electricity from the power grid to meet the system's electricity needs.

[0114]

[0115] (3) Constraints on the rate of abandoned electricity from new energy sources

[0116]

[0117] P loss (t)≥0 (34)

[0118] Where P loss (t) is the abandoned power at time t, Pre (t) is the power generation at time t.

[0119] Step 102: Obtain predicted wind power output and predicted photovoltaic power output.

[0120] First, input the maximum amount of electricity that the photovoltaic power station and wind power station are expected to provide to the hydrogen production and ammonia synthesis system, that is, the predicted wind power output P wt_pre (t) and photovoltaic predicted output P pv_pre (t).

[0121] Specifically, first input the output data of a single wind turbine and photovoltaic power generation as well as the number of units to obtain the predicted wind power output and photovoltaic power output.

[0122] In order to calculate the total predicted output of wind power and photovoltaic power from the output data of a single wind turbine and photovoltaic power plant and their respective numbers, the predicted output of a single device can be multiplied by the corresponding number of devices. Here we assume that we know the predicted output of each wind turbine and photovoltaic device (which may be a predicted value based on historical data, weather forecasts, etc.), as well as the total number of each device.

[0123] Predicted output of wind turbine: Assume that the predicted output of each wind turbine is P wt_pre (t) (unit: kilowatt or megawatt).

[0124] Photovoltaic predicted output: Assume that the predicted output of each photovoltaic panel is P pv_pre (t) (same units as above).

[0125] Determine the total number of equipment, the total number of wind turbines: Assume that the total number of wind turbines is Nw. The total number of photovoltaics (photovoltaic panels are used as an example): Assume that the total number of photovoltaic panels is Npv. In practice, photovoltaic power stations may be measured by the capacity (megawatts) of photovoltaic arrays or photovoltaic power stations. For simplicity, it is assumed that the number of photovoltaic panels is used for calculation.

[0126] Calculate the total predicted output, wind power predicted total output: P pv_pre (t) = Pw_ind × Nw

[0127] Total photovoltaic output forecast: P pv_pre (t) = Ppv_ind × Npv

[0128] Step 103: Set the number of iterations, the number of particles, and the optimization variables contained in each particle, generate hydrogen production flow, ammonia yield, absorbed wind power and absorbed photovoltaic power, and calculate the fitness of each particle.

[0129] Specifically, the position and velocity of the particle can be initialized to generate the hydrogen production flow rate Q h2_in (t), ammonia yield F nh3 (t), wind power P consumedwt (t) and the absorbed photovoltaic power P pv (t).

[0130] It should be noted that microgrid optimization scheduling is a typical power system optimization problem with the characteristics of high dimension, nonlinearity, and multiple constraints. Compared with algorithms such as genetic algorithms, the PSO algorithm has stronger optimization capabilities. At the same time, it is easier to apply when solving optimization problems. Due to its simplicity and effectiveness, the PSO algorithm is widely used in various optimization problems, including continuous optimization, combinatorial optimization, neural network training, pattern recognition, robot path planning, etc. Therefore, the microgrid optimization model in this paper is solved using the particle swarm algorithm. Each particle in the PSO algorithm is described by a position and velocity vector, where the position vector represents a possible solution to the problem, and the velocity vector represents the direction and magnitude of the position change. Assuming that the total number of particles is M, the position and velocity of the nth particle in dimension d are expressed as follows:

[0131]

[0132] The number of particles is the size of the group. A larger group can provide more diversity and help global search, but it also increases the computational cost. Each particle adjusts its speed and position by tracking their previous individual best position and the group best position. These two best positions are expressed as:

[0133]

[0134] Where: P n is the individual optimal position of the nth particle; P g is the group best position obtained from all particles in the previous iteration.

[0135] The key to the particle swarm algorithm is to determine the local optimal value and the global optimal value of the particle, which requires the particle to continuously update the next movement. The particle updates the next movement based on its own experience and the best experience in the group. The update process is:

[0136]

[0137] Among them, w is the inertia weight factor. Increasing the inertia weight factor is not easy to fall into the local minimum, which is convenient for global search. Reducing the inertia weight factor is conducive to local search and the convergence of the algorithm; c1 and c2 are learning factors, which reflect the self-learning ability and social learning ability of particles respectively, and control the degree of attraction of particles to the individual best and global best positions respectively; r1 and r2 are random numbers uniformly distributed in [0,1].

[0138] The updating process of the inertia weight factor is:

[0139]

[0140] Among them, ω start is the initial inertia weight; ω end is the inertia weight when the iteration reaches the maximum number; k is the current number of iterations; T max is the maximum number of iterations.

[0141] The PSO algorithm first initializes a group of particles in the feasible solution space. Each particle represents a potential optimal solution to the optimization problem. The three indicators of position, speed and fitness value are used to represent the characteristics of the particle. The fitness value represents the quality of the particle. The particle moves in the solution space. Each time the position is updated, the fitness value is calculated, and the local optimal value and the global optimal value are updated. The global optimal value after the iteration is completed is the optimal value of the required optimization variable.

[0142] Specifically, the fitness of the particle can be calculated to assign an initial value, and the fitness of the particle is the profit of the hydrogen production and ammonia synthesis system.

[0143] Optionally, the speed, position and inertia weight of each particle may be updated, and the boundary of the particle may be processed so that the optimization variable is within a specified constraint range.

[0144] It can be understood that in the PSO algorithm, each particle represents a possible solution, that is, a set of parameter values ​​(in this problem, it is the hydrogen production flow, ammonia yield, wind power consumed, and photovoltaic power consumed). At the beginning of the algorithm, it is necessary to randomly generate the initial values ​​of these parameters for each particle and set their initial change in the next iteration (that is, speed). Calculate the fitness, that is, profit calculation: The fitness of each particle is determined by applying the solution it represents to the hydrogen production and ammonia synthesis system and calculating the resulting profit. The profit calculation may involve multiple factors, such as production costs, product prices, energy efficiency, and the utilization of renewable energy.

[0145] Furthermore, the particle state, speed, position and inertia weight can be updated: According to the principle of the PSO algorithm, each particle will update its speed and position according to its own historical best position and the global best position in the group. At the same time, the particle's inertia weight will also be updated to adjust its balance between exploration (searching new areas) and utilization (searching near known optimal areas). After updating the position and speed of the particle, it is necessary to check whether these new values ​​meet the constraints of the problem. In this problem, these constraints may include the maximum and minimum values ​​of hydrogen production flow, restrictions on ammonia yield, and the utilization rate of renewable energy. If the new values ​​do not meet the constraints, appropriate adjustments need to be made to ensure that they remain within a valid range.

[0146] It should be noted that the above steps (from calculating fitness to boundary processing) will be repeated in each round of iteration of the PSO algorithm until the predetermined number of iterations is reached or other stopping conditions are met (such as the fitness reaches a stable state). During the iteration process, the particle swarm will gradually converge to the optimal solution or approximate optimal solution to the problem. This process uses the optimization capability of the PSO algorithm to find the optimal solution to maximize the profit of the hydrogen production and ammonia synthesis system while taking into account the supply constraints of renewable energy. By continuously iterating and adjusting the position and speed of the particles, the algorithm is able to explore the solution space of the problem and find the optimal or approximate optimal hydrogen production and ammonia yield parameters that meet all constraints.

[0147] Step 104: Based on the hydrogen production flow rate and ammonia yield, the hydrogen production power and the ammonia synthesis power are calculated respectively.

[0148] Specifically, the hydrogen production power can be determined based on the base power of the hydrogen production equipment, the hydrogen production power consumption constant coefficient and the hydrogen production flow rate, and the synthetic ammonia power can be calculated based on the base power of the synthetic ammonia equipment, the synthetic ammonia power consumption constant coefficient and the ammonia yield.

[0149] Specifically, the hydrogen production flow rate Q h2_in (t) and ammonia yield F nh3 (t) can be used to calculate the hydrogen production power P el (t) and synthetic ammonia power P nh3 (t):

[0150] P el (t) = P el_0 +k h2 *Q h2_in (t) (39)

[0151] P nh3 (t) = P nh3_0 +k nh3 *F nh3 (t) (40)

[0152] Among them, P el_0 is the base power of the hydrogen production equipment; k h2 is the constant power consumption coefficient for hydrogen production; P nh3_0 is the base power of the synthetic ammonia equipment; k nh3 is the constant coefficient of power consumption for synthetic ammonia.

[0153] Step 105: Calculate the number of electrolyzers to be started based on the hydrogen production power, and calculate the amount of abandoned electricity based on the predicted wind power output and photovoltaic power output, the absorbed wind power and the absorbed photovoltaic power.

[0154] Specifically, the number of electrolyzers to be started can be calculated based on the hydrogen production power and the rated power of a single electrolyzer, the predicted wind power output and the predicted photovoltaic power output can be added together, and then the absorbed wind power and the absorbed photovoltaic power can be subtracted to obtain the amount of abandoned power.

[0155] Among them, the number of electrolyzers started n(t) is determined by the hydrogen production power P el (t) obtains:

[0156] n(t)=[P el (t) / P elN_one ] (41)

[0157] Among them, P elN_one It is the rated power of a single electrolytic cell. The brackets represent rounding up.

[0158] Abandoned power P loss (t) The difference between the predicted wind and solar power output and the actual wind and solar power consumed is:

[0159] P loss (t) = P wt_pre (t)+P pv_pre (t)-P wt (t)-P pv (t) 42)

[0160] Step 106: Calculate the energy storage output and energy storage capacity based on the upper and lower limits of the energy storage capacity, the energy storage charging and discharging efficiency, the energy storage initial capacity and the formulated energy storage operation strategy.

[0161] Specifically, the energy storage output and capacity can be calculated based on the formulated energy storage operation strategy.

[0162] Among them, the energy storage operation strategy formulated is as follows: Figure 3 shown.

[0163] First enter the upper and lower limits of energy storage capacity SOC max , SOC min Energy storage charging and discharging efficiency η, energy storage initial capacity SOC sum (0). Calculate the load P load (t) and new energy output P new (t), the difference between the two is the energy storage deficit d bat (t).

[0164] Then, when the load P load (t) greater than the new energy output P new (t), the energy storage is discharged; when the load P load (t) is less than the new energy output P new At (t), the energy storage is charged.

[0165] After that, the energy storage capacity SOC needs to be considered when charging and discharging energy storage sum (t).

[0166] When the energy storage is discharged, if the current energy storage capacity SOC sum (t) Release the energy storage deficit d bat (t) is still higher than the lower limit of energy storage capacity SOC min , then the energy storage output P bat (t) is d bat (t), otherwise it means that the energy storage capacity is not enough to discharge so much electricity, and the energy can only be discharged to the lower limit. The energy storage output P bat (t) is SOC sum (t)-SOC min

[0167] When charging the energy storage, if the current energy storage capacity SOC sum (t) Charge up the energy storage deficit d bat (t) is still lower than the upper limit of energy storage capacity SOC max , then the energy storage output P bat (t) is d bat (t), otherwise it means that the energy storage cannot store so much electricity and can only charge to the upper limit. The energy storage output P bat (t) is SOC sum (t)-SOC max ,

[0168] Finally, according to the energy storage output P bat (t) and the energy storage capacity SOC at the current moment sum (t), the energy storage capacity SOC at the next moment can be calculated sum (t+1).

[0169] SOC sum (t+1) = SOC sum (t)+η*P bat (t) (43)

[0170] Step 107: Calculate the fitness of each particle and compare it with the local optimal solution and the global optimal solution, and update the local optimal solution and the global optimal solution according to the comparison results.

[0171] Step 108: Output the optimal solution set after reaching the maximum number of iterations.

[0172] Specifically, the operation of the entire system can be simulated based on the solution of the current particle (i.e., the operating parameters in each time period), including the calculation of the amount of hydrogen produced by water electrolysis, the amount of ammonia synthesized by hydrogen, the energy storage charging and discharging conditions, and the amount of power wasted in each time period.

[0173] Based on the system simulation results, the total cost of the system (including water electrolysis cost, equipment maintenance cost, energy storage system cost, etc.) and total revenue (sales revenue of hydrogen and ammonia, possible energy storage subsidies, etc.) are calculated. The profit of the system (total revenue minus total cost) is used as the fitness of the particle.

[0174] Update the local optimal solution and the global optimal solution. Local optimal solution: compare the fitness of the current particle with its historical best fitness. If it is better, update the local optimal solution of the particle. Global optimal solution: find the solution with the highest fitness among the local optimal solutions of all particles as the global optimal solution.

[0175] If the maximum number of iterations is reached, the iteration is stopped and the optimal solution set is output. The optimal solution set should include the number of electrolyzers started in each period, the power of hydrogen production from water electrolysis, the power of hydrogen synthesis into ammonia, the energy storage power, the energy storage capacity, the amount of power abandoned, and the cost, income and profit of the system. If the maximum number of iterations is not reached, the particle speed and position are updated continuously for the next round of iterations.

[0176] In each time period, the charging and discharging power of the energy storage system is calculated based on the energy demand of the system and the supply of renewable energy. This needs to take into account the maximum charging and discharging power limit of the energy storage system and the current energy storage capacity status. At the end of each time period, the capacity of the energy storage system is updated, taking into account the charging and discharging situation of that time period. At the same time, it is necessary to ensure that the energy storage capacity does not exceed its maximum capacity limit.

[0177] During the optimization process, it is necessary to ensure that all operating parameters meet the system constraints, such as the maximum power of the electrolyzer, the maximum charge and discharge power and capacity of the energy storage system, the production rate limit of hydrogen and ammonia, etc. Since the output of renewable energy is uncertain, this uncertainty needs to be considered when formulating the energy storage operation plan, and dynamic adjustments may be required during operation.

[0178] It should be noted that the fitness of each particle can be calculated again, compared with the local optimal solution and the global optimal solution, and then updated. The local optimal solution is the optimal solution found in this iteration, and the global optimal solution is the optimal solution found in all current iterations. After reaching the maximum number of iterations, the optimal solution set is output, including the number of electrolyzers started in each period, the power of hydrogen production by water electrolysis, the power of hydrogen synthesis ammonia, the hydrogen production flow, the ammonia yield, the energy storage power, the energy storage capacity, the amount of power abandoned, and the cost, income and profit of the system.

[0179] Figure 2 This is the flow chart of the particle swarm algorithm for the off-grid wind-solar hydrogen production and ammonia synthesis system.

[0180] In the disclosed embodiment, a system model of an off-grid wind-solar-hydrogen-ammonia system is first established, and the wind-solar-hydrogen-ammonia system satisfies the pre-constructed objective function and objective constraints. Then, the predicted wind power output and the predicted photovoltaic output are obtained. Then, the number of iterations, the number of particles, and the optimization variables contained in each particle are set to generate the hydrogen production flow, the ammonia yield, the absorbed wind power, and the absorbed photovoltaic power. The fitness of each particle is calculated. Then, based on the hydrogen production flow and the ammonia yield, the hydrogen production power and the synthetic ammonia power are calculated respectively. Then, based on the hydrogen production power, the number of electrolyzer starts is calculated, and based on the wind power predicted output and the photovoltaic predicted output, the absorbed wind power and the absorbed photovoltaic power, the amount of abandoned electricity is calculated. Then, based on the upper and lower limits of the energy storage capacity, the energy storage charging and discharging efficiency, the initial capacity of the energy storage, and the formulated energy storage operation strategy, the energy storage output and the energy storage capacity are calculated. Then, the fitness of each particle is calculated and compared with the local optimal solution and the global optimal solution. The local optimal solution and the global optimal solution are updated according to the comparison results. Then, after reaching the maximum number of iterations, the optimal solution set is output. The particle swarm algorithm can quickly find the optimal solution, has good global search capabilities, can find the optimal solution in the entire search space, and is not easy to fall into the local optimum. It shows good robustness for many types of optimization problems, and can find acceptable solutions even when the parameter settings are not appropriate. By establishing photovoltaic power station models, wind power station models, water electrolysis hydrogen production models, hydrogen synthesis ammonia section models and energy storage device models, the process of wind and solar hydrogen production and ammonia synthesis under off-grid operation is simulated. Based on typical daily wind and solar data, unit power consumption of hydrogen production and ammonia synthesis processes, related economic data, etc., under the conditions of meeting the real-time power balance of the hydrogen and ammonia production system, the abandonment rate of new energy, the characteristics of the electrolyzer, and the upper and lower limits of energy storage, the particle swarm algorithm is used to optimize the day-ahead scheduling of the wind and solar hydrogen and ammonia production system with the objective function of maximizing engineering profits, and the number of electrolyzer startups, energy storage output, water electrolysis hydrogen production power, hydrogen synthesis ammonia power and related economic data of the electric hydrogen and ammonia system in each period are obtained.

[0181] The operation control model of the off-grid wind-solar hydrogen production and ammonia synthesis system established by the present invention mainly includes a photovoltaic power station model, a wind power station model, a water electrolysis hydrogen production model, a hydrogen synthesis ammonia section model and an energy storage device model. It can simulate the process of wind-solar hydrogen production and ammonia synthesis under off-grid operation. Based on the particle swarm algorithm, the power distribution of energy storage devices, electrolyzers and other equipment, as well as the number of electrolyzers started in each time period, the energy storage output, the water electrolysis hydrogen production power, the hydrogen synthesis ammonia power and related economic data can be obtained. It can meet the real-time power balance of the wind-solar hydrogen production and ammonia synthesis system and maximize the project profit. At the same time, it can improve the utilization and consumption of renewable energy, reduce the abandonment rate of new energy, and is conducive to the green and low-carbon transformation of chemical processes. The off-grid wind-solar hydrogen production and ammonia synthesis system invented in this article is based on the realization of green electricity to green hydrogen, and then prepares synthetic ammonia through green hydrogen. Through this method, not only can the efficient utilization of renewable energy be achieved, but also the development of the green chemical industry can be promoted, helping my country to achieve the "dual carbon goals". The present invention can optimize the operation of the off-grid wind-solar hydrogen production and ammonia synthesis system, improve the efficiency and stability of the system, and maximize the project profit. The system has a wide range of applications. In addition to being applicable to off-grid wind-solar hydrogen production and ammonia synthesis systems, it can also be applied to other similar off-grid energy systems, such as off-grid wind-solar hydrogen production systems, off-grid wind power generation systems, off-grid solar power generation systems, etc.

[0182] In order to implement the above-mentioned embodiments, the present disclosure also proposes an off-grid wind-solar hydrogen production and ammonia synthesis system operation control device.

[0183] Figure 4 This is a structural block diagram of the off-grid wind-solar hydrogen production and ammonia synthesis system operation control device provided in the second embodiment of the present disclosure.

[0184] like Figure 4 As shown, the off-grid wind-solar hydrogen production and ammonia synthesis system operation control device 400 may include:

[0185] A construction module 410 is used to establish a system model of an off-grid wind-solar hydrogen-ammonia system, wherein the wind-solar hydrogen-ammonia system satisfies a pre-constructed objective function and objective constraints;

[0186] An acquisition module 420 is used to acquire predicted wind power output and predicted photovoltaic power output;

[0187] The first calculation module 430 is used to set the number of iterations, the number of particles and the optimization variables contained in each particle, generate the hydrogen production flow rate, the ammonia yield, the wind power consumed and the photovoltaic power consumed, and calculate the fitness of each particle;

[0188] A second calculation module 440 is used to calculate the hydrogen production power and the ammonia synthesis power respectively based on the hydrogen production flow rate and the ammonia yield;

[0189] A third calculation module 450 is used to calculate the number of electrolyzer startups based on the hydrogen production power, and to calculate the amount of abandoned electricity based on the predicted wind power output and the predicted photovoltaic power output, the absorbed wind power and the absorbed photovoltaic power;

[0190] A fourth calculation module 460, for calculating the energy storage output and energy storage capacity based on the upper and lower limits of the energy storage capacity, the energy storage charging and discharging efficiency, the energy storage initial capacity and the formulated energy storage operation strategy;

[0191] An updating module 470 is used to calculate the fitness of each particle, compare it with the local optimal solution and the global optimal solution, and update the local optimal solution and the global optimal solution according to the comparison result;

[0192] The output module 480 is used to output the optimal solution set after reaching the maximum number of iterations.

[0193] Optionally, the building module is specifically used for:

[0194] The photovoltaic power station model, wind power station model, water electrolysis hydrogen production model, hydrogen synthesis ammonia model and energy storage device model are established respectively.

[0195] The objective function includes a plurality of cost functions, and the plurality of cost functions include at least: process electricity cost, operation and maintenance cost, electrolytic cell water consumption cost, staff wages and benefits, and equipment depreciation cost;

[0196] The target constraints include at least: system power balance constraints, green electricity consumption constraints and new energy power abandonment rate constraints.

[0197] Optionally, the second computing module is further used to:

[0198] Updating the velocity, position and inertia weight of each of the particles;

[0199] The boundary of the particle is processed so that the optimization variable is within a specified constraint range.

[0200] Optionally, the second computing module is specifically configured to:

[0201] Determining the hydrogen production power based on the base power of the hydrogen production equipment, the hydrogen production power consumption constant coefficient and the hydrogen production flow rate;

[0202] The synthetic ammonia power is calculated based on the base power of the synthetic ammonia equipment, the synthetic ammonia power consumption constant coefficient and the ammonia yield.

[0203] Optionally, the third calculation module is specifically used to:

[0204] Calculating the number of electrolyzers to be started based on the hydrogen production power and the rated power of a single electrolyzer;

[0205] The predicted wind power output and the predicted photovoltaic power output are added together, and then the absorbed wind power and the absorbed photovoltaic power are subtracted to obtain the amount of abandoned power.

[0206] In the disclosed embodiment, a system model of an off-grid wind-solar-hydrogen-ammonia system is first established, and the wind-solar-hydrogen-ammonia system satisfies the pre-constructed objective function and objective constraints. Then, the predicted wind power output and the predicted photovoltaic output are obtained. Then, the number of iterations, the number of particles, and the optimization variables contained in each particle are set to generate the hydrogen production flow, the ammonia yield, the absorbed wind power, and the absorbed photovoltaic power. The fitness of each particle is calculated. Then, based on the hydrogen production flow and the ammonia yield, the hydrogen production power and the synthetic ammonia power are calculated respectively. Then, based on the hydrogen production power, the number of electrolyzer starts is calculated, and based on the wind power predicted output and the photovoltaic predicted output, the absorbed wind power and the absorbed photovoltaic power, the amount of abandoned electricity is calculated. Then, based on the upper and lower limits of the energy storage capacity, the energy storage charging and discharging efficiency, the initial capacity of the energy storage, and the formulated energy storage operation strategy, the energy storage output and the energy storage capacity are calculated. Then, the fitness of each particle is calculated and compared with the local optimal solution and the global optimal solution. The local optimal solution and the global optimal solution are updated according to the comparison results. Then, after reaching the maximum number of iterations, the optimal solution set is output. The particle swarm algorithm can quickly find the optimal solution, has good global search capabilities, can find the optimal solution in the entire search space, and is not easy to fall into the local optimum. It shows good robustness for many types of optimization problems, and can find acceptable solutions even when the parameter settings are not appropriate. By establishing photovoltaic power station models, wind power station models, water electrolysis hydrogen production models, hydrogen synthesis ammonia section models and energy storage device models, the process of wind and solar hydrogen production and ammonia synthesis under off-grid operation is simulated. Based on typical daily wind and solar data, unit power consumption of hydrogen production and ammonia synthesis processes, related economic data, etc., under the conditions of meeting the real-time power balance of the hydrogen and ammonia production system, the abandonment rate of new energy, the characteristics of the electrolyzer, and the upper and lower limits of energy storage, the particle swarm algorithm is used to optimize the day-ahead scheduling of the wind and solar hydrogen and ammonia production system with the objective function of maximizing engineering profits, and the number of electrolyzer startups, energy storage output, water electrolysis hydrogen production power, hydrogen synthesis ammonia power and related economic data of the electric hydrogen and ammonia system in each period are obtained.

[0207] In order to implement the above embodiments, the present disclosure also proposes a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the operation control method of the off-grid wind-solar hydrogen production and ammonia synthesis system proposed in the above embodiments of the present disclosure is implemented.

[0208] In order to implement the above embodiments, the present disclosure also proposes a non-temporary computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the operation control method of the off-grid wind-solar hydrogen production and ammonia synthesis system proposed in the above embodiments of the present disclosure.

[0209] In order to implement the above embodiments, the present disclosure also proposes a computer program product. When the instruction processor in the computer program product is executed, the operation control method of the off-grid wind-solar hydrogen production and ammonia synthesis system proposed in the above embodiments of the present disclosure is executed.

[0210] Figure 5 A block diagram of an exemplary computer device suitable for implementing embodiments of the present disclosure is shown. Figure 5 The computer device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0211] like Figure 5 As shown, the computer device 12 is in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0212] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnection (PCI) bus.

[0213] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0214] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 5 not shown, usually called a "hard drive"). Although Figure 5 Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read only memory (CD-ROM), a digital versatile disc read only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the various embodiments of the present disclosure.

[0215] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described in the present disclosure.

[0216] The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. In addition, the computer device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the computer device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0217] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the methods mentioned in the above embodiments.

[0218] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0219] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0220] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present disclosure belong.

[0221] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0222] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0223] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0224] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0225] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present disclosure. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present disclosure.

Claims

1. An off-grid wind-solar hydrogen production and ammonia synthesis system operation control method, characterized in that: include: Establishing a system model of an off-grid wind-solar-hydrogen-ammonia system, wherein the wind-solar-hydrogen-ammonia system satisfies a pre-established objective function and objective constraints; Obtain wind power forecast output and photovoltaic forecast output; Set the number of iterations, the number of particles, and the optimization variables contained in each particle, generate hydrogen production flow, ammonia yield, absorbed wind power and absorbed photovoltaic power, and calculate the fitness of each particle; Based on the hydrogen production flow rate and ammonia yield, respectively calculating the hydrogen production power and the ammonia synthesis power; The number of electrolyzers started is calculated based on the hydrogen production power, and the amount of abandoned electricity is calculated based on the predicted wind power output and the predicted photovoltaic power output, the absorbed wind power and the absorbed photovoltaic power; Calculate the energy storage output and capacity based on the upper and lower limits of energy storage capacity, energy storage charging and discharging efficiency, energy storage initial capacity and the formulated energy storage operation strategy; Calculate the fitness of each particle and compare it with the local optimal solution and the global optimal solution, and update the local optimal solution and the global optimal solution according to the comparison results; After reaching the maximum number of iterations, the optimal solution set is output.

2. The method according to claim 1, characterized in that The system model for establishing an off-grid wind-solar hydrogen-ammonia system includes: The photovoltaic power station model, wind power station model, water electrolysis hydrogen production model, hydrogen synthesis ammonia model and energy storage device model are established respectively. The objective function includes a plurality of cost functions, and the plurality of cost functions include at least: process electricity cost, operation and maintenance cost, electrolytic cell water consumption cost, staff wages and benefits, and equipment depreciation cost; The target constraints include at least: system power balance constraints, green electricity consumption constraints and new energy power abandonment rate constraints.

3. The method according to claim 1, characterized in that Before respectively calculating the hydrogen production power and the synthetic ammonia power based on the hydrogen production flow rate and the ammonia yield, the method further includes: Updating the velocity, position and inertia weight of each of the particles; The boundary of the particle is processed so that the optimization variable is within a specified constraint range.

4. The method according to claim 1, characterized in that: Before respectively calculating the hydrogen production power and the synthetic ammonia power based on the hydrogen production flow rate and the ammonia yield, the method includes: Determining the hydrogen production power based on the base power of the hydrogen production equipment, the hydrogen production power consumption constant coefficient and the hydrogen production flow rate; The synthetic ammonia power is calculated based on the base power of the synthetic ammonia equipment, the synthetic ammonia power consumption constant coefficient and the ammonia yield.

5. The method according to claim 1, characterized in that: The calculating of the number of electrolyzer startups based on the hydrogen production power, and the calculating of the amount of abandoned electricity based on the predicted wind power output and the predicted photovoltaic power output, the absorbed wind power and the absorbed photovoltaic power, include: Calculating the number of electrolyzers to be started based on the hydrogen production power and the rated power of a single electrolyzer; The predicted wind power output and the predicted photovoltaic power output are added together, and then the absorbed wind power and the absorbed photovoltaic power are subtracted to obtain the amount of abandoned power.

6. An off-grid wind-solar hydrogen production and ammonia synthesis system operation control device, characterized in that: include: A construction module is used to establish a system model of an off-grid wind-solar-hydrogen-ammonia system, wherein the wind-solar-hydrogen-ammonia system satisfies a pre-constructed objective function and objective constraints; An acquisition module is used to obtain predicted wind power output and predicted photovoltaic power output; The first calculation module is used to set the number of iterations, the number of particles and the optimization variables contained in each particle, generate the hydrogen production flow rate, ammonia yield, absorbed wind power and absorbed photovoltaic power, and calculate the fitness of each particle; A second calculation module is used to calculate the hydrogen production power and the synthetic ammonia power respectively based on the hydrogen production flow rate and the ammonia yield; A third calculation module is used to calculate the number of electrolyzer startups based on the hydrogen production power, and to calculate the amount of abandoned electricity based on the predicted wind power output and photovoltaic power output, the absorbed wind power and the absorbed photovoltaic power; A fourth calculation module is used to calculate the energy storage output and energy storage capacity based on the upper and lower limits of energy storage capacity, energy storage charging and discharging efficiency, energy storage initial capacity and the formulated energy storage operation strategy; The update module is used to calculate the fitness of each particle and compare it with the local optimal solution and the global optimal solution, and update the local optimal solution and the global optimal solution according to the comparison results; The output module is used to output the optimal solution set after reaching the maximum number of iterations.

7. The device according to claim 6, characterized in that The building blocks are specifically used for: The photovoltaic power station model, wind power station model, water electrolysis hydrogen production model, hydrogen synthesis ammonia model and energy storage device model are established respectively. The objective function includes a plurality of cost functions, and the plurality of cost functions include at least: process electricity cost, operation and maintenance cost, electrolytic cell water consumption cost, staff wages and benefits, and equipment depreciation cost; The target constraints include at least: system power balance constraints, green electricity consumption constraints and new energy power abandonment rate constraints.

8. The device according to claim 6, characterized in that The second computing module is further used for: Updating the velocity, position and inertia weight of each of the particles; The boundary of the particle is processed so that the optimization variable is within a specified constraint range.

9. The device according to claim 6, characterized in that The second calculation module is specifically used for: Determining the hydrogen production power based on the base power of the hydrogen production equipment, the hydrogen production power consumption constant coefficient and the hydrogen production flow rate; The synthetic ammonia power is calculated based on the base power of the synthetic ammonia equipment, the synthetic ammonia power consumption constant coefficient and the ammonia yield.

10. The device according to claim 6, characterized in that The third calculation module is specifically used for: Calculating the number of electrolyzers to be started based on the hydrogen production power and the rated power of a single electrolyzer; The predicted wind power output and the predicted photovoltaic power output are added together, and then the absorbed wind power and the absorbed photovoltaic power are subtracted to obtain the amount of abandoned power.

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