Adaptive hydrogen load fluctuation new energy hydrogen production system coordinated control method and device, electronic equipment and storage medium

Abstract

The present application relates to a new energy hydrogen production system coordinated control method and device, electronic equipment and storage medium that adaptively hydrogen load fluctuation, comprising: determining the structure of the new energy hydrogen production system, establishing the equivalent model of the vanadium flow battery, electrolytic cell, hydrogen storage tank and super capacitor;Construct a difference form of state equation, convert the equation into a matrix form, and obtain a rolling optimization model through a state space expression;Construct the objective function, set the constraint condition, and establish the mathematical model of hydrogen energy system operation optimization;Initialize the system, and minimize the objective function based on the rolling optimization control, and propose a system coordinated control method;Verify the analysis example, analyze the system control performance and system robustness by changing the parameters, and obtain the new energy hydrogen production system coordinated control method with the best control performance for coordinated control. The method provided by the present application can adapt to hydrogen load fluctuation, and solve the optimal control method of the new energy hydrogen production system by using rolling optimization control.

Description

Technical Field

[0001] This invention relates to the field of coordinated control of new energy hydrogen production systems, specifically to a coordinated control method, apparatus, electronic equipment, and storage medium for new energy hydrogen production systems that adapt to hydrogen load fluctuations. Background Technology

[0002] The new energy hydrogen production system produces hydrogen through new energy power generation in order to improve the utilization rate of new energy sources and meet the hydrogen load requirements of the system design and the grid load requirements.

[0003] For renewable energy hydrogen production systems, firstly, the electrolyzer control should reliably handle fluctuations in renewable energy output and track the renewable energy's maximum power operating point. Secondly, power coordination issues need to be considered to improve the reliability of the power system. In addition, factors such as the efficiency of hydrogen electrolysis, techno-economic issues, and fluctuations in hydrogen load must also be considered.

[0004] With the rapid growth in hydrogen energy demand, the structure and application of energy systems will undergo significant changes. The uncertainty brought about by hydrogen load fluctuations has a significant impact on the safe and economical operation of energy systems. Unlike the global response of active power frequency regulation in power systems, the uncertainty of hydrogen load fluctuations will cause substantial changes in the operating point of electrolyzers. The deterioration of local electrolyzer operating conditions will lead to overload and shutdown of electrolyzers, affecting their service life, endangering the safety of gas storage tanks and the reliability of hydrogen supply. Furthermore, the power surge caused by large-scale electrolyzer start-ups and shutdowns also threatens the safety and stability of the power system. In recent years, many scholars have conducted research on power coordination control and economic dispatch, but the optimized control of new energy hydrogen production systems adapted to hydrogen load fluctuations urgently needs to be addressed. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a coordinated control method, device, electronic equipment, and storage medium for a new energy hydrogen production system that adapts to hydrogen load fluctuations. Applicable to scenarios with fluctuating hydrogen loads, it utilizes the rolling optimization control principle, treating hydrogen load flow rate, actual maximum output of the new energy source, and the start-up and shutdown status of the electrolyzer as system uncertainties. Through the rolling optimization method, it achieves real-time tracking of the vanadium redox flow battery's state of charge (SOC), hydrogen storage tank pressure, grid load, and supercapacitor power. This invention, through the rolling optimization method, meets hydrogen load demands with optimal operating conditions and minimal power adjustments, optimizing the operating states of the battery and electrolyzer, and reducing electrolyzer downtime.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A coordinated control method for new energy hydrogen production systems that adapts to hydrogen load fluctuations includes:

[0008] Step S1: Determine the structure of the new energy hydrogen production system, and establish the equipment characteristic equations of the new energy hydrogen production system, including vanadium redox flow battery, electrolyzer, hydrogen storage tank and supercapacitor, based on the equipment operating characteristics;

[0009] Step S2: Construct a differential form of the state equation through the dynamic model of the hydrogen energy system, establish a model of the new energy hydrogen production system, transform the equation into matrix form, and directly obtain the rolling optimization model through the state space expression;

[0010] Step S3: Determine the control objectives of the hydrogen energy system, determine the parameter values ​​and reference trajectory, construct the objective function, and establish a mathematical model for optimizing the operation of the hydrogen energy system;

[0011] Step S4: Initialize the reference trajectory and system variables, adjust the working state of each electrolyzer in real time based on the rolling optimization method, and use constant power control to adjust the supercapacitor to meet the energy supply requirements for medium- and long-term and short-term energy storage. A coordinated control method for the new energy hydrogen production system of the electrolyzer is proposed.

[0012] Step S5: Verify and analyze the case study by changing parameters such as the weighting factor of the output variable, the weighting factor of the change increment of the control variable, and the reference time domain. Analyze the changes in the system control performance and the robustness of the system to obtain the optimal control performance of the new energy hydrogen production system, thereby enabling coordinated control of the new energy hydrogen production system.

[0013] Further, step S1 specifically includes:

[0014] Step S11: Determine the structure of the new energy hydrogen production system, which includes a new energy power source, an external power grid, a vanadium redox flow battery, an electrolyzer, a hydrogen storage tank, a supercapacitor, and a hydrogen load;

[0015] Step S12: Establish a dynamic model of the new energy hydrogen production system, wherein the dynamic equivalent model of the vanadium redox flow battery is shown in formula (1):

[0016]

[0017] Among them, S cell P represents the charge of a vanadium redox flow battery. cell Q represents the discharge power of a vanadium redox flow battery. cell σ represents the energy storage capacity of the vanadium redox flow battery, and σ is the self-degradation time coefficient related to the charge of the vanadium redox flow battery.

[0018] The electrolytic cell model is established as shown in formula (2):

[0019]

[0020] in, P is the hydrogen production rate. elecA1 and A2 are linear approximation coefficients, where A1 is the input power of the electrolytic cell.

[0021] The hydrogen storage tank model is established as shown in formula (3):

[0022]

[0023] in, This represents the rate of change of pressure in the hydrogen storage tank. R is the hydrogen production rate, T is the ideal gas constant, and V is the ideal gas temperature.

[0024] The supercapacitor model is established as shown in formula (4):

[0025]

[0026] Where E is the energy absorbed / released by the supercapacitor, A and B are the number of supercapacitors connected in series and parallel, and C is the energy released by the supercapacitor. f U1 represents the capacitance of a single supercapacitor, and U2 represents the initial voltage and the on-state voltage of the supercapacitor.

[0027] Further, step S2 specifically includes:

[0028] Step S21: Construct the difference form of the state equation based on the dynamic model of the hydrogen energy system;

[0029] Based on formula (1), the state difference equation for the state of charge (SOC) of the vanadium redox flow battery is established, as shown in formula (5):

[0030]

[0031] Where Δτ is the time step;

[0032] Based on formulas (2) to (3), the state difference equation for the gas pressure of the hydrogen storage tank is established, as shown in formula (6):

[0033]

[0034] in, For hydrogen load flow rate, and The hydrogen change rate is determined by the hydrogen production power and operating status of each electrolyzer.

[0035] and The expression is represented by a functional, as shown in formulas (7) to (8):

[0036]

[0037]

[0038] Among them, f P (d elec f is a function of the hydrogen production power of the electrolyzer. d (d elec P is a function of the electrolytic cell's operating state. elec Let P be the hydrogen production power of each electrolyzer, δ be the Dirac function, and P be the hydrogen production power of each electrolyzer. heat For cold standby heating power, P sp For hot standby activation power; d elec For the operating status of each electrolytic cell, d elec.i ∈{l, s, w} represent normal operation, cold standby, and hot standby, respectively;

[0039] Considering the system power balance, the power output expression of the power grid is shown in formula (9):

[0040] P grid (t)=P quit (t-1)+||P elec (t-1)||1-P cell (t-1)-P renw (t-1) (9)

[0041] Among them, P quit For the abandoned power of new energy sources, P renw Power output for new energy sources;

[0042] Step S22: Divide the system variables into state variables x, output variables y, control variables u, and disturbance variables p. The state variable x is represented as shown in formula (10):

[0043]

[0044] Among them, S cell For vanadium redox flow battery SOC, p tank P is the gas pressure in the hydrogen storage tank. grid For grid load;

[0045] A series of transformations are made to the control variable u and the disturbance variable p, as shown in formulas (11) to (12):

[0046]

[0047]

[0048] Among them, P sc U is the power of the supercapacitor. dsc This is the terminal voltage of the supercapacitor;

[0049] This leads to the following state equation:

[0050]

[0051] The system state variables can be directly output, therefore the system output equation is shown in equation (14):

[0052]

[0053] Step S23: Transform the system state equation and output equation into matrix form, as shown in formula (15):

[0054]

[0055] Where A is the system state coefficient matrix, B is the control coefficient matrix, C is the output coefficient matrix, and D is the disturbance coefficient matrix. The specific representations of each matrix are shown in formulas (16) to (19):

[0056]

[0057]

[0058]

[0059]

[0060] Step S24: The rolling optimization model is directly obtained from the state-space expression, as shown in formula (20):

[0061]

[0062] in, The output variable value at time t+k is predicted based on the available information at time t; u(t+k-1|t) represents the control command acquired at time t; and p(t+k-1|t) represents the disturbance signal acquired at time t.

[0063] Furthermore, the specific steps of step S3 are as follows:

[0064] Step S31: Determine the control objective of the hydrogen energy system to ensure the reliability of energy supply on medium- and long-term time scales, while ensuring that the vanadium redox flow battery SOC, hydrogen storage tank pressure, grid output, and supercapacitor power should track the scheduling plan, minimize the power adjustment of primary equipment, and improve energy conversion efficiency; construct the objective function as shown in formula (21):

[0065]

[0066] Where N1 is the upper boundary of the prediction domain evaluation, N2 is the lower boundary of the prediction domain evaluation, and N... uTo evaluate the boundary of the control domain, t and j are different times, δ(j) is the weight of the output variable at time j, λ(j) is the weight of the control variable at time j, and Q... y To output the variable weight coefficient matrix, Q u For the control variable weight coefficient matrix, The expected output trajectory of the system within a specific future time range is w(t+k|t), which is the reference trajectory, and Δu is the change value of the control variable.

[0067] Step S32: Determine the parameter values ​​of the upper and lower boundaries N1 and N2 of the prediction domain evaluation to ensure that the reference trajectory w(t+j) is consistent with the dimension of the output variable;

[0068] Step S33: Set the constraints for each element of the control variable, as shown in formula (22):

[0069]

[0070] Among them, P cell.charge and P cell.discharge These represent the maximum charging and discharging power of the vanadium redox flow battery, and the amount of abandoned renewable energy (P). quit (t) should be lower than the maximum theoretical output P of the new energy source. renw (t), P elec.min and P elec.max P represents the minimum and maximum hydrogen production power of the electrolyzer. sc.c，max and P sc.d，max The maximum charging and discharging power of the supercapacitor;

[0071] Set the rate of change for each element of the control variable as shown in formula (23):

[0072]

[0073] in, and These represent the response speed for adjusting the output power of the vanadium redox flow battery downwards and upwards, respectively. and The response speed to adjustments in power output for new energy sources is limited by the MPPT strategy for new energy sources. and The response speed for adjusting the hydrogen production power of the electrolyzer; and The response speed for adjusting the output of supercapacitors downwards and upwards;

[0074] Expand the constraint conditions for the rate of change of the system output variable, as shown in formula (24):

[0075]

[0076] Among them, Scell.min and S cell.max p represents the minimum and maximum state of charge values ​​of the vanadium redox flow cell, respectively. tank.min and p tank.max P represents the minimum and maximum pressure values ​​of the hydrogen storage tank, respectively. grid.min and P grid.max These represent the minimum and maximum power values ​​for purchasing electricity from the power grid, respectively.

[0077] Furthermore, step S4 specifically includes:

[0078] Step S41: Adjust the electrolytic cell in real time based on rolling optimization control. Input the future reference trajectory w(t+j), control variables and current disturbance information. By repeatedly solving the quadratic programming problem, obtain the control signal u(t+1|t+1) for the next moment.

[0079] Step S42: Calculate the power of the electrolytic cell by changing the disturbance variable p of the working state of the electrolytic cell until all electrolytic cells are in normal power state or all normally working electrolytic cells are in hot standby state at the current control moment, and stop iterative solution to meet the energy supply requirements of medium and long time energy storage.

[0080] Step S43: Adjust the working state of the supercapacitor based on constant power control, obtain the current deviation value through the reference power, and use a PI controller to realize the rapid charging and discharging of the supercapacitor to meet the energy supply needs of short-term energy storage.

[0081] Furthermore, step S5 specifically includes:

[0082] Step S51: Set up a specific new energy hydrogen production system and analyze the simulation results;

[0083] Step S52: Change the weighting factor Q of the output variable y1 Q y2 Q y3 Control variable increment weighting factor Q u1 Q u2 Q u3 Using reference time domains N1 and N2, analyze the changes in system control performance;

[0084] Step S53: Change the standard deviation σ of the day-ahead forecast error for renewable energy power. wind Standard deviation of day-ahead forecast error for hydrogen load power The robustness of the system is analyzed to obtain a coordinated control method for a new energy hydrogen production system with optimal control performance.

[0085] The present invention also proposes an apparatus for realizing a coordinated control method for a new energy hydrogen production system, which is divided into a hydrogen energy system framework module, a system rolling optimization module, a system operation planning module, an energy demand calculation module, and a verification and analysis module.

[0086] The hydrogen energy system framework module includes: determining the structure of the new energy hydrogen production system, and establishing the equipment characteristic equations of the new energy hydrogen production system, including vanadium redox flow batteries, electrolyzers, hydrogen storage tanks, and supercapacitors, based on the equipment operating characteristics;

[0087] System rolling optimization module: Constructs a differential form of state equations through the dynamic model of the hydrogen energy system, establishes a model of the new energy hydrogen production system, transforms the equations into matrix form, and obtains the rolling optimization model through state space expression;

[0088] System operation planning module: Determine the control objectives of the hydrogen energy system, determine parameter values ​​and reference trajectories, construct the objective function, and establish a mathematical model for optimizing the operation of the hydrogen energy system;

[0089] Energy demand calculation module: Initialize reference trajectory and system variables, adjust the working status of each electrolyzer in real time based on rolling optimization method, and use constant power control to adjust supercapacitors to meet the energy demand for medium- and long-term and short-term energy storage. It also proposes a coordinated control method for new energy hydrogen production system with iterative control of electrolyzers.

[0090] Verification and Analysis Module: Verify and analyze case studies by changing parameters such as the weighting factor of the output variable, the weighting factor of the control variable change increment, and the reference time domain. Analyze the changes in the system's control performance and the system's robustness to obtain the optimal coordinated control method for the new energy hydrogen production system, thereby enabling coordinated control of the new energy hydrogen production system.

[0091] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the steps of the above-mentioned adaptive hydrogen load fluctuation new energy hydrogen production system coordinated control method.

[0092] The present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps of the above-mentioned adaptive hydrogen load fluctuation new energy hydrogen production system coordinated control method.

[0093] Compared with existing technologies, the invention has the following advantages:

[0094] 1. This invention discloses a coordinated control method for a new energy hydrogen production system that adapts to hydrogen load fluctuations. In order to improve the accuracy of output prediction for future moments, a method is proposed to establish a dynamic model of the hydrogen energy system, construct a differential state equation, and obtain a rolling optimization model using state-space expressions.

[0095] 2. This invention aims to ensure the reliability of energy supply on medium- and long-term time scales, while simultaneously ensuring that the SOC of vanadium redox flow batteries, the gas pressure of hydrogen storage tanks, the power output of the grid, and the power of supercapacitors should follow the scheduling plan as the control objective. It proposes a coordinated control method for new energy hydrogen production systems by solving a quadratic programming problem and minimizing the objective function based on rolling optimization control. The constraints include the gas pressure of the electrolyzer, the charge of the vanadium redox flow batteries, and the power output of the grid.

[0096] 3. This invention uses specific examples to verify the effectiveness of the control method, explores the impact of parameter changes on control performance by changing parameters such as the weighting factor of the output variable, the weighting factor of the change increment of the control variable, and the reference time domain, and analyzes the robustness of the control performance under the fluctuation of new energy and hydrogen load. Attached Figure Description

[0097] Figure 1 This is a flowchart of the adaptive hydrogen load fluctuation coordinated control method for new energy hydrogen production systems in an embodiment of the present invention;

[0098] Figure 2 This is a structural diagram of the new energy hydrogen production system in an embodiment of the present invention;

[0099] Figure 3 This is a flowchart of the rolling optimization control algorithm in an embodiment of the present invention;

[0100] Figure 4 This is a schematic diagram of the daily power curve of new energy sources in an embodiment of the present invention;

[0101] Figure 5 This is a schematic diagram of the daily power curve of hydrogen load in an embodiment of the present invention;

[0102] Figure 6 This is a schematic diagram of the state of charge change curve of the vanadium redox flow battery in an embodiment of the present invention;

[0103] Figure 7 This is a schematic diagram of the pressure change curve of the hydrogen storage tank in an embodiment of the present invention;

[0104] Figure 8 This is a schematic diagram of the power grid load change curve in an embodiment of the present invention. Detailed Implementation

[0105] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0106] This invention provides a coordinated control method for a new energy hydrogen production system that adapts to hydrogen load fluctuations. Under hydrogen load fluctuation scenarios, it utilizes rolling optimization control to meet the grid load and hydrogen load demands with optimal operating conditions and minimal power regulation.

[0107] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below through specific implementations and in conjunction with the accompanying drawings.

[0108] like Figure 1 As shown in the embodiment of the present invention, the adaptive hydrogen load fluctuation-based coordinated control method for new energy hydrogen production systems specifically includes the following steps:

[0109] Step S1: Determine the structure of the new energy hydrogen production system, and establish the equipment characteristic equations of the new energy hydrogen production system, including vanadium redox flow battery, electrolyzer, hydrogen storage tank and supercapacitor, based on the equipment operating characteristics;

[0110] Step S2: Construct a differential form of the state equation through the dynamic model of the hydrogen energy system, establish a model of the new energy hydrogen production system, transform the equation into matrix form, and directly obtain the rolling optimization model through the state space expression;

[0111] Step S3: Determine the control objectives of the hydrogen energy system, determine the parameter values ​​and reference trajectory, construct the objective function, and establish a mathematical model for optimizing the operation of the hydrogen energy system;

[0112] Step S4: Initialize the reference trajectory and system variables, adjust the working state of each electrolyzer in real time based on the rolling optimization method, and use constant power control to adjust the supercapacitor to meet the energy supply requirements for medium- and long-term and short-term energy storage. A coordinated control method for the new energy hydrogen production system of the electrolyzer is proposed.

[0113] Step S5: Verify and analyze the case study by changing parameters such as the weighting factor of the output variable, the weighting factor of the control variable change increment, and the reference time domain. Analyze the changes in system control performance and system robustness to obtain the optimal control performance of the new energy hydrogen production system coordinated control method. The device for the proposed new energy hydrogen production system coordinated control method is divided into a hydrogen energy system framework module, a system rolling optimization module, a system operation planning module, and an energy demand calculation module, and stored on a computer. Parameters of different variables are input to the corresponding modules through sensors to achieve the control objective.

[0114] In this embodiment, step S1 above: determining the structure of the new energy hydrogen production system, and establishing the equipment characteristic equations of the new energy hydrogen production system (vanadium redox flow battery, electrolyzer, hydrogen storage tank, and supercapacitor) based on the equipment operating characteristics, specifically includes:

[0115] Step S11: Determine the structure of the new energy hydrogen production system, including the new energy power source, external power grid, vanadium redox flow battery, electrolyzer, hydrogen storage tank, supercapacitor, and hydrogen load. During normal operation, the system utilizes new energy power generation to produce hydrogen. By adapting to fluctuations in new energy and hydrogen load, it meets the designed hydrogen load demand and grid load demand. Since there is no fuel cell to guarantee electricity demand, a vanadium redox flow battery is installed to ensure power supply security during periods of low new energy demand. Figure 2 As shown;

[0116] Step S12: Establish a dynamic model of the new energy hydrogen production system, wherein the dynamic equivalent model of the vanadium redox flow battery is shown in formula (1):

[0117]

[0118] Among them, S cell P represents the charge of a vanadium redox flow battery. cell Q represents the discharge power of a vanadium redox flow battery. cell σ represents the energy storage capacity of the vanadium redox flow battery, and σ is the self-degradation time coefficient related to the charge of the vanadium redox flow battery.

[0119] The electrolytic cell model is established as shown in formula (2):

[0120]

[0121] in, P is the hydrogen production rate. elec A1 and A2 are linear approximation coefficients, where A1 is the input power of the electrolytic cell.

[0122] The hydrogen storage tank model is established as shown in formula (3):

[0123]

[0124] in, This represents the rate of change of pressure in the hydrogen storage tank. R is the hydrogen production rate, T is the ideal gas constant, and V is the ideal gas temperature.

[0125] The supercapacitor model is established as shown in formula (4):

[0126]

[0127] Where E is the energy absorbed / released by the supercapacitor, A and B are the number of supercapacitors connected in series and parallel, and C is the energy released by the supercapacitor. f U1 represents the capacitance of a single supercapacitor, and U2 represents the initial voltage and the on-state voltage of the supercapacitor.

[0128] After step S1 above, the structure of the new energy hydrogen production system was determined, and equivalent models of vanadium redox flow battery, electrolyzer, hydrogen storage tank and supercapacitor were established.

[0129] In this embodiment, step S2 above involves constructing a difference-form state equation using a dynamic model of the hydrogen energy system, establishing a model for the new energy hydrogen production system, converting the equation into matrix form, and directly obtaining the rolling optimization model through the state-space expression. Specifically, this includes:

[0130] Step S21: Based on the dynamic model of the hydrogen energy system, construct the state equation in differential form. According to formula (1), establish the state difference equation for the vanadium redox flow battery's state of charge (SOC), as shown in formula (5):

[0131]

[0132] Where Δτ is the time step;

[0133] Based on formulas (2) to (3), the state difference equation for the gas pressure of the hydrogen storage tank is established, as shown in formula (6):

[0134]

[0135] in, For hydrogen load flow rate, and The hydrogen change rate is determined by the hydrogen production power and operating status of each electrolyzer.

[0136] and The expression is represented by a functional, as shown in formulas (7) to (8):

[0137]

[0138]

[0139] Among them, f P (P elec f is a function of the hydrogen production power of the electrolyzer. d (d elec P is a function of the electrolytic cell's operating state. elec Let P be the hydrogen production power of each electrolyzer, δ be the Dirac function, and P be the hydrogen production power of each electrolyzer. heat For cold standby heating power, P sp For hot standby activation power; d elec For the operating status of each electrolytic cell, d elec.i ∈{l, s, w} represent normal operation, cold standby, and hot standby, respectively;

[0140] Considering the system power balance, the power output expression of the power grid is shown in formula (9):

[0141] P grid (t)=P quit (t-1)+||P elec (t-1)||1-P cell (t-1)-P renw (t-1) (9)

[0142] Among them, P quit For the abandoned power of new energy sources, P renw Power output for new energy sources;

[0143] Step S22: Divide the system variables into state variables x, output variables y, control variables u, and disturbance variables p. The state variable x is represented as shown in formula (10):

[0144]

[0145] A series of transformations are made to the control variable u and the disturbance variable p, as shown in formulas (11) to (12):

[0146]

[0147]

[0148] Among them, P sc U is the power of the supercapacitor. dsc This is the terminal voltage of the supercapacitor;

[0149] This leads to the following state equation:

[0150]

[0151] The system state variables can be directly output, therefore the system output equation is shown in equation (14):

[0152]

[0153] Step S23: Transform the system state equation and output equation into matrix form, as shown in formula (15):

[0154]

[0155] Where A is the system state coefficient matrix, B is the control coefficient matrix, C is the output coefficient matrix, and D is the disturbance coefficient matrix. The specific representations of each matrix are shown in formulas (16) to (19):

[0156]

[0157]

[0158]

[0159]

[0160] Step S24: The rolling optimization model is directly obtained from the state-space expression, as shown in formula (20):

[0161]

[0162] in, The output variable value at time t+k is predicted based on the available information at time t; u(t+k-1|t) represents the control command acquired at time t; and p(t+k-1|t) represents the disturbance signal acquired at time t.

[0163] After step S2 above, the state equation in difference form is constructed, a series of transformations are performed on the system variables, and the rolling optimization model is obtained using the state-space expression.

[0164] In this embodiment, step S3 above—determining the control objective of the hydrogen energy system, determining parameter values ​​and reference trajectories, constructing the objective function, and establishing a mathematical model for optimizing the operation of the hydrogen energy system—specifically includes:

[0165] Step S31: Determine the control objective of the hydrogen energy system to ensure the reliability of energy supply on medium- and long-term time scales, while ensuring that the vanadium redox flow battery SOC, hydrogen storage tank pressure, grid output, and supercapacitor power should track the scheduling plan, minimize the power adjustment of primary equipment, and improve energy conversion efficiency; construct the objective function as shown in formula (21):

[0166]

[0167] Where N1 is the upper boundary of the prediction domain evaluation, N2 is the lower boundary of the prediction domain evaluation, and N... u To evaluate the boundary of the control domain, t and j are different times, δ(j) is the weight of the output variable at time j, λ(j) is the weight of the control variable at time j, and Q... y To output the variable weight coefficient matrix, Q u For the control variable weight coefficient matrix, The expected output trajectory of the system within a specific future time range is w(t+k|t), which is the reference trajectory, and Δu is the change value of the control variable.

[0168] Step S32: Determine the parameter values ​​of the upper and lower boundaries N1 and N2 of the prediction domain evaluation to ensure that the reference trajectory w(t+j) is consistent with the dimension of the output variable;

[0169] Step S33: Set the constraints for each element of the control variable, as shown in formula (22):

[0170]

[0171] Among them, P cell.charge and P cell.discharge These represent the maximum charging and discharging power of the vanadium redox flow battery, and the amount of abandoned renewable energy (P). quit (t) should be lower than the maximum theoretical output P of the new energy source. renw (t), P elec.min and P elec.max P represents the minimum and maximum hydrogen production power of the electrolyzer. sc.c，max and P sc.d，max The maximum charging and discharging power of the supercapacitor;

[0172] Set the rate of change for each element of the control variable as shown in formula (23):

[0173]

[0174] in, and These represent the response speed for adjusting the output power of the vanadium redox flow battery downwards and upwards, respectively. and The response speed to adjustments in power output for new energy sources is limited by the MPPT strategy for new energy sources. and The response speed for adjusting the hydrogen production power of the electrolyzer; and The response speed for adjusting the output of supercapacitors downwards and upwards;

[0175] Expand the constraint conditions for the rate of change of the system output variable, as shown in formula (24):

[0176]

[0177] Among them, S cell.min and S cell.max p represents the minimum and maximum state of charge values ​​of the vanadium redox flow cell, respectively. tank.min and p tank.max P represents the minimum and maximum pressure values ​​of the hydrogen storage tank, respectively. grid.min and P grid.max These represent the minimum and maximum power values ​​for purchasing electricity from the power grid, respectively.

[0178] After step S3 above, the control objective of the hydrogen energy system was determined. Considering parameter values, reference trajectory and constraints, the objective function was constructed, and a mathematical model for optimizing the operation of the hydrogen energy system was established.

[0179] In this embodiment, step S4 above involves initializing the reference trajectory and system variables, adjusting the working state of each electrolyzer in real time based on a rolling optimization method, and using constant power control to adjust the supercapacitor to meet the energy supply requirements for medium- and long-term and short-term energy storage. A coordinated control method for the new energy hydrogen production system using iterative control of the electrolyzer is proposed, specifically including:

[0180] Step S41: Adjust the electrolytic cell in real time based on rolling optimization control. Input the future reference trajectory w(t+j), control variables and current disturbance information. By repeatedly solving the quadratic programming problem, obtain the control signal u(t+1|t+1) for the next moment.

[0181] Step S42: Calculate the power of the electrolytic cells by changing the disturbance variable p of the operating state of the electrolytic cells until all electrolytic cells are in normal power state or all normally operating electrolytic cells are in hot standby state at the current control moment. Stop the iterative solution to meet the energy supply requirements of medium- and long-term energy storage. The control method is as follows: Figure 3 As shown;

[0182] Step S43: Adjust the working state of the supercapacitor based on constant power control, obtain the current deviation value through the reference power, and use a PI controller to realize the rapid charging and discharging of the supercapacitor to meet the energy supply needs of short-term energy storage.

[0183] After the above step S4, the optimization problem in rolling optimization control is regarded as a standard quadratic programming problem under linear constraints. By solving the quadratic programming problem and repeating the optimization calculation, the control signal at the next moment is obtained. A coordinated control method for new energy hydrogen production system with iterative control of the state of each electrolyzer is proposed.

[0184] In this embodiment, step S5 above: verifying and analyzing the case study, changing parameters such as the weighting factor of the output variable, the weighting factor of the control variable change increment, and the reference time domain, analyzing the changes in system control performance and system robustness, and obtaining a coordinated control method for a new energy hydrogen production system with optimal control performance, specifically including:

[0185] Step S51: Set up a specific new energy hydrogen production system and analyze the simulation results, including the following steps:

[0186] 1) Set up a specific renewable energy hydrogen production system with a total installed capacity of 10MW. Superimpose a normally distributed prediction error onto the day-ahead power forecast curves of renewable energy and hydrogen load. Set the average value of the day-ahead prediction errors of renewable energy power and hydrogen load power to 0, and the standard deviation σ of the day-ahead prediction error of renewable energy power. wind Set at 1000kW, the standard deviation of the day-ahead forecast error for hydrogen load power. The system was set to 1000 mol / h, and the specific parameters are shown in Table 1.

[0187] Table 1

[0188]

[0189]

[0190] 2) Considering the high accuracy of ultra-short-term forecasts, the ultra-short-term forecast error used in this example's rolling optimization control is 0. The daily power curves of new energy and hydrogen loads are as follows: Figures 4-5 As shown;

[0191] 3) The simulation results, including the vanadium redox flow battery state-of-charge change curve and its planned reference trajectory, are as follows: Figure 6 As shown, the pressure change curve of the hydrogen storage tank and its planned reference trajectory are as follows: Figure 7 As shown, the power grid load change curve and its planned reference trajectory are as follows: Figure 8 As shown;

[0192] Step S52: Change the weighting factor Q of the output variable y1 Q y2 Q y3 Control variable increment weighting factor Q u1 Q u2 Q u3 Using reference time domains N1 and N2, analyze the changes in system control performance;

[0193] Step S53: Change the standard deviation σ of the day-ahead forecast error for renewable energy power. wind Standard deviation of day-ahead forecast error for hydrogen load power The robustness of the system was analyzed, and a coordinated control method for the new energy hydrogen production system with optimal control performance was obtained. The simulation results are shown in Table 2.

[0194] Table 2

[0195]

[0196]

[0197] After step S5 above, a specific new energy hydrogen production system is set up to verify the effectiveness of the method and its control performance under extreme conditions. In the actual engineering application, the weight parameters should be adjusted according to the system equipment capacity and control requirements to obtain the best control performance.

[0198] The present invention also proposes an apparatus for realizing a coordinated control method for a new energy hydrogen production system, which is divided into a hydrogen energy system framework module, a system rolling optimization module, a system operation planning module, an energy demand calculation module, and a verification and analysis module.

[0199] The hydrogen energy system framework module includes: determining the structure of the new energy hydrogen production system, and establishing the equipment characteristic equations of the new energy hydrogen production system, including vanadium redox flow batteries, electrolyzers, hydrogen storage tanks, and supercapacitors, based on the equipment operating characteristics;

[0200] System rolling optimization module: Constructs a differential form of state equations through the dynamic model of the hydrogen energy system, establishes a model of the new energy hydrogen production system, transforms the equations into matrix form, and obtains the rolling optimization model through state space expression;

[0201] System operation planning module: Determine the control objectives of the hydrogen energy system, determine parameter values ​​and reference trajectories, construct the objective function, and establish a mathematical model for optimizing the operation of the hydrogen energy system;

[0202] Energy demand calculation module: Initialize reference trajectory and system variables, adjust the working status of each electrolyzer in real time based on rolling optimization method, and use constant power control to adjust supercapacitors to meet the energy demand for medium- and long-term and short-term energy storage. It also proposes a coordinated control method for new energy hydrogen production system with iterative control of electrolyzers.

[0203] Verification and Analysis Module: Verify and analyze case studies by changing parameters such as the weighting factor of the output variable, the weighting factor of the control variable change increment, and the reference time domain. Analyze the changes in the system's control performance and the system's robustness to obtain the optimal coordinated control method for the new energy hydrogen production system, thereby enabling coordinated control of the new energy hydrogen production system.

[0204] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the steps of the above-mentioned adaptive hydrogen load fluctuation new energy hydrogen production system coordinated control method.

[0205] The present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps of the above-mentioned adaptive hydrogen load fluctuation new energy hydrogen production system coordinated control method.

[0206] To improve the accuracy of output control for future timeframes, this invention proposes a method for establishing a dynamic model of the hydrogen energy system, constructing a differential state equation, and deriving a rolling optimization model using state-space expressions. This invention aims to ensure energy supply reliability on medium- to short-term timescales, while simultaneously guaranteeing the SOC of the vanadium redox flow battery, the pressure of the hydrogen storage tank, grid output, and the power tracking and scheduling plan of the supercapacitor. Constraints include the pressure of the electrolyzer, the charge of the vanadium redox flow battery, and the grid output. It proposes a coordinated control method for a new energy hydrogen production system that minimizes the objective function by solving a quadratic programming problem based on rolling optimization control. This invention uses specific examples to verify the effectiveness of the control method, exploring the impact of parameter changes on control performance by altering the weighting factors of output variables, the incremental weighting factors of control variables, and the reference time domain parameters, and analyzing the robustness of control performance under fluctuations in new energy and hydrogen loads.

[0207] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A coordinated control method for a new energy hydrogen production system that adapts to hydrogen load fluctuations, characterized in that, Includes the following steps: Step S1: Determine the structure of the new energy hydrogen production system, and establish an equivalent model of the vanadium redox flow battery, an electrolyzer model, and a hydrogen storage tank model; specifically including: Step S11: Determine the structure of the new energy hydrogen production system, which includes a new energy power source, an external power grid, a vanadium redox flow battery, an electrolyzer, a hydrogen storage tank, and a hydrogen load; Step S12: Establish a dynamic model of the new energy hydrogen production system, wherein the dynamic equivalent model of the vanadium redox flow battery is shown in formula (1): (1) in, This refers to the charge capacity of a vanadium redox flow battery. This refers to the discharge power of the vanadium redox flow battery. For vanadium redox flow battery energy storage capacity, The self-degradation time coefficient related to the charge capacity of the vanadium redox flow battery; The electrolytic cell model is established as shown in formula (2): (2) in, For hydrogen production rate, For the input power of the electrolytic cell, and These are linear approximation coefficients; The hydrogen storage tank model is established as shown in formula (3): (3) in, This represents the rate of change of pressure in the hydrogen storage tank. For hydrogen production rate, Let be the ideal gas constant. The thermodynamic temperature of an ideal gas. Let be the volume of an ideal gas; Step S2: Construct a differential form of the state equation through the dynamic model of the hydrogen energy system, establish a model of the new energy hydrogen production system, transform the equation into matrix form, and directly derive the prediction model through the state space expression. Step S3: Determine the control objective of the hydrogen energy system, determine the parameter values ​​and reference trajectory, set constraints, construct the objective function, and establish a mathematical model for optimizing the operation of the hydrogen energy system; the specific steps are as follows: Step S31: Determine the control objectives of the hydrogen energy system as the vanadium redox flow battery SOC, hydrogen storage tank pressure, and grid output to track the scheduling plan, while maintaining the stable operating conditions of the vanadium redox flow battery, new energy sources, and electrolyzers, minimizing the power adjustment of primary equipment to reduce energy loss and improve energy conversion efficiency; construct the objective function as shown in formula (20): (20) in, To evaluate the upper boundary of the prediction domain, To evaluate the lower boundary of the prediction domain, To evaluate the boundary of the control domain, and For different times, For output variables in Weight of time, To control variables in Weight of time, To output the variable weight coefficient matrix, For the control variable weight coefficient matrix, The expected output trajectory of the system within a specific future time frame. For reference trajectory, To control the changing values ​​of variables; Step S32: Determine the upper and lower boundaries of the prediction domain. The parameter values ​​are selected to ensure the reference trajectory. Consistent with the dimensions of the output variables; Step S33: Set the constraints for each element of the control variable, as shown in formula (21): (21) in, and These represent the maximum charging and discharging power of vanadium redox flow batteries, and the amount of renewable energy curtailed. It should be lower than the maximum theoretical output of new energy sources. , and These are the minimum and maximum hydrogen production capacities of the electrolyzer; Set the rate of change for each element of the control variable as shown in formula (22): (22) in, and These represent the response speed for adjusting the output power of the vanadium redox flow battery downwards and upwards, respectively. and The response speed to adjustments in power output for new energy sources is limited by the MPPT strategy for new energy sources. and The response speed for adjusting the hydrogen production power of the electrolyzer; Expand the constraint conditions for the rate of change of the system output variable, as shown in formula (23): (23) in, and These are the minimum and maximum state of charge values ​​for the vanadium redox flow battery, respectively. and These are the minimum and maximum pressure values ​​of the hydrogen storage tank, respectively. and These represent the minimum and maximum power values ​​purchased from the power grid, respectively. Step S4: Initialize the reference trajectory and system variables, and optimize the objective function by minimizing it based on model predictive control. Modify the input disturbance variables and propose a coordinated control method for the new energy hydrogen production system that iteratively controls the state of each electrolyzer. Step S5: Verify and analyze the case study by changing the weighting factors of the output variables, the weighting factors of the control variable change increment, and the reference time-domain parameters. Analyze the changes in the system control performance and the robustness of the system to obtain the coordinated control method for the new energy hydrogen production system with the best control performance.

2. The adaptive hydrogen load fluctuation-based new energy hydrogen production system coordinated control method according to claim 1, characterized in that, Step S2 includes: Step S21: Construct the difference form of the state equation based on the dynamic model of the hydrogen energy system; Based on formula (1), the state difference equation for the state of charge (SOC) of the vanadium redox flow battery is established, as shown in formula (4): (4) in, For time step; Based on formulas (2) to (3), the state difference equations for the gas pressure in the hydrogen storage tank are established, as shown in formula (5): (5) in, For hydrogen load flow rate, and The hydrogen change rate is determined by the hydrogen production power and operating status of each electrolyzer. and The expression is represented by a functional, as shown in formulas (6) to (7): (6) (7) in, Let be a function of the hydrogen production power of the electrolyzer. This is a function relating to the operating state of the electrolytic cell. The hydrogen production capacity of each electrolyzer, For the Dirac function, For cold standby heating power, Power for hot standby activation; The working status of each electrolytic cell, These represent normal operation, cold standby, and hot standby, respectively. Considering the system power balance, the power output expression of the power grid is shown in formula (8): (8) in, For the abandoned power of new energy sources, Power output for new energy sources; Step S22: Divide the system variables into state variables Output variables Control variables and disturbance variables State variables As shown in formula (9): (9) in, For vanadium redox flow battery SOC, The gas pressure in the hydrogen storage tank. For grid load; For control variables and disturbance variables Make a series of transformations, as shown in formulas (10) to (11): (10) (11) This leads to the following state equation: (12) The system state variables can be directly output, therefore the system output equation is shown in formula (13): (13) Step S23: Transform the system state equation and output equation into matrix form, as shown in formula (14): (14) in, The system state coefficient matrix, This is the control coefficient matrix. To output the coefficient matrix, The perturbation coefficient matrix is ​​shown in equations (15) to (18). (15) (16) (17) (18) Step S24: Directly derive the prediction model from the state-space expression, as shown in formula (19): (19) in, Indicates according to Predicted from available information at time The value of the output variable at time 1; express Control commands acquired in real time; express Perturbation signals acquired at all times.

3. The adaptive hydrogen load fluctuation-based new energy hydrogen production system coordinated control method according to claim 2, characterized in that, Step S4 specifically includes: Step S41: Input future reference trajectory Control quantity at the previous moment Current control quantity and current disturbance information ; Step S42: Minimize the objective function based on model predictive control, solve the quadratic programming problem, repeat the optimization calculation, and obtain the control signal for the next time step. ; Step S43: Calculate the electrolytic cell power by changing the disturbance variable through altering the operating state of the electrolytic cell. The iterative solution continues until all electrolytic cells are at normal power or all normally functioning electrolytic cells are in hot standby mode at the current control moment, at which point the iterative solution stops.

4. The adaptive hydrogen load fluctuation-based new energy hydrogen production system coordinated control method according to claim 3, characterized in that, Step S5 specifically includes: Step S51: Set up a specific new energy hydrogen production system and analyze the simulation results; Step S52: Change the weighting factor of the output variable , , Incremental weighting factor for control variables , , and reference time domain , Analyze the changes in system control performance; Step S53: Change the standard deviation of the day-ahead forecast error for renewable energy power. Standard deviation of day-ahead forecast error for hydrogen load power The robustness of the system is analyzed, and a coordinated control method for a new energy hydrogen production system with optimal control performance is obtained.

5. An apparatus for implementing the adaptive hydrogen load fluctuation coordinated control method for a new energy hydrogen production system according to any one of claims 1-4, characterized in that, include: Hydrogen Energy System Framework Module: Determine the structure of the new energy hydrogen production system, and establish the equipment characteristic equations of the new energy hydrogen production system, including vanadium redox flow battery, electrolyzer, hydrogen storage tank and supercapacitor, based on the equipment operation characteristics; System rolling optimization module: Constructs a differential form of state equations through the dynamic model of the hydrogen energy system, establishes a model of the new energy hydrogen production system, transforms the equations into matrix form, and obtains the rolling optimization model through state space expression; System operation planning module: Determine the control objectives of the hydrogen energy system, determine parameter values ​​and reference trajectories, construct the objective function, and establish a mathematical model for optimizing the operation of the hydrogen energy system; Energy demand calculation module: Initialize reference trajectory and system variables, adjust the working status of each electrolyzer in real time based on rolling optimization method, and use constant power control to adjust supercapacitors to meet the energy demand for medium- and long-term and short-term energy storage. It also proposes a coordinated control method for new energy hydrogen production system with iterative control of electrolyzers. Verification and Analysis Module: Verify and analyze case studies by changing parameters such as the weighting factor of the output variable, the weighting factor of the control variable change increment, and the reference time domain. Analyze the changes in the system's control performance and the system's robustness to obtain the optimal coordinated control method for the new energy hydrogen production system, thereby enabling coordinated control of the new energy hydrogen production system.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the adaptive hydrogen load fluctuation coordinated control method for new energy hydrogen production systems as described in any one of claims 1-4.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the adaptive hydrogen load fluctuation coordinated control method for new energy hydrogen production systems as described in any one of claims 1-4.

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