Wind-solar off-grid direct-current hydrogen production system based on dynamic gradient control

Abstract

The invention belongs to the technical field of renewable energy and hydrogen energy preparation, and provides a wind-solar off-grid direct-current hydrogen production system based on dynamic gradient control, which comprises a hardware sensing layer used for integrating wind-solar off-grid direct-current hydrogen production system hardware; the data communication layer is used for acquiring key operation parameters of the hardware sensing layer; and the system control layer is used for constructing a dynamic gradient feedback control model by adopting a multi-time scale hierarchical control structure in combination with the key operation parameters, and carrying out dynamic gradient control on the wind-solar off-grid direct-current hydrogen production system. A top-to-bottom instruction transmission mechanism is formed among the three layers of control of the system, so that the operation economy and stability of the wind-solar off-grid direct-current hydrogen production system are ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of renewable energy and hydrogen energy production, and specifically relates to a wind-solar off-grid DC hydrogen production system based on dynamic gradient control. Background Art

[0002] As the global energy transition continues to deepen, green hydrogen has become an important carrier for global carbon emission reduction due to its clean, efficient and renewable characteristics. The large-scale off-grid "green electricity hydrogen production" system that includes wind energy, solar energy and hydrogen energy has become an important development direction of the hydrogen-based energy industry. However, wind and solar power sources are affected by weather, day and night and seasonal changes, and the output power has large fluctuations and intermittentity, which poses a challenge to the stable operation of the off-grid system. In the process of power collection, transmission and storage, the existing technology of wind and solar off-grid DC system is difficult to effectively regulate instantaneous fluctuations to achieve efficient energy matching between wind and solar power sources and hydrogen production devices. In addition, due to the lack of a multi-level time scale system dynamic control method, it is impossible to take into account the requirements of the system's instantaneous response and global coordination, resulting in low system energy utilization and poor economic benefits. In response to the above problems, there is an urgent need for a new control system for wind and solar off-grid DC hydrogen production system to realize multi-time scale real-time regulation of wind and solar resources and hydrogen production loads, while ensuring system stability and improving overall operating efficiency and economic benefits. Summary of the Invention

[0003] In response to the problems existing in the above-mentioned prior art, the present invention provides a wind-solar off-grid DC hydrogen production system based on dynamic gradient control. By constructing a three-layer dynamic control strategy of short cycle, medium cycle and long cycle, real-time adjustment of the working status of wind power, photovoltaic, energy storage and hydrogen production units can be achieved on multiple time scales, and real-time monitoring and coordinated control of the system energy flow can be achieved, thereby improving system stability and operation efficiency.

[0004] A wind-solar off-grid DC hydrogen production system based on dynamic gradient control, comprising:

[0005] Hardware perception layer, used to integrate the hardware of wind-solar off-grid DC hydrogen production system;

[0006] The data communication layer is used to obtain the key operating parameters of the hardware perception layer;

[0007] The system control layer is used to adopt a multi-time scale hierarchical control structure in combination with the key operating parameters to construct a dynamic gradient feedback control model to perform dynamic gradient control on the wind-solar off-grid DC hydrogen production system;

[0008] The system control layer includes:

[0009] The top-level control layer uses a minute-long control cycle, builds an optimization objective function using historical key operating parameters, and calculates the operating trajectory vector of the hardware perception layer in combination with system constraints to generate a long-term energy scheduling plan.

[0010] The middle control layer is used to adopt a second-level medium-time control cycle, based on the operation trajectory vector and the key operation parameters, to correct the long-cycle energy scheduling plan through logical judgment and rolling correction mechanism, and obtain voltage gradient information;

[0011] The bottom control layer is used to adopt a preset short-time control cycle, combine the voltage gradient information and the voltage state error to build the dynamic gradient feedback control model, and correct the bus voltage based on the dynamic gradient feedback control model.

[0012] Preferably, the key operating parameters include wind power generation data, photovoltaic power generation data, charge and discharge data, state of charge information, electrolysis hydrogen production power and bus voltage data;

[0013] The hardware perception layer includes:

[0014] A wind power generation unit, configured to generate wind power and generate wind power generation data;

[0015] A photovoltaic power generation unit, used for performing photovoltaic power generation and generating photovoltaic power generation data;

[0016] Energy storage unit, used to store wind and photovoltaic power generation and track system load and wind and solar fluctuations, generating charge and discharge data and state of charge information;

[0017] an electrolytic hydrogen production unit, configured to produce hydrogen by electrolysis based on the electrical energy to generate electrolytic hydrogen production power;

[0018] The DC power distribution module is used to realize DC energy transmission and voltage and current regulation between the wind power generation unit, photovoltaic power generation unit, energy storage unit and electrolytic hydrogen production unit, and generate bus voltage data;

[0019] A sampling module, used for collecting the key operating parameters in real time;

[0020] A communication unit, configured to transmit the key operating parameters in real time;

[0021] The monitoring and control module is used to perform dynamic gradient control on the wind-solar off-grid DC hydrogen production system.

[0022] Preferably, the optimization objective function is constructed based on the unit operation and maintenance cost of energy storage, energy storage output, unit hydrogen production income, electrolyzer working efficiency, hydrogen production efficiency, unit wind and solar power abandonment penalty cost, wind and solar power abandonment power and a preset long-term control period.

[0023] Preferably, the system constraints include system energy balance constraints, energy storage output constraints, energy storage boundary constraints, electrolyzer working range constraints, and hydrogen production flexible gradient constraints; wherein,

[0024] Calculating the energy balance constraint based on the wind power forecast and the photovoltaic power forecast;

[0025] Calculating the energy storage output constraint based on the minimum energy storage output and the maximum energy storage output;

[0026] Calculating the energy storage boundary constraint based on the energy storage initial state of charge, the energy storage capacity, the minimum allowable energy storage state of charge, and the maximum allowable energy storage state of charge;

[0027] Calculating the working range constraint of the electrolytic cell based on the minimum allowable power value of the electrolytic cell and the maximum allowable power value of the electrolytic cell;

[0028] The hydrogen production flexibility gradient constraint is calculated based on the maximum allowable adjustment value of the electrolyzer.

[0029] Preferably, the optimization objective function is solved to obtain the electrolytic cell operation reference value and the energy storage operation reference value, and the operation trajectory vector is obtained in combination with the bus voltage setting value.

[0030] Preferably, in the middle control layer, the process of correcting the long-period energy scheduling plan through logical judgment and rolling correction mechanism includes:

[0031] Calculating a system state vector based on the key operating parameters;

[0032] Performing differential calculation on the system state vector to obtain gradient information; wherein the gradient information includes wind and solar power generation gradient information and voltage gradient information;

[0033] Based on the wind-solar power generation gradient information, using a first-order Taylor expansion, perform wind-solar power generation short-term power estimation to obtain a wind-solar power generation short-term power estimation result;

[0034] Based on the short-term power estimation result of wind and solar power generation and the hydrogen production power of the electrolyzer, a power gap or surplus value is calculated to obtain a power calculation result;

[0035] When the power calculation result is greater than zero, it indicates excess power. It is determined whether the electrolyzer hydrogen production power can be increased while satisfying the electrolyzer working range constraint and the hydrogen production flexible gradient constraint. If so, the electrolyzer hydrogen production power at this time is calculated. Otherwise, it is determined whether the energy storage unit absorbs the excess power to satisfy the energy storage output constraint and the energy storage boundary constraint. If so, the excess power is absorbed and the energy storage absorption power is calculated. Otherwise, the excess power is discarded by abandoning wind and solar power.

[0036] When the power calculation result is less than zero, it means that there is a power gap. It is determined whether the energy storage unit can release power while satisfying the energy storage output constraint and the energy storage boundary constraint. If so, the power is released to the electrolyzer to maintain the hydrogen production plan, and the released power of the energy storage unit is calculated; otherwise, the hydrogen production power of the electrolyzer is reduced, and the hydrogen production power of the electrolyzer at this time is calculated.

[0037] Preferably, in the bottom control layer, the process of constructing the dynamic gradient feedback control model includes:

[0038] Calculate voltage state error based on real-time bus voltage data;

[0039] Based on the voltage state error and the adaptive adjustment factor, respectively calculating proportional, differential and integral dynamic gain matrices based on gradient information;

[0040] The dynamic gradient feedback control model is constructed based on the dynamic gain matrix, the voltage state error, and the voltage gradient information.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows: a top-down instruction transmission mechanism is formed between the three layers of control of the system, which ensures the economic efficiency and stability of the wind-solar off-grid DC hydrogen production system. The top-level control layer uses wind-solar forecasts to perform forward-looking power scheduling, and guides the long-term operation of the system by formulating operating reference trajectories of large-capacity energy storage units and electrolyzers, thereby realizing a system economic optimization scheduling plan. The middle-level control generates gradient information and performs short-term predictions on wind-solar power data to track and correct the operating reference trajectory, ensuring the dynamic energy balance of the system under wind-solar fluctuations. The bottom-level control uses a dynamic gradient feedback control model to control the fast-response energy storage unit to respond in real time to the frequency oscillation caused by wind-solar power fluctuations, maintain transient voltage stability, and ensure stable system operation. The hierarchical optimization across different time scales, the online adaptive tuning mechanism of parameters, and the dynamic control logic of the bottom-level control in the present invention together constitute the significant innovation of the present invention that distinguishes it from the prior art, effectively improving the comprehensive performance of the system under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is a hierarchical diagram of a wind-solar off-grid DC hydrogen production system based on dynamic gradient control according to an embodiment of the present invention;

[0044] Figure 2This is a multi-scale hierarchical control strategy architecture for a wind-solar off-grid DC hydrogen production system based on dynamic gradient control according to an embodiment of the present invention;

[0045] Figure 3 Schematic diagrams comparing the performance of the multi-scale control strategy and traditional control under different operating scenarios of the embodiment of the present invention; (a) is a schematic diagram for comparing hydrogen production; (b) is a schematic diagram for comparing economic benefits; (c) is a schematic diagram for comparing system utilization; and (d) is a schematic diagram for comparing performance improvement percentages.

[0046] Figure 4 Schematic diagram of the system operation under a typical day scenario of an embodiment of the present invention; wherein, (a) is a schematic diagram of the power curve; (b) is a schematic diagram of the energy storage and power abandonment curve; (c) is a schematic diagram of the energy storage SOC curve; and (d) is a schematic diagram of the cumulative hydrogen production curve. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] Example 1:

[0050] like Figure 1 As shown, a wind-solar off-grid DC hydrogen production system based on dynamic gradient control includes: a hardware perception layer, a data communication layer and a system control layer.

[0051] The hardware perception layer is used to integrate the hardware of the wind-solar off-grid DC hydrogen production system.

[0052] The data communication layer is used to obtain key operating parameters of the hardware perception layer.

[0053] The system control layer is used to adopt a multi-time scale hierarchical control structure combined with key operating parameters to build a dynamic gradient feedback control model to perform dynamic gradient control on the wind-solar off-grid DC hydrogen production system.

[0054] A further implementation method is that the key operating parameters include wind power generation data, photovoltaic power generation data, charging and discharging data, state of charge information, electrolysis hydrogen production power and bus voltage data.

[0055] The hardware perception layer includes: wind power generation unit, photovoltaic power generation unit, energy storage unit, electrolysis hydrogen production unit, DC power distribution module, sampling module, communication unit and monitoring and control module.

[0056] The wind power generation unit is used to generate wind power and generate wind power data. In this embodiment, the wind power generation unit is equipped with a variable frequency regulator and wind speed and direction sensors to achieve real-time collection and preliminary adjustment of wind energy signals. The rated power of the wind power generation is 100 kW.

[0057] The photovoltaic power generation unit is used to generate photovoltaic power and generate photovoltaic power generation data. In this embodiment, the photovoltaic power generation unit uses maximum power point tracking (MPPT) technology to achieve real-time energy capture through a light intensity sensor. The rated photovoltaic power generation power is 80 kW.

[0058] Energy storage units store wind and photovoltaic power, track system load and fluctuations in wind and solar power, and generate charge and discharge data and state-of-charge information. These units include large-capacity and fast-response units, both of which generate charge and discharge data and state-of-charge information.

[0059] The large-capacity energy storage unit is a battery, which is used to store electricity generated by wind power and photovoltaic power generation in the medium and long term. It is responsible for medium and long-term energy storage, maintaining SOC balance and smoothing power trends. The maximum capacity of the battery is 240 kWh.

[0060] The fast-response energy storage unit is a supercapacitor, which is used to quickly track system load or wind and solar fluctuations to ensure bus voltage stability. The maximum capacity of the supercapacitor is 10 kWh.

[0061] The electrolytic hydrogen production unit is used to produce hydrogen by electrolysis based on electrical energy to generate electrolytic hydrogen production power. In this embodiment, the electrolytic hydrogen production unit uses an alkaline electrolyzer with a rated power of 150 kW and a hydrogen production rate of 0.018 kg / kWh.

[0062] The DC power distribution module is used to realize DC energy transmission and voltage and current regulation between wind power generation units, photovoltaic power generation units, energy storage units and electrolysis hydrogen production units, and generate bus voltage data.

[0063] Sampling module, used to collect key operating parameters in real time.

[0064] The communication unit is used to transmit key operating parameters in real time. In this embodiment, the sampling module and the communication unit can sample key operating parameters in real time and transmit them in real time via a high-speed bus or industrial communication protocol. The top and middle control layers, which have moderate data volumes, use the Modbus TCP / IP protocol to accommodate the asynchronous transmission requirements of decision-making instructions. The bottom control layer, which has larger data volumes and higher real-time requirements, uses the Profinet IRT protocol to establish a hard real-time communication channel.

[0065] The monitoring and control module is used to perform dynamic gradient control of the off-grid wind and solar power DC hydrogen production system. The monitoring and control module integrates data acquisition, communication, and an embedded processor, and is responsible for executing a multi-time scale control algorithm based on dynamic gradient control.

[0066] The data communication layer uses sampling modules and communication units to obtain key operating parameters from the hardware perception layer, and transmits them to the system control layer in real time through high-speed buses or industrial communication protocols, realizing the collection, aggregation, processing and transmission of various monitoring data, and accurately sending the instructions generated by the system control layer to the actuator.

[0067] The data communication layer uses sampling modules and communication units to obtain wind power generation data, photovoltaic power generation data, energy storage unit charging and discharging data, as well as key operating parameters such as charge status information, electrolysis hydrogen production power, and bus voltage from the hardware perception layer. It integrates historical wind and photovoltaic power generation data and transmits them to the system control layer in real time through a high-speed bus or industrial communication protocol, and accurately sends the instructions generated by the system control layer to the actuator.

[0068] A further embodiment is that the system control layer includes: a top control layer, a middle control layer and a bottom control layer. Figure 2 shown.

[0069] The top-level control layer uses a minute-long control cycle and historical key operating parameters to construct an optimization objective function. Combined with system constraints, it calculates the operating trajectory vector of the hardware perception layer and generates a long-term energy scheduling plan to maximize the system's hydrogen production efficiency. Specifically, the data communication layer uses sampling modules and communication units to obtain key operating parameters such as wind power generation data, photovoltaic power generation data, energy storage unit charge and discharge data, and charge status information, electrolysis hydrogen production power, and bus voltage from the hardware perception layer. It then integrates historical wind and photovoltaic power generation data and transmits it to the system control layer in real time via a high-speed bus or industrial communication protocol. The instructions generated by the system control layer are then accurately issued to the actuators. In this embodiment, the control cycle of the top-level control layer is 15 minutes. The top-level control layer adopts the LSTM-TCN hybrid prediction model. The LSTM module of the prediction model processes the time series wind speed and irradiance data with a time step of 15 minutes. The TCN module captures the long-term periodic characteristics of meteorological data through an expanded convolution kernel with a 2n expansion coefficient. At the same time, the Attention mechanism is designed based on the SENet architecture to dynamically weighted fuse the numerical weather forecast data to ensure that the weight variance is controlled within 0.15.

[0070] A further implementation method is to construct an optimization objective function based on the unit operation and maintenance cost of energy storage, energy storage output, unit hydrogen production income, electrolyzer operating efficiency, hydrogen production efficiency, unit wind and solar power curtailment penalty cost, wind and solar power curtailment power, and a preset long-term control period. The optimization objective function is as follows:

[0071] ,

[0072] Where, is the unit operation and maintenance cost of the battery, To provide power to the battery, is the unit hydrogen production income, For the working efficiency of the electrolyzer, is the hydrogen production power, The penalty cost for unit wind and solar curtailment, is the abandoned wind and solar power, For long-term control cycle.

[0073] A further implementation method is that the system constraints include system energy balance constraints, energy storage output constraints, energy storage boundary constraints, electrolyzer working range constraints and hydrogen production flexible gradient constraints.

[0074] Calculate the energy balance constraints based on the predicted wind power and photovoltaic power:

[0075] ,

[0076] Where, For wind power prediction, Predict power for photovoltaics.

[0077] Based on the minimum and maximum energy storage output, calculate the energy storage output constraint:

[0078] ,

[0079] Where, is the minimum output of the battery, This is the maximum battery output.

[0080] Calculate the energy storage boundary constraints based on the energy storage initial state of charge, energy storage capacity, minimum allowable energy storage state of charge, and maximum allowable energy storage state of charge:

[0081] ,

[0082] Where, is the initial state of charge of the battery, is the battery capacity, is the minimum allowable value of the battery state of charge, It is the maximum allowable value of the battery state of charge.

[0083] Based on the minimum and maximum allowable power of the electrolytic cell, calculate the working range constraints of the electrolytic cell:

[0084] ,

[0085] Where, is the minimum allowable power of the electrolytic cell, The maximum allowable power of the electrolytic cell.

[0086] Based on the maximum allowable adjustment of the electrolyzer, calculate the hydrogen production flexibility gradient constraint:

[0087] ,

[0088] Where, The maximum value allowed for electrolytic cell adjustment.

[0089] A further embodiment is to solve the optimization objective function to obtain the electrolytic cell operation reference value , Energy storage operation reference value , combined with the bus voltage setting value , get the running trajectory vector :

[0090] .

[0091] The top control layer will by It is periodically sent to the medium-cycle or short-cycle controller to guide the system to evolve towards the predetermined goal in the future. is a long-term control cycle, t represents the current time variable, and t0 represents the control start time. In this embodiment, For 15 minutes.

[0092] The middle control layer is used to control the cycle in seconds (10s) based on the trajectory vector As well as key operating parameters, the long-term energy scheduling plan is corrected through logical judgment and rolling correction mechanism, and voltage gradient information is obtained.

[0093] A further implementation method is that, in the middle control layer, the process of correcting the long-term energy scheduling plan through logical judgment and rolling correction mechanism includes:

[0094] Calculate the system state vector based on key operating parameters :

[0095] ,

[0096] Where, is the real-time wind power generation power, is the real-time photovoltaic power generation power, It is the real-time bus voltage data.

[0097] Perform differential calculation on the system state vector to obtain gradient information , real-time analysis of the changing trends of wind and solar power output, battery status, and hydrogen production load. Among them, the gradient information includes wind and solar power generation gradient information and voltage gradient information:

[0098] .

[0099] Assuming that the change of wind and solar power output is approximately linear, based on the wind and solar power generation gradient information, the first-order Taylor expansion is used to estimate the short-term power of wind and solar power generation, and the short-term power estimation result of wind and solar power generation is obtained:

[0100] ,

[0101] Where, is the real-time wind power generation power, is the real-time photovoltaic power generation power, It is the mid-time control cycle.

[0102] Based on the short-term power estimation results of wind and solar power generation and the hydrogen production power of the electrolyzer, the power gap or surplus value is calculated to obtain the power calculation results:

[0103] .

[0104] When the power calculation result is greater than zero ( ), it represents excess power (indicating that wind and solar power generation are in excess and need to be absorbed by batteries or the hydrogen production power is increased). It is determined whether the electrolyzer hydrogen production power can be increased while satisfying the electrolyzer working range constraint and the hydrogen production flexible gradient constraint. If so, the electrolyzer hydrogen production power at this time is calculated:

[0105] .

[0106] Otherwise, determine whether the excess power absorbed by the battery unit meets the energy storage output constraint and the energy storage boundary constraint. If so, absorb the excess power and calculate the absorbed power of the energy storage unit:

[0107] ,

[0108] Otherwise, the excess power is discarded by curtailing wind and solar power.

[0109] When the power calculation result is less than zero ( ), it indicates that there is a power gap (indicating insufficient wind and solar power generation, requiring battery release or reduction of hydrogen production power). It is determined whether the battery unit can release power while meeting the energy storage output constraint and energy storage boundary constraint. If so, power is released to the hydrolysis cell to maintain the hydrogen production plan. The released power of the energy storage unit is calculated as follows:

[0110] .

[0111] Otherwise, reduce the hydrogen production power of the electrolyzer and calculate the hydrogen production power of the electrolyzer at this time:

[0112] .

[0113] The bottom control layer is used to adopt a preset short-term control cycle to build a dynamic gradient feedback control model based on the voltage gradient information and voltage state error for instantaneous changes in environmental parameters such as wind speed and light intensity. The bus voltage is corrected based on the dynamic gradient feedback control model to ensure bus voltage stability and prevent instantaneous imbalance of the system.

[0114] A further implementation method is that, in the bottom control layer, the process of constructing the dynamic gradient feedback control model includes:

[0115] Based on real-time bus voltage data , calculate the voltage state error:

[0116] .

[0117] Based on voltage state error and adaptive adjustment factors to calculate the proportional, differential, and integral dynamic gain matrices based on gradient information.

[0118] The calculation formula of the dynamic gain matrix based on gradient information is:

[0119] ,

[0120] Where, 、 、 is the initial gain, 、 、 In this embodiment, the adaptive adjustment factor is calibrated based on expert experience and multi-scenario experiments.

[0121] Based on the dynamic gain matrix, voltage state error And voltage gradient information, build a dynamic gradient feedback control model:

[0122] ,

[0123] Where, 、 、 They are respectively the dynamic gain matrices of proportion, differentiation and integration based on gradient information.

[0124] Furthermore, the supercapacitor output power is:

[0125] .

[0126] The dynamic gain matrix based on gradient information can adaptively adjust the control gain according to the fluctuation of the system state change rate, thereby accelerating the system response speed.

[0127] Furthermore, the dynamic gradient feedback control model It enables the actual execution unit to respond quickly, quickly suppress power fluctuations and maintain voltage stability.

[0128] Figure 3 Schematic diagrams comparing the performance of the multi-scale control strategy and traditional control under different operating scenarios; (a) shows a comparison of hydrogen production; (b) shows a comparison of economic benefits; (c) shows a comparison of system utilization; and (d) shows a comparison of performance improvement percentages. To fully verify the performance of the hierarchical control strategy proposed in this invention, four different operating scenarios were constructed and tested, covering "typical days," "strong wind days," "strong sunshine days," and "extreme weather conditions." Simulation results show that compared to the traditional PI control method, the control strategy proposed in this invention increases hydrogen production by an average of 8.42% and economic benefits by 7.24%.

[0129] Figure 4 The following diagram illustrates the system operation under a typical daily scenario for an embodiment of the present invention; (a) is a schematic diagram of the power curve; (b) is a schematic diagram of the energy storage and curtailment curve; (c) is a schematic diagram of the energy storage SOC curve; and (d) is a schematic diagram of the cumulative hydrogen production curve. By demonstrating the dynamic processes of key system variables (wind turbine output power, photovoltaic unit output power, electrolyzer power, load power, energy storage power, energy storage SOC, and cumulative hydrogen production) under a typical daily scenario, the efficiency and adaptability of the control strategy proposed in this invention under variable actual operating conditions are further demonstrated.

[0130] In summary, the innovation of the wind-solar off-grid DC hydrogen production system based on dynamic gradient control proposed in this invention lies in:

[0131] 1. Multi-time-scale dynamic hierarchical control is introduced into the off-grid wind-solar direct current hydrogen production system, taking into account both long-term energy optimization and short-term instantaneous dynamic stability control.

[0132] 2. An adaptive control mechanism based on dynamic gradient information feedback is proposed for the off-grid wind and solar power DC hydrogen production system, which establishes a nonlinear relationship between the ramp rate of the hydrogen production equipment and the real-time SOC, thus realizing flexible linkage regulation of the energy storage unit and the hydrogen production load.

[0133] 3. For off-grid wind and solar power scenarios, a dynamic gradient feedback control model is designed to drive the energy storage system to quickly respond to power fluctuations, suppress frequency oscillations and maintain transient voltage stability.

[0134] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A wind-solar off-grid DC hydrogen production system based on dynamic gradient control, characterized in that: include: Hardware perception layer, used to integrate the hardware of wind-solar off-grid DC hydrogen production system; The data communication layer is used to obtain the key operating parameters of the hardware perception layer; The system control layer is used to adopt a multi-time scale hierarchical control structure in combination with the key operating parameters to construct a dynamic gradient feedback control model to perform dynamic gradient control on the wind-solar off-grid DC hydrogen production system; The system control layer includes: The top-level control layer uses a minute-long control cycle, builds an optimization objective function using historical key operating parameters, and calculates the operating trajectory vector of the hardware perception layer in combination with system constraints to generate a long-term energy scheduling plan. The middle control layer is used to adopt a second-level medium-time control cycle, based on the operation trajectory vector and the key operation parameters, to correct the long-cycle energy scheduling plan through logical judgment and rolling correction mechanism, and obtain voltage gradient information; The bottom control layer is used to adopt a preset short-time control cycle, combine the voltage gradient information and the voltage state error to build the dynamic gradient feedback control model, and correct the bus voltage based on the dynamic gradient feedback control model.

2. The system according to claim 1, wherein: The key operating parameters include wind power generation data, photovoltaic power generation data, charge and discharge data, state of charge information, electrolysis hydrogen production power and bus voltage data; The hardware perception layer includes: A wind power generation unit, configured to generate wind power and generate wind power generation data; A photovoltaic power generation unit, used for performing photovoltaic power generation and generating photovoltaic power generation data; Energy storage unit, used to store wind and photovoltaic power generation and track system load and wind and solar fluctuations, generating charge and discharge data and state of charge information; an electrolytic hydrogen production unit, configured to produce hydrogen by electrolysis based on the electrical energy to generate electrolytic hydrogen production power; The DC power distribution module is used to realize DC energy transmission and voltage and current regulation between the wind power generation unit, photovoltaic power generation unit, energy storage unit and electrolytic hydrogen production unit, and generate bus voltage data; A sampling module, used for collecting the key operating parameters in real time; A communication unit, configured to transmit the key operating parameters in real time; The monitoring and control module is used to perform dynamic gradient control on the wind-solar off-grid DC hydrogen production system.

3. The system according to claim 1, wherein: The optimization objective function is constructed based on the unit operation and maintenance cost of energy storage, energy storage output, unit hydrogen production income, electrolyzer working efficiency, hydrogen production efficiency, unit wind and solar power abandonment penalty cost, wind and solar power abandonment power and a preset long-term control period.

4. The system according to claim 3, characterized in that The system constraints include system energy balance constraints, energy storage output constraints, energy storage boundary constraints, electrolyzer working range constraints and hydrogen production flexible gradient constraints; wherein, Calculating the energy balance constraint based on the wind power forecast and the photovoltaic power forecast; Calculating the energy storage output constraint based on the minimum energy storage output and the maximum energy storage output; Calculating the energy storage boundary constraint based on the energy storage initial state of charge, the energy storage capacity, the minimum allowable energy storage state of charge, and the maximum allowable energy storage state of charge; Calculating the working range constraint of the electrolytic cell based on the minimum allowable power value of the electrolytic cell and the maximum allowable power value of the electrolytic cell; The hydrogen production flexibility gradient constraint is calculated based on the maximum allowable adjustment value of the electrolyzer.

5. The system according to claim 1, wherein: The optimization objective function is solved to obtain the electrolytic cell operation reference value and the energy storage operation reference value, and the operation trajectory vector is obtained in combination with the bus voltage setting value.

6. The system according to claim 4, characterized in that In the middle control layer, the process of correcting the long-period energy scheduling plan through logical judgment and rolling correction mechanism includes: Calculating a system state vector based on the key operating parameters; Performing differential calculation on the system state vector to obtain gradient information; wherein the gradient information includes wind and solar power generation gradient information and voltage gradient information; Based on the wind-solar power generation gradient information, using a first-order Taylor expansion, perform wind-solar power generation short-term power estimation to obtain a wind-solar power generation short-term power estimation result; Based on the short-term power estimation result of wind and solar power generation and the hydrogen production power of the electrolyzer, a power gap or surplus value is calculated to obtain a power calculation result; When the power calculation result is greater than zero, it indicates excess power. It is determined whether the electrolyzer hydrogen production power can be increased while satisfying the electrolyzer working range constraint and the hydrogen production flexible gradient constraint. If so, the electrolyzer hydrogen production power at this time is calculated. Otherwise, it is determined whether the energy storage unit absorbs the excess power to satisfy the energy storage output constraint and the energy storage boundary constraint. If so, the excess power is absorbed and the energy storage absorption power is calculated. Otherwise, the excess power is discarded by abandoning wind and solar power. When the power calculation result is less than zero, it means that there is a power gap. It is determined whether the energy storage unit can release power while satisfying the energy storage output constraint and the energy storage boundary constraint. If so, the power is released to the electrolyzer to maintain the hydrogen production plan, and the released power of the energy storage unit is calculated; otherwise, the hydrogen production power of the electrolyzer is reduced, and the hydrogen production power of the electrolyzer at this time is calculated.

7. The system according to claim 6, characterized in that In the bottom control layer, the process of constructing the dynamic gradient feedback control model includes: Calculate voltage state error based on real-time bus voltage data; Based on the voltage state error and the adaptive adjustment factor, respectively calculating proportional, differential and integral dynamic gain matrices based on gradient information; The dynamic gradient feedback control model is constructed based on the dynamic gain matrix, the voltage state error, and the voltage gradient information.

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