Hydrogen energy system and power grid collaborative complementary regulation method and system

Abstract

The invention provides a hydrogen energy system and power grid collaborative complementary adjustment method and system, and relates to the technical field of energy management, and the method comprises the steps: obtaining a power grid load and renewable energy power generation data, calculating a supply-demand difference curve, switching the hydrogen energy system to a hydrogen production mode in a first target time period, and adjusting the hydrogen energy system to target hydrogen production power, switching to the power generation mode in the second target time period, adjusting the output power according to the supply-demand difference value, the hydrogen pressure and the working state of the battery, and recording the operation parameters to generate a cooperative operation evaluation report, thereby achieving the efficient complementation of the power grid and the hydrogen energy system, improving the energy utilization efficiency, and enhancing the power grid adjustment capability.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to a method and system for coordinated and complementary regulation of a hydrogen energy system and a power grid. Background Art

[0002] The volatility and intermittency issues brought about by the integration of renewable energy into the grid require coordinated regulation through a variety of flexibility resources. The coordinated and complementary operation of hydrogen energy systems and power grids has become an important technical approach to solving the stability and flexibility of the power system. It is of great significance for promoting the large-scale consumption of renewable energy and building a clean and low-carbon energy system. However, existing technologies still lack the ability to accurately perceive and predict the supply and demand status of the power grid. The conversion between hydrogen production and power generation modes is often based on simple time period divisions or fixed threshold triggers, making it difficult to flexibly respond to dynamic changes in power grid supply and demand. This leads to low coordination efficiency and fails to fully consider the matching relationship between the operating parameters of the hydrogen energy system itself and the demand of the power grid. The regulation of hydrogen production power and hydrogen fuel cell output power lacks a comprehensive optimization mechanism, making it difficult to simultaneously take into account the regulation needs of the power grid and the safe and efficient operation of the hydrogen energy system. In addition, it is impossible to effectively collect and analyze key data during the coordinated operation process, making it difficult to continuously optimize and adjust the coordination strategy. Therefore, a solution is urgently needed to solve the problems existing in the prior art. Summary of the Invention

[0003] The embodiments of the present invention provide a method and system for coordinated and complementary regulation of a hydrogen energy system and a power grid, which can at least solve some of the problems existing in the prior art.

[0004] A first aspect of an embodiment of the present invention provides a method for coordinated and complementary regulation of a hydrogen energy system and a power grid, comprising: Obtaining grid load data and renewable energy generation data, determining a grid load curve based on the grid load data, and determining a renewable energy generation curve based on the renewable energy generation data; Calculating a grid supply-demand difference curve based on the grid power load curve and the renewable energy power generation curve; Switching the hydrogen energy system to a hydrogen production mode within a first target period, determining a target hydrogen production power according to the grid supply and demand difference curve, and adjusting the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on the hydrogen production operating parameters of the hydrogen energy system; switching the hydrogen energy system to a power generation mode within a second target period, determining an output power of the hydrogen energy system based on the power grid supply-demand difference curve, hydrogen pressure data, and operating status data of the hydrogen fuel cell, and adjusting operating parameters of the hydrogen fuel cell based on the output power; The operating parameters of the hydrogen energy system are recorded to generate operating status data, and an evaluation report on the coordinated operation of the hydrogen energy system and the power grid is generated based on the operating status data.

[0005] In an optional embodiment, Obtaining grid load data and renewable energy generation data, determining a grid load curve based on the grid load data, and determining a renewable energy generation curve based on the renewable energy generation data, including: Dividing the grid load data into multiple time windows according to a time series, and performing data matching on the renewable energy power generation data according to the time windows; Calculating the numerical distribution characteristics of the grid load data and the numerical distribution characteristics of the renewable energy power generation data in each of the time windows, and establishing a correlation matrix between the grid load data and the renewable energy power generation data based on the numerical distribution characteristics; Calculating a deviation coefficient between each data item in the correlation matrix, determining a data verification threshold range according to the deviation coefficient, and generating a data verification rule set based on the data verification threshold range; The grid load data and the renewable energy power generation data are data verified according to the data verification rule set, a grid power load curve is generated according to the grid load data that passes the data verification, and a renewable energy power generation curve is generated according to the renewable energy power generation data that passes the data verification.

[0006] In an optional embodiment, Calculating a grid supply-demand difference curve based on the grid power load curve and the renewable energy power generation curve includes: Extracting load data points from the power grid load curve, calculating the time intervals between adjacent load data points, and generating a load time interval sequence; Extracting power generation data points from the renewable energy power generation curve, calculating the time intervals between adjacent power generation data points, and generating a power generation time interval sequence; determining a reference time sequence based on the load time interval sequence and the power generation time interval sequence; Performing data resampling within the load data segment according to the reference time series, obtaining load data points before and after the corresponding moment for interpolation calculation at each resampling moment to obtain resampled load data; Performing data resampling within the power generation data segment according to the reference time series, obtaining power generation data points before and after the corresponding time for each resampling moment, performing interpolation calculations, and obtaining resampled power generation data; Grid supply and demand difference data is calculated based on the resampled load data and the resampled power generation data, and a grid supply and demand difference curve is generated based on the reference time series and the grid supply and demand difference data.

[0007] In an optional embodiment, Switching the hydrogen energy system to a hydrogen production mode within a first target period, determining a target hydrogen production power according to the power grid supply and demand difference curve, and adjusting the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on the hydrogen production operating parameters of the hydrogen energy system, including: determining a grid power surplus within the first target period based on grid power load data and renewable energy power data within the first target period, and calculating a target hydrogen production power according to the grid power surplus and the grid supply-demand difference curve; Collecting the real-time hydrogen production power of the hydrogen energy system, and calculating the power deviation between the real-time hydrogen production power and the target hydrogen production power; Calculating a hydrogen production operating condition compensation coefficient according to the hydrogen production operating condition parameter, and compensating and correcting the power deviation value based on the hydrogen production operating condition compensation coefficient to obtain a corrected power deviation value; Converting the corrected power deviation value into an electrolytic cell current adjustment instruction and an electrolyte flow adjustment instruction, adjusting the input current of the electrolytic cell according to the electrolytic cell current adjustment instruction and adjusting the flow rate of the electrolyte according to the electrolyte flow adjustment instruction; The adjusted real-time hydrogen production power is collected, and the input current of the electrolyzer and the flow rate of the electrolyte are iteratively adjusted based on the adjusted real-time hydrogen production power until the hydrogen production power of the hydrogen energy system is adjusted to the target hydrogen production power.

[0008] In an optional embodiment, Calculating a hydrogen production operating condition compensation coefficient according to the hydrogen production operating condition parameter, and compensating and correcting the power deviation value based on the hydrogen production operating condition compensation coefficient to obtain a corrected power deviation value, including: Determining a deviation matrix of the hydrogen production operating parameters according to the hydrogen production operating parameters, and calculating a fluctuation characteristic value of each hydrogen production operating parameter according to the deviation matrix; Based on the fluctuation characteristic values, the hydrogen production operating condition parameters are hierarchically classified to obtain a plurality of fluctuation parameter groups; Acquiring historical fluctuation data of the fluctuation parameter group, constructing a dynamic prediction window based on the historical fluctuation data, and using a Kalman filter algorithm to predict parameter fluctuation trends within the dynamic prediction window; Adaptively adjusting the compensation weights of the plurality of fluctuation parameter groups based on the parameter fluctuation trend, and performing dynamic compensation calculations on the plurality of fluctuation parameter groups using an adaptive filtering algorithm to obtain correction coefficients; Establishing a frequency division compensation equation according to the correction coefficient, and obtaining a hydrogen production condition compensation coefficient by iteratively solving the frequency division compensation equation; The power deviation value is added to the hydrogen production operating condition compensation coefficient to obtain a corrected power deviation value.

[0009] In an optional embodiment, Switching the hydrogen energy system to a power generation mode within a second target period, determining the output power of the hydrogen energy system based on the power grid supply-demand difference curve, hydrogen pressure data, and operating status data of the hydrogen fuel cell, and adjusting operating parameters of the hydrogen fuel cell based on the output power, including: calculating an initial grid power deficit based on the load forecast data and the renewable energy power generation forecast data within the second target period, and correcting the initial grid power deficit based on the grid supply-demand difference curve to obtain a target grid power deficit; Based on hydrogen pressure data of the hydrogen storage tank, the hydrogen reserve in the hydrogen storage tank is calculated using the van der Waals equation of state, and the power generation capacity is determined based on the corresponding relationship between the hydrogen reserve and the rated power of the hydrogen fuel cell; constructing a hydrogen fuel cell power output characteristic curve based on the working status data, and determining the output power of the hydrogen energy system in combination with the target grid power deficit, the power generation capacity, and the power output characteristic curve; A hydrogen flow control instruction and a stack current control instruction are generated according to the output power, the opening of the intake valve is adjusted based on the hydrogen flow control instruction, and the input current of the hydrogen fuel cell is adjusted based on the stack current control instruction.

[0010] In an optional embodiment, Constructing a hydrogen fuel cell power output characteristic curve based on the working state data, and determining the output power of the hydrogen energy system in combination with the target grid power deficit, the power generation capacity, and the power output characteristic curve, including: constructing a voltage-current state vector based on the working state data, and calculating a rate of change of the voltage-current state vector in each sampling period; Determining voltage-current transition points according to the change rate, and establishing piecewise continuous state transfer equations between adjacent voltage-current transition points; Substituting the working state data into the state transition equation, calculating a voltage reference value and a current reference value, and constructing a power prediction equation group according to the voltage reference value and the current reference value; Establishing a power output constraint condition based on the target power grid power shortage, substituting the power output constraint condition into the power prediction equation group, and solving the power prediction equation group using nonlinear programming to obtain a power output characteristic curve that satisfies the power output constraint condition; The power output characteristic curve is corrected according to the power generation capacity to obtain a corrected power output characteristic curve, and the output power of the hydrogen energy system is determined based on the corrected power output characteristic curve.

[0011] A second aspect of an embodiment of the present invention provides a hydrogen energy system and a power grid coordinated complementary regulation system, comprising: The first unit is configured to obtain grid load data and renewable energy generation data, determine a grid load curve based on the grid load data, and determine a renewable energy generation curve based on the renewable energy generation data; The second unit is configured to calculate a power grid supply and demand difference curve based on the power grid load curve and the renewable energy power generation curve; a third unit, configured to switch the hydrogen energy system to a hydrogen production mode within a first target period, determine a target hydrogen production power according to the grid supply and demand difference curve, and adjust the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on the hydrogen production operating parameters of the hydrogen energy system; a fourth unit, configured to switch the hydrogen energy system to a power generation mode within a second target period, determine an output power of the hydrogen energy system based on the power grid supply-demand difference curve, hydrogen pressure data, and operating status data of the hydrogen fuel cell, and adjust operating parameters of the hydrogen fuel cell based on the output power; The fifth unit is used to record the operating parameters of the hydrogen energy system to generate operating status data, and generate an evaluation report on the coordinated operation of the hydrogen energy system and the power grid based on the operating status data.

[0012] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including: A processor and a memory for storing processor-executable instructions, wherein the processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0013] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0014] In the present invention, by obtaining grid load data and renewable energy power generation data and calculating the grid supply and demand difference curve, the grid operation status is accurately grasped, which provides a basis for the hydrogen energy system to switch the working mode, effectively improves the scientificity and rationality of grid dispatch, and flexibly adjusts the power output of the hydrogen energy system according to the supply and demand situation of the grid, realizes smooth regulation of grid load, effectively absorbs fluctuations in renewable energy power generation, and improves the stability and reliability of the power system. By recording the operating parameters of the hydrogen energy system and generating an evaluation report, a complete operation monitoring and evaluation system is established, which helps the system to continuously optimize the adjustment strategy, improves the economy and environmental benefits of the entire hydrogen energy and grid coordinated system, and provides a feasible technical path for energy transformation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the flow of the method for coordinated and complementary regulation of a hydrogen energy system and a power grid according to an embodiment of the present invention; Figure 2 This is a flow chart of hydrogen power generation operation control of the method for coordinated complementary regulation of the hydrogen energy system and the power grid according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. 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 shall fall within the scope of protection of the present invention.

[0017] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0018] Figure 1 FIG. 1 is a flow chart of a method for cooperatively and complementaryly regulating a hydrogen energy system and a power grid according to an embodiment of the present invention. Figure 1 As shown, the method includes: Obtaining grid load data and renewable energy generation data, determining a grid load curve based on the grid load data, and determining a renewable energy generation curve based on the renewable energy generation data; Calculating a grid supply-demand difference curve based on the grid power load curve and the renewable energy power generation curve; Switching the hydrogen energy system to a hydrogen production mode within a first target period, determining a target hydrogen production power according to the grid supply and demand difference curve, and adjusting the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on the hydrogen production operating parameters of the hydrogen energy system; switching the hydrogen energy system to a power generation mode within a second target period, determining an output power of the hydrogen energy system based on the power grid supply-demand difference curve, hydrogen pressure data, and operating status data of the hydrogen fuel cell, and adjusting operating parameters of the hydrogen fuel cell based on the output power; The operating parameters of the hydrogen energy system are recorded to generate operating status data, and an evaluation report on the coordinated operation of the hydrogen energy system and the power grid is generated based on the operating status data.

[0019] In an optional embodiment, Obtaining grid load data and renewable energy generation data, determining a grid load curve based on the grid load data, and determining a renewable energy generation curve based on the renewable energy generation data, including: Dividing the grid load data into multiple time windows according to a time series, and performing data matching on the renewable energy power generation data according to the time windows; Calculating the numerical distribution characteristics of the grid load data and the numerical distribution characteristics of the renewable energy power generation data in each of the time windows, and establishing a correlation matrix between the grid load data and the renewable energy power generation data based on the numerical distribution characteristics; Calculating a deviation coefficient between each data item in the correlation matrix, determining a data verification threshold range according to the deviation coefficient, and generating a data verification rule set based on the data verification threshold range; The grid load data and the renewable energy power generation data are data verified according to the data verification rule set, a grid power load curve is generated according to the grid load data that passes the data verification, and a renewable energy power generation curve is generated according to the renewable energy power generation data that passes the data verification.

[0020] Obtain grid load data and renewable energy generation data. Grid load data includes grid electricity consumption at different points in time, such as electricity consumption data recorded every 15 minutes. Renewable energy generation data includes power generation data from renewable energy sources such as solar and wind power at different points in time, also recorded every 15 minutes.

[0021] After acquiring the data, the grid load data is divided into multiple time windows according to the time series. For example, 24 hours of data can be divided into 96 15-minute time windows, or into 24 1-hour time windows. The size of the divided time windows can be flexibly adjusted according to the actual application scenario and data characteristics. For example, during peak periods, a smaller time window such as 15 minutes can be selected, and during off-peak periods, a larger time window such as 1 hour can be selected. For renewable energy power generation data, the system also matches data according to the same time window to ensure that the two types of data correspond to each other in the time dimension. For example, in the time window of 10:00-10:15 on October 15, 2023, the grid load data is 500MW, and the corresponding renewable energy power generation data is 100MW.

[0022] After data matching is complete, the numerical distribution characteristics of the grid load data and renewable energy generation data within each time window are calculated. These numerical distribution characteristics include, but are not limited to, statistical indicators such as mean, standard deviation, median, maximum, and minimum. For example, for the time window of 8:00-9:00 on weekdays, the average grid load for the past 30 days can be calculated to be 800MW with a standard deviation of 50MW; the average renewable energy generation during the same period is 200MW with a standard deviation of 30MW.

[0023] Based on the calculated numerical distribution characteristics, a correlation matrix is ​​constructed between grid load data and renewable energy generation data. The correlation matrix reflects the correlation between the two types of data across different time windows and feature dimensions. Each element of the matrix represents the correlation coefficient between the corresponding dimension, with a value range of [-1, 1], where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation. For example, during the morning hours on weekdays, grid load and solar power generation may exhibit a high positive correlation, with a correlation coefficient of 0.85; whereas during the nighttime hours, grid load and solar power generation may exhibit a low correlation, with a correlation coefficient close to 0.

[0024] Calculate the coefficient of deviation between each data item in the correlation matrix. The coefficient of deviation is a dimensionless indicator that measures the degree of data deviation. It is obtained by calculating the difference between the data and the expected value and normalizing it. The coefficient of deviation between the grid load data and the renewable energy generation data within each time window is calculated. For example, the grid load deviation coefficient for the time window from 9:00 to 10:00 on a weekday is 0.05, indicating that the load data for this period deviates within 5% from the historical data for the same period. The coefficient of deviation for renewable energy generation is 0.15, indicating that the deviation is within 15%.

[0025] Based on the calculated deviation coefficient, the data validation threshold range is determined. Through statistical analysis of historical data, reasonable upper and lower thresholds are set for subsequent data validation. For example, for grid load data, the deviation coefficient threshold range is set to [-0.1, 0.1], meaning that data with a deviation coefficient between -10% and 10% is considered valid. For renewable energy generation data, given its high volatility, a wider threshold range, such as [-0.2, 0.2], can be set.

[0026] Based on the determined data validation threshold range, a data validation rule set is generated. The rule set contains a series of judgment conditions used to identify anomalous data points. For example, Rule 1 could be "If the deviation coefficient of grid load data in the 8:00-9:00 time window on weekdays exceeds 0.1, then mark it as anomalous data"; Rule 2 could be "If the deviation coefficient of solar power generation in the 12:00-13:00 time window on sunny days is less than -0.2, then mark it as anomalous data."

[0027] Grid load data and renewable energy generation data are validated using the generated data validation rule set. Each piece of data is compared against the conditions in the rule set to identify abnormal data that does not meet the rules. For example, the grid load data at 9:15 on October 16, 2023, was detected to be 1200MW, while the historical average for the same period was 800MW. The deviation coefficient was 0.5, exceeding the set threshold range, and was therefore marked as abnormal data.

[0028] Verified grid load data is arranged chronologically to generate a grid load curve. This curve shows how grid load varies at different points in time, visually reflecting peak and trough times. Similarly, based on verified renewable energy generation data, a renewable energy generation curve is generated to illustrate renewable energy generation at different points in time.

[0029] In this embodiment, the original data is verified through a data verification rule set, which can effectively screen out outliers and erroneous data, ensuring the accuracy of subsequent analysis. By establishing a correlation matrix between grid load and renewable energy power generation data, the intrinsic connection between the two types of data is deeply explored, providing a more scientific basis for energy scheduling decisions. The data verification threshold range is dynamically determined based on the deviation coefficient, making the verification process more flexible and adaptable, and able to adapt to changes in data characteristics in different time periods and seasons.

[0030] In an optional embodiment, Calculating a grid supply-demand difference curve based on the grid power load curve and the renewable energy power generation curve includes: Extracting load data points from the power grid load curve, calculating the time intervals between adjacent load data points, and generating a load time interval sequence; Extracting power generation data points from the renewable energy power generation curve, calculating the time intervals between adjacent power generation data points, and generating a power generation time interval sequence; determining a reference time sequence based on the load time interval sequence and the power generation time interval sequence; Performing data resampling within the load data segment according to the reference time series, obtaining load data points before and after the corresponding moment for interpolation calculation at each resampling moment to obtain resampled load data; Performing data resampling within the power generation data segment according to the reference time series, obtaining power generation data points before and after the corresponding time for each resampling moment, performing interpolation calculations, and obtaining resampled power generation data; Grid supply and demand difference data is calculated based on the resampled load data and the resampled power generation data, and a grid supply and demand difference curve is generated based on the reference time series and the grid supply and demand difference data.

[0031] Obtain raw data for the grid's load curve and renewable energy generation curve. The load curve records changes in the grid's load over a period of time (e.g., 24 hours), while the renewable energy generation curve records changes in power generation from renewable energy sources like wind and solar energy over the same period. Because these two types of data are often collected from different monitoring systems with inconsistent sampling intervals, data processing is required to calculate the supply-demand gap.

[0032] Load data points are extracted from the grid load curve, such as {(t1, L1), (t2, L2), ..., (tn, Ln)}, where ti represents a time point and Li represents the load value at that time point. For adjacent load data points, the system calculates the time interval between them, i.e., Δtload_i = ti + 1 - ti, thereby generating a load time interval sequence {Δtload_1, Δtload_2, ..., Δtload_n - 1}. For example, if the times of two adjacent load data points are 10:00 and 10:15, respectively, the time interval is 15 minutes.

[0033] Extract power generation data points from the renewable energy generation curve, such as {(s1, P1), (s2, P2), ..., (sm, Pm)}, where si represents the time point and Pi represents the renewable energy power generation at that time point. Calculate the time interval between adjacent power generation data points, i.e., Δtgen_j = sj+1-sj, to generate the power generation time interval sequence {Δtgen_1, Δtgen_2, ..., Δtgen_m-1}. For example, if the times of two adjacent power generation data points are 10:00 and 10:10, respectively, the time interval is 10 minutes.

[0034] Based on the obtained load and power generation time intervals, a reference time series is determined. This can be determined by taking the minimum of the two time intervals as the reference sampling interval, or by taking the greatest common divisor of the two. In this embodiment, the minimum of the two is chosen. Assuming the load data sampling interval is 15 minutes and the power generation data sampling interval is 10 minutes, the reference time interval is 10 minutes. Based on this, the system generates a reference time series, such as {0:00, 0:10, 0:20, ..., 23:50}.

[0035] Data resampling is performed within the load data segment based on the reference time series. For each time point in the reference time series, if the time point is not in the original load data, the load value at that time point is obtained through interpolation. Specifically, the two original load data points closest to the time point are found, and the load value at that time point is calculated using linear interpolation. For example, for the reference time point 0:10, if the original load data has a load value of 100MW at 0:00 and a load value of 120MW at 0:15, the load value at 0:10 calculated by linear interpolation is: 100+(120-100)×(10 / 15)=113.33MW.

[0036] Data resampling is performed within the power generation data segment based on the reference time series. For each reference time point, if that time point is not in the original power generation data, the power generation value at that time point is also obtained through interpolation. For example, if the reference time point is 0:05, and the original power generation data contains a power generation value of 50MW at 0:00 and a power generation value of 55MW at 0:10, the power generation value at 0:05 is calculated through linear interpolation as: 50 + (55 - 50) × (5 / 10) = 52.5MW.

[0037] After data resampling is complete, the grid supply-demand gap is calculated based on the resampled load and power generation data. For each time point in the baseline time series, the load value at that point is subtracted from the power generation value to obtain the supply-demand gap. For example, if the resampled load value at a certain point in time is 150MW and the power generation value is 100MW, the supply-demand gap at that point in time is 150-100=50MW, indicating a 50MW supply gap on the grid that needs to be supplemented by traditional energy sources or energy storage systems.

[0038] A grid supply-demand difference curve is generated based on a benchmark time series and grid supply-demand difference data. This curve visually illustrates the grid's supply-demand balance over time and can be used to guide energy storage system charging and discharging scheduling, traditional energy generation planning, and grid load adjustments.

[0039] For example, suppose that the power load curve data points of the power grid at a certain time of the day (8:00-12:00) are {(8:00, 200MW), (8:15, 220MW), (8:30, 240MW), (8:45, 230MW), (9:00, 210MW), (9:15, 200MW), (9:30, 190MW), (9:45, 180MW), (10:00, 170 MW), (10:15, 180MW), (10:30, 190MW), (10:45, 200MW), (11:00, 210MW), (11:15, 220MW), (11:30, 230MW), (11:45, 240MW), (12:00, 250MW)}, and the data points of renewable energy power generation curve are {(8:00, 120MW), (8:10, 125MW), (8:20, 130MW), (8:30, 135MW), (8:40, 140MW), (8:50, 145MW), (9:00, 150MW), (9:10, 152MW), (9:20, 155MW), (9:30, 158MW), (9:40, 160MW), (9:50, 162MW), (10:00, 165MW), (10:10, 168MW ), (10:20, 170MW), (10:30, 172MW), (10:40, 175MW), (10:50, 178MW), (11:00, 180MW), (11:10 , 182MW), (11:20, 185MW), (11:30, 187MW), (11:40, 190MW), (11:50, 192MW), (12:00, 195MW)}.

[0040] Through calculation, the benchmark time series is a 10-minute interval sequence {8:00, 8:10, 8:20, ..., 12:00}. After data resampling and supply and demand difference calculation, the data points of the power grid supply and demand difference curve are {(8:00, 80MW), (8:10, 93.3MW), (8:20, 106.7MW), (8:30, 105MW), (8:40, 95MW), (8:50, 85MW), (9:00, 60MW), (9:10, 50MW), (9:20, 40MW), (9:30, 32MW), (9:40, 23.3MW), (9:50, 14.7 MW), (10:00, 5MW), (10:10, 10MW), (10:20, 15MW), (10:30, 18MW), (10:40, 23.3MW), (10:50, 28.7MW), (11:0 0, 30MW), (11:10, 35MW), (11:20, 40MW), (11:30, 43MW), (11:40, 46.7MW), (11:50, 50.3MW), (12:00, 55MW)}.

[0041] In this embodiment, by establishing a benchmark time series, the problem of inconsistent collection time of load data and power generation data is solved, providing a time basis for accurately calculating the supply and demand difference. Interpolation calculation is used to obtain resampled data points, retaining the changing trend characteristics of the original data, while eliminating the errors caused by uneven sampling, improving data accuracy, and the sampled load data and power generation data are completely aligned in the time dimension, enhancing the comparability of the data, making the supply and demand difference calculation more accurate. By generating an accurate power grid supply and demand difference curve, it can intuitively reflect the supply and demand status of the power grid in different time periods, providing an important basis for power grid scheduling decisions, and helping to reduce power waste and improve power grid stability.

[0042] In an optional embodiment, Switching the hydrogen energy system to a hydrogen production mode within a first target period, determining a target hydrogen production power according to the power grid supply and demand difference curve, and adjusting the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on the hydrogen production operating parameters of the hydrogen energy system, including: determining a grid power surplus within the first target period based on grid power load data and renewable energy power data within the first target period, and calculating a target hydrogen production power according to the grid power surplus and the grid supply-demand difference curve; Collecting the real-time hydrogen production power of the hydrogen energy system, and calculating the power deviation between the real-time hydrogen production power and the target hydrogen production power; Calculating a hydrogen production operating condition compensation coefficient according to the hydrogen production operating condition parameter, and compensating and correcting the power deviation value based on the hydrogen production operating condition compensation coefficient to obtain a corrected power deviation value; Converting the corrected power deviation value into an electrolytic cell current adjustment instruction and an electrolyte flow adjustment instruction, adjusting the input current of the electrolytic cell according to the electrolytic cell current adjustment instruction and adjusting the flow rate of the electrolyte according to the electrolyte flow adjustment instruction; The adjusted real-time hydrogen production power is collected, and the input current of the electrolyzer and the flow rate of the electrolyte are iteratively adjusted based on the adjusted real-time hydrogen production power until the hydrogen production power of the hydrogen energy system is adjusted to the target hydrogen production power.

[0043] During the first target period, the target hydrogen production power is determined according to the grid supply and demand difference curve, and the power is adjusted based on the hydrogen production operating parameters of the hydrogen energy system.

[0044] The hydrogen energy system controller switches the hydrogen energy system to hydrogen production mode by receiving a switching command from the grid dispatch system. The controller collects grid load data and renewable energy power data to determine the grid's surplus power during the first target period. Grid load data comes from the smart grid monitoring system, while renewable energy power data comes from the power generation monitoring systems of wind farms and photovoltaic power stations. For example, in a certain area, between 10:00 AM and 3:00 PM, the grid load is 400 MW, while the total wind and photovoltaic power generation during the same period is 500 MW. This means the grid's surplus power is 100 MW.

[0045] The controller calculates the target hydrogen production power based on the grid's supply-demand differential curve. This curve reflects how the difference between power supply and demand in the grid changes over time. It is typically generated by the grid dispatch department based on historical data and power load forecasts. The curve, obtained by the control system, indicates that at the current moment, the power available for allocation to the hydrogen energy system is 30% of the grid's surplus power, or 30 MW. Considering the hydrogen energy system's maximum hydrogen production power is 25 MW, the controller sets the target hydrogen production power at 25 MW.

[0046] After determining the target hydrogen production power, collect the real-time hydrogen production power of the hydrogen energy system. Assuming the current real-time hydrogen production power of the hydrogen energy system is 18MW, the power deviation is 7MW (25MW - 18MW). This deviation indicates that the hydrogen production power needs to be increased by 7MW to reach the target value.

[0047] The control system calculates a hydrogen production operating compensation coefficient based on the hydrogen production operating parameters. These parameters include electrolyzer temperature, pressure, electrolyte concentration, and temperature. For example, the collected electrolyzer temperature is 62°C (standard operating conditions: 70°C), the pressure is 2.8 MPa (standard operating conditions: 3.0 MPa), the electrolyte concentration is 28% (standard operating conditions: 30%), and the electrolyte temperature is 58°C (standard operating conditions: 60°C). Based on the deviation between these parameters and the standard operating conditions, the hydrogen production operating compensation coefficient is calculated to be 0.92, indicating that under the current operating conditions, the hydrogen production efficiency with the same current input is 92% of that under the standard operating conditions.

[0048] The controller compensates for the power deviation based on the hydrogen production compensation factor. The corrected power deviation is 7.61MW (7MW / 0.92), meaning that under the current operating conditions, an additional 7.61MW of input power is required to achieve the additional 7MW of hydrogen production.

[0049] The corrected power deviation is converted into electrolyzer current and electrolyte flow rate control instructions. For the proton exchange membrane electrolyzer used in this example, the control system determines based on the electrolyzer characteristic curve that the electrolyzer current needs to be increased from 1800A to 2350A, while the electrolyte flow rate needs to be increased from 180L / min to 215L / min.

[0050] The controller sends instructions to the electrolyzer power system and electrolyte circulation pump via a programmable logic controller (PLC), adjusting the electrolyzer input current and electrolyte flow rate. The power system gradually increases the output current from 1800A to 2350A in steps of 50A / second. Simultaneously, the electrolyte circulation pump speed is increased from 60Hz to 72Hz, bringing the electrolyte flow rate to 215L / min.

[0051] After the adjustment is completed, the adjusted real-time hydrogen production power is collected. Assume that the real-time hydrogen production power measured after adjustment is 24.2MW, which is still 0.8MW away from the target hydrogen production power of 25MW. The control system continues to iterate and adjust, increasing the electrolyzer current from 2350A to 2390A and the electrolyte flow rate from 215L / min to 218L / min. After re-measurement, the real-time hydrogen production power reaches 24.9MW, and the gap with the target value is narrowed to 0.1MW, which is within the allowable error range and is considered to have reached the target hydrogen production power.

[0052] Throughout the regulation process, the control system continuously monitors safety parameters such as the electrolyzer temperature, electrolyzer pressure, and hydrogen purity to ensure the electrolyzer operates within a safe operating range. For example, the electrolyzer temperature does not exceed 75°C, the electrolyzer pressure does not exceed 3.5MPa, and the hydrogen purity is maintained above 99.99%.

[0053] In this embodiment, the target hydrogen production power is determined by calculating the surplus power of the power grid, so that the hydrogen energy system can fully absorb the excess renewable energy power in the power grid, reduce the phenomenon of wind and solar power abandonment, and improve the utilization efficiency of renewable energy. Through real-time power deviation calculation and hydrogen production operating condition compensation mechanism, the characteristic differences of the electrolyzer under different operating conditions are taken into account, so that the hydrogen production power adjustment is more precise, and the oscillation and error in the adjustment process are reduced. The dual-parameter coordinated adjustment method of electrolyzer current and electrolyte flow is adopted, and closed-loop control is formed through real-time power feedback, so that the hydrogen production power can reach the target value quickly and stably, thereby improving the system operation stability. By considering the hydrogen production operating condition parameters for compensation and correction, the unreasonable operation of the equipment under extreme conditions is avoided, the equipment wear is reduced, and the service life of core equipment such as the electrolyzer is extended.

[0054] In an optional embodiment, Calculating a hydrogen production operating condition compensation coefficient according to the hydrogen production operating condition parameter, and compensating and correcting the power deviation value based on the hydrogen production operating condition compensation coefficient to obtain a corrected power deviation value, including: Determining a deviation matrix of the hydrogen production operating parameters according to the hydrogen production operating parameters, and calculating a fluctuation characteristic value of each hydrogen production operating parameter according to the deviation matrix; hierarchically classifying the hydrogen production operating condition parameters based on the fluctuation characteristic values ​​to obtain a plurality of fluctuation parameter groups; Acquiring historical fluctuation data of the fluctuation parameter group, constructing a dynamic prediction window based on the historical fluctuation data, and using a Kalman filter algorithm to predict parameter fluctuation trends within the dynamic prediction window; Adaptively adjusting the compensation weights of the plurality of fluctuation parameter groups based on the parameter fluctuation trend, and performing dynamic compensation calculations on the plurality of fluctuation parameter groups using an adaptive filtering algorithm to obtain correction coefficients; Establishing a frequency division compensation equation according to the correction coefficient, and obtaining a hydrogen production condition compensation coefficient by iteratively solving the frequency division compensation equation; The power deviation value is added to the hydrogen production operating condition compensation coefficient to obtain a corrected power deviation value.

[0055] Obtain hydrogen production operating parameters and power deviation values. Hydrogen production operating parameters typically include key parameters such as electrolyzer temperature, electrolyte concentration, supply voltage fluctuation, and membrane thickness variation. The power deviation value is the difference between the actual operating power and the set power.

[0056] The process of calculating the hydrogen production operating condition compensation coefficient based on the acquired hydrogen production operating condition parameters begins with constructing a deviation matrix. For example, assuming that the monitored electrolyzer temperature fluctuates within ±3°C based on the standard value of 65°C, the electrolyte concentration fluctuates within ±2% based on the standard value of 30%, and the power supply voltage fluctuates within ±5V based on the standard value of 220V. The differences between the real-time values ​​of the above parameters and the standard values ​​are combined into a deviation matrix. For example, at a certain moment, the deviation matrix may be represented as a temperature deviation of +2°C, a concentration deviation of -1.5%, a voltage deviation of +3V, and so on.

[0057] Based on the deviation matrix, the sliding window method is used to calculate the fluctuation eigenvalue. For example, within a 10-minute time window, the mean deviation, standard deviation, fluctuation frequency, and fluctuation amplitude of each parameter are calculated. For example, the mean deviation of the electrolytic cell temperature is +1.8°C, the standard deviation is 0.5°C, the fluctuation frequency is 0.02Hz, and the fluctuation amplitude is 2.6°C. These data constitute the fluctuation eigenvalue.

[0058] Cluster analysis was used to stratify and classify parameters based on their fluctuation characteristics. Parameters were divided into high-frequency, small-amplitude groups, high-frequency, large-amplitude groups, low-frequency, small-amplitude groups, and low-frequency, large-amplitude groups based on their fluctuation frequency and amplitude. For example, electrolyte concentration was classified as the low-frequency, small-amplitude group (fluctuation frequency 0.005 Hz, amplitude 1.8%), while supply voltage was classified as the high-frequency, small-amplitude group (fluctuation frequency 0.05 Hz, amplitude 4.2 V). This resulted in multiple fluctuation parameter groups.

[0059] For each fluctuating parameter group, historical fluctuation data is obtained. Parameter fluctuation records under similar operating conditions over the past 30 days are extracted from a database to construct a dynamic prediction window. The length of the dynamic window is adaptively adjusted based on the parameter's fluctuation characteristics, with a window of 5 minutes for high-frequency parameters and 15 minutes for low-frequency parameters. A Kalman filter algorithm is applied to predict parameter trends within the window. Using state equations and observation equations, combined with the noise characteristics of historical data, the possible values ​​of the parameters at future moments and their uncertainties are predicted.

[0060] Based on the predicted parameter fluctuation trends, the compensation weights for different fluctuating parameter groups are adaptively adjusted. This weight adjustment follows the principle of "increasing the weight of parameters with severe fluctuations and decreasing the weight of parameters with stable fluctuations." For example, when the electrolytic cell temperature fluctuation intensifies (the standard deviation increases from 0.5°C to 0.8°C), its weight is increased from 0.3 to 0.42. An adaptive filtering algorithm is then applied to each fluctuating parameter group separately. For the high-frequency, small-amplitude group, a high-pass filter is used to eliminate low-frequency interference; for the low-frequency, large-amplitude group, a band-pass filter is used to preserve the characteristic frequency components. Through differentiated processing, a corresponding correction coefficient is calculated for each parameter group. For example, the correction coefficient for the high-frequency, small-amplitude group is 1.08, and the correction coefficient for the low-frequency, large-amplitude group is 0.92.

[0061] A frequency-division compensation equation is established based on the correction coefficients for each parameter group. This equation combines the effects of parameters on different frequency characteristics, formally expressed as a weighted sum of the correction coefficients. Through iterative calculations, the weights are continuously adjusted until the error between the equation output and the actual observed power deviation correction effect is less than a preset threshold (e.g., 0.5%). The resulting hydrogen production compensation coefficient is a scalar value, such as 1.12, that comprehensively considers the influence of each parameter.

[0062] Multiply the power deviation by the hydrogen production compensation factor to complete the compensation correction. For example, if the original power deviation is -2.5kW and the compensation factor is 1.12, the corrected power deviation is -2.8kW. The system applies this corrected value to the hydrogen production system's power control, bringing the actual operating power closer to the set value.

[0063] In this embodiment, the hydrogen production operating parameters are refinedly classified through deviation matrix and fluctuation eigenvalue analysis, so that the compensation calculation is more in line with the actual fluctuation characteristics of different parameters, and the compensation accuracy is significantly improved. Through the dynamic prediction window and adaptive filtering algorithm, the rapid changes in the hydrogen production operating conditions are predicted and compensated, and the system's response speed and adaptability to changes in operating conditions are improved. The parameter groups with different fluctuation characteristics are processed by the frequency division compensation equation, which effectively reduces the complex coupling effects between the hydrogen production operating parameters, making the control logic clearer and more effective. Accurate operating condition compensation avoids the impact of parameter fluctuations on the equipment, reduces the operating pressure of core equipment such as electrolyzers, and effectively extends the service life of the equipment.

[0064] In an optional embodiment, Switching the hydrogen energy system to a power generation mode within a second target period, determining the output power of the hydrogen energy system based on the power grid supply-demand difference curve, hydrogen pressure data, and operating status data of the hydrogen fuel cell, and adjusting operating parameters of the hydrogen fuel cell based on the output power, including: calculating an initial grid power deficit based on the load forecast data and the renewable energy power generation forecast data within the second target period, and correcting the initial grid power deficit based on the grid supply-demand difference curve to obtain a target grid power deficit; Based on hydrogen pressure data of the hydrogen storage tank, the hydrogen reserve in the hydrogen storage tank is calculated using the van der Waals equation of state, and the power generation capacity is determined based on the corresponding relationship between the hydrogen reserve and the rated power of the hydrogen fuel cell; constructing a hydrogen fuel cell power output characteristic curve based on the working status data, and determining the output power of the hydrogen energy system in combination with the target grid power deficit, the power generation capacity, and the power output characteristic curve; A hydrogen flow control instruction and a stack current control instruction are generated according to the output power, the opening of the intake valve is adjusted based on the hydrogen flow control instruction, and the input current of the hydrogen fuel cell is adjusted based on the stack current control instruction.

[0065] The power generation mode switching operation is performed during the second target period. This period usually corresponds to the peak load period of the power grid or the low power generation period of renewable energy. At this time, there is a power gap in the power grid, and the hydrogen energy system needs to provide power support to the power grid through hydrogen fuel cell power generation. The power grid load forecast data and renewable energy power generation forecast data for the second target period are obtained. For example, the load forecast peak of a certain area during the period of 18:00-22:00 is 120MW. The photovoltaic and wind power forecasts during the same period are 10MW and 25MW respectively. The initial power shortage of the power grid is calculated to be 85MW. This initial shortage is corrected based on the real-time monitored power grid supply and demand difference curve. Taking into account the deviation between power grid dispatch and actual operation, the target power shortage of the power grid is determined to be 80MW.

[0066] The hydrogen energy system monitors the pressure data of the hydrogen storage tank in real time through a pressure sensor. For example, taking a hydrogen storage tank as an example, when the measured tank pressure is 35MPa and the temperature is 25°C, the van der Waals equation of state is used to calculate that the total amount of hydrogen in the tank is approximately 1200kg. The van der Waals equation of state takes into account the non-ideal characteristics of actual gas under high pressure conditions. By combining the relationship between the pressure, volume and temperature of hydrogen, the gas constant and the characteristic parameters of hydrogen, the actual mass of hydrogen in the tank is accurately calculated. The system stores a corresponding relationship table between hydrogen reserves and hydrogen fuel cell power generation capacity. According to the query of this table, 1200kg of hydrogen can support a 10MW hydrogen fuel cell to generate electricity continuously for about 12 hours, that is, the current maximum power generation capacity of the system is 10MW.

[0067] The working status data of the hydrogen fuel cell includes parameters such as the stack temperature, humidity, and membrane electrode assembly health status. The current average stack temperature is collected to be 75°C, the relative humidity is 85%, and the membrane electrode assembly health is 92%. Based on these data, the power output characteristic curve of the hydrogen fuel cell is constructed, which describes the system efficiency, response time, and stability indicators corresponding to different output power levels under the current state. Combined with the target grid power shortage of 80MW, the power generation capacity of 10MW, and the optimal operating point shown by the power output characteristic curve, the output power of the hydrogen energy system is determined to be 8MW. This power point can not only ensure a high power generation efficiency (about 52%), but also maintain stable operation of the system.

[0068] After determining the output power, the hydrogen flow rate and stack current need to be precisely controlled to achieve stable power generation. For an output power of 8MW, the required hydrogen flow rate is calculated to be 94Nm³ / h. A hydrogen flow control instruction is generated and sent to the intake valve control unit. The control unit uses a PID control algorithm. Based on the current valve opening of 15% and the target hydrogen flow rate, it is calculated that the valve opening needs to be adjusted to 28%. The valve actuator smoothly adjusts the valve opening according to the instruction to achieve precise control of the hydrogen flow rate. During the adjustment process, the system continuously monitors the actual hydrogen flow rate through the flow meter to ensure that it is stable within the range of 94±2Nm³ / h.

[0069] The system generates stack current control commands. For a hydrogen fuel cell system with a rated voltage of 750V, to output 8MW, the stack current must be controlled at approximately 10,667A. This current control command is sent to the power electronics converter, which uses constant current control mode to gradually ramp the current from an initial value of 0A to a target current of 10,667A at a rate of 200A / s to protect the stack from excessive transient load changes. The entire current regulation process lasts approximately 53 seconds, during which the system monitors stack temperature changes in real time to ensure that it does not exceed the safety threshold of 85°C.

[0070] After the current stabilizes, the stack voltage is continuously monitored. When the voltage stabilizes within the range of 750±5V, the hydrogen fuel cell is confirmed to have reached a stable power generation state. At this point, the hydrogen energy system successfully switches to power generation mode, steadily outputting 8MW of electricity to the grid, alleviating the grid's power shortage. Throughout the power generation process, key parameters such as hydrogen pressure and stack temperature are continuously monitored. When an anomaly is detected, protective measures are immediately implemented, such as reducing output power or performing an emergency shutdown, to ensure safe and reliable operation.

[0071] In this embodiment, the power shortage is calculated by load forecast data and renewable energy power generation forecast data, and is corrected in combination with the grid supply and demand difference curve, so that the hydrogen energy system can accurately respond to the actual needs of the grid, thereby improving the accuracy of grid dispatch. Through the coordinated adjustment of hydrogen flow control instructions and stack current control instructions, precise control of the output power of the hydrogen fuel cell is achieved, reducing power fluctuations and adjustment errors. By precisely controlling the hydrogen flow and stack current, the hydrogen fuel cell operates near the optimal efficiency point, thereby improving the hydrogen-to-electricity conversion efficiency and reducing the system operating cost.

[0072] In an optional embodiment, Constructing a hydrogen fuel cell power output characteristic curve based on the working state data, and determining the output power of the hydrogen energy system in combination with the target grid power deficit, the power generation capacity, and the power output characteristic curve, including: constructing a voltage-current state vector based on the working state data, and calculating a rate of change of the voltage-current state vector in each sampling period; Determining voltage-current transition points according to the change rate, and establishing piecewise continuous state transfer equations between adjacent voltage-current transition points; Substituting the working state data into the state transition equation, calculating a voltage reference value and a current reference value, and constructing a power prediction equation group according to the voltage reference value and the current reference value; Establishing a power output constraint condition based on the target power grid power shortage, substituting the power output constraint condition into the power prediction equation group, and solving the power prediction equation group using nonlinear programming to obtain a power output characteristic curve that satisfies the power output constraint condition; The power output characteristic curve is corrected according to the power generation capacity to obtain a corrected power output characteristic curve, and the output power of the hydrogen energy system is determined based on the corrected power output characteristic curve.

[0073] A voltage-current state vector is constructed based on operating status data. By monitoring the output voltage and current values ​​of the hydrogen fuel cell system under different operating conditions, a series of voltage-current data pairs are formed. For example, at time t1, a data point with a voltage of 65V and a current of 120A is collected. At time t2, a data point with a voltage of 63V and a current of 125A is collected. And so on, a voltage-current state vector containing multiple sampling points is formed. The data is usually stored in the form of a time series, and each data point contains three elements: a timestamp, a voltage value, and a current value.

[0074] Calculate the rate of change of the voltage-current state vector within each sampling period. For every two adjacent sampling points, calculate the voltage and current rates of change, respectively. The voltage rate of change is equal to the difference between the two adjacent sampled voltage values ​​divided by the sampling interval. The current rate of change is calculated similarly. For example, if the voltage at time t1 is 65V and at time t2 is 63V, and the sampling interval is 0.1 seconds, the voltage rate of change is -20V / s, resulting in a rate of change dataset that reflects the system's dynamic response characteristics.

[0075] The voltage-current transition point is determined based on the rate of change, and a piecewise continuous state transfer equation is constructed. When the rate of change of voltage or current exceeds a preset threshold, the point can be determined to be a transition point. For example, the voltage change rate threshold is set to ±15V / s, and the current change rate threshold is set to ±30A / s. When the rate of change of a certain point exceeds these thresholds, it is marked as a transition point. In actual applications, a fuel cell system may detect two transition points at 112A and 138A during the process of current jumping from 100A to 150A, indicating that there is a nonlinear characteristic change near these two points.

[0076] A piecewise continuous state transition equation is established between adjacent voltage-current transition points. For each interval, a polynomial fitting method is used to establish the relationship between voltage and current. For example, for the current range between 112A and 138A, the following relationship can be established by analyzing historical data. Within this interval, the voltage is approximately 70-(current-100) × 0.15 volts. This piecewise expression can more accurately describe the characteristics of the fuel cell at various operating points.

[0077] Substitute the operating state data into the state transition equation to calculate the voltage and current reference values. For each historical operating point, substitute the current value into the state transition equation for the corresponding interval to calculate the theoretical voltage value. This is then compared with the actual voltage value to determine the accuracy of the model and make necessary corrections. For example, for an operating point with a current of 125A, the calculated voltage reference value should be 66.25V, while the actual measured value is 66V. This difference is within an acceptable range, indicating that the model has good fitting accuracy.

[0078] A power prediction equation system is constructed based on the voltage and current reference values. This system consists of three parts: the voltage equation, the current equation, and the power calculation equation. The power calculation follows the principle that electric power equals the product of voltage and current. This system can predict the power output value at any operating point. For example, for an operating point with a voltage of 65V and a current of 120A, the predicted power output is 7800W.

[0079] Power output constraints are established based on the target grid power deficit. These constraints include maximum power limits, minimum power limits, and power change rate limits. For example, when the grid power deficit is 15kW, considering the startup characteristics and safety margins of the hydrogen fuel cell, the system output power range can be set to 10kW to 18kW, with a power change rate not exceeding 2kW / minute.

[0080] Substituting the power output constraints into the power prediction equations, a nonlinear programming solution is used to obtain a power output characteristic curve that satisfies the constraints. During the solution process, an iterative calculation is performed to find the optimal sequence of operating points, enabling the system to both meet grid demand and ensure safe and stable operation. For example, for a 15kW power deficit, a nonlinear programming solution determines that the hydrogen fuel cell should operate at a current of 230A and a voltage of 65.2V, resulting in an output power of 15kW.

[0081] The power output characteristic curve is corrected based on power generation capacity to produce a corrected power output characteristic curve. Power generation capacity is affected by various factors, including hydrogen supply, temperature, and humidity. For example, when the hydrogen supply is insufficient, even though the theoretical output is 18 kW, the actual maximum output is only 15 kW. The power output characteristic curve is dynamically corrected by real-time monitoring of parameters such as hydrogen pressure and stack temperature. In one actual operation, when the system hydrogen pressure dropped below 0.5 MPa, the maximum power output was limited to 12 kW to ensure safe system operation.

[0082] The output power of the hydrogen energy system is determined based on the revised power output characteristic curve. Based on the real-time grid demand and the revised characteristic curve, the optimal power output value is calculated and converted into corresponding voltage and current control signals to drive the hydrogen fuel cell to operate at the target power point. For example, when the grid shortfall is 10kW and the revised maximum output is 12kW, the system will operate at the target power of 10kW, corresponding to an operating point of 200A current and 50V voltage.

[0083] In this embodiment, the nonlinear changes in the operating characteristics of the fuel cell are accurately captured through voltage-current state vector and change rate analysis, so that the power output characteristic curve is more in line with the actual working conditions, and the power control accuracy is greatly improved. By identifying the voltage-current jump point and establishing a piecewise continuous state transfer equation, the dynamic response characteristics of the fuel cell under different working conditions are accurately described, and the system's ability to respond quickly to load changes is improved. The voltage reference value and current reference value calculated based on the state transfer equation provide a stable working reference for the fuel cell, reduce power output fluctuations, and improve system operation stability. By accurately controlling the operating point of the fuel cell, the loss in the energy conversion process is reduced, the hydrogen-to-electricity conversion efficiency is improved, and the system operating cost is reduced.

[0084] Figure 2 This is a flow chart of hydrogen power generation operation control of the method for coordinated complementary regulation of the hydrogen energy system and the power grid according to an embodiment of the present invention.

[0085] A second aspect of an embodiment of the present invention provides a hydrogen energy system and a power grid coordinated complementary regulation system, comprising: The first unit is configured to obtain grid load data and renewable energy generation data, determine a grid load curve based on the grid load data, and determine a renewable energy generation curve based on the renewable energy generation data; The second unit is configured to calculate a power grid supply and demand difference curve based on the power grid load curve and the renewable energy power generation curve; a third unit, configured to switch the hydrogen energy system to a hydrogen production mode within a first target period, determine a target hydrogen production power according to the grid supply and demand difference curve, and adjust the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on the hydrogen production operating parameters of the hydrogen energy system; a fourth unit, configured to switch the hydrogen energy system to a power generation mode within a second target period, determine an output power of the hydrogen energy system based on the power grid supply-demand difference curve, hydrogen pressure data, and operating status data of the hydrogen fuel cell, and adjust operating parameters of the hydrogen fuel cell based on the output power; The fifth unit is used to record the operating parameters of the hydrogen energy system to generate operating status data, and generate an evaluation report on the coordinated operation of the hydrogen energy system and the power grid based on the operating status data.

[0086] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including: A processor and a memory for storing processor-executable instructions, wherein the processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0087] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0088] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for cooperative and complementary regulation of a hydrogen energy system and a power grid, characterized in that: include: Obtaining grid load data and renewable energy generation data, determining a grid load curve based on the grid load data, and determining a renewable energy generation curve based on the renewable energy generation data; Calculating a power grid supply-demand difference curve based on the power grid load curve and the renewable energy power generation curve; Switching the hydrogen energy system to a hydrogen production mode within a first target period, determining a target hydrogen production power according to the grid supply and demand difference curve, and adjusting the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on the hydrogen production operating parameters of the hydrogen energy system; switching the hydrogen energy system to a power generation mode within a second target period, determining an output power of the hydrogen energy system based on the power grid supply-demand difference curve, hydrogen pressure data, and operating status data of the hydrogen fuel cell, and adjusting operating parameters of the hydrogen fuel cell based on the output power; The operating parameters of the hydrogen energy system are recorded to generate operating status data, and an evaluation report on the coordinated operation of the hydrogen energy system and the power grid is generated based on the operating status data.

2. The method according to claim 1, characterized in that Obtaining grid load data and renewable energy generation data, determining a grid load curve based on the grid load data, and determining a renewable energy generation curve based on the renewable energy generation data, including: Dividing the grid load data into multiple time windows according to a time series, and performing data matching on the renewable energy power generation data according to the time windows; Calculating the numerical distribution characteristics of the grid load data and the numerical distribution characteristics of the renewable energy power generation data in each of the time windows, and establishing a correlation matrix between the grid load data and the renewable energy power generation data based on the numerical distribution characteristics; Calculating a deviation coefficient between each data item in the correlation matrix, determining a data verification threshold range according to the deviation coefficient, and generating a data verification rule set based on the data verification threshold range; The grid load data and the renewable energy power generation data are data verified according to the data verification rule set, a grid power load curve is generated according to the grid load data that passes the data verification, and a renewable energy power generation curve is generated according to the renewable energy power generation data that passes the data verification.

3. The method according to claim 1, characterized in that Calculating a grid supply-demand difference curve based on the grid power load curve and the renewable energy power generation curve includes: Extracting load data points from the power grid load curve, calculating the time intervals between adjacent load data points, and generating a load time interval sequence; Extracting power generation data points from the renewable energy power generation curve, calculating the time intervals between adjacent power generation data points, and generating a power generation time interval sequence; determining a reference time sequence based on the load time interval sequence and the power generation time interval sequence; Performing data resampling within the load data segment according to the reference time series, obtaining load data points before and after the corresponding moment for interpolation calculation at each resampling moment to obtain resampled load data; Performing data resampling within the power generation data segment according to the reference time series, obtaining power generation data points before and after the corresponding time for each resampling moment, performing interpolation calculations, and obtaining resampled power generation data; Grid supply and demand difference data is calculated based on the resampled load data and the resampled power generation data, and a grid supply and demand difference curve is generated based on the reference time series and the grid supply and demand difference data.

4. The method according to claim 1, wherein Switching the hydrogen energy system to a hydrogen production mode within a first target time period, determining a target hydrogen production power according to the power grid supply and demand difference curve, and adjusting the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on the hydrogen production operating parameters of the hydrogen energy system, including: determining a grid power surplus within the first target period based on grid power load data and renewable energy power data within the first target period, and calculating a target hydrogen production power according to the grid power surplus and the grid supply-demand difference curve; Collecting the real-time hydrogen production power of the hydrogen energy system, and calculating the power deviation between the real-time hydrogen production power and the target hydrogen production power; Calculating a hydrogen production operating condition compensation coefficient according to the hydrogen production operating condition parameter, and compensating and correcting the power deviation value based on the hydrogen production operating condition compensation coefficient to obtain a corrected power deviation value; Converting the corrected power deviation value into an electrolytic cell current adjustment instruction and an electrolyte flow adjustment instruction, adjusting the input current of the electrolytic cell according to the electrolytic cell current adjustment instruction and adjusting the flow rate of the electrolyte according to the electrolyte flow adjustment instruction; The adjusted real-time hydrogen production power is collected, and the input current of the electrolyzer and the flow rate of the electrolyte are iteratively adjusted based on the adjusted real-time hydrogen production power until the hydrogen production power of the hydrogen energy system is adjusted to the target hydrogen production power.

5. The method according to claim 4, characterized in that Calculating a hydrogen production operating condition compensation coefficient according to the hydrogen production operating condition parameter, and compensating and correcting the power deviation value based on the hydrogen production operating condition compensation coefficient to obtain a corrected power deviation value, including: Determining a deviation matrix of the hydrogen production operating parameters according to the hydrogen production operating parameters, and calculating a fluctuation characteristic value of each hydrogen production operating parameter according to the deviation matrix; Based on the fluctuation characteristic values, the hydrogen production operating condition parameters are hierarchically classified to obtain a plurality of fluctuation parameter groups; Acquiring historical fluctuation data of the fluctuation parameter group, constructing a dynamic prediction window based on the historical fluctuation data, and using a Kalman filter algorithm to predict parameter fluctuation trends within the dynamic prediction window; Adaptively adjusting the compensation weights of the plurality of fluctuation parameter groups based on the parameter fluctuation trend, and performing dynamic compensation calculations on the plurality of fluctuation parameter groups respectively using an adaptive filtering algorithm to obtain correction coefficients; Establishing a frequency division compensation equation according to the correction coefficient, and obtaining a hydrogen production condition compensation coefficient by iteratively solving the frequency division compensation equation; The power deviation value is added to the hydrogen production operating condition compensation coefficient to obtain a corrected power deviation value.

6. The method according to claim 1, wherein Switching the hydrogen energy system to a power generation mode within a second target period, determining the output power of the hydrogen energy system based on the power grid supply-demand difference curve, hydrogen pressure data, and operating status data of the hydrogen fuel cell, and adjusting operating parameters of the hydrogen fuel cell based on the output power, including: calculating an initial grid power deficit based on the load forecast data and the renewable energy power generation forecast data within the second target period, and correcting the initial grid power deficit based on the grid supply and demand difference curve to obtain a target grid power deficit; Based on hydrogen pressure data of the hydrogen storage tank, the hydrogen reserve in the hydrogen storage tank is calculated using the van der Waals equation of state, and the power generation capacity is determined based on the corresponding relationship between the hydrogen reserve and the rated power of the hydrogen fuel cell; constructing a hydrogen fuel cell power output characteristic curve based on the working status data, and determining the output power of the hydrogen energy system in combination with the target grid power deficit, the power generation capacity, and the power output characteristic curve; A hydrogen flow control instruction and a stack current control instruction are generated according to the output power, the opening of the intake valve is adjusted based on the hydrogen flow control instruction, and the input current of the hydrogen fuel cell is adjusted based on the stack current control instruction.

7. The method according to claim 6, characterized in that Constructing a hydrogen fuel cell power output characteristic curve based on the working state data, and determining the output power of the hydrogen energy system in combination with the target grid power deficit, the power generation capacity, and the power output characteristic curve, including: constructing a voltage-current state vector based on the working state data, and calculating a rate of change of the voltage-current state vector in each sampling period; Determining voltage-current transition points according to the change rate, and establishing piecewise continuous state transfer equations between adjacent voltage-current transition points; Substituting the working state data into the state transition equation, calculating a voltage reference value and a current reference value, and constructing a power prediction equation group according to the voltage reference value and the current reference value; Establishing a power output constraint condition based on the target power grid power shortage, substituting the power output constraint condition into the power prediction equation group, and solving the power prediction equation group using nonlinear programming to obtain a power output characteristic curve that satisfies the power output constraint condition; The power output characteristic curve is corrected according to the power generation capacity to obtain a corrected power output characteristic curve, and the output power of the hydrogen energy system is determined based on the corrected power output characteristic curve.

8. A hydrogen energy system and a power grid coordinated complementary regulation system, used to implement the method according to any one of claims 1 to 7, characterized in that: include: The first unit is configured to obtain grid load data and renewable energy generation data, determine a grid load curve based on the grid load data, and determine a renewable energy generation curve based on the renewable energy generation data; The second unit is configured to calculate a power grid supply and demand difference curve based on the power grid load curve and the renewable energy power generation curve; a third unit, configured to switch the hydrogen energy system to a hydrogen production mode within a first target period, determine a target hydrogen production power according to the grid supply and demand difference curve, and adjust the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on the hydrogen production operating parameters of the hydrogen energy system; a fourth unit, configured to switch the hydrogen energy system to a power generation mode within a second target period, determine an output power of the hydrogen energy system based on the power grid supply-demand difference curve, hydrogen pressure data, and operating status data of the hydrogen fuel cell, and adjust operating parameters of the hydrogen fuel cell based on the output power; The fifth unit is used to record the operating parameters of the hydrogen energy system to generate operating status data, and generate an evaluation report on the coordinated operation of the hydrogen energy system and the power grid based on the operating status data.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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