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

Abstract

The application provides a hydrogen energy system and power grid complementary regulation method and system, relates to the technical field of energy management, and comprises the following steps: acquiring 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 and adjusting the hydrogen production power to a target hydrogen production power in a first target period, switching to a power generation mode in a second target period, adjusting the output power according to the supply-demand difference, hydrogen pressure and battery working state, recording operation parameters to generate a cooperative operation evaluation report, and realizing efficient complementation of the power grid and the hydrogen energy system, improving energy utilization efficiency and enhancing power grid regulation capacity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy management, and in particular to a hydrogen energy system and power grid collaborative complementary regulation method and system. BACKGROUND

[0002] The volatility and intermittency problems brought by renewable energy grid connection need to be coordinated and regulated by various flexible resources, and the collaborative complementary operation of hydrogen energy system and power grid becomes an important technical approach to solve the stability and flexibility of power system, which has important significance for promoting large-scale consumption of renewable energy and building clean and low-carbon energy system.

[0003] However, the prior art still has the problems of lacking accurate perception and prediction ability of the power grid supply and demand state, the conversion of hydrogen production and power generation mode is often based on simple time period division or fixed threshold triggering, it is difficult to make flexible response to the dynamic changes of power grid supply and demand, resulting in low collaborative efficiency, failing to fully consider the matching relationship between the working condition parameters of hydrogen energy system and the demand of power grid, lacking comprehensive optimization mechanism for the regulation of hydrogen production power and hydrogen fuel cell output power, it is difficult to simultaneously consider the power grid regulation demand and the safe and efficient operation of hydrogen energy system, and it is difficult to effectively collect and analyze the key data in the collaborative operation process, it is difficult to continuously optimize and adjust the collaborative strategy, and other problems.

[0004] Therefore, there is an urgent need for a solution to solve the problems in the prior art. SUMMARY

[0005] The present application provides a hydrogen energy system and power grid collaborative complementary regulation method and system, which can at least solve some of the problems in the prior art.

[0006] In a first aspect, the present application provides a hydrogen energy system and power grid collaborative complementary regulation method, comprising:

[0007] Obtaining power grid load data and renewable energy generation data, determining a power grid electricity load curve according to the power grid load data, and determining a renewable energy generation curve according to the renewable energy generation data;

[0008] Based on the power grid electricity load curve and the renewable energy generation curve, a power grid supply and demand difference curve is calculated;

[0009] Switching the hydrogen energy system to hydrogen production mode in 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 working condition parameters of the hydrogen energy system;

[0010] switching the hydrogen energy system to a power generation mode in a second target period, determining an output power of the hydrogen energy system according to the power supply-demand difference curve, hydrogen pressure data, and working state data of the hydrogen fuel cell, and adjusting an operating parameter of the hydrogen fuel cell according to the output power;

[0011] recording the operating parameter of the hydrogen energy system to generate operating state data, and generating a hydrogen energy system and power grid cooperative operation evaluation report according to the operating state data.

[0012] In an alternative embodiment,

[0013] obtaining power grid load data and renewable energy generation data, determining a power grid electricity load curve according to the power grid load data, and determining a renewable energy generation curve according to the renewable energy generation data, including:

[0014] dividing the power grid load data into a plurality of time windows according to a time sequence, and matching the renewable energy generation data according to the time windows;

[0015] calculating a numerical distribution feature of the power grid load data and a numerical distribution feature of the renewable energy generation data in each time window, and establishing a correlation matrix of the power grid load data and the renewable energy generation data based on the numerical distribution features;

[0016] 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;

[0017] verifying the power grid load data and the renewable energy generation data according to the data verification rule set, generating a power grid electricity load curve according to the power grid load data that passes the data verification, and generating a renewable energy generation curve according to the renewable energy generation data that passes the data verification.

[0018] In an alternative embodiment,

[0019] based on the power grid electricity load curve and the renewable energy generation curve, calculating a power supply-demand difference curve, including:

[0020] extracting load data points in the power grid electricity load curve, calculating a time interval between adjacent load data points, and generating a load time interval sequence;

[0021] extracting generation data points in the renewable energy generation curve, calculating a time interval between adjacent generation data points, and generating a generation time interval sequence;

[0022] determine a reference time sequence based on the load time interval sequence and the power generation time interval sequence;

[0023] perform data resampling in the load data section according to the reference time sequence, for each resampling time, obtain resampling load data by performing interpolation calculation on load data points before and after the corresponding time;

[0024] perform data resampling in the power generation data section according to the reference time sequence, for each resampling time, obtain resampling power generation data by performing interpolation calculation on power generation data points before and after the corresponding time;

[0025] calculate power supply and demand difference data based on the resampling load data and the resampling power generation data, and generate a power supply and demand difference curve based on the reference time sequence and the power supply and demand difference data.

[0026] In an optional implementation,

[0027] switch the hydrogen energy system to a hydrogen production mode in a first target period, determine a target hydrogen production power according to the power supply and demand difference curve, and adjust the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on hydrogen production working condition parameters of the hydrogen energy system, including:

[0028] determine an amount of power surplus of the power grid in the first target period based on power consumption load data and renewable energy power data of the power grid in the first target period, and calculate a target hydrogen production power according to the amount of power surplus of the power grid and the power supply and demand difference curve;

[0029] collect real-time hydrogen production power of the hydrogen energy system, and calculate a power deviation value of the real-time hydrogen production power and the target hydrogen production power;

[0030] calculate a hydrogen production working condition compensation coefficient according to the hydrogen production working condition parameters, compensate and correct the power deviation value based on the hydrogen production working condition compensation coefficient, and obtain a corrected power deviation value;

[0031] convert the corrected power deviation value into electrolyzer current adjustment instructions and electrolyte flow adjustment instructions, adjust input current of the electrolyzer according to the electrolyzer current adjustment instructions and adjust flow of the electrolyte according to the electrolyte flow adjustment instructions;

[0032] collect the adjusted real-time hydrogen production power, and iteratively adjust input current of the electrolyzer and flow of the electrolyte 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.

[0033] In an optional implementation,

[0034] According to the hydrogen production working condition parameter calculation hydrogen production working condition compensation coefficient, based on the power deviation value is compensated and corrected, obtains the corrected power deviation value, including:

[0035] According to the hydrogen production working condition parameter determination hydrogen production working condition parameter deviation matrix, and according to the deviation matrix calculation each hydrogen production working condition parameter fluctuation characteristic value;

[0036] Based on the fluctuation characteristic value, the hydrogen production working condition parameter is classified, and a plurality of fluctuation parameter groups are obtained;

[0037] The historical fluctuation data of the fluctuation parameter group is obtained, the dynamic prediction window is constructed according to the historical fluctuation data, and the parameter fluctuation trend in the dynamic prediction window is predicted by using Kalman filtering algorithm;

[0038] Based on the parameter fluctuation trend, the compensation weight of the plurality of fluctuation parameter groups is adaptively adjusted, and the adaptive filtering algorithm is used to dynamically compensate and calculate the plurality of fluctuation parameter groups respectively, and the correction coefficient is obtained;

[0039] According to the correction coefficient, the frequency compensation equation is established, and the hydrogen production working condition compensation coefficient is obtained by iteratively solving the frequency compensation equation;

[0040] The power deviation value and the hydrogen production working condition compensation coefficient are obtained, and the corrected power deviation value is obtained.

[0041] In an alternative embodiment,

[0042] In a second target period, the hydrogen energy system is switched to the power generation mode, the output power of the hydrogen energy system is determined according to the power supply-demand difference curve, hydrogen pressure data and working state data of hydrogen fuel cell, and the operating parameters of the hydrogen fuel cell are adjusted according to the output power, including:

[0043] According to the load prediction data and the renewable energy power generation prediction data in the second target period, the initial power shortage of the power grid is calculated, and the initial power shortage of the power grid is corrected according to the power supply-demand difference curve to obtain the target power shortage of the power grid;

[0044] Based on the hydrogen pressure data of the hydrogen storage tank, the hydrogen storage capacity in the hydrogen storage tank is calculated by using the van der Waals state equation, and the power generation capacity is determined based on the corresponding relationship between the hydrogen storage capacity and the rated power of the hydrogen fuel cell;

[0045] Based on the working state data, the hydrogen fuel cell power output characteristic curve is constructed, and the output power of the hydrogen energy system is determined in combination with the target power shortage of the power grid, the power generation capacity and the power output characteristic curve;

[0046] According to the output power, hydrogen flow control instructions and stack current control instructions are generated, the opening of the air inlet valve is adjusted based on the hydrogen flow control instructions, and the input current of the hydrogen fuel cell is adjusted based on the stack current control instructions.

[0047] In an alternative embodiment,

[0048] Based on the working state data, a hydrogen fuel cell power output characteristic curve is constructed, and the output power of the hydrogen energy system is determined in combination with the target power shortage of the power grid, the power generation capacity and the power output characteristic curve, including:

[0049] Based on the working state data, a voltage-current state vector is constructed, and the rate of change of the voltage-current state vector in each sampling period is calculated;

[0050] According to the rate of change, voltage-current jump points are determined, and a piecewise continuous state transition equation is established between adjacent voltage-current jump points;

[0051] The working state data is substituted into the state transition equation to calculate the voltage reference value and the current reference value, and a power prediction equation set is constructed according to the voltage reference value and the current reference value;

[0052] Based on the target power shortage of the power grid, a power output constraint condition is established, the power output constraint condition is substituted into the power prediction equation set, and a nonlinear programming is used to solve the power prediction equation set to obtain a power output characteristic curve that satisfies the power output constraint condition;

[0053] According to the power generation capacity, the power output characteristic curve is modified to obtain a modified power output characteristic curve, and the output power of the hydrogen energy system is determined based on the modified power output characteristic curve.

[0054] In a second aspect of the embodiment of the present application, a hydrogen energy system and power grid collaborative complementary regulation system is provided, comprising:

[0055] The first unit is configured to obtain power grid load data and renewable energy power generation data, determine a power grid electricity load curve based on the power grid load data, and determine a renewable energy power generation curve based on the renewable energy power generation data;

[0056] The second unit is configured to calculate a power grid supply-demand difference curve based on the power grid electricity load curve and the renewable energy power generation curve;

[0057] The third unit is configured to switch the hydrogen energy system to a hydrogen production mode in a first target period, determine a target hydrogen production power based on the power grid supply-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 condition parameters of the hydrogen energy system.

[0058] a fourth unit configured to switch the hydrogen energy system to a power generation mode in a second target time period, determine an output power of the hydrogen energy system according to the power supply-demand difference curve, hydrogen pressure data, and working state data of the hydrogen fuel cell, and adjust operating parameters of the hydrogen fuel cell according to the output power;

[0059] a fifth unit configured to record the operating parameters of the hydrogen energy system to generate working state data, and generate a hydrogen energy system and power grid cooperative operation evaluation report according to the working state data.

[0060] In a third aspect of the embodiments of the present application, an electronic device is provided, comprising:

[0061] a processor and a memory for storing processor-executable instructions, wherein the processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0062] In a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0063] In the present application, by acquiring power grid load data and renewable energy generation data, a power supply-demand difference curve is calculated, the operation state of the power grid is accurately grasped, and a basis for switching the working mode of the hydrogen energy system is provided, the scientificity and rationality of power grid dispatching are effectively improved, the power output of the hydrogen energy system is flexibly adjusted according to the power supply-demand situation, the smooth adjustment of the power grid load is realized, the renewable energy generation fluctuation is effectively absorbed, the stability and reliability of the power system are improved, the operation parameters of the hydrogen energy system are recorded and an evaluation report is generated, a perfect operation monitoring and evaluation system is established, which is helpful for the system to continuously optimize the adjustment strategy, improves the economic efficiency and environmental protection benefit of the entire hydrogen energy and power grid cooperative system, and provides a feasible technical path for energy transformation. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 FIG. 1 is a flowchart of a hydrogen energy system and power grid cooperative complementary adjustment method according to an embodiment of the present application;

[0065] Figure 2 FIG. 2 is a hydrogen energy power generation operation control flowchart of the hydrogen energy system and power grid cooperative complementary adjustment method according to an embodiment of the present application. DETAILED DESCRIPTION

[0066] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0067] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and some embodiments can not be described again for the same or similar concepts or processes.

[0068] Figure 1 The flowchart of the hydrogen energy system and power grid collaborative complementary regulation method of the embodiments of the present application is shown in FIG. 1, which comprises the following steps. Figure 1

[0069] Obtaining power grid load data and renewable energy generation data, determining a power grid electricity load curve according to the power grid load data, and determining a renewable energy generation curve according to the renewable energy generation data;

[0070] Based on the power grid electricity load curve and the renewable energy generation curve, a power grid supply-demand difference curve is calculated;

[0071] Switching the hydrogen energy system to a hydrogen production mode in a first target period, determining a target hydrogen production power according to the power grid supply-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 working condition parameters of the hydrogen energy system;

[0072] Switching the hydrogen energy system to a power generation mode in a second target period, determining an output power of the hydrogen energy system according to the power grid supply-demand difference curve, hydrogen pressure data and working state data of the hydrogen fuel cell, and adjusting the operating parameters of the hydrogen fuel cell according to the output power;

[0073] Recording the operating parameters of the hydrogen energy system to generate operating state data, and generating a hydrogen energy system and power grid collaborative operation evaluation report according to the operating state data.

[0074] In an optional embodiment,

[0075] Obtaining power grid load data and renewable energy generation data, determining a power grid electricity load curve according to the power grid load data, and determining a renewable energy generation curve according to the renewable energy generation data, comprises:

[0076] ​The power grid load data is divided into multiple time windows according to time sequence, and the renewable energy power generation data is matched according to the time windows;

[0077] The numerical distribution characteristics of the power grid load data and the numerical distribution characteristics of the renewable energy power generation data in each time window are calculated, and a correlation matrix of the power grid load data and the renewable energy power generation data is established based on the numerical distribution characteristics;

[0078] The deviation coefficients between the data items in the correlation matrix are calculated, a data verification threshold range is determined according to the deviation coefficients, and a data verification rule set is generated based on the data verification threshold range;

[0079] The power grid load data and the renewable energy power generation data are verified according to the data verification rule set, a power grid power consumption load curve is generated according to the power 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.

[0080] The power grid load data and the renewable energy power generation data are obtained. The power grid load data includes power consumption at different time points, such as power consumption data recorded every 15 minutes; the renewable energy power generation data includes power generation data of renewable energy sources such as solar energy and wind energy at different time points, which can also be recorded every 15 minutes.

[0081] After obtaining the data, the power grid load data is divided into multiple time windows according to time sequence, for example, 24 hours of data can be divided into 96 time windows of 15 minutes, or 24 time windows of 1 hour. The size of the time window can be flexibly adjusted according to the actual application scenario and data characteristics, such as selecting a smaller time window of 15 minutes during peak hours and a larger time window of 1 hour during off-peak hours. For renewable energy power generation data, the system also matches the data according to the same time window to ensure that the two types of data correspond consistently in the time dimension. For example, in the time window of 10:00-10:15 on October 15, 2023, the power grid load data is 500 MW, and the corresponding renewable energy power generation data is 100 MW.

[0082] After data matching, the numerical distribution characteristics of the power grid load data and the renewable energy power generation data in each time window are calculated. The numerical distribution characteristics include but are not limited to mean, standard deviation, median, maximum, minimum, and other statistical indicators. For example, for the time window of 8:00-9:00 on weekdays, the average power grid load in the past 30 days can be calculated as 800 MW with a standard deviation of 50 MW; the average renewable energy power generation in the same period is 200 MW with a standard deviation of 30 MW.

[0083] Based on the calculated numerical distribution characteristics, a correlation matrix of power grid load data and renewable energy generation data is established. The correlation matrix reflects the correlation between the two types of data in different time windows and different feature dimensions. Each element of the matrix represents the correlation coefficient between the corresponding dimensions, with a value range of [-1, 1], where 1 represents complete positive correlation, -1 represents complete negative correlation, and 0 represents no correlation. For example, the power grid load in the morning of weekdays may have a high positive correlation with solar power generation, with a correlation coefficient of 0.85; while the power grid load at night may have a low correlation with solar power generation, with a correlation coefficient close to 0.

[0084] The deviation coefficients between the data items in the correlation matrix are calculated. The deviation coefficient is a dimensionless index that measures the degree of data deviation, obtained by calculating the difference between the data and the expected value and normalizing it. The deviation coefficients of the power grid load data and the renewable energy generation data in each time window are calculated, for example, the power grid load deviation coefficient of a certain weekday from 9:00 to 10:00 is 0.05, indicating that the load data in this period deviates from the historical data in the same period by 5%; while the deviation coefficient of renewable energy generation is 0.15, indicating a deviation of 15%.

[0085] According to the calculated deviation coefficients, the data verification threshold range is determined, and reasonable upper and lower threshold values are set through statistical analysis of historical data for subsequent data verification. For example, for power grid load data, the deviation coefficient threshold range is set to [-0.1, 0.1], i.e. data with a deviation coefficient between -10% and 10% is considered valid; for renewable energy generation data, considering its greater volatility, a wider threshold range can be set, such as [-0.2, 0.2].

[0086] Based on the determined data verification threshold range, a data verification rule set is generated. The rule set contains a series of judgment conditions for identifying abnormal data points. For example, rule 1 can be "if the power grid load data deviation coefficient in the 8:00-9:00 time window of weekdays exceeds 0.1, mark it as abnormal data"; rule 2 can be "if the solar power generation deviation coefficient in the 12:00-13:00 time window of sunny days is less than -0.2, mark it as abnormal data".

[0087] According to the generated data verification rule set, the power grid load data and renewable energy generation data are verified, and each data is compared with the conditions in the rule set to identify abnormal data that does not meet the rules. For example, it is detected that the power grid load data at 9:15 on October 16, 2023 is 1200MW, while the historical average value at the same period is 800MW, with a deviation coefficient of 0.5, which exceeds the set threshold range, so it is marked as abnormal data.

[0088] For the power grid load data verified by the data, it is arranged in chronological order to generate a power grid electricity load curve, which shows the change of power grid electricity load at different time points and can intuitively reflect the electricity peak and valley. Similarly, according to the renewable energy generation data verified by the data, a renewable energy generation curve is generated to show the renewable energy generation at different time points.

[0089] In this embodiment, the original data is verified by the data verification rule set, which can effectively filter out abnormal values and error data, ensure the accuracy of subsequent analysis, and deeply mine the internal relationship between the two types of data by establishing the correlation matrix of power grid load and renewable energy generation data. A more scientific basis is provided for energy dispatching decision-making. The data verification threshold range is dynamically determined based on the bias coefficient, making the verification process more flexible and adaptive, and being able to adapt to the data feature changes of different time periods and different seasons.

[0090] In an optional implementation,

[0091] Based on the power grid electricity load curve and the renewable energy generation curve, the power supply and demand difference curve is calculated, including:

[0092] Extract the load data points in the power grid electricity load curve, calculate the time interval between adjacent load data points, and generate a load time interval sequence;

[0093] Extract the power generation data points in the renewable energy generation curve, calculate the time interval between adjacent power generation data points, and generate a power generation time interval sequence;

[0094] Determine a reference time sequence based on the load time interval sequence and the power generation time interval sequence;

[0095] According to the reference time sequence, perform data resampling in the load data segment. At each resampling time, the load data points before and after the corresponding time are obtained for interpolation calculation to obtain resampled load data.

[0096] According to the reference time sequence, perform data resampling in the power generation data segment. At each resampling time, the power generation data points before and after the corresponding time are obtained for interpolation calculation to obtain resampled power generation data.

[0097] Based on the resampled load data and the resampled power generation data, calculate the power supply and demand difference data, and generate a power supply and demand difference curve based on the reference time sequence and the power supply and demand difference data.

[0098] Obtain raw data of power grid electricity load curve and renewable energy generation curve. The power grid electricity load curve records the electricity load change of the power grid in a period of time (e.g. 24 hours), and the renewable energy generation curve records the power generation change of wind energy, solar energy and other renewable energy in the same period of time. Since the two types of data are often collected from different monitoring systems, their sampling time intervals are usually inconsistent, and data processing is needed to calculate the supply-demand difference.

[0099] Extract load data points from the power grid electricity load curve, such as {(t1, L1), (t2, L2),..., (tn, Ln)}, where ti represents the time point and Li represents the electricity load value at the 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 the two adjacent load data points are 10:00 and 10:15, the time interval is 15 minutes.

[0100] Extract 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 the time point. Calculate the time interval between adjacent generation data points, i.e. Δtgen_j=sj+1-sj, and generate a generation time interval sequence {Δtgen_1, Δtgen_2,..., Δtgen_m-1}. For example, if the times of the two adjacent generation data points are 10:00 and 10:10, the time interval is 10 minutes.

[0101] Based on the obtained load time interval sequence and generation time interval sequence, determine a reference time sequence. The method of determining the reference time sequence can be to take the minimum value of the two time interval sequences as the reference sampling interval, or to take the greatest common divisor of the two. In this embodiment, the minimum value of the two is selected, assuming that the sampling interval of the load data is 15 minutes and the sampling interval of the generation data is 10 minutes, then the reference time interval is 10 minutes. The system generates a reference time sequence accordingly, such as {0:00, 0:10, 0:20,..., 23:50}.

[0102] The data resampling is performed in the load data section according to the benchmark time sequence. For each time point in the benchmark time sequence, if the time point is not in the original load data, the load value at the time point is obtained by interpolation calculation. Specifically, the two nearest original load data points before and after the time point are found, and the load value at the time point is calculated using the linear interpolation method. For example, for the benchmark time point 0:10, if the original load data has a load value of 100 MW at 0:00 and a load value of 120 MW at 0:15, the load value at 0:10 is calculated by linear interpolation as: 100 + (120-100) x (10 / 15) = 113.33 MW.

[0103] The data resampling is performed in the generation data section according to the benchmark time sequence. For each benchmark time point, if the time point is not in the original generation data, the generation value at the time point is also obtained by interpolation calculation. For example, if the benchmark time point is 0:05, the original generation data has a generation value of 50 MW at 0:00 and a generation value of 55 MW at 0:10, the generation value at 0:05 is calculated by linear interpolation as: 50 + (55-50) x (5 / 10) = 52.5 MW.

[0104] After completing the data resampling, the power supply and demand difference data is calculated based on the resampled load data and generation data. For each time point in the benchmark time sequence, the load value at the time point is subtracted from the generation value to obtain the supply and demand difference. For example, if the resampled load value at a certain time point is 150 MW and the generation value is 100 MW, the supply and demand difference at the time point is 150-100 = 50 MW, indicating that the power supply gap at this time is 50 MW, which needs to be supplemented by traditional energy or energy storage system.

[0105] The power supply and demand difference curve is generated based on the benchmark time sequence and the power supply and demand difference data. The power supply and demand difference curve directly shows the supply and demand balance of the power grid in a period of time, and can be used to guide the charging and discharging scheduling of the energy storage system, the formulation of traditional energy generation plan, and the adjustment of power grid load, etc.

[0106] 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)}.

[0107] Through calculation, the reference time sequence is a 10-minute interval sequence {8:00, 8:10, 8:20, …, 12:00}. After data resampling and supply-demand difference calculation, the obtained power grid supply-demand difference curve data points 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, 14.7MW), (10:00, 5MW), (10:10, 10MW), (10:20, 15MW), (10:30, 18MW), (10:40, 23.3MW), (10:50, 28.7MW), (11:00, 30MW), (11:10, 35MW), (11:20, 40MW), (11:30, 43MW), (11:40, 46.7MW), (11:50, 50.3MW), (12:00, 55MW)}.

[0108] In this embodiment, by establishing a reference time sequence, the problem of inconsistent collection time of load data and power generation data is solved, providing a time basis for accurate calculation of supply-demand difference. The resampled data points are obtained by interpolation calculation, which retains the trend characteristics of the original data and eliminates the errors caused by uneven sampling, improves the data accuracy, and aligns the load data and power generation data in the time dimension after sampling, enhances the comparability of the data, makes the supply-demand difference calculation more accurate, and generates an accurate power grid supply-demand difference curve, which can intuitively reflect the supply-demand situation of the power grid at different time periods, provides an important basis for power grid dispatching decision, and helps to reduce power waste and improve power grid stability.

[0109] In an alternative embodiment,

[0110] switching the hydrogen energy system to a hydrogen production mode in a first target time period, determining a target hydrogen production power according to the power grid supply-demand difference curve, and adjusting the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on hydrogen production working condition parameters of the hydrogen energy system, comprising:

[0111] determining a power grid power surplus in the first target time period based on power grid electricity load data and renewable energy power data in the first target time period, and calculating a target hydrogen production power according to the power grid power surplus and the power grid supply-demand difference curve;

[0112] collecting a real-time hydrogen production power of the hydrogen energy system, and calculating a power deviation value of the real-time hydrogen production power and the target hydrogen production power;

[0113] According to the hydrogen production working condition parameter, a hydrogen production working condition compensation coefficient is calculated, and the power deviation value is compensated and corrected based on the hydrogen production working condition compensation coefficient, so as to obtain a corrected power deviation value;

[0114] The corrected power deviation value is converted into electrolytic cell current adjustment instruction and electrolyte flow adjustment instruction, and the input current of the electrolytic cell is adjusted according to the electrolytic cell current adjustment instruction and the flow of the electrolyte is adjusted according to the electrolyte flow adjustment instruction;

[0115] The adjusted real-time hydrogen production power is collected, and the input current of the electrolytic cell and the flow 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.

[0116] In the first target period, the target hydrogen production power is determined according to the power supply-demand difference curve of the power grid, and the power is adjusted based on the hydrogen production working condition parameters of the hydrogen energy system.

[0117] The hydrogen energy system controller switches the hydrogen energy system to the hydrogen production mode by receiving the switching instruction sent by the power grid dispatching system. The controller collects the power consumption load data and renewable energy power data of the power grid to determine the power surplus of the power grid in the first target period. The power consumption load data is obtained from the smart grid monitoring system, and the renewable energy power data is obtained from the power generation monitoring system of the wind farm and photovoltaic power station. Taking a certain region as an example, the power consumption load of the power grid is 400 MW in the period of 10:00-15:00, and the total power generation of wind power and photovoltaic power is 500 MW in the same period, so the power surplus of the power grid is 100 MW.

[0118] The controller calculates the target hydrogen production power in combination with the power supply-demand difference curve of the power grid. The power supply-demand difference curve reflects the law of the difference between power supply and demand in the power grid changing with time, which is usually generated by the power grid dispatching department according to historical data and power load prediction. The power supply-demand difference curve obtained by the control system shows that the power that can be allocated to the hydrogen energy system is 30% of the power surplus of the power grid, i.e. 30 MW, at the current time. Considering that the maximum hydrogen production power of the hydrogen energy system is 25 MW, the controller sets the target hydrogen production power to 25 MW.

[0119] After determining the target hydrogen production power, the real-time hydrogen production power of the hydrogen energy system is collected. Assuming that the real-time hydrogen production power of the hydrogen energy system is 18 MW, the power deviation value is 7 MW (25 MW-18 MW). The deviation value indicates that the hydrogen production power needs to be increased by 7 MW to reach the target value.

[0120] The control system calculates a hydrogen production condition compensation coefficient based on hydrogen production condition parameters. The hydrogen production condition parameters include electrolytic cell temperature, electrolytic cell pressure, electrolyte concentration, and electrolyte temperature, etc. For example, the collected electrolytic cell temperature is 62°C (standard condition is 70°C), the electrolytic cell pressure is 2.8 MPa (standard condition is 3.0 MPa), the electrolyte concentration is 28% (standard condition is 30%), and the electrolyte temperature is 58°C (standard condition is 60°C). According to the deviation of the parameters from the standard condition, the hydrogen production condition compensation coefficient is calculated to be 0.92, indicating that the hydrogen production efficiency under the same current input under the current condition is 92% of the standard condition.

[0121] The controller compensates and corrects the power deviation value based on the hydrogen production condition compensation coefficient. The corrected power deviation value is 7.61 MW (7 MW / 0.92), meaning that under the current condition, an additional input power of 7.61 MW is needed to achieve an additional hydrogen production power of 7 MW.

[0122] The corrected power deviation value is converted into electrolytic cell current adjustment instructions and electrolyte flow adjustment instructions. For the proton exchange membrane electrolytic cell used in this example, the control system determines according to the electrolytic cell characteristic curve that the electrolytic cell current needs to be increased from 1800 A to 2350 A, and the electrolyte flow needs to be increased from 180 L / min to 215 L / min.

[0123] The controller sends instructions to the electrolytic cell power supply system and the electrolyte circulating pump through the programmable logic controller (PLC) to adjust the electrolytic cell input current and the electrolyte flow. The power supply system gradually increases the output current from 1800 A to 2350 A with an increase step of 50 A / s. At the same time, the speed of the electrolyte circulating pump is increased from 60 Hz to 72 Hz, so that the electrolyte flow reaches 215 L / min.

[0124] After the adjustment is completed, the real-time hydrogen production power after adjustment is collected. Assuming that the measured real-time hydrogen production power after adjustment is 24.2 MW, there is still a gap of 0.8 MW from the target hydrogen production power of 25 MW. The control system continues to iteratively adjust the electrolytic cell current from 2350 A to 2390 A and the electrolyte flow from 215 L / min to 218 L / min. After measuring again, the real-time hydrogen production power reaches 24.9 MW, with a gap of 0.1 MW from the target value, which is within the allowable error range, and is considered to have reached the target hydrogen production power.

[0125] During the entire adjustment process, the control system continuously monitors safety parameters such as electrolytic cell temperature, electrolytic cell pressure, and hydrogen purity to ensure that the electrolytic cell operates within a safe operating range. For example, the electrolytic cell temperature does not exceed 75°C, the electrolytic cell pressure does not exceed 3.5 MPa, and the hydrogen purity is maintained above 99.99%.

[0126] In this embodiment, the target hydrogen production power is determined by calculating the power surplus 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 abandoned wind and light, and improve the utilization efficiency of renewable energy. Through real-time power deviation calculation and hydrogen production condition compensation mechanism, the characteristics of electrolytic cell under different conditions are considered, the hydrogen production power regulation is more accurate, the oscillation and error in the regulation process are reduced, the electrolytic cell current and electrolyte flow are used for double parameter cooperative regulation, and the closed loop control is formed through real-time power feedback, so that the hydrogen production power can quickly and stably reach the target value, the system operation stability is improved, the unreasonable operation of equipment under extreme conditions is avoided through considering the compensation correction of hydrogen production condition parameters, the equipment wear is reduced, and the service life of electrolytic cell and other core equipment is prolonged.

[0127] In an alternative embodiment,

[0128] According to the hydrogen production condition parameter, a hydrogen production condition compensation coefficient is calculated, and a modified power deviation value is obtained by compensating and correcting the power deviation value based on the hydrogen production condition compensation coefficient, comprising:

[0129] According to the hydrogen production condition parameter, a deviation matrix of the hydrogen production condition parameter is determined, and a fluctuation characteristic value of each hydrogen production condition parameter is calculated according to the deviation matrix;

[0130] Based on the fluctuation characteristic value, the hydrogen production condition parameters are classified in layers, and a plurality of fluctuation parameter groups are obtained;

[0131] The historical fluctuation data of the fluctuation parameter group is obtained, a dynamic prediction window is constructed according to the historical fluctuation data, and the parameter fluctuation trend in the dynamic prediction window is predicted by using Kalman filtering algorithm;

[0132] Based on the parameter fluctuation trend, the compensation weight of the plurality of fluctuation parameter groups is adaptively adjusted, and the plurality of fluctuation parameter groups are dynamically compensated and calculated by using adaptive filtering algorithm respectively, and a correction coefficient is obtained;

[0133] According to the correction coefficient, a frequency compensation equation is established, and a hydrogen production condition compensation coefficient is obtained by iteratively solving the frequency compensation equation;

[0134] The power deviation value and the hydrogen production condition compensation coefficient are obtained, and a modified power deviation value is obtained.

[0135] The hydrogen production condition parameters and the power deviation value are obtained. The hydrogen production condition parameters usually include electrolytic cell temperature, electrolyte concentration, power supply voltage fluctuation, membrane thickness change and other key parameters. The power deviation value is the difference between the actual running power and the set power.

[0136] The process of calculating the hydrogen production condition compensation coefficient according to the obtained hydrogen production condition parameters starts with constructing a deviation matrix. For example, it is assumed that the electrolytic cell temperature is monitored to fluctuate in the range of ±3°C based on the standard value of 65°C, the electrolyte concentration is monitored to fluctuate in the range of ±2% based on the standard value of 30%, and the power supply voltage is monitored to fluctuate in the range of ±5V based on the standard value of 220V. The differences between the real-time values of the aforementioned parameters and the standard values form the deviation matrix. For example, at a certain time, the deviation matrix can be represented as a temperature deviation of +2°C, a concentration deviation of -1.5%, a voltage deviation of +3V, and the like.

[0137] Based on the deviation matrix, the fluctuation characteristic values are calculated by the sliding window method. For example, within a time window of 10 minutes, 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 form the fluctuation characteristic values.

[0138] According to the fluctuation characteristic values, the parameters are classified by hierarchical clustering analysis. The parameters are divided into high-frequency small-amplitude group, high-frequency large-amplitude group, low-frequency small-amplitude group, and low-frequency large-amplitude group according to the differences in fluctuation frequency and amplitude. For example, the electrolyte concentration is classified into the low-frequency small-amplitude group (fluctuation frequency 0.005Hz, amplitude 1.8%), and the power supply voltage is classified into the high-frequency small-amplitude group (fluctuation frequency 0.05Hz, amplitude 4.2V). In this way, multiple fluctuation parameter groups are formed.

[0139] For each fluctuation parameter group, historical fluctuation data are obtained, and parameter fluctuation records under similar conditions in the past 30 days are extracted from the database to construct a dynamic prediction window. The length of the dynamic window is adaptively adjusted according to the fluctuation characteristics of the parameters, with a high-frequency parameter window set to 5 minutes and a low-frequency parameter window set to 15 minutes. The Kalman filter algorithm is applied to predict the parameter change trend in the window, and through the state equation and observation equation, combined with the noise characteristics of the historical data, the possible value and uncertainty of the parameter at future time are predicted.

[0140] Based on the predicted parameter fluctuation trend, the compensation weights of different fluctuation parameter groups are adaptively adjusted. The weight adjustment follows the principle of "increasing the weight of fluctuation parameters with intense fluctuation and decreasing the weight of fluctuation parameters with stable fluctuation". For example, when the electrolytic cell temperature fluctuation intensifies (the standard deviation increases from 0.5°C to 0.8°C), its weight increases from the original 0.3 to 0.42. The adaptive filtering algorithm is used to process each fluctuation parameter group. 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 retain the characteristic frequency components. Through differential processing, the corresponding correction coefficient is calculated for each parameter group. For example, the correction coefficient of the high-frequency small-amplitude group is 1.08, and the correction coefficient of the low-frequency large-amplitude group is 0.92.

[0141] The frequency compensation equation is established according to the correction coefficients of each parameter group. The equation combines the parameter influences of different frequency characteristics, which is formally expressed as the weighted sum of each correction coefficient. Through iterative calculation, the weight coefficients are continuously adjusted until the error between the equation output value and the actual observed power deviation correction effect is less than a preset threshold (such as 0.5%). The final hydrogen production condition compensation coefficient is a scalar value that comprehensively considers the influence of each parameter, for example, 1.12.

[0142] The power deviation value is multiplied by the hydrogen production condition compensation coefficient to complete the compensation correction. For example, if the original power deviation value is -2.5 kW and the compensation coefficient is 1.12, the corrected power deviation value is -2.8 kW. The system will apply this corrected value to the power control of the hydrogen production system to make the actual operating power closer to the set value.

[0143] In this embodiment, the hydrogen production condition parameters are classified in detail through deviation matrix and fluctuation characteristic value analysis, making the compensation calculation more consistent with the actual fluctuation characteristics of different parameters, significantly improving the compensation accuracy. Through the dynamic prediction window and the adaptive filtering algorithm, the pre-judgment and compensation of the rapid changes of the hydrogen production condition are realized, improving the response speed and adaptability of the system to the changes of the working condition. The parameter groups with different fluctuation characteristics are processed by the frequency compensation equation, effectively reducing the complex coupling influence between the hydrogen production condition parameters, making the control logic more clear and effective, and the accurate working condition compensation avoids the impact of parameter fluctuations on the equipment, reduces the operating pressure of the core equipment such as electrolytic cell, and effectively prolongs the service life of the equipment.

[0144] In an alternative embodiment,

[0145] In the second target period, the hydrogen energy system is switched to power generation mode, the output power of the hydrogen energy system is determined according to the power supply-demand difference curve, hydrogen pressure data, and working state data of the hydrogen fuel cell, and the operating parameters of the hydrogen fuel cell are adjusted according to the output power, including:

[0146] An initial power shortage of the power grid is calculated according to the load prediction data and renewable energy generation prediction data in the second target period, and the initial power shortage of the power grid is corrected according to the power supply-demand difference curve to obtain a target power shortage of the power grid;

[0147] Based on the hydrogen pressure data of the hydrogen storage tank, the hydrogen storage capacity in the hydrogen storage tank is calculated using the van der Waals state equation, and the power generation capacity is determined based on the corresponding relationship between the hydrogen storage capacity and the rated power of the hydrogen fuel cell;

[0148] Based on the working state data, a power output characteristic curve of the hydrogen fuel cell is constructed, and the output power of the hydrogen energy system is determined in combination with the target power shortage of the power grid, the power generation capacity, and the power output characteristic curve.

[0149] The hydrogen flow control instruction and the stack current control instruction are generated according to the output power, the opening degree of the inlet 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.

[0150] The power generation mode switching operation is performed in a second target period, which generally corresponds to a power grid load peak period or a renewable energy power generation trough period, at which 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 the hydrogen fuel cell to obtain power grid load prediction data and renewable energy power generation prediction data in the second target period. For example, the load prediction peak of a certain region in the 18:00-22:00 period is 120 MW, and the predicted power generation of photovoltaic and wind power in the same period is 10 MW and 25 MW respectively, the initial power gap of the power grid is calculated as 85 MW, and the target power gap of the power grid is determined as 80 MW according to the real-time monitored power supply and demand difference curve, considering the power grid scheduling and actual operation deviation.

[0151] The hydrogen energy system monitors the pressure data of the hydrogen storage tank in real time through a pressure sensor. For example, when the measured pressure of the storage tank is 35 MPa and the temperature is 25°C, the total amount of hydrogen in the storage tank is calculated to be about 1200 kg by using the van der Waals equation of state. The van der Waals equation of state considers the non-ideal characteristics of real gas under high pressure conditions, and accurately calculates the actual hydrogen mass in the storage tank through the relationship among the pressure, volume and temperature of hydrogen, combined with the gas constant and hydrogen characteristic parameters. The system stores a corresponding relationship table of hydrogen storage and hydrogen fuel cell power generation capacity, and according to the query of the table, it is known that 1200 kg of hydrogen can support 10 MW of hydrogen fuel cell continuous power generation for about 12 hours, i.e. the current maximum power generation capacity of the system is 10 MW.

[0152] The working state data of the hydrogen fuel cell includes parameters such as stack temperature, humidity, membrane electrode assembly health status, etc. The current average temperature of the stack is 75°C, the relative humidity is 85%, and the health degree of the membrane electrode assembly is 92%. Based on these data, a 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 power gap of the power grid 80 MW, the power generation capacity 10 MW, and the best working point shown by the power output characteristic curve, the output power of the hydrogen energy system is determined as 8 MW, which can not only ensure a high power generation efficiency (about 52%), but also maintain stable operation of the system.

[0153] After determining the output power, it is necessary to accurately control the hydrogen flow and the stack current to realize stable power generation. For an output power of 8 MW, the required hydrogen flow is calculated to be 94 Nm³ / h. A hydrogen flow control instruction is generated, and the instruction is sent to the inlet valve control unit. The control unit uses a PID control algorithm to calculate that the valve opening needs to be adjusted to 28% according to the current valve opening of 15% and the target hydrogen flow. The valve actuator adjusts the valve opening smoothly according to the instruction, realizing accurate control of the hydrogen flow. During the adjustment process, the system continuously monitors the actual hydrogen flow through the flowmeter to ensure that it is stable within the range of 94±2 Nm³ / h.

[0154] A stack current control instruction is generated. For a hydrogen fuel cell system with a rated voltage of 750V, to output 8MW power, the stack current needs to be controlled around 10667A. The current control instruction is sent to the power electronic converter. The converter uses constant current control mode to gradually ramp up the current from the initial value of 0A to the target current of 10667A at a rate of 200A / s to avoid excessive instantaneous load changes on the stack. The entire current adjustment process lasts about 53 seconds, during which the system monitors the stack temperature in real time to ensure that it does not exceed the safety threshold of 85°C.

[0155] After the current is stabilized, the stack voltage is continuously monitored. When the voltage is stable within the range of 750±5V, it is confirmed that the hydrogen fuel cell has reached a stable power generation state. At this time, the hydrogen energy system successfully switches to power generation mode and stably outputs 8MW power to the grid, relieving the power shortage of the grid. During the entire power generation process, key parameters such as hydrogen pressure and stack temperature are continuously monitored. When an anomaly is detected, protective measures such as reducing output power or emergency shutdown are immediately executed to ensure safe and reliable operation.

[0156] In this embodiment, the power shortage is calculated based on load prediction data and renewable energy generation prediction data, and is corrected in combination with the grid supply-demand difference curve, so that the hydrogen energy system can accurately respond to the actual demand of the grid, improving the accuracy of grid dispatching. Through the coordinated adjustment of the hydrogen flow control instruction and the stack current control instruction, accurate control of the output power of the hydrogen fuel cell is realized, reducing power fluctuations and adjustment errors. By accurately controlling the hydrogen flow and the stack current, the hydrogen fuel cell operates near the optimal efficiency point, improving the hydrogen-electric conversion efficiency and reducing the system operating cost.

[0157] In an alternative embodiment,

[0158] Based on the working state data, a hydrogen fuel cell power output characteristic curve is constructed, and the output power of the hydrogen energy system is determined in combination with the target grid power shortage, the power generation capacity, and the power output characteristic curve, including:

[0159] constructing a voltage-current state vector based on the working state data, calculating a change rate of the voltage-current state vector in each sampling period;

[0160] determining voltage-current jump points according to the change rate, and establishing a piecewise continuous state transition equation between adjacent voltage-current jump points;

[0161] substituting the working state data into the state transition equation, calculating voltage reference values and current reference values, and constructing a power prediction equation set based on the voltage reference values and the current reference values;

[0162] establishing a power output constraint condition based on the target power shortage of the power grid, substituting the power output constraint condition into the power prediction equation set, and solving the power prediction equation set by using nonlinear programming to obtain a power output characteristic curve satisfying the power output constraint condition;

[0163] correcting the power output characteristic curve according to the power generation capacity to obtain a corrected power output characteristic curve, and determining the output power of the hydrogen energy system based on the corrected power output characteristic curve.

[0164] Based on working state data, a voltage-current state vector is constructed, and a series of voltage-current data pairs are formed by monitoring the output voltage and current values of the hydrogen fuel cell system under different working conditions. For example, at a certain time t1, a data point with a voltage of 65V and a current of 120A is collected, and at time t2, a data point with a voltage of 63V and a current of 125A is collected, and so on, forming a voltage-current state vector containing multiple sampling points. The data is usually stored in the form of time series, and each data point contains three elements: time stamp, voltage value and current value.

[0165] The change rate of the voltage-current state vector in each sampling period is calculated, and for each two adjacent sampling points, the voltage change rate and the current change rate are calculated respectively. The voltage change rate is equal to the difference between the voltage values of the two adjacent sampling points divided by the sampling time interval, and the calculation method of the current change rate is similar. For example, if the voltage at time t1 is 65V and the voltage at time t2 is 63V, and the sampling interval is 0.1 second, then the voltage change rate is -20V / s, and a change rate data set reflecting the dynamic response characteristics of the system is obtained.

[0166] The voltage-current jump point is determined according to the rate of change, and the piecewise continuous state transition equation is constructed. When the rate of change of voltage or current exceeds the preset threshold, it can be determined that the point is a jump point. For example, the voltage rate of change threshold is set to ± 15V / s, and the current rate of change threshold is set to ± 30A / s. When the rate of change of a certain point exceeds these thresholds, it is marked as a jump point. In actual application, a certain fuel cell system may detect two jump points at 112A and 138A during the jump of current from 100A to 150A, indicating that there is a change in nonlinear characteristics near these two points.

[0167] The piecewise continuous state transition equation is established between adjacent voltage-current jump points. For each interval, a polynomial fitting method is used to establish the relationship expression of voltage and current. For example, for the interval with current ranging from 112A to 138A, the following relationship can be established by analyzing historical data: within this interval, the voltage is about 70-(current-100)×0.15 volts. The piecewise expression can more accurately describe the characteristics of the fuel cell at each working point.

[0168] The working state data is substituted into the state transition equation to calculate the voltage reference value and the current reference value. For each historical working point, the current value is substituted into the state transition equation of the corresponding interval to calculate the theoretical voltage value, which is compared with the actual voltage value to determine the accuracy of the model and make necessary corrections. For example, for a working point with a current value of 125A, the calculated voltage reference value should be 66.25V, and the actual measured value is 66V, the difference is within an acceptable range, indicating that the model has good fitting accuracy.

[0169] The power prediction equation set is constructed according to the voltage reference value and the current reference value. The power prediction equation set includes voltage equation, current equation and power calculation equation. The power calculation follows the basic principle that electric power is equal to the product of voltage and current, and the power output value of any working point can be predicted. For example, for a working point with a voltage of 65V and a current of 120A, the predicted power output is 7800W.

[0170] The power output constraint condition is established based on the target power shortage of the power grid. The power output constraint condition includes maximum power limit, minimum power limit and power rate limit, etc. For example, when the power shortage of the power grid is 15kW, considering the start-up characteristics and safety boundary of the hydrogen fuel cell, the system output power range can be set to 10kW to 18kW, and the power change rate should not exceed 2kW / min.

[0171] The power output constraint condition is substituted into the power prediction equation set, and a nonlinear programming is used to obtain a power output characteristic curve satisfying the constraint condition. In the solving process, an optimal working point sequence is found through iterative calculation, so that the system can meet the grid demand and ensure safe and stable operation. For example, for a power shortage demand of 15kW, through nonlinear programming solving, the hydrogen fuel cell should be operated at a working point of current 230A and voltage 65.2V, and the output power is 15kW.

[0172] The power output characteristic curve is corrected according to the power generation capacity, and a corrected power output characteristic curve is obtained. The power generation capacity is affected by many factors such as hydrogen supply, temperature, humidity, etc. For example, when the hydrogen supply is insufficient, although it can theoretically output 18kW power, the actual maximum output is only 15kW. Through real-time monitoring of hydrogen pressure, stack temperature and other parameters, the power output characteristic curve is dynamically corrected. In a certain actual operation, when the system hydrogen pressure drops below 0.5MPa, the maximum power output is limited to 12kW to ensure safe operation of the system.

[0173] Based on the corrected power output characteristic curve, the output power of the hydrogen energy system is determined, and according to the real-time demand of the grid and the corrected 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 according to the target power point. For example, when the grid shortage is 10kW and the corrected maximum output is 12kW, the system will operate according to the target power of 10kW, and the corresponding working point is current 200A and voltage 50V.

[0174] In this embodiment, through voltage-current state vector and change rate analysis, the nonlinear change of fuel cell working characteristics is accurately captured, the power output characteristic curve is more consistent with the actual working condition, the power control accuracy is greatly improved, the voltage-current jump point is identified and a piecewise continuous state transition equation is established, the dynamic response characteristics of the fuel cell under different working conditions are accurately described, the rapid response ability of the system to load changes is improved, the voltage reference value and current reference value calculated based on the state transition equation provide stable working reference for the fuel cell, reduce power output fluctuation, improve system operation stability, accurately control the working point of the fuel cell, reduce energy conversion loss, improve hydrogen-electric conversion efficiency, and reduce system operation cost.

[0175] Figure 2 The hydrogen energy system and grid collaborative complementary regulation method of the embodiment of the present application is a hydrogen energy power generation operation control flowchart.

[0176] In a second aspect of the embodiment of the present application, a hydrogen energy system and grid collaborative complementary regulation system is provided, comprising:

[0177] The first unit is configured to acquire power grid load data and renewable energy generation data, determine a power grid electricity load curve according to the power grid load data, and determine a renewable energy generation curve according to the renewable energy generation data;

[0178] The second unit is configured to calculate a power grid supply-demand difference curve based on the power grid electricity load curve and the renewable energy generation curve.

[0179] The third unit is configured to switch the hydrogen energy system to a hydrogen production mode in a first target period, determine a target hydrogen production power according to the power grid supply-demand difference curve, and adjust the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on hydrogen production working condition parameters of the hydrogen energy system.

[0180] The fourth unit is configured to switch the hydrogen energy system to a power generation mode in a second target period, determine an output power of the hydrogen energy system according to the power grid supply-demand difference curve, hydrogen pressure data, and working state data of a hydrogen fuel cell, and adjust operation parameters of the hydrogen fuel cell according to the output power.

[0181] The fifth unit is configured to record operation parameters of the hydrogen energy system to generate operation state data, and generate a hydrogen energy system and power grid cooperative operation evaluation report according to the operation state data.

[0182] In a third aspect, the present application provides an electronic device, comprising:

[0183] A processor and a memory for storing processor-executable instructions, wherein the processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0184] In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, implement the method described above.

[0185] The present application can be a method, device, system and / or computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions stored therein, which are used to perform various aspects of the present application.

[0186] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for hydrogen energy system and power grid collaborative complementary regulation, characterized in that, The method comprises the following steps: acquiring power grid load data and renewable energy generation data, determining a power grid electricity load curve based on the power grid load data, and determining a renewable energy generation curve based on the renewable energy generation data, which comprises: dividing the power grid load data into a plurality of time windows according to a time sequence, and matching the renewable energy generation data according to the time windows; calculating the numerical distribution characteristics of the power grid load data and the numerical distribution characteristics of the renewable energy generation data in each time window, and establishing a correlation matrix of the power grid load data and the renewable energy generation data based on the numerical distribution characteristics; calculating the deviation coefficients between the data items in the correlation matrix, determining a data verification threshold range based on the deviation coefficients, and generating a data verification rule set based on the data verification threshold range; verifying the power grid load data and the renewable energy generation data based on the data verification rule set, generating a power grid electricity load curve based on the power grid load data that passes the data verification, and generating a renewable energy generation curve based on the renewable energy generation data that passes the data verification; based on the power grid electricity load curve and the renewable energy generation curve, calculating a power grid supply-demand difference curve; switching the hydrogen energy system to a hydrogen production mode in a first target period, determining a target hydrogen production power based on the power grid supply-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 working condition parameters of the hydrogen energy system; switching the hydrogen energy system to a power generation mode in 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 working state data of the hydrogen fuel cell, and adjusting the operating parameters of the hydrogen fuel cell based on the output power; recording the operating parameters of the hydrogen energy system to generate operating state data, and generating a hydrogen energy system and power grid collaborative operation evaluation report based on the operating state data.

2. The method of claim 1, wherein, Based on the power grid electricity load curve and the renewable energy generation curve, a power grid supply-demand difference curve is calculated, which comprises: extracting load data points in the power grid electricity load curve, calculating the time intervals between adjacent load data points, and generating a load time interval sequence; extracting generation data points in the renewable energy generation curve, calculating the time intervals between adjacent generation data points, and generating a generation time interval sequence; determining a reference time sequence based on the load time interval sequence and the generation time interval sequence; performing data resampling in the load data section according to the reference time sequence, at each resampling time, performing interpolation calculation on the load data points before and after the corresponding time to obtain resampled load data; performing data resampling in the generation data section according to the reference time sequence, at each resampling time, performing interpolation calculation on the generation data points before and after the corresponding time to obtain resampled generation data; based on the resampled load data and the resampled generation data, calculating power grid supply-demand difference data, and based on the reference time sequence and the power grid supply-demand difference data, generating a power grid supply-demand difference curve.

3. The method of claim 1, wherein, switching the hydrogen energy system to a hydrogen production mode in a first target period, determining a target hydrogen production power according to the power supply-demand difference curve, and adjusting the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on hydrogen production working condition parameters of the hydrogen energy system, comprising: determining an electric power surplus of the power grid in the first target period based on power consumption load data and renewable energy power data of the power grid in the first target period, and calculating a target hydrogen production power according to the electric power surplus of the power grid and the power supply-demand difference curve; collecting a real-time hydrogen production power of the hydrogen energy system, and calculating a power deviation value of the real-time hydrogen production power and the target hydrogen production power; calculating a hydrogen production working condition compensation coefficient according to the hydrogen production working condition parameters, and compensating and correcting the power deviation value based on the hydrogen production working condition compensation coefficient to obtain a corrected power deviation value; converting the corrected power deviation value into electrolyzer current adjustment instructions and electrolyte flow adjustment instructions, adjusting input current of the electrolyzer according to the electrolyzer current adjustment instructions and adjusting flow of the electrolyte according to the electrolyte flow adjustment instructions; collecting an adjusted real-time hydrogen production power, and iteratively adjusting the input current of the electrolyzer and the flow of the electrolyte 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.

4. The method of claim 3, wherein, calculating a hydrogen production working condition compensation coefficient according to the hydrogen production working condition parameters, and compensating and correcting the power deviation value based on the hydrogen production working condition compensation coefficient to obtain a corrected power deviation value, comprising: determining a deviation matrix of the hydrogen production working condition parameters according to the hydrogen production working condition parameters, and calculating fluctuation characteristic values of each hydrogen production working condition parameter according to the deviation matrix; classifying the hydrogen production working condition parameters based on the fluctuation characteristic values to obtain a plurality of fluctuation parameter groups; obtaining historical fluctuation data of the fluctuation parameter groups, constructing a dynamic prediction window according to the historical fluctuation data, and predicting a parameter fluctuation trend in the dynamic prediction window by using a Kalman filtering algorithm; adaptively adjusting compensation weights of the plurality of fluctuation parameter groups based on the parameter fluctuation trend, and respectively performing dynamic compensation calculation on the plurality of fluctuation parameter groups by using an adaptive filtering algorithm to obtain correction coefficients; establishing a frequency compensation equation according to the correction coefficients, and obtaining a hydrogen production working condition compensation coefficient by iteratively solving the frequency compensation equation; obtaining a corrected power deviation value by comparing the power deviation value with the hydrogen production working condition compensation coefficient.

5. The method of claim 1, wherein, switching the hydrogen energy system to a power generation mode in a second target period, determining an output power of the hydrogen energy system according to the power supply-demand difference curve, hydrogen pressure data, and working state data of a hydrogen fuel cell, and adjusting operating parameters of the hydrogen fuel cell according to the output power, comprising: calculating an initial electric power shortage of the power grid according to load prediction data and renewable energy power generation prediction data in the second target period, and correcting the initial electric power shortage of the power grid according to the power supply-demand difference curve to obtain a target electric power shortage of the power grid; The hydrogen storage capacity in the hydrogen storage tank is calculated by using a Van der Waals equation of state based on hydrogen pressure data of the hydrogen storage tank, and the power generation capacity is determined based on a correspondence between the hydrogen storage capacity and a rated power of the hydrogen fuel cell; A hydrogen fuel cell power output characteristic curve is constructed based on the working state data, and the output power of the hydrogen energy system is determined in combination of the target power shortage of the power grid, 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 inlet 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.

6. The method of claim 5, wherein, A hydrogen fuel cell power output characteristic curve is constructed based on the working state data, and the output power of the hydrogen energy system is determined in combination of the target power shortage of the power grid, the power generation capacity, and the power output characteristic curve, including: A voltage-current state vector is constructed based on the working state data, and a change rate of the voltage-current state vector in each sampling period is calculated; A voltage-current jump point is determined according to the change rate, and a state transition equation that is continuous in segments between adjacent voltage-current jump points is established; The working state data is substituted into the state transition equation to calculate a voltage reference value and a current reference value, and a power prediction equation set is constructed according to the voltage reference value and the current reference value; A power output constraint condition is established based on the target power shortage of the power grid, the power output constraint condition is substituted into the power prediction equation set, and a power output characteristic curve that satisfies the power output constraint condition is obtained by solving the power prediction equation set using nonlinear programming; 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.

7. Hydrogen energy system and grid complementary regulation system for implementing the method according to any one of the preceding claims 1-6, characterized in that, It includes: A first unit is configured to acquire power grid load data and renewable energy generation data, determine a power grid electricity load curve based on the power grid load data, and determine a renewable energy generation curve based on the renewable energy generation data; A second unit is configured to calculate a power grid supply-demand difference curve based on the power grid electricity load curve and the renewable energy generation curve; A third unit is configured to switch the hydrogen energy system to a hydrogen production mode in a first target period, determine a target hydrogen production power based on the power grid supply-demand difference curve, and adjust the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on hydrogen production working condition parameters of the hydrogen energy system; A fourth unit is configured to switch the hydrogen energy system to a power generation mode in 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 working state data of the hydrogen fuel cell, and adjust operating parameters of the hydrogen fuel cell based on the output power; A fifth unit is configured to record the operating parameters of the hydrogen energy system to generate operating state data, and generate a hydrogen energy system and power grid cooperative operation evaluation report based on the operating state data.

8. An electronic device, comprising: It includes: A processor; A memory for storing processor-executable instructions; The processor is configured to invoke the instructions stored in the memory to execute the method in any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions, when executed by the processor, implement the method in any one of claims 1 to 6.

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