CN120601475A

CN120601475A - Method and system for coordinated and complementary regulation of hydrogen energy system and power grid

Info

Publication number: CN120601475A
Application number: CN202511099724.0A
Authority: CN
Inventors: 何整杰; 沈科炬; 陆晓红; 余金伟; 沈贝石; 林科; 彭佳鹰; 程建华; 周胜; 徐钱乾; 李钟煦; 杨跃平; 方云辉; 张予晨; 陆雅倩
Original assignee: Cixi Power Transmission And Transformation Engineering Co ltd; Cixi Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Current assignee: Cixi Power Transmission And Transformation Engineering Co ltd; Cixi Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Priority date: 2025-08-07
Filing date: 2025-08-07
Publication date: 2025-09-05
Anticipated expiration: 2045-08-07
Also published as: CN120601475B

Abstract

The invention provides a method and a system for adjusting the cooperative complementation of a hydrogen energy system and a power grid, which relate to the technical field of energy management and comprise the following steps: the method comprises the steps of obtaining power grid load and renewable energy power generation data, calculating a supply-demand difference curve, switching a hydrogen energy system to a hydrogen production mode in a first target period, adjusting the hydrogen energy system to target hydrogen production power, switching the hydrogen energy system to a power generation mode in a second target period, adjusting output power according to the supply-demand difference, hydrogen pressure and battery working state, recording operation parameters and generating a cooperative operation evaluation report, so that efficient complementation of a power grid and the hydrogen energy system is realized, energy utilization efficiency is improved, and power grid adjustment capability is enhanced.

Description

Method and system for adjusting hydrogen energy system and power grid in cooperative and complementary mode

Technical Field

The invention relates to the technical field of energy management, in particular to a method and a system for adjusting the coordination and complementation of a hydrogen energy system and a power grid.

Background

The fluctuation and intermittence problems caused by renewable energy grid connection are required to be coordinated and regulated through various flexible resources, and the cooperative and complementary operation of a hydrogen energy system and a power grid becomes an important technical approach for solving the stability and flexibility of the power system, and has important significance for promoting the large-scale consumption of renewable energy and constructing a clean low-carbon energy system;

However, the prior art still lacks accurate sensing and pre-judging capability on the supply and demand states of the power grid, the conversion of the hydrogen production mode and the power generation mode is often triggered based on a simple time period division or a fixed threshold, flexible response is difficult to be carried out on the supply and demand dynamic changes of the power grid, so that the cooperative efficiency is low, the matching relationship between the working condition parameters of the hydrogen energy system and the power grid requirement is not fully considered, the comprehensive optimization mechanism is lacking in the adjustment of the hydrogen production power and the output power of the hydrogen fuel cell, the power grid adjustment requirement and the safe and efficient operation of the hydrogen energy system are difficult to be simultaneously considered, key data in the cooperative operation process cannot be effectively collected and analyzed, and the continuous optimization and adjustment of the cooperative strategy are difficult to be carried out;

accordingly, there is a need for a solution to the problems of the prior art.

Disclosure of Invention

The embodiment of the invention provides a method and a system for adjusting the coordination and complementation of a hydrogen energy system and a power grid, which at least can solve part of problems in the prior art.

In a first aspect of the embodiment of the present invention, a method for adjusting the coordination and complementation of a hydrogen energy system and a power grid is provided, including:

Acquiring power grid load data and renewable energy power generation data, determining a power grid power consumption load curve according to the power grid load data, and determining a renewable energy power generation curve according to the renewable energy power generation data;

calculating to obtain a power grid supply and demand difference curve based on the power grid electricity load curve and the renewable energy power generation curve;

switching a hydrogen energy system to a hydrogen production mode in a first target period, determining 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 hydrogen production working condition parameters of the hydrogen energy system;

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

And recording the operation parameters of the hydrogen energy system to generate operation state data, and generating an evaluation report of the cooperative operation of the hydrogen energy system and the power grid according to the operation state data.

In an alternative embodiment of the present invention,

Acquiring power grid load data and renewable energy power generation data, determining a power grid power consumption load curve according to the power grid load data, and determining a renewable energy power generation curve according to the renewable energy power generation data, wherein the method comprises the following steps of:

Dividing the power grid load data into a plurality of time windows according to a time sequence, and carrying out data matching on the renewable energy power 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 power generation data in each time window, and establishing a correlation matrix of the power 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;

And carrying out data verification on the power grid load data and the renewable energy power generation data according to the data verification rule set, generating a power grid power consumption load curve according to the power grid load data passing the data verification, and generating a renewable energy power generation curve according to the renewable energy power generation data passing the data verification.

In an alternative embodiment of the present invention,

Based on the power grid electricity load curve and the renewable energy power generation curve, calculating to obtain a power grid supply and demand difference curve, wherein the power grid supply and demand difference curve comprises the following components:

Load data points in the power grid electricity load curve are extracted, time intervals between adjacent load data points are calculated, and a load time interval sequence is generated;

extracting power generation data points in the renewable energy power generation curve, calculating 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 generation time interval sequence;

performing data resampling in the load data segment according to the reference time sequence, and performing interpolation calculation on load data points before and after the corresponding time to obtain resampled load data at each resampling time;

performing data resampling in the power generation data segment according to the reference time sequence, and performing interpolation calculation on power generation data points before and after the corresponding time to obtain resampled power generation data at each resampling time;

And calculating power grid supply and demand difference data based on the resampled load data and the resampled power generation data, and generating a power grid supply and demand difference curve based on the reference time sequence and the power grid supply and demand difference data.

In an alternative embodiment of the present invention,

Switching a hydrogen energy system to a hydrogen production mode in a first target period, determining 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 hydrogen production working condition parameters of the hydrogen energy system, wherein the method comprises the following steps:

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

Collecting real-time hydrogen production power of a 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 an electrolytic cell current regulation command and an electrolytic solution flow regulation command, regulating the input current of the electrolytic cell according to the electrolytic cell current regulation command, and regulating the flow of the electrolytic solution according to the electrolytic solution flow regulation command;

And acquiring the regulated real-time hydrogen production power, and iteratively regulating the input current of the electrolytic tank and the flow of the electrolyte based on the regulated real-time hydrogen production power until the hydrogen production power of the hydrogen energy system is regulated to the target hydrogen production power.

In an alternative embodiment of the present invention,

Calculating a hydrogen production working condition compensation coefficient according to the hydrogen production working condition parameter, compensating and correcting the power deviation value based on the hydrogen production working condition compensation coefficient to obtain a corrected power deviation value, wherein the method comprises the following steps:

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 the hydrogen production working condition parameters according to the deviation matrix;

carrying out layered classification on hydrogen production working condition parameters based on the fluctuation characteristic values to obtain a plurality of fluctuation parameter sets;

acquiring historical fluctuation data of the fluctuation parameter set, constructing a dynamic prediction window according to the historical fluctuation data, and predicting parameter fluctuation trend in the dynamic prediction window by using a Kalman filtering algorithm;

based on the parameter fluctuation trend, carrying out self-adaptive adjustment on the compensation weights of the fluctuation parameter sets, and respectively carrying out dynamic compensation calculation on the fluctuation parameter sets by adopting a self-adaptive filtering algorithm to obtain correction coefficients;

Establishing a frequency division compensation equation according to the correction coefficient, and obtaining a hydrogen production working condition compensation coefficient by iteratively solving the frequency division compensation equation;

and obtaining the corrected power deviation value by combining the power deviation value with the hydrogen production working condition compensation coefficient.

In an alternative embodiment of the present invention,

Switching the hydrogen energy system to a power generation mode in a second target period, determining output power of the hydrogen energy system according to the power grid supply and demand difference curve, hydrogen pressure data and working state data of the hydrogen fuel cell, and adjusting operation parameters of the hydrogen fuel cell according to the output power, wherein the method comprises the following steps:

Calculating initial power shortage according to load prediction data and renewable energy power generation prediction data in a second target period, and correcting the initial power shortage according to the power supply and demand difference curve of the power grid to obtain target power shortage;

Calculating the hydrogen reserves in the hydrogen storage tank by utilizing a van der Waals state equation based on the hydrogen pressure data of the hydrogen storage tank, and determining the power generation capacity based on the corresponding relation between the hydrogen reserves and the rated power of the hydrogen fuel cell;

Constructing a power output characteristic curve of the hydrogen fuel cell based on the working state data, and determining the output power of a hydrogen energy system by combining the target power grid power shortage, the power generation capacity and the power output characteristic curve;

And generating a hydrogen flow control command and a pile current control command according to the output power, adjusting the opening of an air inlet valve based on the hydrogen flow control command and adjusting the input current of the hydrogen fuel cell based on the pile current control command.

In an alternative embodiment of the present invention,

Constructing a power output characteristic curve of the hydrogen fuel cell based on the working state data, and determining the output power of the hydrogen energy system by combining the target power grid power shortage, the power generation capacity and the power output characteristic curve, wherein the method comprises the following steps:

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

Determining voltage-current transition points according to the change rate, and establishing a piecewise continuous state transition equation 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 set according to the voltage reference value and the current reference value;

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

And 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.

In a second aspect of the embodiment of the present invention, there is provided a system for adjusting the co-complementation of a hydrogen energy system and a power grid, including:

The first unit is used for acquiring power grid load data and renewable energy power generation data, determining a power grid power utilization load curve according to the power grid load data, and determining a renewable energy power generation curve according to the renewable energy power generation data;

The second unit is used for calculating and obtaining a power grid supply and demand difference curve based on the power grid electricity load curve and the renewable energy power generation curve;

The third unit is used for switching the hydrogen energy system into a hydrogen production mode in a first target period, determining 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;

A fourth unit, configured to switch the hydrogen energy system to a power generation mode in a second target period, determine 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 adjust an operation parameter of the hydrogen fuel cell according to the output power;

and the fifth unit is used for recording the operation parameters of the hydrogen energy system to generate operation state data, and generating an evaluation report of the cooperative operation of the hydrogen energy system and the power grid according to the operation state data.

In a third aspect of an embodiment of the present invention, there is provided an electronic device including:

a processor and a memory for storing processor-executable instructions, wherein the processor is configured to invoke the instructions stored by the memory to perform the method as described previously.

In a fourth aspect of embodiments of the present invention, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method as described above.

According to the invention, the power supply and demand difference curve of the power grid is calculated by acquiring the power grid load data and the renewable energy power generation data, so that the accurate grasp of the running state of the power grid is realized, the basis is provided for the switching of the working mode of the hydrogen energy system, the scientificity and rationality of the power grid dispatching are effectively improved, the power output of the hydrogen energy system is flexibly regulated according to the power grid supply and demand conditions, the smooth regulation of the power grid load is realized, the renewable energy power generation fluctuation is effectively absorbed, the stability and the reliability of the power system are improved, the running parameters of the hydrogen energy system are recorded, the evaluation report is generated, the perfect running monitoring and evaluation system is established, the continuous optimization and regulation strategy of the system is facilitated, the economical efficiency and the environmental protection benefit of the whole hydrogen energy and power grid cooperative system are improved, and a feasible technical path is provided for energy transformation.

Drawings

FIG. 1 is a schematic flow chart of a method for adjusting the coordination and complementation of a hydrogen energy system and a power grid according to an embodiment of the invention;

fig. 2 is a flow chart of hydrogen energy power generation operation control of the method for adjusting the coordination and complementation of the hydrogen energy system and the power grid according to the embodiment of the invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.

Fig. 1 is a schematic flow chart of a method for adjusting the coordination and complementation of a hydrogen energy system and a power grid according to an embodiment of the invention, as shown in fig. 1, the method includes:

In an alternative embodiment of the present invention,

And acquiring power grid load data and renewable energy power generation data. The power grid load data comprise power grid power consumption data of different time points, such as power consumption data recorded once every 15 minutes, and the renewable energy power generation data comprise power generation data of renewable energy sources such as solar energy, wind energy and the like at different time points, and the power generation data can be recorded once every 15 minutes.

After the data is acquired, the grid load data is divided into a plurality of time windows according to a time sequence, for example, 24-hour data can be divided into 96 time windows of 15 minutes or 24 time windows of 1 hour. The size of the divided time window can be flexibly adjusted according to actual application scenes and data characteristics, for example, a smaller time window such as 15 minutes can be selected in the peak period, and a larger time window such as 1 hour can be selected in the valley period. For renewable energy power generation data, the system also performs data matching according to the same time window, so that the two types of data are ensured to be corresponding and consistent in the time dimension. For example, during the time window of 10:00-10:15 on 10.15.2023, the grid load data is 500MW, and the corresponding renewable energy power generation data is 100MW.

And after the data matching is completed, calculating numerical distribution characteristics of the power grid load data and the renewable energy power generation data in each time window. Numerical distribution characteristics include, but are not limited to, statistical indicators of mean, standard deviation, median, maximum, minimum, etc. For example, for a time window of 8:00-9:00 on weekdays, the average power grid load for that period over the last 30 days can be calculated to be 800MW with a standard deviation of 50MW, and the average power generation for the renewable energy source for the same period is 200MW with a standard deviation of 30MW.

And establishing a correlation matrix of the power grid load data and the renewable energy power generation data based on the calculated numerical distribution characteristics. The correlation matrix reflects the correlation of two types of data in different time windows and different characteristic dimensions. Each element of the matrix represents a correlation coefficient between corresponding dimensions, with a range of values [ -1,1], where 1 represents a complete positive correlation, -1 represents a complete negative correlation, and 0 represents no correlation. For example, the grid load and solar power generation during the morning hours of the workday may exhibit a high positive correlation with a correlation coefficient of 0.85, while the grid load and solar power generation during the night may exhibit a low correlation with a correlation coefficient approaching 0.

A coefficient of deviation between the data items in the correlation matrix is calculated. The deviation coefficient is a dimensionless index for measuring the deviation degree of the data, the deviation coefficient is obtained by calculating the difference between the data and an expected value and carrying out normalization processing, the deviation coefficient of the power grid load data and the renewable energy power generation data in each time window is calculated, for example, the power grid load deviation coefficient of a certain working day 9:00-10:00 time window is 0.05, the deviation of the load data and the historical simultaneous period data in the period is within 5%, and the deviation coefficient of the renewable energy power generation is 0.15, and the deviation is within 15%.

And determining a data verification threshold range according to the calculated deviation coefficient, and setting reasonable upper and lower limit thresholds for subsequent data verification through statistical analysis of historical data. For example, for grid load data, a threshold range of deviation coefficients is set to [ -0.1,0.1], i.e. data with deviation coefficients between-10% and 10% are considered valid, and for renewable energy power generation data, a wider threshold range, such as [ -0.2,0.2], may be set, taking into account its greater volatility.

A data verification rule set is generated based on the determined data verification threshold range. The rule set contains a series of criteria for identifying outlier data points. For example, rule 1 may be "if grid load data deviation coefficient of 8:00-9:00 time window on weekday exceeds 0.1, then it is marked as abnormal data", and rule 2 may be "if solar energy power generation amount deviation coefficient of 12:00-13:00 time window on sunny day is lower than-0.2, then it is marked as abnormal data".

And carrying out data verification on the power grid load data and the renewable energy power generation data according to the generated data verification rule set, comparing each piece of data with the conditions in the rule set, and identifying abnormal data which do not accord with the rule. For example, the power grid load data of 9:15 at 2023, 10 and 16 is detected as 1200MW, while the historical contemporaneous mean is 800MW, the deviation factor is 0.5, and is outside the set threshold range, thus it is marked as abnormal data.

And (3) for the power grid load data passing through the data verification, arranging the power grid load data according to time sequence to generate a power grid power consumption load curve, wherein the power grid power consumption load curve shows power grid power consumption load change conditions at different time points, and can intuitively reflect power consumption peaks and valleys. Similarly, a renewable energy power generation curve is generated according to the renewable energy power generation data verified by the data, and the renewable energy power generation conditions at different time points are displayed.

In the embodiment, the original data is verified through the data verification rule set, so that abnormal values and error data can be effectively screened out, the accuracy of subsequent analysis is ensured, the inherent connection between two types of data is deeply excavated through establishing a correlation matrix of power grid load and renewable energy power generation data, a more scientific basis is provided for energy scheduling decision, and a data verification threshold range is dynamically determined based on a deviation coefficient, so that the verification process has more flexibility and adaptability, and can adapt to data characteristic changes in different time periods and seasons.

In an alternative embodiment of the present invention,

And acquiring the original data of a power grid electricity load curve and a renewable energy power generation curve. The power grid power load curve records the power load change condition of the power grid within a period of time (such as 24 hours), and the renewable energy power generation curve records the power generation quantity change condition of renewable energy sources such as wind energy, solar energy and the like within the same period of time. Since these two types of data are often collected from different monitoring systems, their sampling time intervals are often not uniform, and data processing is required to calculate the supply-demand difference.

Load data points are extracted from the grid electrical load curve, such as { (t 1, L1), (t 2, L2), (tn, ln) }, where ti represents the point in time and Li represents the electrical load value at that point in time. 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, & gt, Δtload_n-1}. For example, if the time between two adjacent load data points is 10:00 and 10:15, respectively, then the time interval is 15 minutes.

Power generation data points, such as { (s 1, P1), (s 2, P2), (sm, pm) }, are extracted from the renewable energy power generation curve, where si represents a time point and Pi represents renewable energy power generation amount at the time point. The time interval between adjacent power generation data points is calculated, i.e., Δ tgen _j=sj+1-sj, generating a sequence of power generation time intervals { Δ tgen _1, Δ tgen _2. For example, if the time of two adjacent power generation data points is 10:00 and 10:10, respectively, then the time interval is 10 minutes.

A reference time sequence is determined based on the obtained load time interval sequence and the power generation time interval sequence. The reference time series may be determined by taking the minimum of the two time interval series as the reference sampling interval or by taking the greatest common divisor of the two. In this embodiment, the minimum value of the two is selected, and assuming that the sampling interval of the load data is 15 minutes and the sampling interval of the power generation data is 10 minutes, the reference time interval is 10 minutes. The system generates a reference time series from this, such as {0:00,0:10,0:20, &.23:50 }.

Data resampling is performed within the payload data segments according to the reference time sequence. For each point in time in the reference time series, if the point in time is not in the original load data, the load value of the point in time is obtained by interpolation calculation. Specifically, two nearest original load data points before and after the time point are found, and a linear interpolation method is used for calculating a load value of the time point. For example, for a reference time point of 0:10, if the original load data has a load value of 0:00 of 100MW and a load value of 0:15 of 120MW, the load value at 0:10 is calculated by linear interpolation to be 100+ (120-100) × (10/15) = 113.33MW.

Data resampling is performed within the power generation data segments according to the reference time sequence. For each reference time point, if the time point is not in the original power generation data, the power generation value of the time point is also obtained by interpolation calculation. For example, if the reference time point is 0:05, and the power generation value of 0:00 is 50MW and the power generation value of 0:10 is 55MW in the original power generation data, the power generation value at 0:05 is 50+ (55-50) × (5/10) =52.5 MW by linear interpolation calculation.

And after the data resampling is completed, calculating power grid supply and demand difference data based on the resampled load data and the power generation data. For each time point in the reference time series, calculating the load value minus the power generation value at the time point to obtain the supply-demand difference. For example, if the resampling load value at a certain time point is 150MW and the power generation value is 100MW, the supply-demand difference at the certain time point is 150-100=50mw, which means that the power grid supply gap is 50MW at this time, and the power grid supply gap needs to be supplemented by a conventional energy source or energy storage system.

And generating a power grid supply and demand difference curve based on the reference time sequence and the power grid supply and demand difference data. The power grid supply and demand difference curve intuitively shows the supply and demand balance condition of the power grid in a period of time, and can be used for guiding operations such as charge and discharge scheduling of an energy storage system, traditional energy power generation planning, power grid load adjustment and the like.

For example, assume that the grid has a power load curve data point {(8：00,200MW),(8：15,220MW),(8：30,240MW),(8：45,230MW),(9：00,210MW),(9：15,200MW),(9：30,190MW),(9：45,180MW),(10：00,170MW),(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 a renewable energy power generation curve data point {(8：00,200MW),(8：15,220MW),(8：30,240MW),(8：45,230MW),(9：00,210MW),(9：15,200MW),(9：30,190MW),(9：45,180MW),(10：00,170MW),(10：15,180MW),(10：30,190MW),(10：45,200MW),(11：00,210MW),(11：15,220MW),(11：30,230MW),(11：45,240MW),(12：00,250MW)}, at some time period (8:00-12:00) of the day {(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)}.

By calculation, the reference time series is a 10 minute interval series {8:00,8:10,8:20,. }. The obtained power grid supply and demand difference curve data points are obtained by data resampling and supply and demand difference calculation {(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)}.

In the embodiment, the problem that the collection time of the load data is inconsistent with that of the power generation data is solved by establishing a reference time sequence, a time basis is provided for accurately calculating the supply and demand difference value, the resampling data points are obtained by adopting interpolation calculation, the change trend characteristics of the original data are reserved, meanwhile, errors caused by uneven sampling are eliminated, the data precision is improved, the sampled load data and the power generation data are completely aligned in the time dimension, the comparability of the data is enhanced, the supply and demand difference value calculation is more accurate, the supply and demand conditions of a power grid in different time periods can be intuitively reflected by generating an accurate power grid supply and demand difference value curve, an important basis is provided for power grid scheduling decision, and the method is beneficial to reducing power waste and improving the power grid stability.

In an alternative embodiment of the present invention,

And in the first target period, determining target hydrogen production power according to the power grid supply and demand difference curve, and performing power adjustment based on hydrogen production working condition parameters of the hydrogen energy system.

And the hydrogen energy system controller is used for switching the hydrogen energy system to a hydrogen production mode by receiving a switching instruction sent by the power grid dispatching system. The controller collects grid electrical load data and renewable energy power data for determining a grid electrical power margin within a first target period. The power grid electricity load data come from a smart grid monitoring system, and the renewable energy source power data come from a wind power plant and a power generation monitoring system of a photovoltaic power station. Taking a certain area as an example, in the period of 10:00-15:00, the power grid electricity load is 400MW, and the total power generation amount of wind power and photovoltaic in the same period is 500MW, so that the power grid electricity surplus is 100MW.

The controller calculates the target hydrogen production power by combining the power grid supply and demand difference curve. The power supply and demand difference curve reflects the law of the change of the difference of power supply and demand in the power grid along with time, is usually generated by a power grid dispatching department according to historical data and power load prediction, and the power supply and demand difference curve of the power grid obtained by a control system displays that the power which can be distributed to a hydrogen energy system at the current moment is 30% of the power surplus of the power grid, namely 30MW. The controller sets the target hydrogen production power to 25MW considering that the maximum hydrogen production power of the hydrogen energy system is 25MW.

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

The control system calculates the hydrogen production working condition compensation coefficient according to the hydrogen production working condition parameters. The parameters of hydrogen production conditions include cell temperature, cell pressure, electrolyte concentration, electrolyte temperature, etc., and the cell temperature is illustratively 62 ℃ (70 ℃ in standard conditions), cell pressure is 2.8MPa (3.0 MPa in standard conditions), electrolyte concentration is 28% (30% in standard conditions), and electrolyte temperature is 58 ℃ (60 ℃ in standard conditions) as collected. According to the deviation of the parameter and the standard working condition, the compensation coefficient of the hydrogen production working condition is calculated to be 0.92, and the hydrogen production efficiency under the same current input is 92% of the standard working condition under the current working condition.

And the controller compensates and corrects the power deviation value based on the hydrogen production working condition compensation coefficient. The corrected power deviation value is 7.61MW (7 MW/0.92), which means that under the current working condition, 7.61MW of input power is required to be increased to achieve additional 7MW of hydrogen production power.

And converting the corrected power deviation value into an electrolytic cell current regulation command and an electrolytic flow regulation command. For the proton exchange membrane electrolyzer used in this example, the control system determines from the electrolyzer characteristic curve that it is necessary to increase the electrolyzer current from 1800A to 2350A while increasing the electrolyte flow from 180L/min to 215L/min.

The controller sends instructions to the power supply system of the electrolytic tank and the electrolyte circulating pump through a Programmable Logic Controller (PLC) to adjust the input current of the electrolytic tank and the flow of the electrolyte. The power supply system steps the output current from 1800A to 2350A in steps of 50A/sec. Meanwhile, the rotating speed of the electrolyte circulating pump is increased from 60Hz to 72Hz, so that the flow rate of the electrolyte reaches 215L/min.

And after the adjustment is finished, collecting the adjusted real-time hydrogen production power. Assuming that the real-time hydrogen production power measured after adjustment is 24.2MW, a difference of 0.8MW from the target hydrogen production power of 25MW remains. The control system continues the iterative adjustment, increasing the cell current from 2350A to 2390A and the electrolyte flow from 215L/min to 218L/min. After the measurement again, the real-time hydrogen production power reaches 24.9MW, the difference from the target value is reduced to 0.1MW, and the target hydrogen production power is considered to be reached within the allowable error range.

In the whole regulating process, the control system continuously monitors the safety parameters such as the temperature of the electrolytic cell, the pressure of the electrolytic cell, the purity of hydrogen and the like, and ensures that the electrolytic cell operates in a safe working condition range. If the temperature of the electrolytic tank is not more than 75 ℃, the pressure of the electrolytic tank is not more than 3.5MPa, and the purity of the hydrogen is maintained to be more than 99.99 percent.

In the embodiment, the target hydrogen production power is determined by calculating the power grid power surplus, so that a hydrogen energy system can fully absorb redundant renewable energy power in a power grid, the wind abandoning and light abandoning phenomenon is reduced, the utilization efficiency of renewable energy sources is improved, the characteristic difference of an electrolytic cell under different working conditions is considered through a real-time power deviation calculation and hydrogen production working condition compensation mechanism, the hydrogen production power is more accurately regulated, the oscillation and error in the regulating process are reduced, a double-parameter cooperative regulation mode of the electrolytic cell current and the electrolyte flow is adopted, closed-loop control is formed through real-time power feedback, 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 working conditions is avoided by compensating and correcting by considering the hydrogen production working condition parameters, the equipment abrasion is reduced, and the service life of core equipment such as the electrolytic cell is prolonged.

In an alternative embodiment of the present invention,

And obtaining hydrogen production working condition parameters and power deviation values. The hydrogen production working condition parameters generally comprise key parameters such as the temperature of an electrolytic tank, the concentration of electrolyte, the fluctuation of power supply voltage, the film thickness change and the like. The power deviation value is the difference between the actual running power and the set power.

The process of calculating the hydrogen production condition compensation coefficient from the acquired hydrogen production condition parameters starts with constructing a deviation matrix, and by way of example, it is assumed that the fluctuation range of the monitored temperature of the electrolytic cell is + -3 ℃ on the basis of the standard value of 65 ℃, the fluctuation range of the concentration of the electrolyte on the basis of the standard value of 30% is + -2%, and the fluctuation range of the power supply voltage on the basis of the standard value of 220V is + -5V. And forming a deviation matrix by the difference between the real-time value and the standard value of the parameter. For example, at some point the bias matrix may be expressed as temperature bias +2 ℃, concentration bias-1.5%, voltage bias +3v, etc.

The fluctuation feature value is calculated by a sliding window method based on the deviation matrix, and, illustratively, the mean deviation, standard deviation, fluctuation frequency, and fluctuation amplitude of each parameter are calculated within a time window of 10 minutes. For example, the average deviation of the cell temperature was +1.8 ℃, the standard deviation was 0.5 ℃, the fluctuation frequency was 0.02Hz, and the fluctuation amplitude was 2.6 ℃. These data constitute the fluctuation feature value.

And carrying out hierarchical classification on the parameters by adopting cluster analysis according to the fluctuation characteristic values. Parameters are divided into a high-frequency small-amplitude group, a high-frequency large-amplitude group, a low-frequency small-amplitude group and a low-frequency large-amplitude group according to the fluctuation frequency and the fluctuation amplitude. For example, the electrolyte concentration is classified into a low-frequency small-amplitude group (fluctuation frequency 0.005Hz, amplitude 1.8%), and the supply voltage is classified into a high-frequency small-amplitude group (fluctuation frequency 0.05Hz, amplitude 4.2V). This forms a plurality of sets of fluctuation parameters.

And for each fluctuation parameter group, acquiring historical fluctuation data, extracting parameter fluctuation records under similar working conditions in the past 30 days from a database, and constructing a dynamic prediction window. The length of the dynamic window is adaptively adjusted according to the fluctuation characteristics of parameters, the high-frequency parameter window is set to be 5 minutes, the low-frequency parameter window is set to be 15 minutes, a Kalman filtering algorithm is applied to predict the parameter change trend in the window, and the possible value and the uncertainty of the predicted parameter at the future moment are predicted by combining the noise characteristics of historical data through a state equation and an observation equation.

And based on the predicted parameter fluctuation trend, carrying out self-adaptive adjustment on the compensation weights of different fluctuation parameter groups. The weight adjustment follows the principle of increasing the weight of the fluctuation intense parameter and decreasing the weight of the fluctuation stable parameter. For example, when the temperature fluctuation of the electrolytic cell is aggravated (the standard deviation is increased from 0.5 ℃ to 0.8 ℃), the weight of the electrolytic cell is increased from 0.3 to 0.42, and each fluctuation parameter group is processed by adopting an adaptive filtering algorithm. For the high-frequency small-amplitude group, a high-pass filter is adopted to eliminate low-frequency interference, and for the low-frequency large-amplitude group, a band-pass filter is adopted to keep characteristic frequency components. By the differentiation processing, a 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.

And establishing a frequency division compensation equation according to the correction coefficient of each parameter set. The equation combines the parameter effects of different frequency characteristics, expressed formally as a weighted sum of correction coefficients, and the weight coefficients are continuously adjusted by iterative calculation until the error of the equation output value and the actual observed power deviation correction effect is less than a preset threshold (e.g., 0.5%). The final hydrogen production condition compensation coefficient is a scalar value, such as 1.12, which comprehensively considers the influence of each parameter.

And multiplying the power deviation value by a hydrogen production working condition compensation coefficient to complete compensation correction. 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 modified value to the power control of the hydrogen production system to bring the actual operating power closer to the set point.

In the embodiment, the hydrogen production working condition parameters are finely classified through analysis of the deviation matrix and the fluctuation eigenvalue, so that compensation calculation is more fit with actual fluctuation characteristics of different parameters, the compensation precision is remarkably improved, the pre-judgment and compensation of the rapid change of the hydrogen production working condition are realized through a dynamic prediction window and a self-adaptive filtering algorithm, the response speed and the adaptability of the system to the change of the working condition are improved, parameter sets with different fluctuation characteristics are processed through a frequency division compensation equation, the complex coupling influence among the hydrogen production working condition parameters is effectively reduced, the control logic is clearer and more effective, the impact of parameter fluctuation on equipment is avoided through accurate working condition compensation, the operation pressure of core equipment such as an electrolytic tank is lightened, and the service life of the equipment is effectively prolonged.

In an alternative embodiment of the present invention,

And executing power generation mode switching operation in a second target period, wherein the period generally corresponds to a power grid load peak period or a renewable energy power generation valley period, a power gap exists in the power grid, and the hydrogen energy system is required to provide power support for the power grid through hydrogen fuel cell power generation to acquire power grid load prediction data and renewable energy power generation prediction data in the second target period. For example, a load prediction peak value of a certain area is 120MW in a period of 18:00-22:00, meanwhile, the photovoltaic and wind power prediction generating capacity of the certain area is 10MW and 25MW respectively, an initial power grid power shortage is calculated to be 85MW, the initial shortage is corrected according to a power grid supply and demand difference curve monitored in real time, and a target power grid power shortage is determined to be 80MW in consideration of power grid scheduling and actual operation deviation.

The hydrogen energy system monitors the pressure data of the hydrogen storage tank in real time through the pressure sensor, and illustratively, taking a certain hydrogen storage tank as an example, when the pressure of the storage tank is measured to be 35MPa and the temperature is measured to be 25 ℃, the total hydrogen amount in the storage tank is calculated to be about 1200kg by adopting a van der Waals state equation. The van der Waals state equation considers the non-ideal characteristic of the actual gas under the high pressure condition, and accurately calculates the actual hydrogen mass in the storage tank by combining the gas constant and the hydrogen characteristic parameter according to the relationship among the pressure, the volume and the temperature of the hydrogen. The system stores a corresponding relation table of hydrogen reserves and the power generation capacity of the hydrogen fuel cell, and according to the table, 1200kg of hydrogen can support the 10MW hydrogen fuel cell to continuously generate power for about 12 hours, namely the current maximum power generation capacity of the system is 10MW.

The working state data of the hydrogen fuel cell comprises parameters such as the temperature of a galvanic pile, the humidity, the health state of a membrane electrode assembly and the like, the average temperature of the galvanic pile is 75 ℃, 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 index corresponding to different output power levels in the current state. The output power of the hydrogen energy system is 8MW by combining the target power grid power shortage of 80MW, the power generation capacity of 10MW and the optimal working point displayed by the power output characteristic curve, and the power point can ensure higher power generation efficiency (about 52%), and can keep the system to stably operate.

After determining the output power, the hydrogen flow and stack current need to be precisely controlled to achieve stable power generation. For an output power of 8MW, the required hydrogen flow was calculated to be 94Nm3/h. A hydrogen flow control instruction is generated and sent to the intake valve control unit. The control unit adopts a PID control algorithm, and calculates the valve opening to be adjusted to 28% according to the current valve opening 15% and the target hydrogen flow. The valve actuating mechanism stably adjusts the valve opening according to the instruction, so that the accurate control of the hydrogen flow is realized, and the system continuously monitors the actual flow of the hydrogen through the flowmeter in the adjusting process, so that the hydrogen is ensured to be stable within the range of 94+/-2 Nm3/h.

Generating a current control command of a pile, for a hydrogen fuel cell system with rated voltage of 750V, controlling the pile current to be about 10667A in order to output 8MW power, sending the current control command to a power electronic converter, gradually increasing the current from an initial value of 0A to a target current of 10667A by the converter in a constant current control mode, controlling the rising rate to be 200A/s, and avoiding the pile from bearing excessive transient load change. The entire current regulation process lasts about 53 seconds, during which the system monitors the stack temperature change in real time, ensuring that the safety threshold is not exceeded at 85 ℃.

After the current is stabilized, the stack voltage is continuously monitored, and when the voltage is stabilized within the range of 750+/-5V, the hydrogen fuel cell is confirmed to reach a stable power generation state. At the moment, the hydrogen energy system is successfully switched to a power generation mode, 8MW power is stably output to the power grid, and the power shortage of the power grid is relieved. In the whole power generation process, key parameters such as hydrogen pressure, stack temperature and the like are continuously monitored, and when abnormality is detected, protection measures are immediately executed, such as output power reduction or emergency shutdown, so that safe and reliable operation is ensured.

In the embodiment, the power shortage is calculated through the load prediction data and the renewable energy power generation prediction data, and the power shortage is corrected by combining with the power grid supply and demand difference curve, so that the hydrogen energy system can accurately respond to the actual demand of the power grid, the accuracy of power grid dispatching is improved, the accurate control of the output power of the hydrogen fuel cell is realized through the cooperative adjustment of the hydrogen flow control instruction and the electric pile current control instruction, the power fluctuation and the adjustment error are reduced, the hydrogen fuel cell is operated near the optimal efficiency point through the accurate control of the hydrogen flow and the electric pile current, the hydrogen-electricity conversion efficiency is improved, and the system operation cost is reduced.

In an alternative embodiment of the present invention,

A voltage-current state vector is constructed based on the operating state data, 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 operating conditions. For example, a data point with a voltage of 65V and a current of 120A is collected at a time t1, a data point with a voltage of 63V and a current of 125A is collected at a time t2, and so on, to form a voltage-current state vector containing a plurality of sampling points, the data is typically stored in a time series form, and each data point contains three elements of a time stamp, a voltage value and a current value.

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

And determining a voltage-current transition point according to the change rate, constructing a piecewise continuous state transition equation, and judging the point as the transition point when the change rate of the voltage or the current exceeds a preset threshold value. For example, a voltage change rate threshold value of + -15V/s and a current change rate threshold value of + -30A/s are set, and when the change rate of a certain point exceeds these threshold values, they are marked as transition points. In practice, a certain fuel cell system may detect two transition points at 112A and 138A during the current jump from 100A to 150A, indicating that there is a nonlinear characteristic change near these two points.

And establishing a piecewise continuous state transition equation between adjacent voltage-current transition points, and establishing a voltage-current relational expression by adopting a polynomial fitting method for each interval. For example, for a section in which the current ranges between 112A and 138A, a relationship can be established by analyzing the history data, in which the voltage is about 70- (current-100) ×0.15 volts, the piecewise expression can describe the characteristics of the fuel cell at each operating point more accurately.

Substituting the working state data into a state transition equation, calculating a voltage reference value and a current reference value, substituting the current value of each historical working point into the state transition equation of a corresponding section, calculating a theoretical voltage value, comparing the theoretical voltage value with an actual voltage value, determining the accuracy of a model and carrying out necessary correction. For example, for a current value of 125A at an operating point, the calculated voltage reference should be 66.25V, the actual measurement 66V, and the difference within an acceptable range, indicating that the model has good fitting accuracy.

And constructing a power prediction equation set according to the voltage reference value and the current reference value, wherein the power prediction equation set comprises a voltage equation, a current equation and a power calculation equation, and the power calculation follows the basic principle that the electric power is equal to the product of the voltage and the current, so that the power output value of any working point can be predicted. For example, for an operating point of 65V and 120A current, the predicted power output is 7800W.

A power output constraint condition is established based on the target grid power deficiency, the power output constraint condition comprising a maximum power limit, a minimum power limit, a power change rate limit, and the like. For example, when the grid power shortage is 15kW, the system output power range may be set to 10kW to 18kW in consideration of the start-up characteristics and the safety margin of the hydrogen fuel cell, with the power change rate not exceeding 2 kW/min.

Substituting the power output constraint condition into a power prediction equation set, and solving by adopting a nonlinear programming to obtain a power output characteristic curve meeting the constraint condition. In the solving process, the optimal working point sequence is found through iterative calculation, so that the system can meet the power grid requirement and ensure safe and stable operation. For example, solving by nonlinear programming for a power deficit demand of 15kW, results in a hydrogen fuel cell that should operate at an operating point with current of 230A and voltage of 65.2V, with an output power of 15kW.

And correcting the power output characteristic curve according to the power generation capacity to obtain a corrected power output characteristic curve. The power generation capacity is affected by various factors such as the hydrogen supply amount, temperature, humidity, and the like. For example, when the hydrogen supply is insufficient, even though it is theoretically possible to output 18kW of power, the actual maximum output is only 15kW. And dynamically correcting the power output characteristic curve by monitoring parameters such as hydrogen pressure, stack temperature and the like in real time. In a certain actual operation, when the hydrogen pressure of the system is reduced to below 0.5MPa, the maximum power output is limited to 12kW, so that the safe operation of the system is ensured.

And determining the output power of the hydrogen energy system based on the corrected power output characteristic curve, calculating an optimal power output value according to the real-time requirement of the power grid and the corrected characteristic curve, converting the optimal power output value into corresponding voltage and current control signals, and driving the hydrogen fuel cell to operate according to the target power point. For example, when the grid shortage is 10kW and the modified maximum output is 12kW, the system will operate at a target power of 10kW with a corresponding operating point of current 200A and voltage 50V.

In the embodiment, through analysis of the voltage-current state vector and the change rate, the nonlinear change of the working characteristic of the fuel cell is accurately captured, so that the power output characteristic curve is more fit with the actual working condition, the power control precision is greatly improved, the dynamic response characteristic of the fuel cell under different working conditions is accurately described through identifying the voltage-current transition point and establishing a piecewise continuous state transition equation, the quick response capability of the system to the load change is improved, the stable working reference is provided for the fuel cell based on the voltage reference value and the current reference value calculated by the state transition equation, the power output fluctuation is reduced, the system operation stability is improved, the loss in the energy conversion process is reduced through accurately controlling the working point of the fuel cell, the hydrogen-electricity conversion efficiency is improved, and the system operation cost is reduced.

The present invention may be a method, apparatus, system, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for performing various aspects of the present invention.

It should be noted that the above embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the technical solution described in the above embodiments may be modified or some or all of the technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiments of the present invention.

Claims

1. The method for adjusting the coordination and complementation of the hydrogen energy system and the power grid is characterized by comprising the following steps of:

2. The method of claim 1, wherein obtaining grid load data and renewable energy generation data, determining a grid electrical load profile from the grid load data, and determining a renewable energy generation profile from the renewable energy generation data, comprises:

3. The method of claim 1, wherein calculating a grid supply-demand difference curve based on the grid electrical load curve and the renewable energy generation curve comprises:

4. The method of claim 1, wherein switching the hydrogen energy system to the hydrogen production mode for a first target period of time, determining a target hydrogen production power from the grid supply-demand difference curve, adjusting the hydrogen production power of the hydrogen energy system to the target hydrogen production power based on hydrogen production operating parameters of the hydrogen energy system, comprising:

5. The method of claim 4, wherein calculating a hydrogen production operating condition compensation coefficient from the hydrogen production operating condition parameter, compensating and correcting the power offset value based on the hydrogen production operating condition compensation coefficient to obtain a corrected power offset value, comprising:

6. The method of claim 1, wherein switching the hydrogen energy system to the power generation mode for a second target period of time, determining an output power of the hydrogen energy system based on the grid supply-demand difference curve, hydrogen pressure data, and operating state data of the hydrogen fuel cell, and adjusting an operating parameter of the hydrogen fuel cell based on the output power, comprises:

7. The method of claim 6, wherein constructing a hydrogen fuel cell power output characteristic based on the operating state data, and determining an output power of a hydrogen energy system in combination with the target grid power deficiency, the power generation capacity, and the power output characteristic, comprises:

8. A system for regulating the co-operation of a hydrogen energy system with an electric network, for implementing the method according to any one of the preceding claims 1 to 7, characterized in that it comprises:

9. An electronic device, comprising:

a processor;

A memory for storing processor-executable instructions;

Wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1 to 7.