A direct current off-grid type renewable energy hydrogen production energy management method and system

Abstract

The application discloses a direct-current off-grid type renewable energy hydrogen production energy management method and system, relates to the technical field of renewable energy management, and comprises the following steps: constructing a hydrogen production energy management module of a direct-current voltage networking; performing load characteristic analysis, obtaining energy demand of hydrogen production facilities, and identifying high-demand and low-demand periods; collecting wind power and sunlight data, evaluating potential output capacity of wind power generation and photovoltaic power generation; determining wind turbines, photovoltaic panels and energy storage devices based on analysis results of the energy demand and the output capacity, determining a hydrogen production operation mode, and performing energy management. The direct-current off-grid type renewable energy hydrogen production energy management method provided by the application improves energy utilization efficiency, reduces energy waste during off-peak periods, enhances adaptability and stability, and deals with different situations of energy peaks and troughs. The method optimizes energy storage and output strategies, reduces operation costs, improves economic benefits, and meets energy demand.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of renewable energy management, in particular to a direct current off-grid renewable energy hydrogen production energy management method and system. BACKGROUND

[0002] In the scenario of high penetration of new energy access or with new energy as the main power source, it is necessary to strengthen the multi-element direct utilization of renewable energy, utilize new energy direct power supply, wind-solar-hydrogen storage coupling, flexible load and other technologies, promote the integration of new energy development, transmission and terminal consumption through development and utilization mode innovation, and build a batch of new energy supply and utilization of high proportion of wind-solar-hydrogen integrated projects, which has become a new way of large-scale consumption of new energy. Under the condition of new energy and hydrogen production load fluctuation, there are problems of insufficient power supply reliability, voltage instability risk and power failure and network collapse when the tie-in channel is disconnected, especially under the restriction of new energy high proportion of self-generation and self-use project, it is necessary to improve the stability and reliability of wind-solar-hydrogen integrated system, and reduce the disturbance of large power grid.

[0003] According to the different sources of electric energy, the renewable energy hydrogen production technology can be divided into grid-connected hydrogen production and off-grid hydrogen production. Grid-connected hydrogen production is to connect the electric energy generated by wind-solar units to the power grid or partially connect it to the power grid, and then take electric energy from the power grid. It is mainly applied to large-scale abandoned light and wind consumption and energy storage. Off-grid hydrogen production refers to directly providing electric energy generated by wind-solar units to the hydrogen production equipment for electrolysis of water to produce hydrogen, which is mainly applied to distributed hydrogen production. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is that the existing grid-connected and off-grid hydrogen production methods have the problems of low energy utilization efficiency and poor grid stability, and how to realize the efficient direct utilization and optimization of storage of new energy.

[0006] To solve the above technical problems, the present application provides the following technical scheme: a direct current off-grid renewable energy hydrogen production energy management method, comprising:

[0007] A hydrogen production energy management module of direct current voltage networking is constructed. Load characteristic analysis is performed to obtain the energy demand of hydrogen production facilities and identify high demand and low demand periods. Wind and sunlight data are collected to evaluate the potential output capacity of wind power generation and photovoltaic power generation. Based on the analysis results of energy demand and output capacity, the wind turbine, photovoltaic panel and energy storage device are determined, the hydrogen production operation mode is determined, and energy management is performed.

[0008] As a preferred scheme of the direct current off-grid renewable energy hydrogen production energy management method, the hydrogen production energy management module of direct current voltage networking comprises an upper layer, a middle layer and a lower layer.

[0009] The upper layer is used for operation management / scheduling / optimization / prediction by the scheduling management system, and issues operation instructions and operation constraints to the lower layer micro source / load and its local controller; the middle layer is used for determining the operation mode of hydrogen production according to the upper layer instruction and the lower layer state, and managing and scheduling the lower layer equipment by the energy management module; and the lower layer is used for data measurement / operation control / state feedback of renewable energy / energy storage / load by the micro source / load and its local controller.

[0010] As a preferred scheme of the direct off-grid renewable energy hydrogen production energy management method, wherein: the load characteristic analysis includes collecting energy consumption data of the hydrogen production facility recorded by hours in a past preset time period; collecting environmental data in the same period, including temperature, humidity, and wind speed; and recording equipment state data, including maintenance history and efficiency change;

[0011] The energy demand of the hydrogen production facility is obtained by constructing an energy consumption prediction model.

[0012] The historical energy consumption data is collected, the environmental data is collected, the operation mode and maintenance data of the hydrogen production facility are recorded, the energy consumption fluctuation is explained, the collected data is preprocessed, the historical energy consumption data is subjected to Fourier analysis, the Akaike information criterion AIC is used to select the parameters of the ARIMA model, the linear regression method is used to determine the influence coefficient of environmental factors on energy consumption, the energy consumption prediction value of the time is calculated according to the determined parameters and real-time environmental data, the prediction result is compared with the actual energy consumption data to verify the accuracy of the model, the model parameters are adjusted according to the actual situation, the prediction performance is optimized, and the model parameters are re-predicted according to the adjusted model parameters; the average value and the standard deviation are calculated by using the historical prediction results, and the high demand threshold and the low demand threshold are set.

[0013] As a preferred scheme of the direct off-grid renewable energy hydrogen production energy management method, wherein: the evaluation of the potential output capacity of wind power generation and photovoltaic power generation includes constructing an output capacity evaluation model.

[0014] A wind speed meter and a solar radiation meter are installed at the location of the hydrogen production facility to collect wind speed and sunshine intensity, and the collected data is preprocessed;

[0015] The integral of the square of the wind speed in a preset time window is calculated to quantify the continuous contribution of the wind speed to the energy output, the sunshine data is subjected to logarithmic transformation to improve the sensitivity and response of the model to the change of the sunshine intensity;

[0016] The prediction model is selected based on the data characteristics, the model is trained, and the initial parameters of the neural network are set, including the number of layers, the number of neurons in each layer, and the type of activation function;

[0017] The model is trained using historical data sets, and the model parameters are optimized through a backpropagation algorithm to minimize prediction errors.

[0018] The model performance is evaluated using cross-validation techniques.

[0019] The prediction accuracy of the model is evaluated by the mean square error and the determination coefficient.

[0020] Real-time wind speed and solar intensity data are input into the model to obtain real-time energy output prediction.

[0021] As a preferred scheme of the direct off-grid type renewable energy hydrogen production energy management method, the determination of wind turbines, photovoltaic panels and energy storage devices includes identifying time period combinations, including high demand and sufficient energy supply, high demand and energy shortage, low demand and sufficient energy supply, and low demand and energy shortage.

[0022] For each time period combination, configuration analysis is performed to develop a configuration scheme, determine device requirements, and evaluate the matching degree of existing device configuration and predicted demand.

[0023] For high demand and energy shortage periods, the number of wind turbines and photovoltaic panels needed to be increased is calculated to make up for the energy gap; for low demand and sufficient energy supply periods, excess energy is managed by adjusting the operation strategy rather than increasing the number of devices; and for high demand and sufficient energy supply periods and low demand and energy shortage periods, the devices are ensured to operate effectively.

[0024] The energy storage device is determined according to the changes in energy supply and demand in each time period, and the required energy storage capacity is determined when in high demand and energy shortage periods, and the energy storage device is configured to ensure energy supply.

[0025] When in low demand and sufficient energy supply periods, the energy storage strategy is executed to maximize energy capture.

[0026] As a preferred scheme of the direct off-grid type renewable energy hydrogen production energy management method, the determination of the operation mode of hydrogen production includes analyzing the matching degree of device configuration and predicted demand in each time period combination, and determining the operation mode based on the matching degree, the operation mode including maximum output mode, energy saving mode, balance mode and emergency mode.

[0027] As a preferred scheme of the direct off-grid type renewable energy hydrogen production energy management method, the energy management includes increasing the discharge rate of the energy storage device when the operation mode is the maximum output mode, real-time monitoring and adjusting the energy output to not exceed the maximum carrying capacity of the facility, and automatically transferring the energy exceeding the immediate demand to the energy storage system for storage.

[0028] When the operation mode is the energy-saving mode, the operation frequency of the hydrogen production facility is reduced to reduce energy consumption, increase energy storage preparation, and optimize energy distribution by reducing or temporarily shutting down the power use of non-critical equipment to ensure the energy supply of critical operations;

[0029] When the operation mode is the balance mode, the charge and discharge behavior of the energy storage device is dynamically adjusted to smooth transient energy fluctuations, automatically balance the energy flow to the hydrogen production activity or the energy storage, and maintain the stability of the system operation;

[0030] When the operation mode is the emergency mode, a preset emergency energy management strategy is started, the standby energy system is immediately enabled, and the energy storage device is preferentially dispatched to support the operation of the critical facility.

[0031] Another object of the present application is to provide a direct current off-grid renewable energy hydrogen production energy management system, which can effectively dispatch and optimize energy resources in a direct current off-grid environment by constructing a direct current off-grid renewable energy hydrogen production energy management system, enhance the adaptability to environmental changes, improve the hydrogen production efficiency, reduce energy waste, and achieve the goal of improving the overall energy management efficiency and system reliability.

[0032] To solve the above technical problems, the present application provides the following technical solutions: a direct current off-grid renewable energy hydrogen production energy management system, comprising: an energy management module, a demand analysis module, and a potential evaluation module; the energy management module is used to construct a hydrogen production energy management module with direct current voltage networking; the demand analysis module is used to perform load characteristic analysis, obtain the energy demand of the hydrogen production facility, and identify high-demand and low-demand periods; the potential evaluation module is used to collect wind power and solar radiation data, and evaluate the potential output capacity of wind power generation and photovoltaic power generation; the energy management module is used to determine the wind turbine, photovoltaic panel, and energy storage device based on the analysis results of the energy demand and output capacity, determine the hydrogen production operation mode, and perform energy management.

[0033] A computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the direct current off-grid renewable energy hydrogen production energy management method as described above when executing the computer program.

[0034] A computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of the direct current off-grid renewable energy hydrogen production energy management method as described above.

[0035] The direct off-grid renewable energy hydrogen production energy management method provided by the application optimizes energy distribution through load characteristic analysis, improves energy utilization efficiency, and reduces energy waste during off-peak periods. The potential output capacity of wind power generation and photovoltaic power generation is predicted to optimize energy production and storage and adapt to the variability of wind and sunlight conditions.

[0036] By precisely matching the supply and demand configuration of appropriate energy equipment, adaptability and stability are enhanced to cope with different situations of energy peaks and troughs. The energy storage and output strategy is optimized to reduce operating costs, improve economic benefits, and meet energy demand. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0038] Figure 1 A direct off-grid renewable energy hydrogen production energy management method is provided for an embodiment of the application. DETAILED DESCRIPTION

[0039] In order to make the above-mentioned purposes, features and advantages of the application more apparent and easy to understand, the specific embodiments of the application will be described in detail below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the application.

[0040] In the following description, many specific details are set forth in order to provide a thorough understanding of the application, but the application can also be implemented in other ways different from the description, and those skilled in the art can make similar generalizations without departing from the spirit of the application. Therefore, the application is not limited to the specific embodiments disclosed below.

[0041] Embodiment 1

[0042] Reference Figure 1 For an embodiment of the application, a direct off-grid renewable energy hydrogen production energy management method is provided, comprising:

[0043] The hydrogen production energy management module of direct current voltage networking is constructed; load characteristic analysis is carried out to obtain the energy demand of the hydrogen production facility, and high demand and low demand periods are identified; wind and sunlight data are collected to evaluate the potential output capacity of wind power generation and photovoltaic power generation; the wind turbine, photovoltaic panel and energy storage device are determined based on the analysis results of the energy demand and output capacity, the hydrogen production operation mode is determined, and energy management is carried out.

[0044] The hydrogen production energy management module of direct current voltage networking includes upper, middle and lower layers; the upper layer is a dispatching management system for operation management / dispatching / optimization / prediction, and issues operation instructions and operation constraints to the lower layer micro source / load and its local controller; the middle layer is an energy management module that determines the operation mode of hydrogen production according to the upper layer instructions and lower layer state, and manages and dispatches the lower layer equipment; the lower layer is a micro source / load and its local controller for renewable energy / energy storage / load data measurement / operation control / state feedback.

[0045] The upper layer is responsible for load characteristic analysis, wind and sunlight data collection and evaluation. This layer mainly processes a large amount of data for analysis and prediction, including but not limited to energy demand analysis, weather conditions, and energy production potential. The dispatching management system of the upper layer will process this information and generate a prediction and demand model of energy supply based on this data, and then form an operation strategy and instruction.

[0046] The middle layer receives analysis results and strategy instructions from the upper layer, and determines the optimal configuration and layout of wind turbines, photovoltaic panels and energy storage devices based on these information. Ensure that all device configurations meet the optimal mode of energy output and demand prediction. The middle layer makes decisions on the configuration and operation strategy of energy equipment by analyzing the data received from the upper layer, to achieve the best energy utilization efficiency and cost-effectiveness.

[0047] The lower layer is responsible for actually executing the strategies formulated by the middle and upper layers. It includes actually operating wind turbines, photovoltaic panels and managing energy storage devices.

[0048] Load characteristic analysis includes collecting energy consumption data of the hydrogen production facility recorded by hour in the past preset time period; collecting environmental data in the same period, including temperature, humidity, wind speed; recording equipment state data, including maintenance history and efficiency change.

[0049] Obtaining the energy demand of the hydrogen production facility includes constructing an energy consumption prediction model, which is represented as:

[0050] ,

[0051] wherein, represents the predicted energy consumption at time , and The coefficients of the Fourier series quantitatively describe the amplitude of periodic fluctuations in energy consumption data at different frequencies. They are obtained through the Fourier transform of historical energy consumption data. Indicates period, The number of periodic items is determined based on the seasonal characteristics of the data. This invention utilizes seasonal variations throughout the year. Indicates the period length. This represents an autoregressive integral moving average model used to predict trends and non-periodic fluctuations in energy consumption data. It is historical energy consumption data. It is the order of the autoregressive term. It is the difference order. It is the order of the moving average term. Represents the regression coefficients of environmental variables. A data vector representing environmental variables, including temperature and humidity, obtained from real-time data from environmental monitoring equipment.

[0052] Collect historical energy consumption data Collect environmental data Record the operation mode and maintenance data of the hydrogen production facility, interpret energy consumption fluctuations, and preprocess the collected data; use historical energy consumption data to perform Fourier analysis to determine... and ; Select parameters for fitting the ARIMA model using the Akaike Information Criterion (AIC). The influence coefficient of environmental factors on energy consumption was determined using linear regression. Based on the established parameters and real-time environmental data Calculation time Energy consumption forecast The predicted results are compared with actual energy consumption data to verify the model's accuracy. The model parameters are adjusted according to the actual situation to optimize the prediction performance. The prediction is then re-established based on the adjusted model parameters. ;use Calculate the average of historical forecast results and standard deviation Set the high demand threshold to The low demand threshold is set to ,in, Determined by historical data analysis; if A high demand threshold is used to determine a period of high demand. A low demand threshold is used to determine a low demand period; otherwise, it is considered a regular demand period.

[0053] The rationale for the three-part addition is that each explains a different aspect of the energy consumption variation. The Fourier series deals with inherent periodic variations, the ARIMA model deals with random fluctuations and trends, and the exogenous variables deal with variations caused by environmental factors. Combined, they provide energy consumption forecasts.

[0054] Traditional energy consumption analysis methods typically include simple historical averages or use basic linear trend forecasting. The drawbacks of traditional methods include: inadequate handling of periodic variations: simple historical averages or linear models cannot effectively handle seasonal and daily periodic variations in the data. Ignoring random fluctuations and external influences: simple historical averages or linear models cannot adapt to the randomness and external environmental changes in energy consumption forecasting.

[0055] Evaluating the potential output capacity of wind power and photovoltaic power generation includes building an output capacity evaluation model, represented as:

[0056] ,

[0057] ,

[0058] where, represents the total predicted energy output at time , represents the wind speed at time , represents the solar intensity at time , represents the model parameters, represents the integral of the square of the wind speed within a period , representing the persistent effect of wind, represents the logarithmic transformation of solar intensity, used to smooth the solar data and enhance the non-linear expression ability of the model, represents the environmental influence function, is a coefficient obtained through experimental data, represents the exponential function, representing the normalized value, converting the linear regression output into a number between 0 and 1.

[0059] In actual energy models, the cumulative effect of wind speed and the immediate effect of solar radiation are combined, allowing the model to capture both the long-term cumulative effect of wind energy and the short-term variation effect of solar energy.

[0060] By separately processing wind speed and solar intensity, it can more flexibly adapt to different environmental conditions and changes, increasing the accuracy and applicability of the prediction. For example, on a cloudy and windy day, the model can accurately calculate the high energy output due to the persistent presence of wind, while handling the fluctuations in photovoltaic output caused by changes in cloud cover.

[0061] Using wind speed as the integral variable and solar intensity as the instantaneous variable, the model can effectively predict energy output at different time scales. It increases adaptability to complex environmental changes.

[0062] Install anemometers and pyranometers at the location of the hydrogen production facility to collect wind speed and solar intensity data. Data preprocessing is performed on the collected data. Data preprocessing includes data cleaning: outlier processing, checking data through automated scripts, identifying and removing outliers such as data during equipment failure.

[0063] Missing value processing: for missing data points, time series interpolation methods can be used to fill in missing data points to ensure data integrity.

[0064] Data normalization: standardization processing, normalize or standardize wind speed and solar intensity data, convert data to a unified scale (e.g. between 0 and 1) to eliminate the impact of different measurement units.

[0065] Calculate the integral of the square of the wind speed within a predetermined time window to quantify the sustained contribution of wind speed to energy output. Apply a logarithmic transformation to the solar data to improve the model's sensitivity and response to changes in light intensity. Based on data characteristics, select a prediction model. The present invention uses a multi-layer perceptron (MLP) to handle non-linear relationships and high-dimensional data. Model training is performed by setting the initial parameters of the neural network, including the number of layers, the number of neurons in each layer, and the type of activation function. Use historical data sets to train the model and optimize model parameters through the backpropagation algorithm to minimize prediction error. Use cross-validation techniques to evaluate model performance. Evaluate the prediction accuracy of the model using indicators such as mean squared error (MSE) and coefficient of determination (R²). Input real-time wind speed and solar intensity data into the model to obtain immediate energy output predictions. When ≥ the first output capacity threshold, it is determined to be an energy supply sufficient period, when < the second output capacity threshold, it is determined to be an energy shortage risk period.

[0066] The determination of the wind turbines, photovoltaic panels and energy storage devices includes identifying time period combinations including high demand and energy sufficiency, high demand and energy shortage, low demand and energy sufficiency, and low demand and energy shortage; performing configuration analysis on each time period combination, formulating configuration schemes, determining device requirements, and evaluating the matching degree of existing device configurations and predicted requirements; determining wind turbines and photovoltaic panels, calculating the number of wind turbines and photovoltaic panels required to be increased to make up for the energy gap for high demand and energy shortage time periods; for low demand and energy sufficiency time periods, managing excess energy by adjusting operation strategies rather than increasing devices; ensuring effective operation of devices during high demand and energy sufficiency time periods and low demand and energy shortage time periods; determining energy storage devices, determining the required energy storage capacity according to the changes in energy supply and demand in each time period, configuring energy storage devices to ensure energy supply when in high demand time periods and energy shortage risk time periods; and executing energy storage strategies to maximize energy capture when in low demand time periods and energy sufficiency time periods. If both conditions are met, configure energy storage devices to ensure energy supply and execute energy storage strategies to maximize energy capture.

[0067] Configuring energy storage devices to ensure energy supply includes extracting energy from devices and ensuring that these devices are fully charged before this time period. The output of the energy storage device is dynamically adjusted according to real-time energy demand and supply. Machine learning algorithms are used to optimize the charging and discharging cycles of the energy storage device, reduce energy loss and improve response speed, automatically start the backup power system when energy shortage is about to occur, and ensure that all control systems and sensors function normally in extreme weather or other adverse conditions.

[0068] Executing energy storage strategies to maximize energy capture includes storing excess energy in energy storage devices when sunlight is strong or wind speed is high, using high-frequency pulse charging or gradual charging techniques to improve energy conversion efficiency and reduce heat loss, adjusting the charging strategy of the energy storage device according to predicted energy output data, and starting slow charging after the peak of the sun to extend the life of the battery. Automatically adjust energy collection and storage strategies based on predicted weather changes.

[0069] The determination of the operation mode of hydrogen production includes analyzing the matching degree of device configurations and predicted requirements in each time period combination, analyzing whether the number and capacity of wind turbines, photovoltaic panels and energy storage devices meet the current and predicted energy demand, and determining the operation mode based on the matching degree, the operation mode including a maximum output mode, an energy saving mode, a balance mode and an emergency mode.

[0070] After determining the wind turbines, photovoltaic panels and energy storage devices, the data is re-collected to update the values after executing the adjustment strategy and The obtained values are preprocessed, and the difference values .

[0071] ,

[0072] where, represents the difference.

[0073] Based on historical data analysis, considering seasonal changes, weather forecasts and possible changes in equipment efficiency, set energy management thresholds, including energy surplus threshold , energy balance threshold and energy shortage threshold .

[0074] In this invention is set to 0, indicating that the energy is exactly balanced; is set to 3%; is set to -3%, indicating a situation where energy supply is significantly insufficient.

[0075] The maximum output mode is satisfied when , indicating that there is enough energy for efficient hydrogen production.

[0076] The energy-saving mode is satisfied when , indicating that energy is relatively tight and energy-saving measures need to be taken.

[0077] The balance mode is satisfied when , the energy supply and demand are basically balanced, and it is suitable for normal operation.

[0078] The emergency mode is satisfied when , the energy is severely insufficient, and the standby system needs to be started or emergency measures need to be taken.

[0079] According to the actual energy usage and prediction data, dynamically adjust the threshold and operation mode, use machine learning and pattern recognition technology to continuously optimize and calculation, improve the accuracy of prediction.

[0080] Energy management includes actively increasing the discharge rate of energy storage devices in the maximum output mode to provide the maximum amount of energy for hydrogen production facilities, supporting efficient hydrogen production operations. It is automatically activated when the energy supply is sufficient. To prevent equipment overload, real-time monitoring of energy output ensures that it does not exceed the maximum carrying capacity of the facility. At the same time, energy exceeding immediate demand will be automatically transferred to the energy storage system for storage, or sold to other users through smart grid technology, thereby optimizing energy utilization.

[0081] In the balance mode, the goal is to maintain the balance of energy supply and demand to ensure the stability of the system operation. Adjust the charging and discharging behavior of the energy storage device to smooth out transient energy fluctuations. Based on real-time monitoring of energy production and consumption data, automatically balance energy flow to hydrogen production activities or energy storage to achieve optimal system efficiency and response.

[0082] In energy-saving mode, measures are taken to reduce energy consumption, reduce the operating frequency of the hydrogen production facility to reduce energy consumption, and increase energy storage preparation, optimize energy distribution, and ensure energy supply for critical operations by reducing power usage or temporarily shutting down non-critical equipment.

[0083] In emergency mode, preset emergency energy management strategies are activated, including immediately activating backup energy systems (such as diesel generators, etc.) to quickly supplement the energy required for critical operations. In addition, energy storage devices will be prioritized to support the operation of critical facilities such as hydrogen production lines. Non-critical loads are automatically disconnected to ensure the endurance and safety of critical operations.

[0084] Embodiment 2

[0085] One embodiment of the present application provides a direct current off-grid renewable energy hydrogen production energy management system, comprising: an energy management module, a demand analysis module, and a potential assessment module.

[0086] The energy management module is used to construct a direct current voltage network hydrogen production energy management module.

[0087] The demand analysis module is used to analyze load characteristics and obtain energy demand of the hydrogen production facility, and identify high demand and low demand periods.

[0088] The potential assessment module is used to collect wind and sunlight data and assess the potential output capacity of wind power generation and photovoltaic power generation.

[0089] The energy management module is used to determine wind turbines, photovoltaic panels, and energy storage devices based on the analysis results of energy demand and output capacity, determine the hydrogen production operating mode, and perform energy management.

[0090] Embodiment 3

[0091] One embodiment of the present application differs from the previous two embodiments in that:

[0092] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the technical solutions that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0093] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a list of executable instructions for implementing logic functions, which can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus or device, such as a computer-based system, a system including a processor or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or in conjunction with these instructions execution systems, apparatus or devices. For the purpose of this specification, the "computer-readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by or in connection with an instruction execution system, apparatus or device, or in conjunction with these instruction execution systems, apparatus or devices.

[0094] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CD ROM). In addition, the computer readable medium can even be paper or other suitable medium on which the program can be printed, because the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by editing, interpreting or processing, if necessary, in other suitable ways, to be electronically obtained and then stored in the computer memory.

[0095] It should be understood that portions of the present application can be implemented in hardware, software, firmware, or combinations thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, implementation can be with any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0096] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A DC off-grid type renewable energy hydrogen production energy management method, characterized by, Comprise: A hydrogen production energy management module for direct current voltage networking; The hydrogen production energy management module for direct current voltage networking comprises an upper layer, a middle layer and a lower layer; The upper layer is a scheduling management system for operation management / scheduling / optimization / prediction, and issues operation instructions and operation constraints to the lower layer micro source / load and its local controller; The middle layer is an energy management module that determines the operation mode of hydrogen production according to the upper layer instructions and the lower layer state, and manages and schedules the lower layer equipment; The lower layer is a micro source / load and its local controller for renewable energy / energy storage / load data measurement / operation control / state feedback; Load characteristic analysis is performed to obtain the energy demand of the hydrogen production facility and identify high demand and low demand periods; The load characteristic analysis comprises collecting energy consumption data of the hydrogen production facility recorded by hours in a past preset time period; Collect environmental data for the same period, including temperature, humidity, and wind speed; Record equipment state data, including maintenance history and efficiency changes; The energy demand of the hydrogen production facility is obtained by constructing an energy consumption prediction model; Collect historical energy consumption data, collect environmental data, record the operation mode and maintenance data of the hydrogen production facility, explain energy consumption fluctuations, and perform data preprocessing on the collected data; perform Fourier analysis using historical energy consumption data; select parameters for fitting ARIMA models through Akaike Information Criterion (AIC); use linear regression method to determine the influence coefficient of environmental factors on energy consumption; calculate the energy consumption prediction value according to the determined parameters and real-time environmental data; compare the prediction results with the actual energy consumption data to verify the accuracy of the model, adjust the model parameters according to the actual situation, optimize the prediction performance, and re-predict based on the adjusted model parameters; calculate the average value and standard deviation using historical prediction results, and set high demand threshold and low demand threshold; Collect wind and sunlight data to evaluate the potential output capacity of wind power and photovoltaic power generation; Based on the analysis results of energy demand and output capacity, determine the wind turbine, photovoltaic panel and energy storage device, determine the hydrogen production operation mode, and perform energy management; The determination of the wind turbine, photovoltaic panel and energy storage device includes identifying time period combinations including high demand and sufficient energy supply, high demand and energy shortage, low demand and sufficient energy supply, and low demand and energy shortage; Perform configuration analysis for each time period combination, develop a configuration scheme, determine device requirements, and evaluate the matching degree of existing device configuration and predicted demand; Determine the wind turbine and photovoltaic panel, calculate the number of wind turbines and photovoltaic panels needed to be added to make up for the energy gap for high demand and energy shortage periods; for low demand and sufficient energy supply periods, manage excess energy by adjusting operation strategies rather than adding devices; ensure effective operation of devices in high demand and sufficient energy supply periods and low demand and energy shortage periods; Determine the energy storage device, determine the required energy storage capacity according to the changes in energy supply and demand in each period, and configure the energy storage device to ensure energy supply when in high demand and energy shortage periods; When in low demand and sufficient energy supply periods, execute energy storage strategies to maximize energy capture; The determining the operation mode of hydrogen production includes analyzing the matching degree of the equipment configuration and the predicted demand in each time period combination, determining the operation mode based on the matching degree, and the operation mode includes a maximum output mode, an energy saving mode, a balance mode, and an emergency mode; The energy management includes increasing the discharge rate of the energy storage equipment when the operation mode is the maximum output mode, monitoring and adjusting the energy output in real time to not exceed the maximum carrying capacity of the facility, and automatically transferring the energy exceeding the immediate demand to the energy storage system for storage; When the operation mode is the energy saving mode, the operation frequency of the hydrogen production facility is reduced to reduce energy consumption, energy storage preparation is increased, and energy distribution is optimized by reducing or temporarily shutting down the power use of non-critical equipment to ensure the energy supply of critical operations; When the operation mode is the balance mode, the charge and discharge behavior of the energy storage equipment is dynamically adjusted to smooth transient energy fluctuations, and the energy flow to hydrogen production activities or energy storage is automatically balanced to maintain the stability of system operation; When the operation mode is the emergency mode, the preset emergency energy management strategy is started, the standby energy system is immediately enabled, and the energy storage equipment is preferentially dispatched to support the operation of critical facilities.

2. The DC off-grid renewable energy hydrogen generation energy management method of claim 1, wherein: The evaluation of the potential output capacity of wind power generation and photovoltaic power generation includes constructing an output capacity evaluation model; Wind speed meters and solar radiation meters are installed at the location of the hydrogen production facility to collect wind speed and sunshine intensity, and the collected data is preprocessed; The integral of the square of the wind speed in a preset time window is calculated to quantify the continuous contribution of wind speed to energy output, and the sunshine data is processed using a logarithmic transformation to improve the sensitivity and response of the model to changes in sunshine intensity; Based on the characteristics of the data, a prediction model is selected, the model is trained, and the initial parameters of the neural network are set, including the number of layers, the number of neurons in each layer, and the type of activation function; The model is trained using a historical data set, and the model parameters are optimized using a backpropagation algorithm to minimize prediction error; The model performance is evaluated using cross-validation techniques; The prediction accuracy of the model is evaluated by the mean square error and the coefficient of determination; Real-time wind speed and sunshine intensity data is input into the model to obtain an immediate energy output prediction.

3. A system employing the direct current off-grid type renewable energy hydrogen production energy management method according to any one of claims 1 to 2, characterized in that, It includes: An energy management module, a demand analysis module, and a potential evaluation module; The energy management module is used to construct a direct current voltage networking hydrogen energy management module; The demand analysis module is used to analyze load characteristics, obtain energy demand of the hydrogen production facility, and identify high demand and low demand periods; The potential evaluation module is used to collect wind and sunshine data and evaluate the potential output capacity of wind power generation and photovoltaic power generation; The energy management module is used to determine wind turbines, photovoltaic panels, and energy storage equipment based on the analysis results of energy demand and output capacity, determine the operation mode of hydrogen production, and perform energy management.

4. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to implement the steps of the direct current off-grid renewable energy hydrogen production energy management method of any one of claims 1-2.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the direct current off-grid renewable energy hydrogen production energy management method of any one of claims 1-2.

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