New energy flexible hydrogen production system multi-stage dynamic optimization method and system

Abstract

The invention discloses a multi-stage dynamic optimization method and system for a new energy flexible hydrogen production system, and the method comprises the steps: a day-ahead scheduling plan stage: formulating a next-day scheduling plan based on day-ahead prediction data; in the real-time monitoring stage, the output condition of the new energy power generation unit is monitored in real time through a sensor network; in the dynamic adjustment stage, the intelligent energy management system is used for dynamically adjusting the water electrolysis hydrogen production unit; in the hydrogen management and optimization stage, a hydrogen storage and transportation unit is intelligently managed; and in the multi-stage circulation optimization stage, the operation efficiency and economy of the system are continuously optimized through a closed loop feedback mechanism. The system comprises a new energy power generation unit, a water electrolysis hydrogen production unit, an intelligent energy management system and a hydrogen storage and transportation unit. Through the steps of real-time monitoring, dynamic adjustment, hydrogen management and optimization and multi-stage circulation optimization, the energy utilization efficiency is remarkably improved, the hydrogen production cost is reduced, the flexibility and stability of the system are enhanced, and powerful support is provided for development of the hydrogen energy industry.

Description

Technical Field

[0001] The present invention relates to the field of new energy utilization and hydrogen energy technology, and particularly to a method and system for flexibly producing hydrogen using renewable energy such as wind energy and solar energy, and improving hydrogen production efficiency and system economy through a multi-stage dynamic optimization method. Background Art

[0002] In the context of the global energy transition, hydrogen energy, as an important part of clean energy, is gradually becoming a key technical path for realizing the transformation of the energy structure and addressing climate change. Hydrogen energy has significant advantages such as high calorific value, zero emissions, and wide sources, and is regarded as an important part of the future energy system. However, traditional electrolytic water hydrogen production technology mainly relies on large grid power supply, and this mode faces many challenges in terms of energy utilization efficiency, hydrogen production cost, and energy sustainability.

[0003] Limitations of Traditional Electrolytic Water Hydrogen Production Technology Traditional electrolytic water hydrogen production technology depends on a stable large grid power supply, and its hydrogen production efficiency and cost are affected by multiple factors such as the stability of grid power supply, electricity price fluctuations, and grid construction costs. First of all, the power of the large grid mainly comes from fossil energy, which leads to the problem of indirect carbon emissions in the process of electrolytic water hydrogen production, contrary to the original intention of hydrogen energy as clean energy. Secondly, the stability of grid power supply directly affects the operation efficiency and lifespan of the electrolyzer. Power fluctuations may cause the electrolyzer to start and stop frequently, increasing equipment losses and maintenance costs. Moreover, with the peak-valley changes of the grid load, the electricity price also fluctuates, which further increases the uncertainty of hydrogen production cost.

[0004] Challenges and Opportunities of Renewable Energy Hydrogen Production To solve the above problems, using renewable energy such as wind energy and solar energy for electrolytic water hydrogen production has become a potential solution. Wind energy and solar energy, as clean and renewable energy sources, have advantages such as rich resources and wide distribution. However, these energy sources are volatile and intermittent, and their power generation and power generation time are difficult to accurately predict and control, which poses quite a challenge to electrolytic water hydrogen production.

[0005] On the one hand, the volatility of renewable energy leads to unstable power supply, and the electrolyzer needs to frequently adjust its operating state to adapt to power changes, which not only reduces the hydrogen production efficiency but also may damage the electrolyzer. On the other hand, the intermittency of renewable energy makes the power supply have time uncertainty. How to efficiently produce hydrogen when the power is sufficient and ensure the stability of hydrogen supply when the power is insufficient has become an urgent problem to be solved. Summary of the Invention

[0006] To solve the above problems, the present invention proposes a multi-stage dynamic optimization method and system for a new energy flexible hydrogen production system, aiming to improve the utilization efficiency of renewable energy, reduce the hydrogen production cost, and enhance the flexibility and stability of the system.

[0007] To achieve the above object, the present invention provides the following technical solutions: A multi-stage dynamic optimization method for a new energy flexible hydrogen production system, comprising the following steps: a) Day-ahead scheduling plan stage: Based on day-ahead prediction data, including new energy power generation potential prediction and hydrogen demand prediction, formulate the scheduling plan for the next day, determine the operating power of the electrolyzer, hydrogen production time, and the charge and discharge strategies of energy storage devices, so as to ensure that while meeting the hydrogen demand, the renewable energy is maximally utilized for hydrogen production and the hydrogen production cost is minimized; b) Real-time monitoring stage: Real-time monitor the output of the new energy power generation unit through a sensor network. The new energy power generation unit includes a wind power generation system and a photovoltaic power generation system, and collect and process the wind power generation power, photovoltaic power generation power, and related weather and environmental data; c) Dynamic adjustment stage: Based on the data collected in the real-time monitoring stage, use an intelligent energy management system to dynamically adjust the electrolytic water hydrogen production unit. The adjustment contents include electrolytic current, voltage, and the working state of the electrolyzer, so as to match the real-time output of the new energy power generation unit and maximize the utilization of renewable energy for hydrogen production; d) Hydrogen management and optimization stage: According to the output of the hydrogen production unit and the hydrogen demand prediction, intelligently manage the hydrogen storage and transportation unit to ensure the safe storage, efficient transportation, and on-demand supply of hydrogen; at the same time, further fine-tune the output plan of the electrolytic water hydrogen production unit according to the hydrogen inventory and demand feedback; e) Multi-stage cyclic optimization stage: Repeat the above day-ahead scheduling plan stage to hydrogen management and optimization stage to form a closed-loop feedback mechanism, and continuously cycle and optimize the overall operation efficiency and economy of the new energy flexible hydrogen production system.

[0008] Furthermore, the intelligent energy management system further includes a prediction model, and this prediction model further has the following components: New energy power generation potential prediction module: Using historical weather data, current weather conditions, geographical location information, and the technical parameters of the new energy power generation unit, predict the wind power generation potential and photovoltaic power generation potential in the next period of time through machine learning algorithms or statistical models, including the predicted average power generation, maximum / minimum power generation, and possible fluctuation ranges; Hydrogen demand forecasting module: Based on historical hydrogen consumption data, seasonal demand variations, production plans of industrial users, hydrogen energy application trends in the transportation sector, and policy guidance factors, it uses time series analysis or neural networks to predict the market demand for hydrogen and the peak and valley periods of demand over a certain period in the future. Pre-scheduling decision-making module: Combining the results of new energy power generation potential forecasting and hydrogen demand forecasting, it formulates the operation plan for the water electrolysis hydrogen production unit, including start-up / shutdown times, electrolysis power setting, and the calling strategy for possible backup power sources or energy storage systems; meanwhile, it plans the operation of the hydrogen storage and transportation unit, including hydrogen filling times, pressure management of storage tanks, and scheduling of transportation vehicles, to ensure stable hydrogen supply and maximize cost-effectiveness. Forecast uncertainty management module: Considering the uncertainty of the forecasting model itself and the unpredictability of external factors such as weather changes and equipment failures, it sets safety margins and emergency response plans to deal with forecast deviations or emergencies, maintaining the flexibility and robustness of the system.

[0009] Furthermore, it also includes a fault warning and emergency response mechanism. When a fault is detected in the new energy power generation unit or the water electrolysis hydrogen production unit, it automatically adjusts the system operation strategy to ensure the continuity and safety of the system.

[0010] A new energy flexible hydrogen production system includes: New energy power generation unit: At least including a wind power generation system and a photovoltaic power generation system, used to provide the electricity required for hydrogen production. Water electrolysis hydrogen production unit: Receives electricity from the new energy power generation unit and electrolyzes water molecules into hydrogen and oxygen. Intelligent energy management system: Real-time monitors the power output of the new energy power generation unit, dynamically adjusts the operation parameters of the water electrolysis hydrogen production unit, and manages the hydrogen storage and transportation unit at the same time. Hydrogen storage and transportation unit: Used to safely store the hydrogen produced by the water electrolysis hydrogen production unit and transport it efficiently according to demand.

[0011] Furthermore, the intelligent energy management system also includes a remote monitoring and data analysis module, used to remotely monitor the operation status of the system and analyze historical data to optimize future operation strategies.

[0012] Furthermore, it also includes an energy trading platform connected to the intelligent energy management system, used to sell electricity to the power grid when there is an excess of new energy or purchase electricity from the power grid when there is a shortage of electricity to balance the system's energy demand.

[0013] Advantages of the present invention: 1. Improve the utilization rate of new energy By monitoring the output of new energy power generation units in real time and intelligently predicting their power generation potential, the system can make full use of new energy power generation, reduce phenomena such as curtailment of photovoltaic power and curtailment of wind power, and improve the utilization rate of new energy.

[0014] 2. Optimize hydrogen production cost Intelligently adjusting the hydrogen production strategy according to the prediction results, such as adjusting the operating power and hydrogen production time of the electrolyzer, can ensure that while meeting the hydrogen demand, the hydrogen production cost is minimized.

[0015] 3. Enhance system flexibility The system has the ability of flexible hydrogen production and can respond and adjust quickly according to the fluctuations of new energy power generation and the changes in hydrogen demand, enhancing the flexibility and adaptability of the system.

[0016] 4. Improve the efficiency of hydrogen storage and transportation By intelligently managing the hydrogen storage unit and monitoring the pressure, temperature and hydrogen storage volume of the storage tank in real time, the system can ensure the safe storage and on-demand supply of hydrogen. At the same time, intelligently planning the scheduling and routes of transportation vehicles can reduce transportation costs and improve the efficiency of hydrogen storage and transportation.

[0017] 5. Achieve continuous system optimization Establish a closed-loop feedback mechanism, collect real-time monitoring data, prediction results, the effects of hydrogen production strategy adjustment, and feedback information on hydrogen management and transportation, and use optimization algorithms to iteratively upgrade and improve the system, which can continuously improve the operating efficiency and economy of the system.

[0018] 6. Promote the development of the hydrogen energy industry The application of this method and system can promote the rapid development of the hydrogen energy industry, reduce the production cost of hydrogen energy, improve the utilization efficiency of hydrogen energy, and provide strong support for the commercialization and large-scale application of the hydrogen energy industry.

[0019] 7. Improve environmental benefits By efficiently using new energy to produce hydrogen, the dependence on traditional fossil fuels is reduced, greenhouse gas emissions are lowered, and environmental benefits are improved. At the same time, as a clean energy, the wide application of hydrogen energy helps to promote the transformation and upgrading of the energy structure.

[0020] In summary, the multi-stage dynamic optimization method and system for a new energy flexible hydrogen production system have significant beneficial effects. They not only improve the utilization rate of new energy and hydrogen production efficiency, reduce production costs, but also enhance the flexibility and adaptability of the system, providing strong support for the development of the hydrogen energy industry. At the same time, the application of this method and system also helps to promote the transformation and upgrading of the energy structure and improve environmental benefits. Brief Description of the Drawings

[0021] Figure 1Schematic flow diagram of a multi-stage dynamic optimization method and system for a new energy flexible hydrogen production system according to the present invention.

[0022] Figure 2 Schematic structural diagram of a multi-stage dynamic optimization method and system for a new energy flexible hydrogen production system according to the present invention. Detailed implementation manners

[0023] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in 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. Obviously, the described embodiments are some but not all of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0024] Embodiment

[0025] I. System overview The new energy flexible hydrogen production system is a comprehensive system integrating a variety of advanced technologies, aiming to efficiently, flexibly and stably utilize renewable energy for hydrogen production. This system mainly consists of the following core units, and each unit works in coordination to jointly achieve the optimized management of the hydrogen production process.

[0026] New energy power generation unit The new energy power generation unit is the energy source of the system, mainly including a wind power generation system and a photovoltaic power generation system. These systems use the natural wind energy and solar energy to generate electricity and provide the required power for the water electrolysis hydrogen production unit. To ensure the stability and reliability of power generation, the new energy power generation unit is equipped with monitoring and control equipment, which can real-time monitor key parameters such as power generation power, voltage, and current, and automatically adjust the power generation strategy according to weather conditions and environmental factors.

[0027] 2. Water electrolysis hydrogen production unit The water electrolysis hydrogen production unit is the core part of the system, responsible for electrolyzing water into hydrogen and oxygen. This unit consists of an electrolytic cell, a power supply system, a cooling system, and a hydrogen separation and purification system. The electrolytic cell is the key equipment for water electrolysis hydrogen production, and its performance directly affects the hydrogen production efficiency and cost. To match the real-time output of the new energy power generation unit, the water electrolysis hydrogen production unit needs to have the ability to dynamically adjust operating parameters, such as electrolysis current, voltage, and the working state of the electrolytic cell. In addition, to ensure the safety and stability of the hydrogen production process, the water electrolysis hydrogen production unit is also equipped with safety protection devices and fault diagnosis systems.

[0028] 3. Intelligent energy management system The intelligent energy management system is the "brain" of the system, responsible for real-time monitoring of the output of new energy power generation units, predicting the new energy power generation potential and hydrogen demand in the next period based on the prediction model. Based on this information, the intelligent energy management system dynamically adjusts the operating parameters of the electrolytic water hydrogen production unit to ensure the efficiency and flexibility of the hydrogen production process. At the same time, the intelligent energy management system also manages the hydrogen storage and transportation unit, formulates transportation plans according to hydrogen demand and storage conditions, and ensures the timely supply of hydrogen and maximizes cost-effectiveness. The intelligent energy management system usually adopts advanced algorithms and technologies, such as machine learning, big data analysis and optimization algorithms, to achieve the intelligent management and optimal decision-making of the system.

[0029] 4. Hydrogen storage and transportation unit The hydrogen storage and transportation unit is responsible for storing and transporting the hydrogen produced by the electrolytic water hydrogen production unit. The hydrogen storage unit adopts high-pressure gaseous storage or liquid storage methods to ensure the safe storage and long-term preservation of hydrogen. The hydrogen transportation unit selects appropriate transportation methods according to hydrogen demand and transportation distance, such as pipeline transportation, tanker transportation or ship transportation. To ensure the safe transportation and timely delivery of hydrogen, the hydrogen storage and transportation unit is equipped with safety monitoring devices and emergency response systems to deal with possible safety risks and emergencies.

[0030] In summary, the new energy flexible hydrogen production system realizes an efficient, flexible and stable hydrogen production process by real-time monitoring the output of new energy power generation units, dynamically adjusting the operating parameters of the electrolytic water hydrogen production unit, and managing the hydrogen storage and transportation unit. This system not only improves energy utilization efficiency, reduces hydrogen production costs, but also provides strong support for the development of the hydrogen energy industry.

[0031] Specific implementation steps

[0032] 1. Day-ahead scheduling plan stage Based on day-ahead prediction data (including new energy power generation potential prediction and hydrogen demand prediction), formulate the scheduling plan for the next day, determine the operating power of the electrolyzer, hydrogen production time, and charge and discharge strategies of energy storage devices, so as to ensure that while meeting hydrogen demand, renewable energy is maximally utilized for hydrogen production and hydrogen production costs are minimized.

[0033] Specific implementation process: 1.1 Data collection and integration The intelligent energy management system collects monitoring data of power generation power, voltage, current, weather conditions (wind speed, wind direction, light intensity), and environmental parameters (temperature, humidity) from new energy power generation units, electrolytic water hydrogen production units, and external data sources (weather stations); at the same time, the system also obtains relevant historical data in the past period from the historical database, including new energy power generation potential, hydrogen demand, and equipment operating conditions.

[0034] 1.2 Application of Prediction Model Using historical data and real-time monitoring data, select appropriate prediction algorithms (time series analysis, regression analysis, machine learning models) to build a prediction model; use historical data to train and optimize the prediction model, and adjust the model parameters to improve the accuracy of prediction.

[0035] 1.3 Formulate Scheduling Plan According to the prediction results, the intelligent energy management system formulates the scheduling plan for the next day, including the operating power of the electrolyzer, the hydrogen production time, and the charging and discharging strategies of energy storage devices, ensuring that while meeting the hydrogen demand, the renewable energy is maximally utilized for hydrogen production and the hydrogen production cost is minimized.

[0036] 2. Real-time Monitoring Phase Equipment Configuration: Install sensors at key positions in the new energy power generation units (wind power generation system and photovoltaic power generation system) to monitor key parameters such as power generation power, voltage, and current in real time. At the same time, collect weather data (wind speed, wind direction, light intensity) and environmental data (temperature, humidity); Data Processing: Transmit the data collected by the sensors to the intelligent energy management system for preprocessing and cleaning to remove outliers and noise, ensuring the accuracy and reliability of the data.

[0037] Specific Implementation Process: 2.1 Equipment Configuration Sensor Selection and Installation: Install sensors at key positions (wind turbine blades, generators, anemometers) in the wind power generation system to monitor wind speed, wind direction, generator power, voltage, and current parameters; Install sensors at key positions (solar panels, inverters, environmental monitoring stations) in the photovoltaic power generation system to monitor light intensity, solar panel temperature, power generation power, voltage, and current parameters; Data Acquisition Equipment: Configure data collectors or data loggers to collect the data transmitted by the sensors and convert it into digital signals for storage and transmission.

[0038] 2.2 Data Collection Weather Data: Obtain weather data such as wind speed, wind direction, light intensity, temperature, and humidity through weather stations or weather data providers, and these data can be transmitted to the intelligent energy management system in real time through the Internet; Environmental Data: Install environmental monitoring equipment such as temperature sensors and humidity sensors near the new energy power generation units to monitor environmental data in real time.

[0039] 2.3 Data Transmission Wired transmission: Transmit the data collected by sensors and data acquisition devices to the intelligent energy management system through a wired network (such as Ethernet, optical fiber); Wireless transmission: For new energy power generation units in remote areas or where wiring is difficult, wireless communication technologies (such as Wi-Fi, LoRa, 4G / 5G) can be used to transmit data to the intelligent energy management system.

[0040] 2.4 Data processing Data preprocessing: Preprocess the collected data, including data cleaning, denoising, and outlier detection, to ensure the accuracy and reliability of the data; Data format conversion: Convert the preprocessed data into a format recognizable by the intelligent energy management system, such as JSON, XML; Data storage: Store the processed data in a database for subsequent analysis and use.

[0041] 2.5 Types of parameters, weather data, and environmental data Parameter types Wind power generation system parameters: wind speed, wind direction, generator power, voltage, current; Photovoltaic power generation system parameters: light intensity, solar panel temperature, power generation, voltage, current.

[0042] Weather data types Wind speed: represents the intensity of the wind, usually measured in meters per second (m / s); Wind direction: represents the direction of the wind, usually expressed in degrees, such as 0 degrees for north wind and 90 degrees for east wind; Light intensity: represents the intensity of sunlight, usually measured in watts per square meter (W / m²); Temperature: represents the temperature of the environment, usually measured in degrees Celsius (°C) or degrees Fahrenheit (°F); Humidity: represents the water vapor content in the air, usually expressed as relative humidity (%).

[0043] Environmental data types Temperature: same as above, represents the temperature of the environment near the new energy power generation unit; Humidity: same as above, represents the humidity of the environment near the new energy power generation unit; Other environmental parameters: such as air pressure, air quality index (AQI), and these parameters can be monitored according to specific requirements.

[0044] In summary, the specific implementation process of the real-time monitoring stage includes steps such as equipment configuration, data collection, data transmission, and data processing. At the same time, the parameters to be monitored, weather data, and environmental data types are also diverse, and the accuracy and reliability of these data are crucial for subsequent analysis and decision-making.

[0045] 3. Dynamic adjustment stage Application of prediction model: The intelligent energy management system uses the built-in prediction model to predict the new energy generation potential and hydrogen demand in the next period of time based on real-time monitoring data and historical data; Adjustment of hydrogen production strategy: According to the prediction results, the intelligent energy management system dynamically adjusts the operating parameters of the water electrolysis hydrogen production unit, such as electrolysis current, voltage, and the working state of the electrolyzer, to match the real-time output of the new energy generation unit and meet the hydrogen demand; Fault warning and response: The system monitors the operating status of the water electrolysis hydrogen production unit in real time. Once an abnormality or fault is detected, it immediately triggers the warning mechanism and automatically adjusts the system operation strategy to ensure the continuity and safety of the system.

[0046] Specific implementation process: 3.1 Application of prediction model Data collection and integration The intelligent energy management system collects real-time monitoring data of power generation power, voltage, current, weather conditions (wind speed, wind direction, light intensity), and environmental parameters (temperature, humidity) from the new energy generation unit, water electrolysis hydrogen production unit, and external data sources (weather stations); At the same time, the system also obtains relevant historical data in the past period of time from the historical database, including new energy generation potential, hydrogen demand, and equipment operating conditions.

[0047] Data analysis and preprocessing Clean and preprocess the collected real-time monitoring data to remove outliers and noise to ensure the accuracy and reliability of the data; Conduct statistical analysis on the historical data to identify the periodic changes, trends, and correlations with other factors of the new energy generation potential and hydrogen demand.

[0048] Prediction model construction and training Select a suitable prediction algorithm (time series analysis, regression analysis, machine learning model) to construct a prediction model based on historical data and real-time monitoring data; Use historical data to train and optimize the prediction model, and adjust the model parameters to improve the prediction accuracy; This prediction model further consists of the following: New Energy Power Generation Potential Prediction Module: Using historical weather data, current weather conditions, geographical location information, and technical parameters of new energy power generation units, predict the wind power generation potential and photovoltaic power generation potential in the future for a certain period through machine learning algorithms or statistical models, including the predicted average power generation, maximum / minimum power generation, and possible fluctuation ranges; Hydrogen Demand Prediction Module: Based on historical hydrogen consumption data, seasonal demand changes, production plans of industrial users, hydrogen energy application trends in the transportation field, and policy-oriented factors, use time series analysis or neural networks to predict the market demand for hydrogen in the future for a certain period, as well as the peak and valley periods of demand; Pre-scheduling Decision Module: Combine the results of new energy power generation potential prediction and hydrogen demand prediction to formulate the operation plan of the water electrolysis hydrogen production unit, including start / stop times, electrolysis power setting, and the calling strategy of possible backup power sources or energy storage systems; at the same time, plan the operation of the hydrogen storage and transportation unit, including hydrogen filling time, pressure management of storage tanks, and scheduling of transportation vehicles, to ensure stable hydrogen supply and maximize cost-effectiveness; Prediction Uncertainty Management Module: Considering the uncertainty of the prediction model itself and the unpredictability of external factors such as weather changes and equipment failures, set safety margins and emergency response plans to cope with prediction deviations or emergencies, and maintain the flexibility and robustness of the system.

[0049] Future Prediction Using the trained prediction model, according to real-time monitoring data and historical data, predict the new energy power generation potential and hydrogen demand in the future for a certain period; Output the prediction results to the intelligent energy management system to provide a basis for adjusting the hydrogen production strategy.

[0050] 3.2 Hydrogen Production Strategy Adjustment Strategy Formulation According to the predicted new energy power generation potential and hydrogen demand, the intelligent energy management system formulates the hydrogen production strategy, including the setting of operating parameters such as electrolysis current, voltage, and the working state of the electrolyzer; During the strategy formulation process, consider the real-time output of the new energy power generation unit to ensure that the hydrogen production process can match the volatility of new energy power generation.

[0051] Dynamic Adjustment During the hydrogen production process, the intelligent energy management system monitors the output of the new energy power generation unit and the operating state of the water electrolysis hydrogen production unit in real time; According to the deviation between the real-time monitoring data and the prediction results, dynamically adjust the operating parameters such as electrolysis current, voltage, and the working state of the electrolyzer to optimize the hydrogen production efficiency and cost.

[0052] Optimization and Feedback During the implementation of the hydrogen production strategy, the intelligent energy management system continuously collects feedback data, including hydrogen production efficiency, energy consumption, and equipment operation status; According to the feedback data, the hydrogen production strategy is optimized and adjusted to improve hydrogen production efficiency and reduce costs.

[0053] 3.3 Fault Warning and Response Real-time Monitoring The intelligent energy management system monitors the operation status of the water electrolysis hydrogen production unit in real time, including key parameters such as electrolyzer temperature, pressure, and current density; At the same time, the system also monitors the operation status of the new energy power generation unit, such as the rotation speed of the wind turbine and the temperature of the photovoltaic panel.

[0054] Fault Warning When the operation status of the water electrolysis hydrogen production unit or the new energy power generation unit is monitored to be abnormal, the intelligent energy management system immediately triggers the warning mechanism; The system judges the fault type and severity according to the preset fault warning rules and issues corresponding warning signals.

[0055] Automatic Response After the fault warning is triggered, the intelligent energy management system automatically adjusts the system operation strategy, such as reducing the electrolysis current and pausing the hydrogen production process, to avoid further expansion of the fault or damage to the equipment; At the same time, the system sends fault alarm information to the operation and maintenance personnel so that they can take further repair and maintenance measures in a timely manner.

[0056] Fault Recording and Analysis The intelligent energy management system records the process and results of each fault warning and automatic response; Based on the fault records and analysis results, the operation and maintenance personnel conduct regular maintenance and inspection of the system to improve the reliability and stability of the system.

[0057] In summary, the implementation process of the dynamic adjustment stage includes three main steps: prediction model application, hydrogen production strategy adjustment, and fault warning and response. By combining real-time monitoring data, historical data, and prediction models, the intelligent energy management system can dynamically adjust the hydrogen production strategy and respond to possible fault situations to ensure the efficient, stable, and safe operation of the new energy flexible hydrogen production system.

[0058] 4. Hydrogen Management and Optimization Stage Hydrogen Storage Management: According to the output of the hydrogen production unit and the hydrogen demand forecast, the hydrogen storage unit is intelligently managed to ensure the safe storage and on-demand supply of hydrogen. The system monitors the pressure, temperature, and hydrogen storage volume of the storage tank in real time to avoid safety hazards such as overpressure, overheating, or hydrogen leakage; Hydrogen transportation optimization: Based on hydrogen demand and transportation distance, intelligently plan the scheduling and routes of transportation vehicles to ensure the timely delivery of hydrogen and minimize transportation costs. The system also considers external conditions such as traffic conditions and weather factors to dynamically adjust the transportation plan.

[0059] Specific implementation process: 4.1 Implementation process of hydrogen storage management Hydrogen production and demand forecasting Utilize historical data and advanced forecasting algorithms (such as time series analysis, machine learning models) to predict the output of the hydrogen production unit, and at the same time predict hydrogen demand based on market demand and the operating conditions of hydrogen-consuming devices; The forecasting results should be updated regularly to reflect the latest market and technological changes.

[0060] Intelligent management of hydrogen storage units According to the forecasting results, intelligently manage hydrogen storage units, including the selection of storage tanks, filling and discharging plans; The system should monitor the pressure, temperature and hydrogen storage volume of the storage tanks in real time to ensure operation within a safe range; When the pressure or temperature exceeds the set threshold, the system should automatically alarm and take corresponding measures, such as opening the relief valve and starting the cooling system.

[0061] Investigation and prevention of potential safety hazards Regularly maintain and inspect the storage tanks, including the integrity and airtightness of the tank body, valves, pipeline components; Equip with combustible gas detectors and alarm systems to monitor hydrogen leakage in real time; Formulate emergency plans, including leakage handling, personnel evacuation and fire fighting measures, to ensure rapid response in case of emergencies.

[0062] 4.2 Implementation process of hydrogen transportation optimization Transportation demand analysis and planning Based on hydrogen demand and transportation distance, intelligently plan the scheduling and routes of transportation vehicles; Consider the advantages and disadvantages of different transportation methods, such as cylinder supply being suitable for small-scale demands and tube trailer supply being suitable for large-scale demands, and select the appropriate transportation method.

[0063] Dynamic adjustment of transportation plan The system monitors external conditions such as traffic conditions and weather factors in real time to dynamically adjust the transportation plan; In case of traffic congestion or bad weather conditions, the system should automatically adjust the transportation route or delay the transportation plan to ensure the safe delivery of hydrogen.

[0064] Optimization of transportation costs Utilize advanced optimization algorithms (such as path optimization algorithms and vehicle scheduling algorithms) to reduce transportation costs; Consider the cost differences of different transportation modes, such as vehicle rental costs, fuel costs, and maintenance costs, and select the appropriate transportation mode to reduce costs.

[0065] Transportation process monitoring and tracking The system monitors the position, speed, and temperature parameters of transportation vehicles in real time to ensure the safety and reliability of the transportation process; When a vehicle breaks down or an abnormal situation occurs, the system should automatically alarm and take corresponding measures, such as starting a standby vehicle and contacting maintenance personnel.

[0066] In summary, hydrogen storage management and hydrogen transportation optimization are important tasks in the hydrogen management and optimization stage. Through the above implementation process, intelligent management and optimization of hydrogen storage and transportation can be achieved, improving the safety and economy of hydrogen, providing strong support for the development of the hydrogen energy industry, while reducing transportation costs and increasing economic benefits.

[0067] 5. Multi-stage cyclic optimization stage Establishment of a feedback mechanism: The system establishes a closed-loop feedback mechanism to continuously collect real-time monitoring data, prediction results, the effects of hydrogen production strategy adjustments, and feedback information on hydrogen management and transportation; Application of optimization algorithms: Use optimization algorithms (such as genetic algorithms and particle swarm algorithms) to analyze and process the feedback data, identify the bottlenecks and problems in the system operation, and propose improvement and optimization suggestions; System iterative upgrade: According to the results and suggestions of the optimization algorithms, iterate and upgrade the system to continuously improve the operation efficiency and economy of the system.

[0068] Specific implementation process: 5.1 Establishment of a feedback mechanism Data collection and integration The intelligent energy management system establishes a comprehensive data collection and integration mechanism, and real-time monitoring data, prediction results, the effects of hydrogen production strategy adjustments, and feedback information on hydrogen management and transportation can be accurately and completely collected into the system; Real-time monitoring data includes, but is not limited to, the output data of new energy power generation units, the operation data of electrolytic water hydrogen production units, and the monitoring data of hydrogen storage and transportation; Prediction result data includes the predicted values of new energy power generation potential and hydrogen demand and their deviations from the actual values; Data on the effects of hydrogen production strategy adjustments includes the adjusted hydrogen production efficiency, energy consumption, and cost; Feedback information on hydrogen management and transportation includes the implementation status of transportation plans, transportation costs, and the safety of hydrogen storage.

[0069] Data Quality Control Perform quality control on the collected data to ensure data accuracy, integrity, and consistency; Adopt data verification and data cleaning methods to remove outliers and fill in missing values to improve data quality.

[0070] Feedback Mechanism Construction Establish a closed-loop feedback mechanism to promptly feedback the collected data to each module and component of the system; Set feedback thresholds and triggering conditions. When the data reaches or exceeds the threshold, automatically trigger the feedback mechanism for corresponding adjustments and optimizations.

[0071] 5.2 Application of Optimization Algorithms Algorithm Selection and Implementation Based on the system characteristics and optimization goals, select appropriate optimization algorithms, such as genetic algorithms, particle swarm algorithms, and neural network optimization algorithms; Implement the optimization algorithms and integrate them into the intelligent energy management system for analyzing and processing feedback data.

[0072] Data Analysis and Processing Use the optimization algorithms to analyze and process the feedback data to identify the bottlenecks and problems in the system operation; Analyze the impact of different parameters on the system performance to determine the optimization direction and goals.

[0073] Generation of Optimization Suggestions Generate improvement and optimization suggestions based on the results of the optimization algorithms; The content of the suggestions includes adjusting the hydrogen production strategy, optimizing the hydrogen storage and transportation plan, and improving the equipment operation status.

[0074] 5.3 System Iterative Upgrade Formulation of Iteration Plan Based on the optimization suggestions, formulate a system iterative upgrade plan; Determine the goals, content, schedule, and responsible person for the iterative upgrade.

[0075] System Development and Testing According to the iteration plan, develop and upgrade the system; During the development process, ensure code quality, system stability, and security; After the development is completed, conduct system testing, including unit testing, integration testing, and system testing, to ensure that the upgraded system can operate normally.

[0076] System Deployment and Verification Deploy the upgraded system to the actual operating environment; Verify and evaluate the system to ensure that the performance of the upgraded system is improved and meets the expected goals.

[0077] Continuous monitoring and optimization The upgraded system enters the stage of continuous monitoring and optimization; Continuously collect new feedback data and conduct continuous analysis and optimization using optimization algorithms; According to the actual situation, iteratively upgrade and improve the system to continuously improve the operating efficiency and economy of the system.

[0078] Through the above implementation process, multi-stage cyclic optimization can be achieved, continuously improving the performance and economy of the intelligent energy management system, and providing strong support for the development of the hydrogen energy industry.

[0079] III. System benefit analysis The implementation of the multi-stage dynamic optimization method and system for the new energy flexible hydrogen production system can bring the following benefits: Improve energy utilization efficiency: By real-time monitoring and dynamically adjusting the hydrogen production strategy, maximize the use of renewable energy for hydrogen production and reduce energy waste.

[0080] Reduce hydrogen production costs: By optimizing the hydrogen production process and managing hydrogen storage and transportation, reduce hydrogen production costs and improve economic benefits.

[0081] Enhance system flexibility and stability: Use the intelligent energy management system to achieve dynamic adjustment and optimized management of the hydrogen production process, enhance the flexibility and stability of the system, and cope with the volatility and intermittency of new energy.

[0082] Promote the development of the hydrogen energy industry: The popularization and application of the new energy flexible hydrogen production system contribute to the development and growth of the hydrogen energy industry and make contributions to energy transformation and green development.

[0083] IV. Conclusion The implementation of the multi-stage dynamic optimization method and system for the new energy flexible hydrogen production system is an effective way to achieve efficient, flexible and stable hydrogen production. Through the implementation of steps such as the day-ahead scheduling plan, real-time monitoring, dynamic adjustment, hydrogen management and optimization, and multi-stage cyclic optimization, it can significantly improve energy utilization efficiency, reduce hydrogen production costs, enhance system flexibility and stability, and provide strong support for the development of the hydrogen energy industry.

[0084] Specific application examples: I. Background of the application example Taking the XX Chemical xx Green Hydrogen Demonstration Project as an example, this project will elaborate in detail on the specific application of the multi-stage dynamic optimization method and system for the new energy flexible hydrogen production system based on this example.

[0085] II. Multi-stage dynamic optimization method for the new energy flexible hydrogen production system Phase 1: Day-ahead Scheduling Plan Based on day-ahead prediction data (including new energy power generation potential prediction and hydrogen demand prediction), formulate the scheduling plan for the next day, determine the operating power of the electrolyzer, hydrogen production time, and charge and discharge strategies of energy storage devices, so as to ensure that while meeting the hydrogen demand, renewable energy is maximally utilized for hydrogen production and the hydrogen production cost is minimized.

[0086] Phase 2: Real-time Monitoring and Prediction Real-time Monitoring: The system monitors in real time the output of photovoltaic power generation units, the operating status of electrolytic water hydrogen production units, monitoring data of hydrogen storage and transportation, etc.; Prediction Analysis: Use prediction algorithms such as time series analysis and machine learning models to predict the new energy power generation potential and hydrogen demand. The prediction results will be used as the basis for subsequent hydrogen production strategy adjustment and optimization of hydrogen storage and transportation plans.

[0087] Specific Values: Accuracy rate of photovoltaic power generation unit output prediction: ≥90% Accuracy rate of hydrogen demand prediction: ≥85% Phase 3: Hydrogen Production Strategy Adjustment Strategy Formulation: According to the prediction results, intelligently adjust the hydrogen production strategy, including the operating power of the electrolyzer, hydrogen production time, etc., to maximize the utilization of new energy power generation while meeting the hydrogen demand; Optimized Control: Through the intelligent energy management system, achieve the synchronous response matching of the hydrogen production unit and the new energy power generation unit, and improve the adaptability of hydrogen production to new energy fluctuations.

[0088] Specific Values: Adjustment range of electrolyzer operating power: 0 - 100% Adjustment accuracy of hydrogen production time: ≤1 hour Phase 4: Optimization of Hydrogen Storage and Transportation Storage Management: According to the hydrogen demand and prediction results, intelligently manage the hydrogen storage capacity unit, avoid safety hazards, and ensure the safe storage of hydrogen and the optimization of on-demand transportation supply; Transportation System Plan: According to the real-time monitoring of the dynamic hydrogen storage tank, adjust the demand, pressure, transportation, distance, temperature and accuracy; intelligently plan the specific transportation values of hydrogen vehicles for scheduling and routes to ensure the timely delivery of hydrogen to the storage tank and the minimization of transportation monitoring costs; System considers external conditions such as traffic conditions and weather factors: ±0.1MPa Reduction ratio of hydrogen transportation cost: ≥10% Phase 5: Feedback and Iterative Optimization Feedback mechanism: Establish a closed-loop feedback mechanism to collect real-time monitoring data, prediction results, feedback information on the adjustment effect of the hydrogen production strategy, and hydrogen management and transportation; Optimization algorithm: Use optimization algorithms such as genetic algorithms and particle swarm algorithms to analyze and process the feedback data, identify the bottlenecks and problems in the system operation, and propose improvement and optimization suggestions; Iterative upgrade: According to the results and suggestions of the optimization algorithm, iteratively upgrade and improve the system to continuously improve the operation efficiency and economy of the system.

[0089] Specific values: System iteration period: ≤ 6 months System performance improvement ratio: ≥ 5% III. Specific application examples of the system The XX green hydrogen demonstration project adopted the above multi-stage dynamic optimization method for the new energy flexible hydrogen production system and achieved the following specific applications: Intelligent prediction and scheduling: The system predicts the power generation potential in the next period according to the real-time output and historical data of the photovoltaic power generation unit; According to the hydrogen demand prediction results, intelligently adjust the operating power and hydrogen production time of the electrolyzer to ensure the stability and economy of hydrogen supply.

[0090] Intelligent hydrogen storage and transportation: The system monitors the pressure, temperature and hydrogen storage volume of the hydrogen storage tank in real time to ensure storage safety; According to the hydrogen demand and transportation distance, intelligently plan the scheduling and routes of transportation vehicles to reduce transportation costs.

[0091] System iteration and optimization: During the project operation, continuously collect feedback data and use optimization algorithms to iteratively upgrade and improve the system; Through the application of optimization algorithms, the system performance has been continuously improved, ensuring the long-term stable operation of the project.

[0092] Economic and environmental benefits: The project produces 20,000 tons of green hydrogen annually, reduces carbon emissions by 485,000 tons, which is equivalent to planting about 280,000 trees annually; Through measures such as intelligent prediction and scheduling, intelligent hydrogen storage and transportation, and system iteration and optimization, the project has achieved a win-win situation in economic and environmental benefits.

[0093] In summary, the application of the multi-stage dynamic optimization method and system for the new energy flexible hydrogen production system in the XX green hydrogen demonstration project has achieved remarkable results, providing strong support for the development of the hydrogen energy industry.

[0094] The above describes the present invention and its implementation manners. Such description is not restrictive. What is shown in the drawings is only one of the implementation manners of the present invention, and the actual structure is not limited thereto. In general, if those of ordinary skill in the art are inspired by it and, without departing from the gist of the present invention, design similar structural manners and embodiments to this technical solution without creative efforts, they shall fall within the protection scope of the present invention.

Claims

1. A multi-stage dynamic optimization method for a new energy flexible hydrogen production system, characterized in that, It includes the following steps: a) Day-ahead scheduling plan stage: Based on day-ahead prediction data, including new energy power generation potential prediction and hydrogen demand prediction, formulate the scheduling plan for the next day, determine the operating power of the electrolyzer, hydrogen production time, and the charge and discharge strategies of energy storage devices, so as to ensure that while meeting the hydrogen demand, renewable energy is maximally utilized for hydrogen production and the hydrogen production cost is minimized; b) Real-time monitoring stage: Real-time monitor the output of new energy power generation units through a sensor network. The new energy power generation units include a wind power generation system and a photovoltaic power generation system, and collect and process wind power generation power, photovoltaic power generation power, and relevant weather and environmental data; c) Dynamic adjustment stage: Based on the data collected in the real-time monitoring stage, use an intelligent energy management system to dynamically adjust the electrolytic water hydrogen production unit. The adjustment content includes electrolytic current, voltage, and the working state of the electrolyzer to match the real-time output of the new energy power generation unit and maximize the utilization of renewable energy for hydrogen production; d) Hydrogen management and optimization stage: According to the output of the hydrogen production unit and the hydrogen demand prediction, intelligently manage the hydrogen storage and transportation unit to ensure the safe storage, efficient transportation, and on-demand supply of hydrogen; at the same time, further fine-tune the output plan of the electrolytic water hydrogen production unit according to the hydrogen inventory and demand feedback; e) Multi-stage cyclic optimization stage: Repeat the above day-ahead scheduling plan stage to hydrogen management and optimization stage to form a closed-loop feedback mechanism, and continuously cycle and optimize the overall operating efficiency and economy of the new energy flexible hydrogen production system.

2. A multi-stage dynamic optimization method for a new energy flexible hydrogen production system according to claim 1, characterized in that: The intelligent energy management system also includes a prediction model, which further has the following components: New energy power generation potential prediction module: Use historical weather data, current weather conditions, geographical location information, and technical parameters of new energy power generation units to predict the wind power generation potential and photovoltaic power generation potential in the next period of time through machine learning algorithms or statistical models, including the predicted average power generation, maximum / minimum power generation, and possible fluctuation ranges; Hydrogen demand prediction module: Based on historical hydrogen consumption data, seasonal demand changes, production plans of industrial users, hydrogen energy application trends in the transportation field, and policy guidance factors, use time series analysis or neural networks to predict the market demand for hydrogen in the next period of time, as well as the peak and valley periods of demand; Pre-scheduling decision-making module: Combine the results of new energy power generation potential prediction and hydrogen demand prediction to formulate the operation plan of the electrolytic water hydrogen production unit, including start / stop time, electrolysis power setting, and the calling strategy of possible backup power sources or energy storage systems; at the same time, plan the operation of the hydrogen storage and transportation unit, including hydrogen filling time, pressure management of storage tanks, and scheduling of transportation vehicles, to ensure the stable supply of hydrogen and maximize cost-effectiveness; Prediction uncertainty management module: Considering the uncertainty of the prediction model itself and the unpredictability of external factors such as weather changes and equipment failures, set safety margins and emergency response plans to cope with prediction deviations or emergencies and maintain the flexibility and robustness of the system.

3. A multi-stage dynamic optimization method for a new energy flexible hydrogen production system according to claim 1, characterized in that: It also includes a fault warning and emergency response mechanism. When a fault occurs in the new energy power generation unit or the electrolytic water hydrogen production unit, the system operation strategy is automatically adjusted to ensure the continuity and safety of the system.

4. A new energy flexible hydrogen production system, characterized in that, It includes: New energy power generation unit: It includes at least a wind power generation system and a photovoltaic power generation system, which are used to provide the electricity required for hydrogen production; Electrolytic water hydrogen production unit: It receives the electricity from the new energy power generation unit and electrolyzes water molecules into hydrogen and oxygen; Intelligent energy management system: According to the method described in any one of claims 1 to 3, it monitors the output of the new energy power generation unit in real time, dynamically adjusts the operation parameters of the electrolytic water hydrogen production unit, and manages the hydrogen storage and transportation unit at the same time; Hydrogen storage and transportation unit: It is used to safely store the hydrogen produced by the electrolytic water hydrogen production unit and transport it efficiently according to the demand.

5. A new energy flexible hydrogen production system according to claim 4, characterized in that: The intelligent energy management system also includes a remote monitoring and data analysis module, which is used to remotely monitor the operation status of the system and analyze historical data to optimize future operation strategies.

6. The new energy flexible hydrogen production system according to claim 5, characterized in that: It also includes an energy trading platform connected to the intelligent energy management system, which is used to sell electricity to the power grid when there is an excess of new energy, or purchase electricity from the power grid when the power is insufficient, so as to balance the energy demand of the system.

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