Energy management method and system for green electricity hydrogen production and hydrogenation all-in-one machine

Abstract

The invention relates to an energy management method and system for a green electricity hydrogen production and hydrogenation all-in-one machine, and relates to the technical field of hydrogen energy application, the system comprises an upper layer control system and a communication interface module, collaborative optimization scheduling is executed in the day-ahead stage, the rolling stage and the real-time stage; the communication interface module supports IEC104, private UDP and other industrial protocols, and realizes real-time data interaction with a green electricity prediction system and a hydrogen production DCS system. The method comprises the following steps: making an electrolytic cell start-stop and power plan in a day-ahead stage; periodically correcting prediction and demand errors in a rolling stage; the power of the electrolytic cell is dynamically adjusted to fluctuate along with green electricity in the real-time stage. According to the invention, cross-section and multi-time scale coordination control is realized, and the green power consumption rate, the system operation economy and the hydrogen energy supply reliability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydrogen energy application, in particular, especially relates to a green electricity hydrogen production and hydrogenation integrated machine energy management method and system. BACKGROUND

[0002] The green electricity hydrogen production and hydrogenation integrated machine couples power, control, chemical industry and other links, as a complex device of multi-energy flow coupling, needs to coordinate green electricity such as photovoltaic and wind power, water electrolysis hydrogen production, hydrogen storage, hydrogenation and other links, but the adaptability of conventional energy management system and green electricity hydrogen production and hydrogenation integrated machine still faces challenges. On the one hand, the theoretical level optimization algorithm is difficult to match the green electricity hydrogen production and hydrogenation integrated machine; on the other hand, the conventional energy management system (EMS) research less involves the communication and data interaction problems among multi-section heterogeneous systems (photovoltaic inverter, electrolytic cell, hydrogenation machine), and it is difficult to meet the real-time collaboration needs of complex engineering scenarios. Therefore, it is urgent to develop an energy management system suitable for green electricity hydrogen production and hydrogenation integrated machine to realize cross-section, multi-time scale coordinated management and efficient and economic operation. SUMMARY

[0003] According to the above technical problems, a green electricity hydrogen production and hydrogenation integrated machine energy management method and system are provided.

[0004] The technical means adopted by the present application are as follows: A green electricity hydrogen production and hydrogenation integrated machine energy management system, comprising: An upper control system, configured to execute optimization and instruction issuing of operation strategy in day-ahead, rolling and real-time stages; A communication interface module, configured to realize data interaction between the green electricity prediction system and the hydrogen production DCS system; An optimization scheduling module integrated in the upper control system, configured to execute optimization scheduling calculation with minimization of operation cost as the core in day-ahead and rolling stages based on green electricity prediction data, hydrogenation demand, hydrogen storage state and equipment operation state; A real-time control module integrated in the upper control system, configured to dynamically adjust the electrolytic cell operation power according to the deviation of green electricity real-time power and electrolytic cell reference power, and realize second-level matching of hydrogen production power and green electricity fluctuation; The upper control system executes optimization scheduling with minimization of total operation cost as the target based on the green electricity prediction data, hydrogenation demand, hydrogen storage state and electrolytic cell operation state data collected by the communication interface module, and outputs electrolytic cell start-stop state and reference power / current instruction; and in the real-time stage, dynamically adjusts the electrolytic cell power according to the deviation of green electricity real-time power and the reference power, and realizes power tracking.

[0005] Further, the communication interface module comprises: An interface with the green electricity prediction system adopts an IEC104 protocol and is used for receiving green electricity output prediction data; An interface with the hydrogen production DCS system adopts a private UDP protocol and is used for issuing device start-stop instructions, power / current set values and receiving device state feedback.

[0006] Further, the optimization scheduling module is further configured to: A mathematical programming model with minimization of total operation cost as the target is established, and the target function includes electrolytic cell start-stop cost, energy consumption cost and hydrogen storage cost; A multi-objective optimization mode is set, including a green electricity consumption rate maximization mode, a hydrogen production power fluctuation minimization mode and an economic operation mode, and switching according to operation scenarios is supported; Multiple types of physical and operation constraints are applied, including power balance constraints, electrolytic cell power upper and lower limit constraints, hydrogen production rate constraints, hydrogen storage tank pressure and capacity constraints, device start-stop logic and state switching constraints.

[0007] Further, the real-time control module further includes: A power deviation calculation unit is used for real-time calculation of the difference between green electricity actual output and electrolytic cell reference power; A power distribution logic unit is used for dynamic adjustment of the power distribution proportion of each electrolytic cell based on a preset fluctuation production following strategy; A state feedback unit is used for collection of electrolytic cell actual start-stop state, operation temperature and purification device timing information, and uploading to the upper control system for rolling scheduling correction.

[0008] Further, the upper control system collects data through the communication interface module, coordinates the collaborative operation of the optimization scheduling module and the real-time control module, and forms a closed-loop control logic of day-ahead planning, rolling correction and real-time tracking.

[0009] Further, it further includes: A data storage and management module is used for unified storage of prediction data, operation instructions, actual operation data and optimization results, and supports time series data query and analysis; A visual human-computer interface is used for display of system real-time operation state, green electricity consumption rate, hydrogen production, hydrogen storage state and economic indicators, and supports manual instruction intervention and mode switching.

[0010] The application also discloses a green electricity hydrogen production and hydrogenation integrated machine energy management method based on the system. Step S1: Real-time collection of green electricity prediction data, hydrogenation demand information, hydrogen storage tank state and hydrogen production system device operation parameters through a multi-protocol communication interface; Step S2: In the day-ahead stage, based on the next day green electricity prediction and production demand, an optimization calculation is performed to minimize the operation cost, and an electrolytic cell start-stop plan and a reference power in each period are formulated; Step S3: In the rolling stage, based on the updated green electricity prediction and the actual operation state, the operation strategy of the remaining period is re-optimized periodically to correct the yield and demand deviation; Step S4: In the real-time stage, according to the deviation between the actual power of green electricity and the reference power of electrolytic cell, the running power of electrolytic cell is dynamically adjusted to realize real-time tracking of power; Step S5: The real-time operation deviation is accumulated, and the rolling scheduling update is triggered after reaching the preset time period to form a closed-loop cooperative optimization of day-ahead, rolling, and real-time.

[0011] Compared with the prior art, the present application has the following advantages: 1. The existing theoretical optimization algorithm is difficult to match the complex operation characteristics of the actual green electricity hydrogen production and hydrogenation integrated machine. The present application builds a refined model including power balance, device start-stop logic, hydrogen production constraints, separation purification and compression storage process constraints, so that the optimization scheduling result is more consistent with the engineering practice and can directly drive the equipment to execute.

[0012] 2. The present application builds a data communication framework compatible with IEC104, private UDP and other multi-protocols, realizes stable and real-time data intercommunication between the green electricity prediction system, the hydrogen production DCS system and the integrated automation system, and provides a basis for cross-section coordinated control.

[0013] 3. Compared with the two-level architecture of day-ahead planning and real-time control in the existing patents, the day-ahead planning cannot timely correct the green electricity prediction deviation, and the real-time control can only passively follow the fluctuations, resulting in low hydrogen production efficiency and energy waste, and the two-level architecture is prone to disconnection between planning and actuality. The present application designs a three-level cooperative framework of day-ahead planning, rolling scheduling and real-time control to meet the real-time cooperation needs of complex engineering scenarios. The day-ahead scheduling lays the foundation for the basic strategy, the real-time control realizes the second-level power tracking, and the rolling scheduling periodically corrects the prediction error and operation deviation. The three form a closed loop of information feedback and strategy update, significantly improving the system's ability to cope with green electricity volatility and demand uncertainty, and realizing efficient and economic operation. The present application introduces a rolling scheduling stage, re-optimizes the strategy of the remaining period based on the latest green electricity prediction and actual operation state (such as electrolytic cell power deviation cumulative value) periodically, and synchronizes the corrected instructions to the real-time control module. This closed-loop feedback mechanism effectively makes up for the disconnection problem caused by prediction error in day-ahead planning, so that the system can dynamically adapt to green electricity fluctuations and demand changes, thereby significantly improving the green electricity consumption rate and hydrogen supply reliability.

[0014] 4. Existing technologies tend to favor the design characteristics of large-scale integrated systems (such as coal chemical coupling and photovoltaic hydrogen storage stations). This invention is specifically adapted to the miniaturization and integration requirements of green electricity hydrogen production and refueling integrated machines, avoiding redundant architecture and cost waste in large systems. This invention strengthens the end-to-end collaboration of single-device hydrogen production, storage, and refueling, resulting in simpler control logic and more direct response. Furthermore, it focuses on the independent operation of a single integrated machine, eliminating the need for a station-level monitoring backend, and can independently complete the entire process of prediction, scheduling, and control, adapting to distributed and mobile hydrogen refueling scenarios. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A schematic diagram of the control logic and data interaction of the integrated hydrogen production and refueling machine using green electricity.

[0017] Figure 2 A communication and interaction framework for the integrated hydrogen production and refueling machine using green electricity.

[0018] Figure 3 This is an integrated schematic diagram of a hydrogen refueling station for a low-altitude aircraft, which is an extended embodiment of the present invention. Detailed Implementation

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0022] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0023] In the description of this invention, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is generally based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this invention and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this invention. The directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0024] For ease of description, spatial relative terms such as "above," "over," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation besides the orientation of the device as described in the figures. For example, if the device in the figures is inverted, a device described as "above" or "above" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0025] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0026] like Figure 1 , Figure 2 As shown, the energy management system of the green electricity hydrogen production and refueling integrated machine in this embodiment specifically includes an upper control system, a communication interface module, an optimization scheduling module, a real-time control module, a data storage and management module, and a visual human-machine interface. Each module realizes data communication and command flow through a bus. The upper control system is specifically an Upper Control System (UCS), which internally deploys an optimization scheduling algorithm and a logic control algorithm. The optimization algorithm serves the day-ahead plan and rolling scheduling, laying the foundation for the basic operation strategy. The logic control algorithm is used to formulate the operation mode in the real-time stage to ensure coordinated management across sections and multiple time scales.

[0027] When the upper-level control system collects data through the communication interface module, it needs to standardize the format of the heterogeneous system data, unify the data timestamps and units, and ensure the consistency of data from different sources. When coordinating the collaborative operation of the optimization scheduling module and the real-time control module, an instruction priority mechanism is established, with real-time control instructions having higher priority than day-ahead / rolling scheduling instructions to avoid conflicts, and finally forming a closed-loop control logic of day-ahead planning, rolling correction, and real-time tracking.

[0028] The communication interface module is used to realize data interaction with the green electricity prediction system and the hydrogen production DCS system; further, the communication interface module includes: an interface with the green electricity prediction system, which adopts the IEC104 protocol, for receiving green electricity output prediction data; and an interface with the hydrogen production DCS system, which adopts the private UDP protocol, for issuing equipment start / stop commands, power / current setpoints and receiving equipment status feedback.

[0029] The communication interface module constructs a multi-protocol compatible data communication framework, supporting data access and time-series data processing for common industrial protocols, forming a unified data management and control logic. When communicating with the green electricity prediction system, it periodically receives ultra-short-term and short-term green electricity prediction data, with a reception cycle configurable to 5 minutes per time. When communicating with the hydrogen production DCS system, in addition to implementing start / stop commands, power / current setpoint issuance, and equipment status feedback, it also supports alarm information transmission. When situations such as excessive electrolyzer temperature or abnormal hydrogen storage tank pressure occur, the DCS system pushes an alarm signal to the communication interface module via a private UDP protocol, which is then forwarded to the upper-level control system to trigger an early warning.

[0030] The optimization scheduling module is integrated into the upper-level control system. Based on green electricity forecast data, hydrogen demand, hydrogen storage status, and equipment operating status, it performs optimization scheduling calculations with the core objective of minimizing operating costs during the day-ahead and rolling phases. Furthermore, the optimization scheduling module is further configured to: establish a mathematical programming model with the objective of minimizing total operating costs, where the objective function includes the electrolyzer start-up and shutdown costs, energy consumption costs, and hydrogen storage costs; set up multi-objective optimization modes, including a green electricity utilization maximization mode, a hydrogen production power fluctuation minimization mode, and an economical operation mode, supporting switching according to the operating scenario; and apply various physical and operational constraints, including power balance constraints, electrolyzer power upper and lower limit constraints, hydrogen production rate constraints, hydrogen storage tank pressure and capacity constraints, and equipment start-up and shutdown logic and state switching constraints. In addition, the optimization objective is to minimize total operating costs, including electrolyzer start-up / shutdown costs, etc. The main constraints also include: (1) the power balance of the integrated hydrogen production and hydrogen refueling machine, the upper and lower limits of the electrolyzer power, hydrogen production constraints, and the constraints of electrolyzer start-up, shutdown and state switching; (2) the constraints of temperature, pressure, etc. of the separation and purification module, timer modeling and operating logic constraints; (3) the hydrogen charging and discharging constraints and pressure constraints of the compression storage module. The core variables are: (1) the operating status and power of the electrolyzer; (2) the hydrogen charging and discharging flow rate, etc.

[0031] The optimization scheduling module establishes an optimization model using mathematical programming, then calls a professional solver to solve and output the optimal solution. The hydrogen storage status is calculated by reading the pressure value of the storage tank, and the equipment operating status includes the initial start-up and shutdown status and temperature of the electrolyzer, as well as the temperature and timing of the purification unit. Among the applied constraints, the separation and purification module also includes timer modeling and operational logic constraints to ensure the purification process coordinates with the hydrogen production process. The rolling phase's operating cycle extends to the end of the day, with variable data points decreasing as the rolling process progresses. Each time the rolling phase occurs, the latest green electricity prediction data is read from the green electricity prediction system, and combined with historical operating status, the unit operating status and reference power for subsequent periods are updated to reduce production errors.

[0032] The real-time control module is integrated into the upper-level control system. It dynamically adjusts the electrolyzer's operating power based on the deviation between the real-time green electricity power and the electrolyzer's reference power, achieving second-level matching between hydrogen production power and green electricity fluctuations. Further, the real-time control module includes: a power deviation calculation unit for real-time calculation of the difference between the actual green electricity output and the electrolyzer's reference power; a power allocation logic unit for dynamically adjusting the power allocation ratio of each electrolyzer based on a preset fluctuating production strategy; and a status feedback unit for collecting the actual start-up and shutdown status of the electrolyzers, operating temperature, and purification unit timing information, and uploading this data to the upper-level control system for rolling scheduling corrections. During the real-time control phase, the operating logic is set based on the deviation between the real-time green electricity power and the electrolyzer power, and power is allocated according to the actual start-up and shutdown status of the electrolyzers. The operating mode is to follow green electricity fluctuations, meaning the electrolyzer power matches the green electricity power.

[0033] The real-time control module operates strictly according to the principle of following green electricity fluctuations, meaning that the power of the electrolyzer is matched with the power of the green electricity. The power deviation calculation unit has a calculation cycle of 1 second to ensure a second-level response to green electricity power fluctuations. When adjusting the power of each electrolyzer, the power allocation logic unit must consider the current load, rated power, and efficiency characteristics of the electrolyzer to avoid single electrolyzers operating beyond their range. In addition to the actual start-up and shutdown status of the electrolyzer, operating temperature, and purification device timing information, the data collected by the status feedback unit also includes the actual power / current value of the electrolyzer, which is uploaded to the upper-level control system in real time to accumulate deviations.

[0034] The upper-level control system collects data through the communication interface module and coordinates the collaborative operation of the optimization scheduling module and the real-time control module to form a closed-loop control logic of daily planning, rolling correction, and real-time tracking.

[0035] The specific implementation of collaborative operation is as follows: the electrolytic cell start-up and shutdown plan and reference power established in the previous stage serve as the initial operating basis for the real-time control module; the status feedback data of the real-time control module serves as the input for rolling scheduling, and when real-time operation lasts for 20 minutes, rolling scheduling is triggered to correct errors; the updated operating strategy of rolling scheduling is synchronously sent to the real-time control module to adjust the reference power of the electrolytic cells in subsequent periods, realizing a complete closed loop of planning, execution, feedback, and correction. The specific steps of the collaborative framework can be as follows: 1) The daily schedule is planned to collect data such as the initial state of the electrolyzer and green electricity prediction, and input them into the UCS system to execute the optimization algorithm, obtain the output of different time periods, and send the electrolyzer start / stop command for the current time period to the hydrogen production DCS system, while saving the reference power of the electrolyzer.

[0036] 2) Second-level control: Read the actual output of green electricity and the reference power of the electrolyzer. The UCS system outputs the real-time power / current of the electrolyzer according to the operating logic and sends it to the hydrogen production DCS system.

[0037] 3) Real-time operation: The hydrogen production and refueling DCS system operates according to the instructions issued by the upper level, records the power / current values, accumulates the deviation between the actual value and the instruction value, and returns to step 2). If the real-time operation lasts for 20 minutes, it proceeds to step 4.

[0038] 4) Rolling scheduling to correct errors: Due to errors in green electricity forecasting and hydrogen demand, hydrogen production output deviates from the load. Therefore, the actual operating status of the hydrogen production system and the latest green electricity forecast are obtained to re-formulate the operating strategy, such as the start-up and shutdown status of the electrolyzer, and the process is returned to step 2.

[0039] The system also includes: a data storage and management module for uniformly storing predicted data, operating instructions, actual operating data and optimization results, and supporting time-series data query and analysis; and a visual human-machine interface for displaying the system's real-time operating status, green electricity consumption rate, hydrogen production, hydrogen storage status and economic indicators, and supporting manual instruction intervention and mode switching.

[0040] The data storage and management module uses a time-series database to store data, with storage periods including real-time data (second-level storage) and historical data (archived by day). It supports multi-dimensional queries and exports by time range and data type. In addition to displaying core indicators, the visual human-machine interface also supports manual switching of multi-objective optimization modes and manual fine-tuning of the reference power of the electrolytic cell. In case of emergency, emergency shutdown commands can be issued through the interface, with priority higher than all automatic control commands.

[0041] This invention also discloses an energy management method for a green electricity-to-hydrogen and hydrogen refueling integrated machine based on the above system, comprising the following steps: Step S1: Real-time collection of green electricity forecast data, hydrogen demand information, hydrogen storage tank status and hydrogen production system equipment operating parameters through multi-protocol communication interface; In step S1, green electricity forecast data is collected from the green electricity forecast system through the IEC104 protocol, including the next day's ultra-short-term and short-term green electricity output forecast values; hydrogen demand information is the next day's production demand and hydrogen demand manually written; the hydrogen storage tank status is collected by pressure sensor to collect pressure value, and then the hydrogen storage capacity is calculated; the hydrogen production system equipment operating parameters include the initial start-up and shutdown status of the electrolyzer, operating temperature, temperature and timing of the purification unit, actual power / current of the electrolyzer, etc., collected from the hydrogen production DCS system through the private UDP protocol. In the day-ahead stage, the inputs include: (1) the predicted value of the next day's green electricity output from the green electricity forecast system such as photovoltaic and wind power; (3) the next day's production demand and hydrogen demand written; (4) the pressure value of the storage tank is read to calculate the hydrogen storage capacity; (5) the initial start-up and shutdown status and temperature of the electrolyzer and the temperature and timing of the purification unit, etc.

[0042] Step S2: In the day-ahead phase, based on the next day's green electricity forecast and production demand, optimization calculations are performed with the goal of minimizing operating costs to formulate electrolyzer start-up and shutdown plans and reference power for each time period. In Step S2, the optimization calculation calls the mathematical programming model and solver of the optimization scheduling module. The input data includes the next day's green electricity forecast data collected in Step S1, hydrogen refueling demand information, initial status of hydrogen storage tanks, and initial operating parameters of equipment. The output results include the electrolyzer start-up and shutdown plans and reference power for each time period, as well as the hydrogen storage tank charging and discharging plans, to ensure that hydrogen production and hydrogen refueling demand are matched.

[0043] Step S3: During the rolling phase, the operating strategy for the remaining time period is periodically re-optimized based on the updated green electricity forecast and actual operating status, and the deviation between output and demand is corrected. The rolling cycle of step S3 is 20 minutes, and the trigger condition is that the cumulative real-time running time reaches 20 minutes. The updated green electricity forecast data is the latest ultra-short-term forecast value, and the actual operating status is the actual start-up and shutdown status of the electrolyzer, actual power / current, cumulative deviation, real-time pressure of the hydrogen storage tank, and operating status of the purification unit fed back by the real-time control module. During the re-optimization, the objective function remains unchanged, the initial state parameters in the constraints are dynamically adjusted, and the corrected start-up and shutdown status of the electrolyzer and the reference power are output.

[0044] Step S4: In the real-time stage, the operating power of the electrolyzer is dynamically adjusted according to the deviation between the actual power of the green electricity and the reference power of the electrolyzer, so as to achieve real-time power tracking. In step S4, the actual power of the green electricity is collected in real time by the power sensor, and the collection period is 1 second. The power deviation is calculated by the power deviation calculation unit of the real-time control module. The power allocation logic unit dynamically adjusts the power allocation ratio of each electrolyzer based on the green electricity fluctuation strategy to ensure that the total power matches the actual power of the green electricity. During the adjustment process, the upper and lower limits of the electrolyzer power must be followed to avoid overshoot. In the real-time stage, the inputs of the upper control system include: (1) the actual start-stop status of the electrolyzer; (2) the reference power / current of the electrolyzer; (3) the real-time power of the green electricity, etc. The control system uses all input variables to give the output according to the designed algorithm logic: the power / current of the electrolyzer, and sends it to the hydrogen production system for execution.

[0045] Step S5: Accumulate real-time operational deviations and trigger rolling scheduling updates after a preset time period, forming a closed-loop collaborative optimization of day-ahead, rolling, and real-time operations. In Step S5, real-time operational deviations are accumulated by the status feedback unit, and deviation calculation is performed by integrating the difference between the reference power and the actual power; the preset time period is consistent with the rolling phase period, which is 20 minutes; after triggering the rolling scheduling update, the accumulated deviation is used as a correction factor input into the rolling optimization model to ensure that subsequent operating strategies are more in line with actual working conditions, ultimately forming a closed-loop collaborative optimization mechanism that lays the foundation for day-ahead planning, provides second-level response for real-time control, and corrects deviations through rolling scheduling.

[0046] To concretely demonstrate the technical advantages of this invention in low-altitude aircraft application scenarios, the energy management method of the green electricity hydrogen production and refueling integrated machine is deeply embedded into the entire operation process of a mobile hydrogen production and refueling integrated machine suitable for low-altitude aircraft such as hydrogen-powered drones. The method is explained in detail how it solves the core safety problems in this specific scenario and highlights its irreplaceable value.

[0047] The energy management method and system for the integrated green electricity hydrogen production and refueling machine disclosed in this invention provide core control logic and a collaborative foundation for the realization of the aforementioned mobile green electricity autonomous hydrogen refueling station. Specifically, in this drone hydrogen refueling application scenario, the technical solution of this invention is integrated into and drives the intelligent operation of the entire system in the following manner: This invention acts as the intelligent brain, enabling energy coordination across multiple time scales. The upper-level control system (UCS) and its integrated optimization scheduling and real-time control modules constitute the decision-making center of the hydrogen refueling station. In the day-ahead phase, the system formulates the most economically optimal hydrogen production and storage plan based on wind and solar power forecasts and flight mission plans. In the real-time phase, through second-level power tracking control, it ensures that the electrolyzer power accurately follows fluctuating photovoltaic / wind power output, maximizing the local consumption of green electricity. In the rolling phase, the operating strategy is dynamically adjusted periodically based on the latest drone demand and ultra-short-term weather forecasts. This three-level coordination mechanism ensures that the system maintains efficient and economical operation despite the dual challenges of the intermittency of renewable energy and the randomness of hydrogen use by drones.

[0048] This invention serves as a means to facilitate data interaction across multiple systems. The communication interface module, compatible with multiple protocols (such as IEC 104, proprietary UDP, and 4G / 5G), enables the integration and communication of internal and external data. Internally, it collects real-time equipment status data from the hydrogen production DCS, energy storage units, and compressed storage modules. Externally, it reliably receives hydrogen refueling requests and task information from the UAV scheduling cloud platform and integrates local wind and solar power prediction data. This framework solves the communication challenges between heterogeneous equipment across multiple sections, such as photovoltaic inverters, electrolyzers, compressors, and hydrogen refueling robotic arms, providing a stable and reliable data foundation for cross-section coordinated control.

[0049] Based on its optimized model and real-time logic, this invention enables the system to not only manage energy flow but also directly schedule hydrogen and task flows. When a drone lands and makes a request, the system automatically triggers and guides an intelligent robotic arm to complete the tank replacement operation based on the real-time hydrogen storage status and equipment availability. Simultaneously, the status feedback unit continuously collects multi-parameter data such as pressure, flow rate, and concentration during the tank replacement process, forming a closed-loop feedback with the energy management system to jointly ensure operational safety and efficiency.

[0050] Specifically, taking the integrated hydrogen production and refueling machine for low-altitude aircraft equipped with this system as an example: Figure 3 As shown, this embodiment of the invention discloses an integrated hydrogen production and refueling machine suitable for low-altitude aircraft, including a photovoltaic module, an energy storage module, a hydrogen production unit, a storage unit, a hydrogen diaphragm compressor module, a control module, an intelligent robotic arm module, and auxiliary units; The photovoltaic module is used to convert solar energy into electrical energy; its core function is solar power generation, converting renewable energy into electrical energy to provide a green power source for the system. The energy storage module is used to store the electrical energy generated by the photovoltaic module and to supply power to the system equipment; specifically, it is electrically connected to the photovoltaic module to store excess electrical energy, realize power supply stability regulation, and ensure continuous operation of the system. The hydrogen production unit is used to produce high-purity hydrogen from tap water. It includes a water purification module, a water alkali tank module, a water replenishment module, an alkali circulation module, an electrolyzer module connected in sequence, as well as a hydrogen separation and cooling module, a hydrogen purification and cooling module, and an oxygen separation and cooling module connected to the electrolyzer module. The water purification module is used to purify tap water, remove impurities, and avoid affecting the lifespan of the electrolyzer and the purity of hydrogen. The water replenishment module is used to automatically replenish purified tap water according to the liquid level in the water scale tank module; The alkaline solution tank module is used to store a mixture of alkaline solution and purified water, providing raw materials for the electrolytic cell module; The alkali circulation module is used to realize the recycling of alkali in the electrolytic cell module and improve the utilization rate of raw materials. An electrolytic cell module, used to electrolyze a mixture to produce hydrogen and oxygen under DC drive; The hydrogen separation and cooling module is used to separate and cool the hydrogen produced by electrolysis, and to remove moisture and impurities. The hydrogen purification and cooling module is used to further purify and cool hydrogen, ensuring a hydrogen purity of ≥99.97%. The oxygen separation and cooling module is used to separate and cool the oxygen produced by electrolysis, enabling safe venting.

[0051] In the drone scenario, the upper control system (UCS) defined above is the highest decision-making body; while the control module (PLC) of the drone itself is a local actuator that receives UCS commands and executes actions such as robotic arms.

[0052] The above scheme involves purging all oxygen, which is still wasteful. Further, an oxygen storage tank can be installed to store the oxygen produced as a byproduct of electrolysis. In northern regions with low winter temperatures, an oxygen-assisted combustion heating module can be installed to heat the electrolyzer to a certain extent. In winter or low-temperature environments, a small amount of oxygen can be burned with fuel to quickly heat the electrolyzer to the operating temperature, further addressing the problem of low hydrogen production efficiency at low temperatures.

[0053] Optionally, in this embodiment, the water replenishment module is directly connected to the hydrogen separation cooling module and the oxygen separation cooling module, so that while replenishing water to the system, it can also cool down the high-temperature alkaline solution.

[0054] The storage unit includes a high-pressure hydrogen storage module and a high-pressure hydrogen storage tank module for low-altitude aircraft, used to store high-purity hydrogen; wherein the storage tank of the low-altitude aircraft hydrogen storage tank module can be replaced independently.

[0055] The hydrogen diaphragm compressor module is connected to the hydrogen purification and cooling module, the high-pressure hydrogen storage module, and the high-pressure hydrogen storage tank module for low-altitude aircraft, respectively, for hydrogen pressurization, storage and refueling. The control module is controlled by a PLC and is electrically connected to each module to realize the automated operation and mode switching of the system; optionally, a touch control screen is configured for human-machine interaction.

[0056] The intelligent robotic arm module is used to automatically replace the high-pressure hydrogen storage tank for low-altitude aircraft; specifically, it is used to grab the 35MPa hydrogen storage tank for low-altitude aircraft and automatically replace the storage tank with the low-altitude aircraft without manual operation.

[0057] The auxiliary unit includes a rectifier power supply module, a mechanical ventilation module, a chiller module, and an air compressor module. The rectifier power supply module is electrically connected to the photovoltaic module, energy storage module, and electrolytic cell module to achieve stable power supply. The mechanical ventilation module is used for ventilation when the hydrogen concentration exceeds the standard. The chiller module provides cooling water for the modules that need to be cooled. The air compressor module provides instrument air for the pneumatic valves.

[0058] In this embodiment, the high-pressure hydrogen is specifically designed for 35 MPa hydrogen storage tank drones commonly used in the prior art. In other optional embodiments, the design can be adjusted according to the specific requirements of the drone.

[0059] Furthermore, the system has two operating modes: hydrogen production and storage, and hydrogen refueling. The control module automatically switches between these modes based on the pressure of the high-pressure hydrogen storage module: when the pressure of the high-pressure hydrogen storage module is lower than the preset high pressure, the system switches to hydrogen production and storage mode; when the pressure of the high-pressure hydrogen storage module reaches the preset high pressure, the system switches to hydrogen refueling mode.

[0060] Furthermore, the electrical energy generated by the photovoltaic module is distributed in two ways: one way is supplied to the electrolytic cell module after being stabilized by the rectifier power supply module; the other way is supplied to the energy storage module. When there is sufficient solar energy, the energy storage module supplies power to other devices except the rectifier power supply module, and when there is insufficient solar energy, it supplies power to all devices in the system.

[0061] Furthermore, foldable small wind power modules can be integrated on the top or side of the integrated unit to form a wind-solar complementary power supply with the photovoltaic module, in order to cope with the situation where solar energy is significantly affected by day and night and weather, and hydrogen production is unstable.

[0062] Furthermore, it can also include a hydrogen emergency power generation unit using proton exchange membrane fuel cells. This unit utilizes backup hydrogen stored in a 35MPa large storage module to provide temporary power when wind and solar power are insufficient, ensuring the operation of core equipment such as control modules and mechanical ventilation. In this case, an energy management submodule integrated into the control module prioritizes the use of photovoltaic power, supplemented by wind power when photovoltaic power is insufficient, and triggers hydrogen emergency power generation when both are insufficient.

[0063] Furthermore, the mechanical ventilation module is electrically connected to the hydrogen detector. When the hydrogen detector detects that the hydrogen concentration inside the system exceeds the safe range, the control module starts the exhaust fan of the mechanical ventilation module until the hydrogen concentration drops to the safe range.

[0064] Furthermore, the high-pressure hydrogen storage tank module for low-altitude aircraft includes at least one independently replaceable high-pressure hydrogen storage tank, and the intelligent robotic arm module can grasp the storage tank and complete the replacement operation with the low-altitude aircraft.

[0065] Furthermore, the alkali circulation module is used to realize the recycling of alkali in the electrolytic cell module, and the water replenishment module automatically replenishes purified tap water according to the liquid level of the water-alkali tank module.

[0066] Furthermore, the hydrogen purified and cooled by the hydrogen purification module has a purity of ≥99.97%, which meets the hydrogen standards for low-altitude aircraft.

[0067] The above-mentioned method for using the integrated hydrogen production and refueling machine applicable to low-altitude aircraft includes the following steps: The hydrogen production and storage process is as follows: Tap water is purified by the water purification module, then mixed with circulating alkali from the water replenishment module and the alkali circulation module before entering the electrolyzer module. The electrolyzer module produces hydrogen through electrolysis powered by the rectifier power supply module. The generated hydrogen is processed by the hydrogen separation and cooling module and the hydrogen purification and cooling module, then pressurized to a preset high pressure by the hydrogen diaphragm compressor module and stored in the high-pressure hydrogen storage module. The generated oxygen is treated by the oxygen separation and cooling module and then discharged into the atmosphere. The hydrogen refueling process is as follows: Hydrogen in the high-pressure hydrogen storage module is refueled through the hydrogen diaphragm compressor module to the high-pressure hydrogen storage tank module for low-altitude aircraft, where the pressure is lower than the preset high pressure. Refueling stops when the pressure of the storage tank reaches the preset high pressure. If all storage tanks in the high-pressure hydrogen storage tank module for low-altitude aircraft reach the preset high pressure, the hydrogen generated in the hydrogen production and storage mode is temporarily vented until there is a storage tank to be refueled.

[0068] Furthermore, during the operation of the electrolytic cell module, the control module controls the power source of the electrolytic cell module based on the data obtained by the photovoltaic module. When there is sufficient solar energy, the photovoltaic module directly supplies power to the rectifier power supply, and the excess electrical energy is stored in the energy storage module. When there is insufficient solar energy, the energy storage module automatically switches to supply power to the entire system.

[0069] Furthermore, when the remaining hydrogen amount of the low-altitude aircraft falls below a preset value, it sends a hydrogen refueling request to the integrated machine via wireless communication. After the control module receives the request, when the aircraft lands in the area designated by the integrated machine, the intelligent robotic arm module is activated to grab the full-pressure tank in the high-pressure hydrogen storage tank group, remove the old tank, and install a new tank for the low-altitude aircraft.

[0070] In a specific implementation, during hydrogen refueling, when the pressure of the 35MPa large storage module reaches 35MPa, and the control module detects that the pressure of any tank in the 35MPa low-altitude aircraft hydrogen storage tank group module is lower than 35MPa, the system switches to hydrogen refueling mode; the hydrogen diaphragm compressor module switches to refueling condition, and the hydrogen in the 35MPa large storage module is refueled to the tank to be refueled, and the refueling rate remains stable; when the tank pressure reaches 35MPa, the control module closes the refueling valve and switches to the next tank to be refueled, until all tanks reach 35MPa; The hydrogen-powered drone lands in the designated area of ​​the integrated unit and sends a replacement request to the control module via wireless communication; The control module controls the intelligent robotic arm module to move to the tank group module, grab the full-pressure tank, accurately position it to the drone tank installation position, and complete the replacement operation of disassembling the old tank and installing the new tank. After the replacement is completed, the intelligent robotic arm module puts the old tank back into the tank group module, the control module records the tank status, and refills the tank when the pressure of the old tank is lower than 35MPa.

[0071] In some optional implementations, the aircraft lands in the designated area of ​​the integrated machine using GPS / BeiDou positioning. The control module confirms the aircraft's tank installation position using visual sensors. The intelligent robotic arm module then grabs a fully pressurized tank from the 35MPa tank group, precisely moves it to the aircraft's tank installation position, disassembles the old tank, and installs the new tank, with the entire process taking less than 3 minutes. After the replacement is complete, the robotic arm returns the old tank to its refueling position in the tank group, and the control module records the pressure of the old tank for subsequent hydrogen refueling. The aircraft sends a tank installation confirmation signal, the control module indicates successful replacement, and the aircraft can take off. Simultaneously, the touch control screen updates the number of remaining fully pressurized tanks in the tank group.

[0072] This technology uses PLC control, which has a high degree of automation. In some expandable cases, AI algorithms are integrated into the control module to collect key data from various modules in real time, including electrolyzer voltage, current, compressor temperature, robotic arm repeatability accuracy, and hydrogen leakage concentration trend. This data is used to establish a fault prediction model, which can better provide early warning of the lifespan of vulnerable parts and identify abnormal operating trends.

[0073] When this technology is widely applied, a 5G communication module can be integrated into the control system to establish real-time interaction with multiple low-altitude aircraft regarding hydrogen refueling needs and resource status. The integrated system broadcasts the remaining number of fully pressurized storage tanks and the hydrogen production rate to aircraft within a certain radius. Based on their remaining battery power and flight mission, aircraft send hydrogen refueling reservation requests to the integrated system. The system sorts these requests by urgency and distance, generating an optimal refueling queue to avoid congestion caused by multiple aircraft waiting. At this point, the control system integrates a scheduling algorithm, combined with GPS positioning data, to plan the aircraft landing sequence.

[0074] In a drone logistics or power line inspection network covering an area of ​​50-100 kilometers, one or more mobile green energy hydrogen production and refueling integrated machines based on this invention can be deployed. These integrated machines can be deployed on logistics center rooftops, substation open spaces, field support points, etc., serving as distributed hydrogen refueling nodes within the network. Their core task is to autonomously produce and store hydrogen for the hydrogen-powered drones operating on the go, utilizing local wind and solar power, and to quickly replace the drone's hydrogen tanks using a fully automated robotic arm, enabling continuous operation with unlimited range for the drones.

[0075] The operation of this system forms an intelligent closed loop from energy prediction to task scheduling and then to automatic tank replacement. The specific process is as follows: Phase 1: Current Planning and Preparation UCS obtains the next day's weather and wind and solar power generation forecast curves from local meteorological micro-stations; it obtains the next day's scheduled flight mission plans from the drone scheduling platform and estimates the possible hydrogen refueling demand for each time period.

[0076] The optimized scheduling module aims to minimize operating costs and maximize green energy consumption, formulating a 24-hour hydrogen production plan for the following day. For example, the plan is to produce hydrogen at full capacity during the day when there is sufficient sunlight, compressing and storing the hydrogen in large-capacity buffer tanks and backup drone quick-change tanks; at night, if wind power is available, the system will operate at low power to maintain its status.

[0077] Based on demand forecasts, some hydrogen was pre-filled from the buffer tank into several drone quick-change tanks to bring them to full pressure, ready for the drones to arrive.

[0078] Phase Two: Real-time Operation and Power Point Tracking (Continuous Execution 7x24 Hours) The real-time control module continuously monitors the actual instantaneous power of photovoltaic and wind power.

[0079] Strictly following the logic of adapting to fluctuations in green electricity, the input power of the electrolyzer is dynamically adjusted. When wind and solar power output is high, the electrolyzer power is automatically increased to maximize local consumption; when wind and solar power output decreases, the power is smoothly reduced. The entire process has a response time of seconds, ensuring that the hydrogen production process is synchronized with the fluctuations in renewable energy, achieving truly green hydrogen production.

[0080] Real-time data collection of equipment status (temperature, pressure, purity, etc.) ensures safe operation.

[0081] Phase 3: Rolling Scheduling and Demand Response (Periodic Triggering, such as every 15 minutes) The system obtains the latest real-time location and remaining hydrogen forecast of the drone from the scheduling platform every 15 minutes (rolling cycle), and reads the latest ultra-short-term wind and solar forecasts.

[0082] The rolling scheduling unit was triggered. For example, it was discovered that Drone No. 3, which was originally scheduled to return in the afternoon, needed to refuel with hydrogen earlier, at noon, due to an extended mission; at the same time, radar showed that cloud cover would cause a decrease in photovoltaic output in the afternoon. The system immediately recalculated, adjusted the hydrogen production power curve and hydrogen storage allocation strategy, prioritized the upcoming hydrogen refueling needs of the drones, and may activate the backup power mode.

[0083] The revised instructions were immediately sent to the real-time control module and the robotic arm preparation queue, while the latest service capabilities of this site were pushed to the scheduling platform for the platform to optimize the global drone path.

[0084] This demonstrates the advantages of the three-level collaborative framework of the present invention, which is based on the day-ahead, rolling, and real-time approach. Through periodic rolling corrections, it effectively addresses the dual uncertainties brought about by green electricity prediction errors and changes in UAV missions.

[0085] Phase Four: Automated Hydrogen Refueling Services The drone sends a hydrogen refueling request to the integrated machine via a communication link and lands on the designated platform. The system confirms the location of the drone and the hydrogen tank interface through visual positioning.

[0086] Before the robotic arm moves, the system automatically detects the hydrogen concentration and equipment status in the hydrogen refueling area. After confirming safety, the robotic arm accurately grabs the full-pressure quick-change tank.

[0087] The robotic arm completes the entire process from unloading the old tank to installing the new tank within 1-3 minutes. The old tank is then returned to its filling position, and its pressure data is automatically recorded.

[0088] Once the tank replacement is complete, the system marks the drone service as finished, updates the inventory, and uses the old tank pressure data as the new demand input to trigger subsequent refueling tasks.

[0089] In summary, this invention relies entirely on locally generated renewable green electricity to produce hydrogen, eliminating dependence on the power grid and external hydrogen transportation, and significantly reducing the total life cycle cost and carbon footprint of hydrogen.

[0090] This invention automates the entire process from hydrogen production and storage to refueling, requiring no manual intervention. It is suitable for deployment in remote and harsh environments, significantly reducing operation and maintenance costs. Tank replacement takes only 3 minutes, far exceeding the efficiency of charging.

[0091] By deeply integrating with the front-end drone dispatch cloud platform and the back-end energy forecasting system, it is no longer an isolated device, but becomes an active node in the smart energy network, capable of proactively responding to network needs and optimizing overall operational efficiency.

[0092] The multi-parameter safety monitoring and three-level collaborative control integrated in this invention ensure long-term autonomous, safe and stable operation of equipment in complex outdoor environments.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An energy management system for a green electricity-to-hydrogen-refueling integrated machine, characterized in that, include: The upper-level control system is used to optimize operating strategies and issue commands during the day-ahead, rolling, and real-time phases; The communication interface module is used to enable data interaction with the green electricity prediction system and the hydrogen production DCS system; The optimized scheduling module, integrated into the upper-level control system, is used to perform optimized scheduling calculations with the core objective of minimizing operating costs during the day-ahead and rolling phases, based on green electricity forecast data, hydrogen refueling demand, hydrogen storage status, and equipment operating status. The real-time control module, integrated into the upper-level control system, is used to dynamically adjust the operating power of the electrolyzer based on the deviation between the real-time power of the green electricity and the reference power of the electrolyzer, so as to achieve second-level matching between the hydrogen production power and the fluctuation of the green electricity. The upper-level control system, based on the green electricity prediction data, hydrogen demand, hydrogen storage status and electrolyzer operation status data collected by the communication interface module, performs optimized scheduling with the goal of minimizing total operating cost, and outputs the electrolyzer start / stop status and reference power / current commands. In the real-time phase, the electrolytic cell power is dynamically adjusted based on the deviation between the real-time green electricity power and the reference power to achieve power tracking.

2. The energy management system for the integrated green electricity hydrogen production and refueling machine according to claim 1, characterized in that, The communication interface module includes: The interface with the green power prediction system adopts the IEC104 protocol for receiving green power output prediction data. The interface with the hydrogen production DCS system uses a proprietary UDP protocol for issuing equipment start / stop commands, power / current setpoints, and receiving equipment status feedback.

3. The energy management system for the integrated green electricity hydrogen production and refueling machine according to claim 1, characterized in that, The optimized scheduling module is further configured as follows: A mathematical programming model is established with the goal of minimizing total operating costs. The objective function includes the start-up and shutdown costs of the electrolyzer, energy consumption costs, and hydrogen storage costs. Set up a multi-objective optimization mode, including the green electricity consumption rate maximization mode, the hydrogen production power fluctuation minimization mode, and the economic operation mode, and support switching according to the operation scenario; Multiple physical and operational constraints are applied, including power balance constraints, upper and lower limits of electrolyzer power constraints, hydrogen production rate constraints, hydrogen storage tank pressure and capacity constraints, equipment start-up and shutdown logic and state switching constraints.

4. The energy management system for the integrated green electricity hydrogen production and refueling machine according to claim 1, characterized in that, The real-time control module further includes: The power deviation calculation unit is used to calculate the difference between the actual output of green electricity and the reference power of the electrolyzer in real time. The power allocation logic unit dynamically adjusts the power allocation ratio of each electrolytic cell based on a preset production strategy that follows fluctuations. The status feedback unit is used to collect the actual start-up and shutdown status of the electrolyzer, operating temperature, and purification unit timing information, and upload them to the upper-level control system for rolling scheduling and correction.

5. The energy management system for the integrated green electricity hydrogen production and refueling machine according to claim 1, characterized in that, The upper-level control system collects data through the communication interface module and coordinates the collaborative operation of the optimization scheduling module and the real-time control module to form a closed-loop control logic of daily planning, rolling correction, and real-time tracking.

6. The energy management system for the integrated green electricity hydrogen production and refueling machine according to claim 1, characterized in that, Also includes: The data storage and management module is used to uniformly store forecast data, running instructions, actual running data and optimization results, and supports time series data query and analysis. A visual human-machine interface is used to display the system's real-time operating status, green electricity consumption rate, hydrogen production, hydrogen storage status, and economic indicators, and supports manual command intervention and mode switching.

7. A method for energy management of an integrated green electricity hydrogen production and refueling machine based on the energy management system of any one of claims 1 to 6, characterized in that, Includes the following steps: Step S1: Collect green electricity forecast data, hydrogen refueling demand information, hydrogen storage tank status and hydrogen production system equipment operating parameters in real time through multi-protocol communication interfaces; Step S2: In the day-ahead phase, based on the green electricity forecast and production demand for the next day, perform optimization calculations with the goal of minimizing operating costs, and formulate electrolyzer start-up and shutdown plans and reference power for each time period; Step S3: During the rolling phase, the operating strategy for the remaining period is periodically optimized based on the updated green electricity forecast and actual operating status, and the deviation between output and demand is corrected. Step S4: In the real-time stage, the operating power of the electrolyzer is dynamically adjusted according to the deviation between the actual power of the green electricity and the reference power of the electrolyzer, so as to achieve real-time power tracking; Step S5: Accumulate real-time operational deviations and trigger rolling scheduling updates after reaching a preset time period, forming a closed-loop collaborative optimization of daily, rolling, and real-time processes.

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