Green hydrogen coupled coal chemical industry integrated scheduling control method and system

Abstract

This application discloses a green hydrogen coupled coal chemical integrated scheduling and control method and system. The method includes: outputting control commands for wind farms, photovoltaic farms, and energy storage systems, and generating real-time commands for total hydrogen production load; generating power commands and start / stop commands for each electrolyzer; forming APC control commands for the electrolyzers and green hydrogen output optimization setting commands; optimizing the internal control loops of each electrolyzer using APC control; and forming hydrogen-to-carbon ratio control commands based on the green hydrogen output optimization setting commands and real-time status feedback from the coal chemical methanol synthesis unit. Using the scheme in this application, a full-chain, multi-level intelligent scheduling and advanced control system is constructed, encompassing renewable energy forecasting, source-grid-load-storage coordination, fine control of the electrolyzer cluster, and downstream coal chemical hydrogen-to-carbon ratio optimization. This achieves deep coupling and efficient collaboration between the green energy production end and the coal chemical application end.

Description

Technical Field

[0001] This application generally relates to the field of intelligent control and optimal scheduling technology for multi-energy systems. More specifically, this application relates to a green hydrogen coupled coal chemical integrated scheduling and control method and system. Background Technology

[0002] As the global energy structure accelerates its transformation towards low-carbon and clean energy, the production of "green hydrogen" through water electrolysis using renewable energy sources (such as wind and solar power) and its coupling with the traditional coal chemical industry has become a key technological path for promoting the low-carbon upgrading and sustainable development of coal-based industries. However, in current practice, the integrated application of green hydrogen with coal chemical systems still faces many technical challenges, resulting in problems such as poor overall system energy efficiency and unstable operation.

[0003] Existing technologies in green hydrogen coupled coal chemical engineering mainly face the following bottlenecks:

[0004] First, the dynamic matching efficiency between renewable energy and hydrogen electrolysis is low: renewable energy sources such as wind and solar power are significantly intermittent and volatile. The optimal operating temperature range of traditional alkaline electrolyzers is typically 80℃-90℃, and their thermal management systems struggle to effectively cope with the drastic fluctuations in renewable energy power generation, causing the electrolyzer's operating temperature to deviate from the optimal range, thus reducing hydrogen production efficiency by 20% to 35%. Simultaneously, fluctuating power inputs pose significant challenges to the stable control of key operating parameters of the electrolyzer system, such as liquid level, temperature, pressure, and alkali circulation.

[0005] Secondly, the response lag in hydrogen-to-carbon ratio control in coal chemical processes: In coal chemical production processes (such as methanol synthesis), the hydrogen-to-carbon ratio of the syngas (typically requiring an H2 / CO ratio within the range of 1.8-2.2) is a key parameter affecting product yield and quality. Traditionally, the adjustment of the hydrogen-to-carbon ratio relies on parameter adjustments to units such as gasifiers and water-gas shift converters. Currently, the common control loops exhibit a response delay of 5 to 8 minutes. This lag cannot promptly match changes in the supply of green hydrogen, leading to a reduction in the synthesis efficiency of downstream products such as methanol by more than 12%.

[0006] Third, there is insufficient coordinated control of multi-energy systems: A unified and efficient energy flow management and coordinated scheduling strategy is lacking throughout the entire chain of "wind and solar power generation - electrolysis hydrogen production - hydrogen storage and transportation - coal chemical applications." The start-up and shutdown scheduling of equipment at each stage often has an error of up to ±15%, especially under conditions of drastic changes in wind and solar power output (such as rapid increases or decreases), which can easily trigger cascading system failures and affect the overall stability and reliability of operation.

[0007] Fourth, low equipment integration leads to additional energy consumption: Currently, in most green hydrogen coupled coal chemical projects, the electrolysis hydrogen production unit and the coal chemical production unit are physically isolated. This separate layout causes significant pressure loss during long-distance pipeline transportation of the produced hydrogen, typically as high as 0.8 MPa to 1.2 MPa. To compensate for this pressure loss, the hydrogen compressor needs to consume a large amount of electrical energy, which can even exceed 18% of the total system energy consumption, further reducing the overall economic efficiency of the system.

[0008] In view of this, there is an urgent need to provide an integrated scheduling and control scheme for green hydrogen coupled with coal chemical industry, which can overcome the above-mentioned defects, realize system-level collaborative control and scheduling of green hydrogen and coal chemical industry, so as to improve overall energy efficiency and ensure the stable operation of green hydrogen and coal chemical industry. Summary of the Invention

[0009] In order to at least solve one or more of the technical problems mentioned above, this application proposes a green hydrogen coupled coal chemical integrated scheduling and control scheme in several aspects.

[0010] In the first aspect, this application provides a green hydrogen coupled coal chemical integrated scheduling and control method, including: integrating wind and solar power forecast data, real-time wind, solar, hydrogen, and storage data, interface configuration data, equipment models, and constraints to generate a top-level scheduling plan, wherein the top-level scheduling plan includes planned grid-connected power, planned wind and solar power generation, planned energy storage power, planned total hydrogen production power, and electrolyzer start-up and shutdown planning; receiving planned grid-connected power, planned wind and solar power generation, planned energy storage power, and planned total hydrogen production power, and combining wind and solar power forecast data to perform coordinated scheduling of source, grid, load, and storage, outputting control commands for wind farms, photovoltaic farms, and energy storage systems, and generating real-time hydrogen production load commands; and using electrolyzer start-up and shutdown planning and real-time hydrogen production load commands... The system generates power commands and start / stop commands for each electrolyzer based on actual limits on electrolyzer load and rate, and electrolyzer status data. These start / stop commands include both start and stop commands. Each electrolyzer is controlled based on its power command, start / stop command, and status data. An APC controller generates APC control commands and green hydrogen delivery optimization settings for each electrolyzer. The internal control loops of each electrolyzer are optimized using these APC control commands. Finally, a hydrogen-to-carbon ratio control command is generated based on the green hydrogen delivery optimization settings and real-time status feedback from the coal chemical methanol synthesis unit, and sent to the coal chemical methanol synthesis unit and water-gas conversion unit for execution.

[0011] In some embodiments, the wind and solar power prediction data of the power station is obtained through the following steps: establishing and training a wind and solar power prediction model; based on meteorological data, environmental perception data, historical wind power generation data and historical photovoltaic power generation data, using the trained wind and solar power prediction model to establish wind power generation prediction trends and photovoltaic power generation prediction trends at multiple time scales.

[0012] In some embodiments, the equipment model includes a wind turbine model, a photovoltaic array model, an energy storage system model, and an electrolyzer model.

[0013] In some embodiments, the following steps are performed during the generation of the top-level scheduling plan: by receiving wind and solar forecast data, real-time wind, solar, hydrogen, and storage data, interface configuration data, equipment models, and constraints, data verification and initial operating status determination are completed; based on the operating objectives in the interface configuration data, combined with the equipment models and constraints, a preliminary top-level scheduling plan for the day is generated; periodically, based on the latest wind and solar forecast data and real-time wind, solar, hydrogen, and storage data, combined with the equipment models and constraints, the preliminary top-level scheduling plan for the day is continuously optimized to form the top-level scheduling plan for each time period within the day.

[0014] In some embodiments, during the process of outputting control commands for wind farms, photovoltaic power plants, and energy storage systems, and generating real-time commands for total hydrogen production load, the following steps are performed: calculating the expected power generation deviation by comparing the planned wind and solar power generation with the actual wind and solar power generation; obtaining the amplitude and direction of power regulation for wind farms, photovoltaic power plants, and energy storage systems based on the power generation deviation; and determining the control commands for wind farms, photovoltaic power plants, and energy storage systems, as well as the real-time commands for total hydrogen production load, based on the operating targets in the interface configuration data and the amplitude and direction of power regulation.

[0015] In some embodiments, the following steps are performed during the generation of power commands and start / stop commands for each electrolyzer: Based on the electrolyzer start / stop plan and electrolyzer status data, select currently available electrolyzers that meet the planned scheduling conditions; calculate the net power adjustment amount that needs to be increased or decreased by comparing the real-time hydrogen production load command with the current load of the electrolyzers; based on the operating target in the interface configuration data, generate start / stop commands for each electrolyzer using the selected currently available electrolyzers that meet the planned scheduling conditions and the net power adjustment amount that needs to be increased or decreased; distribute the real-time hydrogen production load command among the electrolyzers currently executing the start command to form the power commands for the corresponding electrolyzers.

[0016] In some embodiments, the APC control commands include temperature control commands, pressure control commands, and liquid level control commands.

[0017] In some embodiments, during the process of generating the electrolyzer APC control command and the green hydrogen delivery optimization setting command, the following steps are performed: dynamically generating the electrolyzer temperature control command, pressure control command, and liquid level control command through electrolyzer status data, electrolyzer operating parameters, and preset control strategies; based on the operating target in the interface configuration data, and through the current status of the green hydrogen injection buffer tank and the green hydrogen production, adjusting the flow rate of green hydrogen delivered to the buffer tank to achieve the optimal hydrogen-to-carbon ratio, thereby obtaining the green hydrogen delivery optimization setting command.

[0018] In some embodiments, the process of generating a hydrogen-to-carbon ratio control command includes the following steps: calculating the actual hydrogen-to-carbon ratio currently entering the methanol synthesis unit based on the real-time status feedback from the coal chemical methanol synthesis unit; calculating the deviation between the actual hydrogen-to-carbon ratio and the optimal hydrogen-to-carbon ratio; and generating a hydrogen-to-carbon ratio control command based on the deviation between the actual hydrogen-to-carbon ratio and the optimal hydrogen-to-carbon ratio.

[0019] In a second aspect, this application provides a green hydrogen coupled coal chemical integrated scheduling and control system, which employs the green hydrogen coupled coal chemical integrated scheduling and control method as described in any embodiment of the first aspect for green hydrogen coupled coal chemical integrated scheduling and control. The system includes: a wind-solar-hydrogen-storage coordinated scheduling and electrolyzer start-up and shutdown planning module, configured to integrate wind and solar forecast data, wind-solar-hydrogen-storage real-time data, interface configuration data, equipment models, and constraints to generate a top-level scheduling plan, wherein the top-level scheduling plan includes planned grid-connected power, planned wind and solar power generation, planned energy storage power, planned total hydrogen production power, and electrolyzer start-up and shutdown planning; a source-grid-load-storage coordinated control system, configured to receive planned grid-connected power, planned wind and solar power generation, planned energy storage power, and planned total hydrogen production power, and, in conjunction with wind and solar forecast data, perform source-grid-load-storage coordinated scheduling, output control commands for wind farms, photovoltaic farms, and energy storage systems, and generate real-time commands for total hydrogen production load to be sent to the hydrogen production cluster control system; and a hydrogen production cluster control system, configured to... The system is configured to perform flexible group control of the hydrogen production cluster based on electrolyzer start / stop planning, real-time hydrogen production load commands, actual limits on electrolyzer load and rate, and electrolyzer status data fed back from the hydrogen production DCS module. This generates power commands and start / stop commands for each electrolyzer within the hydrogen production DCS module. The hydrogen production DCS module is configured to control each electrolyzer based on its power commands, start / stop commands, and status data, and generates APC control commands and green hydrogen delivery optimization settings via an APC controller. The electrolyzer APC control system is configured to optimize the internal control loops of each electrolyzer based on the APC control commands and feed back its status to the hydrogen production DCS module. Finally, a hydrogen-to-carbon ratio APC control system is configured to generate hydrogen-to-carbon ratio control commands based on green hydrogen delivery optimization settings and real-time status feedback from the coal chemical methanol synthesis unit, and send these commands to the coal chemical methanol synthesis unit and the water-gas conversion unit for execution.

[0020] Through the green hydrogen coupled coal chemical integrated scheduling and control scheme provided above, this application embodiment constructs a full-chain, multi-level intelligent scheduling and advanced control system, encompassing renewable energy forecasting, source-grid-load-storage coordination, fine control of electrolyzer clusters, and downstream coal chemical hydrogen-carbon ratio optimization. This achieves deep coupling and efficient collaboration between the green energy production end and the coal chemical application end. By integrating forecast data and real-time status, and conducting top-level unified planning and hierarchical optimization control, it not only significantly improves the absorption rate of renewable energy sources such as wind and solar power and the overall energy conversion efficiency, but also reduces system operating costs. Simultaneously, through refined power allocation, start-up and shutdown management, and APC optimization of the electrolyzers, as well as real-time precise control of the hydrogen-carbon ratio in the coal chemical section, it ensures the economic efficiency of green hydrogen production and the stability and efficiency of the chemical process, effectively reducing the carbon footprint of chemical products. Furthermore, this method comprehensively considers the equipment models, operational constraints, and dynamic characteristics of each link, enhancing the operational flexibility, safety, reliability, and overall economic benefits of the entire green hydrogen coupled coal chemical system under varying operating conditions, ultimately promoting the intelligent and green transformation of energy and chemical processes.

[0021] Furthermore, in some embodiments, during the generation of the top-level scheduling plan, firstly, rigorous data verification and precise determination of the initial operating state lay a solid and reliable data foundation for all subsequent planning and decision-making, ensuring the accuracy of the planning's starting point. Secondly, preliminary day-ahead planning based on clear operational objectives enables the prediction of future operational trends and the initial optimization of resource allocation, reflecting the strategic and guiding nature of the plan. Thirdly, a periodic rolling optimization mechanism is introduced, which can dynamically adjust and optimize the scheduling scheme based on the latest forecast data and real-time operating conditions. This greatly enhances the adaptability and response speed of the scheduling plan to actual changes, making the scheduling plans for each time period within a day more accurate, flexible, and realistic. This significantly improves the efficiency, stability, and economy of the entire system operation, and ensures that the plan is always feasible under equipment capacity and constraints.

[0022] Furthermore, in some embodiments, the generation of power and start / stop commands for each electrolyzer enables refined, dynamic, and goal-oriented management of the electrolyzer cluster, allowing for precise and efficient response to real-time hydrogen production load commands issued from the upper level. First, by filtering based on start / stop planning and real-time status, it ensures that only currently healthy, available electrolyzer resources that conform to the overall scheduling strategy are utilized, improving system reliability and operational orderliness. Second, the net power adjustment is clearly calculated, providing a clear quantitative basis for subsequent start / stop decisions and load allocation, ensuring the accuracy of adjustments. Third, decisions on the start / stop of specific electrolyzers are based on operational targets, rather than simply adding or subtracting, reflecting intelligent control and optimization depth, contributing to better overall benefits. Finally, the total load command is effectively allocated to the selected operating electrolyzers, ensuring the precise achievement of the overall hydrogen production task and laying the foundation for subsequent APC optimization control of each electrolyzer, thereby improving the response speed, energy utilization efficiency, and automation level of the entire hydrogen production process.

[0023] Furthermore, in some embodiments, dual optimization from hydrogen production unit to chemical application is achieved through fine-tuning of individual electrolyzers internally and optimizing green hydrogen delivery strategies externally. By dynamically generating APC control commands for internal parameters such as temperature, pressure, and liquid level of each electrolyzer, it is possible to ensure that each electrolyzer operates stably, efficiently, and safely near its designed or preset optimal operating point, thereby maximizing the hydrogen production efficiency of individual units, extending equipment life, and ensuring the quality and stability of green hydrogen production. Simultaneously, based on overall operational goals and combined with the real-time status of the green hydrogen injection buffer tank and actual green hydrogen production, the green hydrogen flow rate to the buffer tank is intelligently adjusted to optimize the hydrogen-to-carbon ratio downstream (such as in a coal chemical methanol synthesis unit), thereby generating optimized green hydrogen delivery settings commands. This proactively and precisely matches the supply of green hydrogen with the actual needs of chemical processes (especially the key hydrogen-to-carbon ratio parameter), ensuring not only the efficiency, product quality, and process stability of downstream chemical production, but also significantly improving the effective utilization rate of valuable green hydrogen resources and the overall economic benefits and operational flexibility of the entire green hydrogen-coupled coal chemical system. It provides a key control means for achieving deep integration and synergistic effects between clean energy production and traditional chemical processes. Attached Figure Description

[0024] The above and other objects, features, and advantages of exemplary embodiments of this application will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of this application are illustrated by way of example and not limitation, and the same or corresponding reference numerals denote the same or corresponding parts, wherein:

[0025] Figure 1 An exemplary flowchart of the integrated scheduling and control method for green hydrogen coupled with coal chemical industry according to an embodiment of this application is shown;

[0026] Figure 2 An exemplary flowchart illustrating the generation of a top-level scheduling plan according to an embodiment of this application is shown;

[0027] Figure 3 An exemplary flowchart illustrating the generation of power commands and start / stop commands for each electrolytic cell according to an embodiment of this application is shown.

[0028] Figure 4 An exemplary structural block diagram of the green hydrogen-coupled coal chemical integrated scheduling and control system according to an embodiment of this application is shown. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] It should be understood that the terms "comprising" and "including" used in the specification and claims of this application indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0031] It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this specification and claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this specification and claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0032] Figure 1 An exemplary flowchart of the integrated scheduling and control method 100 for green hydrogen coupling coal chemical industry according to an embodiment of this application is shown.

[0033] like Figure 1 As shown, in step S110, the wind and solar forecast data, real-time wind, solar and hydrogen storage data, interface configuration data, equipment models and constraints are integrated to generate a top-level scheduling plan. The top-level scheduling plan includes the planned grid-connected power, the planned wind and solar power generation power, the planned energy storage power, the planned total hydrogen production power, and the electrolyzer start-up and shutdown plan.

[0034] In the embodiments of this application, a wind and solar prediction model is established and trained during the generation of wind and solar power prediction data for power stations. Then, based on meteorological data, environmental perception data, historical wind power generation data, and historical photovoltaic power generation data, the trained wind and solar power prediction model is used to establish multi-timescale wind power generation prediction trends and photovoltaic power generation prediction trends.

[0035] In the embodiments of this application, the wind power generation forecast trend and photovoltaic power generation forecast trend at multiple time scales include second-level wind power generation forecast trend, second-level photovoltaic power generation forecast trend, 15-minute wind power generation forecast trend, 15-minute photovoltaic power generation forecast trend, etc.

[0036] Through the process of generating wind and solar power forecast data, systematic data analysis and model training transform raw, multi-source, heterogeneous data into structured and quantitative predictions of future power generation, significantly improving the scientific rigor and reliability of the forecasts. Secondly, the generated multi-timescale forecast trends can meet the accuracy requirements of different levels of dispatch decisions (such as second-level and 15-minute-level forecasts), enabling the system to anticipate potential changes in wind and solar power output earlier. Especially for large-scale fluctuations within minutes, this allows valuable time for rapid response and advance dispatch of adjustable resources such as electrolyzers, effectively improving the renewable energy absorption rate, ensuring a stable supply to downstream loads (such as hydrogen production), and ultimately enhancing the operational flexibility, stability, and economic efficiency of the entire energy system.

[0037] In the embodiments of this application, the real-time data of wind, solar, hydrogen and storage includes real-time data of wind power generation, real-time data of photovoltaic power generation, green hydrogen production, real-time data of energy storage system, etc.

[0038] In the embodiments of this application, the interface configuration data includes running targets and strategy parameters, as well as time and period parameters.

[0039] Specifically, the operational objectives and strategic parameters include hydrogen production targets, economic targets, green electricity consumption targets, operational modes, and equipment scheduling priorities. Hydrogen production targets include the daily / weekly planned total hydrogen production volume and the target liquid level range for buffer tanks. Economic targets include the set maximum acceptable hydrogen production cost, the expected grid connection tariff revenue threshold, and strategic parameters for participating in electricity market ancillary services. Green electricity consumption targets include the expected minimum local wind and solar power integration rate. Operational modes include different scheduling modes, such as prioritizing maximizing green hydrogen production, prioritizing optimal system operational economics, or prioritizing grid-friendly interaction. Equipment scheduling priorities include the activation priority of different equipment (such as specific wind turbines, photovoltaic arrays, energy storage units, and electrolyzer groups) when multiple resources are available.

[0040] Specifically, time and cycle parameters include scheduling cycle parameters and planned maintenance time parameters. Scheduling cycle parameters include the start and end times of day-ahead scheduling and the frequency of intraday rolling optimization. Planned maintenance time parameters include manually entered or selected equipment planned maintenance time windows.

[0041] In the embodiments of this application, the equipment models include wind turbine models, photovoltaic array models, energy storage system models, electrolyzer models, etc.

[0042] In the embodiments of this application, the constraints include grid interaction constraints, equipment operation constraints, and energy storage operation constraints. Specifically, grid interaction constraints include the maximum on-grid / off-grid power limit set by the operator for a specific time period. Equipment operation constraints include the maximum number of allowed start-stop cycles for the electrolyzer, the minimum stable operating time, and the maximum load change rate. Energy storage operation constraints include the minimum / maximum state of charge (SOC) limit for the energy storage device and the daily allowed number of charge-discharge cycles.

[0043] For details on the specific process of generating the top-level scheduling plan in the embodiments of this application, please refer to [link / reference]. Figure 2 .

[0044] Figure 2 An exemplary flowchart illustrating the generation of a top-level scheduling plan according to an embodiment of this application is shown.

[0045] like Figure 2 As shown, in step S210, data verification and initial operating status determination are completed by receiving wind and solar forecast data, real-time wind, solar, hydrogen, and storage data, interface configuration data, equipment models, and constraints. In step S220, based on the operating objectives in the interface configuration data, combined with the equipment models and constraints, a preliminary day-ahead top-level scheduling plan is generated. In step S230, the preliminary day-ahead top-level scheduling plan is periodically optimized based on the latest wind and solar forecast data and real-time wind, solar, hydrogen, and storage data, combined with the equipment models and constraints, to form top-level scheduling plans for each time period within the day.

[0046] In the embodiments of this application, during the process of data verification and determination of the initial operating state, the received data is first subjected to validity checks (such as range checks and consistency checks), abnormal data is removed or corrected, and missing data is filled in (such as using interpolation or prediction). Then, data from different sources and at different time scales are timestamped to ensure data consistency within the same optimization time step. Next, based on real-time data from wind, solar, hydrogen, and storage systems, the initial operating state of the optimization cycle is determined, such as the initial SOC of the energy storage system, the initial pressure / capacity of the hydrogen storage tank, and the current operating state of each electrolyzer.

[0047] In the embodiments of this application, during the execution of step S220, an objective function is constructed based on the operational objectives set in the interface configuration data (such as minimizing operating costs, maximizing green hydrogen production, maximizing renewable energy absorption rate, or a weighted combination of these objectives). Based on the equipment model, constraints describing the operating characteristics and capacity limitations of each piece of equipment are established, such as: wind and solar power generation not exceeding their predicted output and installed capacity; energy storage charging and discharging power, SOC range, and cycle life; and the minimum / maximum operating power, load change rate, start-up and shutdown conditions, and hydrogen production efficiency of the electrolyzer. Based on these constraints and limitations, system-level mandatory constraints are established, such as: power exchange with the grid within permissible limits; and the safety pressure and capacity limits of the hydrogen storage system. Then, the above objective function and constraints are integrated into a mathematical model, and a suitable optimization algorithm solver is used to solve this mathematical model to obtain the optimal solution for a set of decision variables (i.e., future planned grid-connected power, planned wind and solar power generation, planned energy storage charging and discharging power, planned total hydrogen production, start-up and shutdown status of each electrolyzer, and power allocation), making the objective function optimal while satisfying all constraints. As a result, a preliminary top-level scheduling plan has been developed.

[0048] In the embodiments of this application, during the execution of step S230, an intraday rolling optimization model is constructed. The latest wind and solar forecast data, real-time wind, solar, hydrogen and storage data, and the preliminary top-level scheduling plan for the day are taken as inputs. The constraints and constraints corresponding to the equipment model are taken as constraints of the intraday rolling optimization model. According to the operational objectives of each stage set in the interface configuration data (such as minimizing wind and solar curtailment), the preliminary top-level scheduling plan for the day is rolled to form the top-level scheduling plan for each time period of the day.

[0049] Specifically, the top-level scheduling plan for each time period within the day can be a top-level scheduling plan for each 15-minute time period within the day.

[0050] Step S110 integrates and verifies multi-source data, ensuring the quality of data input and the accuracy of the initial state for the scheduling plan, thus providing a solid foundation for subsequent optimization. Based on clear operational objectives (such as hydrogen production targets, economic efficiency, and green electricity consumption) and equipment / system constraints, a preliminary day-ahead top-level scheduling plan is generated through mathematical modeling and optimization. This not only achieves strategic pre-deployment for the next 24 hours of operation, ensuring the overall optimality and feasibility of the plan, but also considers practical factors such as equipment scheduling priorities and planned maintenance. The day-ahead plan is periodically revised and optimized based on the latest wind and solar forecasts and real-time operational data, resulting in a scheduling plan for each time period within a day that closely reflects reality. This mechanism greatly enhances the system's adaptability and response speed to uncertainties such as wind and solar fluctuations and load changes, enabling timely adjustments to strategies to minimize wind and solar curtailment and maximize the utilization of renewable energy. The planning process fully considered grid interaction constraints, equipment (such as the number of start-ups and shutdowns of electrolyzers and the rate of load change) operation constraints, and energy storage (such as the range of state of charge and the number of cycles) operation constraints, ensuring that all dispatching commands are executed within a safe and permissible range, and guaranteeing the stable and reliable operation of the entire system.

[0051] Through the above-mentioned refined data processing, forecasting, multi-objective day-ahead optimization, and intraday rolling correction, the renewable energy absorption rate can be effectively improved, the energy storage system charging and discharging strategy can be optimized, and the start-up and shutdown of the electrolyzer and the load can be rationally arranged. Thus, while ensuring the completion of core tasks such as hydrogen production, the system operating cost can be minimized, the green hydrogen production can be maximized, or the overall economic benefits can be optimized, which strongly promotes the efficient, economical and green operation of the green hydrogen coupled coal chemical integrated system.

[0052] After completing step S110, in step S120, the planned grid-connected power, wind and solar power generation, energy storage power and planned total hydrogen production power are received. Combined with wind and solar forecast data, the coordinated scheduling of source, grid, load and storage is carried out, and control commands for wind farms, photovoltaic farms and energy storage systems are output. Real-time commands for total hydrogen production load are generated.

[0053] In the embodiments of this application, during the process of outputting control commands for wind farms, photovoltaic power plants, and energy storage systems, and generating real-time commands for total hydrogen production load, firstly, the expected power generation deviation is calculated by comparing the planned wind and solar power generation with the actual wind and solar power generation. Based on this deviation, the amplitude and direction of power regulation for wind farms, photovoltaic power plants, and energy storage systems are obtained. Next, based on the operational targets in the interface configuration data, the control commands for wind farms, photovoltaic power plants, and energy storage systems, as well as the real-time commands for total hydrogen production load, are determined through the amplitude and direction of power regulation.

[0054] In the embodiments of this application, during the process of determining the magnitude and direction of power regulation, after calculating the expected power generation deviation, a comprehensive assessment of the overall power balance in the current and very short future period is conducted based on the planned grid-connected power, planned energy storage power, and planned total hydrogen production power. Based on this comprehensive assessment, the system determines whether there is a power deficit (i.e., insufficient power generation to meet demand) or a power surplus (i.e., power generation exceeding demand). If the assessment result is a power deficit, the regulation direction is to increase energy supply or reduce load; if there is a power surplus, the regulation direction is to reduce energy supply or increase consumption (such as energy storage charging or increasing hydrogen production). The specific value of this power deficit or power surplus, combined with the expected power generation deviation, determines the magnitude of the regulation required.

[0055] In the embodiments of this application, during the process of determining control commands for wind farms, photovoltaic power plants, and energy storage systems, as well as determining real-time commands for total hydrogen production load, a coordinated control strategy is formed based on the operational objectives in the interface configuration data (e.g., minimizing deviation, maximizing green electricity utilization, and ensuring system stability). This strategy comprehensively considers the interactions between the source (wind and solar), grid (exchange with the main grid), load (hydrogen production load), and storage (energy storage system), resulting in equipment-level control commands and real-time commands for total hydrogen production load. Specifically, equipment-level control commands include control commands for wind farms, control commands for photovoltaic power plants, and control commands for energy storage systems. For example, control commands for wind farms include active power setpoints, wind curtailment commands, and voltage / reactive power control commands. Control commands for photovoltaic power plants include active power setpoints, solar curtailment commands, and voltage / reactive power control commands. Control commands for energy storage systems include charge / discharge power setpoints and operating mode switching commands.

[0056] Through step S120, based on real-time reception of various planned power indicators and accurate prediction of wind and solar power, the system dynamically assesses the current power balance status and accurately calculates the power generation deviation. Based on this deviation and the overall balance demand (power deficit or surplus), the system can determine the magnitude and direction of regulation. Then, according to preset operating objectives (such as minimizing deviation, maximizing green electricity utilization, and ensuring system stability), it can intelligently formulate and issue specific control commands for wind power, photovoltaic, and energy storage systems, as well as real-time commands for the total hydrogen production load. By combining wind and solar forecast data, the system can predict power generation deviations and thus conduct forward-looking regulation rather than passive response, improving the accuracy of control. Based on the overall power balance status, a comprehensive assessment and collaborative decision-making of wind, solar, storage, and hydrogen (load) is carried out, realizing optimized interaction between source, grid, load, and storage. At the same time, the generation of control commands closely revolves around the operating objectives configured on the interface, giving the regulation behavior a clear optimization direction (such as maximizing green electricity consumption and ensuring grid friendliness), reflecting intelligent decision-making. Furthermore, it can generate specific control commands at the equipment level (such as power setpoint, wind and solar curtailment commands, and charging and discharging commands), enabling refined management and efficient scheduling of all components of the energy system. By accurately predicting deviations, assessing balances, and coordinating regulation, it effectively addresses the volatility of renewable energy, ensuring the efficient and stable operation of the entire integrated wind, solar, and hydrogen storage system.

[0057] After completing step S120, in step S130, power commands and start / stop commands for each electrolyzer are generated based on electrolyzer start / stop planning, real-time hydrogen production load commands, electrolyzer load, actual limits on electrolyzer rate, and electrolyzer status data. The start / stop commands include start commands and stop commands.

[0058] In the embodiments of this application, the specific steps involved in step S130 can be found in the following references. Figure 3 .

[0059] Figure 3 An exemplary flowchart illustrating an embodiment of this application is shown, illustrating the generation of power commands and start / stop commands for each electrolytic cell.

[0060] like Figure 3As shown, in step S310, based on the electrolyzer start-up / shutdown plan and electrolyzer status data, currently available electrolyzers that meet the planned scheduling conditions are selected. In step S320, the net power adjustment amount that needs to be increased or decreased is calculated by comparing the real-time hydrogen production load command with the current load of the electrolyzers. In step S330, based on the operating target in the interface configuration data, start-up / shutdown commands for each electrolyzer are generated by selecting currently available electrolyzers that meet the planned scheduling conditions and the net power adjustment amount that needs to be increased or decreased. In step S340, the real-time hydrogen production load command is distributed among the electrolyzers currently executing the start-up command to form the power command for the corresponding electrolyzer.

[0061] In the embodiments of this application, "available and meeting the planning and scheduling conditions" means that the cell is healthy and can be used for scheduling, that is, it is in a non-faulty, non-maintenance state and meets the basic requirements for electrolytic cell start-up and shutdown planning.

[0062] In the embodiments of this application, during the execution of step S330, the running target in the interface configuration data is a preset priority strategy. Based on the preset priority strategy (obtained by considering startup cost, response speed, cumulative running time, efficiency curves, etc.), specific electrolytic cells are selected for startup and shutdown. Simultaneously, the startup and shutdown operation sequence is generated according to the time requirements for the startup and shutdown process in the actual limitations of the electrolytic cell rate.

[0063] In the embodiments of this application, during step S340, for the electrolyzers determined to be operational (including those already in operation and continuing to operate, and those newly started and about to be put into operation), the real-time hydrogen production load command is allocated to the electrolyzers determined to be operational. During the allocation process, the actual limitations of the electrolyzer load, the rated capacity of the electrolyzer rate, the efficiency characteristics, and the upper and lower operating limits of the electrolyzer rate are considered, ensuring that the total power of all electrolyzers accurately tracks the real-time hydrogen production load command.

[0064] In step S130, based on the start-up and shutdown plan and the real-time status data of the electrolyzers, the currently healthy, available electrolyzers that meet the scheduling plan are first selected to ensure the effective execution of subsequent instructions. By comparing the total hydrogen production load instruction with the actual total load of the current electrolyzers, the net power adjustment that needs to be increased or decreased is accurately calculated. Based on preset operating targets (such as priority strategies, comprehensively considering start-up costs, response speed, cumulative operating time, efficiency, etc.) and the actual rate limits of the electrolyzers (start-up and shutdown time requirements), the specific electrolyzers to be started or stopped are intelligently determined, and the corresponding start-up and shutdown sequence is generated. The real-time hydrogen production load instruction is reasonably allocated among all the electrolyzers currently determined to be operating, forming a specific power instruction for each electrolyzer, while considering the current load, rate limits, rated capacity, efficiency characteristics, and operating upper and lower limits of each electrolyzer. This achieves independent and precise control of each electrolyzer, rather than extensive management, and makes intelligent start-up and shutdown decisions through preset strategies. Prioritizing the selection of electrolyzers for start-up and shutdown based on factors such as cost, efficiency, and lifespan, and considering efficiency characteristics during load allocation, helps improve the overall economic efficiency and effectiveness of the hydrogen production system. Furthermore, ensuring that the total power output of all operating electrolyzers accurately tracks real-time total load commands guarantees a rapid response and stable output to energy fluctuations in the hydrogen production process.

[0065] Meanwhile, the decision-making and instruction issuance process fully considers the state and actual limitations of the electrolytic cell (such as rate, capacity, and upper and lower operating limits), which helps ensure the safe and reliable operation of the equipment and avoids exceeding limits or improper operation. In addition, it follows the upper-level start-up and shutdown plan while making flexible adjustments based on real-time load instructions and equipment status, achieving a good combination of planning and actual operating conditions.

[0066] After step S130 is completed, in step S140, each electrolyzer is controlled based on the power command of each cell, the start and stop command of each electrolyzer, and the status data of the electrolyzer. The APC controller generates the electrolyzer APC control command and the green hydrogen delivery optimization setting command.

[0067] In the embodiments of this application, the electrolytic cell APC control commands include temperature control commands, pressure control commands, liquid level control commands, etc.

[0068] In the embodiments of this application, during the process of generating the electrolyzer APC control command and the green hydrogen delivery optimization setting command, the temperature control command, pressure control command, and liquid level control command of the electrolyzer are dynamically generated through electrolyzer status data, electrolyzer operating parameters, and preset control strategies. Based on the operating target in the interface configuration data, and by adjusting the flow rate of green hydrogen delivered to the buffer tank to achieve the optimal hydrogen-to-carbon ratio, the green hydrogen delivery optimization setting command is obtained through the current state of the green hydrogen injection buffer tank and the green hydrogen production rate.

[0069] In the embodiments of this application, during the process of dynamically generating temperature control commands, pressure control commands, and liquid level control commands for the electrolyzer using electrolyzer status data, electrolyzer operating parameters, and preset control strategies, feedback data from the electrolyzer APC control system and electrolyzer operating parameters such as temperature, pressure, and liquid level directly collected from sensors in each electrolyzer are received. These electrolyzer operating parameters are compared with preset standard ranges of electrolyzer process parameters (i.e., safe operating limits and optimal efficiency ranges) in the system. For example, the electrolyte temperature has its optimal operating range; excessively high or low temperatures will affect electrolysis efficiency and equipment lifespan. The hydrogen outlet pressure and liquid level also need to fluctuate within a specific range. Simultaneously, the specific needs of the auxiliary system based on the current electrolyzer status (such as start-up and shutdown processes, load levels) are considered, such as preheating requirements during startup or enhanced cooling requirements during high-load operation. High-load operation generates a large amount of heat, which may require the APC to enhance the cooling system's capacity to maintain temperature stability. Changes in the gas production rate during load variations will affect pressure and liquid level, requiring prediction and instruction of the APC to perform feedforward adjustment.

[0070] Based on the comparison results between the electrolyzer's operating parameters and the preset standard range of electrolyzer process parameters in the system, as well as the state of the electrolyzer, the optimal set points for auxiliary parameters such as temperature, pressure, and liquid level are calculated using internally fixed control logic (such as deviation-driven, rule-based judgment, or predictive feedforward algorithms). These precise setting instructions are then sent to the electrolyzer's APC control system, which is responsible for driving the specific actuators to complete fine adjustments, ensuring that the electrolyzer operates safely and efficiently under optimal conditions.

[0071] In the embodiments of this application, during the process of generating the green hydrogen delivery optimization setting instruction, the actual total hydrogen production capacity and sustainable supply potential of the entire electrolyzer cluster are accurately assessed. The system actively acquires or receives explicit demand signals from the downstream hydrogen-to-carbon ratio (HCH) APC control system or higher-level production planning systems, such as the target flow rate, pressure, and key HCH molar ratio requirements of downstream process units for green hydrogen. The current status of green hydrogen injection into the buffer tank is also taken into consideration. Based on a comprehensive understanding of upstream supply capacity and downstream usage demand, and to achieve the optimal HCH ratio, the system intelligently determines the current optimal green hydrogen allocation scheme. Finally, this green hydrogen delivery optimization setting instruction, containing the flow rate, pressure, or other scheduling parameters of green hydrogen sent to the buffer tank, is sent to the HCH APC control system to guide it in efficiently and economically receiving and utilizing green hydrogen, thereby optimizing the operation of the entire process chain.

[0072] In step S140, based on the power commands, start / stop commands, and real-time status data of each electrolyzer, the system dynamically generates and issues targeted Advanced Process Control (APC) commands, such as temperature control, pressure control, and level control commands. This ensures that key process parameters (temperature, pressure, level, etc.) inside the electrolyzer are maintained within optimal and safe ranges. The system intelligently determines the green hydrogen delivery flow rate and parameters based on the actual green hydrogen production, the status of the buffer tank, and the requirements of downstream processes (such as the hydrogen-to-carbon ratio), generating optimized green hydrogen delivery setting commands to achieve the optimal hydrogen-to-carbon ratio, thereby optimizing the operation of the entire subsequent process chain.

[0073] Through APC control, the temperature, pressure, and liquid level of the electrolyzer are precisely and dynamically adjusted to ensure it always operates within the optimal process parameter range. This not only prevents equipment damage or efficiency reduction due to parameter exceeding limits but also enables feedforward adjustment and optimization of auxiliary systems (such as cooling) based on load changes (such as start-up, shutdown, and high load), ensuring the safe and efficient operation of the electrolyzer under various operating conditions. The green hydrogen delivery optimization setting command is not simply the output of hydrogen gas, but rather a smart decision based on a comprehensive understanding of upstream hydrogen production capacity and downstream (such as the hydrogen-to-carbon ratio APC) specific needs, aiming to achieve the optimal hydrogen-to-carbon ratio. This ensures that green hydrogen can be efficiently and economically received and utilized by downstream processes, achieving chain optimization from hydrogen production to utilization. The system uses internally fixed control logic (such as deviation-driven, rule-based judgment, and predictive feedforward algorithms) to calculate the APC setpoint and intelligently decides on green hydrogen allocation based on comprehensive supply and demand information, demonstrating a high degree of intelligence and a certain degree of foresight. By receiving feedback from the electrolyzer's APC control system and sensor data, a closed-loop control system is formed. This system continuously compares actual parameters with set targets, allowing for fine-tuning and ensuring control accuracy and system stability. Precise control of the electrolyzer's operating parameters helps guarantee the purity and stable production of green hydrogen.

[0074] After step S140 is completed, in step S150, the internal control loop of each electrolytic cell is optimized by APC control based on the electrolytic cell APC control command.

[0075] In the embodiments of this application, each electrolyzer is typically equipped with basic internal (or local) control loops, such as a temperature control loop (maintaining temperature by adjusting cooling water / heater), a pressure control loop (maintaining pressure by adjusting hydrogen production outlet valve), and a level control loop (maintaining level by adjusting water supply valve). These internal control loops can accurately and quickly track the electrolyzer APC control commands. Based on the electrolyzer APC control commands, the electrolyzer APC control system automatically adjusts the load of a single electrolyzer, executes automatic start-stop, and automatically switches the cell status. It also achieves optimized control of the internal control loop of a single cell under frequent load changes, ensuring the stability of key control parameters. Under extreme weather conditions and hydrogen consumption variations, it achieves one-button start-stop / fully automatic unmanned operation, seamless switching between manual and automatic operation, and black-screen operation of the unit, achieving the goal of reduced manpower, reduced workload, and increased efficiency, effectively reducing operating costs and operational burden.

[0076] In step S150, the electrolyzer APC control system translates the APC control commands (such as optimized temperature, pressure, and level setpoints) issued from the upper level into precise guidance and optimized execution of the basic control loops (temperature, pressure, and level control) within each electrolyzer. This enables individual electrolyzers to achieve automatic load adjustment, automated start-up and shutdown procedures, and automatic switching of operating states. It ensures that even under conditions of frequent and drastic load changes, the key control parameters within the electrolyzer remain stable. This achieves automatic load adjustment, automatic start-up and shutdown, and automatic state switching at the individual electrolyzer level, significantly reducing the need for manual intervention. Even when drastic changes in external conditions (such as weather or hydrogen demand) cause frequent load fluctuations, the internal control loops can be optimized through APC to accurately track commands, ensuring the stability of key parameters such as temperature, pressure, and level, thereby guaranteeing the safe and efficient operation of the electrolyzer. The advanced level of automation control, especially the one-button start / stop and fully automatic unmanned operation under extreme conditions, as well as the smooth and seamless switching between manual and automatic operation, enables the unit to operate without a screen (i.e., it can operate stably without continuous operator monitoring of the screen). By achieving minimal staffing, the burden is greatly reduced and efficiency is increased, thereby effectively reducing operating costs and operational workload, and improving the economy and manageability of the entire hydrogen production unit.

[0077] After step S150 is completed, in step S160, the hydrogen-carbon ratio control command is generated by sending the optimization setting command through green hydrogen and the real-time status feedback from the coal chemical methanol synthesis unit, and then sent to the coal chemical methanol synthesis unit and the water-gas conversion unit for execution.

[0078] In the embodiments of this application, during the process of generating the hydrogen-to-carbon ratio control command, firstly, the actual hydrogen-to-carbon ratio currently entering the methanol synthesis unit is calculated based on the real-time status feedback from the coal chemical methanol synthesis unit. Next, the deviation between the actual hydrogen-to-carbon ratio and the optimal hydrogen-to-carbon ratio is calculated. Then, the hydrogen-to-carbon ratio control command is generated based on the deviation between the actual hydrogen-to-carbon ratio and the optimal hydrogen-to-carbon ratio.

[0079] In the embodiments of this application, the real-time status feedback from the coal chemical methanol synthesis unit includes the actual green hydrogen flow rate entering the coal chemical methanol synthesis unit (measured after the green hydrogen is injected into the buffer tank), the actual gray hydrogen flow rate entering the coal chemical methanol synthesis unit, and the flow rate and composition (CO, CO2, etc.) of carbon-containing raw materials (such as synthesis gas).

[0080] In calculating the current actual hydrogen-carbon ratio, the hydrogen-carbon ratio APC control system uses real-time collected green hydrogen flow rate, gray hydrogen flow rate, and carbon-containing feedstock flow rate to dynamically calculate the current actual total hydrogen to carbon molar ratio entering the coal chemical methanol synthesis unit.

[0081] In the process of generating hydrogen-carbon ratio control commands based on the deviation between the actual and optimal hydrogen-carbon ratios, the hydrogen-carbon ratio APC control system assesses its ability to rapidly replenish or reduce the supply of green hydrogen based on the deviation between the actual and optimal ratios and the current state of the green hydrogen injection buffer tank. The hydrogen-carbon ratio APC control calculates the amount of adjustment needed to the green hydrogen flow rate into the injection buffer tank to achieve the optimal hydrogen-carbon ratio. By adjusting the green hydrogen flow rate into the injection buffer tank, it indirectly or directly affects the amount of green hydrogen ultimately entering the methanol synthesis unit, thus achieving rapid and precise adjustment of the hydrogen-carbon ratio.

[0082] By precisely controlling the amount of green hydrogen injected into the buffer tank, the hydrogen-to-carbon ratio APC control system can effectively manage the green hydrogen inventory in the buffer tank, thereby quickly and flexibly adjusting the amount of green hydrogen actually replenished from the buffer tank to the methanol synthesis feed gas, thus suppressing fluctuations in the hydrogen-to-carbon ratio.

[0083] In step S160, the hydrogen-to-carbon ratio APC control system receives the green hydrogen delivery optimization setting command as guidance for upstream green hydrogen supply. Combined with real-time status data from the coal-to-methanol synthesis unit (including actual green hydrogen flow rate, ash hydrogen flow rate, carbon-containing feedstock flow rate and its composition), it dynamically and accurately calculates the actual total hydrogen to carbon molar ratio currently entering the methanol synthesis unit. The calculated actual hydrogen-to-carbon ratio is compared with the preset optimal hydrogen-to-carbon ratio, and the deviation between the two is analyzed. Based on this deviation, and considering the current status of the green hydrogen injection buffer tank (i.e., the ability to quickly replenish or reduce green hydrogen supply), the hydrogen-to-carbon ratio APC system calculates the green hydrogen flow rate in the injection buffer tank that needs to be adjusted to achieve the optimal hydrogen-to-carbon ratio. By regulating the amount of green hydrogen entering the buffer tank, the amount of green hydrogen ultimately entering the coal-to-methanol synthesis unit is indirectly or directly affected. This generates and sends a hydrogen-to-carbon ratio control command to the coal-to-methanol synthesis unit, achieving rapid and precise adjustment of the hydrogen-to-carbon ratio.

[0084] Through the above process, the hydrogen-to-carbon ratio (H / C ratio) of the feedstock for methanol synthesis, a key chemical process, can be precisely controlled at its optimal setpoint. This is crucial for maximizing methanol yield, optimizing catalyst performance, reducing byproducts, and improving overall process efficiency. The H / C ratio APC system can dynamically calculate and adjust based on real-time feedback data from the methanol plant, rather than relying on fixed or lagging information, thus better adapting to changes and disturbances in actual operating conditions. By precisely controlling the amount of green hydrogen injected into the green hydrogen buffer tank to manage the buffer tank inventory, the system can quickly and flexibly adjust the amount of green hydrogen supplied from the buffer tank for methanol synthesis. This not only meets the need for precise adjustment of the H / C ratio but also effectively mitigates large fluctuations in the H / C ratio that may be caused by fluctuations in green hydrogen supply or changes in downstream demand, enhancing process stability.

[0085] In summary, through the green hydrogen coupled coal chemical integrated scheduling and control scheme provided above, this application embodiment constructs a full-chain, multi-level intelligent scheduling and advanced control system, encompassing renewable energy forecasting, source-grid-load-storage coordination, fine control of electrolyzer clusters, and downstream coal chemical hydrogen-carbon ratio optimization. This achieves deep coupling and efficient collaboration between the green energy production end and the coal chemical application end. By integrating forecast data and real-time status, and conducting top-level unified planning and hierarchical optimization control, it not only significantly improves the absorption rate of renewable energy sources such as wind and solar power and the overall energy conversion efficiency, but also reduces system operating costs. Simultaneously, through refined power allocation, start-up and shutdown management, and APC optimization of the electrolyzers, as well as real-time precise control of the hydrogen-carbon ratio in the coal chemical section, it ensures the economic efficiency of green hydrogen production and the stability and efficiency of the chemical process, effectively reducing the carbon footprint of chemical products. Furthermore, this method comprehensively considers the equipment models, operational constraints, and dynamic characteristics of each link, enhancing the operational flexibility, safety, reliability, and overall economic benefits of the entire green hydrogen coupled coal chemical system under varying operating conditions, ultimately promoting the intelligent and green transformation of energy and chemical processes.

[0086] Furthermore, in some embodiments, during the generation of the top-level scheduling plan, firstly, rigorous data verification and precise determination of the initial operating state lay a solid and reliable data foundation for all subsequent planning and decision-making, ensuring the accuracy of the planning's starting point. Secondly, preliminary day-ahead planning based on clear operational objectives enables the prediction of future operational trends and the initial optimization of resource allocation, reflecting the strategic and guiding nature of the plan. Thirdly, a periodic rolling optimization mechanism is introduced, which can dynamically adjust and optimize the scheduling scheme based on the latest forecast data and real-time operating conditions. This greatly enhances the adaptability and response speed of the scheduling plan to actual changes, making the scheduling plans for each time period within a day more accurate, flexible, and realistic. This significantly improves the efficiency, stability, and economy of the entire system operation, and ensures that the plan is always feasible under equipment capacity and constraints.

[0087] Furthermore, in some embodiments, the generation of power and start / stop commands for each electrolyzer enables refined, dynamic, and goal-oriented management of the electrolyzer cluster, allowing for precise and efficient response to real-time hydrogen production load commands issued from the upper level. First, by filtering based on start / stop planning and real-time status, it ensures that only currently healthy, available electrolyzer resources that conform to the overall scheduling strategy are utilized, improving system reliability and operational orderliness. Second, the net power adjustment is clearly calculated, providing a clear quantitative basis for subsequent start / stop decisions and load allocation, ensuring the accuracy of adjustments. Third, decisions on the start / stop of specific electrolyzers are based on operational targets, rather than simply adding or subtracting, reflecting intelligent control and optimization depth, contributing to better overall benefits. Finally, the total load command is effectively allocated to the selected operating electrolyzers, ensuring the precise achievement of the overall hydrogen production task and laying the foundation for subsequent APC optimization control of each electrolyzer, thereby improving the response speed, energy utilization efficiency, and automation level of the entire hydrogen production process.

[0088] Furthermore, in some embodiments, dual optimization from hydrogen production unit to chemical application is achieved through fine-tuning of individual electrolyzers internally and optimizing green hydrogen delivery strategies externally. By dynamically generating APC control commands for internal parameters such as temperature, pressure, and liquid level of each electrolyzer, it is possible to ensure that each electrolyzer operates stably, efficiently, and safely near its designed or preset optimal operating point, thereby maximizing the hydrogen production efficiency of individual units, extending equipment life, and ensuring the quality and stability of green hydrogen production. Simultaneously, based on overall operational goals and combined with the real-time status of the green hydrogen injection buffer tank and actual green hydrogen production, the green hydrogen flow rate to the buffer tank is intelligently adjusted to optimize the hydrogen-to-carbon ratio downstream (such as in a coal chemical methanol synthesis unit), thereby generating optimized green hydrogen delivery settings commands. This proactively and precisely matches the supply of green hydrogen with the actual needs of chemical processes (especially the key hydrogen-to-carbon ratio parameter), ensuring not only the efficiency, product quality, and process stability of downstream chemical production, but also significantly improving the effective utilization rate of valuable green hydrogen resources and the overall economic benefits and operational flexibility of the entire green hydrogen-coupled coal chemical system. It provides a key control means for achieving deep integration and synergistic effects between clean energy production and traditional chemical processes.

[0089] This application also provides a green hydrogen coupled coal chemical integrated scheduling and control system, which can be implemented using the aforementioned green hydrogen coupled coal chemical integrated scheduling and control method 100, or other methods. This application does not impose any restrictions on this.

[0090] Figure 4 An exemplary structural block diagram of the green hydrogen-coupled coal chemical integrated scheduling and control system according to an embodiment of this application is shown.

[0091] like Figure 4 As shown, the system 400 includes a wind-solar-hydrogen-storage coordinated scheduling and electrolyzer start-up and shutdown planning module 410, a source-grid-load-storage coordinated control system 420, a hydrogen production cluster control system 430, a hydrogen production DCS module 440, an electrolyzer APC control system 450, and a hydrogen-carbon ratio APC control system 460.

[0092] Specifically, the wind-solar-hydrogen-storage coordination scheduling and electrolyzer start-up and shutdown planning module 410 is configured to integrate wind and solar forecast data, real-time wind-solar-hydrogen-storage data, interface configuration data, equipment models and constraints to generate a top-level scheduling plan. The top-level scheduling plan includes planned grid-connected power, planned wind and solar power generation, planned energy storage power, planned total hydrogen production power, and electrolyzer start-up and shutdown planning.

[0093] Specifically, the source-grid-load-storage coordinated control system 420 is configured to receive planned grid-connected power, planned wind and solar power generation, planned energy storage power, and planned total hydrogen production power. It combines wind and solar forecast data to perform coordinated scheduling of source, grid, load, and storage, output control commands for wind farms, photovoltaic farms, and energy storage systems, and generate real-time commands for total hydrogen production load to be sent to the hydrogen production cluster control system 430.

[0094] Specifically, the hydrogen production cluster control system 430 is configured to perform flexible group control of the hydrogen production cluster by using electrolyzer start-up and shutdown planning, real-time command of total hydrogen production load, actual limits on electrolyzer load and electrolyzer rate, and electrolyzer status data fed back from the hydrogen production DCS module 440, and to generate power commands and start-up and shutdown commands for each electrolyzer in the hydrogen production DCS module 440.

[0095] Specifically, the hydrogen production DCS module 440 is configured to control each electrolyzer based on the power command of each cell, the start and stop command of each electrolyzer, and the status data of the electrolyzer, and to generate the electrolyzer APC control command and the green hydrogen delivery optimization setting command through the APC controller.

[0096] Specifically, the electrolyzer APC control system 450 is configured to optimize the internal control loop of each electrolyzer based on the electrolyzer APC control command, and to feed back its status to the hydrogen production DCS module 440.

[0097] Specifically, the hydrogen-to-carbon ratio APC control system 460 is configured to generate hydrogen-to-carbon ratio control commands by sending optimization setting commands through green hydrogen and real-time status feedback from the coal chemical methanol synthesis unit, and send them to the coal chemical methanol synthesis unit and water-gas conversion unit for execution.

[0098] When the system 400 employs the aforementioned integrated scheduling and control method 100 for green hydrogen coupled coal chemical industry, the aforementioned steps S110 are executed through the wind-solar-hydrogen-storage coordinated scheduling and electrolyzer start-up / shutdown planning module 410; steps S120 are executed through the source-grid-load-storage coordinated control system 420; steps S130 are executed through the hydrogen production cluster control system 430; steps S140 are executed through the hydrogen production DCS module 440; steps S150 are executed through the electrolyzer APC control system 450; and steps S160 are executed through the hydrogen-to-carbon ratio APC control system 460. The specific execution process can be found above and will not be repeated here.

[0099] While numerous embodiments of this application have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will arise for those skilled in the art without departing from the spirit and intent of this application. It should be understood that various alternatives to the embodiments of this application described herein may be employed in the practice of this application. The appended claims are intended to define the scope of protection of this application and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. A green hydrogen-coupled coal chemical integrated scheduling and control method, characterized in that, include: By integrating wind and solar forecast data, real-time wind, solar, hydrogen storage data, interface configuration data, equipment models, and constraints, a top-level scheduling plan is generated. The top-level scheduling plan includes planned grid-connected power, planned wind and solar power generation, planned energy storage power, planned total hydrogen production power, and electrolyzer start-up and shutdown planning. It receives planned grid-connected power, planned wind and solar power generation, planned energy storage power, and planned total hydrogen production power. Combined with wind and solar forecast data, it coordinates and schedules the power generation, grid, load, and storage, outputs control commands for wind farms, solar farms, and energy storage systems, and generates real-time commands for total hydrogen production load. By using electrolyzer start-stop planning, real-time hydrogen production load commands, electrolyzer load, actual limits on electrolyzer rate, and electrolyzer status data, power commands and start-stop commands for each electrolyzer are generated, wherein the start-stop commands include start commands and stop commands. Each electrolyzer is controlled based on its power command, start / stop command, and status data. The APC controller generates APC control commands and green hydrogen delivery optimization settings for each electrolyzer. Optimize the internal control loop of each electrolytic cell based on the APC control commands of the electrolytic cell; and The hydrogen-to-carbon ratio control command is generated by sending optimization setting instructions through green hydrogen and real-time status feedback from the coal chemical methanol synthesis unit, and then sent to the coal chemical methanol synthesis unit and water-gas conversion unit for execution.

2. The integrated scheduling and control method for green hydrogen coupled coal chemical industry according to claim 1, characterized in that, The site's wind and solar forecast data is obtained through the following steps: Establish and train a landscape prediction model; Based on meteorological data, environmental perception data, historical wind power generation data, and historical photovoltaic power generation data, a multi-timescale wind power generation forecast trend and photovoltaic power generation forecast trend are established using a trained wind and solar power forecast model.

3. The integrated scheduling and control method for green hydrogen coupled coal chemical industry according to claim 1, characterized in that, The equipment models include wind turbine models, photovoltaic array models, energy storage system models, and electrolyzer models.

4. The integrated scheduling and control method for green hydrogen coupled coal chemical industry according to claim 1 or 3, characterized in that, The following steps are performed during the generation of the top-level scheduling plan: By receiving wind and solar forecast data, real-time wind, solar, hydrogen, and storage data, interface configuration data, equipment models, and constraints, the system completes data verification and determines the initial operating status. Based on the operational objectives in the interface configuration data, combined with the equipment model and constraints, a preliminary top-level scheduling plan for the current day is generated. Periodically, based on the latest wind and solar forecast data and real-time wind, solar and hydrogen storage data, combined with equipment models and constraints, the preliminary top-level scheduling plan for the day is continuously optimized to form the top-level scheduling plan for each time period of the day.

5. The integrated scheduling and control method for green hydrogen coupled coal chemical industry according to claim 1, characterized in that, In the process of outputting control commands for wind farms, photovoltaic power plants, and energy storage systems, and generating real-time commands for total hydrogen production load, the following steps are performed: The expected power generation deviation is calculated by comparing the planned power generation of wind and solar power with the actual power generation of wind and solar power. Based on the power generation deviation, the magnitude and direction of power regulation of wind farms, photovoltaic farms and energy storage systems are obtained. Based on the operational targets in the interface configuration data, control commands for wind farms, photovoltaic farms, and energy storage systems are determined by the magnitude and direction of power regulation, as well as real-time commands for the total hydrogen production load.

6. The integrated scheduling and control method for green hydrogen coupled coal chemical industry according to claim 1, characterized in that, The following steps are performed during the generation of power commands and start / stop commands for each electrolytic cell: Based on the electrolytic cell start-up and shutdown plan and electrolytic cell status data, select the currently available electrolytic cells that meet the planning and scheduling conditions. The net power adjustment that needs to be increased or decreased is calculated by comparing the real-time command of the total hydrogen production load with the current load of the electrolyzer. Based on the running objectives in the interface configuration data, start and stop instructions for each electrolytic cell are generated by selecting currently available electrolytic cells that meet the planning and scheduling conditions and the net power adjustment amount that needs to be increased or decreased. The real-time command for total hydrogen production load is distributed among the electrolyzers currently executing the start-up command, forming the power command for the corresponding electrolyzer.

7. The integrated scheduling and control method for green hydrogen coupled coal chemical industry according to claim 1, characterized in that, The APC control commands include temperature control commands, pressure control commands, and liquid level control commands.

8. The integrated scheduling and control method for green hydrogen coupled coal chemical industry according to claim 1 or 7, characterized in that, During the process of generating the electrolyzer APC control command and the green hydrogen delivery optimization setting command, the following steps are performed: Temperature control commands, pressure control commands, and liquid level control commands for the electrolyzer are dynamically generated based on the electrolyzer status data, electrolyzer operating parameters, and preset control strategies. Based on the operational goals in the interface configuration data, the current status and output of the green hydrogen injection buffer tank are used to adjust the flow rate of green hydrogen sent to the buffer tank to achieve the optimal hydrogen-to-carbon ratio, thereby obtaining the green hydrogen delivery optimization setting command.

9. The integrated scheduling and control method for green hydrogen coupled coal chemical industry according to claim 1, characterized in that, The following steps are performed during the process of generating the hydrogen-to-carbon ratio control command: The actual hydrogen-to-carbon ratio entering the methanol synthesis unit is calculated based on the real-time status feedback from the coal chemical methanol synthesis unit. Calculate the deviation between the actual hydrogen-to-carbon ratio and the optimal hydrogen-to-carbon ratio; Hydrogen-carbon ratio control commands are generated based on the deviation between the actual hydrogen-carbon ratio and the optimal hydrogen-carbon ratio.

10. A green hydrogen-coupled coal chemical integrated scheduling and control system, characterized in that, The integrated scheduling and control of green hydrogen coupled coal chemical industry is performed using the green hydrogen coupled coal chemical industry integrated scheduling and control method as described in any one of claims 1-9, wherein the system comprises: The wind-solar-hydrogen-storage coordination and scheduling and electrolyzer start-up and shutdown planning module is configured to integrate wind and solar forecast data, real-time wind-solar-hydrogen-storage data, interface configuration data, equipment models and constraints to generate a top-level scheduling plan. The top-level scheduling plan includes planned grid-connected power, planned wind and solar power generation, planned energy storage power, planned total hydrogen production power and electrolyzer start-up and shutdown planning. The source-grid-load-storage coordinated control system is configured to receive planned grid-connected power, planned wind and solar power generation, planned energy storage power, and planned total hydrogen production power. It combines wind and solar forecast data to coordinate and schedule source-grid-load-storage, output control commands for wind farms, photovoltaic farms, and energy storage systems, and generate real-time commands for total hydrogen production load to be sent to the hydrogen production cluster control system. The hydrogen production cluster control system is configured to perform flexible group control of the hydrogen production cluster by using electrolyzer start-stop planning, real-time command of total hydrogen production load, actual limits of electrolyzer load and electrolyzer rate, and electrolyzer status data fed back from the hydrogen production DCS module. It generates power commands and start-stop commands for each electrolyzer in the hydrogen production DCS module. The hydrogen production DCS module is configured to control each electrolyzer based on power commands for each cell, start / stop commands for each electrolyzer, and status data of each electrolyzer. It also generates APC control commands for each electrolyzer and optimized setting commands for green hydrogen delivery through the APC controller. An electrolyzer APC control system is configured to optimize the internal control loops of each electrolyzer based on APC control commands and to feed back its status to the hydrogen production DCS module; and The hydrogen-to-carbon ratio APC control system is configured to generate hydrogen-to-carbon ratio control commands by sending optimization setting commands through green hydrogen and real-time status feedback from the coal chemical methanol synthesis unit, and send them to the coal chemical methanol synthesis unit and water-gas conversion unit for execution.

Images