Layered operation control method and system of bio-natural gas preparation and storage integrated system

Abstract

The application discloses a hierarchical operation control method and system for a bio-natural gas production and storage integrated system, and the method comprises the following steps: acquiring system operation states and external environment information; constructing a monthly planning layer, optimizing cross-seasonal gas storage targets based on long-term prediction, and considering slow process dynamics of anaerobic fermentation; constructing an hourly scheduling layer, formulating hydrogen production and power generation plans based on short-term prediction and model prediction control, and introducing a hydrogen synthesis hydrogen injection rate change rate penalty; constructing a minute-level real-time control layer, rolling tracking plans based on ultra-short-term prediction, and designing a rapid response loop to respond to sudden green electricity consumption, peak regulation and frequency regulation demands; and feeding back real-time operation data to the upper layer for adaptive updating of model parameters. The application integrates multi-scale prediction information, coordinates fast and slow processes, realizes efficient consumption of green electricity and flexible support of the power grid, and can be widely applied to the optimal operation control of bio-natural gas production and storage systems.

Description

Technical Field

[0001] This invention belongs to the field of renewable energy system optimization operation control technology, and in particular relates to a hierarchical operation control method and system for an integrated biogas production, storage and utilization system. Background Technology

[0002] The intermittency and volatility of renewable energy pose significant challenges to the safe and stable operation of the power grid, and the problem of wind and solar curtailment is becoming increasingly prominent. There is an urgent need to develop large-scale, long-cycle energy storage technologies to achieve the spatial and temporal transfer of electricity. Against this backdrop, electricity-to-biogas technology has emerged. By converting surplus green electricity into hydrogen, and then coupling it with biomass to produce biogas, it not only solves the problem of green electricity consumption but also produces green fuel, and has become an important development direction in the field of renewable energy.

[0003] Unlike traditional single-conversion pathways, the system involved in this invention employs a two-stage hydrogenation synthesis route: First, the green hydrogen produced by electrolysis is injected into an anaerobic fermentation system, where it undergoes first-stage biosynthesis through the action of hydrotrophic methanogenic bacteria, increasing the methane concentration in the biogas; then, the remaining hydrogen is reacted with the methane-rich gas produced in the first stage in a second-stage catalytic methanation reaction to generate biogas. The advantage of this technical route lies in the deep coupling of green electricity, green hydrogen, and local biogas and other biomass energy sources, achieving both efficient conversion and storage of green electricity and enhancing the utilization value of biomass energy.

[0004] However, the optimized operation and control of this system faces challenges such as complex coupling across multiple time scales, multi-level predictive information, and constraints from the slow process of biosynthesis: electrolysis for hydrogen production can respond in seconds, catalytic reactions require minute-level temperature control, anaerobic fermentation is a slow process on a monthly scale, and gas storage can achieve cross-seasonal energy transfer, with dynamics of different time scales being coupled together; wind and solar power output forecasts have multiple time scales, including long-term, short-term, and ultra-short-term, while grid demand also varies intraday, cross-day, and seasonally; as a slow process, anaerobic fermentation's gas production rate cannot be quickly adjusted and is affected by the amount of hydrogen injected, resulting in a lag; the control strategy must consider this slow dynamic to avoid impacting the biological system. Currently, there is a lack of an optimized control method for an integrated biogas production, storage, and utilization system that can systematically integrate multi-scale predictive information and coordinate different process stages with varying speeds. Summary of the Invention

[0005] This invention provides a hierarchical operation control method and system for an integrated biogas production, storage and utilization system, which solves the technical problems of difficulty in coupling multiple time scales, insufficient coordination of fast and slow processes, and insufficient utilization of prediction information in the prior art.

[0006] In a first aspect, the present invention provides a hierarchical operation control method for an integrated biogas production, storage, and utilization system, comprising:

[0007] The system acquires operational status information and external environmental information of the integrated biogas production, storage and utilization system. The external environmental information includes long-term forecast data of renewable energy power generation, short-term forecast data of renewable energy power generation, grid dispatch instructions and time-of-use electricity price information.

[0008] A monthly planning layer is constructed, and with historical operating data and long-term forecast data of renewable energy power generation as inputs, and with monthly time as the cycle and the goal of maximizing monthly revenue, the monthly gas production plan of the gas production unit, the monthly gas storage plan of the gas storage unit, and the monthly gas consumption plan of the gas consumption unit are optimized.

[0009] An hourly-level scheduling layer is constructed, with the monthly gas production plan, the monthly gas storage plan, and the monthly gas consumption plan as constraints passed to the hourly-level scheduling layer. The short-term forecast data, the power grid dispatch instructions, and the time-of-use electricity price information are used as inputs to identify the current operating mode of the integrated biogas production, storage, and consumption system. Based on the identified operating mode, a corresponding optimized scheduling model is constructed and solved to generate hourly-level scheduling instructions.

[0010] A minute-level real-time control layer is constructed, using the real-time operating status information of the integrated biogas production, storage and utilization system as feedback input. Real-time closed-loop control is performed on the gas production unit, gas storage unit and gas utilization unit in a minute cycle, so that the real-time operating parameters track the scheduling instructions, and the real-time operating data is fed back to the hour-level scheduling layer and the monthly-level planning layer for rolling optimization in the next cycle, forming a closed-loop control.

[0011] Secondly, the present invention provides a hierarchical operation control system for an integrated biogas production, storage, and utilization system, comprising:

[0012] The acquisition module is configured to acquire the operating status information and external environment information of the integrated biogas production, storage and utilization system. The external environment information includes long-term forecast data of renewable energy power generation, short-term forecast data of renewable energy power generation, grid dispatch instructions and time-of-use electricity price information.

[0013] The first construction module is configured to build a monthly planning layer, and takes historical operating data and long-term forecast data of renewable energy power generation as input, takes monthly time as the cycle, and takes the maximum monthly income as the goal to optimize and formulate the monthly gas production plan of the gas production unit, the monthly gas storage plan of the gas storage unit, and the monthly gas consumption plan of the gas consumption unit.

[0014] The second construction module is configured to build an hourly scheduling layer. The monthly gas production plan, the monthly gas storage plan, and the monthly gas consumption plan are passed to the hourly scheduling layer as constraints. The module takes the short-term forecast data, the power grid dispatch instructions, and the time-of-use electricity price information as inputs to identify the current operating mode of the integrated biogas production, storage, and consumption system. Based on the identified operating mode, the module constructs and solves the corresponding optimized scheduling model to generate hourly scheduling instructions.

[0015] The third module is configured to build a minute-level real-time control layer. It uses the real-time operating status information of the integrated biogas production, storage and utilization system as feedback input, and performs real-time closed-loop control of the gas production unit, gas storage unit and gas utilization unit in a minute cycle. This enables the real-time operating parameters to track the scheduling instructions, and feeds the real-time operating data back to the hour-level scheduling layer and the monthly-level planning layer for rolling optimization in the next cycle, forming a closed-loop control.

[0016] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the hierarchical operation control method for an integrated biogas production, storage and utilization system according to any embodiment of the present invention.

[0017] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the hierarchical operation control method for an integrated biogas production, storage and utilization system according to any embodiment of the present invention.

[0018] This application discloses a hierarchical operation control method and system for an integrated biogas production, storage, and utilization system. By constructing a three-tiered hierarchical control architecture—monthly planning, hourly scheduling, and minute-level real-time control—it achieves full-timescale optimization from cross-seasonal energy storage to real-time response. At the monthly planning level, cross-seasonal gas storage targets are optimized based on long-term meteorological forecasts, considering the dynamics of the slow anaerobic fermentation process. At the hourly scheduling level, hydrogen production and power generation plans are formulated using model predictive control based on day-ahead forecasts, incorporating penalties for changes in the biosynthetic hydrogen injection rate. At the minute-level real-time control level, plans are tracked rollingly based on ultra-short-term forecasts, and a fast response loop is designed to address sudden demands for green energy consumption, peak shaving, and frequency regulation. The three layers coordinate through a feedforward-feedback mechanism, with actual operational deviations used for adaptive updates of model parameters. This invention integrates multi-scale forecast information, coordinates fast and slow processes, and achieves efficient green energy consumption and flexible grid support. It can be widely applied to the optimized operation control of biogas production, storage, and utilization systems, solving the problems of difficult multi-timescale coupling, insufficient coordination of fast and slow processes, and inadequate utilization of forecast information in existing technologies. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0020] Figure 1 A flowchart illustrating a hierarchical operation control method for an integrated biogas production, storage, and utilization system, provided in an embodiment of the present invention;

[0021] Figure 2 A structural block diagram of a hierarchical operation control system for an integrated biogas production, storage, and utilization system provided in an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0023] 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, not all embodiments. 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.

[0024] Please see Figure 1 The diagram shows a flowchart of a hierarchical operation control method for an integrated biogas production, storage and utilization system according to this application.

[0025] like Figure 1 As shown, the hierarchical operation control method for an integrated biogas production, storage, and utilization system specifically includes the following steps:

[0026] Step S101: Obtain the operating status information and external environment information of the integrated biogas production, storage and utilization system. The external environment information includes long-term forecast data of renewable energy power generation, short-term forecast data of renewable energy power generation, grid dispatch instructions and time-of-use electricity price information.

[0027] In this step, the operational status information includes the real-time operating parameters of the electrolysis hydrogen production module, hydrogen storage module, primary biosynthesis module, secondary chemical synthesis module, gas storage module, and power generation module. Specifically:

[0028] Electrolytic hydrogen production module: Collect the input power (MW), direct current (A), direct current voltage (V), hydrogen production (kg / h), electrolytic cell temperature (°C), pressure (MPa), etc. of the electrolytic cell. These data are uploaded to the real-time database of the control system in real time through the PLC (programmable logic controller) or sensors supporting the electrolytic cell, using industrial communication protocols such as ModbusTCP / RTU and Profinet.

[0029] Hydrogen storage module: Collect the hydrogen inventory (kg), pressure (MPa), temperature (°C), inlet and outlet valve states, etc. of the hydrogen storage tank. Usually obtained through pressure sensors and liquid level gauges (for high-pressure hydrogen storage, a mass flowmeter or pressure-temperature compensation algorithm can be used to calculate the inventory), and the data is collected by the IO module.

[0030] Primary biosynthesis module: Collect the temperature (°C), pH value, stirring rate (rpm), biogas production (Nm³ / h), methane concentration in biogas (%), hydrogen injection flow rate (kg / h), etc. of the anaerobic fermentation tank. These parameters are measured by the on-line analytical instruments (such as infrared gas analyzers and pH meters) and flow meters supporting the fermentation tank, and are transmitted to the control system through RS485 or wirelessly.

[0031] Secondary chemical synthesis module: Collect the inlet hydrogen flow rate, inlet methane flow rate, reaction temperature, pressure, outlet gas composition (methane concentration), catalyst bed temperature distribution, etc. of the catalytic reactor. Analyzing equipment such as gas chromatographs and thermal conductivity detectors is used, and the data is obtained through an OPCUA server or a dedicated acquisition software.

[0032] Gas storage module: Collect the methane inventory (Nm³), pressure, temperature of the gas storage tank (such as high-pressure spherical tank or underground gas storage), as well as the flow rate and valve opening of the inlet and outlet gas pipelines. Measured by radar level gauges, pressure transmitters, turbine flow meters, etc., and the data is uploaded through RTU (remote terminal unit).

[0033] Power generation module: Collect the input natural gas flow rate (Nm³ / h), output electric power (MW), unit efficiency, exhaust temperature, rotational speed, etc. of the generator set (such as gas internal combustion engine or gas turbine). Collected through the generator control panel or independent sensors, and accessed to the control system using IEC61850 or Modbus protocol.

[0034] The frequency of all operational status information acquisition is set according to control requirements: for the real-time control layer, updates are required at the second or minute level (such as electrolytic cell power, grid frequency, etc.); for the hourly scheduling layer, minute or hourly data can be accepted; for the monthly planning layer, daily or monthly cumulative values ​​are usually calculated. After preprocessing (such as filtering, bad value removal, and unit conversion), the data is stored in a time-series database (such as InfluxDB, PISystem) for use by the upper-level optimization control model.

[0035] External environmental information includes long-term and short-term forecasts for renewable energy generation, grid dispatch instructions, and time-of-use pricing information. The methods for obtaining this information are as follows:

[0036] Long-term forecast data for renewable energy generation refers to the forecast of total wind and solar power generation on a monthly or even quarterly scale. This is typically obtained from professional meteorological service providers (such as the European Centre for Medium-Range Weather Forecasts (ECMWF) and the China Meteorological Administration), including monthly average wind speeds and solar irradiance for the next 3-6 months. Combined with historical output curves and unit maintenance plans of wind farms and solar power plants, monthly total power generation forecasts are generated using statistical upscaling methods (such as analogy based on typical year data) or machine learning models (such as Long Short-Term Memory networks, LSTM). Data is in CSV or JSON format, automatically retrieved at the beginning of each month via API or FTP, and imported into the monthly planning layer database.

[0037] Short-term forecast data for renewable energy generation refers to the forecast of wind and solar power output from the current day to the next few hours. It is typically generated using numerical weather prediction (NWP) models (such as WRF models) combined with local micro-weather station data, and updated every 6 hours or hourly. The forecast results include hourly output curves (unit: MW) for the next 24-72 hours, with a resolution of 15 minutes or 1 hour. Data is obtained through dedicated interfaces provided by the power dispatch center (such as the DL / T634.5104 protocol) or APIs from third-party forecasting service providers. In addition, locally deployed ultra-short-term forecasting systems (based on cloud image recognition or rolling time series models) can be used to perform high-frequency corrections to the output for the next 4 hours.

[0038] Power grid dispatch instructions include peak-shaving instructions, frequency regulation instructions, and emergency power curtailment instructions. A communication link is established with the power grid dispatch center's energy management system (EMS), using the IEC60870-5-104 or IEC61850 MMS protocol, to receive real-time instructions from the dispatch center regarding active power setpoints, frequency regulation deviation coefficients, peak-shaving capacity requirements, etc. Instructions typically include timestamps and priority identifiers, which the system must parse according to the protocol and store in a real-time database. Frequency regulation instructions often require a second-level response time; therefore, the communication link must have low latency characteristics (such as dedicated fiber optic cables).

[0039] Time-of-use (TOU) electricity pricing information refers to the time series of electricity purchase and sales prices published by the power grid. For systems participating in the electricity market, day-ahead market clearing prices can be obtained through the data service platform of the power trading center, or fixed-time pricing can be generated based on the peak-valley pricing policy published by the government. The data format is a time-price pair (yuan / MWh), usually updated daily in JSON or XML format. The system retrieves the data periodically via HTTP requests, or it can be manually imported and stored in a relational database (such as MySQL). The electricity price information is used in the revenue maximization objective function of the hourly scheduling layer.

[0040] Step S102: Construct a monthly planning layer, and use historical operating data and long-term forecast data of renewable energy power generation as inputs, with monthly time as the cycle and the goal of maximizing monthly revenue, to optimize and formulate the monthly gas production plan of the gas production unit, the monthly gas storage plan of the gas storage unit, and the monthly gas consumption plan of the gas consumption unit.

[0041] In this step, the monthly-level planning layer's optimization scheduling model aims to maximize monthly revenue, and its expression is:

[0042] ,

[0043] In the formula, To optimize the total number of months within the period, For the first The expected monthly electricity revenue is calculated based on the total monthly electricity generation and the projected electricity price. For the first The expected monthly hydrogen production cost is calculated based on the total monthly hydrogen production and the projected electricity price. For the first Monthly gas storage costs This is the penalty coefficient for slow processes. This is the penalty coefficient for slow processes;

[0044] The constraints include monthly energy balance, gas storage capacity constraints, and slow dynamic constraints on biosynthesis, expressed as follows:

[0045] ,

[0046] ,

[0047] ,

[0048] In the formula, For the first End-of-month gas storage tank energy status For the first End-of-month gas storage tank energy status For the first The total energy stored through hydrogen production and conversion each month For the first Total energy released by monthly power generation This is the maximum energy capacity of the gas storage tank. The efficiency of the bioconversion of hydrogen to methane. For the first Monthly average maximum acceptable hydrogen injection rate for the microbial system For the first Total number of hours in a month.

[0049] Step S103: Construct an hourly-level scheduling layer. The monthly gas production plan, the monthly gas storage plan, and the monthly gas consumption plan are passed to the hourly-level scheduling layer as constraints. The short-term forecast data, the power grid dispatch instructions, and the time-of-use electricity price information are used as inputs to identify the current operating mode of the integrated biogas production, storage, and consumption system. Based on the identified operating mode, a corresponding optimized scheduling model is constructed and solved to generate hourly-level scheduling instructions.

[0050] In this step, the identification of the operating mode is based on: the comparison results between the short-term forecast value of renewable energy power generation and the system's preset absorption threshold, the type identifier of the grid dispatch instruction, and the time period category of the time-of-use price;

[0051] When the short-term forecast value of renewable energy power generation exceeds the preset absorption threshold of the integrated biogas production, storage and utilization system, it is identified as a green electricity consumption mode.

[0052] When the received power grid dispatch command is a peak shaving command or a valley filling command, it is identified as a power grid peak shaving mode;

[0053] When the received power grid dispatch command is a primary frequency regulation command or a secondary frequency regulation command, it is identified as a power grid frequency regulation mode.

[0054] The optimized scheduling model constructed under the green energy consumption mode includes:

[0055] The optimization objective is to maximize the absorption of surplus green electricity or minimize the curtailment of wind and solar power.

[0056] The start-up / shutdown status and load rate of the electrolytic hydrogen production equipment are used as decision variables.

[0057] The optimized scheduling model constructed under the power grid peak-shaving mode includes:

[0058] The optimization objective is to maximize the benefits of peak shaving compensation or minimize the difference between peak and valley loads in the system.

[0059] The gas storage unit's charging and discharging power and the gas generator set's output power are used as decision variables;

[0060] The optimized scheduling model constructed under the aforementioned power grid frequency regulation mode includes:

[0061] The optimization objective is to minimize frequency deviation or maximize FM mileage gains.

[0062] The rapid charging and discharging rate of the gas storage unit and the regulation rate of the gas generator set are used as decision variables.

[0063] It should be noted that the optimized scheduling model at the hourly scheduling layer is a model predictive control model, and the objective function of the model predictive control model is:

[0064] ,

[0065] ,

[0066] ,

[0067] In the formula, For time period Electricity sales price, For time period Generator unit grid-connected power, For time period The electricity purchase price, For time period Hydrogen production capacity of electrolyzer, , , All are weighting coefficients. Electricity purchase and sale price 1.5 to 2 times that, The value of causes the deviation of the gas storage at the end of the day. Keep it within ±5%. The typical value ranges from 0.1 to 1.0. For time period The total output of the landscape forecast, This represents the maximum power that the power grid can accept. This represents the methane inventory in the gas storage tank at the end of the day. The target gas storage volume at the end of the day. For time step, The target energy status of the gas storage tank for the month. The penalty coefficient for wind and solar power curtailment. The lower heating value of methane, For time period The proportion of hydrogen allocated to primary biosynthesis For time period The proportion of hydrogen allocated to primary biosynthesis;

[0068] The constraints of the model predictive control model include hydrogen material balance, natural gas material balance, power generation balance, equipment capacity constraints, hydrogen distribution constraints, biosynthetic hydrogen injection rate change rate constraints, hydrogen storage tank state boundary, gas storage tank state boundary, and periodic boundary conditions.

[0069] The expression for hydrogen material balance is:

[0070] ,

[0071] ,

[0072] ,

[0073] In the formula, For time period Hydrogen storage tank hydrogen level For time period Hydrogen storage tank hydrogen level The efficiency of hydrogen production in an electrolyzer. For time period The flow rate of biosynthesized hydrogen injected. For time period The flow rate of hydrogen gas injected during chemical synthesis;

[0074] The expression for natural gas material balance is:

[0075] ,

[0076] In the formula, For time period Methane storage tank contents For time period Methane storage tank contents The conversion efficiency from hydrogen to methane in secondary chemical synthesis. For time period Natural gas flow used for power generation For time period Natural gas flow rate sold directly;

[0077] The expression for power generation balance is:

[0078] ,

[0079] In the formula, For generator set electrical efficiency;

[0080] The expression for the equipment capacity constraint is:

[0081] ,

[0082] ,

[0083] ,

[0084] In the formula, The rated power of the electrolytic cell, This refers to the generator's rated power.

[0085] The expression for the hydrogen distribution constraint is:

[0086] ,

[0087] ,

[0088] In the formula, The proportion of hydrogen allocated to chemical synthesis during time period k;

[0089] The expression for the constraint on the rate of change of biosynthetic hydrogen injection rate is:

[0090] ,

[0091] In the formula, The maximum allowable rate of change for a microbial system;

[0092] The expressions for the state boundaries of the hydrogen storage tank and the gas storage tank are as follows:

[0093] ,

[0094] ,

[0095] In the formula, Let k be the amount of hydrogen stored in the hydrogen storage tank during time period k. This is the maximum capacity of the hydrogen storage tank. The amount of natural gas stored in storage tank k during time period k. This is the maximum capacity of the gas storage tank;

[0096] The expression for periodic boundary conditions is:

[0097] ,

[0098] ,

[0099] In the formula, This represents the hydrogen inventory at the beginning of the day. This refers to the hydrogen inventory in the storage tank at the end of the day. This represents the methane inventory at the beginning of the day.

[0100] Step S104: Construct a minute-level real-time control layer. Using the real-time operating status information of the integrated biogas production, storage, and utilization system as feedback input, perform real-time closed-loop control on the gas production unit, gas storage unit, and gas utilization unit in a minute-by-minute cycle. This enables the real-time operating parameters to track the scheduling instructions and feeds the real-time operating data back to the hour-level scheduling layer and the monthly-level planning layer for rolling optimization in the next cycle, thus forming a closed-loop control.

[0101] In this step, the minute-level real-time control layer employs a model predictive control algorithm. Using the scheduling command as a reference trajectory and the current real-time operating status information as initial conditions, it continuously optimizes the control sequence within a preset time domain and issues the first control action to the actuators of the gas production unit, gas storage unit, and gas consumption unit. The objective function of the model predictive control algorithm is:

[0102] ,

[0103] In the formula, For the current moment t in relation to the future The predicted value of the generator's grid-connected power at any given time. For the future Reference trajectory of generator power at any given time. For the current moment t in relation to the future Predicted hydrogen production power of the electrolyzer at any given time. For the future Reference trajectory of hydrogen production power of the electrolyzer at any given time. To predict the number of time periods within the window, This is the predicted value of the power grid frequency deviation. The penalty for real-time wind and solar curtailment is calculated based on ultra-short-term wind and solar forecasts. , All are weighting coefficients.

[0104] In summary, the method in this application achieves full-timescale optimization from cross-seasonal energy storage to real-time response by constructing a three-tiered hierarchical control architecture of "monthly planning, hourly scheduling, and minute-level real-time control." At the monthly planning level, cross-seasonal gas storage targets are optimized based on long-term meteorological forecasts, considering the dynamics of the slow anaerobic fermentation process. At the hourly scheduling level, hydrogen production and power generation plans are formulated using model predictive control based on day-ahead forecasts, incorporating penalties for changes in the biosynthetic hydrogen injection rate. At the minute-level real-time control level, plans are tracked rollingly based on ultra-short-term forecasts, and a fast response loop is designed to address sudden demands for green energy consumption, peak shaving, and frequency regulation. The three layers coordinate through a feedforward-feedback mechanism, with actual operational deviations used for adaptive updates of model parameters. This invention integrates multi-scale forecast information, coordinates fast and slow processes, and achieves efficient green energy consumption and flexible grid support. It can be widely applied to the optimized operation and control of biogas production, storage, and utilization systems, solving the problems of difficult multi-timescale coupling, insufficient coordination of fast and slow processes, and inadequate utilization of forecast information in existing technologies.

[0105] Please see Figure 2 The diagram shows a structural block diagram of a hierarchical operation control system for an integrated biogas production, storage and utilization system according to this application.

[0106] like Figure 2 As shown, the hierarchical operation control system 200 includes an acquisition module 210, a first construction module 220, a second construction module 230, and a third construction module 240.

[0107] The acquisition module 210 is configured to acquire the operating status information and external environment information of the integrated biogas production, storage, and utilization system. The external environment information includes long-term forecast data for renewable energy power generation, short-term forecast data for renewable energy power generation, grid dispatch instructions, and time-of-use pricing information. The first construction module 220 is configured to construct a monthly planning layer, using historical operating data and long-term forecast data for renewable energy power generation as input, with a monthly time period and the goal of maximizing monthly revenue, to optimize and formulate monthly gas production plans for the gas production unit, monthly gas storage plans for the gas storage unit, and monthly gas consumption plans for the gas consumption unit. The second construction module 230 is configured to construct an hourly scheduling layer, using the monthly gas production plan, the monthly gas storage plan, and the monthly... The gas consumption plan is transmitted as a constraint to the hourly scheduling layer. Using the short-term forecast data, the grid dispatch instructions, and the time-of-use electricity price information as inputs, the current operating mode of the integrated biogas production, storage, and utilization system is identified. Based on the identified operating mode, a corresponding optimized scheduling model is constructed and solved to generate hourly scheduling instructions. The third construction module 240 is configured to construct a minute-level real-time control layer. Using the real-time operating status information of the integrated biogas production, storage, and utilization system as feedback input, it performs real-time closed-loop control of the gas production unit, gas storage unit, and gas consumption unit on a minute-by-minute cycle. This ensures that real-time operating parameters track the scheduling instructions, and the real-time operating data is fed back to the hourly scheduling layer and the monthly planning layer for rolling optimization in the next cycle, forming a closed-loop control.

[0108] It should be understood that Figure 2 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0109] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the hierarchical operation control method for the integrated biogas production, storage and utilization system in any of the above method embodiments.

[0110] In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows:

[0111] The system acquires operational status information and external environmental information of the integrated biogas production, storage and utilization system. The external environmental information includes long-term forecast data of renewable energy power generation, short-term forecast data of renewable energy power generation, grid dispatch instructions and time-of-use electricity price information.

[0112] A monthly planning layer is constructed, and with historical operating data and long-term forecast data of renewable energy power generation as inputs, and with monthly time as the cycle and the goal of maximizing monthly revenue, the monthly gas production plan of the gas production unit, the monthly gas storage plan of the gas storage unit, and the monthly gas consumption plan of the gas consumption unit are optimized.

[0113] An hourly-level scheduling layer is constructed, with the monthly gas production plan, the monthly gas storage plan, and the monthly gas consumption plan as constraints passed to the hourly-level scheduling layer. The short-term forecast data, the power grid dispatch instructions, and the time-of-use electricity price information are used as inputs to identify the current operating mode of the integrated biogas production, storage, and consumption system. Based on the identified operating mode, a corresponding optimized scheduling model is constructed and solved to generate hourly-level scheduling instructions.

[0114] A minute-level real-time control layer is constructed, using the real-time operating status information of the integrated biogas production, storage and utilization system as feedback input. Real-time closed-loop control is performed on the gas production unit, gas storage unit and gas utilization unit in a minute cycle, so that the real-time operating parameters track the scheduling instructions, and the real-time operating data is fed back to the hour-level scheduling layer and the monthly-level planning layer for rolling optimization in the next cycle, forming a closed-loop control.

[0115] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and application programs required for at least one function; the stored data area may store data created based on the use of the hierarchical operation control system of the integrated biogas production, storage, and utilization system. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely located relative to a processor, which can be connected to the hierarchical operation control system of the integrated biogas production, storage, and utilization system via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0116] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby realizing the hierarchical operation control method of the integrated biogas production, storage, and utilization system described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the hierarchical operation control system of the integrated biogas production, storage, and utilization system. The output device 340 may include a display screen or other display device.

[0117] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0118] In one implementation, the aforementioned electronic device is applied in a hierarchical operation control system of an integrated biogas production, storage, and utilization system, serving as a client, and includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0119] The system acquires operational status information and external environmental information of the integrated biogas production, storage and utilization system. The external environmental information includes long-term forecast data of renewable energy power generation, short-term forecast data of renewable energy power generation, grid dispatch instructions and time-of-use electricity price information.

[0120] A monthly planning layer is constructed, and with historical operating data and long-term forecast data of renewable energy power generation as inputs, and with monthly time as the cycle and the goal of maximizing monthly revenue, the monthly gas production plan of the gas production unit, the monthly gas storage plan of the gas storage unit, and the monthly gas consumption plan of the gas consumption unit are optimized.

[0121] An hourly-level scheduling layer is constructed, with the monthly gas production plan, the monthly gas storage plan, and the monthly gas consumption plan as constraints passed to the hourly-level scheduling layer. The short-term forecast data, the power grid dispatch instructions, and the time-of-use electricity price information are used as inputs to identify the current operating mode of the integrated biogas production, storage, and consumption system. Based on the identified operating mode, a corresponding optimized scheduling model is constructed and solved to generate hourly-level scheduling instructions.

[0122] A minute-level real-time control layer is constructed, using the real-time operating status information of the integrated biogas production, storage and utilization system as feedback input. Real-time closed-loop control is performed on the gas production unit, gas storage unit and gas utilization unit in a minute cycle, so that the real-time operating parameters track the scheduling instructions, and the real-time operating data is fed back to the hour-level scheduling layer and the monthly-level planning layer for rolling optimization in the next cycle, forming a closed-loop control.

[0123] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0124] 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A hierarchical operation control method of a bio-natural gas production and storage integrated system, characterized by, include: The system acquires operational status information and external environmental information of the integrated biogas production, storage and utilization system. The external environmental information includes long-term forecast data of renewable energy power generation, short-term forecast data of renewable energy power generation, grid dispatch instructions and time-of-use electricity price information. A monthly planning layer is constructed, and with historical operating data and long-term forecast data of renewable energy power generation as inputs, and with monthly time as the cycle and the goal of maximizing monthly revenue, the monthly gas production plan of the gas production unit, the monthly gas storage plan of the gas storage unit, and the monthly gas consumption plan of the gas consumption unit are optimized. An hourly-level scheduling layer is constructed, with the monthly gas production plan, the monthly gas storage plan, and the monthly gas consumption plan as constraints passed to the hourly-level scheduling layer. The short-term forecast data, the power grid dispatch instructions, and the time-of-use electricity price information are used as inputs to identify the current operating mode of the integrated biogas production, storage, and consumption system. Based on the identified operating mode, a corresponding optimized scheduling model is constructed and solved to generate hourly-level scheduling instructions. The optimized scheduling model of the hourly-level scheduling layer is a model predictive control model. The objective function of the model predictive control model is: ， ， ， In the formula, For time period Electricity sales price, For time period Generator unit grid-connected power, For time period The electricity purchase price, For time period Hydrogen production capacity of electrolyzer, , , All are weighting coefficients. Electricity purchase and sale price 1.5 to 2 times that, The value of causes the deviation of the gas storage at the end of the day. Keep it within ±5%. The typical value ranges from 0.1 to 1.

0. For time period The total output of the landscape forecast, This represents the maximum power that the power grid can accept. This represents the methane inventory in the gas storage tank at the end of the day. The target gas storage volume at the end of the day. For time step, The target energy status of the gas storage tank for the month. The penalty coefficient for wind and solar power curtailment. The lower heating value of methane, For time period The proportion of hydrogen allocated to primary biosynthesis For time period The proportion of hydrogen allocated to primary biosynthesis; The constraints of the model predictive control model include hydrogen material balance, natural gas material balance, power generation balance, equipment capacity constraints, hydrogen distribution constraints, biosynthetic hydrogen injection rate change rate constraints, hydrogen storage tank state boundary, gas storage tank state boundary, and periodic boundary conditions. The expression for natural gas material balance is: ， In the formula, For time period Methane storage tank contents For time period Methane storage tank contents The conversion efficiency from hydrogen to methane in secondary chemical synthesis. For time period Natural gas flow used for power generation For time period Natural gas flow rate sold directly; A minute-level real-time control layer is constructed, using the real-time operating status information of the integrated biogas production, storage, and utilization system as feedback input. Real-time closed-loop control is performed on the gas production unit, storage unit, and utilization unit in minute cycles, ensuring that real-time operating parameters track the scheduling commands. Real-time operating data is fed back to the hourly scheduling layer and the monthly planning layer for rolling optimization in the next cycle, forming a closed-loop control. The minute-level real-time control layer employs a model predictive control algorithm, using the scheduling commands as a reference trajectory and the current real-time operating status information as initial conditions. It continuously optimizes the control sequence within a preset time domain and issues the first control action to the actuators of the gas production unit, storage unit, and utilization unit. The objective function of the model predictive control algorithm is: ， In the formula, For the current moment t in relation to the future The predicted value of the generator's grid-connected power at any given time. For the future Reference trajectory of generator power at any given time. For the current moment t in relation to the future Predicted hydrogen production power of the electrolyzer at any given time. For the future Reference trajectory of hydrogen production power of the electrolyzer at any given time. To predict the number of time periods within the window, This is the predicted value of the power grid frequency deviation. The penalty for real-time wind and solar curtailment is calculated based on ultra-short-term wind and solar forecasts. , All are weighting coefficients.

2. The hierarchical operation control method for an integrated biogas production, storage, and utilization system according to claim 1, characterized in that, The identification criteria for the operating mode include: the comparison results between the short-term forecast value of renewable energy power generation and the system's preset absorption threshold, the type identifier of the grid dispatch instruction, and the time period category of the time-of-use electricity price; When the short-term forecast value of renewable energy power generation exceeds the preset absorption threshold of the integrated biogas production, storage and utilization system, it is identified as a green electricity consumption mode. When the received power grid dispatch command is a peak shaving command or a valley filling command, it is identified as a power grid peak shaving mode; When the received power grid dispatch command is a primary frequency regulation command or a secondary frequency regulation command, it is identified as a power grid frequency regulation mode.

3. The hierarchical operation control method for an integrated biogas production, storage, and utilization system according to claim 2, characterized in that, The optimized scheduling model constructed under the green energy consumption mode includes: The optimization objective is to maximize the absorption of surplus green electricity or minimize the curtailment of wind and solar power. The start-up / shutdown status and load rate of the electrolytic hydrogen production equipment are used as decision variables. The optimized scheduling model constructed under the power grid peak-shaving mode includes: The optimization objective is to maximize the benefits of peak shaving compensation or minimize the difference between peak and valley loads in the system. The gas storage unit's charging and discharging power and the gas generator set's output power are used as decision variables; The optimized scheduling model constructed under the aforementioned power grid frequency regulation mode includes: The optimization objective is to minimize frequency deviation or maximize FM mileage gains. The rapid charging and discharging rate of the gas storage unit and the regulation rate of the gas generator set are used as decision variables.

4. The hierarchical operation control method for an integrated biogas production, storage, and utilization system according to claim 1, characterized in that, The monthly-level planning layer's optimization scheduling model aims to maximize monthly revenue, and its expression is: ， In the formula, To optimize the total number of months within the period, For the first The expected monthly electricity revenue is calculated based on the total monthly electricity generation and the projected electricity price. For the first The expected monthly hydrogen production cost is calculated based on the total monthly hydrogen production and the projected electricity price. For the first Monthly gas storage costs This is the penalty coefficient for slow processes; The constraints include monthly energy balance, gas storage capacity constraints, and slow dynamic constraints on biosynthesis, expressed as follows: ， ， ， In the formula, For the first End-of-month gas storage tank energy status For the first End-of-month gas storage tank energy status For the first The total energy stored through hydrogen production and conversion each month For the first Total energy released by monthly power generation This is the maximum energy capacity of the gas storage tank. The efficiency of the bioconversion of hydrogen to methane. For the first Monthly average maximum acceptable hydrogen injection rate for the microbial system For the first Total number of hours in a month.

5. The hierarchical operation control method for an integrated biogas production, storage, and utilization system according to claim 1, characterized in that, The expression for hydrogen material balance is: ， ， ， In the formula, For time period Hydrogen storage tank hydrogen level For time period Hydrogen storage tank hydrogen level The efficiency of hydrogen production in an electrolyzer. For time period The flow rate of biosynthesized hydrogen injected. For time period The flow rate of hydrogen gas injected during chemical synthesis; The expression for power generation balance is: ， In the formula, For generator set electrical efficiency; The expression for the equipment capacity constraint is: ， ， ， In the formula, The rated power of the electrolytic cell, This refers to the generator's rated power. The expression for the hydrogen distribution constraint is: ， ， In the formula, The proportion of hydrogen allocated to chemical synthesis during time period k; The expression for the constraint on the rate of change of biosynthetic hydrogen injection rate is: ， In the formula, The maximum allowable rate of change for a microbial system; The expressions for the state boundaries of the hydrogen storage tank and the gas storage tank are as follows: ， ， In the formula, Let k be the amount of hydrogen stored in the hydrogen storage tank during time period k. This is the maximum capacity of the hydrogen storage tank. The amount of methane in the gas storage tank during time period k. This is the maximum capacity of the gas storage tank; The expression for periodic boundary conditions is: ， ， In the formula, This represents the hydrogen inventory at the beginning of the day. This refers to the hydrogen inventory in the storage tank at the end of the day. This represents the methane inventory at the beginning of the day.

6. A hierarchical operation control system for an integrated biogas production, storage, and utilization system, characterized in that, include: The acquisition module is configured to acquire the operating status information and external environment information of the integrated biogas production, storage and utilization system. The external environment information includes long-term forecast data of renewable energy power generation, short-term forecast data of renewable energy power generation, grid dispatch instructions and time-of-use electricity price information. The first construction module is configured to build a monthly planning layer, and takes historical operating data and long-term forecast data of renewable energy power generation as input, takes monthly time as the cycle, and takes the maximum monthly income as the goal to optimize and formulate the monthly gas production plan of the gas production unit, the monthly gas storage plan of the gas storage unit, and the monthly gas consumption plan of the gas consumption unit. The second construction module is configured to construct an hourly-level scheduling layer. The monthly gas production plan, the monthly gas storage plan, and the monthly gas consumption plan are passed to the hourly-level scheduling layer as constraints. The short-term forecast data, the power grid dispatch instructions, and the time-of-use electricity price information are used as inputs to identify the current operating mode of the integrated biogas production, storage, and consumption system. Based on the identified operating mode, a corresponding optimized scheduling model is constructed and solved to generate hourly-level scheduling instructions. The optimized scheduling model of the hourly-level scheduling layer is a model predictive control model. The objective function of the model predictive control model is: ， ， ， In the formula, For time period Electricity sales price, For time period Generator unit grid-connected power, For time period The electricity purchase price, For time period Hydrogen production capacity of electrolyzer, , , All are weighting coefficients. Electricity purchase and sale price 1.5 to 2 times that, The value of causes the deviation of the gas storage at the end of the day. Keep it within ±5%. The typical value ranges from 0.1 to 1.

0. For time period The total output of the landscape forecast, This represents the maximum power that the power grid can accept. This represents the methane inventory in the gas storage tank at the end of the day. The target gas storage volume at the end of the day. For time step, The target energy status of the gas storage tank for the month. The penalty coefficient for wind and solar power curtailment. The lower heating value of methane, For time period The proportion of hydrogen allocated to primary biosynthesis For time period The proportion of hydrogen allocated to primary biosynthesis; The constraints of the model predictive control model include hydrogen material balance, natural gas material balance, power generation balance, equipment capacity constraints, hydrogen distribution constraints, biosynthetic hydrogen injection rate change rate constraints, hydrogen storage tank state boundary, gas storage tank state boundary, and periodic boundary conditions. The expression for natural gas material balance is: ， In the formula, For time period Methane storage tank contents For time period Methane storage tank contents The conversion efficiency from hydrogen to methane in secondary chemical synthesis. For time period Natural gas flow used for power generation For time period Natural gas flow rate sold directly; The third module is configured to construct a minute-level real-time control layer. Using the real-time operating status information of the integrated biogas production, storage, and utilization system as feedback input, it performs real-time closed-loop control of the gas production unit, storage unit, and utilization unit on a minute-by-minute cycle. This ensures that real-time operating parameters track the scheduling commands and feeds back real-time operating data to the hourly scheduling layer and the monthly planning layer for rolling optimization in the next cycle, forming a closed-loop control. The minute-level real-time control layer employs a model predictive control algorithm. Using the scheduling commands as a reference trajectory and the current real-time operating status information as initial conditions, it continuously optimizes the control sequence within a preset time domain and issues the first control action to the actuators of the gas production unit, storage unit, and utilization unit. The objective function of the model predictive control algorithm is: ， In the formula, For the current moment t in relation to the future The predicted value of the generator's grid-connected power at any given time. For the future Reference trajectory of generator power at any given time. For the current moment t in relation to the future Predicted hydrogen production power of the electrolyzer at any given time. For the future Reference trajectory of hydrogen production power of the electrolyzer at any given time. To predict the number of time periods within the window, This is the predicted value of the power grid frequency deviation. The penalty for real-time wind and solar curtailment is calculated based on ultra-short-term wind and solar forecasts. , All are weighting coefficients.

7. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method described in any one of claims 1 to 5.

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