Microgrid optimization method and system for green-hydrogen coupling coal chemical industry

Abstract

The invention belongs to the technical field of energy transformation, and provides a green hydrogen coupling coal chemical industry microgrid optimization method and system, and the method comprises the following steps: S1, obtaining the input data of a green hydrogen coupling coal chemical industry microgrid; s2, constructing an optimization model of the green hydrogen coupling coal chemical industry micro-grid based on a Pyomo optimization modeling language; s3, setting an economic objective function in the optimization model; s4, calling a solver to solve the optimization model to obtain the optimal operation strategy and the equipment scale of the equipment in the micro-grid; through modular design, a Pyomo optimization modeling language and a large-scale optimization solver are utilized, various factors such as an electric power system, a hydrogen system, a steam system, coal chemical industry, cost and environment are comprehensively considered, optimal scheduling and distribution of electric power, steam and hydrogen and optimal design of equipment scale are realized, and the optimal scheduling and distribution of the electric power, the steam and the hydrogen are realized. The efficiency and the accuracy of model solving are remarkably improved, the stability and the economical efficiency of the system are ensured, and the carbon emission is minimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy transformation, and specifically relates to a microgrid optimization method and system for coupling green hydrogen with coal chemical industry. Background Art

[0002] With the transformation of the global energy structure and the proposal of sustainable development goals, green hydrogen, as a clean energy source, has become increasingly prominent in the energy system. At the same time, the coal chemical industry, as a traditional high-energy-consuming and high-carbon-emission industry, is also facing the pressure of transformation and upgrading. The microgrid optimization method for coupling green hydrogen with coal chemical industry aims to achieve the deep integration of green hydrogen and coal chemical industry by optimizing the production and distribution of electricity, steam, and hydrogen, and improve the economy and environmental protection of the energy system.

[0003] However, the existing green hydrogen-coupled coal chemical system lacks an efficient optimization method, and it is difficult to minimize carbon emissions while ensuring the stability and economy of the system. Traditional optimization methods often have a long calculation cycle, high model complexity, and are difficult to adapt to the volatility and uncertainty of renewable energy. Therefore, it is particularly important to develop a microgrid optimization method and system for coupling green hydrogen with coal chemical industry that can comprehensively consider the balance of electricity, steam, and hydrogen, optimize the equipment scale and operation strategy at the same time, and have high calculation efficiency. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a microgrid optimization method and system for coupling green hydrogen with coal chemical industry to solve the problems in the prior art that the existing green hydrogen-coupled coal chemical system lacks an efficient optimization method and it is difficult to minimize carbon emissions while ensuring the stability and economy of the system.

[0005] A microgrid optimization method for coupling green hydrogen with coal chemical industry includes the following steps:

[0006] S1. Obtain input data:

[0007] The input data includes, but is not limited to, the demand and price of the power system, the power generation parameters of renewable energy sources (wind power, photovoltaic), the relevant parameters of thermal power, the parameters of the energy storage system, the parameters of the hydrogen system, and the parameters of coal chemical hydrogen production and by-product production.

[0008] The input data is imported through an Excel file and preprocessed in the model initialization stage to ensure the accuracy and consistency of the data.

[0009] S2. Construct an optimization model:

[0010] Construct an optimization model for the microgrid of coupling green hydrogen with coal chemical industry based on the Pyomo optimization modeling language.

[0011] Define sets (including time set, equipment set and product set), parameters (such as time-related parameters, power system parameters, hydrogen system parameters, etc.), and variables (continuous variables, integer variables and auxiliary variables);

[0012] Set the economic objective function, aiming to minimize the total cost (including fixed cost, variable cost, curtailment cost and revenue) or maximize the net revenue (the difference between total revenue and total cost), and consider carbon emission constraints (such as annual carbon emission cap and green power / green hydrogen ratio constraints);

[0013] Set various constraint conditions, including power balance, steam balance, hydrogen balance, equipment limitations and environmental constraints, etc.;

[0014] S3. Solve the optimization model:

[0015] Call a solver (such as Gurobi or SCIP) to solve the optimization model and obtain the optimal operation strategy and equipment scale of the equipment in the microgrid;

[0016] S4. Output the optimization results:

[0017] Output the time series data of power dispatch (generation, energy storage, curtailment), steam flow (consumption and output of each equipment), hydrogen production and storage according to the solution results;

[0018] Output the comprehensive results of equipment scale, total cost and carbon emissions.

[0019] A microgrid optimization system for green hydrogen coupled with coal chemical industry, comprising:

[0020] A data input unit for obtaining the input data of the green hydrogen coupled coal chemical microgrid;

[0021] A model construction unit for constructing an optimization model based on Pyomo, defining sets, parameters, variables, objective functions and constraint conditions;

[0022] A solving unit for calling a solver to solve the optimization model;

[0023] A result output unit for outputting the time series data of power dispatch, steam flow, hydrogen production and storage according to the solution results, as well as the comprehensive results of equipment scale, total cost and carbon emissions.

[0024] Preferably, it further includes a user interaction interface for displaying the model calculation results and providing user operation interfaces for users to perform operations such as model parameter setting, result viewing and analysis.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. The present invention adopts a modular design, divides the optimization process into four modules: data input, model creation, model solution, and result extraction. Data transmission and interaction are carried out between modules through interfaces, which improves the scalability and maintainability of the system.

[0027] 2. The present invention constructs an optimization model using the Pyomo optimization modeling language and calls a large-scale optimization solver (such as Gurobi or SCIP) for solution, significantly improving the efficiency and accuracy of model solution.

[0028] 3. The present invention comprehensively considers various factors such as power systems, hydrogen systems, steam systems, coal chemical industry, costs, and the environment, realizes the optimal scheduling and allocation of electricity, steam, and hydrogen, as well as the optimal design of equipment scale. Brief Description of the Drawings

[0029] Figure 1 is the system architecture diagram of the present invention;

[0030] Figure 2 is the system deployment structure diagram of the present invention;

[0031] Figure 3 is the model calculation flow chart of the present invention;

[0032] Figure 4 is the model calculation and processing flow chart of the present invention. Detailed Embodiments

[0033] The following further describes the embodiments of the present invention in detail in conjunction with the embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0034] Embodiment: The present invention provides a microgrid optimization method for coupling green hydrogen with coal chemical industry, including:

[0035] Step 1. Data Input and Preprocessing

[0036] 1. Data Input:

[0037] Create an Excel file (such as Inputs.xlsx), and enter the electricity system requirements and prices, renewable energy generation parameters, thermal power related parameters, energy storage system parameters, hydrogen system parameters, and coal chemical hydrogen production and by-product production parameters in a predetermined format.

[0038] Read the data in the Excel file through a Python script and import it into the model.

[0039] 2. Data Preprocessing:

[0040] Clean and verify the input data to ensure the accuracy and consistency of the data.

[0041] Convert time-related parameters (such as time resolution, simulation period, step size) into a format recognizable by the model.

[0042] Step 2. Model construction

[0043] 1. Define sets:

[0044] Time set: Represents discrete time steps within the simulation period.

[0045] Device set: Includes thermal power, wind power, photovoltaic, energy storage, electrolyzers, coal gasification equipment, etc.

[0046] Product set: Includes hydrogen, by-products (such as aviation kerosene, diesel, naphtha, etc.).

[0047] 2. Define parameters:

[0048] Time-related parameters: Such as time resolution, simulation period, step size, etc.

[0049] Power system parameters: Such as power demand, price, generation capacity factor, curtailment cost, etc.

[0050] Hydrogen system parameters: Such as hydrogen demand, price, electrolysis / gasification efficiency, storage and transportation parameters, etc.

[0051] Steam system parameters: Such as steam consumption and output coefficients of each device.

[0052] Coal chemical parameters: Such as by-product price, equipment efficiency, etc.

[0053] Cost parameters: Such as fixed cost, variable cost, replacement cost, etc.

[0054] Environmental parameters: Such as carbon emission constraints, CO2 price, etc.

[0055] 3. Define variables:

[0056] Continuous variables: Such as the output of each device (power generation, hydrogen production), energy storage / hydrogen storage status, etc.

[0057] Integer variables: Such as the installed capacity of equipment (if discrete scale is involved).

[0058] Auxiliary variables: Such as curtailment power, curtailment hydrogen, steam flow, etc.

[0059] 4. Set the objective function:

[0060] According to the optimization objective (minimize the total cost or maximize the net profit), construct the corresponding objective function.

[0061] Considering the carbon emission constraints, incorporate the carbon emission cost into the objective function.

[0062] 5. Set constraint conditions:

[0063] Power balance constraint: Ensure the balance between power generation and consumption.

[0064] Steam balance constraint: Ensure the balance between steam supply and demand.

[0065] Hydrogen balance constraint: Ensure the balance between hydrogen supply and demand.

[0066] Equipment limit constraint: Such as upper and lower limits of installed capacity, operating load range, ramping constraint, etc.

[0067] Environmental constraint: Such as the total carbon emissions not exceeding the annual emission limit, green power / green hydrogen ratio constraint, etc.

[0068] Step 3. Model solution

[0069] 1. Invoke the solver:

[0070] Select a suitable solver (such as Gurobi or SCIP) and configure the solver parameters.

[0071] Invoke the solver to solve the optimization model and obtain the optimal operation strategy and equipment scale of the equipment.

[0072] 2. Result processing:

[0073] Analyze the results output by the solver and extract the time series data of power dispatch, steam flow, hydrogen production and storage.

[0074] Calculate comprehensive results such as equipment scale, total cost, carbon emissions, etc.

[0075] Step 4. Result output and user interaction

[0076] 1. Result output:

[0077] Output the optimization results to the user in an appropriate form (such as charts, reports, etc.).

[0078] Provide a detailed viewing function for time series data and comprehensive results.

[0079] 2. User interaction:

[0080] Provide a user interaction interface that allows users to perform operations such as model parameter setting, result viewing and analysis.

[0081] Support users to adjust model parameters as needed and re-run the optimization model to obtain new optimization results.

[0082] As can be seen from the above, through modular design, using the Pyomo optimization modeling language and large-scale optimization solvers, this method comprehensively considers various factors such as power systems, hydrogen systems, steam systems, coal chemical industry, costs, and the environment, realizes the optimal scheduling and allocation of power, steam, and hydrogen, as well as the optimal design of equipment scale, significantly improves the efficiency and accuracy of model solving, ensures the stability and economy of the system, and minimizes carbon emissions.

[0083] Among them, data input requires the following input data: the capital cost and operating cost of equipment, power market prices and demands, resource data of wind energy and solar energy, technical parameters and performance indicators of equipment, etc. The input data is mainly imported through Excel files and preprocessed in Model_Initialization.py. To clearly express model input, it is divided into the following main categories and grouped according to physical meaning and function:

[0084] (1) Time-related parameters

[0085] Time resolution: t_resolution (number of time steps)

[0086] Simulation period: t_sim_period (total simulation time range)

[0087] Time step length: t_step_length (length of each time step)

[0088] (2) Power system input

[0089] 1) Power demand and price

[0090] Committed electricity demand: Committed_demand(t) (grid agreement electricity demand) Feed-in tariff for wind and solar power: elc_mark_committed_supply_price(t) Spot price for wind and solar power feed-in: elc_mark_spot_price(t)

[0091] Demand load curve: demand_load(t)

[0092] Power price change: ele_price(t)

[0093] Upper limit of grid power consumption: ele_consumption_max_limit

[0094] Backup network cost: backup_charge

[0095] Green electricity proportion constraint: elec_green_percentage_cons

[0096] 2) Renewable energy power generation

[0097] Wind power capacity factor: e_wind_genprofile_t(t)

[0098] Photovoltaic capacity factor: e_pv_genprofi le_t(t)

[0099] Upper and lower limits of wind power installed capacity: wind_max_mw, wind_min_mw

[0100] Upper and lower limits of photovoltaic installed capacity: solar_max_mw, solar_min_mw

[0101] Wind and PV curtailment rate: wind_PV_curtai l_share

[0102] Unit cost of curtailment: elc_curtai lment_cost(t)

[0103] 3) Thermal power related

[0104] Upper and lower limits of thermal power installed capacity: CPP_max_mw, CPP_min_mw

[0105] Minimum stable output of thermal power: CPP_min_gen_lvl

[0106] Thermal power ramp constraint: r_up_limit

[0107] Thermal power generation efficiency: CPP_efficiency

[0108] Emission coefficient of fuel coal: CO2_coal_intensity

[0109] Calorific value of fuel coal: fuel_coal_calorific_value 4) Energy storage system

[0110] Upper and lower limits of energy storage scale: ess_max_limit_1, ess_min_limit_1

[0111] Energy storage charge and discharge efficiency: ess_char_efficiency_1, ess_disch_efficiency_1 Energy storage initial power: ess_ini_storage_1

[0112] Energy storage depth of discharge: ess_min_storage_limit_1

[0113] Energy storage capacity configuration ratio: ess_capacity_share_cons

[0114] Energy storage duration: ess_dura_limit_1

[0115] (3) Hydrogen system input

[0116] 1) Hydrogen demand and price

[0117] Hydrogen demand variation: hydro_demand_load(t)

[0118] Hydrogen price variation: hydro_price(t)

[0119] Hydrogen selling price: hydro_saletom_price(t)

[0120] Hydrogen curtailment cost: hydro_curtailment_cost(t)

[0121] Green hydrogen ratio constraint: Hydro_green_percentage_cons

[0122] 2) Hydrogen production by electrolyzing water

[0123] Upper and lower limits of electrolyzer scale: Hydro_max_cap, Hydro_min_cap

[0124] Load range of hydrogen production equipment: Hydro_max_load, Hydro_min_load

[0125] Green hydrogen carbon emission constraint: Green_hydro_carbon_cons

[0126] Water consumption per unit of hydrogen energy: Hydro_water_intensity

[0127] Electricity consumption per unit of hydrogen energy: Hydro_elec_intensity

[0128] Upper limit of water use: water_use_max_limit

[0129] Water price 3) Hydrogen production by coal gasification

[0130] Upper and lower limits of coal gasification scale: Hydro_coal_max_cap, Hydro_coal_min_cap Unit coal consumption per unit of hydrogen energy: Hydro_coal_intensity

[0131] Calorific value of raw coal: coal_Calorific_value

[0132] Carbon emission coefficient of raw coal: CO2_resouce_coal_intensity

[0133] Equipment adjustment range: Hydro_load_adjust_limit

[0134] Number of adjustments: Hydro_load_adjust_times

[0135] Related to syngas:

[0136] o Ratio: Syngas1_ratio

[0137] o Water consumption: Syngas_ReclaimedWater_input

[0138] o Coal consumption: Syngas_RowCoal_input

[0139] o Oxygen consumption: Syngas_Oxygen_input

[0140] o CO2 consumption: Syngas_CO2_input

[0141] o Wastewater rate: Syngas_Wastewater_input

[0142] o Hydrogen content: Syngas_Hydrogen_ratio

[0143] Redundancy factor of coal gasification: coalgas_Standby_Redundancy_factor

[0144] 4) Hydrogen storage and transportation

[0145] Upper and lower limits of hydrogen storage scale: Hydro_storage_max_limit, Hydro_storage_min_limit Hydrogen storage filling / release speed: Hydro_charge_flow_cap, Hydro_discharge_flow_cap Hydrogen storage depth of hydrogen release: Hydro_Soc_min_limit

[0146] Hydrogen storage power consumption: Hydro_storage_ele

[0147] Initial hydrogen storage volume: Hydro_storage_initial_limit

[0148] Hydrogen storage pressure and capacity: Hydro_storage_pressure, Hydro_storage_capacity

[0149] Hydrogen storage steam usage: Hydro_storage_LPS_input

[0150] Hydrogen transmission pipeline parameters:

[0151] o Cross-sectional area: Hydro_trans_pineline_SectionArea

[0152] o Pressure: Hydro_trans_pressure

[0153] o Pressure drop: Hydro_trans_pressure_drop

[0154] o Distance: Hydro_trans_distance

[0155] o Steam usage: Hydro_trans_LPS_input

[0156] Hydrogen transmission limit: Hydro_coupling_max_limit

[0157] (4) Steam system input

[0158] Steam price and consumption:

[0159] o Coal gasification steam: gasification_vapor_9MP_once_factor,

[0160] gasification_vapor_045MP_discontinuity_factor

[0161] o Shift steam: convert_vapor_41MP_once_factor,

[0162] convert_vapor_11MP_once_factor, etc.

[0163] o Oil product synthesis / processing steam: OilSynthesisUnit_vapor_9MP_produce_factor, OilProcessUnit_vapor_41MP_produce_factor, etc.

[0164] Seasonal steam data:

[0165] o Steam drive

[0166] o Electric drive

[0167] (5) Coal chemical industry and by-products

[0168] Coal price: coal_price(t)

[0169] By-product price:

[0170] o Non-electric products: Non_electric_Type_1_value(t)

[0171] o Hydro-upgrading Aeronautical Coal: Hydroupgrading_AeronauticalCoal_value(t)

[0172] o Hydro-upgrading petrol: Hydroupgrading_petrol_value(t)

[0173] o Hydro-upgrading Naphtha: Hydroupgrading_Naphtha_value(t),

[0174] OilProcessUnit_RefinedNaphtha_value(t), etc.

[0175] o Asphalt Upgrading Pitch Coke: AsphaltUpgrading_PitchCoke_value(t)

[0176] o Ammonium sulfate: CatalystPrep_NH42SO4_value(t)

[0177] o Liquefied petroleum gas

[0178] o Sulfur

[0179] o Liquid ammonia

[0180] Equipment scale and efficiency:

[0181] o Shift: convert_scale_max, convert_scale_min,

[0182] Syngas_Shift_Efficiency

[0183] o Oil synthesis: OilSynthesisUnit_scale_max, OilSynthesisUnit_scale_min o Coal liquefaction: CTL_scale_max, CTL_scale_min, etc.

[0184] (6) Cost parameters

[0185] Fixed costs (annual average investment + operation and maintenance):

[0186] o Thermal power: CPP_capex_fixed_cost

[0187] o Wind power: wind_capex_fixed_cost

[0188] o Photovoltaic: solar_capex_fixed_cost

[0189] o Energy storage: ess_capex_fixed_cost

[0190] o Electrolyzer: electrolyzer_capex_fixed_cost o Coal gasification: coalgas_capex_fixed_cost, etc.

[0191] Variable cost:

[0192] o Thermal power: CPP_var_cost

[0193] o Photovoltaic: solar_var_cost

[0194] o Wind power: wind_var_cost o Energy storage: ess_var_cost, etc.

[0195] Replacement cost: replacement_cost

[0196] (7) Environmental constraints

[0197] Carbon dioxide price: CO2_Price

[0198] Annual carbon emission constraint: CO2_max_limit

[0199] CO2 emissions per 10,000 yuan of added value: CO2_emissions

[0200] (8) Equipment scale and standby

[0201] Scale factor: CPP_scale_factor, wind_scale_factor, etc. Scale unit: CPP_unit_capa, Wind_unit_capa, etc.

[0202] Capacity standby: CPP_backup_capa, wind_backup_capa, etc.

[0203] A microgrid optimization system for green hydrogen coupled coal chemical industry, comprising:

[0204] A data input unit for obtaining input data of the green hydrogen coupled coal chemical industry microgrid;

[0205] A model construction unit that constructs an optimization model based on Pyomo, defining sets, parameters, variables, objective functions, and constraint conditions;

[0206] A solution unit that calls a solver to solve the optimization model;

[0207] A result output unit that outputs time series data of power dispatching, steam flow, hydrogen production and storage, as well as comprehensive results of equipment scale, total cost, and carbon emissions according to the solution results;

[0208] A user interface that is used to display the model calculation results and provide user operation interfaces for users to perform operations such as model parameter setting, result viewing, and analysis.

[0209] The above embodiments will be further described in detail below with reference to the accompanying drawings.

[0210] 1) System architecture

[0211] The system architecture is as Figure 1 shown. The system architecture diagram includes the following:

[0212] 1. The infrastructure layer is the basic configuration for ensuring the operation of the system.

[0213] 2. The data layer records data such as model data, log data, and operation records stored in the system.

[0214] 3. The support layer is mainly middleware for ensuring the docking of the model system with external systems.

[0215] 4. The cost optimization model system for coal chemical coupling with green power and green hydrogen includes functional models such as project management, model scheme management, device database management, and model data display management.

[0216] 5. The access layer labels the users of the current operating system.

[0217] 6. The external docking model describes the functions and operations of the model calculation layer.

[0218] As can be seen from the above, through a clearly layered design, the system architecture organically integrates key components such as infrastructure, data storage, system support, core function models, user access, and external docking. The infrastructure layer provides a stable foundation for the system operation, the data layer ensures the secure storage and efficient retrieval of information, and the support layer, as middleware, promotes seamless docking between internal and external systems. The core function model layer integrates key business logics such as project management, model solution management, device database management, and data display, achieving comprehensive coverage of the optimization of the green hydrogen coupled coal chemical microgrid. The access layer provides a convenient operation interface for users, while the external docking model enhances the flexibility and scalability of the system. This architecture not only improves the maintainability and scalability of the system, but also ensures the efficiency and accuracy of the optimization process, providing strong technical support for the intelligent and green development of the green hydrogen coupled coal chemical industry.

[0219] 2) System Deployment Structure

[0220] The system deployment structure is as Figure 2 shown. This figure details various server configurations required by the system and its network environment, including the specific configuration parameters and deployment methods of application servers, database servers, model calculation servers, and load balancers. This structured deployment method ensures the high availability and load balancing of the system, effectively enhancing the data processing and model calculation capabilities, and providing a solid hardware foundation for the optimization of the green hydrogen coupled coal chemical microgrid.

[0221] As can be seen from the above, through Figure 2 the deployment structure, the system can efficiently process a large amount of input data, quickly build and optimize models, and stably output optimization results, providing timely and accurate support for the decision-making of the green hydrogen coupled coal chemical industry, and significantly improving the economy and environmental protection of the energy system.

[0222] The system deployment network structure is as Figure 3 shown. This process starts from the bottom-level data management and maintenance, goes through project creation, model technical solution formulation, equipment parameter entry, function conversion and model calculation, and finally reaches multiple links such as data parsing, storage, and display. This process ensures the accuracy of model construction and the efficiency of calculation, providing a scientific basis for optimizing the operation strategy and equipment scale of the green hydrogen coupled coal chemical microgrid.

[0223] As can be seen from the above, through Figure 3 the model calculation process, the system can systematically integrate and analyze various types of data, accurately build an optimization model that meets actual needs, and obtain the optimal solution through an efficient solution algorithm. This not only improves the scientificity and rationality of decision-making, but also effectively reduces the operation cost and environmental impact of the system, promoting the sustainable development of the green hydrogen coupled coal chemical industry.

[0224] The system deployment configuration is as follows:

[0225]

[0226] 3) Model calculation process

[0227] The model calculation and processing process is as Figure 4 shown. The model calculation and processing process includes:

[0228] 1. Perform low-level data management and maintenance on the device equipment.

[0229] 2. Create a project in the system and supplement project parameters.

[0230] 3. Create a model technical solution under the project, select the device equipment, and supplement the equipment economic parameters and low-level parameters.

[0231] 4. Perform function conversion and entry on a single device and conduct function model calculation.

[0232] 5. Import the time-series calculation parameters related to the device equipment

[0233] 6. Perform model calculation on the model solution data.

[0234] 7. Perform data parsing and storage on the model algorithm data.

[0235] 8. Display the model data for the data stored in the model calculation.

[0236] As can be seen from the above, the model calculation and processing process, through systematic steps, from low-level data management and maintenance to project creation, model technical solution formulation, and then to equipment parameter entry and function model calculation, finally realizes the comprehensive calculation and result parsing and storage of the model solution data. This series of processes ensures the accuracy and integrity of the data, improves the refinement of model construction and the accuracy of calculation, provides a scientific and reliable decision-making basis for the optimization of the green hydrogen-coupled coal chemical microgrid, and effectively promotes the improvement of the economy and environmental protection of the energy system.

[0237] An embodiment of the present application provides an electronic device, applicable to the above-mentioned microgrid optimization method for green hydrogen-coupled coal chemical industry, including:

[0238] A memory, used to protect computer programs and data;

[0239] A processor, used to run the system program.

[0240] An embodiment of the present application provides a computer storage medium, applicable to the above-mentioned microgrid optimization method for green hydrogen-coupled coal chemical industry, and performs hierarchical confidentiality management on the above-mentioned system and data according to the requirements of confidentiality management.

[0241] Those skilled in the art should understand that the embodiments of the present application can be provided as a system or a computer program product. Therefore, the present application can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0242] The present application is described with reference to the flowcharts and / or block diagrams of devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0243] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0244] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0245] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0246] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0247] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0248] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, article or device comprising the element.

[0249] The standard parts used in the present invention can all be purchased from the market. The special-shaped parts can be customized according to the description in the specification and the drawings. The specific connection methods of each part all adopt conventional means such as bolts, rivets, welding, etc. that are mature in the prior art. The machines, parts and devices all adopt conventional models in the prior art. Coupled with the circuit connection adopting the conventional connection method in the prior art, details are not described herein. The content not described in detail in this specification belongs to the prior art well-known to those of ordinary skill in the art.

[0250] The embodiments of the present invention are given for the purposes of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A microgrid optimization method for coupling green hydrogen with coal chemical industry, characterized in that: It includes the following steps: S1. Obtain the input data of the green hydrogen-coupled coal chemical microgrid. The input data includes the demand and price of the power system, the power generation parameters of renewable energy, the relevant parameters of thermal power, the parameters of the energy storage system, the parameters of the hydrogen system, and the parameters of coal chemical hydrogen production and by-product production; S2. Based on the Pyomo optimization modeling language, construct an optimization model of the green hydrogen-coupled coal chemical microgrid, define sets including a time set, an equipment set, and a product set, as well as multiple types of parameters including the power system, the hydrogen system, the steam system, coal chemical industry, cost, and environment. At the same time, set continuous variables, integer variables, and auxiliary variables; S3. Set an economic objective function in the optimization model, aiming to minimize the total cost or maximize the net income, and consider the carbon emission constraint; at the same time, set various constraint conditions including power balance, steam balance, hydrogen balance, equipment limitation, and environmental constraint; S4. Call a solver to solve the optimization model to obtain the optimal operation strategy and equipment scale of the equipment in the microgrid; S5. According to the solution results, output the time series data of power dispatching, steam flow, hydrogen production and storage, as well as the comprehensive results of equipment scale, total cost, and carbon emissions.

2. The microgrid optimization method for coupling green hydrogen with coal chemical industry according to claim 1, wherein: The input data is imported through an Excel file and preprocessed in the model initialization stage to ensure the accuracy and consistency of the data.

3. The microgrid optimization method for coupling green hydrogen with coal chemical industry according to claim 1, characterized in that: The optimization model adopts a modular design, including a data input module, a model creation module, a model solution module, and a result extraction module. Data transmission and interaction are carried out between the modules through interfaces.

4. The microgrid optimization method for coupling green hydrogen with coal chemical industry according to claim 1, wherein: When the economic objective function minimizes the total cost, it considers fixed costs, variable costs, power / hydrogen curtailment costs, and revenues; when maximizing the net income, it considers the difference between the total revenue and the total cost.

5. The microgrid optimization method for coupling green hydrogen with coal chemical industry according to claim 1, wherein: The carbon emission constraint conditions include the annual carbon emission upper limit and the green power / green hydrogen ratio constraint to ensure the environmental friendliness of the system operation.

6. The microgrid optimization method for coupling green hydrogen with coal chemical industry according to claim 1, characterized in that: The solver is a large-scale optimization solver, such as Gurobi or SCIP, to improve the efficiency and accuracy of model solution.

7. A microgrid optimization system for coupling green hydrogen with coal chemical industry, characterized in that: It is applicable to a microgrid optimization method for a green hydrogen-coupled coal chemical industry as described in any one of claims 1-6, including: It includes a data input unit, a model construction unit, a solution unit, and a result output unit; the data input unit is used to obtain input data; the model construction unit constructs an optimization model based on Pyomo; the solution unit calls a solver to solve the optimization model; the result output unit outputs relevant data according to the solution results.

8. The microgrid optimization system for coupling green hydrogen with coal chemical industry according to claim 7, wherein: It further includes a user interaction interface for displaying the model calculation results and providing user operation interfaces so that users can perform operations such as model parameter setting, result viewing, and analysis.

9. The microgrid optimization system for coupling green hydrogen with coal chemical industry according to claim 7, characterized in that: The system is deployed on multiple servers including an application server, a database server, a model calculation server, and a load balancer to achieve high availability and load balancing.

10. The microgrid optimization system for coupling green hydrogen with coal chemical industry according to claim 7, wherein: The system supports multiple deployment methods, including local deployment and cloud deployment, to meet the needs and scenarios of different users.

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