Wind-light-hydrogen-biomass oxygen-enriched combustion coupled negative carbon system multi-energy collaborative optimization scheduling method

Abstract

The invention relates to the technical field of comprehensive energy system application, in particular to a wind-light-hydrogen-biomass oxygen-enriched combustion coupled negative carbon system multi-energy collaborative optimization scheduling method, which comprises the following steps of: constructing a comprehensive energy system mathematical model integrating wind power, photovoltaic power, an electrolytic tank, hydrogen energy storage, a biomass oxygen-enriched combustion boiler, a carbon capture and a methanation reactor; establishing an optimization function which aims at the lowest total operation cost of the system, the lowest total carbon emission amount in the whole process and the highest renewable energy utilization rate, and determining a weight by adopting a subjective and objective combination method; and finally, predicting wind and light output and load demands by using an optimal machine learning algorithm, embedding a mixed integer linear programming model for solving, and outputting an optimal scheduling instruction. According to the invention, multi-energy complementation and'hydrogen-oxygen-carbon 'closed-loop utilization are realized, and the system economy, the renewable energy consumption capability and the negative carbon efficiency are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of integrated energy system application, in particular to a wind-solar-hydrogen-biomass oxygen-rich combustion coupled carbon-negative system multi-energy collaborative optimization scheduling method. BACKGROUND

[0002] In recent years, the global climate change trend is severe, and the problem of temperature warming caused by carbon emissions has attracted widespread attention from the international community. It is crucial to further comprehensively promote the implementation of the "double carbon" strategy and build a new energy system. The energy structure undergoes a huge transformation, and in the process of building a new energy power system, there are profound changes in the source, network, load and storage of each link. Establishing a clean and low-carbon, safe and efficient new energy system, realizing low-carbon transformation and efficient energy system have become the research focus of all circles. Under this background, integrated energy system as a "multi-energy complementary" platform integrating multiple types of energy production, conversion, transmission and consumption provides an important path for building a clean, low-carbon, supply-demand collaborative and economically efficient energy utilization mode.

[0003] Under the "double carbon" target, there are problems of wind and light abandonment and lack of flexibility after large-scale access of wind, light and electricity in industrial parks; traditional carbon capture and storage (CCS) has high energy consumption and high cost, and is difficult to support deep decarbonization. P2G technology can convert excess renewable electricity into hydrogen and methane, realizing cross-time and space energy transfer; the third generation of oxygen-rich combustion capture (OCC) technology can significantly increase CO2 concentration and reduce separation energy consumption, but the energy consumption cost of air separation device for providing oxygen for oxygen-rich combustion is large, and if the by-product oxygen of the electrolytic tank is directly used for biomass oxygen-rich combustion, a "hydrogen-oxygen-carbon" closed loop can be formed. The optimization scheduling of multi-energy coupling system lacks effective handling of multi-objective conflicts and uncertainties, making it difficult to realize the coordination of deep decarbonization and economic operation. With the development of artificial intelligence, machine learning technology is increasingly widely used in energy systems. Because wind and solar energy and the demand for electric and thermal loads in industrial parks are constantly changing, it is crucial to predict production and demand in order to plan and schedule energy in advance. Exploring the characteristics of different machine learning and integrating multiple machine learning to improve prediction performance is the trend of machine learning development.

[0004] Existing solutions focus on a single coupling path, and lack a unified prediction-optimization-scheduling framework for a wind-solar-hydrogen-biomass carbon-negative system. There is an urgent need for a collaborative optimization method that can simultaneously consider economic efficiency, environmental friendliness and system flexibility. SUMMARY

[0005] The purpose of the present application is to provide a wind-solar-hydro-biomass oxygen-enriched combustion coupled negative carbon system multi-energy collaborative optimization scheduling method, which realizes the collaborative scheduling of electricity, heat, hydrogen and oxygen by system modeling, multi-objective optimization and machine learning prediction, effectively improves the renewable energy consumption, and reduces the operation cost and carbon emission.

[0006] To achieve the above technical purposes and effects, the present application is realized by the following technical solutions: A wind-solar-hydro-biomass oxygen-enriched combustion coupled negative carbon system multi-energy collaborative optimization scheduling method, the method realizes the collaborative scheduling of electricity, heat, hydrogen and oxygen by constructing a comprehensive energy system model, establishing a multi-objective optimization function, and combining machine learning prediction and mathematical programming solution, specifically including the following steps: S1: System modeling: a comprehensive energy system mathematical model integrating wind power generation, photovoltaic power generation, electrolytic tank, hydrogen fuel cell, hydrogen storage tank, oxygen storage tank, heat storage tank, biomass oxygen-enriched combustion boiler, carbon dioxide capture device and methane reactor is established; the mathematical model represents the correlation between energy flow and material flow of each device.

[0007] S2: Establishing electrolysis-oxygen-enriched combustion and capture path: the electrolytic tank produces high-purity oxygen while producing hydrogen, and the oxygen is stabilized by the buffer oxygen storage tank and then directly supplied to the biomass boiler for combustion reaction with fuel. Remove the independent air separation device, increase the flame temperature and burnout degree without increasing the energy consumption of additional oxygen production, increase the volume fraction of flue gas carbon dioxide, reduce the unit separation energy consumption of the subsequent carbon capture unit, and couple with the methanation reactor to realize the closed loop conversion and energy storage of carbon in the system.

[0008] S3: Multi-objective optimization function construction: based on the comprehensive energy system mathematical model, a multi-objective optimization problem including the total operation cost of the system, the total carbon emission of the whole process and the renewable energy utilization rate is constructed; the optimal-worst method is used to obtain the subjective weight of each target, and the criterion importance calculation method is used to obtain the objective weight of each target, and the subjective and objective weights are fused to weight the sum of the multi-objective to form a single comprehensive objective function.

[0009] S4: Prediction and solution: using machine learning algorithm, based on historical meteorological data and system operation data, the wind power, photovoltaic power, electric load and heat load in the future period are predicted; the prediction results are taken as known parameters and embedded into a mixed integer linear programming model including power balance constraints, device operation constraints and energy storage constraints of electricity, heat, hydrogen and oxygen, and the comprehensive objective function is solved, so as to output the optimal scheduling instruction which makes the total operation cost, carbon emission and renewable energy utilization rate lowest.

[0010] Furthermore, the establishment of the mathematical model of the integrated energy system in step S1 specifically includes: Establish a functional model relating the output power of a wind turbine to wind speed: In the formula: for Wind speed at any given moment; In a given Output power at any given time (kW); Pr is the rated output power; To cut in wind speed; Rated wind speed; To cut off the wind speed; Establish a functional model relating the output power of a photovoltaic generator to solar irradiance and battery temperature: In the formula: For photovoltaic power stations The actual output power at any given time; Let be the solar radiation intensity received by the photovoltaic cell area at time t; and let A be the photovoltaic cell area. The nominal efficiency is given under standard test conditions; β is the temperature coefficient. For photovoltaic cells in The operating temperature at any given time; Th represents the ambient temperature. Establish a functional model relating the power consumption of the electrolyzer to the hydrogen and oxygen production: In the formula, Hydrogen gas produced by the electrolyzer at time t; The power of the electrolyzer in the water electrolysis process; Oxygen produced in the electrolytic cell at time t; and These are the minimum and maximum power of the electrolytic cell, respectively. This represents the maximum power change rate of the electrolytic cell; , For the efficiency of the electrolyzer and the calorific value of hydrogen; Establish a functional model relating the power generation, heat generation, and hydrogen consumption of a hydrogen fuel cell: In the formula, , , These represent the power generation, heat production, and hydrogen consumption of FC at time t, respectively. , To improve hydrogen consumption power generation rate and waste heat utilization rate, Let t be the amount of hydrogen consumed by the fuel cell at time t; and These represent the minimum and maximum power generation of a hydrogen fuel cell, respectively. Construct mathematical models for energy storage in hydrogen storage tanks, oxygen storage tanks, and thermal storage tanks; Mathematical model of hydrogen storage tank: In the formula, The amount of hydrogen stored in HST during time period t; , The hydrogen charging and discharging rates of HST during time period t; , The maximum hydrogen charging rate and hydrogen decharging rate of HST; , These are binary variables, representing the charging and discharging state parameters of HST during time period t; =1、 =0 indicates that HST is in a hydrogen-filling state, and the opposite indicates that it is in a hydrogen-depleting state; , The minimum and maximum hydrogen storage capacities of HST; , The hydrogen charging and discharging efficiency of HST.

[0011] Mathematical model of oxygen storage tank: In the formula, Let OS be the oxygen storage capacity during time period t. The oxygen production of EL during time period t; Let be the amount of oxygen consumed by Bb at time t; and These represent the minimum and maximum oxygen storage capacity of OS, respectively.

[0012] Mathematical model of thermal storage tank: In the formula, Thermal storage tank charging power; Thermal storage tank heat release power; Thermal storage tank heat storage capacity; Lower limit of thermal storage tank capacity; Maximum capacity of thermal storage tanks; , Lower limit of thermal storage and heat release power; , Upper limit of heat storage and heat release power.

[0013] Construct a mathematical model for a biomass boiler that includes an oxygen-enriched combustion mechanism and an oxygen-enriched CO2 capture system. Biomass boiler mainly meets the heat load required by the park. The introduction of oxygen-enriched combustion significantly increases the flame temperature, accelerates the pyrolysis and combustion reaction of the fuel, and makes the biomass combustion more sufficient. Oxygen-enriched environment can improve combustion efficiency, reduce pollutant emissions, enhance fuel adaptability and optimize operation, which is an important technical direction for efficient and clean utilization of biomass energy. The mathematical model of biomass boiler is as follows: In the formula, is the thermal output of Bb at t period; is the thermal efficiency of biomass unit; is the biomass heat value; is the consumption of biomass fuel of Bb at t period; is the oxygen consumption of Bb at t period; is the oxygen consumption ratio coefficient of biomass fuel.

[0014] Oxygen-enriched capture technology increases the concentration of CO2 in flue gas by high concentration of oxygen, realizes simple and efficient carbon capture, and has lower cost than pre-combustion capture and post-combustion capture. The mathematical model of oxygen-enriched capture is as follows: In the formula, CO2 generated by the unit at t period, is the capture amount of CO2 of CCS at t period, is the amount of CO2 not captured by CCS, is the amount of CO2 stored by CCS at t period, is the amount of CO2 used for methanation by CCS at t period, is the electric power consumed by CCS at t period; is the electric power consumed by CCS to capture CO2 at t period; is the fixed electric power consumed by CCS at t period; , are the efficiency and power consumption rate of CO2 captured by CCS, respectively.

[0015] A methane reaction and waste heat recovery model using captured CO2 and hydrogen to synthesize methane is constructed; Part of the CO2 in the flue gas captured by oxygen-enriched combustion and part of the hydrogen in HES are used to generate methane in the MR device, which is supplied to local residents, realizes carbon cycle, plays the advantages of hydrogen energy storage system, and realizes multi-stage utilization of energy. The mathematical model of MR is as follows: In the formula, is the amount of H2 participating in the reaction at t period in the methanation process; is the amount of methane generated; is the total heat generated by methanation at t period; is the heat recovery efficiency. is the direct utilization efficiency of the waste heat in the methanation process in the time period t; is the heat that can be recovered and reused by the waste heat recovery system in the time period t; is the coefficient of the waste heat corresponding to the methanation reaction; is the direct utilization efficiency of the waste heat in the methanation process; is the waste heat recovery efficiency.

[0016] Further, the water electrolysis process in the step S2 includes: water dissociation of hydrogen ions and oxygen ions, the hydrogen ions moving to the cathode to generate hydrogen, and the oxygen ions moving to the anode to generate oxygen. For every 1 kg of hydrogen generated, 7-8 kg of oxygen is generated at the same time. The byproduct oxygen from the electrolytic tank is directly sent to the biomass oxygen-enriched combustion boiler after pressure stabilization by the oxygen storage tank, and the pressure stabilization interval is , ], wherein is the minimum tank pressure allowed for oxygen-enriched combustion, is the upper limit pressure allowed within the requirements of equipment design and safety valve setting, and the oxygen purity is ≥ 99%. In the wind and light surplus and low electricity price period, the scheduling model preferentially increases the electrolytic tank load to supplement the hydrogen and oxygen inventory, so that the oxygen storage amount is maintained within , ], wherein is the target inventory determined according to the oxygen-enriched working condition of the boiler design, and Δ is the upper and lower tolerance set to avoid frequent start and stop. In the period of high heat load and insufficient wind and light, the "oxygen storage release → boiler oxygen-enriched load increase → fuel cell power (heat) supplement" is executed to realize the rapid response to the heat load and carbon capture load.

[0017] Further, the oxygen-enriched combustion process in the step S2 includes: forming oxygen-enriched combustion under the condition that the biomass flow rate and the oxygen flow rate are in a ratio of 1:1.371, the combustion efficiency is 99%-100%, and the heat generated is directly supplied to the user or enters the heat storage tank for adjustment. In addition, since there is a risk of boiler damage due to high flame temperature in the oxygen-enriched combustion process, a flue gas circulation technology is adopted to adjust the flame temperature by using the oxidizing agent in the exhaust gas to reduce the risk of boiler damage.

[0018] Further, the total system operation cost in the step S3 includes: biomass fuel cost, operation and maintenance cost of all equipment, penalty cost due to abandoned wind and light, carbon trading cost, and income from hydrogen and methane sales, and the cost-benefit function model is: The total operation cost of the park includes biomass boiler fuel cost , abandoned wind and light cost, CO2 sequestration cost , abandoned wind and light cost , equipment operation and maintenance cost , carbon trading cost , methane transaction cost , electricity purchase cost In the operation cycle T, the total operation cost of the integrated energy system is minimized The objective function is the minimum.

[0019] In the formula, is the biomass unit t time operation fuel cost, is the biomass unit fuel price coefficient; is the carbon sequestration cost coefficient, is the carbon capture unit count variable; , respectively represent the system unit wind, light abandoned penalty cost coefficient; , respectively represent the abandoned wind, light quantity at t time; , , , , , , , respectively represent Bb 、WT、PV、EL、HST、OST、HFC、MR、CCS and TST device unit time unit power operation maintenance cost; represents the carbon transaction cost at t time; represents the carbon trading price, represents the carbon quota at t time; represents the methane market transaction price; represents the market electricity price at t time, represents the electricity purchase power at t time.

[0020] Further, the calculation of the total carbon emission in the whole process in step S3 includes the carbon emission of biomass fuel from transportation to processing, the carbon emission of wind turbine and photovoltaic generator in its life cycle, and the direct carbon emission of biomass combustion, and the amount of carbon dioxide captured by the carbon dioxide capture device and the amount of carbon dioxide consumed by the methane reactor are deducted.

[0021] Further, the fusion mode of subjective and objective weights in step S3 is: multiply the subjective weight vector and the objective weight vector element by element, and normalize the new vector obtained after multiplication to obtain the final comprehensive weight for weighted summation.

[0022] Further, the machine learning algorithm in step S3 is selected by comparing the prediction accuracy from the algorithm set consisting of artificial neural network, BP neural network, autoregressive integrated moving average model, random forest, particle swarm optimization artificial neural network and particle swarm optimization BP neural network.

[0023] Further, the constraint conditions of the mixed integer linear programming model in step S4 further include: power ramp rate constraints of electrolytic cell, hydrogen fuel cell, biomass boiler and methane reactor, and capacity upper and lower limit and charging and discharging rate constraints of hydrogen storage tank, oxygen storage tank and heat storage tank.

[0024] Further, combined with the integrated energy system model, an integrated energy system scheduling model considering cost benefit, new energy utilization rate and negative carbon emission benefit is constructed, including: Combined with the integrated energy system mathematical model, a cost benefit function model considering biomass fuel cost, equipment operation and maintenance cost, abandoned wind and light cost, carbon trading cost and hydrogen and methane sales revenue is constructed; Combined with the integrated energy system mathematical model, a new energy consumption function model considering maximum utilization of wind energy and solar energy is constructed; Combined with the integrated energy system model, an emission function model of the whole process of biomass transportation and processing, biomass combustion, WT / PV production and construction process, oxygen-enriched combustion carbon capture effect and MR device absorbing part of CO2 is constructed; Based on BWM+CRITIC distribution weight coefficient, the cost function, carbon emission function and energy utilization function are weighted and summed to construct the final system objective function.

[0025] Further, the step S4 includes: historical meteorological and load data acquisition; data cleaning, normalization and feature extraction; comparing six different machine learning prediction of wind, light output and electric and heat load demand, including artificial neural network (ANN), BP neural network (BP), autoregressive integrated moving average model (ARIMA), random forest (RF), particle swarm optimization artificial neural network (PSO-ANN) and particle swarm optimization BP neural network (PSO-BP); embedding the optimal prediction result into the mixed integer linear programming model; simulating and solving the optimization scheduling result to obtain the system optimal scheduling result.

[0026] Further, the method uses the oxygen by-product of the electrolytic cell for oxygen-enriched combustion of the biomass boiler, improves the combustion efficiency and the concentration of carbon dioxide in the flue gas, thereby reducing the energy consumption of subsequent carbon dioxide capture; at the same time, the captured carbon dioxide and hydrogen produced by the electrolytic cell are synthesized into methane by the methane reactor, realizing the system internal recycling and energy storage of carbon elements, which together constitute the core operation mechanism of the negative carbon system.

[0027] The beneficial effects of this invention are: This invention integrates wind, solar, hydrogen, and biomass multi-energy coupling, utilizing the electro-gas conversion process to efficiently utilize hydrogen and byproduct oxygen, thus advancing the negative carbon system from "single energy utilization" to "multi-energy synergistic emission reduction." By directly supplying byproduct oxygen from electrolysis to the biomass boiler, high-concentration CO2 flue gas can be generated in the combustion section without the need for additional air separation devices, and energy consumption is significantly reduced through simplified capture processes. After coupling, the system effectively improves CO2 separation efficiency and shortens the capture path while ensuring thermal and power requirements, thereby reducing the unit energy consumption and operating cost of the capture system and achieving deep synergy between oxygen-enriched combustion and carbon capture.

[0028] This invention combines machine learning to predict solar and wind power generation and the required electricity and heat loads of the industrial park, ultimately obtaining the daily wind and solar power output and electricity and heat load demands. The optimal prediction results are embedded into a mixed-integer linear programming model, and the optimized scheduling results are solved through simulation. The BWM+CRITIC method integrates expert subjective and data objective weighting, organically weighting the three objectives of "minimizing operating costs," "minimizing total carbon emissions," and "maximizing wind and solar utilization." After solving the multi-objective scheduling problem, the system achieves high renewable energy consumption and park-level negative carbon operation while balancing economic and environmental considerations, achieving a comprehensive effect of energy conservation, emission reduction, and sustainable development.

[0029] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is an overall flowchart of the present invention; Figure 2 This is a diagram of the oxygen-enriched combustion flue gas circulation of the present invention; Figure 3 This is a schematic diagram illustrating the proposed prediction method using six machine learning algorithms of the present invention; Figure 4 A schematic diagram showing the optimal prediction results for wind and solar power output and electricity and heat load demand; Figure 5 The diagram shows the results of the operation of the method of the present invention; a represents the result of power supply and demand balance, b represents the result of heat supply and demand balance, c represents the result of hydrogen supply and demand balance, and d represents the result of hydrogen supply and demand balance. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0033] Example 1 The multi-energy coordinated optimization scheduling method for a wind-solar-hydrogen-biomass oxygen-enriched combustion coupled negative carbon system described in this embodiment includes the following steps: S1: Construct a comprehensive energy system model integrating wind and solar power output, hydrogen energy storage system, biomass oxygen-enriched combustion boiler, oxygen-enriched capture process, and methanation process; S2: The electrolyzer produces high-purity oxygen while generating hydrogen. After being stabilized by a buffer oxygen storage tank, the oxygen is directly supplied to the biomass boiler to undergo combustion with the fuel. Eliminating the independent air separation device increases the flame temperature and burnout without increasing additional oxygen production energy consumption, thereby increasing the volume fraction of carbon dioxide in the flue gas and reducing the unit separation energy consumption of the subsequent carbon capture unit.

[0034] S3: Combining the integrated energy system model, with the objectives of minimizing operating costs, minimizing carbon emissions throughout the process, and maximizing the utilization rate of new energy sources, a multi-objective weighted function is constructed by comprehensively weighting the optimal-worst method and the criterion importance calculation method and then summing the weights. S4: Based on historical meteorological data and the operating status of wind and solar turbines, the effects of different machine learning algorithms on predicting wind and solar power output and system electricity and heat load demand are compared. The optimal prediction result is embedded into a mixed integer linear programming (MILP) model, and the optimized scheduling result is simulated and solved to obtain the optimal scheduling result of the system.

[0035] In step S1, a comprehensive energy system mathematical model is constructed, integrating wind and solar power output, hydrogen energy storage system, biomass oxy-fuel combustion boiler, oxy-fuel capture process, and methanation process, as detailed below: Construct mathematical models for the power output of wind turbine generators and photovoltaic generators; The mathematical model for WT is as follows: In the formula: for Wind speed at any given moment; In a given Output power at any given time (kW); Pr is the rated output power; To cut in wind speed; Rated wind speed; To cut off the wind speed.

[0036] The mathematical model for PV is as follows: In the formula: For photovoltaic power stations The actual output power at any given time; Let be the solar radiation intensity received by the photovoltaic cell area at time t; and let A be the photovoltaic cell area. The nominal efficiency is given under standard test conditions; β is the temperature coefficient. For photovoltaic cells in The operating temperature at any given time; Th represents the ambient temperature.

[0037] Construct a mathematical model for a hydrogen energy storage system that includes strategies for hydrogen and oxygen production by electrolyzing water in an electrolyzer, combined heat and power generation in a hydrogen fuel cell, and utilization of oxygen as a byproduct. The mathematical model of the electrolyzer is as follows: In the formula, Hydrogen gas produced by the electrolyzer at time t; The power of the electrolyzer in the water electrolysis process; Oxygen produced in the electrolytic cell at time t; and These are the minimum and maximum power of the electrolytic cell, respectively. This represents the maximum power change rate of the electrolytic cell; , For the efficiency of the electrolyzer and the calorific value of hydrogen; Mathematical model of hydrogen fuel cell: In the formula, , , These represent the power generation, heat production, and hydrogen consumption of FC at time t, respectively. , To improve hydrogen consumption power generation rate and waste heat utilization rate, Let t be the amount of hydrogen consumed by the fuel cell at time t; and These represent the minimum and maximum power output of a hydrogen fuel cell, respectively.

[0038] Construct mathematical models for energy storage in hydrogen storage tanks, oxygen storage tanks, and thermal storage tanks; Mathematical model of hydrogen storage tank: In the formula, The amount of hydrogen stored in HST during time period t; , The hydrogen charging and discharging rates of HST during time period t; , The maximum hydrogen charging rate and hydrogen decharging rate of HST; , These are binary variables, representing the charging and discharging state parameters of HST during time period t; =1、 =0 indicates that HST is in a hydrogen-filling state, and the opposite indicates that it is in a hydrogen-depleting state; , The minimum and maximum hydrogen storage capacities of HST; , The hydrogen charging and discharging efficiency of HST.

[0039] Mathematical model of oxygen storage tank: In the formula, Let OS be the oxygen storage capacity during time period t. The oxygen production of EL during time period t; Let be the amount of oxygen consumed by Bb at time t; and These represent the minimum and maximum oxygen storage capacity of OS, respectively.

[0040] Mathematical model of thermal storage tank: In the formula, Thermal storage tank charging power; Thermal storage tank heat release power; Thermal storage tank heat storage capacity; Lower limit of thermal storage tank capacity; Maximum capacity of thermal storage tanks; , Lower limit of thermal storage and heat release power; , Upper limit of heat storage and heat release power.

[0041] Construct a mathematical model for a biomass boiler that includes an oxygen-enriched combustion mechanism and an oxygen-enriched CO2 capture system. Biomass boilers primarily meet the heat load requirements of the industrial park. Introducing oxygen-enriched combustion significantly increases flame temperature, accelerates fuel pyrolysis and combustion reactions, and ensures more complete biomass combustion. An oxygen-enriched environment improves combustion efficiency, reduces pollutant emissions, enhances fuel adaptability, and optimizes operation, representing an important technological direction for the efficient and clean utilization of biomass energy. The mathematical model for a biomass boiler is as follows: In the formula, The thermal output of Bb during time period t; For the thermal efficiency of biomass units; It is the calorific value of biomass; Let Bb be the amount of biomass fuel consumed during time period t; Let Bb be the amount of oxygen consumed during time period t. This is the oxygen consumption ratio coefficient for biomass fuel.

[0042] Oxygen-enriched carbon capture technology increases the CO2 concentration in flue gas by using a high concentration of oxygen, achieving simple and efficient carbon capture at a lower cost than pre-combustion and post-combustion capture. The mathematical model for oxygen-enriched carbon capture is as follows: In the formula, CO2 produced by the unit during time period t Let be the CO2 capture amount of CCS during time period t. This represents the amount of CO2 not captured by CCS. The amount of CO2 stored in CCS during period t. This represents the amount of CO2 used by CCS for methanation during time period t. The electrical power consumed during the CCS t period; The CCS t time period is used to capture the electrical power consumed by CO2; The electrical power consumed by CCS during the fixed time period t; , The figures represent the efficiency and power consumption of CO2 captured by CCS, respectively.

[0043] Construct a model for the synthesis of methane using captured CO2 and hydrogen, and the recovery of waste heat. Oxygen-enriched combustion captures some CO2 from the flue gas and some hydrogen from the hydrogen storage system (HES) to generate methane in the MR unit, which is then supplied to local residents, achieving a carbon cycle, leveraging the advantages of hydrogen energy storage systems, and realizing multi-stage energy utilization. The mathematical model for MR is as follows: In the formula, The amount of H2 that participates in the reaction during time period t in the methanation process; This represents the amount of methane produced. The total heat generated by methanation during time period t; For heat recovery efficiency. The residual heat that sustains the reaction directly during the methanation process within time period t; The heat that can be recovered and reused by the waste heat recovery system within time period t; This is the coefficient corresponding to the residual heat of the methanation reaction; Methanation maintains its own direct utilization efficiency in the reaction; Waste heat recovery efficiency.

[0044] In step S2, an electrolysis-oxygen-enriched combustion and capture path is established: the electrolyzer produces high-purity oxygen while generating hydrogen. The oxygen, after being stabilized by a buffer oxygen storage tank, is directly supplied to the biomass boiler to undergo combustion with the fuel. The independent air separation device is removed, increasing the flame temperature and burnout without increasing additional oxygen production energy consumption, thereby increasing the volume fraction of carbon dioxide in the flue gas and reducing the unit separation energy consumption of the subsequent carbon capture unit. Specifically: Water dissociates into hydrogen and oxygen ions. Hydrogen ions move to the cathode to produce hydrogen gas, while oxygen ions move to the anode to produce oxygen gas. For every 1 kg of hydrogen gas produced, 7-8 kg of oxygen are generated simultaneously. The byproduct oxygen from the electrolysis cell is directly fed into the biomass oxygen-enriched combustion boiler after being pressure-stabilized in an oxygen storage tank. The pressure stabilization range is [insert range here]. , ],in The minimum tank pressure required to allow oxygen-enriched combustion. The upper limit pressure is set within the allowable range of equipment design and safety valve settings, and the oxygen purity is ≥99%. During periods of surplus wind and solar power and low electricity prices, the dispatch model prioritizes increasing the electrolyzer load to replenish hydrogen and oxygen stocks, maintaining oxygen storage at [a certain level]. , [Inside, among which] The target inventory is determined according to the oxygen-enriched operating conditions of the boiler design, and Δ represents the upper and lower tolerances set to avoid frequent start-ups and shutdowns. During periods of high heat load and insufficient wind and solar power, the process follows the sequence of "oxygen release → boiler oxygen enrichment to increase load → fuel cell power supplementation (heat supplementation)" to achieve rapid response to heat load and carbon capture load.

[0045] The oxygen-enriched combustion process in step S2 includes: oxygen-enriched combustion is achieved under the condition that the ratio of biomass flow rate to oxygen flow rate is 1:1.371, with a combustion efficiency of 99%-100%. The generated heat is directly supplied to users or enters a heat storage tank for regulation. Furthermore, since there is a risk of boiler damage due to high flame temperature during oxygen-enriched combustion, flue gas recirculation technology is used to regulate the flame temperature with oxidants in the exhaust gas, reducing the risk of boiler damage.

[0046] In step S3, combining the mathematical model of the integrated energy system, with the objectives of minimizing operating costs, minimizing total carbon emissions, and maximizing the utilization rate of new energy sources, a weighted sum is calculated after weighting using the optimal-worst method and the criterion importance calculation method. Specifically: Based on the mathematical model of the integrated energy system, a cost-benefit function model is constructed that considers biomass fuel costs, equipment operation and maintenance costs, wind and solar curtailment costs, carbon trading costs, and revenue from hydrogen and methane sales. The total operating cost of the park includes the fuel cost of the biomass boiler. Costs of wind and solar power curtailment and CO2 storage Costs of wind and solar power curtailment Equipment operation and maintenance costs Carbon trading costs Methane transaction costs Electricity purchase cost Within the operating cycle T, the total operating cost of the integrated energy system is considered. The minimum is the objective function.

[0047] In the formula, The fuel cost of operating the biomass unit at time t. This refers to the fuel price coefficient for biomass power generation units; This is the carbon sequestration cost coefficient. For counting variables of carbon capture units; , These represent the penalty cost coefficients for wind and solar power curtailment per unit of the system, respectively; , These represent the amount of wind and solar power curtailment at time t, respectively. , , , , , , , These represent the unit power operation and maintenance costs per unit time period for Bb, WT, PV, EL, HST, OST, HFC, MR, CCS, and TST devices, respectively. This represents the carbon trading cost at time t; Indicates the carbon trading price, This represents the carbon allowance at time t; This indicates the market price of methane; This represents the market electricity price at time t. This represents the power purchased at time t.

[0048] Based on the mathematical model of the integrated energy system, a new energy consumption function model considering the maximum utilization of wind and solar energy is constructed; In the formula, The utilization rate of new energy per unit time; WTPV power generation per unit time; The amount of WTPV power generation utilized per unit time; , This refers to the amount of wind and solar power curtailed per unit time.

[0049] Based on the mathematical model of the integrated energy system, a full-process carbon emission function model is constructed, which includes emissions from biomass transportation and processing, biomass combustion, WTPV production and construction, carbon capture efficiency of oxygen-enriched combustion, and CO2 absorption by the MR device. In the formula, This refers to the carbon emissions per unit time during the transportation and processing of biomass. Emission factor; , The carbon emissions from the production and construction of WTPV. , Emission factor; This represents the system's cumulative total carbon emissions.

[0050] Based on the weighting coefficients assigned by BWM+ CRITIC, the cost function, carbon emission function, and energy utilization function are weighted and summed.

[0051] BWM requires experts to directly compare the relative importance of each indicator with the best and worst indicators to obtain a more consistent subjective weighting.

[0052] The optimal index in the set is determined by experts based on the actual situation. and worst-case indicators For each metric Experts provide the best indicators relatively Preference value ,in Similarly, for each indicator Experts gave Compared to the worst-case indicator Preference value ,in Let the weight of each indicator be... satisfy( 0 and Based on the principle of consistency, two objectives are constructed: , To minimize both deviations simultaneously, a model can be constructed as follows: This makes it possible for all : , The optimal solution is obtained by solving the problem. , =1,2,...n.

[0053] The CRITIC method determines the objective weights of indicators based on the information entropy of the indicator data itself and the correlation between indicators. For a given sample data matrix... (in Indicates the decision-making object, (Representing indicators), and then normalizing them: Then, the weight of each indicator is calculated based on the standardized data: Then calculate the indicators. Entropy value: .in, To prevent small constants with a logarithm of 0, the degree of difference in the indicators is reflected in the complement of the information entropy. Furthermore, considering the correlation between indicators, the indicators The contrast intensity can be expressed as: Finally, the objective weight vector of the indicators is obtained by comparing the intensity normalization: .

[0054] Combining the BWM and CRITIC methods, the two weight vectors are fused using the product method and then normalized to obtain the comprehensive weights: Construct the system's final objective function: In the formula, This represents the total daily operating cost of the system. This represents the system's cumulative total carbon emissions. The utilization rate of new energy per unit time , , This indicates the weight of each objective.

[0055] In step S4, based on historical meteorological data and the operating status of wind and solar turbines, the effects of different machine learning algorithms on predicting wind and solar power output and system electricity and heat load demand are compared. The optimal prediction result is embedded into a mixed integer linear programming model, and the optimized scheduling result is simulated and solved to obtain the optimal scheduling result of the system.

[0056] Based on historical meteorological data and wind and solar turbine operating status models, a pre-set machine learning algorithm is used to predict wind and solar power generation. Hourly average values ​​are used to predict solar and wind power generation, as well as the required electricity and heat load for the industrial park. The ML algorithm is trained using historical data from the fourth quarter and then validated to ultimately obtain the daily wind and solar power output and electricity and heat load requirements.

[0057] Six machine learning algorithms were used for prediction comparison, including ANN, BP, ARIMA, RF, PSO-ANN, and PSO-BP. The proposed method for prediction using machine learning algorithms is as follows: First, wind and solar power output and historical electricity and heat load data are collected and preprocessed. After feature extraction, the data are divided into training, validation, and test sets. Next, an appropriate machine learning model is selected for training and parameter tuning. Finally, the model's prediction performance is evaluated using the test set, and the predicted values ​​for wind and solar power output and electricity and heat loads are output.

[0058] The optimal prediction results are embedded into a mixed integer linear programming model, and the optimized scheduling results are simulated and solved. The constraints of the MILP model include comprehensive energy system constraints, including internal electrical, thermal, and hydrogen power balance constraints, carbon capture constraints, and equipment output constraints.

[0059] Electric power balance constraints: In the formula, Purchase electricity for period t; Let t represent the user's electrical load during the time period.

[0060] Thermal power balance constraint: In the formula, The user's heat load during time period t; Hydrogen power balance constraint: Oxygen power balance constraint: Example 2 like Figure 1 As shown in this embodiment, the overall process of a multi-energy collaborative optimization scheduling method for a carbon-negative system coupled with wind-solar-hydrogen-biomass oxygen-enriched combustion includes: First, constructing a comprehensive energy system mathematical model that includes a wind farm, a photovoltaic power station, an electrolyzer, a hydrogen fuel cell cogeneration, a hydrogen storage tank, an oxygen storage tank, a thermal storage tank, a biomass boiler, an oxygen-enriched capture unit, a methane reactor, and waste heat recovery and utilization; then, with the objectives of "minimizing the total operating cost of the system," "minimizing the carbon emissions throughout the process," and "maximizing the utilization rate of new energy sources," determining the weights through the fusion of the optimal-worst method and the CRITIC method, and constructing a multi-objective weighted optimization function; next, using six different machine learning prediction algorithms—ANN, BP, ARIMA, RF, PSO-ANN, and PSO-BP—to perform rolling predictions of wind power output, photovoltaic power output, and electricity-heat load for the next 24 hours, and embedding the optimal prediction results into an uncertainty margin model; finally, integrating the above prediction model, uncertainty margin, and multi-objective optimization model into a mixed integer linear programming problem, and calling the Gurobi solver to obtain the optimal scheduling strategy.

[0061] Figure 2 This invention illustrates the flue gas circulation diagram of oxygen-enriched combustion in an embodiment of the present invention. Oxygen-enriched combustion and capture can effectively achieve CO2 capture, and the cost is lower than that of pre-combustion and post-combustion capture technologies for carbon capture. However, high-oxygen combustion requires an air separation oxygen generator, resulting in higher oxygen production costs. Therefore, this invention considers that P2G technology can produce oxygen while producing hydrogen, and its combined operation with an oxygen-enriched combustion boiler can reduce the system's operating costs. The oxygen produced by the first-stage electrolysis reaction of P2G is input into the boiler, allowing biomass to burn fully in an oxygen-enriched environment, improving combustion efficiency while obtaining high-concentration CO2 flue gas. After dust removal, desulfurization, and condensation treatment, the flue gas enters the carbon capture device to capture CO2. The captured CO2 is divided into two parts: one part of CO2, together with the hydrogen produced by water electrolysis, is used as a raw material for the methanation reaction; the remaining CO2 is captured using carbon sequestration technology to reduce CO2 emissions.

[0062] Figure 3 This invention demonstrates the workflow of six machine learning algorithms used in the prediction of wind and solar power output and electricity-heat load in embodiments of the present invention: First, historical wind speed, solar radiation, temperature, and load data are preprocessed and features are extracted; second, BP neural networks (BP), random forests (RF), artificial neural networks (ANN), ARIMA adaptive neural networks, and particle swarm optimization (PSO) are constructed to iteratively optimize the weights and thresholds of ANN and BP networks to obtain the PSO-BP model; then, the six models are cross-validated and their performance is evaluated on the same test set, and their RMSE, MAE, and R are compared. 2 Based on these indicators, the algorithm prediction value with the best overall performance is finally selected and embedded into the subsequent optimization process. Through the above process, this invention can ensure prediction accuracy while taking into account computational efficiency, providing high-quality wind and solar power output and load demand prediction data for subsequent multi-objective optimization scheduling.

[0063] Figure 4 This paper demonstrates the optimal performance of the PSO-BP algorithm in predicting wind and solar power output and electricity and heat load demand in this embodiment of the invention. Among six candidate models, the PSO-BP algorithm achieved the best performance indicators: root mean square error (RMSE) 3.77, mean absolute error (MAE) 2.16, and coefficient of determination (R²) 0.988, which are superior to pure BP, ANN, RF, ARIMA, and PSO-ANN, respectively. This result verifies the superior performance of the PSO-optimized BP neural network in wind power, electricity load, and heat load prediction tasks, providing high-precision and robust input data for subsequent multi-energy collaborative optimization scheduling. Figure 4 a shows the wind power output time series comparison. It can be seen that the PSO-BP prediction curve (dashed line) and the measured value (solid line) are highly consistent, which can accurately capture the power change trend caused by wind speed fluctuations.Figure 4 b represents the photovoltaic output comparison. PSO-BP effectively reflects the sudden changes and attenuation process of power generation during peak solar irradiance periods. Figure 4 c represents the predicted electricity load, and the model maintains a detailed fit even when switching between peak and off-peak electricity consumption. Figure 4 d represents the predicted heat load. The PSO-BP curve can synchronously follow changes in temperature and heat demand, with the overall deviation controlled within an acceptable range. These results demonstrate that the PSO-BP algorithm used in this invention maintains a sensitive response to power fluctuations under various operating modes while also ensuring stability and robustness, providing high-precision input data for multi-energy collaborative optimization scheduling.

[0064] like Figure 5 As shown, in terms of electrical energy ( Figure 5 a) During nighttime when wind power is abundant and electricity prices are low, the electrolyzer produces a large amount of hydrogen and oxygen, utilizing surplus electricity; during the early morning off-peak period when wind power is insufficient, the system supplements electricity through purchase and ensures oxygen-enriched heating from the biomass boiler; during the daytime peak solar power period, almost no external purchase is needed; in the evening and at night when both solar and wind power are weak, the system again relies on purchased electricity and the coordinated efforts of fuel cells and hydrogen storage to smooth out load fluctuations. Regarding thermal energy ( Figure 5 (b) During the early morning peak heat load, the oxygen-enriched biomass boiler is the main supplier, supplemented by waste heat from the thermal storage tank, fuel cell, and methane reactor. During the midday off-peak heat load, wind and solar power output is sufficient, and the boiler stores excess heat in the thermal storage tank after meeting the current demand, preparing for subsequent peak periods. Regarding hydrogen supply and demand ( Figure 5 c) The produced hydrogen is prioritized to drive fuel cell power generation and combined heat and power (CHP), reducing electricity purchases and fuel consumption; the remaining hydrogen, besides meeting the oxygen enrichment needs of boilers, is synthesized from captured CO2 in a methane reactor and supplied to residential gas supply, while the remainder is injected into hydrogen storage tanks for inter-period scheduling, achieving multi-stage utilization of hydrogen energy. Regarding oxygen supply and demand ( Figure 5 d) Oxygen produced as a byproduct of electrolysis is directly fed into the boiler for combustion. Excess oxygen is stored in an oxygen storage tank when wind and solar power are in surplus and released when needed to maintain oxygen-enriched combustion. Overall scheduling results demonstrate that this invention, through the close coupling of subsystems such as wind power, photovoltaics, hydrogen storage, biomass oxygen-enriched combustion, carbon capture, and methanation, achieves precise matching of the four major subsystems (electricity, heat, hydrogen, and oxygen) under multi-energy complementarity conditions. This realizes the synergistic optimization goals of high-proportion renewable energy consumption, stable electricity and heat supply, and park-level net carbon emissions.

[0065] Table 1. Cost Comparison Analysis of Different Schemes (10 4 CNY) Table 1 shows the module costs and total operating costs for each of the five scenarios within a scheduling cycle in this embodiment of the invention. The operation and maintenance costs include the maintenance costs of each device within the system during operation. Scenario 1 does not have any wind or solar power consumption measures, therefore the penalty cost for wind and solar power curtailment is the highest, reaching 237,000 yuan. After adding a hydrogen energy storage system, excess wind and solar power is converted into hydrogen energy for storage, and the utilization rate of new energy is basically 100%. Scenarios 1 and 2 do not incur storage costs, but because emissions exceed the quota, carbon emission rights need to be purchased from external sources, resulting in additional carbon trading costs. Adding carbon capture incurs storage costs, while generating some revenue through the sale of carbon emission credits. During park operation, due to the uncertain output and load changes of wind and solar power, it is sometimes necessary to purchase electricity from the grid to meet load demand. Scenario 5 has the highest electricity purchase cost, reaching 258,300 yuan. This is because the system needs to purchase a large amount of electricity to supply the electrolyzer to produce oxygen to maintain the oxygen-enriched combustion of the biomass boiler. However, this expenditure is offset by the revenue generated from the production of methane for residents due to the significant increase in hydrogen production. Meanwhile, during the multi-objective optimization process, the economic benefits of purchasing electricity to generate methane when electricity prices are low are also a reason for purchasing more electricity. The total cost of Scenario 5 decreased by 339.17%, 23.90%, 13.36%, and 6.21% respectively compared to the previous four scenarios, thus proving that the integrated energy negative carbon park multi-energy synergistic optimization scheduling proposed in this invention, which combines hydrogen energy storage and carbon capture, has superior economic performance.

[0066] Table 2 Total Daily Carbon Footprint in Different Scenarios Table 2 shows the total daily carbon footprint of the embodiments under different scenarios. The carbon emissions of each scenario show significant differences. Scenario 1 and Scenario 2 have total emissions of 213.33 tons and 204.48 tons, respectively, indicating that conventional biomass combustion and the mode of introducing only hydrogen energy storage have limited emission reduction effects. With the addition of carbon capture technology, the emissions of Scenario 3 and Scenario 4 are 59.24 tons and 55.60 tons, respectively, nearly four times lower than Scenario 1 and Scenario 2, effectively reducing carbon dioxide emissions. Scenario 5, with the synergy of oxy-fuel combustion and hydrogen energy storage, further improves combustion efficiency and capture efficiency, resulting in a low hourly emission curve overall, demonstrating strong carbon-negative potential, with a total emission of only 32.75 tons. This example considers the premise of zero carbon emissions throughout the entire life cycle of biomass, and each scenario exhibits "carbon-negative" performance within 24 hours. Scenario 1 and Scenario 2, without carbon capture technology, still show a net emission state and do not show carbon-negative benefits; Scenario 3, Scenario 4, and Scenario 5 significantly improve carbon-negative capabilities, fully realizing CO2 "absorption". This is equivalent to reducing CO2 in the environment by 127.8 tons per day during the operation of the park. This fully demonstrates the important role of multi-energy synergistic optimization scheduling of the integrated energy negative carbon park, which combines hydrogen energy storage and carbon capture, in achieving the park's goals of deep emission reduction and negative carbon emissions.

[0067] In summary, this invention proposes a multi-energy synergistic optimization scheduling method for a carbon-negative system coupled with wind, solar, hydrogen, and biomass oxy-fuel combustion. This method includes: constructing a comprehensive energy system mathematical model integrating wind power, photovoltaics, electrolyzers, hydrogen storage, a biomass oxy-fuel combustion boiler, carbon capture, and a methanation reactor; establishing an optimization function with the objectives of minimizing total system operating cost, minimizing total carbon emissions, and maximizing renewable energy utilization, and determining weights using a combination of subjective and objective methods; finally, using a machine learning algorithm to predict wind and solar power output and load demand, embedding a mixed-integer linear programming model for solution, and outputting the optimal scheduling command. This invention achieves multi-energy complementarity and a closed-loop utilization of hydrogen, oxygen, and carbon, significantly improving system economy, renewable energy absorption capacity, and carbon-negative efficiency.

[0068] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A multi-energy collaborative optimization scheduling method for a wind-solar-hydrogen-biomass oxygen-enriched combustion coupled negative carbon system, characterized in that, The method achieves coordinated scheduling of multiple energy sources, including electricity, heat, hydrogen, and oxygen, by constructing a comprehensive energy system model, establishing a multi-objective optimization function, and combining machine learning prediction with mathematical programming solutions. Specifically, it includes the following steps: S1: System Modeling: Establish a comprehensive energy system mathematical model integrating wind power generation, photovoltaic power generation, electrolyzer, hydrogen fuel cell, hydrogen storage tank, oxygen storage tank, thermal storage tank, biomass oxygen-enriched combustion boiler, carbon dioxide capture device, and methane reactor; the mathematical model characterizes the energy flow and material flow between the various devices; S2: Establish an electrolysis-oxygen-enriched combustion and capture path: The electrolyzer produces high-purity oxygen while producing hydrogen. After being stabilized by a buffer oxygen storage tank, the oxygen is directly supplied to the biomass boiler to undergo combustion reaction with the fuel. The independent air separation device is eliminated, and the flame temperature and burnout are increased without increasing the additional energy consumption for oxygen production. This increases the volume fraction of carbon dioxide in the flue gas, reduces the unit separation energy consumption of the subsequent carbon capture unit, and couples with the methanation reactor to achieve closed-loop conversion and energy storage of carbon within the system. S3: Construction of Multi-Objective Optimization Function: Based on the mathematical model of the integrated energy system, a multi-objective optimization problem is constructed, including the total operating cost of the system, the total carbon emissions of the whole process, and the utilization rate of renewable energy. The subjective weights of each objective are obtained by the optimal-worst method, and the objective weights of each objective are obtained by the criterion importance calculation method. The subjective and objective weights are integrated and the multi-objectives are weighted and summed to form a single comprehensive objective function. S4: Prediction and Solution: Using machine learning algorithms, based on historical meteorological data and system operation data, the wind power generation, photovoltaic power generation, electrical load, and thermal load for future periods are predicted. The prediction results are used as known parameters and embedded into a mixed integer linear programming model that includes power balance constraints for electricity, heat, hydrogen, and oxygen, equipment operation constraints, and energy storage constraints. The comprehensive objective function is then solved to output the optimal scheduling instruction that minimizes the total system operating cost, carbon emissions, and maximizes renewable energy utilization.

2. The multi-energy coordinated optimization scheduling method for a carbon-negative system coupled with wind-solar-hydrogen-biomass oxygen-enriched combustion as described in claim 1, characterized in that: The specific steps in step S1 of establishing the mathematical model of the integrated energy system include: Establish a functional model relating the output power of a wind turbine to wind speed: In the formula: for Wind speed at any given moment; In a given Output power at any given time (kW); Pr is the rated output power; To cut in wind speed; Rated wind speed; To cut off the wind speed; Establish a functional model relating the output power of a photovoltaic generator to solar irradiance and battery temperature: In the formula: For photovoltaic power stations The actual output power at any given time; Let be the solar radiation intensity received by the photovoltaic cell area at time t; and let A be the photovoltaic cell area. The nominal efficiency is given under standard test conditions; β is the temperature coefficient. For photovoltaic cells in The operating temperature at any given time; Th represents the ambient temperature. Establish a functional model relating the power consumption of the electrolyzer to the hydrogen and oxygen production: In the formula, Hydrogen gas produced by the electrolyzer at time t; The power of the electrolyzer in the water electrolysis process; Oxygen produced in the electrolytic cell at time t; and These are the minimum and maximum power of the electrolytic cell, respectively. This represents the maximum power change rate of the electrolytic cell; , For the efficiency of the electrolyzer and the calorific value of hydrogen; Establish a functional model relating the power generation, heat generation, and hydrogen consumption of a hydrogen fuel cell: In the formula, , , These represent the power generation, heat production, and hydrogen consumption of FC at time t, respectively. , To improve hydrogen consumption power generation rate and waste heat utilization rate, Let t be the amount of hydrogen consumed by the fuel cell at time t; and These represent the minimum and maximum power generation of a hydrogen fuel cell, respectively. Construct mathematical models for energy storage in hydrogen storage tanks, oxygen storage tanks, and thermal storage tanks; Mathematical model of hydrogen storage tank: In the formula, The amount of hydrogen stored in HST during time period t; , The hydrogen charging and discharging rates of HST during time period t; , The maximum hydrogen charging rate and hydrogen decharging rate of HST; , These are binary variables, representing the charging and discharging state parameters of HST during time period t; =1、 =0 indicates that HST is in a hydrogen-filling state, and the opposite indicates that it is in a hydrogen-depleting state; , The minimum and maximum hydrogen storage capacities of HST; , To improve the hydrogen charging and discharging efficiency of HST; Mathematical model of oxygen storage tank: In the formula, Let OS be the oxygen storage capacity during time period t. The oxygen production of EL during time period t; Let be the amount of oxygen consumed by Bb at time t; and These represent the minimum and maximum oxygen storage capacity of OS, respectively. Mathematical model of thermal storage tank: In the formula, Thermal storage tank charging power; Thermal storage tank heat release power; Thermal storage tank heat storage capacity; Lower limit of thermal storage tank capacity; Maximum capacity of thermal storage tanks; , Lower limit of thermal storage and heat release power; , Upper limit of heat storage and heat release power; Construct a mathematical model for a biomass boiler that includes an oxygen-enriched combustion mechanism and an oxygen-enriched CO2 capture system. Biomass boilers primarily meet the heat load requirements of the industrial park. Introducing oxygen-enriched combustion significantly increases flame temperature, accelerates fuel pyrolysis and combustion reactions, and ensures more complete biomass combustion. An oxygen-enriched environment improves combustion efficiency, reduces pollutant emissions, enhances fuel adaptability, and optimizes operation, representing a crucial technological direction for the efficient and clean utilization of biomass energy. The mathematical model for the biomass boiler is as follows: In the formula, The thermal output of Bb during time period t; For the thermal efficiency of biomass units; It is the calorific value of biomass; Let Bb be the amount of biomass fuel consumed during time period t; Let Bb be the amount of oxygen consumed during time period t. The oxygen consumption ratio coefficient for biomass fuel; Oxygen-enriched carbon capture technology increases the CO2 concentration in flue gas by using a high concentration of oxygen, achieving simple and efficient carbon capture at a lower cost than pre-combustion and post-combustion capture. The mathematical model for oxygen-enriched carbon capture is as follows: In the formula, CO2 produced by the unit during time period t Let be the CO2 capture amount of CCS during time period t. This represents the amount of CO2 not captured by CCS. The amount of CO2 stored in CCS during period t. This represents the amount of CO2 used by CCS for methanation during time period t. The electrical power consumed during the CCS t period; The CCS t time period is used to capture the electrical power consumed by CO2; The electrical power consumed by CCS during the fixed time period t; , The figures represent the efficiency and power consumption of CO2 captured by CCS, respectively. Construct a model for the synthesis of methane using captured CO2 and hydrogen, and the recovery of waste heat. Oxygen-enriched combustion captures some CO2 from the flue gas and some hydrogen from the hydrogen storage system (HES) to generate methane in the MR unit, which is then supplied to local residents, achieving a carbon cycle and leveraging the advantages of hydrogen energy storage systems to realize multi-stage energy utilization. The mathematical model for MR is as follows: In the formula, The amount of H2 that participates in the reaction during time period t in the methanation process; This represents the amount of methane produced. The total heat generated by methanation during time period t; For heat recovery efficiency; The residual heat that sustains the reaction directly during the methanation process within time period t; The heat that can be recovered and reused by the waste heat recovery system within time period t; This is the coefficient corresponding to the residual heat of the methanation reaction; Methanation maintains its own direct utilization efficiency in the reaction; Waste heat recovery efficiency.

3. The multi-energy collaborative optimization scheduling method for a carbon-negative system coupled with wind-solar-hydrogen-biomass oxygen-enriched combustion as described in claim 1, characterized in that, The water electrolysis process in step S2 includes: water dissociation into hydrogen ions and oxygen ions; hydrogen ions move to the cathode to produce hydrogen gas, and oxygen ions move to the anode to produce oxygen gas; 7-8 kg of oxygen are produced for every 1 kg of hydrogen gas generated. The byproduct oxygen from the electrolysis cell is directly fed into the biomass oxygen-enriched combustion boiler after being pressure-stabilized in an oxygen storage tank. The pressure stabilization range is [insert range here]. , ],in The minimum tank pressure required to allow oxygen-enriched combustion. The upper limit pressure is set within the allowable range of equipment design and safety valve settings, and the oxygen purity is ≥99%. During periods of surplus wind and solar power and low electricity prices, the dispatch model prioritizes increasing the electrolyzer load to replenish hydrogen and oxygen stocks, maintaining oxygen storage at [a certain level]. , [Inside, among which] The target inventory is determined according to the oxygen-enriched operating conditions of the boiler design, and Δ represents the upper and lower tolerances set to avoid frequent start-ups and shutdowns. During periods of high heat load and insufficient wind and solar power, the process follows the sequence of "oxygen release → boiler oxygen enrichment to increase load → fuel cell power replenishment (heat replenishment)" to achieve rapid response to heat load and carbon capture load. Under the condition that the ratio of biomass flow rate to oxygen flow rate is 1:1.371, oxygen-enriched combustion is formed with a combustion efficiency of 99%-100%. The heat generated is directly supplied to users or enters the heat storage tank for regulation. Flue gas recirculation technology is used to use the oxidant in the exhaust gas to regulate the flame temperature and reduce the risk of boiler damage.

4. The multi-energy collaborative optimization scheduling method for a carbon-negative system coupled with wind-solar-hydrogen-biomass oxygen-enriched combustion as described in claim 1, characterized in that: The total system operating cost in step S3 includes: biomass fuel cost, operation and maintenance cost of all equipment, penalty cost due to wind and solar power curtailment, carbon trading cost, and revenue from hydrogen and methane sales. Cost-benefit function model: The total operating cost of the park includes the fuel cost of the biomass boiler. Costs of wind and solar power curtailment and CO2 storage Costs of wind and solar power curtailment Equipment operation and maintenance costs Carbon trading costs Methane transaction costs Electricity purchase cost Within the operating cycle T, the total operating cost of the integrated energy system is used as the benchmark. The minimum is the objective function; In the formula, The fuel cost of operating the biomass unit at time t. This refers to the fuel price coefficient for biomass power generation units; This is the carbon sequestration cost coefficient. For counting variables of carbon capture units; , These represent the penalty cost coefficients for wind and solar power curtailment per unit of the system, respectively. , These represent the amount of wind and solar power curtailment at time t, respectively. , , , , , , , These represent the unit power operation and maintenance costs per unit time period for Bb, WT, PV, EL, HST, OST, HFC, MR, CCS, and TST devices, respectively. This represents the carbon trading cost at time t; Indicates the carbon trading price, This represents the carbon allowance at time t; This indicates the market price of methane; This represents the market electricity price at time t. This represents the power purchased at time t.

5. The multi-energy collaborative optimization scheduling method for a carbon-negative system coupled with wind-solar-hydrogen-biomass oxygen-enriched combustion as described in claim 1, characterized in that: The calculation of total carbon emissions in step S3 includes carbon emissions from the transportation and processing of biomass fuel, carbon emissions from wind turbines and photovoltaic generators during their life cycle, and direct carbon emissions from biomass combustion, and deducts the amount of carbon dioxide captured by the carbon dioxide capture device and the amount of carbon dioxide consumed by the methane reactor.

6. The multi-energy coordinated optimization scheduling method for a carbon-negative system coupled with wind-solar-hydrogen-biomass oxygen-enriched combustion as described in claim 1, characterized in that: The method for fusing subjective and objective weights in step S3 is as follows: the subjective weight vector and the objective weight vector are multiplied element by element, and the new vector obtained after multiplication is normalized to obtain the final comprehensive weight for weighted summation.

7. The multi-energy coordinated optimization scheduling method for a carbon-negative system coupled with wind-solar-hydrogen-biomass oxygen-enriched combustion as described in claim 1, characterized in that: The constraints of the mixed integer linear programming model in step S4 also include: power ramp-up rate constraints for the electrolyzer, hydrogen fuel cell, biomass boiler and methane reactor, as well as capacity upper and lower limits and charge / discharge rate constraints for the hydrogen storage tank, oxygen storage tank and thermal storage tank.

8. The multi-energy coordinated optimization scheduling method for a negative carbon system coupled with wind-solar-hydrogen-biomass oxygen-enriched combustion as described in claim 1, characterized in that: Step S4 includes: collecting historical meteorological and load data; data cleaning, normalization and feature extraction; comparing six different machine learning methods to predict wind, solar power output and electricity and heat load demand, namely artificial neural network, BP neural network, autoregressive integral moving average model, random forest, particle swarm optimization artificial neural network and particle swarm optimization BP neural network; embedding the optimal prediction result into a mixed integer linear programming model; simulating and solving the optimized scheduling result to obtain the optimal scheduling result of the system.

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