An optimal scheduling method of integrated energy system

Abstract

The application provides an optimal scheduling method of a comprehensive energy system, and relates to the field of comprehensive energy system operation control, and comprises the following steps: obtaining operation state data and load prediction data; constructing a comprehensive energy system optimal scheduling model containing carbon-oxygen bidirectional circulation collaborative constraints, source side multi-degree-of-freedom adjustment constraints and load side multi-dimensional response constraints; solving the model to generate multi-energy flow scheduling instructions and issuing the instructions to physical devices to control the operation state thereof. Through carbon-oxygen resource closed-loop recycling and source-load bilateral flexible adjustment, the low-carbon economy, adjustment flexibility and renewable energy consumption capacity of the system are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system operation control, and more specifically to an optimized scheduling method for integrated energy systems. Background Technology

[0002] Currently, in the process of low-carbon transformation of integrated energy systems, carbon capture technology is usually combined with power-to-gas (EPG) technology to achieve emission reduction and multi-energy complementarity. However, existing carbon capture technologies mostly adopt post-combustion capture, which has high capture energy consumption and low carbon dioxide concentration. Furthermore, its synergy with EPG systems is often limited to unidirectional utilization, lacking a bidirectional material cycle mechanism for oxygen and carbon dioxide, thus limiting the overall energy efficiency improvement of the system. Simultaneously, existing dispatch strategies mainly rely on demand response on the load side, while source-side cogeneration units and other equipment typically operate under fixed heat-to-power ratios or hydrogen blending ratios, resulting in limited adjustment freedom and difficulty in adapting to power fluctuations under high-proportion renewable energy integration. This leads to insufficient system adjustment flexibility and underutilization of renewable energy absorption capacity. Therefore, there is an urgent need for an optimized dispatch method for integrated energy systems that can achieve deep recycling of carbon and oxygen resources and possess flexible adjustment capabilities on both the source and load sides. Summary of the Invention

[0003] To address the problems of low efficiency in carbon capture and power-to-gas synergy and insufficient flexibility in source-load regulation in existing technologies, this application proposes an optimized scheduling method for an integrated energy system to achieve bidirectional recycling of carbon and oxygen resources and joint flexible regulation of source and load with multiple degrees of freedom, thereby improving the system's low-carbon economy and renewable energy absorption capacity.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: An optimization scheduling method for an integrated energy system includes: acquiring operational status data and load forecast data of the integrated energy system; constructing an optimized scheduling model for the integrated energy system based on the operational status data and the load forecast data; the optimized scheduling model for the integrated energy system includes a carbon-oxygen bidirectional cycle synergistic constraint, a source-side multi-degree-of-freedom adjustment constraint, and a load-side multi-dimensional response constraint; wherein, the carbon-oxygen bidirectional cycle synergistic constraint is used to limit the closed-loop material balance relationship between oxygen supply and carbon dioxide consumption between the oxygen-enriched combustion capture unit and the power-to-gas unit; the source-side multi-degree-of-freedom adjustment constraint is used to limit the joint adjustment boundary of the thermal power output ratio and hydrogen doping ratio of the power supply unit; the load-side multi-dimensional response constraint is used to limit the joint response boundary of the time-series transfer and cross-energy substitution on the load side; solving the optimized scheduling model for the integrated energy system to generate multi-energy flow scheduling instructions; and issuing the multi-energy flow scheduling instructions to the physical devices in the integrated energy system to control the operational status of the physical devices.

[0005] Preferably, the carbon-oxygen bidirectional cycle synergistic constraint includes an oxygen supply balance constraint and a carbon dioxide distribution constraint; the oxygen supply balance constraint is used to limit the supply and demand balance relationship between the oxygen consumption of the oxygen-enriched combustion capture unit and the by-product oxygen of the power-to-gas unit and the oxygen production of the air separation oxygen production unit; the carbon dioxide distribution constraint is used to limit the distribution relationship between the carbon capture amount of the oxygen-enriched combustion capture unit and the methanation consumption and carbon sequestration amount of the power-to-gas unit.

[0006] Preferably, the method for constructing the oxygen supply balance constraint and the carbon dioxide distribution constraint includes: constructing the oxygen supply balance constraint based on the unit oxygen production energy consumption of the air separation oxygen production unit and the by-product oxygen production of the water electrolysis equipment in the power-to-gas unit, such that the total oxygen volume fed into the oxygen-enriched combustion capture unit is equal to the sum of the by-product oxygen volume of the water electrolysis equipment, the oxygen production volume of the air separation oxygen production unit, and the oxygen filling and releasing volume of the oxygen storage unit; and constructing the carbon dioxide distribution constraint based on the total carbon emissions of the coal-fired power generation unit in the oxygen-enriched combustion capture unit and the carbon capture efficiency of the carbon capture equipment, such that the mass of carbon dioxide captured by the carbon capture equipment is equal to the sum of the mass of carbon dioxide fed into the methanation reactor in the power-to-gas unit and the mass of carbon dioxide directly sealed.

[0007] Preferably, the source-side multi-degree-of-freedom adjustment constraint includes a two-degree-of-freedom adjustment constraint for the cogeneration unit and a hydrogen doping adjustment constraint for the gas-fired boiler; the two-degree-of-freedom adjustment constraint is used to limit the joint dynamic change relationship between the heat and power output ratio and the hydrogen doping volume ratio of the cogeneration unit within a preset adjustment range; the hydrogen doping adjustment constraint is used to limit the dynamic change relationship between the hydrogen doping molar mass ratio of the gas-fired boiler within a preset hydrogen doping range.

[0008] Preferably, the method for constructing the dual-degree-of-freedom adjustment constraint and the hydrogen blending adjustment constraint includes: constructing the dual-degree-of-freedom adjustment constraint based on the upper and lower limits of the electrical output, thermal output, and total output ramp-up of the cogeneration unit, as well as the upper and lower limits of the thermal power output ratio and the upper and lower limits of the hydrogen blending volume ratio, to break the fixed thermal power ratio operation limitation of the cogeneration unit; constructing the hydrogen blending adjustment constraint based on the upper and lower limits of the thermal output, thermal output ramp-up, and the upper and lower limits of the hydrogen blending molar mass ratio of the gas boiler; wherein, the hydrogen blending ratio of the cogeneration unit and the gas boiler is jointly optimized according to the dynamic matching relationship between renewable energy output and load demand.

[0009] Preferably, the load-side multidimensional response constraints include price-based time-series shift constraints and substitution-based cross-energy substitution constraints; the price-based time-series shift constraints are used to limit the time-series power adjustment boundaries of loads that can be reduced or transferred based on energy price signals; the substitution-based cross-energy substitution constraints are used to limit the energy switching boundaries of loads that can be substituted between different energy types.

[0010] Preferably, the method for constructing the price-based time-series transition constraint and the substitution-based cross-energy substitution constraint includes: constructing the price-based time-series transition constraint based on the price demand elasticity matrix, where the price demand elasticity matrix characterizes the response sensitivity of load in each time period to changes in energy prices in different time periods, and determining the power adjustment amount of the load that can be reduced and the load that can be transferred by combining the upper limit constraint of load adjustment amount; constructing the substitution-based cross-energy substitution constraint based on the electrothermal substitution coefficient and energy utilization rate, and determining the cross-energy power conversion amount of the substitutable load by combining the upper and lower limits of the substitution amount of substitutable electrical load and substitutable thermal load; and introducing a user satisfaction constraint into the load-side multidimensional response constraint, limiting the load change amount before and after the demand response to meet a preset satisfaction threshold.

[0011] Preferably, the objective function of the integrated energy system optimization scheduling model is to minimize the total system operating cost. The total system operating cost includes gas purchase cost, coal consumption cost, carbon sequestration cost, tiered carbon trading cost, wind curtailment penalty cost, and operation and maintenance cost. When solving the integrated energy system optimization scheduling model, binary variables are introduced to linearize the tiered carbon trading cost, and the Big M method is used to linearize the nonlinear product terms in the integrated energy system optimization scheduling model, transforming the mixed integer nonlinear model into a mixed integer linear model for solution.

[0012] Furthermore, this invention also provides an optimized scheduling device for an integrated energy system, comprising: a data acquisition module for acquiring operational status data and load forecast data of the integrated energy system; a model building module for constructing an optimized scheduling model for the integrated energy system based on the operational status data and the load forecast data; the optimized scheduling model for the integrated energy system includes carbon-oxygen bidirectional cycle synergistic constraints, source-side multi-degree-of-freedom adjustment constraints, and load-side multi-dimensional response constraints; wherein, the carbon-oxygen bidirectional cycle synergistic constraints are used to limit the closed-loop material balance relationship between oxygen supply and carbon dioxide consumption between the oxygen-enriched combustion capture unit and the power-to-gas unit; the source-side multi-degree-of-freedom adjustment constraints are used to limit the joint adjustment boundary of the thermal power output ratio and hydrogen doping ratio of the power supply unit; the load-side multi-dimensional response constraints are used to limit the joint response boundary of the time-series transfer and cross-energy substitution on the load side; and a solution and control module for solving the optimized scheduling model for the integrated energy system, generating multi-energy flow scheduling instructions, and issuing the multi-energy flow scheduling instructions to the physical devices in the integrated energy system to control the operational status of the physical devices.

[0013] Furthermore, the present invention also provides an integrated energy system, comprising: oxygen-enriched combustion and capture physical equipment, including a coal-fired power generation unit, an air separation oxygen production unit, an oxygen storage unit, and a carbon capture unit; an electricity-to-gas physical equipment, including a water electrolysis unit and a methanation reaction unit; energy supply physical equipment, including a combined heat and power unit and a gas-fired boiler; energy storage physical equipment, including a battery, a thermal storage tank, and a hydrogen storage tank; and an optimization scheduling device for the integrated energy system as described above; the optimization scheduling device is communicatively connected to the oxygen-enriched combustion and capture physical equipment, the electricity-to-gas physical equipment, the energy supply physical equipment, and the energy storage physical equipment, respectively, and is used to issue multi-energy flow scheduling commands to control the operating status of each physical equipment.

[0014] Beneficial effects: This invention constructs a two-way carbon-oxygen cycle with synergistic constraints. It utilizes the byproduct oxygen generated from water electrolysis in the electro-to-gas unit to feed back into the oxygen-enriched combustion capture unit, while simultaneously using the high-concentration carbon dioxide captured in the oxygen-enriched combustion as feedstock for methanation in the electro-to-gas unit, thus forming a closed-loop material cycle of oxygen and carbon dioxide. This two-way synergistic mechanism not only reduces the operating energy consumption of the air separation oxygen production unit but also reduces the need for purchased natural gas, fundamentally improving the system's carbon capture efficiency and resource utilization level, achieving a dual optimization of low carbon emissions and economic efficiency.

[0015] This invention introduces multi-degree-of-freedom adjustment constraints on the source side, endowing combined heat and power (CHP) units with the ability to jointly adjust the heat-to-power ratio and hydrogen blending ratio, as well as the dynamic adjustment capability of the hydrogen blending ratio in gas-fired boilers. This breaks the rigid operational limitations of traditional units that operate on a "heat-driven power generation" or fixed hydrogen blending ratio. This mechanism enables source-side energy supply equipment to flexibly adjust the energy conversion path and fuel ratio according to real-time changes in renewable energy output and load demand, effectively expanding the space for renewable energy consumption and enhancing the system's resilience in responding to power fluctuations.

[0016] This invention establishes multidimensional load-side response constraints, combining price-based time-series transfer responses with substitution-based cross-energy substitution responses, and introduces user satisfaction constraints as boundary conditions. This multidimensional load regulation mechanism not only guides users to smooth peak and valley loads in the time dimension but also achieves electrical and thermal complementarity in the energy type dimension. While ensuring user experience, it further explores the flexible adjustment potential of the load side and promotes the spatiotemporal equilibrium matching of multiple energy flows.

[0017] This invention transforms the mixed-integer nonlinear model containing the aforementioned multiple constraints into a mixed-integer linear model for solution, ensuring the computability and global optimality of the scheduling strategy in engineering practice. The generated scheduling instructions can accurately control the operating status of physical equipment, ensuring the effective implementation of theoretical optimization results in actual integrated energy systems. Attached Figure Description

[0018] Figure 1 This is a flowchart of the integrated energy system optimization scheduling method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the integrated energy system architecture according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the principle of oxygen-enriched combustion capture and electro-gas co-operation in an embodiment of the present invention; Figure 4 This is a daily variation curve of electrical-thermal load and wind power according to an embodiment of the present invention; Figure 5 This is a 24-hour variation curve of time-of-use electricity price and heat price according to an embodiment of the present invention; Figure 6 This is a graph showing the change in electrical load power before and after demand response according to an embodiment of the present invention; Figure 7 This is a graph showing the change in heat load power before and after the demand response according to an embodiment of the present invention; Figure 8 This is a graph showing the change in hydrogen doping ratio over time before and after the source-charge response in an embodiment of the present invention. Figure 9 This is a comparison curve of the flexible response of the CHP thermoelectric ratio before and after in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0021] Example 1 like Figure 1 As shown, this embodiment provides an optimized scheduling method for an integrated energy system. This method constructs an optimization model incorporating multiple physical constraints and generates actual control commands to achieve coordinated control of multi-energy flow devices within the integrated energy system. The method specifically includes the following steps: Step S1: Obtain the operating status data and load forecast data of the integrated energy system.

[0022] Specifically, operational status data reflects the current physical conditions of the system, including but not limited to the real-time output power, start-up / shutdown status, and fuel consumption rate of coal-fired power generating units, combined heat and power units, gas-fired boilers, water electrolysis equipment, and methanation reaction equipment; as well as the state of charge, thermal storage level, gas storage pressure, and storage percentage of energy storage devices such as batteries, thermal storage tanks, hydrogen storage tanks, and oxygen storage equipment; and network status parameters such as grid node voltage, heating network supply and return water temperature, and gas network node pressure. This data is typically acquired in real-time by sensors, smart meters, and data acquisition and monitoring control systems deployed in the physical field. Load forecast data covers the electricity load demand, heat load demand, and output forecasts of renewable energy sources such as wind power and photovoltaics for one or more future scheduling cycles. In this embodiment, the scheduling cycle can be set to 24 hours, with a unit scheduling duration of 1 hour, or it can be adjusted to 15 minutes or other time granularities according to actual control accuracy requirements. By acquiring the above multi-dimensional real-time and forecast data, accurate physical boundary conditions and input basis are provided for subsequent optimization decisions.

[0023] Step S2: Based on the operating status data and load forecast data, construct an integrated energy system optimization scheduling model; the integrated energy system optimization scheduling model includes carbon-oxygen bidirectional cycle coordination constraints, source-side multi-degree-of-freedom adjustment constraints, and load-side multi-dimensional response constraints.

[0024] Among them, the carbon-oxygen bidirectional cycle synergistic constraint is used to limit the closed-loop material balance relationship between oxygen supply and carbon dioxide consumption between the oxygen-enriched combustion capture unit and the power-to-gas unit; the source-side multi-degree-of-freedom adjustment constraint is used to limit the joint adjustment boundary of the thermal power output ratio and hydrogen doping ratio of the power supply unit; and the load-side multi-dimensional response constraint is used to limit the joint response boundary of the time-series transfer and cross-energy substitution on the load side.

[0025] Specifically, this step is not simply mathematical modeling, but rather a process of translating the physical operating mechanism and safety boundaries of the integrated energy system into mathematical language. The carbon-oxygen bidirectional cycle synergistic constraint ensures, from a material flow perspective, the dynamic balance between the source and destination of oxygen required for oxygen-enriched combustion, and the rational allocation of carbon dioxide capture between methanation utilization and storage, preventing equipment shutdowns or excessive emissions due to material imbalances. The source-side multi-degree-of-freedom adjustment constraint breaks the rigid limitations of traditional fixed heat-to-power ratios or fixed hydrogen blending ratios for units. Considering physical safety boundaries such as ramp rate and upper and lower output limits, it defines the feasible adjustment domain for cogeneration units and gas-fired boilers within the two-dimensional space of heat-to-power ratio and hydrogen blending ratio. The load-side multi-dimensional response constraint quantifies the user's energy flexibility into an executable adjustment range, including load time-series shifting capabilities based on price signals, substitution and conversion capabilities between different energy forms such as electricity and heat, and introduces a satisfaction threshold to ensure user experience. These three types of constraints together constitute the feasible solution space of the optimization model, ensuring that the generated scheduling strategy is theoretically optimal while also being physically safe and executable.

[0026] Step S3: Solve the integrated energy system optimization scheduling model and generate multi-energy flow scheduling instructions.

[0027] Specifically, since this model typically falls under the category of mixed-integer linear programming or mixed-integer nonlinear programming problems, it can be solved using commercial solvers such as CPLEX and Gurobi, or open-source optimization algorithms. The solution process involves finding the combination of decision variables that minimizes the total operating cost of the system while satisfying the aforementioned physical constraints. The generated multi-energy flow scheduling instructions are not abstract energy allocation plans, but rather specific parameter sequences directly corresponding to the control interfaces of the underlying equipment. For example, for each scheduling period, the instruction set includes the target net output setpoints for coal-fired power generating units, the oxygen production setpoints for air separation oxygen production units, the input power setpoints for water electrolysis units, the inlet flow valve opening of the methanation reactor, the target electrical and thermal power values ​​of cogeneration units and their corresponding hydrogen blending ratio setpoints, the target thermal output value and hydrogen blending molar mass ratio of gas-fired boilers, and the charging and discharging power and charging / discharging rates of energy storage equipment. These instructions form a bridge connecting the upper-level optimization decision-making with the lower-level physical execution.

[0028] Step S4: The multi-energy flow scheduling command is sent to the physical devices in the integrated energy system to control the operating status of the physical devices.

[0029] Specifically, this step demonstrates the practical transformation effect of the present invention's technical solution on the physical world. The optimized scheduling device transmits the scheduling instructions generated in step S3 to the local controllers or direct actuators of each physical device via industrial Ethernet, fieldbus, or wireless communication networks. For example, when an instruction requires increasing the hydrogen blending ratio of the cogeneration unit, the control system automatically adjusts the opening of the hydrogen injection valve and the natural gas regulating valve, changing the fuel composition entering the combustion chamber, thereby altering the unit's combustion characteristics and carbon emission intensity in real time. When an instruction requires increasing the power of the water electrolysis equipment to absorb wind curtailment, the power electronic converter adjusts the DC-side current to increase the hydrogen production rate, while the by-product oxygen flow rate increases accordingly and is transported through pipelines to the oxygen storage equipment or oxygen-enriched combustion unit. When an instruction requires adjusting the charging and discharging state of the oxygen storage equipment, the corresponding compressor or expander valve group performs opening or closing actions, realizing the spatiotemporal transfer of oxygen. Through this closed-loop control mechanism, the mathematical variables in the optimized scheduling model are precisely transformed into the mechanical actions, electrical parameter changes, or chemical reaction rate adjustments of the physical devices, thereby achieving substantial control over the low-carbon, economical, and flexible operation of the integrated energy system. It should be understood that although this embodiment describes a specific instruction issuance method, other implementations may also adopt architectures such as edge computing node hierarchical control and cloud-edge collaborative control, as long as the optimized instructions can achieve actual control over the physical device's operating status.

[0030] Example 2 In some embodiments, the carbon-oxygen bidirectional cycle synergistic constraint includes an oxygen supply balance constraint and a carbon dioxide allocation constraint. The oxygen supply balance constraint defines the supply-demand balance between the oxygen consumption of the oxy-fuel combustion capture unit and the by-product oxygen of the electro-gas conversion unit and the oxygen production of the air separation unit. The carbon dioxide allocation constraint defines the allocation of carbon capture by the oxy-fuel combustion capture unit between the methanation consumption and carbon sequestration of the electro-gas conversion unit. This constraint system constitutes the core mathematical expression for achieving a closed-loop material flow in this invention. Its purpose is to deeply couple the oxy-fuel combustion system and the electro-gas conversion system at the material level through precise material balance equations, rather than merely a simple interaction at the energy level.

[0031] Specifically, the method for constructing oxygen supply balance constraints and carbon dioxide distribution constraints includes: based on the unit oxygen production energy consumption of the air separation oxygen production unit and the by-product oxygen production of the water electrolysis equipment in the electro-to-gas unit, constructing oxygen supply balance constraints so that the total oxygen volume fed into the oxygen-enriched combustion and capture unit is equal to the sum of the by-product oxygen volume of the water electrolysis equipment, the oxygen production volume of the air separation oxygen production unit, and the oxygen filling and releasing volume of the oxygen storage unit. Combined with... Figure 3The cooperative operation principle illustrated here essentially establishes a dynamic balance mechanism for multi-source oxygen supply. In traditional technologies, the oxygen required for oxygen-enriched combustion relies entirely on the energy-intensive air separation unit. However, in this invention, the byproduct oxygen generated during hydrogen production in the water electrolysis unit is incorporated into the supply system, while an oxygen storage unit is introduced as a buffer. When wind power output is high and the water electrolysis unit is operating at full load, the generated byproduct oxygen not only meets immediate demand but can also be stored in the oxygen storage unit; when wind power output is insufficient, the oxygen storage unit releases oxygen to fill the gap. This design allows the system to prioritize the use of low-cost byproduct oxygen to replace high-cost air separation oxygen production, significantly reducing the overall oxygen production energy consumption of the oxygen-enriched combustion capture unit. It should be understood that although this embodiment describes a specific combination of oxygen sources, in other embodiments, if there are byproduct oxygen sources from industries such as steel or chemicals in the vicinity of the system, they can also be included in this balance constraint, as long as the real-time matching of the total oxygen supply and the oxygen consumption of oxygen-enriched combustion is met.

[0032] Specifically, a portion of the electricity generated by the coal-fired power generating unit (TPU) is used for system operation energy consumption, while the remainder is fed into the power grid to supply the electrical load. Operational energy consumption includes the energy consumption of the carbon capture unit (CCE) and the air separation unit (ASU), as detailed below: In the formula: , For time period t, the total power and net power generated by the TPU are respectively. Energy consumption for capturing a unit mass of CO2 using CCE; Let t be the mass of CO2 captured by CCE during time period t; Energy consumption for producing a unit volume of oxygen from an ASU; Let t be the volume of oxygen produced by ASU during time period t.

[0033] Combining the relationship between carbon capture efficiency and carbon emissions, This can be further expressed as: In the formula: For CCE carbon capture efficiency, The carbon emission intensity of TPU.

[0034] The oxygen supply balance constraint can be expressed as: In the formula: , , , The oxygen volume required by the TPU during time period t, the oxygen volume generated by the electrolyzer, and the oxygen volume stored and released in the oxygen storage tank; The volume of oxygen required to generate a unit power for the TPU.

[0035] Furthermore, the method for constructing oxygen supply balance constraints and carbon dioxide allocation constraints also includes: constructing carbon dioxide allocation constraints based on the total carbon emissions of the coal-fired power generation unit in the oxygen-enriched combustion capture unit and the carbon capture efficiency of the carbon capture equipment, such that the mass of carbon dioxide captured by the carbon capture equipment is equal to the sum of the mass of carbon dioxide fed into the methanation reactor in the power-to-gas unit and the mass of carbon dioxide directly stored. For example... Figure 3 As shown, this constraint defines the boundary of carbon flow. After the flue gas from the oxy-fuel combustion capture unit is treated by the carbon capture equipment, high-concentration carbon dioxide is separated. This carbon dioxide is not entirely stored as waste; instead, it is dynamically allocated based on the real-time absorption capacity of the methanation reactor in the power-to-gas unit. When hydrogen supply is sufficient and heat load demand is high, more carbon dioxide is fed into the methanation reactor to participate in the methane synthesis reaction, achieving both the chemical utilization of carbon resources and the recovery of reaction heat for heating. When hydrogen supply is limited or the methanation reactor reaches its processing limit, the excess carbon dioxide enters the storage stage to ensure that emission reduction targets are achieved. This allocation mechanism avoids the resource waste or equipment idleness problems caused by the independent operation of carbon capture and power-to-gas in existing technologies, ensuring the efficient recycling of carbon within the system.

[0036] Specifically, the carbon dioxide allocation constraint can be expressed as: In the formula: , For the period t, TPU generates total carbon emissions and net carbon emissions. , Let t represent the mass of CO2 consumed by the methane reactor and the amount of carbon seized. The above formula shows that the carbon dioxide captured by the carbon capture device is dynamically allocated between methanation and direct sequestration, achieving efficient recycling of carbon resources.

[0037] This embodiment establishes the material basis for the bidirectional carbon-oxygen cycle at the mathematical model level through the joint construction of the two types of constraints mentioned above. The oxygen supply balance constraint solves the problem of the source of combustion-supporting agents and how to obtain them at low cost, while the carbon dioxide allocation constraint solves the problem of the destination of carbon emissions and how to utilize them at high value. The combined effect of the two enables the integrated energy system optimization scheduling model to automatically find the optimal matching strategy for oxygen production, oxygen consumption, carbon capture, and carbon utilization while meeting the physical safety boundaries, thereby supporting the overall low-carbon economic goals of the system.

[0038] Example 3 In some embodiments, the source-side multi-degree-of-freedom regulation constraints include dual-degree-of-freedom regulation constraints for the cogeneration unit and hydrogen blending regulation constraints for the gas-fired boiler. The dual-degree-of-freedom regulation constraints limit the combined dynamic relationship between the cogeneration unit's heat and electricity output ratio and the hydrogen blending volume ratio within a preset regulation range; the hydrogen blending regulation constraints limit the dynamic relationship between the hydrogen blending molar mass ratio of the gas-fired boiler within a preset hydrogen blending range. This constraint system aims to unlock the flexible regulation potential of energy supply equipment from the source side, breaking the constraints of traditional rigid unit operation modes on the overall system flexibility by endowing key operating parameters with dynamic optimization capabilities.

[0039] Specifically, the construction methods for the two-degree-of-freedom adjustment constraints and hydrogen doping adjustment constraints include: constructing two-degree-of-freedom adjustment constraints based on the upper and lower limits of the electrical output, thermal output, and total output ramp-up of the cogeneration unit, as well as the upper and lower limits of the thermoelectric output ratio and the hydrogen doping volume ratio, to break the fixed thermoelectric ratio operation limitation of the cogeneration unit. Combined with... Figure 2 The system architecture shown depicts a combined heat and power (CHP) unit that is no longer a passive device operating solely along a fixed heat-to-power ratio curve. Instead, it is an active regulating unit that freely seeks optimization within a multi-dimensional feasible domain defined by electrical power boundaries, thermal power boundaries, ramp rate boundaries, and safe adjustment ranges for heat-to-power ratio and hydrogen blending ratio. At the physical implementation level, this means that the scheduling model can independently or collaboratively adjust the hydrogen-to-natural gas volume ratio entering the combustion chamber, as well as the turbine extraction rate or back pressure parameters, based on real-time energy supply and demand conditions. For example, when the system is in a period of high wind power generation and low electrical load at night, the model can instruct the CHP unit to increase the heat-to-power output ratio to its upper limit, for example... Figure 9 During the period from 0:00 to 9:00, the heat-to-power ratio is maintained at around 2.0, which significantly reduces power output while meeting heat load demand, thus freeing up valuable grid capacity for wind power absorption. Conversely, during the midday peak power load period when wind power output is insufficient, the model can lower the heat-to-power output ratio to its lower limit, for example... Figure 9 The power supply rate drops to 1.0 at 11:00 AM to prioritize power supply to the generating units. The resulting heat shortage is then supplemented by gas-fired boilers or thermal storage tanks.

[0040] Specifically, the hydrogen production model for the water electrolysis stage in the electro-gas conversion unit can be expressed as: In the formula: The volume of hydrogen produced by the electrolyzer during time period t; For electrolytic cell efficiency; The electrical power consumed by the electrolytic cell during time period t; Thermoelectric conversion coefficient; Hydrogen has a low calorific value; The density of hydrogen gas is given.

[0041] The model for the methanation stage can be represented as: In the formula: , For time period t, there is the volume of methane generated by MR and the volume of hydrogen used for methanation. , For MR efficiency and methanation reaction heat recovery utilization rate; It has the low heating value of methane; The density of methane; The heat power generated by the methanation reaction during time period t; The heat of reaction for methanation; is the molar mass of methane.

[0042] The flexible response model for a combined heat and power (CHP) unit can be expressed as: In the formula: , , Let t represent the electrical power, thermal power, and total power generated by CHP during time period t. , CHP consumption during time period t The power generated; , For the electrical efficiency and thermal efficiency of CHP; , The hydrogen doping volume ratio and thermoelectric ratio of CHP at time t; , and , Upper and lower limits of electrical output and upper and lower limits of thermal output; , The upper and lower limits of CHP's total output for ramping; For t Total power generated by CHP during period 1; , and , The upper and lower limits are the volume ratio and thermoelectric ratio of CHP.

[0043] Furthermore, the construction methods for the two-degree-of-freedom adjustment constraints and hydrogen doping adjustment constraints also include: constructing hydrogen doping adjustment constraints based on the upper and lower limits of the thermal output of the gas-fired boiler, the upper and lower limits of the thermal output ramp-up, and the upper and lower limits of the hydrogen doping molar mass ratio; wherein, the hydrogen doping ratio of the cogeneration unit and the gas-fired boiler is jointly optimized based on the dynamic matching relationship between renewable energy output and load demand. Figure 8As shown, the hydrogen blending molar mass ratio of gas-fired boilers is no longer a fixed design parameter, but a scheduling variable that changes dynamically over time. During periods of ample wind power output (e.g., 0:00-8:00 and 19:00-24:00), the optimization model automatically increases the hydrogen blending ratio of gas-fired boilers and even combined heat and power units to a higher level (e.g., 20%), making full use of surplus hydrogen produced by water electrolysis to replace fossil fuels. This promotes the local utilization of wind power curtailment and reduces the system's direct carbon emissions. However, during periods of low wind power output and high electrical load (e.g., 10:00-18:00), to avoid competition between the electrolyzer and the main grid for limited power resources, the model automatically lowers the hydrogen blending ratio to the minimum value required to maintain stable combustion (e.g., around 2%), relying instead on natural gas to ensure the reliability of heat supply. It should be understood that although this embodiment uses the hydrogen volume ratio and hydrogen molar mass ratio as the specific adjustment objects, in other embodiments, equivalent parameters such as the hydrogen calorific value ratio and the lower heating value of the mixed fuel can also be used to characterize the adjustment state of the fuel composition, as long as they can reflect the ratio of hydrogen to fossil fuel and be controlled by the output of renewable energy.

[0044] Specifically, the flexible response model for gas-fired boilers (GB) can be expressed as: In the formula: The heat power generated by GB during time period t; , GB consumption during time period t and The power generated; The heat generation efficiency of GB; The molar mass ratio of hydrogen doping in GB during time period t; , , The relative molecular mass; , GB heat output upper and lower; , The upper and lower limits of the GB climbing slope; , The upper and lower limits of the hydrogen molar ratio in GB standards are used. The hydrogen molar ratio of gas-fired boilers is optimized according to the dynamic matching relationship between renewable energy output and load demand, and is also limited by the upper and lower limits of heat output and the ramp-up rate.

[0045] This embodiment transforms combined heat and power units and gas-fired boilers from single-capacity equipment into flexible resources with multi-dimensional adjustment capabilities by constructing the aforementioned source-side multi-degree-of-freedom adjustment constraints. Figure 9 The dramatic fluctuations in the heat-to-electricity ratio curve and Figure 8The temporal variation of the hydrogen blending ratio visually demonstrates the application trajectory of this flexibility in actual scheduling. Compared to the traditional fixed-parameter operation mode, this joint dynamic adjustment mechanism enables the integrated energy system to adapt to power fluctuations caused by the high proportion of renewable energy access to the greatest extent possible, while ensuring the balance between power and heat load supply and demand. This effectively alleviates the source-load mismatch contradiction and significantly improves the system's renewable energy absorption capacity and low-carbon economic operation level.

[0046] Example 4 In some embodiments, load-side multidimensional response constraints include price-based time-series shifting constraints and substitution-based cross-energy substitution constraints. Price-based time-series shifting constraints define the time-series power adjustment boundaries for loads that can be reduced or transferred based on energy price signals; substitution-based cross-energy substitution constraints define the energy switching boundaries for substitutable loads between different energy sources. This constraint system aims to tap into the flexible adjustment potential from the load side, transforming rigid user energy consumption behavior into a flexible resource that can participate in system optimization through a dual mechanism of price guidance and cross-energy complementarity, while simultaneously considering both user experience and adjustment effectiveness.

[0047] Specifically, the construction methods for price-based time-series shift constraints and substitution-based cross-energy substitution constraints include: constructing price-based time-series shift constraints based on the price demand elasticity matrix. The price demand elasticity matrix characterizes the response sensitivity of load at different time periods to changes in energy prices at different time periods, and determines the power adjustment amounts of loads that can be reduced or transferred by combining the upper limit constraint of load adjustment. In this embodiment, loads that can be reduced refer to loads that can withstand a certain degree of interruption or reduced operation during peak electricity price periods, and their response characteristics are mainly manifested in a reduction in total volume; loads that can transfer are those whose total energy consumption remains unchanged, but whose energy consumption time can be shifted from high-price periods to low-price periods, possessing lateral time adjustment capabilities. The price demand elasticity matrix, as a two-dimensional parameter table, accurately characterizes the spatiotemporal coupling characteristics of this response behavior, that is, the load adjustment amount at a certain moment depends not only on the current electricity price change, but also on the cross-influence of electricity price changes at other time periods. For example, when the electricity price rises significantly during the midday period, the self-elasticity coefficient in the matrix is ​​negative, driving a decrease in load during that period; at the same time, if the electricity price is lower during the night period, the mutual elasticity coefficient is positive, guiding some load to shift to the night. Figure 6As shown in the power load change curves before and after demand response, during the high-price period from 8:00 to 22:00, the post-response power load curve is significantly lower than the initial load curve, and the corresponding bar chart shows that both the load that can be reduced and the load that can be transferred exhibit negative adjustment. Conversely, during the low-price periods from 0:00 to 8:00 and from 22:00 to 24:00, the load curve shows a positive increase, achieving a significant peak-shaving and valley-filling effect. It should be understood that although this embodiment uses electricity load as an example, this modeling logic is also applicable to price-based responses to heat load; simply replace the electricity price with the heat price and use the corresponding heat load elasticity matrix.

[0048] Specifically, the price elasticity of demand matrix is ​​defined as: In the formula: Let be the elasticity matrix; t, j=1, 2, 3, ..., n, n=24; The element in row t and column j represents the elasticity coefficient of the electricity load in time period t with respect to the electricity price in time period j. , The initial electrical load and the change in electrical load after DR; , The initial electricity price and the change in electricity price after DR. Based on this elasticity matrix, the power adjustment amount that can reduce load can be expressed as: In the formula: The initial CL value for time period t; Let CL be the price-demand elasticity matrix, which is a diagonal matrix; Let j be the electricity price during period j. Similarly, let SL be the change in time period t after the demand response. for: In the formula: The initial SL value for time period t; Let SL be the price-demand elasticity matrix. The above formula shows that the load adjustment in each time period depends not only on the electricity price change in the current time period, but also on the cross-influence of electricity price changes in other time periods, reflecting the spatiotemporal coupling characteristics of demand response.

[0049] Furthermore, the construction methods for price-based time-series transition constraints and substitution-based cross-energy substitution constraints also include: constructing substitution-based cross-energy substitution constraints based on the electrothermal substitution coefficient and energy utilization rate, and determining the cross-energy power conversion amount of substitutable loads by combining the upper and lower limits of the substitution amount of substitutable electrical load and substitutable thermal load. Substitutable loads refer to loads that can use both electricity and heat to meet the same energy demand, such as heating and air conditioning, and domestic hot water systems. These loads have vertical cross-energy regulation capabilities. The electrothermal substitution coefficient reflects the equivalent conversion relationship between unit electricity and unit heat in terms of end-user energy consumption, while the energy utilization rate considers the differences in conversion efficiency of different energy supply equipment. During the optimization scheduling process, when the electricity price is relatively low compared to the heat price (such as during nighttime periods when wind power generation is high), the model tends to increase the output of electric heating equipment, converting some heat load into electrical load, thereby promoting the consumption of renewable energy; conversely, when the electricity price is high and the heat price is low, gas boilers or thermal storage tanks are given priority for heating, reducing the use of electric heating equipment. Figure 7 As shown, the heat load curve after demand response exhibited a significant reshaping in shape between 11:00 and 20:00, which is the result of the combined effect of the electricity-heat substitution mechanism and price signals. Through this cross-energy substitution, the system breaks down the barriers between the independent operation of the electricity and heat networks, achieving deep coupling and complementary optimization of multiple energy flows on the user side.

[0050] Specifically, the alternative demand response model can be expressed as: In the formula: , The amount of electricity and heat load that can be replaced during time period t; The electrothermal substitution coefficient; The calorific value is a unit of electrical energy or thermal energy. This refers to the utilization rate of electrical and thermal energy.

[0051] The maximum substitutability constraint for this type of load also needs to be considered: In the formula: , The upper and lower limits of the amount of substitute electrical load; , These are the upper and lower limits of the amount of heat load that can be substituted.

[0052] In summary, the electrical load after DR in time period t can be obtained. and heat load for: In the formula: , , Let t be the initial heat load during time period t, the amount of heat load change that can be reduced after DR, and the amount of heat load change that can be transferred after DR. The above formula comprehensively reflects the superimposed adjustment effect of price-based response and substitution-based response on the load.

[0053] It is particularly important to emphasize the introduction of a user satisfaction constraint into the load-side multidimensional response constraint, limiting the load change before and after demand response to meet a preset satisfaction threshold. In practical engineering applications, load regulation cannot come at the expense of unlimited user comfort or energy experience. The user satisfaction constraint, as a hard boundary condition, is typically defined as the cumulative load deviation before and after the response relative to the original total load, which must not exceed a preset lower limit (e.g., 90% or 95%). This constraint directly limits the feasible solution space of the optimization model, preventing the algorithm from generating overly aggressive scheduling instructions in pursuit of extreme economic goals. For example, even if electricity prices are extremely high during a certain period, if the user's available load reduction has reached the limit allowed by satisfaction, the model will not continue to reduce that load, but will instead seek source-side regulation or other energy storage methods to balance the system. This design ensures that the scheduling strategy proposed in this invention is not only theoretically optimal, but also acceptable and sustainable in engineering practice. Figure 6 and Figure 7 Overall, under the constraint of satisfaction, the system still achieved significant adjustment results, reducing the peak-to-valley difference of electricity load from 140MW to 73MW and the peak-to-valley difference of heat load from 125MW to 56.5MW. This fully demonstrates that, under the premise of ensuring user experience, the load-side multi-dimensional response mechanism can still provide huge flexibility support for the integrated energy system.

[0054] Specifically, the user satisfaction constraint can be expressed as: In the formula: and Satisfaction with electrical and thermal loads; and The change in load before and after the electrical and thermal load DR during time period t; and This constraint represents the minimum satisfaction level for both electrical and thermal loads. It ensures that demand response does not come at the expense of excessive user comfort, providing a hard boundary for the user experience in the optimization model.

[0055] Example 5 In some embodiments, the objective function of the integrated energy system optimization scheduling model is to minimize the total system operating cost. Specifically, the total system operating cost encompasses gas purchase cost, coal consumption cost, carbon sequestration cost, tiered carbon trading cost, wind curtailment penalty cost, and... Operation and maintenance costs. Specifically, the objective function can be expressed as: In the formula: The scheduling period; For natural gas prices; The volume of natural gas purchased by the system during time period t; The price of coal for TPU; The amount of coal consumed per unit of power generated by the TPU; The cost of sealing a unit mass of CO2; The cost of carbon trading during period t; This is the wind curtailment penalty coefficient; The wind curtailment power of the system during time period t; k represents the set of power supply equipment, ASU, and CEE; k represents the set of energy storage equipment. , Let be the maintenance coefficients for equipment i and equipment k; Let t be the operating power of device i during time period t; , Let t represent the charging and discharging capacity of energy storage device k during time period t.

[0056] The construction of this objective function is not merely a simple summation of economic indicators, but rather a process that guides the system towards low-carbon, efficient, and flexible operation through multi-dimensional cost signals. Specifically, gas purchase costs and coal consumption costs reflect the direct costs of fossil fuel consumption, driving the system to prioritize the use of renewable energy and internally recycled resources; carbon sequestration costs internalize the external environmental costs of carbon emissions, prompting the system to minimize net emissions; wind curtailment penalty costs act as a virtual regulatory pressure, forcing the optimization model to find pathways to absorb wind power within the feasible domain, avoiding the waste of clean energy; and operation and maintenance costs ensure a balance between the economic efficiency and lifespan of equipment operation. It is particularly noteworthy that tiered carbon trading costs play a crucial role in guiding low-carbon development in this invention. Unlike fixed carbon price mechanisms, tiered costs exhibit a step-like upward curve as carbon emissions increase. This non-linear price signal can more accurately incentivize the system to proactively take deep emission reduction measures when approaching emission thresholds, rather than merely meeting emission standards.

[0057] Specifically, in the tiered carbon trading cost model, carbon emission allowances and actual carbon emissions can be expressed as follows: In the formula, , , For period t, IES's carbon emission allowance, actual carbon emissions, and carbon emission trading amount; , , , Carbon emission allowances per unit power supply of TPU, per unit power supply of CHP, per unit heat supply of CHP, and per unit heat supply of GB. , For time period t, the carbon emissions of CHP and GB are respectively. The base price for carbon trading; The interval length; The compensation coefficient; This is the penalty coefficient.

[0058] In addition, the integrated energy system optimization scheduling model also includes energy balance constraints and energy storage device constraints. The energy balance constraint can be expressed as: In the formula: The output of the wind turbine during time period t; , The charging and discharging power of the battery during time period t; , For time period t, the electric power consumed and the heat power generated by the electric heating boiler are respectively. , The electricity and heat demand on the load side during time period t; , The charging and discharging power of the thermal storage tank during time period t; , and , The CHP and GB consumed during time period t Volume and CHP, GB consumption volume; , The volume of hydrogen stored and released in the hydrogen storage tank during time period t.

[0059] The constraints of energy storage equipment, taking oxygen storage tanks as an example, can be expressed as follows: In the formula: Let t be the capacity of the oxygen storage tank during time period t; These are the upper and lower limits of the oxygen storage tank capacity; The charging and discharging status of the oxygen storage tank during time period t; This refers to the maximum filling and discharging volume of the oxygen storage tank; This refers to the initial capacity of the oxygen storage tank at the end of the scheduling period; This represents the capacity of the oxygen storage tank during time period t-1. The energy storage constraints of batteries, thermal storage tanks, and hydrogen storage tanks have similar structures, all including a capacity dynamic balance equation, mutual exclusion constraints between charge and discharge states, maximum charge and discharge rate constraints, and capacity consistency constraints at the beginning and end of the scheduling cycle.

[0060] When solving the integrated energy system optimal scheduling model, binary variables are introduced to linearize the tiered carbon trading cost, and the Big M method is used to linearize the nonlinear product terms in the integrated energy system optimal scheduling model, transforming the mixed-integer nonlinear model into a mixed-integer linear model for solution. Because the original model contains tiered functions and product terms of equipment start-up / shutdown states and continuous output variables, the model exhibits strong nonlinear characteristics. Direct solution is prone to getting trapped in local optima or facing the curse of computational dimensionality, making it difficult to meet the timeliness requirements of actual engineering scheduling. Therefore, this embodiment adopts a strict linearization reconstruction strategy. For the tiered carbon trading cost, a set of auxiliary binary variables is introduced to characterize the current carbon emission range of the system, and the original piecewise nonlinear cost function is transformed into a weighted sum of multiple linear intervals, making the carbon trading cost mathematically equivalent to a combination of a series of linear constraints. For nonlinear product terms, such as the coupling relationship between the start-up / shutdown states of cogeneration units and their actual output, the Big M method is used to introduce a sufficiently large positive constant M, relaxing the product terms into a set of linear inequality constraints. When the binary variable is 0, the inequality forces the continuous variable to be 0; when the binary variable is 1, the inequality allows the continuous variable to take values ​​freely within its physical upper and lower limits, thus accurately restoring the logical coupling relationship between variables.

[0061] It should be understood that although this embodiment describes a specific processing method based on the Big M method and piecewise linearization, in other embodiments, the special ordered set (SOS2) method can be used to process the step function, or tighter linearization techniques such as convex hull relaxation can be used to narrow the feasible region gap, depending on the performance characteristics of the solver. The key is to transform the nonlinear model into a form that can be efficiently solved by commercial solvers. The core value of this linearization process lies in its ability to make the originally complex mixed-integer nonlinear programming problem acceptable to mature commercial mathematical programming solvers such as CPLEX and Gurobi. This significantly reduces computation time while ensuring the global optimal solution, meeting the engineering practicality requirements of day-ahead or intraday rolling scheduling in integrated energy systems. Compared to heuristic methods such as genetic algorithms and particle swarm optimization, this solution strategy based on precise mathematical transformations not only avoids the tedious process of parameter tuning but, more importantly, provides theoretical proof of optimality, ensuring the credibility and authority of the scheduling scheme in terms of economic and low-carbon performance.

[0062] Example 6 This embodiment provides an optimized scheduling device for an integrated energy system. This device is a specific implementation of the optimized scheduling method described in embodiments 1 to 5 at the functional module level, aiming to support the engineering deployment of scheduling strategies and the protection of software products through a modular functional architecture. Figure 1 The illustrated process logic is mapped to specific data processing units in this device, which includes a data acquisition module, a model building module, and a solution and control module. It should be understood that each module in this embodiment is a functional unit implemented based on computer program instructions. It can be integrated into a single central processing unit or edge computing gateway, or it can be distributed between a cloud server and a local controller, as long as it can implement the data processing and control functions defined in the following modules.

[0063] The data acquisition module is used to acquire operational status data and load forecast data of the integrated energy system. Specifically, this module acts as the sensing interface between the optimization dispatching device and the physical world. In engineering implementation, the data acquisition module typically includes multiple communication protocol adaptation subunits, such as driver interfaces supporting IEC 61850, Modbus TCP, or OPC UA protocols, to interface with low-level sensing devices such as field data acquisition and monitoring control systems, smart meters, gas flow meters, and temperature sensors, to read operational status data in real time, such as coal-fired generator output, oxygen storage tank level, and DC-side power of electrolyzers. Simultaneously, this module also includes an external data access subunit, which acquires wind and solar power output forecast curves and regional electric and thermal load forecast data from meteorological service platforms and power grid dispatching centers via API interfaces or dedicated networks. Through cleaning, time alignment, and outlier removal of multi-source heterogeneous data, the data acquisition module provides standardized and highly reliable physical boundary inputs for subsequent optimization decisions, ensuring that the dispatching model is built on real system operating conditions.

[0064] The model building module is used to construct an integrated energy system optimization scheduling model based on operational status data and load forecast data. This model includes carbon-oxygen bidirectional cycle synergistic constraints, source-side multi-degree-of-freedom adjustment constraints, and load-side multi-dimensional response constraints. Specifically, the carbon-oxygen bidirectional cycle synergistic constraints define the closed-loop material balance relationship between oxygen supply and carbon dioxide consumption between the oxygen-enriched combustion capture unit and the power-to-gas conversion unit; the source-side multi-degree-of-freedom adjustment constraints define the joint adjustment boundary between the thermal power output ratio and hydrogen doping ratio of the power supply units; and the load-side multi-dimensional response constraints define the joint response boundary between the time-series transfer and cross-energy substitution on the load side. In essence, the model building module is the core of this device's decision-making process, encapsulating the constraint construction logic described in Examples 2 to 4. This module does not simply store mathematical formulas but transforms the operating mechanism of the physical equipment into a computer-recognizable algebraic constraint structure. For example, when constructing the carbon-oxygen bidirectional cycle synergistic constraint, the module dynamically generates a set of oxygen supply and demand balance equations based on the real-time acquired byproduct oxygen production from the water electrolysis unit and energy consumption parameters from the air separation oxygen production unit, and updates the carbon dioxide distribution inequality in real time based on carbon capture efficiency. When constructing the source-side multi-degree-of-freedom adjustment constraint, the module automatically generates the vertex coordinates of the feasible domains for the heat-to-power ratio and hydrogen doping ratio based on the current ramp-up capability and safe operating range of the cogeneration unit, forming a convex hull constraint or polygonal operating region description. This method of codifying physical laws enables the optimization model to adaptively adjust with changes in system operating conditions, ensuring that the scheduling strategy always remains within the physical safety boundary.

[0065] The solution and control module is used to solve the optimal scheduling model of the integrated energy system, generate multi-energy flow scheduling commands, and issue these commands to the physical devices in the integrated energy system to control their operating status. Specifically, this module implements a closed-loop transformation from the mathematically optimal solution to physical control actions. In the solution phase, the module integrates a mixed-integer linear programming engine, receiving standard model files output by the model building module and parameter instances provided by the data acquisition module, calculating the combination of decision variables that minimizes the total system operating cost within a millisecond to second timescale. In the control phase, the module includes a command parsing and issuing subunit, converting the abstract variables obtained from the solution (such as the target hydrogen blending ratio and target thermal power) into control signals recognizable by the underlying devices. For example, the target hydrogen blending ratio is converted into the opening percentage setpoint of the hydrogen regulating valve, and the target charging and discharging power is converted into the active power reference value of the energy storage converter, and then sent to the local controllers of each physical device via industrial Ethernet or fieldbus. This command issuing mechanism, directly targeting physical actuators, establishes the technical attributes of this device as an industrial control system, distinguishing it from a simple information processing or mathematical calculation tool.

[0066] This embodiment constructs a complete "perception-decision-execution" technical closed loop through the collaborative work of the three functional modules described above. The data acquisition module ensures the timeliness of the input, the model building module ensures the security and compliance of the decision-making through embedded physical constraints, and the solution and control module ensures the physical executability of the optimization results. This modular architecture not only facilitates software iteration, upgrades, and maintenance, but more importantly, it provides a product-based protection model for integrated energy system optimization and scheduling methods that is independent of specific hardware carriers. This allows software products or embedded devices containing the above functional modules to be provided, sold, or offered for sale even without directly implementing the method steps to be included within the scope of protection of this invention, thereby effectively enhancing the commercial value and legal defense depth of the technical solution.

[0067] Example 7 like Figure 2 As shown, this embodiment provides an integrated energy system, which is the physical entity on which the aforementioned optimized scheduling method operates. It achieves coordinated control of multiple energy flows through deep coupling of hardware and software. The integrated energy system includes oxygen-enriched combustion and capture physical equipment, electricity-to-gas physical equipment, energy supply physical equipment, energy storage physical equipment, and an optimized scheduling device for the integrated energy system as described in Embodiment 6.

[0068] Specifically, the oxygen-enriched combustion capture equipment includes coal-fired power generating units, air separation oxygen production equipment, oxygen storage equipment, and carbon capture equipment. In this embodiment, the coal-fired power generating unit is modified for oxygen-enriched combustion, with its combustion chamber inlet connected to a mixing pipeline for oxygen and circulating flue gas, enabling it to burn in a high-concentration oxygen environment and produce high-concentration carbon dioxide flue gas. The air separation oxygen production equipment serves as the basic oxygen source, producing oxygen from the air through cryogenic separation or pressure swing adsorption processes. The oxygen storage equipment typically employs high-pressure gaseous storage tanks or cryogenic liquid storage tanks to mitigate the time mismatch between oxygen supply and demand. The carbon capture equipment compresses, purifies, and liquefies the flue gas emitted from the coal-fired power generating unit. These devices collectively constitute the carbon and oxygen mass flow source of the system, and their operating status directly determines the system's carbon emission intensity and oxygen supply capacity.

[0069] The electro-gas conversion equipment includes water electrolysis equipment and methanation reaction equipment. The water electrolysis equipment is preferably an alkaline electrolyzer or a proton exchange membrane electrolyzer. Its DC side is connected to renewable energy sources such as wind power, while its AC side or DC bus is connected to the power grid. It has rapid power regulation capabilities and can convert electrical energy into hydrogen with oxygen as a byproduct. The methanation reaction equipment is filled with nickel-based or other catalysts, which promote the exothermic reaction between hydrogen and carbon dioxide to produce methane under high temperature and high pressure conditions. This equipment not only consumes the carbon dioxide captured by the carbon capture equipment but also recovers the heat of reaction for use in the heating network, making it a key hub for realizing multi-energy conversion from electricity to gas to heat.

[0070] The energy supply equipment includes a combined heat and power (CHP) unit and a gas-fired boiler. In this embodiment, the CHP unit employs a flexible configuration with hydrogen-blended combustion and adjustable extraction / back pressure. Its fuel inlet pipeline is equipped with a natural gas-hydrogen ratio regulating valve group, and the steam side has an adjustable extraction valve or back pressure regulating mechanism, thus providing dual physical degrees of freedom for adjusting both the heat-to-power ratio and the hydrogen blending ratio. The gas-fired boiler is also equipped with a hydrogen-blended burner and a corresponding fuel blending control system, capable of dynamically adjusting the fuel composition according to dispatch instructions to adapt to different heat load demands and hydrogen consumption targets. These two types of equipment, as the main energy output terminals of the system, directly support the physical execution of the multi-degree-of-freedom adjustment strategy on the source side through their flexibility.

[0071] Energy storage devices include batteries, thermal storage tanks, and hydrogen storage tanks. Batteries are used for short-term, high-frequency regulation of electrical energy, smoothing out fluctuations in renewable energy output. Thermal storage tanks utilize the sensible heat of water or the latent heat of phase change materials to store thermal energy, decoupling the rigid thermoelectric connection of combined heat and power units. Hydrogen storage tanks provide buffer storage for hydrogen across seasons or long periods, ensuring a continuous and stable supply for methanation reactions and hydrogen blending combustion. These energy storage devices are connected to the main system through their respective charge / discharge, heat / charge, and gas / charge interfaces, providing time-dimensional translation capabilities for optimized scheduling.

[0072] The optimized scheduling device communicates with the oxygen-enriched combustion capture equipment, the electro-gas conversion equipment, the energy supply equipment, and the energy storage equipment, respectively, to issue multi-energy flow scheduling commands to control the operating status of each physical device. Specifically, this communication connection can be achieved through industrial communication protocols such as Industrial Ethernet, Profibus-DP, Modbus TCP, or OPC UA, constructing an information perception and control network covering the entire system. The scheduling commands generated by the optimized scheduling device are not abstract data streams, but are converted into specific physical signals that can be recognized by the underlying controller. For example, for a combined heat and power unit, the command is parsed into active power setpoints, reactive power setpoints, extraction steam pressure setpoints, and hydrogen flow regulating valve opening percentages; for an electrolysis water unit, the command is expressed as the DC current reference value or power setpoint of the rectifier cabinet; for oxygen storage equipment and hydrogen storage tanks, the command corresponds to compressor start / stop signals, valve opening commands, or mass flow controller setpoints. Simultaneously, the operating parameters (such as temperature, pressure, flow rate, liquid level, power, etc.) collected in real time by sensors from each physical device are also fed back to the optimization scheduling device via the same communication network, serving as the state input for the scheduling decision at the next moment. It should be understood that although this embodiment describes a specific communication protocol and control signal format, alternative solutions such as wireless sensor networks, 5G industrial private networks, or edge computing gateways can be used in other embodiments, as long as closed-loop control of the operating status of each physical device by the optimization scheduling device can be achieved. Through the above-described integrated hardware and software architecture design, this invention transforms abstract optimization algorithms into precise control of physical devices, ensuring the safe, low-carbon, and economical operation of the integrated energy system at the physical level.

[0073] Example 8 To verify the effectiveness of the integrated energy system optimization scheduling method proposed in this invention in practical applications and the contribution of its core technical features, this embodiment designs six progressive simulation scenarios for comparative analysis based on the system architecture and model construction methods described in embodiments 1 to 7. The scheduling duration T = 24 hours, with a unit scheduling duration of 1 hour. The system's purchased natural gas price is 3.5 yuan / m³. 3 The cost of CO2 storage is 40 yuan / t. In the tiered carbon trading model, the base price for carbon trading is 250 yuan / t, the interval length is 50 t, and both the reward and compensation coefficients are 0.25. The electricity and heat load demand curves and the wind power output forecast curves are as follows: Figure 4 As shown. Time-of-use energy price is as follows. Figure 5 As shown in the table below: The six scenarios are as follows: Table 1 Scene Information Scenario 1 serves as the baseline scenario, employing traditional post-combustion carbon capture with flexible passive load response. Scenario 2 modifies the carbon capture technology in Scenario 1 to oxy-fuel combustion capture. Scenario 3, building upon Scenario 2, introduces the coordinated operation of oxy-fuel combustion capture and power-to-gas conversion. Scenario 4 adds multi-dimensional demand response on the load side to Scenario 3. Scenario 5 further introduces flexible adjustment of the hydrogen doping ratio on the source side, building upon Scenario 4. Scenario 6 represents the complete technical solution of this invention, namely, adding flexible adjustment of the heat-to-power ratio of a combined heat and power (CHP) unit to Scenario 5. All scenarios use the same load forecast data and renewable energy output forecast data, with a scheduling cycle of 24 hours and a unit scheduling duration of 1 hour to ensure the objectivity and comparability of the comparison results.

[0074] Specifically, by comparing the scheduling results of scenario 1 and scenario 2, the superiority of oxygen-enriched combustion capture technology over traditional post-combustion carbon capture technology can be verified, as shown in Table 2.

[0075] Table 2 Comparison of scheduling results for each scenario Data shows that the total system cost of Scenario 2 is 49,600 yuan lower than that of Scenario 1, while the actual carbon emissions are reduced by approximately 24.40%. More significantly, the carbon trading cost shifts from an expenditure of 214,200 yuan in Scenario 1 to a gain of 3,900 yuan in Scenario 2. The underlying mechanism of this change is that the oxy-fuel combustion capture unit significantly increases the concentration of carbon dioxide in the flue gas through high-concentration oxygen combustion, thereby significantly reducing the energy consumption of the carbon capture equipment and improving capture efficiency. Although the introduction of air separation oxygen production equipment increases some electricity consumption and operation and maintenance costs, the improved carbon capture efficiency allows the system to obtain more carbon allowance surplus, thus realizing economic returns through the carbon trading market. Meanwhile, the wind curtailment cost of Scenario 2 is 61,100 yuan lower than that of Scenario 1, indicating that the oxy-fuel combustion system itself has better regulation characteristics and can promote the consumption of renewable energy to a certain extent.

[0076] Further comparison of scenarios 2 and 3 aims to verify the economic and environmental benefits of the carbon-oxygen bidirectional cycle synergistic constraint. In scenario 3, by establishing a closed-loop material flow between the oxygen-enriched combustion capture unit and the electro-gas conversion unit, the system's gas purchase cost is significantly reduced by RMB 337,800 compared to scenario 2, with a corresponding reduction in total cost of RMB 263,500, and carbon emissions are further reduced by 40.03 tons. This significant performance improvement stems from a synergistic mechanism at two levels: firstly, the methanation reactor in the electro-gas conversion unit directly utilizes the high-concentration carbon dioxide captured by oxygen-enriched combustion as raw material, replacing part of the purchased natural gas and achieving internal recycling of carbon resources; secondly, the by-product oxygen generated by the water electrolysis equipment is returned to the oxygen-enriched combustion capture unit, reducing the operating load and energy consumption of the air separation oxygen production equipment. In addition, the heat released by the methanation reaction is also used to share the heating pressure, indirectly reducing the fossil fuel consumption and carbon emissions of the gas boiler. This result strongly demonstrates that the carbon-oxygen bidirectional cycle synergistic constraint in this invention is not a simple module superposition, but rather produces substantial resource complementarity and energy efficiency multiplication effects.

[0077] Building upon Scenario 3, Scenario 4 introduces load-side multidimensional response constraints, demonstrating the potential for tapping into load-side flexibility resources. The total system cost of Scenario 4 is reduced by 192,100 yuan compared to Scenario 3, with wind curtailment costs plummeting from 94,100 yuan to 12,600 yuan, and carbon emissions reduced by 58 tons. Combined with... Figure 6 and Figure 7 As shown in the load curve changes before and after the demand response, price-based time-series shifting constraints guide the load that can be reduced or shifted to actively decrease power during peak electricity price periods and increase electricity consumption during off-peak periods. This reduces the peak-to-valley difference in electricity load from 140MW to 73MW and the peak-to-valley difference in heat load from 125MW to 56.5MW. Substitution-based cross-energy substitution constraints, guided by electricity and heat price signals, achieve dynamic complementarity between electric heating equipment and gas-fired boiler output. This source-load interaction mechanism effectively smooths out fluctuations in the net load curve, providing greater absorption capacity for intermittent renewable energy sources such as wind power and avoiding wind curtailment waste caused by source-load mismatch.

[0078] The comparison between Scenario 5 and Scenario 4 focuses on the value of flexible adjustment of the hydrogen blending ratio on the source side. In Scenario 5, the hydrogen blending ratio of the combined heat and power (CHP) unit and the gas-fired boiler is no longer fixed, but dynamically optimized based on wind power output and load demand. For example... Figure 8As shown, during nighttime hours when wind power is abundant (0:00-8:00 and 19:00-24:00), the hydrogen blending ratio automatically increases to around 20%, fully utilizing surplus wind power to produce hydrogen to replace natural gas. Conversely, during peak daytime hours when wind power is scarce (10:00-18:00), the hydrogen blending ratio automatically decreases to the minimum limit to avoid competition between the electrolyzer and the main grid. This strategy further reduces carbon emissions by 38.18 tons in Scenario 5 compared to Scenario 4, with a total cost reduction of 21,600 yuan. This demonstrates that using the hydrogen blending ratio as a scheduling variable rather than a fixed parameter allows energy supply equipment to more accurately adapt to the spatiotemporal distribution characteristics of renewable energy, maximizing low-carbon benefits while ensuring energy supply reliability.

[0079] Finally, Scenario 6, as a complete implementation of the present invention, introduces flexible adjustment of the heat-to-power ratio of the combined heat and power unit based on Scenario 5. Comparative results show that Scenario 6 achieves the goal of complete wind curtailment with zero cost, reducing the total cost by RMB 132,200 compared to Scenario 5, and reducing carbon emissions by 83.63 tons. Figure 9 As shown, the dynamic adjustment of the heat-to-power ratio completely breaks the rigid constraints of traditional units that rely on either heat-driven power generation or power-driven heat generation. During the night and evening hours when heat load is high, the heat-to-power ratio is maintained at a high level of around 2.0, with the unit prioritizing heat supply and wind power supplementing the power shortage. During the midday hours when electricity load is high, the heat-to-power ratio is lowered to 1.0, with the unit prioritizing power supply and gas-fired boilers or thermal storage tanks filling the heat shortage. This dual-degree-of-freedom adjustment capability transforms the cogeneration unit from a passive production unit into an active and flexible adjustment resource, forming a multi-dimensional system elastic support system together with load-side response and hydrogen blending adjustment.

[0080] In summary, through quantitative comparison of six progressive scenarios, the independent contributions and synergistic effects of the core technical features of this invention can be clearly observed. From scenario 1 to scenario 6, the total system operating cost was reduced by a cumulative 659,000 yuan, a decrease of 12.24%; actual carbon emissions were reduced by a cumulative 1,000.6 tons, a decrease of 31.27%; and ultimately, full utilization of renewable energy was achieved. These data fully demonstrate that the comprehensive energy system optimization scheduling method constructed in this invention, which includes carbon-oxygen bidirectional cycle synergistic constraints, source-side multi-degree-of-freedom adjustment constraints, and load-side multi-dimensional response constraints, can effectively solve the problems of high carbon capture energy consumption, insufficient resource recycling, and insufficient source-load adjustment flexibility in existing technologies. It has achieved significant and unexpected technical effects in terms of low carbon emissions, economy, and flexibility. It should be understood that the above scenario settings and data are only illustrative examples. In actual engineering applications, the specific cost savings and emission reductions will vary with factors such as system scale, equipment parameters, energy prices, and resource endowments. However, as long as the optimization scheduling architecture and constraint construction logic described in this invention are adopted, the corresponding technical advantages can be obtained.

[0081] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Those skilled in the art should understand that the specific descriptions of the integrated energy system optimization scheduling method, device, and system in the foregoing embodiments, including but not limited to the construction method of carbon-oxygen bidirectional cycle collaborative constraints, the implementation logic of source-side multi-degree-of-freedom adjustment constraints, the modeling method of load-side multi-dimensional response constraints, and the transformation strategy of mixed-integer linear models, are all exemplary descriptions listed to fully disclose the technical solution of the present invention, and are not intended to limit the technical concept of the present invention. Any person skilled in the art, within the technical scope disclosed in the present invention, can easily conceive of variations or substitutions based on the core inventive concept of the present invention. For example, using an equivalent material balance equation to replace a specific oxygen supply balance constraint, using other types of flexible resources to expand the source-load adjustment dimension, or applying different mathematical programming algorithms to solve the model, etc., should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An optimized scheduling method for an integrated energy system, characterized in that, include: Obtain the operating status data and load forecast data of the integrated energy system; Based on the operational status data and the load forecast data, an integrated energy system optimization scheduling model is constructed; the integrated energy system optimization scheduling model includes carbon-oxygen bidirectional cycle coordination constraints, source-side multi-degree-of-freedom adjustment constraints, and load-side multi-dimensional response constraints. The carbon-oxygen bidirectional cycle synergistic constraint is used to limit the closed-loop material balance relationship between oxygen supply and carbon dioxide consumption between the oxygen-enriched combustion capture unit and the power-to-gas unit; the source-side multi-degree-of-freedom adjustment constraint is used to limit the joint adjustment boundary of the thermal power output ratio and hydrogen doping ratio of the power supply unit; and the load-side multi-dimensional response constraint is used to limit the joint response boundary of the time-series transfer and cross-energy substitution on the load side. Solve the integrated energy system optimization scheduling model to generate multi-energy flow scheduling instructions; The multi-energy flow scheduling command is sent to the physical devices in the integrated energy system to control the operating status of the physical devices.

2. The optimized scheduling method for an integrated energy system according to claim 1, characterized in that, The carbon-oxygen bidirectional cycle synergistic constraint includes oxygen supply balance constraint and carbon dioxide distribution constraint; The oxygen supply balance constraint is used to limit the supply and demand balance between the oxygen consumption of the oxygen-enriched combustion capture unit and the by-product oxygen production of the electric-to-gas unit and the oxygen production of the air separation oxygen production unit. The carbon dioxide allocation constraint is used to limit the allocation relationship between the carbon capture amount of the oxygen-enriched combustion capture unit and the methanation consumption and carbon sequestration amount of the electro-gas conversion unit.

3. The optimized scheduling method for an integrated energy system according to claim 2, characterized in that, The methods for constructing the oxygen supply balance constraint and the carbon dioxide allocation constraint include: Based on the unit oxygen production energy consumption of the air separation oxygen production unit and the by-product oxygen production of the water electrolysis equipment in the electric-to-gas unit, the oxygen supply balance constraint is constructed so that the total oxygen volume fed into the oxygen-enriched combustion and capture unit is equal to the by-product oxygen volume of the water electrolysis equipment, the oxygen production volume of the air separation oxygen production unit, and the oxygen filling and releasing volume of the oxygen storage unit. Based on the total carbon emissions of the coal-fired power generation unit in the oxygen-enriched combustion capture unit and the carbon capture efficiency of the carbon capture equipment, the carbon dioxide allocation constraint is constructed such that the mass of carbon dioxide captured by the carbon capture equipment is equal to the sum of the mass of carbon dioxide fed into the methanation reactor in the power-to-gas unit and the mass of carbon dioxide directly stored.

4. The optimized scheduling method for an integrated energy system according to claim 1, characterized in that, The source-side multi-degree-of-freedom adjustment constraints include the dual-degree-of-freedom adjustment constraints of cogeneration units and the hydrogen doping adjustment constraints of gas-fired boilers. The dual-degree-of-freedom adjustment constraint is used to limit the joint dynamic change relationship between the heat and power output ratio and the hydrogen doping volume ratio of the cogeneration unit within a preset adjustment range. The hydrogen doping regulation constraint is used to limit the dynamic change of the hydrogen doping molar mass ratio of the gas-fired boiler within a preset hydrogen doping range.

5. The optimized scheduling method for an integrated energy system according to claim 4, characterized in that, The method for constructing the two-degree-of-freedom adjustment constraint and the hydrogen-doping adjustment constraint includes: Based on the upper and lower limits of the electrical output, thermal output, and total output ramp-up of the cogeneration unit, as well as the upper and lower limits of the thermoelectric output ratio and the upper and lower limits of the hydrogen doping volume ratio, the dual-degree-of-freedom adjustment constraint is constructed to break the fixed thermoelectric ratio operation limitation of the cogeneration unit. Based on the upper and lower limits of the thermal output of the gas-fired boiler, the upper and lower limits of the thermal output ramp-up, and the upper and lower limits of the hydrogen doping molar mass ratio, the hydrogen doping adjustment constraints are constructed. The hydrogen blending ratio of the combined heat and power unit and the gas boiler is jointly optimized based on the dynamic matching relationship between renewable energy output and load demand.

6. The optimized scheduling method for an integrated energy system according to claim 1, characterized in that, The load-side multidimensional response constraints include price-type time-series transition constraints and substitution-type transenergy substitution constraints; The price-based time-series shifting constraint is used to define the time-series power adjustment boundaries for loads that can be reduced or shifted based on energy price signals. The alternative cross-energy substitution constraint is used to define the energy switching boundary between different energy types for substitutable loads.

7. The optimized scheduling method for an integrated energy system according to claim 6, characterized in that, The methods for constructing the price-type time-series transition constraints and the substitution-type cross-energy substitution constraints include: The price-based time-series transfer constraint is constructed based on the price demand elasticity matrix, which characterizes the response sensitivity of load in each time period to changes in energy prices in different time periods. The power adjustment amount of the load that can be reduced and the load that can be transferred is determined in combination with the upper limit constraint of load adjustment. The substitution-type cross-energy substitution constraint is constructed based on the electrothermal substitution coefficient and energy utilization rate, and the cross-energy power conversion amount of the substitutable load is determined by combining the upper and lower limits of the substitution amount of the substitutable electrical load and the substitutable thermal load. A user satisfaction constraint is introduced into the load-side multidimensional response constraint to limit the load change before and after the demand response to meet a preset satisfaction threshold.

8. The optimized scheduling method for an integrated energy system according to claim 1, characterized in that, The objective function of the integrated energy system optimization scheduling model is to minimize the total operating cost of the system. The total operating cost of the system includes gas purchase cost, coal consumption cost, carbon sequestration cost, tiered carbon trading cost, wind curtailment penalty cost, and operation and maintenance cost. When solving the integrated energy system optimization scheduling model, binary variables are introduced to linearize the tiered carbon trading cost, and the Big M method is used to linearize the nonlinear product terms in the integrated energy system optimization scheduling model, transforming the mixed integer nonlinear model into a mixed integer linear model for solution.

9. An optimized scheduling device for an integrated energy system, characterized in that, include: The data acquisition module is used to acquire the operating status data and load forecast data of the integrated energy system; The model building module is used to construct an integrated energy system optimization scheduling model based on the operating status data and the load forecast data. The integrated energy system optimization scheduling model includes carbon-oxygen bidirectional cycle synergistic constraints, source-side multi-degree-of-freedom adjustment constraints, and load-side multi-dimensional response constraints. Among them, the carbon-oxygen bidirectional cycle synergistic constraints are used to limit the closed-loop material balance relationship between oxygen supply and carbon dioxide consumption between the oxygen-enriched combustion capture unit and the power-to-gas unit; the source-side multi-degree-of-freedom adjustment constraints are used to limit the joint adjustment boundary of the thermal power output ratio and hydrogen doping ratio of the power supply unit; and the load-side multi-dimensional response constraints are used to limit the joint response boundary of the time-series transfer and cross-energy substitution on the load side. The solution and control module is used to solve the integrated energy system optimization scheduling model, generate multi-energy flow scheduling instructions, and send the multi-energy flow scheduling instructions to the physical devices in the integrated energy system to control the operating status of the physical devices.

10. An integrated energy system, characterized in that, include: Oxygen-enriched combustion capture physical equipment includes coal-fired power generation units, air separation oxygen production equipment, oxygen storage equipment, and carbon capture equipment; Electro-gas conversion physical equipment, including water electrolysis equipment and methanation reaction equipment; Energy supply equipment, including combined heat and power units and gas-fired boilers; Energy storage physical devices, including batteries, thermal storage tanks, and hydrogen storage tanks; And the optimized scheduling device for the integrated energy system as described in claim 9; The optimized scheduling device is communicatively connected to the oxygen-enriched combustion capture physical device, the electricity-to-gas physical device, the energy supply physical device, and the energy storage physical device, respectively, and is used to issue multi-energy flow scheduling commands to control the operating status of each physical device.

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