Comprehensive energy system optimization method based on OCC-P2G-dynamic hydrogen doping coupling

Abstract

The invention discloses an integrated energy system optimization method based on OCC-P2G-dynamic hydrogen doping coupling, and relates to the technical field of energy system optimization. The method comprises the following steps: firstly, constructing an oxygen-enriched combustion trapping system model, a two-stage electricity-to-gas system model and a fuel gas dynamic hydrogen doping mathematical model to realize cyclic utilization of oxygen, carbon dioxide, hydrogen and reaction heat in the system; secondly, a stepped carbon transaction mechanism is introduced, and a nonlinear piecewise function model between the carbon transaction cost and the net carbon emission is constructed; and finally, with the goal of minimizing the total operation cost of the system including the carbon transaction cost, establishing an optimal scheduling model of the integrated energy system and solving the optimal scheduling model. Through the synergistic effect of physical coupling and stepped economic excitation, the renewable energy consumption capacity, the overall energy efficiency of the system, the operation economy and the scheduling flexibility are effectively improved, and the energy structure is driven to be deeply transformed to low carbon.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy system optimization, in particular, to an OCC-P2G-dynamic hydrogen blending coupled comprehensive energy system optimization method. BACKGROUND

[0002] As a key carrier for realizing multi-energy complementation and energy cascade utilization, comprehensive energy system (IES) has attracted much attention in promoting low-carbon energy transformation. Existing research has made many explorations in improving the low-carbon and economic performance of IES, such as coupling combustion post carbon capture (CCS) technology with power-to-gas (P2G) technology to utilize renewable energy to produce hydrogen and consume captured CO2; or introducing oxygen-rich combustion capture (OCC) technology into IES to reduce carbon capture energy consumption; or studying hydrogen blending of gas turbine units to reduce carbon emission intensity. However, these existing schemes often have inherent defects of single technology or insufficient coupling. Specifically, OCC, P2G and hydrogen blending technology are not systematically modeled, resulting in the inability of deep recycling of hydrogen, oxygen, carbon and reaction heat and other resources, limiting the renewable energy consumption capacity and overall energy efficiency of the system; or a fixed hydrogen blending ratio of the gas is adopted, which cannot flexibly adapt to source and load fluctuations, weakening the flexibility of system operation; in addition, in the incentive mechanism, most studies use the traditional fixed unit price carbon trading mode, which has limited guiding effect on system emission reduction and energy structure optimization. The above defects together result in the fact that the existing comprehensive energy system still faces significant challenges in resource comprehensive utilization efficiency, high proportion of renewable energy consumption, operation economy and dispatching flexibility. Therefore, it is urgent to develop a systematic optimization and dispatching method that can deeply integrate carbon capture, power-to-gas and dynamic hydrogen blending technology, and supplemented by an efficient and economic incentive strategy.

[0003] The above information disclosed in the background section is only for the purpose of enhancing the understanding of the background of the present application, therefore it can include information that does not constitute the prior art known to those of ordinary skill in the art. SUMMARY

[0004] The present application aims to provide an OCC-P2G-dynamic hydrogen blending coupled comprehensive energy system optimization method, by constructing an OCC-P2G-dynamic hydrogen blending deeply coupled comprehensive energy system model, a step-by-step carbon trading mechanism for optimizing parameters, and a multi-cost minimization optimization and dispatching model, to realize efficient resource utilization, improve renewable energy consumption capacity, optimize energy structure, reduce carbon emissions and system total cost, and ultimately realize low-carbon and efficient operation of IES.

[0005] In a first aspect, the technical solution provided in the embodiments of the present application is an OCC-P2G-dynamic hydrogen blending coupled comprehensive energy system optimization method, including the following steps: An oxygen-enriched combustion capture system model is constructed by retrofitting an existing post-combustion carbon capture power plant for oxygen-enriched combustion; a two-stage electricity-to-gas system model is constructed including an electrolysis stage and a methanation stage; a dynamic hydrogen-doping gas model is constructed by retrofitting a gas turbine unit for hydrogen-doping; Based on the oxygen-enriched combustion capture system model, the two-stage electricity-to-gas system model, and the dynamic hydrogen-doping gas model, a comprehensive energy system model is constructed; Based on the comprehensive energy system model, the system carbon quota and the system actual carbon emissions at each time are obtained; based on the absolute value of the difference between the system carbon quota and the system actual carbon emissions, multiple carbon trading price intervals are divided to construct a stepwise carbon trading mechanism model representing carbon trading costs; According to the stepwise carbon trading mechanism model, a target function is constructed to minimize the sum of carbon trading costs, carbon sequestration costs, coal costs, gas purchase costs, and wind curtailment costs, and based on the energy balance constraints and device operation constraints set by the comprehensive energy system model, a comprehensive energy system optimization scheduling model is constructed and solved by a CPLEX solver to obtain system optimization strategies.

[0006] As a preferred embodiment, the oxygen-enriched combustion capture system model includes an oxygen-enriched combustion unit, an air separation oxygen device, an oxygen storage tank, and a carbon capture device; The air separation oxygen device and the oxygen storage tank are connected in parallel to form an oxygen supply unit for providing oxygen required for combustion to the oxygen-enriched combustion unit; The exhaust end of the oxygen-enriched combustion unit is connected to the carbon capture device for transporting high-concentration carbon dioxide generated by combustion to the carbon capture device for capture.

[0007] As a preferred embodiment, the two-stage electricity-to-gas system model includes an electrolytic cell and a methanation device connected in sequence; The electrolytic cell is configured to consume electrical energy to perform an electrolysis reaction to produce hydrogen and oxygen; the oxygen is supplied to the oxygen supply unit, The methanation device is configured to react part of the hydrogen produced by the electrolytic cell with the carbon dioxide captured by the carbon capture device to generate methane and reaction heat; the methane is supplied to the gas network of the comprehensive energy system model, and the reaction heat is supplied to the heat network of the comprehensive energy system model.

[0008] As a preferred embodiment, the dynamic hydrogen-doping gas model includes a hydrogen-doping gas turbine and a hydrogen-doping gas boiler; The hydrogen-doping gas turbine and the hydrogen-doping gas boiler are configured to receive another part of the hydrogen from the electrolytic cell and mix with externally supplied natural gas at a dynamically adjustable volume ratio for combustion; The hydrogen-doped proportion of the hydrogen-doped gas turbine and the hydrogen-doped gas boiler model is dynamically adjusted according to the electric load demand and the renewable energy output state in the integrated energy system, and the hydrogen-doped proportion changes within a preset safe operation range.

[0009] Preferably, the hydrogen-doped proportion of the hydrogen-doped gas turbine and the hydrogen-doped gas boiler model is dynamically adjusted according to the electric load demand and the renewable energy output state in the integrated energy system, and the hydrogen-doped proportion changes within a preset safe operation range; comprising the following steps: The hydrogen-doped proportion of the hydrogen-doped gas turbine and the hydrogen-doped proportion of the hydrogen-doped gas boiler are taken as optimization variables in the integrated energy system optimization scheduling model; In the optimization scheduling model, the correlation rules between the hydrogen-doped proportion and the system operation state are established, so that when the system is in the electric load valley or the renewable energy output surplus period, the hydrogen-doped proportion is increased by optimization to promote the electrolytic cell to consume surplus electricity; when the system is in the electric load peak period, the hydrogen-doped proportion is reduced by optimization to reduce the forced output load of the electrolytic cell; The first safe operation range is set for the hydrogen-doped proportion of the hydrogen-doped gas turbine, and the second safe operation range is set for the hydrogen-doped proportion of the hydrogen-doped gas boiler, and the corresponding variable upper and lower limit constraints are set in the optimization scheduling model.

[0010] Preferably, the system carbon quota and the system actual carbon emission at each moment are obtained based on the integrated energy system model; a plurality of carbon trading price intervals are divided based on the absolute value of the difference between the system carbon quota and the system actual carbon emission to construct a stepwise carbon trading mechanism model representing carbon trading cost; comprising the following steps: Based on the output data of the oxy-combustion unit, the hydrogen-doped gas turbine and the hydrogen-doped gas boiler in the integrated energy system model and the preset carbon emission right allocation coefficient, the system carbon quota at each moment is calculated; Based on the methane consumption of the hydrogen-doped gas turbine and the hydrogen-doped gas boiler in the integrated energy system model, the carbon emission per unit volume of methane combustion, the total power output of the oxy-combustion unit, the unit carbon emission intensity of the oxy-combustion unit and the carbon capture level of the oxy-combustion capture system model, the system actual carbon emission at each moment is calculated; the difference between the system actual carbon emission at each moment and the system carbon quota is calculated to obtain the system carbon emission trading amount; A stepwise carbon trading cost model is constructed, wherein according to the positive and negative and size of the system carbon emission trading amount, its numerical range is divided into a plurality of continuous intervals, and different carbon trading prices are set for each interval.

[0011] As preferred, the positive and negative and size of the system carbon emission trading amount is divided into a plurality of continuous intervals according to the value range, and different carbon trading unit prices are set for each interval, including: If the system carbon emission trading amount is positive, the carbon trading unit price is increased step by step with the increase of the excess amount; If the system carbon emission trading amount is negative, the carbon trading unit price is increased step by step with the increase of the surplus amount.

[0012] As preferred, the target function of minimizing the sum of carbon trading cost, carbon sequestration cost, coal burning cost, gas purchase cost and wind curtailment cost is constructed according to the step-by-step carbon trading mechanism model, including the following steps: The total carbon trading cost of the integrated energy system model in the dispatching period is calculated based on the step-by-step carbon trading mechanism model; The total carbon sequestration cost in the dispatching period is calculated based on the carbon sequestration amount and the unit carbon sequestration cost; The total coal burning cost in the dispatching period is calculated based on the output of the oxy-combustion unit, the coal price, the unit coal consumption and the operation and maintenance cost; The total gas purchase cost in the dispatching period is calculated based on the amount of natural gas purchased and consumed by the hydrogen-doped gas turbine and the hydrogen-doped gas boiler from the outside and the natural gas unit price; The total wind curtailment penalty cost in the dispatching period is calculated based on the difference between the wind power predicted output and the actual wind power consumption in the integrated energy system model and the wind curtailment penalty unit price; The target function is constructed to minimize the sum of the total carbon trading cost, the total carbon sequestration cost, the total coal burning cost, the total gas purchase cost and the total wind curtailment penalty cost.

[0013] As preferred, the energy balance constraint condition includes: Electric power balance constraint, heat power balance constraint, natural gas balance constraint, hydrogen balance constraint and oxygen balance constraint.

[0014] As preferred, the equipment operation constraint condition includes: oxy-combustion capture unit constraint and gas turbine constraint, gas boiler constraint and energy storage device constraint.

[0015] The present application has at least the following substantial beneficial effects: (1) In view of the problems that OCC, P2G and hydrogen blending technology lack deep cooperation in the existing integrated energy system, and the resource recycling of hydrogen, oxygen, carbon and reaction heat is insufficient, the application builds an OCC-P2G-dynamic hydrogen blending deeply coupled integrated energy system model, realizes efficient methanation of high-concentration CO2 captured by OCC and hydrogen produced by P2G electrolysis water, supplies oxygen produced by P2G to OCC system to reduce the energy consumption of air separation oxygen generation, connects the methanation reaction heat to the heat network to share the heating load, and the dynamic hydrogen blending strategy adapts to the supply of hydrogen source and the fluctuation of source and load, forming a carbon-hydrogen-oxygen-heat multi-energy flow closed loop cycle, which significantly improves the resource comprehensive utilization efficiency and renewable energy consumption capacity, and fundamentally breaks through the performance bottleneck of single technology or shallow coupling.

[0016] (2) In view of the problem that fixed hydrogen blending ratio cannot flexibly respond to system source and load dynamic changes, leading to forced output of electrolytic cell and insufficient system operation flexibility, the application takes the hydrogen blending ratio of hydrogen-blended gas turbine and hydrogen-blended gas boiler as an optimization variable into the scheduling model, establishes dynamic correlation rules of hydrogen blending ratio, electric load demand and renewable energy output state, adjusts the hydrogen blending ratio to promote the consumption of excess electricity by electrolytic cell during low load valley or wind power surplus period, and adjusts the hydrogen blending ratio to reduce the operating pressure of electrolytic cell during load peak period, while setting a safe hydrogen blending interval to ensure combustion stability, effectively solving the poor adaptability of the system under the fixed hydrogen blending mode, and greatly improving the scheduling flexibility and operation safety of the integrated energy system.

[0017] (3) In view of the problem that the traditional fixed unit price carbon trading mechanism has limited guiding effect on system emission reduction and energy structure optimization, the application builds a stepped carbon trading mechanism of optimization parameters, divides multi-interval differential trading unit price based on the difference between system carbon quota and actual emission, and the higher the excess emission, the higher the penalty unit price, and the more the quota surplus, the higher the reward unit price, combined with the low-carbon technology basis of OCC-P2G-dynamic hydrogen blending coupling model, strongly guides the system to preferentially schedule low-carbon hydrogen-blended gas units and optimize oxygen-enriched combustion unit output, not only realizes significant reduction of total carbon emissions, but also achieves deep unification of low carbon and economy through carbon trading income offsetting technology transformation and operation cost, breaking through the technical difficulties of balancing emission reduction and cost under the traditional incentive mechanism.

[0018] The above invention content is only a summary of the technical solution of the application, in order to more clearly understand the technical means of the application, which can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application are described below. BRIEF DESCRIPTION OF DRAWINGS

[0019] Other features, objects, and advantages of the application will become more apparent from the following detailed description when read in connection with the following drawings, in which like numerals designate like elements in the several figures. The drawings are intended to be illustrative, and not limiting of the application. Like numerals designate like elements in the several views.

[0020] Figure 1 A flow chart of an OCC-P2G-dynamic hydrogen blending coupled comprehensive energy system optimization method according to an embodiment of the application.

[0021] Figure 2 A structure system schematic diagram of a comprehensive energy system model according to an embodiment of the application. DETAILED DESCRIPTION

[0022] In order to make the objectives, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only the best mode of the present application, which are used to explain the present application and do not limit the protection scope of the present application. All other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present application.

[0023] Before the example embodiments are discussed in more detail, it should be mentioned that some of the example embodiments are described as processes or methods depicted as flow charts. While the process is described as sequential process, many of the operations (or steps) can be performed in parallel, concurrently or at the same time. In addition, the order of the operations can be re-arranged. The process can be terminated when its operations are completed, but could also have additional steps not included in the figure; the process can correspond to methods, functions, procedures, subroutines, subprograms, etc.

[0024] Embodiment 1: A technical solution provided in an embodiment of the present application is an OCC-P2G-dynamic hydrogen blending coupled comprehensive energy system optimization method, as shown in Figure 1 , comprising the following steps: Performing oxygen-enriched combustion reconstruction on a post-combustion carbon capture power plant to build an oxygen-enriched combustion capture system model; building a two-stage electric gas conversion system model including an electrolytic water stage and a methanation stage; performing hydrogen blending reconstruction on a gas turbine unit to build a gas dynamic hydrogen blending mathematical model; Building a comprehensive energy system model based on the oxygen-enriched combustion capture system model, the two-stage electric gas conversion system model and the gas dynamic hydrogen blending mathematical model.

[0025] As an optional embodiment, as shown in Figure 2The structural system diagram of the integrated energy system model is shown, which is mainly composed of an oxygen-enriched combustion capture system model, a two-stage electric-to-gas system model, and a dynamic hydrogen-doped gas mathematical model. The oxygen-enriched combustion capture system model is a modification of a traditional thermal power unit, and an air separation unit (ASU) and an oxygen storage tank (OST) are introduced. The thermal load is mainly supplied by a gas turbine, a gas boiler (GB), and an electric heating boiler in cooperation. P2G, as an energy conversion device in the IES, produces hydrogen in the first stage, which is used for hydrogen-doped gas turbine and methanation, and oxygen, which is used for oxygen-enriched combustion. Methane and reaction heat produced in the second stage are used to meet part of the gas load and thermal load demand. Energy storage devices include energy storage in four energy forms: electricity, heat, hydrogen, and oxygen.

[0026] The air separation unit and the oxygen storage tank are connected in parallel to form an oxygen supply unit for providing oxygen required for combustion for the oxygen-enriched combustion unit. The exhaust end of the oxygen-enriched combustion unit is connected to the carbon capture device for transporting high-concentration carbon dioxide produced by combustion to the carbon capture device for capture.

[0027] It can be understood that, in order to break through the bottleneck of isolated operation of carbon capture, electric-to-gas, and hydrogen energy utilization in the existing integrated energy system, and to break the resource flow, the embodiment constructs a deep coupling physical architecture integrating oxygen-enriched combustion capture (OCC), two-stage electric-to-gas (P2G), and dynamic hydrogen-doping, and introduces multiple types of energy storage devices. In this embodiment, the air separation unit (ASU) and the oxygen storage tank (OST) are connected in parallel to form a flexible oxygen supply unit to ensure stable oxygen supply to the oxygen-enriched combustion unit, and the high-concentration carbon dioxide exhaust from the unit is directly connected to the carbon capture device for efficient capture. At the same time, through system design, the oxygen produced by water electrolysis in the first stage of P2G is introduced into the above oxygen supply unit, the captured carbon dioxide is introduced into the second stage of P2G for methanation, and the produced hydrogen is distributed to the hydrogen-doped gas turbine and boiler and the methanation reactor as needed. This design realizes the resource utilization of by-products and the cascade recovery of energy through the directional coupling and circulation of material flow and energy flow (oxygen-carbon-hydrogen-heat).

[0028] Specifically, the oxygen-enriched combustion capture system model is established as shown in formulas (1)-(3): The operation energy consumption of the oxygen-enriched combustion carbon capture system composed of the carbon capture and air separation unit at time t, is the sum of the carbon capture device energy consumption and the air separation device energy consumption which can be expressed as:

[0029] In the formula: The power required to capture a unit mass of CO2; Let t be the mass of CO2 captured by the carbon capture device at time t; The power required to produce one unit of oxygen for an ASU; Let be the amount of oxygen produced by ASU at time t.

[0030] Oxygen produced by the ASU can be supplied to the OCC unit as needed, or stored in oxygen storage tanks. The oxygen supply situation is as follows:

[0031] In the formula: The amount of oxygen required for an OCC unit to generate a unit of power; Let t be the total output power of the OCC unit at time t; The amount of oxygen produced by the electrolytic cell at time t; These represent the amounts of oxygen stored in and released from the oxygen storage tank at time t, respectively. t Oxygen reserves at time 1.

[0032] CO2 emissions from OCC unit at time t The operating model for the carbon capture device is as follows:

[0033] In the formula: for carbon emission intensity; Let t be the total CO2 produced by the OCC unit, gas turbine, and gas boiler at time t; Let t be the carbon emissions of the gas turbine. Let t be the carbon emissions from the gas-fired boiler. The carbon capture level of the carbon capture device; The amount of CO2 supplied to the methane reactor by the carbon capture unit; Let t be the amount of carbon stored at time t.

[0034] As an optional embodiment, the two-stage electro-gas system model includes an electrolyzer and a methanation device connected in sequence; The electrolyzer is configured to consume electrical energy to perform an electrolysis reaction of water to produce hydrogen and oxygen; the oxygen is supplied to the oxygen supply unit. The methanation equipment is configured to react a portion of the hydrogen produced by the electrolyzer with the carbon dioxide captured by the carbon capture device to generate methane and heat of reaction; the methane is supplied to the gas network of the integrated energy system model, and the heat of reaction is supplied to the heat network of the integrated energy system model.

[0035] It can be understood that, in order to overcome the defects that the traditional electricity-to-gas (P2G) technology only focuses on one-way conversion of electricity to gas, the by-products (oxygen, reaction heat) are not systematically utilized, and the overall energy efficiency is limited, the embodiment is designed by two-stage decoupling and directional integration of the P2G process. The embodiment is to build an electrolytic cell and a methanation device connected in turn, and to establish precise interfaces with the oxygen, carbon, and heat networks in the system: the electrolytic cell is configured to consume electricity (especially surplus renewable energy power) to perform water electrolysis, simultaneously producing hydrogen and oxygen; the oxygen is directly supplied to the oxygen supply unit of the oxygen-enriched combustion capture system as part of its high-purity oxygen source. At the same time, the methanation device is configured to use part of the hydrogen produced by the electrolytic cell and the carbon dioxide captured from the carbon capture device to perform a catalytic reaction, generate methane and release reaction heat; the generated methane is injected into the gas network of the integrated energy system, and the reaction heat is introduced into the heat network of the integrated energy system. The P2G process is transformed from a single "electricity-gas" conversion unit to a multi-energy flow hub that simultaneously links the "oxygen network", "carbon network", "gas network" and "heat network". Through the above mechanism, the by-product oxygen produced by the electrolytic cell is directly utilized, reducing the energy consumption burden of the air separation oxygen device and reducing the total operating cost of the system; the methanation process simultaneously realizes the recycling of carbon resources (conversion of captured CO2 into combustible gas) and the recovery of low-grade heat energy, respectively alleviating the gas purchase pressure and heat supply pressure of the system, and improving the energy cascade utilization efficiency; the two-stage integrated model significantly enhances the flexibility and value of the P2G technology in terms of absorbing fluctuating renewable energy and participating in the coordinated scheduling of multi-energy networks.

[0036] Specifically, the two-stage electricity-to-gas system model is established as shown in equations (4) to (6): The electricity-to-gas technology includes two main stages of water electrolysis and methanation. In the water electrolysis stage, water is decomposed into hydrogen and oxygen by an electrolysis device. This process not only promotes the consumption of renewable energy, but also delivers the generated oxygen to the oxygen-enriched combustion carbon capture unit, thereby achieving effective utilization of oxygen resources. In the methanation stage, CO2 captured by the oxygen-enriched combustion carbon capture technology reacts with hydrogen produced by electrolysis to synthesize methane. The chemical formula of the two stages is:

[0037] From equation It can be seen that the volume ratio of hydrogen and oxygen produced in the first stage of water electrolysis is 2:1. The first stage P2G mathematical model is:

[0038] In the formula, are the volumes of hydrogen and oxygen produced by the electrolytic cell at time t, respectively. is the thermal-electric conversion coefficient; is the electrolyzer's electric-hydrogen conversion efficiency; is the power consumed by the electrolyzer at time t; is the heat generated by burning unit mass of hydrogen; is the density of hydrogen.

[0039] The second stage P2G mathematical model is:

[0040] In the formula: is the volume of methane generated by methanation at time t; is the methanation efficiency; is the volume of hydrogen used for methanation at time t; is the heat generated by burning unit mass of methane; is the density of methane; is the efficiency of methane production heat; is the heat of reaction of methanation; is the molar mass of methane.

[0041] As an optional embodiment, the gas dynamic hydrogen blending mathematical model includes a hydrogen-blended gas turbine and a hydrogen-blended gas boiler; The hydrogen-blended gas turbine and the hydrogen-blended gas boiler are configured to receive another part of hydrogen from the electrolyzer and combust with externally supplied natural gas in a dynamically adjustable volume ratio; The hydrogen-blended ratio of the hydrogen-blended gas turbine and the hydrogen-blended gas boiler model is dynamically adjusted according to the electric load demand and the renewable energy output state in the integrated energy system, and the hydrogen-blended ratio changes within a preset safe operation range.

[0042] As an optional embodiment, the hydrogen-blended ratio of the hydrogen-blended gas turbine and the hydrogen-blended gas boiler model is dynamically adjusted according to the electric load demand and the renewable energy output state in the integrated energy system, and the hydrogen-blended ratio changes within a preset safe operation range; comprising the following steps: The hydrogen-blended ratio of the hydrogen-blended gas turbine and the hydrogen-blended ratio of the hydrogen-blended gas boiler are taken as optimization variables and included in the integrated energy system optimization scheduling model; In the optimization scheduling model, the correlation rules between the hydrogen-blended ratio and the system operation state are established, so that when the system is in a low valley of electric load or a period of surplus renewable energy output, the hydrogen-blended ratio is increased through optimization to promote the consumption of surplus electricity by the electrolyzer; when the system is in a period of electric load peak, the hydrogen-blended ratio is reduced through optimization to reduce the forced output load of the electrolyzer; A first safe operation range is set for the hydrogen blending ratio of the hydrogen-blended gas turbine, a second safe operation range is set for the hydrogen blending ratio of the hydrogen-blended gas boiler, and corresponding variable upper and lower limit constraints are set in the optimization scheduling model.

[0043] It can be understood that, for the existing comprehensive energy system, the hydrogen blending ratio of the gas is fixed and cannot respond to real-time fluctuations of the source and load, so that the electrolytic cell may be forced to generate power during the power consumption peak, and hydrogen energy cannot be fully consumed during the surplus of renewable energy, thereby restricting the economic efficiency and scheduling flexibility of system operation. The embodiment introduces a gas dynamic hydrogen blending operation strategy based on an optimization model. In the embodiment, the hydrogen blending ratios of the hydrogen-blended gas turbine and the hydrogen-blended gas boiler are taken as key optimization decision variables and are included in a unified comprehensive energy system optimization scheduling model, and a strong correlation optimization rule between the hydrogen blending ratio and the real-time state of the system (electric load demand and renewable energy output) is established: when the system is in a low valley of electric load or a period of surplus renewable energy output, the optimization model actively adjusts the hydrogen blending ratio to a high level to encourage the electrolytic cell to consume more surplus electric energy to produce hydrogen, thereby improving the utilization rate of renewable energy and reducing the cost of abandoned wind power; when the system is in a period of high electric load, the optimization adjusts the hydrogen blending ratio to a low level to avoid unnecessary forced output load of the electrolytic cell for maintaining a fixed hydrogen blending ratio, thereby relieving the power supply pressure during the peak period and reducing the total energy consumption of the system.

[0044] Specifically, a gas dynamic hydrogen blending mathematical model is constructed as shown in formulas (7) and (8): A hydrogen-blended gas turbine model is constructed: when the hydrogen blending volume ratio of the gas turbine is in the interval of 10% to 20%, the combustor can maintain a safe and stable combustion state. The mathematical model is as follows:

[0045] In the formulas, and are the heat generated by the combustion of methane and hydrogen in the gas turbine at time t, respectively; and are the volumes of methane and hydrogen consumed by the gas turbine at time t, respectively; and are the electric power and thermal power generated by the gas turbine at time t, respectively; is the hydrogen blending ratio of the gas turbine at time t.

[0046] A hydrogen-blended gas boiler model is constructed: similar to the gas turbine, in order to maintain a safe and stable combustion state, the hydrogen blending ratio of the gas boiler is set to be in the range of 2% to 20%. The mathematical model of the hydrogen-blended gas boiler is as follows:

[0047] In the formulas, and QCH4(t) and QCH4(t) respectively represent the heat generated by the combustion of methane and hydrogen in the gas boiler at time t; VCH4(t) and VCH4(t) respectively represent the volume of methane and hydrogen consumed by the gas boiler at time t; P(t) represents the heat power generated by the gas boiler at time t; H(t) represents the hydrogen blending ratio of the gas boiler at time t.

[0048] Based on the integrated energy system model, the system carbon quota and the system actual carbon emission at each moment are obtained; and based on the absolute value of the difference between the system carbon quota and the system actual carbon emission, a plurality of carbon trading price intervals are divided to construct a step-type carbon trading mechanism model representing the carbon trading cost.

[0049] As an optional embodiment, based on the integrated energy system model, the system carbon quota and the system actual carbon emission at each moment are obtained; and based on the absolute value of the difference between the system carbon quota and the system actual carbon emission, a plurality of carbon trading price intervals are divided to construct a step-type carbon trading mechanism model representing the carbon trading cost; which comprises the following steps: Based on the output data of the oxy-combustion unit, the hydrogen-blended gas turbine and the hydrogen-blended gas boiler in the integrated energy system model, and in combination with a preset carbon emission right allocation coefficient, the system carbon quota at each moment is calculated; Based on the methane consumption of the hydrogen-blended gas turbine and the hydrogen-blended gas boiler in the integrated energy system model, the carbon emission per unit volume of methane combustion, the total power output of the oxy-combustion unit, the carbon emission intensity per unit of the oxy-combustion unit and the carbon capture level of the oxy-combustion capture system model, the system actual carbon emission at each moment is calculated; and the difference between the system actual carbon emission and the system carbon quota at each moment is calculated to obtain the system carbon emission trading amount. A step-type carbon trading cost model is constructed, wherein according to the positive and negative and size of the system carbon emission trading amount, the numerical range thereof is divided into a plurality of continuous intervals, and different carbon trading prices are set for each interval.

[0050] As an optional embodiment, the dividing of the numerical range of the system carbon emission trading amount into a plurality of continuous intervals according to the positive and negative and size thereof and the setting of different carbon trading prices for each interval comprise: If the system carbon emission trading amount is positive, the carbon trading price is stepwise increased with the increase of the excess amount; If the system carbon emission trading amount is negative, the carbon trading price is stepwise increased with the increase of the surplus amount.

[0051] ​It can be understood that, in order to solve the problem that the traditional carbon trading mechanism adopts a fixed unit price and has limited incentive effect on the low-carbon transformation of the integrated energy system, and cannot effectively guide the deep optimization of the energy structure within the system, the embodiment builds a step-byed carbon trading cost model closely related to the real-time operation state of the system. The embodiment first accurately calculates the system carbon quota at each moment according to the preset carbon emission right distribution coefficient of each unit (oxygen-enriched combustion unit, hydrogen-doped gas turbine and boiler) and the actual output thereof; at the same time, based on the carbon emission of fossil fuel (methane) consumed by the gas equipment and the actual carbon capture level of the oxygen-enriched combustion capture system, the actual carbon emission of the system is dynamically calculated, and then the difference (i.e. carbon trading amount) is obtained. On this basis, for the excess emission (the trading amount is positive), the unit price increases step by step with the increase of the excess amount, forming an increasing punitive cost; for the quota surplus (the trading amount is negative), the unit price increases step by step with the increase of the surplus amount, forming an increasing reward benefit. The carbon trading cost is changed from a simple linear proportion with the emission to a nonlinear strong correlation with a segmented slope and an increasing or decreasing marginal cost; through the above mechanism, the model injects a strong dynamic economic signal into the system optimization process, when the system tries to minimize the total cost, the high-intensity marginal penalty will force the algorithm to greatly reduce the output of high-carbon units (such as traditional coal-fired units), and the high-intensity marginal reward will encourage the algorithm to increase the deep low-carbon measures (such as increasing the hydrogen-doping ratio and increasing the carbon capture level) as much as possible, thereby fundamentally driving the energy supply structure to transform towards low-carbon and zero-carbon from the operation strategy while pursuing economy, and achieving high coordination between environmental and economic goals.

[0052] Specifically, the step-by-step carbon trading mechanism model is as shown in formulas (9)-(12): The system carbon quota model is established:

[0053] In the formula: is the IES carbon allocation at time t; and are the carbon emission right allocation of the gas turbine unit power supply and heating power supply, respectively; is the carbon emission right allocation of the gas boiler unit heating power supply; is the carbon emission right allocation of the OCC unit power supply.

[0054] The actual carbon emission of the system is calculated:

[0055] In the formula: is the CO2 emission of unit volume of methane combustion; is the actual carbon emission of the system at time t.

[0056] Carbon emission trading amount of the computing system:

[0057] In the formula: is the carbon emission trading amount of the system at time t.

[0058] A ladder-type carbon trading mechanism model is established:

[0059] In the formula: is the carbon trading cost of the system at time t, positive representing purchase and negative representing sale; is the carbon trading base price; L is the interval length; is the compensation coefficient; is the penalty coefficient.

[0060] According to the ladder-type carbon trading mechanism model, a target function of minimizing the sum of carbon trading cost, carbon sequestration cost, coal-fired cost, gas purchase cost and wind power curtailment cost is constructed, and based on the energy balance constraint condition and the equipment operation constraint condition set by the integrated energy system model, an integrated energy system optimization scheduling model is constructed and solved by a CPLEX solver to obtain the system optimization strategy.

[0061] As an optional embodiment, the target function of minimizing the sum of carbon trading cost, carbon sequestration cost, coal-fired cost, gas purchase cost and wind power curtailment cost according to the ladder-type carbon trading mechanism model comprises the following steps: The total carbon trading cost of the integrated energy system model in the scheduling period is calculated based on the ladder-type carbon trading mechanism model; The total carbon sequestration cost in the scheduling period is calculated based on the carbon sequestration amount and the unit carbon sequestration cost; The total coal-fired cost in the scheduling period is calculated based on the output of the oxygen-enriched combustion unit and the coal-fired price, unit coal consumption and operation and maintenance cost; The total gas purchase cost in the scheduling period is calculated based on the amount of natural gas purchased and consumed by the hydrogen-doped gas turbine and hydrogen-doped gas boiler from the outside and the natural gas unit price; The total wind power curtailment penalty cost in the scheduling period is calculated based on the difference between the wind power predicted output and the actual wind power consumption in the integrated energy system model and the wind power curtailment penalty unit price; The target function is constructed to minimize the sum of the total carbon trading cost, the total carbon sequestration cost, the total coal-fired cost, the total gas purchase cost and the total wind power curtailment penalty cost.

[0062] It can be understood that, in order to overcome the defects of single objective function (often only focusing on traditional economic indicators or single environmental protection indicators) in the existing comprehensive energy system optimization scheduling, and difficult to achieve multiple goals such as low carbon, economy and high efficiency under complex operation constraints, a multi-dimensional composite cost objective function integrating environmental protection policy cost, energy conversion and procurement cost and renewable energy utilization efficiency is constructed in the embodiment. The embodiment integrates the stepped carbon trading cost, carbon sequestration cost, coal-fired cost, gas purchase cost and wind curtailment penalty cost into a unified mathematical expression, and sets the minimization of the sum of the expression as the optimization goal. The core technical principle of the design is to unify the multiple sub-goals (such as minimizing carbon emissions and minimizing operation cost) that may conflict with each other into additive cost items, and use the optimization ability of the optimization algorithm to automatically seek the global optimal balance point between these interrelated and mutually offset cost dimensions. Through the above mechanism, the external carbon policy pressure (through the stepped carbon price), the internal carbon management decision (sequestration or not), the main primary energy consumption (coal, gas) and the renewable energy consumption level are all internalized into the "cost language" of the system, forcing the optimization scheduling model to comprehensively weigh the immediate and cumulative impact on the five cost dimensions when making decisions on unit output and energy allocation each time. Therefore, the system operation strategy is driven to evolve spontaneously in the direction of reducing total carbon emissions, improving wind and light consumption rate, reducing dependence on fossil fuels and controlling total operation cost, and finally realizing the deep integration and overall optimization of environmental benefits, energy security benefits and economic benefits.

[0063] Specifically, the objective function of the application takes the sum of carbon trading cost, carbon sequestration cost, coal-fired cost, gas purchase cost and wind curtailment cost as the goal. The objective function is as follows:

[0064] In the formula: Total cost; Carbon trading cost; Carbon sequestration cost; Coal-fired cost; Gas purchase cost; Wind curtailment cost.

[0065] Carbon trading and carbon sequestration cost:

[0066] In the formula: Scheduling period; Cost of sequestrating unit mass of CO2.

[0067] Coal-fired cost:

[0068] Wherein: a is the price of coal; b is the coal consumption of OCC unit per unit of power generation; d is the operation and maintenance cost of OCC unit per unit of power generation.

[0069] Gas purchase cost:

[0070] Wherein: is the unit price of natural gas; is the volume of natural gas purchased by the system at time t.

[0071] Wind curtailment cost:

[0072] Wherein: is the penalty cost of unit wind curtailment; is the wind curtailment of the system at time t.

[0073] As an optional embodiment, the energy balance constraint condition comprises: An electric power balance constraint, a thermal power balance constraint, a natural gas balance constraint, a hydrogen balance constraint, and an oxygen balance constraint.

[0074] Specifically, the energy balance constraint is shown in formula (18): the system internally includes five kinds of energy flow, i.e., electricity, heat, natural gas, hydrogen, and oxygen, and the balance relationship of the five kinds of energy flow is given below:

[0075] Wherein: is the output of wind turbine at time t; and is the electric power consumed by the electric heating boiler and the heat generated by the electric heating boiler at time t; and is the electric and heat demand of the load side at time t; and is the charging and discharging heat power of the heat storage device at time t; and is the amount of hydrogen stored and released by the hydrogen storage device at time t, respectively.

[0076] As an optional embodiment, the device operation constraint condition comprises: an oxygen-enriched combustion capture unit constraint and a gas turbine constraint, a gas boiler constraint, and an energy storage device constraint.

[0077] The OCC unit constraint, i.e., the oxygen-enriched combustion capture unit constraint:

[0078] Wherein: and are the upper and lower limits of the output of the OCC unit; Upper and lower ramping limits for OCC units and Maximum and minimum operating power for carbon capture device and Maximum and minimum operating power for ASU

[0079] Gas turbine constraints

[0080] wherein and Upper and lower limits for gas turbine electrical output and Upper and lower limits for gas turbine thermal output Total output of gas turbine at time t and Upper and lower ramping limits for total output of gas turbine

[0081] Gas boiler constraints

[0082] wherein and Upper and lower limits for thermal output of gas boiler and Upper and lower ramping limits for thermal output of gas boiler

[0083] Energy storage device constraints The system comprises electricity storage, heat storage, hydrogen storage and oxygen storage devices, which have similar energy storage device constraints. The following embodiment lists the constraints of the electricity storage device, and the rest of the energy storage device constraints can be designed by those skilled in the art according to their working conditions and environments:

[0084] wherein Capacity of electricity storage device at time t and Upper and lower limits for capacity of electricity storage device and State of charge of electricity storage device at time t and Maximum charge and discharge power of electricity storage device and Capacity of electricity storage device at the beginning and at the end of the scheduling time, respectively

[0085] Further, the carbon trading and OCC-P2G-dynamic hydrogen blending coupled comprehensive energy system optimization scheduling model constructed by the embodiment of the present application is a mixed integer nonlinear model, which cannot be directly solved and needs to be converted into a linear model for solving. In view of formula (12), the piecewise function is linearized by introducing a binary variable, and in view of formula (22) in the form of product of binary variable and continuous variable, the big M method is used for linearization, and then the system optimization strategy is obtained by solving through the Matlab platform and using the CPLEX solver.

[0086] Through the description of the above embodiments, those skilled in the art can understand that, for the convenience and brevity of description, only the above division of each functional module is taken as an example, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the specific device is divided into different functional modules to complete all or part of the functions described above.

[0087] In the embodiments provided in the present application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the above-described embodiments of the structure are only illustrative, for example, the division of the modules or units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another structure, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed ones can be through some interfaces, indirect coupling or communication connection of structures or units, and can be electrical, mechanical or other forms.

[0088] The above specific embodiments are the preferred embodiments of the present application, and the specific implementation range of the present application is not limited thereto. The scope of the present application includes but is not limited to the above specific embodiments, and equivalent changes made according to the shape and structure of the present application are within the protection scope of the present application.

Claims

1. An integrated energy system optimization method based on OCC-P2G-dynamic hydrogen blending coupling, characterized in that: The method comprises the following steps: An oxygen-enriched combustion capture system model is constructed by retrofitting an existing post-combustion carbon capture power plant for oxygen-enriched combustion; A two-stage electricity-to-gas system model is constructed, which comprises an electrolysis stage and a methanation stage; A gas turbine unit is retrofitted for hydrogen blending to construct a dynamic hydrogen-blended gas model; Based on the oxygen-enriched combustion capture system model, the two-stage electricity-to-gas system model, and the dynamic hydrogen-blended gas model, a comprehensive energy system model is constructed; Based on the comprehensive energy system model, the system carbon quota and the actual carbon emissions at each time are obtained; Based on the absolute value of the difference between the system carbon quota and the actual carbon emissions, multiple carbon trading price intervals are divided to construct a stepwise carbon trading mechanism model representing carbon trading costs; According to the stepwise carbon trading mechanism model, a target function is constructed to minimize the sum of carbon trading costs, carbon sequestration costs, coal costs, gas purchase costs, and wind curtailment costs, and based on the energy balance constraints and equipment operation constraints set by the comprehensive energy system model, a comprehensive energy system optimization scheduling model is constructed and solved by a CPLEX solver to obtain the system optimization strategy.

2. The OCC-P2G-dynamic hydrogen blending coupled comprehensive energy system optimization method according to claim 1, wherein: The oxygen-enriched combustion capture system model comprises an oxygen-enriched combustion unit, an air separation oxygen generation device, an oxygen storage tank, and a carbon capture device; The air separation oxygen generation device and the oxygen storage tank are connected in parallel to form an oxygen supply unit, which provides oxygen required for combustion for the oxygen-enriched combustion unit; The exhaust end of the oxygen-enriched combustion unit is connected to the carbon capture device for transporting high-concentration carbon dioxide generated by combustion to the carbon capture device for capture.

3. The OCC-P2G-dynamic hydrogen blending coupled comprehensive energy system optimization method according to claim 2, wherein: The two-stage electricity-to-gas system model comprises an electrolytic cell and a methanation device connected in sequence; The electrolytic cell is configured to consume electrical energy to perform an electrolysis reaction to generate hydrogen and oxygen, and the oxygen is supplied to the oxygen supply unit, The methanation device is configured to react part of the hydrogen generated by the electrolytic cell with the carbon dioxide captured by the carbon capture device to generate methane and reaction heat; the methane is supplied to the gas network of the comprehensive energy system model, and the reaction heat is supplied to the heat network of the comprehensive energy system model.

4. The OCC-P2G-dynamic hydrogen blending coupled comprehensive energy system optimization method according to claim 3, wherein: The dynamic hydrogen-blended gas model comprises a hydrogen-blended gas turbine and a hydrogen-blended gas boiler; The hydrogen-blended gas turbine and the hydrogen-blended gas boiler are configured to receive another part of the hydrogen from the electrolytic cell and mix with externally supplied natural gas at a dynamically adjustable volume ratio for combustion; The hydrogen blending ratio of the hydrogen-blended gas turbine and the hydrogen-blended gas boiler model is dynamically adjusted according to the electrical load demand within the comprehensive energy system and the renewable energy output state, and the hydrogen blending ratio changes within a pre-set safe operating range.

5. The OCC-P2G-dynamic hydrogen blending coupled integrated energy system optimization method according to claim 3, wherein: the hydrogen blending ratios of the hydrogen-blended gas turbine and the hydrogen-blended gas boiler model are dynamically adjusted according to the electrical load demand and the renewable energy output state in the integrated energy system, and the hydrogen blending ratios change within a preset safe operation range; and the method comprises the following steps: incorporating the hydrogen blending ratios of the hydrogen-blended gas turbine and the hydrogen-blended gas boiler into the integrated energy system optimization scheduling model as optimization variables; establishing a correlation rule between the hydrogen blending ratios and the system operation state in the optimization scheduling model, so that when the system is in a low electrical load valley or a renewable energy output surplus period, the hydrogen blending ratios are increased by optimization to promote the electrolyzer to consume the surplus electrical energy; and when the system is in an electrical load peak period, the hydrogen blending ratios are decreased by optimization to reduce the forced output load of the electrolyzer; setting a first safe operation range for the hydrogen blending ratio of the hydrogen-blended gas turbine and a second safe operation range for the hydrogen blending ratio of the hydrogen-blended gas boiler, and setting corresponding variable upper and lower limit constraints in the optimization scheduling model.

6. The OCC-P2G-dynamic hydrogen blending coupled integrated energy system optimization method according to claim 1, wherein: the system carbon quota and the system actual carbon emission at each moment are obtained based on the integrated energy system model; a plurality of carbon trading price intervals are divided based on the absolute value of the difference between the system carbon quota and the system actual carbon emission to construct a stepwise carbon trading mechanism model representing the carbon trading cost; and the method comprises the following steps: calculating the system carbon quota at each moment based on the output data of the oxy-combustion unit, the hydrogen-blended gas turbine and the hydrogen-blended gas boiler in the integrated energy system model, and a preset carbon emission right allocation coefficient; calculating the system actual carbon emission at each moment based on the methane consumption of the hydrogen-blended gas turbine and the hydrogen-blended gas boiler, the carbon emission per unit volume of methane combustion, the total power output of the oxy-combustion unit, the carbon emission intensity per unit of the oxy-combustion unit and the carbon capture level of the oxy-combustion capture system model; calculating the difference between the system actual carbon emission and the system carbon quota at each moment to obtain the system carbon emission trading amount; and constructing a stepwise carbon trading cost model, wherein the numerical range of the system carbon emission trading amount is divided into a plurality of continuous intervals according to its sign and size, and different carbon trading prices are set for each interval.

7. The OCC-P2G-dynamic hydrogen blending coupled integrated energy system optimization method according to claim 6, wherein: the step of dividing the numerical range of the system carbon emission trading amount into a plurality of continuous intervals according to its sign and size, and setting different carbon trading prices for each interval, comprises: if the system carbon emission trading amount is positive, the carbon trading price is stepwise increased with the increase of the excess amount; and if the system carbon emission trading amount is negative, the carbon trading price is stepwise increased with the increase of the surplus amount. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 8. The OCC-P2G-dynamic hydrogen blending coupled integrated energy system optimization method according to claim 4, wherein: the objective function of minimizing the sum of carbon trading cost, carbon sequestration cost, coal-fired cost, gas purchasing cost and wind curtailment cost is constructed based on the stepwise carbon trading mechanism model, including the following steps: calculating the total carbon trading cost of the integrated energy system model in the dispatch period based on the stepwise carbon trading mechanism model; calculating the total carbon sequestration cost in the dispatch period based on the carbon sequestration amount and the unit carbon sequestration cost; calculating the total coal-fired cost in the dispatch period based on the output of the oxy-combustion unit and the coal price, unit coal consumption and operation and maintenance cost; calculating the total gas purchasing cost in the dispatch period based on the amount of natural gas purchased and consumed by the hydrogen-blended gas turbine and hydrogen-blended gas boiler from the outside and the natural gas unit price; calculating the total wind curtailment penalty cost in the dispatch period based on the difference between the wind power predicted output and the actual wind power consumption in the integrated energy system model and the wind curtailment penalty unit price; constructing the objective function as minimizing the sum of the total carbon trading cost, total carbon sequestration cost, total coal-fired cost, total gas purchasing cost and total wind curtailment penalty cost.

9. The OCC-P2G-dynamic hydrogen blending coupled integrated energy system optimization method according to claim 8, wherein: the energy balance constraint includes: electrical power balance constraint, thermal power balance constraint, natural gas balance constraint, hydrogen balance constraint and oxygen balance constraint.

10. The OCC-P2G-dynamic hydrogen blending coupled integrated energy system optimization method according to claim 8, wherein: the device operation constraint includes: oxy-combustion capture unit constraint, gas turbine constraint, gas boiler constraint and energy storage device constraint.

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