Multi-target collaborative optimization scheduling method for hydrogen-containing comprehensive energy system

Abstract

The invention provides a multi-target collaborative optimization scheduling method for a hydrogen-containing comprehensive energy system, and the method comprises the steps: building a hydrogen energy diversified utilization model containing electrolytic hydrogen production, hydrogen-to-methane production, hydrogen-to-electricity conversion, carbon capture, hydrogen storage and thermoelectric hydrogen doping, and fully absorbing surplus wind power new energy; on the basis, economy and low-carbon property are taken into consideration, a hydrogen-containing comprehensive energy system multi-target optimization scheduling model with minimum total operation cost and minimum carbon emission as targets is established, the model is solved in combination with an epsilon-constraint method and a TOPSIS decision-making method, and quantity-quality cooperative operation of the comprehensive energy system is promoted. Under the condition that the hydrogen doping ratio of fuel gas is fixed, the total cost and the carbon emission amount are reduced along with the increase of the hydrogen doping ratio of the CHP unit, and when the hydrogen doping ratio reaches up to 20%, the economic benefit and the environmental benefit of the IES are the best. Compared with a traditional IES optimization scheduling problem, multi-objective optimization can better promote quantity-quality cooperative operation of the IES, the overall performance of the comprehensive energy system is improved, and the method can be widely applied and popularized.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy optimization and scheduling, and specifically relates to a multi-objective collaborative optimization and scheduling method for a hydrogen-containing integrated energy system. Background Art

[0002] The contradiction between energy consumption and green and sustainable development is becoming increasingly severe. Building a new energy system, reducing carbon emissions, and increasing the absorption of new energy sources have become urgent technical challenges that need to be overcome. Hydrogen, as a low-carbon, clean, secondary green energy source, has broad application prospects in the optimized operation of integrated energy systems (IES). The use of hydrogen energy is an ideal vehicle for achieving large-scale development and utilization of new energy. Coupling and coordinating hydrogen energy with other energy sources in an integrated energy system is of great significance for promoting IES operational flexibility and reducing carbon emissions. However, some existing research on the use of hydrogen energy is insufficiently comprehensive and in-depth.

[0003] With the rapid development of integrated energy systems and the deepening of energy transition, some research efforts have moved beyond simply pursuing scheduling strategies focused solely on economic efficiency. The large-scale integration of wind and solar renewable energy, policy mandates for carbon reduction and emissions reduction, and the diversification of user energy usage patterns have all placed higher demands on the economic efficiency, flexibility, environmental benefits, and reliability of the system. The optimal scheduling of integrated energy systems has gradually shifted from single-objective optimization to multi-objective coordinated optimization, aiming to balance multiple objectives, including minimizing economic costs and carbon emissions.

[0004] Therefore, studying the multi-objective collaborative optimization scheduling of hydrogen-containing integrated energy systems is of great significance to achieving the coordinated operation of the integrated energy systems in terms of quantity and quality. Summary of the Invention

[0005] The purpose of the present invention is to provide a multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system to solve the above-mentioned technical problems existing in the prior art.

[0006] To this end, the technical solutions provided by the present invention are as follows:

[0007] The multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system includes the following steps:

[0008] Step 1) establishing a hydrogen energy diversified utilization model including hydrogen electrolysis, hydrogen to methane, hydrogen to electricity, carbon capture, hydrogen storage, and hydrogen blending with natural gas;

[0009] Step 2) Based on the hydrogen energy diversified utilization model, a multi-objective optimization scheduling model for the hydrogen-containing integrated energy system is established with the minimum total system operating cost and the minimum carbon emissions as the objective function;

[0010] Step 3) Combining the ε-constraint method and the TOPSIS decision-making method, MATLAB calls CPLEX to solve the multi-objective optimization scheduling model of the hydrogen-containing integrated energy system, determine the optimal compromise solution of the Pareto front, and obtain the total system operating cost, carbon emissions, and the optimal output plan for each device in each time period;

[0011] Among them, the equipment includes electrolyzer EL, methane reactor MR, hydrogen fuel cell HFC, hydrogen storage tank HTS, gas boiler GB and combined heat and power unit CHP.

[0012] The electrolysis hydrogen production in step 1) is to use the surplus wind power resources in the electrolyzer device to produce hydrogen by electrolyzing water. The model is:

[0013]

[0014] Where η EL is the energy conversion efficiency of the electrolytic cell EL; P EL,e (t) is the electric power consumed by EL at time t; P EL,e (t) is the hydrogen power generated by EL at time t;

[0015] In step 1), hydrogen to methane is generated in a methane reactor by combining hydrogen and CO2 emitted by the gas turbine captured by the carbon capture device, and then transported to the cogeneration unit as fuel. The model is:

[0016] 4H2+CO2→2H2O+CH4 (2)

[0017]

[0018] Where: η MR is the energy conversion efficiency of the methane reactor MR; is the hydrogen power consumed by MR at time t; P MR,g (t) is the gas power generated by MR at time t; P CCS,e (t) is the electrical energy consumed by the carbon capture device; η CO2 Energy consumption required to treat unit mass of CO2, MW·h / t; E CCS (t) is the weight of carbon dioxide captured by the carbon capture device CCS; P CCS,sum (t) is the total power consumed by CCS at time t; P base is the basic energy consumption of CCS; h-e is the thermoelectric conversion coefficient, which is 3600MJ / MW·h; is the density of CO2; is the calorific value of natural gas, MJ / m 3 .

[0019] Step 1) The hydrogen-to-electricity conversion is to generate electricity by consuming hydrogen through a hydrogen fuel cell. The model is as follows:

[0020]

[0021] Where: η HFC The power generation efficiency of hydrogen fuel cells HFC; is the input hydrogen power of HFC at time t; P HFC,e (t) is the output power of HFC at time t;

[0022] Step 1) The hydrogen storage is to store excess wind power in the form of hydrogen energy through a hydrogen storage tank. The model is as follows:

[0023]

[0024] S HTS (t) = S HTS (t-1)+P HTS (t) (9)

[0025] Where: are the charging and discharging efficiencies of the hydrogen storage tank HTS, respectively; are the charging and discharging power of the hydrogen storage tank during period t; P HTS (t) is the storage power of the hydrogen storage tank during period t; S HTS (t) is the storage capacity at time t; S HTS (t-1) is the storage capacity at time t-1.

[0026] Step 1) The carbon capture is to capture the CO2 emitted by the gas boiler GB through a carbon capture device. The model is as follows:

[0027] P GB,h (t) = η GB P GB,g (t) (10)

[0028] Where: η GB is the energy conversion efficiency of the gas boiler GB; P GB,h (t) is the thermal power output by GB at time t; P GB,g (t) is the natural gas power consumed by GB at time t;

[0029] In step 1), the gas-hydrogen blending is a cogeneration unit that generates electricity and heat by blending gas-hydrogen. The model is as follows:

[0030]

[0031] 0≤ξ(t)≤20% (13)

[0032] P CHP,e (t)+PCHP,h (t) = η CHP P CHP (t) (14)

[0033] λ min P CHP,e (t)≤P CHP,h (t)≤λ max P CHP,e (t) (15)

[0034] Where: P CHP (t), P CHP,g (t), are the total gas power input to the CHP unit, the power of the gas boiler burning natural gas, and the power of the gas boiler burning hydrogen; ξ(t), L CH4 Respectively represent the ratio of hydrogen blended with fuel gas, the lower calorific value of hydrogen, and the lower calorific value of natural gas; P CHP,e (t), P CHP,h (t) are the power generation and heating power of the CHP unit respectively; η CHP is the working efficiency of the CHP unit; min ,λ max They are the upper and lower limits of the heat-to-electricity ratio of the CHP unit respectively.

[0035] Step 2) Modeling of the multi-objective optimization scheduling model of the hydrogen-containing integrated energy system includes objective functions and constraints;

[0036] The objective functions include the system total operating cost objective function f1 and the carbon emission objective function f2;

[0037] The constraints include wind power output constraints, gas boiler output constraints and ramp constraints, cogeneration unit output constraints and ramp constraints, electrolyzer output constraints and ramp constraints, hydrogen fuel cell output constraints and ramp constraints, methane reactor output constraints and ramp constraints, carbon capture device output constraints and ramp constraints, hydrogen storage tank constraints and power balance constraints;

[0038] Among them, the power balance constraints include electric power balance constraints, thermal power balance constraints, gas power balance constraints and hydrogen power balance constraints.

[0039] System total operating cost objective function

[0040] Carbon emission objective function:

[0041] f2=min(E grid +E GB +E CHP -E CCS) (17)

[0042] Where f1 is the total operating cost; F g,buy Gas purchase cost refers to the natural gas purchased by the system to meet the needs of cogeneration units, gas boilers and gas loads; is the carbon trading cost; F e,buy The cost of electricity purchase refers to the amount of electricity purchased from the main grid to meet the electricity load demand;

[0043] f2 is the actual carbon emissions of IES; E grid 、E GB 、E CHP are the actual carbon emissions of purchased coal-fired power, gas boilers, and cogeneration units; E CCS the amount of carbon emissions absorbed by carbon capture plants;

[0044] in,

[0045]

[0046]

[0047] Where, f coal is the power generation cost of the coal-fired unit; t is the unit scheduling step; T is the scheduling period; f gas is the purchase price of natural gas; P e,buy (t) Energy consumption power provided by the power grid; P g,buy (t) Energy consumption power provided to the gas grid; is the carbon trading benchmark price, d is the length of the carbon emission interval; α is the price growth rate, E IES,t P is the actual carbon emissions of carbon trading; GB,g (t) is the natural gas power consumed by the gas boiler GB at time t, P CHP,g (t) is the natural gas power consumed by the cogeneration unit at time t; is the carbon emission coefficient per unit power generation of the coal-fired unit, t / MW·h; They are the carbon content per unit calorific value of natural gas and the carbon oxidation rate of natural gas respectively; 44 / 12 represents the ratio of the relative molecular mass of CO2 to carbon.

[0048] b. Constraints

[0049] (1) Wind power output constraint: 0<P WT (t)<P wind (t)

[0050] Where, P WT (t) is the actual output of the wind turbine in the energy system; P wind is the predicted electric power output of the wind turbine;

[0051] (2) Gas boiler output constraints and ramp constraints:

[0052]

[0053] Where: The upper limit of gas consumption power of gas boiler; P is the upper and lower limits of the gas boiler's climbing capacity; GB,g (t+1) is the gas consumption power of the gas boiler at time t+1;

[0054] (3) Output constraints and ramp constraints for cogeneration units:

[0055]

[0056] Where: is the maximum operating condition of the cogeneration unit; P is the upper and lower limits of the ramp rate of the cogeneration unit; CHP (t) is the total gas power input to the cogeneration unit at time t, P CHP (t+1) is the total gas power input to the cogeneration unit at time t+1;

[0057] (4) Electrolyzer output constraints and climbing constraints:

[0058]

[0059] Where: The upper limit of the electric power input to the electrolyzer equipment; P is the upper and lower limits of the climbing power of the electrolyzer during operation; EL,e (t) is the electric power consumed by the electrolytic cell EL at time t, P EL,e (t+1) is the electric power consumed by EL at time t+1;

[0060] (5) Hydrogen fuel cell output constraints and climbing constraints:

[0061]

[0062] Where: The upper limit of the hydrogen power input to the hydrogen fuel cell; The upper and lower limits of the climbing power of the hydrogen fuel cell during operation; is the input hydrogen power of HFC at time t+1;

[0063] (6) Methane reactor output constraints and ramp constraints:

[0064]

[0065] Where: is the upper limit of hydrogen consumption power of methane reactor; The upper and lower limits of the ramp power of the methane reactor during operation;

[0066] (7) Output constraints and ramp constraints for carbon capture devices:

[0067]

[0068] Where: The maximum operating condition for the carbon capture equipment; P is the upper and lower limits of the climbing power of the carbon capture device during operation; CCS,e (t) the electrical energy consumed by the carbon capture plant;

[0069] (8) Hydrogen storage tank constraints:

[0070]

[0071] S HTS (0) = S HTS (T) (41)

[0072] Where: The lower and upper limits of the hydrogen storage tank capacity; The maximum single charge and discharge power of the hydrogen storage equipment; S is the hydrogen filling and releasing status of the hydrogen storage tank at time t, and the value is 0 or 1, and both cannot be 1 at the same time, indicating that the hydrogen storage tank cannot be charged and released at the same time; HTS (0) is the initial capacity of the hydrogen storage device; are the charging and discharging power of the hydrogen storage tank during period t; S HTS (t) is the storage capacity at time t;

[0073] (9) Power balance constraints

[0074] Electric power balance constraints:

[0075] P e.buy (t)+P WG (t)+P CHP,e (t)+P HFC,e (t) = P load,e (t)+P EL,e (t)+P CCS,sum (t)

[0076] Where, P load,e (t) is the electrical load demand of the system; P WG (t) is the electric power output by the wind turbine; P CHP,e (t), is the power generation power of the combined heat and power unit CHP; P HFC,e(t) is the output power of HFC at time t; P EL,e (t) is the electric power consumed by EL at time t; P CCS,sum (t) is the total electricity consumed by the carbon capture system CCS at time t;

[0077] Thermal power balance constraints:

[0078] P CHP,h (t)+P GB,h (t) = P load,h (t) (43)

[0079] Where, P load,h (t) is the heat load requirement of the system; P CHP,h (t) are the heating power of CHP units; P GB,h (t) is the thermal power output by GB at time t;

[0080] Gas power balance constraints:

[0081] P g,buy (t)+P MR,g (t) = P CHP,g (t)+P GB,g (t)+P load,g (t) (44)

[0082] Where, P load,g (t) is the gas load demand of the system; P MR,g (t) is the gas power generated by the methane reactor MR at time t;

[0083] Hydrogen power balance constraints:

[0084]

[0085] Where, P EL,e (t) is the hydrogen power generated by the hydrogen fuel cell EL at time t; is the hydrogen power consumed by MR at time t; is the input hydrogen power of HFC at time t; The power of hydrogen combustion in gas boiler; are the charging and discharging power of the hydrogen storage tank during period t respectively.

[0086] In summary, the multi-objective optimization scheduling model of the hydrogen-containing integrated energy system can be simplified into the following form:

[0087]

[0088] Where x is the set of decision variables in IES; G(x) is the inequality constraint of the multi-objective optimization model; H(x) is the equality constraint of the multi-objective optimization model.

[0089] Step 3) Combine the ε-constraint method and the TOPSIS decision method, and use MATLAB to call CPLEX to solve and determine the optimal compromise solution of the Pareto frontier; the solution process is as follows Figure 3 The specific steps are as follows:

[0090] ε-constraint method to solve the Pareto front:

[0091] (1) Optimize and solve equation (47) to determine the minimum value of the total system operating cost f1, denoted as f1 min , and obtain the optimal solution x; at this time, the maximum carbon emissions of the system are calculated by x Thus, the solution is obtained

[0092]

[0093] (2) Formula (48) is optimized with f2 as the objective function. Its constraints are the same as those of Formula (47). The minimum value of the system carbon emissions f2 is obtained by solving it, which is recorded as Then, the decision variable x at this time is used to solve f1, and the maximum value of the total operating cost f1 is obtained max ,Right now

[0094]

[0095] (3) Take f1 as the reference target and divide the range of the objective function f2 into a intervals, thereby generating a+1 equal points. Each equal point corresponds to a solution on the Pareto front. Solving equation (49) can obtain the optimal solution N of the Pareto front corresponding to the i-th equal point. i， By continuously adjusting the value of i and repeatedly solving the problem, we can finally obtain the complete Pareto frontier solution set of Equation (46);

[0096]

[0097] Where i is the equal-division point within the range of f2; i = 0, 1, 2, …, a.

[0098] The specific process of obtaining the optimal compromise solution using the TOPSIS decision-making method is as follows:

[0099] (1) Forward process the original matrix consisting of the complete Pareto front solution set:

[0100] According to the two optimization objectives as extremely small indicators, they are converted:

[0101]

[0102] In the formula, the left side of the equation is the result after the original objective function value is forward-processed;

[0103] The matrix after forward transformation is expressed as:

[0104]

[0105] (2) Standardize the forward matrix:

[0106]

[0107] Where j = 1, 2, indicating that there are two objective functions; the standardized matrix Z is expressed as:

[0108]

[0109] (3) Calculate the positive ideal solution of each indicator based on the obtained standardized matrix and negative ideal solutions

[0110] Calculation is as follows:

[0111]

[0112] (4) Based on the obtained positive and negative ideal solutions, calculate the relationship between each object in the matrix and the positive ideal solution. and negative ideal solutions The distance between them is as follows:

[0113]

[0114] Where, is the distance from each object to the positive ideal solution; is the distance from each object to the negative ideal solution;

[0115] (5)Use and Measure the relative closeness between the object to be evaluated and the ideal solution h i :

[0116]

[0117] According to h i Sort by size, h i The higher the value, the closer the solution is to the positive ideal solution, and the farther it is from the negative ideal solution, and the better its overall performance is. Therefore, h is selected. i The largest Pareto optimal solution is taken as the compromise optimal solution.

[0118] The beneficial effects of the present invention are:

[0119] The multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system provided by the present invention fully absorbs surplus wind power new energy by establishing a diversified hydrogen energy utilization model including electrolysis hydrogen production, hydrogen to methane, hydrogen-to-electricity, carbon capture, hydrogen storage, and thermoelectric hydrogenation. On this basis, taking into account both economy and low carbon, a multi-objective optimization scheduling model for a hydrogen-containing integrated energy system is established with the goal of minimizing total operating costs and carbon emissions. The model is solved by combining the ε-constraint method and the TOPSIS decision-making method to promote the coordinated operation of the integrated energy system in terms of quantity and quality.

[0120] The proposed method and the hydrogen energy diversification model employ a gas-to-hydrogen blending strategy that can fully absorb surplus wind power and enhance the flexibility and safety of IES operations. When the gas-to-hydrogen blending ratio is fixed, total costs and carbon emissions decrease as the ratio of CHP units increases. When the hydrogen blending ratio reaches a maximum of 20%, the economic and environmental benefits of IES are optimal. Furthermore, compared to fixed hydrogen blending, the variable hydrogen blending strategy effectively avoids the issue of forced hydrogen allocation for hydrogen-blending units, providing support for gas-to-hydrogen blending technology.

[0121] The proposed method combines the ε-constraint method with the TOPSIS method to obtain a compromise optimal solution. This method effectively balances the conflicting and trade-off relationship between the two objective functions, total operating cost and carbon emissions. By leveraging the advantages of the two single objective functions, carbon emissions decreased by 2.626% compared to the economic operation strategy, and total operating costs decreased by 1.152% compared to the low-carbon operation strategy. Compared with the traditional IES optimization scheduling problem, multi-objective optimization can better promote the coordinated operation of IES in terms of quantity and quality, improving the overall performance of integrated energy systems and is suitable for widespread application and promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] Figure 1 This is a framework diagram of the integrated energy system of the present invention;

[0123] Figure 2 This is a model diagram of the diversified utilization of hydrogen energy of the present invention;

[0124] Figure 3 This is a flow chart of the multi-objective solution algorithm of the present invention;

[0125] Figure 4 This is a diagram showing the solution results of the multi-objective optimization algorithm of the present invention;

[0126] Figure 5 This is a graph showing the predicted output of electric, thermal, and gas loads and wind power according to the present invention;

[0127] Figure 6 is the total operating cost and carbon emissions under different hydrogen blending ratios of the present invention;

[0128] Figure 7This is a graph showing the carbon emissions of purchased coal-fired power and gas-fired units at a fixed hydrogen blending ratio according to the present invention;

[0129] Figure 8 This is a graph showing the carbon emission quota and hydrogen fuel cell hydrogen consumption power at a fixed hydrogen blending ratio according to the present invention;

[0130] Figure 9 This is a hydrogen consumption power diagram under different hydrogen doping schemes of the present invention;

[0131] Figure 10 This is a diagram of hydrogen production power of the electrolyzer under different operation strategies of the present invention;

[0132] Figure 11 This is a diagram of the electricity and hydrogen power optimization results under different operating strategies of the present invention. DETAILED DESCRIPTION

[0133] The following describes the embodiments of the invention through specific embodiments. Those skilled in the art can easily understand other advantages and effects of the invention from the contents disclosed in this specification.

[0134] Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings. However, the invention may be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided so as to provide a thorough and complete disclosure of the invention and fully convey the scope of the invention to those skilled in the art. The terms used in the exemplary embodiments shown in the drawings are not intended to limit the invention.

[0135] Unless otherwise specified, the terms used herein (including technical terms) have the meanings commonly understood by those skilled in the art. In addition, it is understood that terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0136] Example 1

[0137] The present invention provides a multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system, comprising the following steps:

[0138] Step 1) establishing a hydrogen energy diversified utilization model including hydrogen electrolysis, hydrogen to methane, hydrogen to electricity, carbon capture, hydrogen storage, and hydrogen blending with natural gas;

[0139] Step 2) Based on the hydrogen energy diversified utilization model, a multi-objective optimization scheduling model for the hydrogen-containing integrated energy system is established with the minimum total system operating cost and the minimum carbon emissions as the objective function;

[0140] Step 3) Combining the ε-constraint method and the TOPSIS decision-making method, MATLAB calls CPLEX to solve the multi-objective optimization scheduling model of the hydrogen-containing integrated energy system, determine the optimal compromise solution of the Pareto front, and obtain the total system operating cost, carbon emissions, and the optimal output plan for each device in each time period;

[0141] Among them, the equipment includes electrolyzer EL, methane reactor MR, hydrogen fuel cell HFC, hydrogen storage tank HTS, gas boiler GB and combined heat and power unit CHP.

[0142] To fully leverage the multi-energy coupling characteristics and high proportion of renewable energy access in integrated energy systems, this paper proposes a multi-objective collaborative optimization scheduling model for hydrogen-based integrated energy systems. Based on the low-carbon and clean nature of hydrogen energy, a model for diversified hydrogen utilization is established to explore the impact of the hydrogen blending ratio in natural gas on IES operation. Taking into account both the economic and low-carbon nature of the integrated energy system, a multi-objective optimization scheduling model is constructed with the goals of minimizing total operating costs and carbon emissions. Combining the ε-constraint method and the TOPSIS decision-making method, the solution is solved by calling CPLEX using Matlab. This paper can balance the economic and low-carbon nature of integrated energy systems.

[0143] Example 2

[0144] Based on Example 1, this example provides a multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system, comprising the following steps:

[0145] Step 1) Based on the low-carbon and clean characteristics of hydrogen energy, a diversified hydrogen energy utilization model including electrolysis hydrogen production, hydrogen to methane, hydrogen to electricity, carbon capture, hydrogen storage and gas-hydrogen blending is established to fully absorb the surplus wind power new energy.

[0146] The structure of the hydrogen energy diversified utilization model is as follows: Figure 2 As shown, in the hydrogen production link, the electrolyzer device uses surplus wind power resources to produce hydrogen by electrolyzing water; in the hydrogen use link, hydrogen and CO2 emitted by the gas unit captured by the carbon capture device are used as raw materials for the methane reactor to generate methane and transport it to the cogeneration unit as fuel. In addition, hydrogen fuel cells consume hydrogen to generate electricity, and cogeneration units use gas mixed with hydrogen to generate electricity and heat. Both are also important hydrogen consumers; in the hydrogen storage link, hydrogen storage tanks store excess wind power in the form of hydrogen energy and release it during periods of wind resource shortage or high load demand, realizing energy shifting and shouldering the energy supply pressure for the system. The specific modeling steps are as follows:

[0147] (1) Electrolyzer (EL)

[0148]

[0149] Where η ELis the energy conversion efficiency of the electrolytic cell EL; P EL,e (t) is the electric power consumed by EL at time t; P EL,e (t) is the hydrogen power generated by EL at time t;

[0150] (2) Methane reactor (MR)

[0151] 4H2+CO2→2H2O+CH4 (2)

[0152]

[0153] P CCS,sum (t) = P CCS,e (t)+P base (5)

[0154]

[0155] Where: η MR is the energy conversion efficiency of the methane reactor MR; is the hydrogen power consumed by MR at time t; P MR,g (t) is the gas power generated by MR at time t; P CCS,e (t) is the electrical energy consumed by the carbon capture device; η CO2 Energy consumption required to treat unit mass of CO2, MW·h / t; E CCS (t) is the weight of carbon dioxide captured by the carbon capture device CCS; P CCS,sum (t) is the total power consumed by CCS at time t; P base is the basic energy consumption of CCS; h-e is the thermoelectric conversion coefficient, which is 3600MJ / MW·h; is the density of CO2; is the calorific value of natural gas, MJ / m 3 .

[0156] (3) Hydrogen fuel cells (HFC)

[0157]

[0158] Where: η HFC The power generation efficiency of hydrogen fuel cells HFC; is the input hydrogen power of HFC at time t; P HFC,e (t) is the output power of HFC at time t;

[0159] (4) Hydrogen Storage Tank Model (HTS)

[0160]

[0161] S HTS(t) = S HTS (t-1)+P HTS (t) (9)

[0162] Where: are the charging and discharging efficiencies of the hydrogen storage tank HTS, respectively; are the charging and discharging power of the hydrogen storage tank during period t; P HTS (t) is the storage power of the hydrogen storage tank during period t; S HTS (t) is the storage capacity at time t; S HTS (t-1) is the storage capacity at time t-1.

[0163] (5) Gas boiler model (GB)

[0164] P GB,h (t) = η GB P GB,g (t) (10)

[0165] Where: η GB is the energy conversion efficiency of the gas boiler GB; P GB,h (t) is the thermal power output by GB at time t; P GB,g (t) is the natural gas power consumed by GB at time t;

[0166] (6) Combined heat and power gas hydrogen blending model (CHP)

[0167]

[0168] 0≤ξ(t)≤20% (13)

[0169] P CHP,e (t)+P CHP,h (t) = η CHP P CHP (t) (14)

[0170] λ min P CHP,e (t)≤P CHP,h (t)≤λ max P CHP,e (t) (15)

[0171] Where: P CHP (t), P CHP,g (t), are the total gas power input to the CHP unit, the power of the gas boiler burning natural gas, and the power of the gas boiler burning hydrogen; ξ(t), L CH4 Respectively represent the ratio of hydrogen blended with fuel gas, the lower calorific value of hydrogen, and the lower calorific value of natural gas; P CHP,e (t), PCHP,h (t) are the power generation and heating power of the CHP unit respectively; η CHP is the working efficiency of the CHP unit; min ,λ max They are the upper and lower limits of the heat-to-electricity ratio of the CHP unit respectively.

[0172] Related research has shown that controlling the hydrogen blend ratio in natural gas to below 20% in gas turbines ensures safe and stable combustion. When modeling cogeneration units, the sum of the power and heat production efficiencies is assumed to be constant, and the heat-to-electricity ratio is adjustable within a certain range. This allows for flexible adjustment of the power-to-heat output ratio based on real-time power and heat demand.

[0173] Step 2: Taking the minimum total system operating cost and carbon emissions as the objective function, a multi-objective optimization scheduling model for the hydrogen-containing integrated energy system is established.

[0174] The integrated energy system model framework constructed is as follows Figure 1 As shown. The energy balance relationship of electricity, gas, heat and hydrogen in the hydrogen-containing integrated energy system is as follows: In terms of electricity supply and demand, the external power grid, wind turbines, cogeneration units and hydrogen fuel cells jointly meet the electricity demand of electric loads, electrolyzers and carbon capture devices, among which the electrolyzers and carbon capture devices only consume the power of wind turbines; in terms of thermal energy supply and demand, the cogeneration units and gas boilers meet the thermal load demand; in terms of natural gas supply and demand, the external gas grid and methane reactor provide natural gas for gas turbines, gas boilers and gas loads; in terms of hydrogen energy supply and demand, the electrolyzer produces hydrogen by electrolyzing water to provide hydrogen energy for hydrogen storage tanks, methane reactors, hydrogen fuel cells and gas-to-hydrogen blending;

[0175] Based on the law of conservation of energy and the coupling relationship, we add operational constraints and take minimizing the total operating cost of the system as the objective function to establish an optimal scheduling model for the hydrogen-containing integrated energy system:

[0176] a. Objective function

[0177] The total operating cost objective function is as follows:

[0178]

[0179] Where f1 is the total operating cost; F g,buy The cost of purchasing gas; is the carbon trading cost; F e,buy The cost of purchasing electricity;

[0180] Carbon emission objective function:

[0181] f2=min(E grid +E GB +E CHP -ECCS ) (17)

[0182] f2 is the actual carbon emissions of IES; E grid 、E GB 、E CHP are the actual carbon emissions of purchased coal-fired power, gas boilers, and cogeneration units; E CCS the amount of carbon emissions absorbed by carbon capture plants;

[0183] (1)F e,buy Power purchase cost: refers to the amount of electricity purchased by the system from the main power grid to meet the power load demand.

[0184]

[0185] (2)F g,buy Gas purchase cost: refers to the natural gas purchased by the system to meet the needs of cogeneration units, gas boilers and gas loads.

[0186]

[0187] (3) Carbon trading costs adopt a tiered carbon trading mechanism to accumulate carbon emissions into total operating costs in the form of carbon taxes, further constraining carbon emissions.

[0188]

[0189] (4) Carbon emission accounting: When calculating carbon emissions, the present invention takes into account the actual carbon emissions generated by the energy supply entity during use.

[0190]

[0191] Where, f coal is the power generation cost of the coal-fired unit; t is the unit scheduling step; T is the scheduling period; f gas is the purchase price of natural gas; P e,buy (t) Energy consumption power provided by the power grid; P g,buy (t) Energy consumption power provided to the gas grid; is the carbon trading benchmark price, d is the length of the carbon emission interval; α is the price growth rate, E IES,t P is the actual carbon emissions of carbon trading; GB,g (t) is the natural gas power consumed by the gas boiler GB at time t, P CHP,g (t) is the natural gas power consumed by the cogeneration unit at time t; is the carbon emission coefficient per unit power generation of the coal-fired unit, t / MW·h; They are the carbon content per unit calorific value of natural gas and the carbon oxidation rate of natural gas respectively; 44 / 12 represents the ratio of the relative molecular mass of CO2 to carbon.

[0192] Wind power output constraint condition: 0<P WT (t)<P wind (t)

[0193] Where, P WT (t) is the actual output of the wind turbine in the energy system; P wind is the predicted electric power output of the wind turbine;

[0194] Gas boiler output constraints and ramp constraints:

[0195]

[0196]

[0197] Where: The upper limit of gas consumption power of gas boiler; P is the upper and lower limits of the gas boiler's climbing capacity; GB,g (t+1) is the gas consumption power of the gas boiler at time t+1;

[0198] Output constraints and ramp constraints for cogeneration units:

[0199]

[0200] Where: is the maximum operating condition of the cogeneration unit; P is the upper and lower limits of the ramp rate of the cogeneration unit; CHP (t) is the total gas power input to the cogeneration unit at time t, P CHP (t+1) is the total gas power input to the cogeneration unit at time t+1;

[0201] Electrolyzer output constraints and climbing constraints:

[0202]

[0203] Where: The upper limit of the electric power input to the electrolyzer equipment; P is the upper and lower limits of the climbing power of the electrolyzer during operation; EL,e (t) is the electric power consumed by the electrolytic cell EL at time t, P EL,e (t+1) is the electric power consumed by EL at time t+1;

[0204] Hydrogen fuel cell output constraints and climbing constraints:

[0205]

[0206] Where: The upper limit of the hydrogen power input to the hydrogen fuel cell; The upper and lower limits of the climbing power of the hydrogen fuel cell during operation; is the input hydrogen power of HFC at time t+1;

[0207] Methane reactor output constraints and ramp constraints:

[0208]

[0209] Where: is the upper limit of hydrogen consumption power of methane reactor; The upper and lower limits of the ramp power of the methane reactor during operation;

[0210] Output constraints and ramp constraints for carbon capture devices:

[0211]

[0212] Where: The maximum operating condition for the carbon capture equipment; P is the upper and lower limits of the climbing power of the carbon capture device during operation; CCS,e (t) the electrical energy consumed by the carbon capture plant;

[0213] Hydrogen storage tank constraints:

[0214]

[0215] S HTS (0) = S HTS (T) (41)

[0216] Where: The lower and upper limits of the hydrogen storage tank capacity; The maximum single charge and discharge power of the hydrogen storage equipment; S is the hydrogen filling and releasing status of the hydrogen storage tank at time t, and the value is 0 or 1, and both cannot be 1 at the same time, indicating that the hydrogen storage tank cannot be charged and released at the same time; HTS (0) is the initial capacity of the hydrogen storage device; are the charging and discharging power of the hydrogen storage tank during period t; S HTS (t) is the storage capacity at time t;

[0217] Electric power balance constraints:

[0218] P e.buy (t)+P WG (t)+P CHP,e (t)+P HFC,e (t) = P load,e(t)+P EL,e (t)+P CCS,sum (t) (42)

[0219] Where, P load,e (t) is the electrical load demand of the system; P WG (t) is the electric power output by the wind turbine; P CHP,e (t), is the power generation power of the combined heat and power unit CHP; P HFC,e (t) is the output power of HFC at time t; P EL,e (t) is the electric power consumed by EL at time t; P CCS,sum (t) is the total electricity consumed by the carbon capture system CCS at time t;

[0220] Thermal power balance constraints:

[0221] P CHP,h (t)+P GB,h (t) = P load,h (t) (43)

[0222] Where, P load,h (t) is the heat load requirement of the system; P CHP,h (t) are the heating power of CHP units; P GB,h (t) is the thermal power output by GB at time t;

[0223] Gas power balance constraints:

[0224] P g,buy (t)+P MR,g (t) = P CHP,g (t)+P GB,g (t)+P load,g (t) (44)

[0225] Where, P load,g (t) is the gas load demand of the system; P MR,g (t) is the gas power generated by the methane reactor MR at time t;

[0226] Hydrogen power balance constraints:

[0227]

[0228] Where, P EL,e (t) is the hydrogen power generated by the hydrogen fuel cell EL at time t; is the hydrogen power consumed by MR at time t; is the input hydrogen power of HFC at time t; The power of hydrogen combustion in gas boiler; are the charging and discharging power of the hydrogen storage tank during period t respectively.

[0229] The multi-objective optimization scheduling model of the hydrogen-containing integrated energy system is simplified to the following form:

[0230]

[0231] Where x is the set of decision variables in IES; G(x) is the inequality constraint of the multi-objective optimization model; H(x) is the equality constraint of the multi-objective optimization model.

[0232] Step 3) First, solve the Pareto frontier using the ε-constraint method, and then obtain the optimal compromise solution using the TOPSIS decision method; Figure 4 As shown;

[0233] The specific process of solving the Pareto frontier using the ε-constraint method is as follows:

[0234] (1) Optimize and solve equation (47) to determine the minimum value of the total system operating cost f1, denoted as f1 min , and obtain the optimal solution x; at this time, the maximum carbon emissions of the system are calculated by x Thus, the solution is obtained

[0235]

[0236] (2) Formula (48) is optimized with f2 as the objective function to obtain the minimum value of the system carbon emissions f2, which is recorded as Then, the decision variable x at this time is used to solve f1, and the maximum value of the total operating cost f1 is obtained max ,Right now

[0237]

[0238] (3) The solutions M1 and M2 obtained through the above two steps constitute the two extreme points of the Pareto frontier of the original formula (46). and are the minimum and maximum values ​​of the objective function f2 in the complete Pareto front, so the value range of f2 is The ε-constraint method takes f1 as the reference target and divides the value range of the objective function f2 into a intervals, thereby generating a+1 equal points. Each equal point corresponds to a solution on the Pareto front. Solving equation (49) can obtain the optimal solution N of the Pareto front corresponding to the i-th equal point. i， By continuously adjusting the value of i and repeatedly solving the problem, we can finally obtain the complete Pareto frontier solution set of Equation (46);

[0239]

[0240] Where i is the equal-division point within the range of f2; i = 0, 1, 2, …, a.

[0241] b. The specific process of obtaining the optimal compromise solution using the TOPSIS decision-making method is as follows:

[0242] Determine the target category: if the optimization target is as small as possible, it is classified as an extremely small indicator; if the optimization target is as large as possible, it is classified as an extremely large indicator; if the optimization target is intended to approach a specific value, it is classified as an intermediate indicator; if the optimization target is required to fall into a specific interval, it is classified as an interval indicator.

[0243] (1) Forward process the original matrix consisting of the complete Pareto front solution set:

[0244] In the TOPSIS method, in order to ensure the consistency of the evaluation indicators, all indicators need to be uniformly positively processed, that is, all indicators are uniformly converted into extremely large indicators. The two optimization objectives involved in this chapter are both extremely small indicators, and their conversion method is shown in formula (50):

[0245]

[0246] In the formula, the left side of the equation is the result after the original objective function value is forward-processed;

[0247] The matrix after forward transformation is expressed as:

[0248]

[0249] Where R i1 represents the i-th solution of f1, R i2 represents the i-th solution of f2.

[0250] (2) Standardize the forward matrix:

[0251]

[0252] Where j = 1, 2, indicating that there are two objective functions; the standardized matrix Z is expressed as:

[0253]

[0254] (3) Calculate the positive ideal solution of each indicator based on the obtained standardized matrix and negative ideal solutions

[0255] Calculation is as follows:

[0256]

[0257] (4) Based on the obtained positive and negative ideal solutions, calculate the relationship between each object in the matrix and the positive ideal solution. and negative ideal solutions The distance between them is as follows:

[0258]

[0259] Where, is the distance from each object to the positive ideal solution; is the distance from each object to the negative ideal solution;

[0260] (5)Use and Measure the relative closeness between the object to be evaluated and the ideal solution h i :

[0261]

[0262] According to h i Sort by size, h i The higher the value, the closer the solution is to the positive ideal solution, and the farther it is from the negative ideal solution, and the better its overall performance is. Therefore, h is selected. i The largest Pareto optimal solution is taken as the compromise optimal solution.

[0263] In order to further verify the effect of the present invention, under the condition of meeting system constraints, a comparative analysis is conducted on the impact of different gas hydrogen blending ratios and different optimization scheduling strategies on system operation; different scenarios are set to obtain the total operating cost of the integrated energy system, carbon emissions, the optimal output plan for each equipment in each time period, and other results.

[0264] The model is simulated and analyzed with a 24-hour dispatch cycle. The wind power curve and load demand curve are shown in Figure 5 Since existing hydrogen blending technologies have significant limitations on the hydrogen blending ratio, this paper studies the impact of the hydrogen blending ratio on the economic and environmental benefits of IES in a hydrogen diversified utilization model, providing support for fuel gas hydrogen blending technology.

[0265] Figure 6 The figure shows the CO2 emissions and total cost of the IES system at different hydrogen blending ratios. All calculations assume a fixed hydrogen blending ratio and adopt a single-objective operation strategy, with the objective function being total operating cost. The figure shows that as the hydrogen blending ratio increases, the system's total cost and carbon emissions gradually decrease. When the CHP unit's hydrogen blending ratio reaches its maximum of 20%, the IES achieves optimal economic and environmental benefits.

[0266] To deeply analyze the reasons behind this trend, Figure 7 The carbon emissions of purchased coal-fired power and gas-fired units under a fixed hydrogen blending ratio are given. Figure 8The IES carbon emission quota and hydrogen fuel cell hydrogen consumption are shown. As the hydrogen blending ratio increases, the hydrogen content of the fuel cell decreases, reducing its power supply capacity. The shortfall in electricity demand is then met by purchasing coal-fired power, leading to increased carbon emissions. However, the high carbon emission quota for coal-fired power offsets some of this carbon emission. Furthermore, hydrogen, a clean and pollution-free alternative to natural gas, further reduces carbon emissions. At the same time, hydrogen consumption in cogeneration units increases, gas purchases decrease, and costs decrease. Furthermore, natural gas accounts for a much higher proportion of energy supply than coal-fired power. Therefore, despite a slight increase in electricity purchase costs, overall costs and carbon emissions are on a downward trend. Therefore, when using a fixed hydrogen blending ratio, appropriately setting the hydrogen blending ratio for cogeneration units can effectively control the system's carbon emission intensity while ensuring economic benefits.

[0267] Table 1 Optimization results under different hydrogen doping schemes

[0268]

[0269] However, due to the fixed hydrogen blending ratio, hydrogen is forced to be distributed when hydrogen is blended into the fuel gas of the cogeneration unit. This results in the hydrogen energy diversified utilization model being unable to flexibly schedule hydrogen use. Other hydrogen-consuming equipment is also restricted, resulting in suboptimal operating results. Therefore, the present invention considers variable hydrogen blending when setting different scenarios and compares and analyzes it with fixed hydrogen blending, in this case setting the hydrogen blending ratio to 20%. The optimization results under different hydrogen blending schemes are shown in Table 1.

[0270] Table 1 shows that the total operating cost of variable hydrogen blending is reduced by 670 yuan, and carbon emissions change slightly, which is negligible. The changes in gas and electricity costs are opposite: the purchase of natural gas with variable hydrogen blending increases by 83.301 MW·h, a 1.057% increase; the purchase of coal-fired power decreases by 53.267 MW·h, a 1.826% decrease.

[0271] Combine Figure 9Analysis of the hydrogen consumption curves for the combined heat and power (CHP) units shows that, for a constant 20% hydrogen blending ratio, during the 9:00-20:00 period, when electricity demand is high and power supply pressure increases, the variable hydrogen blending system allocates as much hydrogen from the hydrogen storage tank as possible to the hydrogen fuel cell for power generation, and the gas-to-hydrogen blending system ceases operation. During other periods, when electricity demand is low and heat demand is high, the gas-to-hydrogen blending ratio is increased, allowing the CHP units to flexibly adjust to the maximum hydrogen blending ratio. This flexible scheduling strategy of variable hydrogen blending allows the system to coordinate resource allocation and achieve the goal of minimizing total operating costs. In contrast, under a fixed hydrogen blending ratio, the gas-to-hydrogen blending system forces the CHP units to operate at full load at all times. The hydrogen blended into the CHP units partially replaces natural gas as fuel, resulting in reduced IES gas purchase costs, reduced hydrogen available for the hydrogen fuel cell, and increased electricity purchase costs. This significantly impacts the rational scheduling of hydrogen within the hydrogen energy diversification model.

[0272] Taking into account the flexibility and safety of system operation, the variable hydrogen blending operation mode is more suitable for the operation of hydrogen-containing integrated energy systems.

[0273] In order to explore the performance differences between single-objective operation strategies and multi-objective operation strategies in actual applications, three different scenarios are proposed in this implementation for comparative analysis.

[0274] Scenario 1: Considering a single economic goal, that is, minimizing the operating cost of IES; Scenario 2: Considering a single low-carbon goal, that is, minimizing the carbon emissions of IES; Scenario 3: Adopting a multi-objective operation strategy. The scheduling results are shown in Table 2.

[0275] Table 2 Optimization results under different operation strategies

[0276]

[0277] According to the data analysis in Table 2, compared with the economic operation strategy in Scenario 1, the low-carbon operation strategy in Scenario 2 can reduce carbon emissions by 3.648%, but at the same time increase operating costs by 1.766%. This result indicates that there is no significant correlation between the various objective functions, and there is a certain trade-off between economic efficiency and low-carbon goals. In addition, the multi-objective operation strategy in Scenario 3 shows a good compromise in terms of total operating costs and carbon emissions. Its operating costs are reduced by 42,540 yuan compared to Scenario 2, a decrease of 1.152%, and its carbon emissions are reduced by 23.995 tons compared to Scenario 1, a decrease of 2.626%.

[0278] In order to further explore the internal mechanism of different optimization strategies, Figure 10 and Figure 11The hydrogen production power and electricity-hydrogen power balance of the electrolyzer under different scenarios are shown. Scenario 1, aimed at system economics, requires a high proportion of wind power for the electricity load to reduce electricity purchase costs. This limits the wind power resources available to the electrolyzer, resulting in relatively low hydrogen production power and maximum carbon emissions. In contrast, Scenario 2, aimed at low carbon efficiency, frees the system from the strict constraints of operating costs. As a result, the electrolyzer consumes more wind power to produce clean hydrogen, while the power supply gap is replaced by purchased coal-fired power, resulting in a significant increase in total economic costs.

[0279] Furthermore, in this scenario, the cogeneration unit is blended with high proportions of hydrogen at all times. To meet the hydrogen blending ratio, natural gas consumption is forced to increase, further increasing operating costs. However, this strategy demonstrates outstanding environmental benefits, minimizing carbon emissions. Thus, single-objective optimization can often achieve optimal results for a specific objective, but its overall performance is slightly insufficient. Furthermore, there is no clear correlation between the two selected objective functions, and they exhibit contradictory characteristics.

[0280] Scenario 3, however, considers both total system operating costs and carbon emissions, effectively balancing the advantages of Scenario 1 and Scenario 2. Compared with the economic operation strategy, carbon emissions are reduced by 2.626%, and compared with the low-carbon operation strategy, total operating costs are reduced by 1.152%. This scenario strikes a balance between economic efficiency and low carbon efficiency, promoting the coordinated operation of IES in terms of both quantity and quality.

[0281] The above examples are merely illustrative of the invention and do not constitute a limitation on the scope of protection of the invention. Any design that is identical or similar to the invention falls within the scope of protection of the invention.

Claims

1. A multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system, characterized by: The following steps are involved: Step 1) establishing a hydrogen energy diversified utilization model including hydrogen electrolysis, hydrogen to methane, hydrogen to electricity, carbon capture, hydrogen storage, and hydrogen blending with natural gas; Step 2) Based on the hydrogen energy diversified utilization model, a multi-objective optimization scheduling model for the hydrogen-containing integrated energy system is established with the minimum total system operating cost and the minimum carbon emissions as the objective function; Step 3) Combining the ε-constraint method and the TOPSIS decision-making method, MATLAB calls CPLEX to solve the multi-objective optimization scheduling model of the hydrogen-containing integrated energy system, determine the optimal compromise solution of the Pareto front, and obtain the total system operating cost, carbon emissions, and the optimal output plan for each device in each time period; Among them, the equipment includes electrolyzer EL, methane reactor MR, hydrogen fuel cell HFC, hydrogen storage tank HTS, gas boiler GB and combined heat and power unit CHP.

2. The multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system according to claim 1 is characterized by: The electrolysis hydrogen production in step 1) is to use the surplus wind power resources in the electrolyzer device to produce hydrogen by electrolyzing water. The model is: Where η EL is the energy conversion efficiency of the electrolytic cell EL; P EL,e (t) is the electric power consumed by EL at time t; P EL,e (t) is the hydrogen power generated by EL at time t; In step 1), hydrogen to methane is generated in a methane reactor by combining hydrogen and CO2 emitted by the gas turbine captured by the carbon capture device, and then transported to the cogeneration unit as fuel. The model is: 4H2+CO2→2H2O+CH4 (2) P CCS,sum (t)=P CCS,e (t)+P base (5) Where: η MR is the energy conversion efficiency of the methane reactor MR; is the hydrogen power consumed by MR at time t; P MR,g (t) is the gas power generated by MR at time t; P CCS,e (t) is the electrical energy consumed by the carbon capture device; η CO2 Energy consumption required to treat unit mass of CO2, MW·h / t; E CCS (t) is the weight of carbon dioxide captured by the carbon capture device CCS; P CCS,sum (t) is the total power consumed by CCS at time t; P base is the basic energy consumption of CCS; h-e is the thermoelectric conversion coefficient, which is 3600MJ / MW·h; is the density of CO2; is the calorific value of natural gas, MJ / m 3 .

3. The multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system according to claim 1 is characterized in that: Step 1) The hydrogen-to-electricity conversion is to generate electricity by consuming hydrogen through a hydrogen fuel cell. The model is as follows: Where: η HFC The power generation efficiency of hydrogen fuel cells HFC; is the input hydrogen power of HFC at time t; P HFC,e (t) is the output power of HFC at time t; Step 1) The hydrogen storage is to store excess wind power in the form of hydrogen energy through a hydrogen storage tank. The model is as follows: Where: are the charging and discharging efficiencies of the hydrogen storage tank HTS, respectively; are the charging and discharging power of the hydrogen storage tank during period t; P HTS (t) is the storage power of the hydrogen storage tank during period t; S HTS (t) is the storage capacity at time t; S HTS (t-1) is the storage capacity at time t-1.

4. The multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system according to claim 1 is characterized in that: Step 1) The carbon capture is to capture the CO2 emitted by the gas boiler GB through a carbon capture device. The model is as follows: P GB,h (t)=η GB P GB,g (t) (10) Where: η GB is the energy conversion efficiency of the gas boiler GB; P GB,h (t) is the thermal power output by GB at time t; P GB,g (t) is the natural gas power consumed by GB at time t; In step 1), the gas-hydrogen blending is a cogeneration unit that generates electricity and heat by blending gas-hydrogen. The model is as follows: 0≤ξ(t)≤20% (13) P CHP,e (t)+P CHP,h (t)=η CHP P CHP (t) (14) λ min P CHP,e (t)≤P CHP,h (t)≤λ max P CHP,e (t) (15) Where: P CHP (t), P CHP,g (t), are the total gas power input to the CHP unit, the power of the gas boiler burning natural gas, and the power of the gas boiler burning hydrogen; ξ(t), L CH4 Respectively represent the ratio of hydrogen blended with fuel gas, the lower calorific value of hydrogen, and the lower calorific value of natural gas; P CHP,e (t), P CHP,h (t) are the power generation and heating power of the CHP unit respectively; η CHP is the working efficiency of the CHP unit; min ,λ max They are the upper and lower limits of the heat-to-electricity ratio of the CHP unit respectively.

5. The multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system according to claim 1 is characterized in that: Step 2) Modeling of the multi-objective optimization scheduling model of the hydrogen-containing integrated energy system includes objective functions and constraints; The objective functions include the system total operating cost objective function f1 and the carbon emission objective function f2; The constraints include wind power output constraints, gas boiler output constraints and ramp constraints, cogeneration unit output constraints and ramp constraints, electrolyzer output constraints and ramp constraints, hydrogen fuel cell output constraints and ramp constraints, methane reactor output constraints and ramp constraints, carbon capture device output constraints and ramp constraints, hydrogen storage tank constraints and power balance constraints; Among them, the power balance constraints include electric power balance constraints, thermal power balance constraints, gas power balance constraints and hydrogen power balance constraints.

6. The multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system according to claim 5 is characterized by: System total operating cost objective function Carbon emission objective function f2=min(E grid +E GB +E CHP -E CCS )(17) Where f1 is the total operating cost; F g,buy Gas purchase cost refers to the natural gas purchased by the system to meet the needs of cogeneration units, gas boilers and gas loads; is the carbon trading cost; F e,buy The cost of electricity purchase refers to the amount of electricity purchased from the main grid to meet the electricity load demand; f2 is the actual carbon emissions of IES; E grid 、E GB 、E CHP are the actual carbon emissions of purchased coal-fired power, gas boilers, and cogeneration units; E CCS the amount of carbon emissions absorbed by carbon capture plants; in, Where, f coal is the power generation cost of the coal-fired unit; t is the unit scheduling step; T is the scheduling period; f gas is the purchase price of natural gas; P e,buy (t) Energy consumption power provided by the power grid; P g,buy (t) Energy consumption power provided to the gas grid; is the carbon trading benchmark price, d is the length of the carbon emission interval; α is the price growth rate, E IES,t P is the actual carbon emissions of carbon trading; GB,g (t) is the natural gas power consumed by the gas boiler GB at time t, P CHP,g (t) is the natural gas power consumed by the cogeneration unit at time t; is the carbon emission coefficient per unit power generation of the coal-fired unit, t / MW·h; They are the carbon content per unit calorific value of natural gas and the carbon oxidation rate of natural gas respectively; 44 / 12 represents the ratio of the relative molecular mass of CO2 to carbon.

7. The multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system according to claim 6 is characterized in that: Wind power output constraint condition: 0<P WT (t)<P wind (t) Where, P WT (t) is the actual output of the wind turbine in the energy system; P wind is the predicted electric power output of the wind turbine; Gas boiler output constraints and ramp constraints: Where: The upper limit of gas consumption power of gas boiler; P is the upper and lower limits of the gas boiler's ramp rate; GB,g (t+1) is the gas consumption power of the gas boiler at time t+1; Output constraints and ramp constraints for cogeneration units: Where: is the maximum operating condition of the cogeneration unit; P is the upper and lower limits of the ramp rate of the cogeneration unit; CHP (t) is the total gas power input to the cogeneration unit at time t, P CHP (t+1) is the total gas power input to the cogeneration unit at time t+1; Electrolyzer output constraints and climbing constraints: Where: The upper limit of the electric power input to the electrolyzer equipment; P is the upper and lower limits of the climbing power of the electrolyzer during operation; EL,e (t) is the electric power consumed by the electrolytic cell EL at time t, P EL,e (t+1) is the electric power consumed by EL at time t+1; Hydrogen fuel cell output constraints and climbing constraints: Where: The upper limit of the hydrogen power input to the hydrogen fuel cell; The upper and lower limits of the climbing power of the hydrogen fuel cell during operation; is the input hydrogen power of HFC at time t+1; Methane reactor output constraints and ramp constraints: Where: is the upper limit of hydrogen consumption power of methane reactor; The upper and lower limits of the ramp power of the methane reactor during operation; Output constraints and ramp constraints for carbon capture devices: Where: The maximum operating condition for the carbon capture equipment; P is the upper and lower limits of the climbing power of the carbon capture device during operation; CCS,e (t) the electrical energy consumed by the carbon capture plant; Hydrogen storage tank constraints: S HTS (0)=S HTS (T) (41) Where: The lower and upper limits of the hydrogen storage tank capacity; The maximum single charge and discharge power of the hydrogen storage equipment; S is the hydrogen filling and releasing status of the hydrogen storage tank at time t, and the value is 0 or 1, and both cannot be 1 at the same time, indicating that the hydrogen storage tank cannot be charged and released at the same time; HTS (0) is the initial capacity of the hydrogen storage device; are the charging and discharging power of the hydrogen storage tank during period t; S HTS (t) is the storage capacity at time t; Electric power balance constraints: P e.buy (t)+P WG (t)+P CHP,e (t)+P HFC,e (t)=P load,e (t)+P EL,e (t)+P CCS,sum (t) (42) Where, P load,e (t) is the electrical load demand of the system; P WG (t) is the electric power output by the wind turbine; P CHP,e (t), is the power generation power of the combined heat and power unit CHP; P HFC,e (t) is the output power of HFC at time t; P EL,e (t) is the electric power consumed by EL at time t; P CCS,sum (t) is the total electricity consumed by the carbon capture system CCS at time t; Thermal power balance constraints: P CHP,h (t)+P GB,h (t)=P load,h (t) (43) Where, P load,h (t) is the heat load requirement of the system; P CHP,h (t) are the heating power of CHP units; P GB,h (t) is the thermal power output by GB at time t; Gas power balance constraints: P g,buy (t)+P MR,g (t)=P CHP,g (t)+P GB,g (t)+P load,g (t) (44) Where, P load,g (t) is the gas load demand of the system; P MR,g (t) is the gas power generated by the methane reactor MR at time t; Hydrogen power balance constraints: Where, P EL,e (t) is the hydrogen power generated by the hydrogen fuel cell EL at time t; is the hydrogen power consumed by MR at time t; is the input hydrogen power of HFC at time t; The power of hydrogen combustion in gas boiler; are the charging and discharging power of the hydrogen storage tank during period t respectively.

8. The multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system according to claim 7 is characterized in that: The multi-objective optimization scheduling model of the hydrogen-containing integrated energy system is simplified to the following form: Where x is the set of decision variables in IES; G(x) is the inequality constraint of the multi-objective optimization model; H(x) is the equality constraint of the multi-objective optimization model.

9. The multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system according to claim 8, characterized in that: Step 3) First, the Pareto frontier is solved by the ε-constraint method, and then the optimal compromise solution is obtained by the TOPSIS decision method; The specific process of solving the Pareto frontier using the ε-constraint method is as follows: (1) Optimize and solve equation (47) to determine the minimum value of the total system operating cost f1, denoted as f1 min , and obtain the optimal solution x; at this time, the maximum carbon emissions of the system are calculated by x Thus, the solution is obtained (2) Formula (48) is optimized with f2 as the objective function to obtain the minimum value of the system carbon emissions f2, which is recorded as Then, the decision variable x at this time is used to solve f1, and the maximum value of the total operating cost f1 is obtained max ,Right now (3) Take f1 as the reference target and divide the range of the objective function f2 into a intervals, thereby generating a+1 equal points. Each equal point corresponds to a solution on the Pareto front. Solving equation (49) can obtain the optimal solution N of the Pareto front corresponding to the i-th equal point. i， By continuously adjusting the value of i and repeatedly solving the problem, we can finally obtain the complete Pareto frontier solution set of Equation (46); Where i is the equal-division point within the range of f2; i = 0, 1, 2, …, a.

10. The multi-objective collaborative optimization scheduling method for a hydrogen-containing integrated energy system according to claim 9, characterized in that: The specific process of obtaining the optimal compromise solution using the TOPSIS decision-making method is as follows: (1) Forward process the original matrix consisting of the complete Pareto front solution set: According to the two optimization objectives as extremely small indicators, they are converted: In the formula, the left side of the equation is the result after the original objective function value is forward-processed; The matrix after forward transformation is expressed as: (2) Standardize the forward matrix: Where j = 1, 2, indicating that there are two objective functions; the standardized matrix Z is expressed as: (3) Calculate the positive ideal solution of each indicator based on the obtained standardized matrix and negative ideal solutions Calculation is as follows: (4) Based on the obtained positive and negative ideal solutions, calculate the relationship between each object in the matrix and the positive ideal solution. and negative ideal solutions The distance between them is as follows: Where, is the distance from each object to the positive ideal solution; is the distance from each object to the negative ideal solution; (5)Use and Measure the relative closeness between the object to be evaluated and the ideal solution h i : According to h i Sort by size, h i The higher the value, the closer the solution is to the positive ideal solution, and the farther it is from the negative ideal solution, and the better its overall performance is. Therefore, h is selected. i The largest Pareto optimal solution is taken as the compromise optimal solution.

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