Comprehensive energy system optimization method and system based on electricity-to-gas and carbon capture equipment

Abstract

The invention belongs to the technical field of comprehensive energy system optimization, and discloses a comprehensive energy system optimization method and system based on power-to-gas and carbon capture equipment. The comprehensive energy system comprising two-stage power-to-gas equipment, the carbon capture equipment, electric energy storage equipment and the like is constructed, and the coupling relation and adjusting characteristics between load energy conversion are combined; a low-carbon economic dispatching model is constructed, objective functions are established by considering power purchase cost, gas purchase cost and the like, a constraint set is established by considering thermoelectric power balance, system capacity, carbon capture and the like in a comprehensive energy system, and a multi-objective evolutionary algorithm is adopted to carry out optimization solution on a plurality of objective functions. According to the technical scheme, the total operation cost and the carbon emission of the system are comprehensively considered, the low-carbon and environment-friendly characteristics of hydrogen energy are fully played, the renewable energy consumption of the system is improved, the multi-target intelligent algorithm is adopted for solving, economic benefits are considered, the method is environmentally friendly, and the total operation cost and the CO2 emission of the system are reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system optimization, and specifically relates to an integrated energy system optimization method and system based on power-to-gas and carbon capture equipment. Background Art

[0002] In order to achieve the "dual carbon" goal, my country is accelerating the construction of a clean, low-carbon, safe and efficient energy system. The low-carbon economic dispatch of the integrated energy system aims to coordinate and optimize heterogeneous energy and promote the complementary integration of energy such as electricity, hydrogen and natural gas. It is the main means to promote energy transformation. The integrated energy system that can realize the hierarchical utilization of energy has become an important carrier for achieving this vision. However, due to the asynchrony between the source and the load, the integrated energy system that aggregates large-scale renewable energy has an unignorable problem of renewable energy abandonment. Hydrogen energy, as the ultimate answer to solving energy problems worldwide, has put the research on hydrogen production from renewable energy electrolysis on the agenda in countries around the world. The emerging power to gas (P2G) technology and hydrogen storage system are an effective solution to energy problems.

[0003] The current integrated energy system dispatch is mainly studied around economy and low carbon. Carbon capture and storage technology separates carbon dioxide from the emission source and transports it to a safe storage site to achieve long-term isolation from the atmosphere. Specific technical routes include post-combustion capture, pre-combustion capture and oxygen-enriched combustion capture. The capture energy of the carbon capture power plant mainly comes from steam extracted from the power generation cycle or directly using the power plant electricity. Compared with traditional coal-fired power plants, carbon capture power plants also have more superior peak-shaving characteristics. The use of carbon capture power plants can effectively increase the access capacity of renewable energy and save peak-shaving costs.

[0004] In summary, the current integrated energy system optimization methods have the following shortcomings: In terms of economy, the existing optimization methods often take the minimum total operating cost of the system as the optimization goal, study the coordinated operation of multiple energy systems, and ignore the low-carbon research; in terms of low-carbon, the existing research ignores the recycling of carbon dioxide, the coordination of unit output, etc., and cannot flexibly control the unit output. Proposing an integrated energy system optimization method that takes into account both the economy and low-carbon of the integrated energy system and the coordinated operation of each part of the system is of great significance to promoting the consumption of renewable energy and building a clean and efficient energy system. Summary of the invention

[0005] In order to solve the problems mentioned in the above background technology, the present invention proposes an integrated energy system optimization method and system based on power-to-gas and carbon capture equipment. The method introduces a step-type demand response incentive mechanism to adaptively optimize system parameters on the basis of comprehensively considering the coupling relationship and regulation characteristics between load energy conversion, and uses a multi-objective grey wolf algorithm to optimize and solve the system supply and demand side and the step-type demand incentive mechanism.

[0006] In a first aspect, the present invention provides a method for optimizing an integrated energy system based on a power-to-gas and carbon capture device, the method comprising the following steps: Step 1: Build a comprehensive energy system including two-stage power-to-gas equipment, carbon capture equipment, wind and solar power generation equipment, cogeneration equipment, gas boilers, electric boilers, and electric energy storage equipment; Step 2: Based on the coupling relationship and regulation characteristics between load energies, a ladder-type carbon trading mechanism is introduced, and the system carbon quota and actual carbon emissions are calculated to model the ladder-type carbon trading mechanism; Step 3: Establish an objective function, taking into account the electricity purchase cost, gas purchase cost, carbon trading cost and operation and maintenance cost, and taking the minimum total cost and the lowest carbon emission as the optimization goal in the integrated energy system; Step 4: Establish a constraint set based on thermal power balance, electrical power balance, system capacity constraints and carbon capture constraints in the integrated energy system; Step 5: According to the multi-objective evolutionary algorithm based on NSGA-Ⅲ, various objective constraints are considered in MATLAB to solve the objective function.

[0007] Specifically, in step 1, mathematical modeling is performed on each part of the integrated energy system to establish an electrolyzer model based on dynamic hydrogen production efficiency, a hydrogen fuel cell mathematical model, a hydrogen storage tank mathematical model, a methane reactor mathematical model, a gas turbine mathematical model, and an integrated flexible carbon capture power plant model.

[0008] Specifically, the mathematical models of each part are established through the following steps: Step 11: Use polynomial simulation to formulate the dynamic characteristics of the electrolyzer conversion efficiency under the running time, and establish an electrolyzer model based on the dynamic hydrogen production efficiency. The specific model is as follows: , In the formula, and are the electrical power input and hydrogen power output of electrolyzer i in period t, is the energy conversion efficiency of electrolyzer i, , and are the coefficients of the electrolyzer dynamic hydrogen production efficiency polynomial, is the rated power of electrolytic cell i, is the volume of hydrogen output by electrolyzer i during period t, is the volumetric higher calorific value of hydrogen; Step 12: Establish a hydrogen fuel cell mathematical model. The specific formula is as follows: , In the formula, , are the electrical and thermal conversion powers of the hydrogen fuel cell at time t, , are the electrical and thermal conversion efficiencies of hydrogen fuel cells, is the amount of hydrogen input into the hydrogen fuel cell at time t, is the maximum amount of hydrogen input; Step 13: The hydrogen storage tank model does not allow for simultaneous charging and discharging of hydrogen. Under this constraint, a mathematical model of the hydrogen storage tank is established. The specific formula is as follows: , In the formula, is the storage capacity of the hydrogen storage tank at time t, , are the maximum and minimum capacities of the hydrogen storage tank, , are the amount of hydrogen charged and discharged in the hydrogen storage tank at time t, , are the efficiency of hydrogen charging and discharging, , They are the upper limits of charging and discharging of the hydrogen storage tank at time t respectively; Step 14: Establish a mathematical model of a methane reactor. The specific formula is as follows: , In the formula, and are the hydrogen power input and methane power output of methane reactor j in time period t, is the energy conversion efficiency of methane reactor j, and are the volume of methane output and the volume of hydrogen consumed by methane reactor j in period t, respectively; Step 15: The hydrogen-mixed gas turbine performs energy conversion by consuming the hydrogen-mixed natural gas. The specific formula of the gas turbine mathematical model is as follows: , In the formula, is the output of gas turbine a during period t, is the energy conversion efficiency of gas turbine a, is the volume of mixed gas consumed by gas turbine a, is the volumetric high calorific value of the mixed gas; Step 16: Establish a comprehensive flexible carbon capture power plant model. The specific formula is as follows: , In the formula, is the total output power of the carbon capture unit w during period t, is the fixed energy consumption of the carbon capture unit w in period t, is the operating energy consumption of the carbon capture unit w during period t, is the net power output of the carbon capture unit w at time t, is the actual carbon emission coefficient of the carbon capture unit w, is the carbon capture coefficient of the carbon capture unit, is the maximum working state coefficient of the regeneration tower and compressor, is the total amount of CO2 captured by the carbon capture unit w, is the amount of CO2 produced by the carbon capture unit w during period t, The energy consumed by the carbon capture unit to capture CO2, is the flue gas split ratio, are the amount of CO2 to be captured provided by the solution storage of carbon capture power plant w during period t, It is the maximum output of thermal power unit.

[0009] Specifically, step 2 includes: Step 21: Calculate the system carbon quota. The specific formula is as follows: , In the formula, For carbon quotas, is the output of thermal power unit c during period t, is the output of gas turbine a during period t, is the power consumed by the gas load at the gas grid node b during period t, is the hydrogen production power of the electrolyzer injected into node m during period t, is the purchased hydrogen power injected into node m during period t, is the carbon quota coefficient of thermal power unit c, is the carbon quota coefficient corresponding to the consumption of natural gas, is the number of thermal power units, is the number of gas units, is the number of gas load nodes, The number of nodes for injecting hydrogen into the gas grid; Step 22: Calculate the actual carbon emissions. The specific formula is as follows: , In the formula, is the mass of CO2 consumed by the methane reactor to synthesize methane during period t, is the methane power output by methane reactor j during period t, is the molar volume of the gas, is the molar mass of CO2, is the actual carbon emissions of the thermal power unit in period t, is the carbon emission coefficient of thermal power units, is the output of thermal power unit c during period t, is the total amount of CO2 captured by the carbon capture unit w, is the carbon emission coefficient of natural gas, The actual carbon emissions corresponding to the consumption of natural gas include the actual carbon emissions corresponding to the natural gas load and the gas turbine. is the volumetric higher calorific value of natural gas, is the number of carbon capture units, is the number of methane reactors, is the number of hydrogen mixing nodes; Step 23: Construct a mathematical model of the ladder-type carbon trading mechanism. The specific formula is as follows: The net carbon emissions of the system are equal to the actual carbon emissions minus the system carbon quota: , In the formula, is the net carbon emissions of the system, is the amount of CO2 purchased from methane reactor j; The mathematical model of the ladder-type carbon trading mechanism is as follows: , In the formula, is the carbon trading base price, is the price growth rate of the ladder-type carbon trading mechanism, is the interval length of carbon emissions, is the total cost of the ladder-type carbon trading.

[0010] Specifically, in step 3, the economic optimization goal is to minimize the total cost, which includes the following steps: Step 31: Calculate the operating cost of the energy supply side of the system. The calculation formula is: , in: , In the formula, F, F1, F2, and F3 are the operating cost of the system energy supply side, the penalty for abandoned wind and solar power, the operation and maintenance cost, and the external energy purchase cost. is the penalty factor for discarding the vth energy source, is the amount of the vth energy source discarded at time t, is the operation and maintenance coefficient of the i-th equipment, is the output of the i-th device at time t, is the price of the u-th purchased energy at time t, is the interaction amount of the u-th purchased energy at time t, T is the simulation period, V, I, and U are the energy source, equipment, and purchased energy sets respectively; Step 32: Calculate the total system operating cost based on the operating cost of the system energy supply side and the total cost of the ladder-type carbon trading. The calculation formula is: , Where C is the total cost of system operation, is the total cost of the tiered carbon trading.

[0011] Specifically, in step 3, the environmental optimization goal is to minimize the system carbon emissions. The specific formula is as follows: , in: , In the formula, , , , They are the actual carbon emissions of the integrated energy system, power grid purchases, gas turbines, and gas boilers. is the carbon capture capacity of the carbon capture system, The carbon emissions caused by purchasing a unit of electricity from the upper power grid, is the power purchased at time t, The carbon emissions per unit of electricity produced by gas turbines, The carbon emissions caused by the production of unit heat by gas boilers, , are the powers of the gas turbine and gas boiler at time t respectively.

[0012] Specifically, in step 4, the supply and demand balance constraint formula is as follows: , In the formula, , are the output of photovoltaic power and wind power at time t, , are the charge and discharge amounts of the energy storage device at time t, , are the power consumption and heating capacity of the heat pump at time t, , are the outputs of the gas boiler and waste heat boiler at time t, , are the output of the gas turbine and the power grid at time t, , They are the output and heating capacity of the hydrogen fuel cell at time t, , are the power consumption of the carbon capture power plant and electrolyzer at time t, , are the electric and thermal loads of the integrated energy system at time t respectively; The energy storage device constraint formula is as follows: , In the formula, is the capacity of the energy storage device at time t, , are the minimum and maximum capacities of the energy storage device, respectively. , are the charging and discharging efficiency of the energy storage device, is the self-loss coefficient, , are the upper limits of the charging and discharging power of the energy storage device at time t, , are the charging and discharging power states of the energy storage device at time t, respectively, which are Boolean variables; The upper and lower limit constraint formulas for equipment operating power are as follows: , In the formula, , are the minimum and maximum operating power of the i-th device, is the power of the i-th device at time t; The equipment running climbing constraint formula is as follows: , In the formula, , are the power of the i-th device at time t+1 and time t, respectively. is the climbing upper limit of the i-th device.

[0013] Specifically, step 5 includes: Step 51, read system information, including the current operating status of each unit and equipment, the coal consumption information of the unit, and the forecast information of wind power and photovoltaic power in the next 5 minutes; Step 52, forming multiple non-dominated layers by fast non-dominated sorting, and placing the non-dominated layers F into a new population layer by layer, so that the population reaches a preset size; Step 53: Solve the objective function based on the compromise solution selection of fuzzy logic.

[0014] Specifically, step 53 includes: Step 531, using the fuzzy set theory compromise solution, the membership function is: , In the formula, is the membership function of the i-th solution and the k-th objective function, , is the maximum and minimum value of k objective functions, is the value of the kth objective function. For minimizing the objective function, the minimum value is the optimal solution; Step 532: The normalized membership function calculation formula of each solution is: , Where I and K are the sets of non-dominated solutions and objective functions, respectively; Step 533: define the minimum normalized membership function as the optimal solution.

[0015] In a second aspect, the present invention also provides an integrated energy system optimization system based on power-to-gas and carbon capture equipment, the system comprising: a system construction module, a model construction module, an objective function construction module, a constraint set construction module and a solution module; System building module, used to build a comprehensive energy system including two-stage power-to-gas equipment, carbon capture equipment, wind and solar power generation equipment, cogeneration equipment, gas boilers, electric boilers, and electric energy storage equipment; The model building module is used to introduce a ladder-type carbon trading mechanism based on the coupling relationship and regulation characteristics between load energies, calculate the system carbon quota and actual carbon emissions, and model the ladder-type carbon trading mechanism; The objective function building module is used to establish the objective function, taking into account the electricity purchase cost, gas purchase cost, carbon trading cost and operation and maintenance cost, and taking the minimum total cost and the minimum carbon emission as the optimization goal in the integrated energy system; A constraint set building module, used to build constraint sets based on thermal power balance, electrical power balance, system capacity constraints and carbon capture constraints in an integrated energy system; The solving module is used to solve the objective function in MATLAB by considering various objective constraints according to the multi-objective evolutionary algorithm based on NSGA-Ⅲ.

[0016] Compared with the prior art, the beneficial effects of the present invention are at least as follows: The present invention applies a multi-objective evolutionary algorithm based on NSGA-III to the problem of solving the optimal scheduling of an integrated energy system to deal with typical multi-objective high-dimensional optimization problems. Compared with the traditional heuristic random search algorithm, the algorithm proposed in the present invention requires fewer parameters and converges faster when solving the problem of rapid optimization of multiple objective functions. The multi-objective optimization based on Pareto can more comprehensively measure the overall state of the system and flexibly schedule system resources, which is more suitable for solving high-dimensional problems.

[0017] The present invention solves the problem that traditional inventions cannot take into account both economic and low-carbon performance and the coordinated operation of various parts in the system, based on the coupling relationship between various parts in the system and the ladder-type carbon trading mechanism. The multi-objective evolutionary algorithm based on NSGA-Ⅲ is used to solve the objective function. On the basis of taking into account economic and low-carbon performance, the coordinated operation of various parts in the system is achieved, which is of great significance to promoting the consumption of renewable energy and building a clean and efficient energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0019] Figure 1 It is a flow chart of the comprehensive energy system optimization method based on power-to-gas and carbon capture equipment of the present invention; Figure 2 A structural diagram of a comprehensive energy system according to an embodiment of the present invention; Figure 3 The wind and solar power output curve and the new energy output curve of the integrated energy system of the embodiment of the present invention; Figure 4 A table of configuration parameters of various parts of the integrated energy system according to an embodiment of the present invention; Figure 5 A comparison table of simulation results of an example of a comprehensive energy system according to an embodiment of the present invention; Figure 6 The electric power balance curve of the integrated energy system according to the embodiment of the present invention; Figure 7 The thermal power balance curve of the integrated energy system according to the embodiment of the present invention; Figure 8 A cost analysis diagram of a comprehensive energy system according to an embodiment of the present invention; Fig. 9 It is a modular schematic diagram of the integrated energy system optimization system based on power-to-gas and carbon capture equipment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the specific embodiments described herein are only used to explain the present invention and are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0021] It should be noted that if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0022] Figure 1 FIG. 1 is a flow chart of an embodiment of a comprehensive energy system optimization method based on power-to-gas and carbon capture equipment provided by the present invention, and the flow chart specifically includes the following steps: Step 1: Build a comprehensive energy system including two-stage power-to-gas equipment, carbon capture equipment, wind and solar power generation equipment, cogeneration equipment, gas boilers, electric boilers, and electric energy storage equipment.

[0023] Specifically, in step 1, mathematical modeling is performed on each part of the integrated energy system to establish an electrolyzer model based on dynamic hydrogen production efficiency, a hydrogen fuel cell mathematical model, a hydrogen storage tank mathematical model, a methane reactor mathematical model, a gas turbine mathematical model, and an integrated flexible carbon capture power plant model.

[0024] Specifically, the mathematical models of each part are established through the following steps: Step 11: Use polynomial simulation to formulate the dynamic characteristics of the electrolyzer conversion efficiency under the running time, and establish an electrolyzer model based on the dynamic hydrogen production efficiency. The specific model is as follows: , In the formula, and are the electrical power input and hydrogen power output of electrolyzer i in period t, is the energy conversion efficiency of electrolyzer i, , and are the coefficients of the electrolyzer dynamic hydrogen production efficiency polynomial, is the rated power of electrolytic cell i, is the volume of hydrogen output by electrolyzer i during period t, is the volumetric higher calorific value of hydrogen.

[0025] Step 12: Establish a hydrogen fuel cell mathematical model. The specific formula is as follows: , In the formula, , are the electrical and thermal conversion powers of the hydrogen fuel cell at time t, , are the electrical and thermal conversion efficiencies of hydrogen fuel cells, is the amount of hydrogen input into the hydrogen fuel cell at time t, The maximum amount of hydrogen to be input.

[0026] Step 13: The hydrogen storage tank model does not allow for simultaneous charging and discharging of hydrogen. Under this constraint, a mathematical model of the hydrogen storage tank is established. The specific formula is as follows: , In the formula, is the storage capacity of the hydrogen storage tank at time t, , are the maximum and minimum capacities of the hydrogen storage tank, , are the amount of hydrogen charged and discharged in the hydrogen storage tank at time t, , are the efficiency of hydrogen charging and discharging, , They are the upper limits of charging and discharging of the hydrogen storage tank at time t respectively.

[0027] Step 14: Establish a mathematical model of a methane reactor. The specific formula is as follows: , In the formula, and are the hydrogen power input and methane power output of methane reactor j in time period t, is the energy conversion efficiency of methane reactor j, and are respectively the volume of methane output and the volume of hydrogen consumed by methane reactor j in period t.

[0028] Step 15: The hydrogen-mixed gas turbine performs energy conversion by consuming the hydrogen-mixed natural gas. The specific formula of the gas turbine mathematical model is as follows: , In the formula, is the output of gas turbine a during period t, is the energy conversion efficiency of gas turbine a, is the volume of mixed gas consumed by gas turbine a, It is the volumetric high calorific value of the mixed gas.

[0029] Step 16: Establish a comprehensive flexible carbon capture power plant model. The specific formula is as follows: , In the formula, is the total output power of the carbon capture unit w during period t, is the fixed energy consumption of the carbon capture unit w in period t, is the operating energy consumption of the carbon capture unit w during period t, is the net power output of the carbon capture unit w at time t, is the actual carbon emission coefficient of the carbon capture unit w, is the carbon capture coefficient of the carbon capture unit, is the maximum working state coefficient of the regeneration tower and compressor, is the total amount of CO2 captured by the carbon capture unit w, is the amount of CO2 produced by the carbon capture unit w during period t, The energy consumed by the carbon capture unit to capture CO2, is the flue gas split ratio, are the amount of CO2 to be captured provided by the solution storage of carbon capture power plant w during period t, It is the maximum output of thermal power unit.

[0030] The electrolyzer produces hydrogen by electrolyzing water, which can convert the random and volatile wind power into high-quality hydrogen energy. Its conversion efficiency will change dynamically due to changes in operating time and operating power. Use the formula shown in step 11 to establish an electrolyzer model based on dynamic hydrogen production efficiency.

[0031] The conversion efficiency of the methanation reaction is related to multiple factors, such as temperature and pressure. It is the second stage in the two-stage process of refining the power-to-gas conversion. This paper takes fixed values ​​for modeling. The mathematical model of the methane reactor is shown in the formula in step 14.

[0032] The flexible flue gas diversion ratio in the carbon capture power plant works in coordination with the liquid storage container. On the one hand, it alleviates the "peak shaving and valley filling" pressure of the system. On the other hand, it realizes CO2 time shifting and decouples the CO2 absorption and regeneration process. The comprehensive flexible carbon capture power plant model is shown in the formula in step 16.

[0033] Step 2: Based on the coupling relationship and regulation characteristics between load energies, a ladder-type carbon trading mechanism is introduced, and the system carbon quota and actual carbon emissions are calculated to model the ladder-type carbon trading mechanism.

[0034] Based on the coupling relationship and regulation characteristics between load energies, a ladder-type carbon trading mechanism is introduced, the system carbon quota and actual carbon emissions are calculated, the ladder-type carbon trading mechanism is modeled, and a mathematical model that considers the synergy of various parts within the system is constructed.

[0035] Specifically, step 2 includes: Step 21: Calculate the system carbon quota. The specific formula is as follows: , In the formula, For carbon quotas, is the output of thermal power unit c in period t, is the output of gas turbine a during period t, is the power consumed by the gas load at the gas grid node b during period t, is the hydrogen production power of the electrolyzer injected into node m during period t, is the purchased hydrogen power injected into node m during period t, is the carbon quota coefficient of thermal power unit c, is the carbon quota coefficient corresponding to the consumption of natural gas, is the number of thermal power units, is the number of gas units, is the number of gas load nodes, is the number of nodes that inject hydrogen into the gas grid.

[0036] Step 22: Calculate the actual carbon emissions. The specific formula is as follows: , In the formula, is the mass of CO2 consumed by the methane reactor to synthesize methane during period t, is the methane power output by methane reactor j during period t, is the molar volume of the gas, is the molar mass of CO2, is the actual carbon emissions of the thermal power unit in period t, is the carbon emission coefficient of thermal power units, is the output of thermal power unit c during period t, is the total amount of CO2 captured by the carbon capture unit w, is the carbon emission coefficient of natural gas, The actual carbon emissions corresponding to the consumption of natural gas include the actual carbon emissions corresponding to the natural gas load and the gas turbine. is the volumetric higher calorific value of natural gas, is the number of carbon capture units, is the number of methane reactors, is the number of hydrogen mixing nodes.

[0037] Step 23: Construct a mathematical model of the ladder-type carbon trading mechanism. The specific formula is as follows: The net carbon emissions of the system are equal to the actual carbon emissions minus the system carbon quota: , In the formula, is the net carbon emissions of the system, is the amount of CO2 purchased from methane reactor j; The mathematical model of the ladder-type carbon trading mechanism is as follows: , In the formula, is the carbon trading base price, is the price growth rate of the ladder-type carbon trading mechanism, is the interval length of carbon emissions, is the total cost of the ladder-type carbon trading.

[0038] The subjects with carbon emission rights allocation in the system include conventional units, gas units and gas load, so the system carbon quota is calculated using the formula shown in step 21.

[0039] The methane reactor needs to consider CO2 when synthesizing methane, and the CO2 consumed by the methane reactor needs to be considered when calculating the carbon emissions. Therefore, the actual carbon emissions are calculated using the formula shown in step 22.

[0040] Step 3: Establish an objective function, taking into account electricity purchase costs, gas purchase costs, carbon trading costs, and operation and maintenance costs, with the minimum total cost and the lowest carbon emissions as the optimization goals in the integrated energy system.

[0041] Specifically, in step 3, the economic optimization goal is to minimize the total cost, which includes the following steps: Step 31: Calculate the operating cost of the energy supply side of the system. The calculation formula is: , in: , In the formula, F, F1, F2, and F3 are the operating cost of the system energy supply side, the penalty for abandoned wind and solar power, the operation and maintenance cost, and the external energy purchase cost. is the penalty factor for discarding the vth energy source, is the amount of the vth energy source discarded at time t, is the operation and maintenance coefficient of the i-th equipment, is the output of the i-th device at time t, is the price of the u-th purchased energy at time t, is the interaction amount of the u-th purchased energy at time t, T is the simulation period, and V, I, and U are the energy source, equipment, and purchased energy collection, respectively.

[0042] Step 32: Calculate the total system operating cost based on the operating cost of the system energy supply side and the total cost of the ladder-type carbon trading. The calculation formula is: , Where C is the total cost of system operation, is the total cost of the tiered carbon trading.

[0043] Exemplarily, the value of the simulation period T is 24 hours.

[0044] Specifically, in step 3, the environmental optimization goal is to minimize the system carbon emissions. The specific formula is as follows: , in: , In the formula, , , , They are the actual carbon emissions of the integrated energy system, power grid purchases, gas turbines, and gas boilers. is the carbon capture capacity of the carbon capture system, The carbon emissions caused by purchasing a unit of electricity from the upper power grid, is the power purchased at time t, The carbon emissions per unit of electricity produced by gas turbines, The carbon emissions caused by the production of unit heat by gas boilers, , are the powers of the gas turbine and gas boiler at time t respectively.

[0045] Step 4: Establish a constraint set based on the thermal power balance, electrical power balance, system capacity constraints and carbon capture constraints in the integrated energy system.

[0046] Specifically, in step 4, the supply and demand balance constraint formula is as follows: , In the formula, , are the output of photovoltaic power and wind power at time t, , are the charge and discharge amounts of the energy storage device at time t, , are the power consumption and heating capacity of the heat pump at time t, , are the outputs of the gas boiler and waste heat boiler at time t, , are the output of the gas turbine and the power grid at time t, , They are the output and heating capacity of the hydrogen fuel cell at time t, , are the power consumption of the carbon capture power plant and electrolyzer at time t, , are the electric and thermal loads of the integrated energy system at time t respectively; The energy storage device constraint formula is as follows: , In the formula, is the capacity of the energy storage device at time t, , are the minimum and maximum capacities of the energy storage device, respectively. , are the charging and discharging efficiency of the energy storage device, is the self-loss coefficient, , are the upper limits of the charging and discharging power of the energy storage device at time t, , are the charging and discharging power states of the energy storage device at time t, respectively, which are Boolean variables; The upper and lower limit constraint formulas for equipment operating power are as follows: , In the formula, , are the minimum and maximum operating power of the i-th device, is the power of the i-th device at time t; The equipment running climbing constraint formula is as follows: , In the formula, , are the power of the i-th device at time t+1 and time t, respectively. is the climbing upper limit of the i-th device.

[0047] Step 5: According to the multi-objective evolutionary algorithm based on NSGA-Ⅲ, various objective constraints are considered in MATLAB to solve the objective function.

[0048] According to the multi-objective evolutionary algorithm based on the third-generation non-sorting genetic algorithm (NSGA-Ⅲ), various objective constraints are considered in MATLAB to solve the objective function and obtain a solution set that takes into account both economy and low carbon and the coordinated operation of various parts in the system.

[0049] Specifically, step 5 includes: Step 51: Read system information, including the current operating status of each unit and equipment, the coal consumption information of the unit, and the forecast information of wind power and photovoltaic power for the next 5 minutes.

[0050] Step 52: Form multiple non-dominated layers through fast non-dominated sorting, and put the non-dominated layers F into the new population layer by layer, so that the population reaches a preset size.

[0051] Step 53: Solve the objective function based on the compromise solution selection of fuzzy logic.

[0052] Specifically, step 53 includes: Step 531, using the fuzzy set theory compromise solution, the membership function is: , In the formula, is the membership function of the i-th solution and the k-th objective function, , is the maximum and minimum value of k objective functions, is the value of the kth objective function. For minimizing the objective function, the minimum value is the optimal solution.

[0053] Step 532: The normalized membership function calculation formula of each solution is: , Where I and K are the sets of non-dominated solutions and objective functions, respectively.

[0054] Step 533: define the minimum normalized membership function as the optimal solution.

[0055] The methods for dealing with multi-objective optimization usually include: (1) converting multiple objectives into a single objective by introducing the concept of weight; (2) considering the relative importance of the objective function and improving the low-priority objective function without reducing the value of the high-priority objective function; (3) solving the Pareto frontier based on the Pareto theory and making decisions in the Pareto solution set.

[0056] The NSGA-III algorithm is a third-generation non-dominated sorting genetic optimization algorithm for multi-objective optimization problems formed by combining the Pareto theory. The specific steps of the multi-objective evolutionary algorithm based on NSGA-III involved in the present invention are as follows: (1) Initialization. Read system information, including the current operating status of each unit and equipment, the unit's coal consumption information, and the forecast information of wind power and photovoltaic power in the next 5 minutes.

[0057] (2) Non-dominated solution screening and stratification. Multiple non-dominated layers are formed by fast non-dominated sorting, and the non-dominated layer F is placed into the new population layer by layer to make the population reach a certain size. In most cases, only part of the last non-dominated layer placed into the new population can enter the new population. In order to ensure the diversity of solutions, the NSGA-Ⅱ algorithm uses the method of crowding sorting to screen the last layer, but crowding sorting is quite difficult to deal with multi-objective problems. Therefore, the NSGA-Ⅲ algorithm introduces widely distributed reference points. The essence is to divide the grid in space to judge the crowding degree of each solution, and then screen out solutions with smaller crowding to ensure the diversity of the population. The present invention uses the reference point method and ensures the widely distributed characteristics of the set reference point to carry out screening in the last layer. After setting the reference point, considering the difference in the numerical scale of each objective function, it is necessary to normalize it and change the numerical scale of each objective function to between 0 and 1. Finally, the normalized solutions are connected to the reference points, and the reference line (the line connecting the reference point and the coordinate origin) is used to assist in the judgment. The number of solutions connected to the reference points is used to judge the density of the solution distribution in the optimization space, and then the solutions with sparse spatial distribution are selected to enter the next generation population.

[0058] (3) Compromise solution selection based on fuzzy logic. The best solution has the smallest distance to the optimal solution and the largest distance to the worst solution. The present invention adopts the compromise solution based on fuzzy set theory.

[0059] The smallest normalized membership function is the optimal solution. It should be noted that in the present invention, due to the difference in predicted power of photovoltaic and wind power, the parameters of the objective function will change, the maximum and minimum values ​​of the function will also change, and the membership function will also be different. The present invention will reasonably coordinate the operating status of each device in the system according to the current state of the system and the wind and solar power output prediction.

[0060] The Pareto solution set of the two objective functions of the system is solved by a multi-objective evolutionary algorithm based on NSGA-Ⅲ, and reasonable decisions are made in the Pareto solution set based on the energy change trend on the energy supply side through fuzzy set theory.

[0061] In order to verify the reliability and effectiveness of the present invention, a certain area in Shandong Province was taken as the research object. The carbon emission factor of electricity purchased from the power grid was 0.872kg / (kW·h), and the carbon emission factor of natural gas purchased from outside was 1.96kg / m 3 The planning time period is set to 24 hours a day, with a step length of 1 hour. The structure of the integrated energy system is shown in Figure 2, new energy units include photovoltaic and wind turbines, equipped with cogeneration devices, gas turbines, P2G devices and other parts. Thermal power units and new energy units generate electricity to supply the power load. When the load is too heavy, the difference is made up by purchasing electricity from the power network; when the power load is light, the excess power will be stored and consumed locally instead of being connected to the grid. P2G devices include electrolyzers, hydrogen storage equipment, hydrogen fuel cells, hydrogen mixing devices and methane reactors. Hydrogen is transported through the natural gas network. Referring to existing research papers, the hydrogen blending ratio is controlled at 24%. Gas turbines can also consume combustible gases to power the system. The wind and solar output curve of the system is as follows: Figure 3 As shown, the configuration parameters of each part of the system are shown in Figure 4 .

[0062] In order to show the superiority of the comprehensive energy system in economic benefits and environmental friendliness, the total cost is further reduced, and the operation and maintenance costs and carbon emissions are also reduced by comparing with the carbon trading ladder-type demand response incentive mechanism without considering parameter adaptive optimization. Figure 5 The total cost of the system includes electricity purchase cost, gas purchase cost, carbon trading cost and operation and maintenance cost.

[0063] from Figure 5 It can be seen from the comparison table of simulation results of the example in that, compared with the system without introducing the step-type demand response incentive mechanism and performing parameter adaptive optimization, the cost of purchasing electricity and gas for the system proposed in the present invention has decreased, the total cost has been reduced by about 14.9%, and the carbon emissions have been reduced by 23.1%.

[0064] Figure 3 The new energy output curve of the system shows that the wind power output at night is slightly stronger than that during the day, and the photovoltaic output curve is concentrated between 6:00 and 19:00 every day. The photovoltaic output in other time periods can be basically ignored. Figure 6 It can be seen from the electric power balance curve that when the power supply is insufficient at night, a large amount of electricity needs to be purchased from the power grid. At this time, the purchase of electricity accounts for a dominant position in the system power supply, supplemented by cogeneration and wind power generation. During the daytime, the photovoltaic output is concentrated, and the amount of electricity purchased from the power grid is significantly reduced. Cogeneration accounts for a dominant position in the power supply. If there is any surplus electricity at this time, it will be stored in the energy storage device, which can be called out for use during the power supply low period.

[0065] Figure 7 This is the thermal power balance curve of the system. It can be seen that during the nighttime period, the cogeneration power is relatively low, and it mainly relies on electric boilers and gas boilers to work, and the carbon capture power is maintained at a low level; during the daytime, the cogeneration power increases, and the carbon capture power increases at this time. The captured CO2 can continue to participate in the power-to-gas stage to cope with the high heat load during the day. The scheduling of this system flexibly balances supply and demand and reduces the waste of excess energy. Figure 8This is a system cost analysis chart. It can be seen that the gas purchase cost accounts for more than half of the total cost, and the gas and electricity purchase costs account for the vast majority of the total cost. In comparison, the carbon trading cost accounts for a very small proportion, which is due to the optimized step-by-step carbon trading strategy.

[0066] In summary, the integrated energy optimization method and system based on power-to-gas and carbon capture proposed in the present invention, compared with the traditional integrated energy system, introduces a two-stage power-to-gas, carbon capture, hydrogen fuel cell and hydrogen storage tank composed of a refined utilization system of hydrogen energy, giving full play to the low-carbon and environmentally friendly characteristics of hydrogen energy, improving the system's renewable energy consumption, and reducing the carbon emissions and operating costs of the integrated energy system. Compared with the traditional algorithm optimization operation, the multi-objective evolutionary algorithm based on NSGA-Ⅲ combined with the hardware equipment support of power-to-gas and carbon capture used in the present invention has reduced the total operating cost by 14.9% and CO2 emissions by 23.1%.

[0067] Fig. 9 FIG. 1 is a schematic diagram of a structure of an embodiment of a comprehensive energy system optimization system based on power-to-gas and carbon capture equipment provided by the present invention. Fig. 9 As shown, the system includes: a system building module, a model building module, an objective function building module, a constraint set building module and a solution module.

[0068] System building modules are used to build a comprehensive energy system that includes two-stage power-to-gas equipment, carbon capture equipment, wind and solar power generation equipment, cogeneration equipment, gas boilers, electric boilers, and electric energy storage equipment.

[0069] The model building module is used to introduce a ladder-type carbon trading mechanism according to the coupling relationship and regulation characteristics between load energies, and calculate the system carbon quota and actual carbon emissions to model the ladder-type carbon trading mechanism.

[0070] The objective function construction module is used to establish the objective function, taking into account the electricity purchase cost, gas purchase cost, carbon trading cost and operation and maintenance cost, and taking the minimum total cost and the lowest carbon emission as the optimization goal in the integrated energy system.

[0071] The constraint set building module is used to establish the constraint set according to the thermal power balance, electrical power balance, system capacity constraints and carbon capture constraints in the integrated energy system.

[0072] The solving module is used to solve the objective function in MATLAB by considering various objective constraints according to the multi-objective evolutionary algorithm based on NSGA-Ⅲ.

[0073] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0074] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0075] The above embodiments only express the preferred implementation mode of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. An integrated energy system optimization method based on power-to-gas and carbon capture equipment, characterized in that: The steps include: Step 1: Build a comprehensive energy system including two-stage power-to-gas equipment, carbon capture equipment, wind and solar power generation equipment, cogeneration equipment, gas boilers, electric boilers, and electric energy storage equipment; Step 2: Based on the coupling relationship and regulation characteristics between load energies, a ladder-type carbon trading mechanism is introduced, and the system carbon quota and actual carbon emissions are calculated to model the ladder-type carbon trading mechanism; Step 3: Establish an objective function, taking into account the electricity purchase cost, gas purchase cost, carbon trading cost and operation and maintenance cost, and taking the minimum total cost and the minimum carbon emission as the optimization goals in the integrated energy system; Step 4: Establish a constraint set according to the thermal power balance, electrical power balance, system capacity constraint and carbon capture constraint in the integrated energy system; Step 5: According to the multi-objective evolutionary algorithm based on NSGA-Ⅲ, various objective constraints are considered in MATLAB to solve the objective function.

2. The method according to claim 1, characterized in that In the step 1, mathematical modeling is performed on each part of the integrated energy system to establish an electrolyzer model based on dynamic hydrogen production efficiency, a hydrogen fuel cell mathematical model, a hydrogen storage tank mathematical model, a methane reactor mathematical model, a gas turbine mathematical model, and an integrated flexible carbon capture power plant model.

3. The method according to claim 2, characterized in that The mathematical models of each part are established through the following steps: Step 11: Use polynomial simulation to formulate the dynamic characteristics of the electrolyzer conversion efficiency under the running time, and establish an electrolyzer model based on the dynamic hydrogen production efficiency. The specific model is as follows: ， In the formula, and are the electrical power input and hydrogen power output of electrolyzer i in period t, is the energy conversion efficiency of electrolyzer i, , and are the coefficients of the electrolyzer dynamic hydrogen production efficiency polynomial, is the rated power of electrolytic cell i, is the volume of hydrogen output by electrolyzer i during period t, is the volumetric higher calorific value of hydrogen; Step 12: Establish a hydrogen fuel cell mathematical model. The specific formula is as follows: ， In the formula, , are the electrical and thermal conversion powers of the hydrogen fuel cell at time t, , are the electrical and thermal conversion efficiencies of hydrogen fuel cells, is the amount of hydrogen input into the hydrogen fuel cell at time t, is the maximum amount of hydrogen input; Step 13: The hydrogen storage tank model does not allow for simultaneous charging and discharging of hydrogen. Under this constraint, a mathematical model of the hydrogen storage tank is established. The specific formula is as follows: ， In the formula, is the storage capacity of the hydrogen storage tank at time t, , are the maximum and minimum capacities of the hydrogen storage tank, , are the amount of hydrogen charged and discharged in the hydrogen storage tank at time t, , are the efficiency of hydrogen charging and discharging, , They are the upper limits of charging and discharging of the hydrogen storage tank at time t respectively; Step 14: Establish a mathematical model of a methane reactor. The specific formula is as follows: ， In the formula, and are the hydrogen power input and methane power output of methane reactor j in time period t, is the energy conversion efficiency of methane reactor j, and are the methane volume output and hydrogen volume consumed by methane reactor j during period t, is the volumetric higher calorific value of hydrogen, is the volumetric higher calorific value of methane; Step 15: The hydrogen-mixed gas turbine performs energy conversion by consuming the hydrogen-mixed natural gas. The specific formula of the gas turbine mathematical model is as follows: ， In the formula, is the output of gas turbine a during period t, is the energy conversion efficiency of gas turbine a, is the volume of mixed gas consumed by gas turbine a, is the volumetric high calorific value of the mixed gas; Step 16: Establish a comprehensive flexible carbon capture power plant model. The specific formula is as follows: ， In the formula, is the total output power of the carbon capture unit w during period t, is the fixed energy consumption of the carbon capture unit w in period t, is the operating energy consumption of the carbon capture unit w during period t, is the net power output of the carbon capture unit w at time t, is the actual carbon emission coefficient of the carbon capture unit w, is the carbon capture coefficient of the carbon capture unit, is the maximum working state coefficient of the regeneration tower and compressor, is the total amount of CO2 captured by the carbon capture unit w, is the amount of CO2 produced by the carbon capture unit w during period t, The energy consumed by the carbon capture unit to capture CO2, is the flue gas split ratio, are the amount of CO2 to be captured provided by the solution storage of carbon capture power plant w during period t, It is the maximum output of thermal power unit.

4. The method according to claim 1, characterized in that The step 2 comprises: Step 21: Calculate the carbon quota of the system. The specific formula is as follows: ， In the formula, For carbon quotas, is the output of thermal power unit c during period t, is the output of gas turbine a during period t, is the power consumed by the gas load at the gas grid node b during period t, is the hydrogen production power of the electrolyzer injected into node m during period t, is the purchased hydrogen power injected into node m during period t, is the carbon quota coefficient of thermal power unit c, is the carbon quota coefficient corresponding to the consumption of natural gas, is the number of thermal power units, is the number of gas units, is the number of gas load nodes, The number of nodes for injecting hydrogen into the gas grid; Step 22: Calculate the actual carbon emissions. The specific formula is as follows: ， In the formula, is the mass of CO2 consumed by the methane reactor to synthesize methane during period t, is the methane power output by methane reactor j during period t, is the molar volume of the gas, is the molar mass of CO2, is the actual carbon emissions of the thermal power unit in period t, is the carbon emission coefficient of thermal power units, is the output of thermal power unit c during period t, is the total amount of CO2 captured by the carbon capture unit w, is the carbon emission coefficient of natural gas, The actual carbon emissions corresponding to the consumption of natural gas include the actual carbon emissions corresponding to the natural gas load and the gas turbine. is the volumetric higher calorific value of natural gas, is the number of carbon capture units, is the number of methane reactors, is the number of hydrogen mixing nodes; Step 23: construct a mathematical model of the ladder-type carbon trading mechanism. The specific formula is as follows: The net carbon emissions of the system are equal to the actual carbon emissions minus the system carbon quota: ， In the formula, is the net carbon emissions of the system, is the amount of CO2 purchased from methane reactor j; The mathematical model of the ladder-type carbon trading mechanism is as follows: ， In the formula, is the carbon trading base price, is the price growth rate of the ladder-type carbon trading mechanism, is the interval length of carbon emissions, is the total cost of the ladder-type carbon trading.

5. The method according to claim 1, characterized in that In step 3, the economic optimization goal is to minimize the total cost, and the steps include: Step 31: Calculate the operating cost of the energy supply side of the system. The calculation formula is: ， in: ， In the formula, F, F1, F2, and F3 are the operating cost of the system energy supply side, the penalty for abandoned wind and solar power, the operation and maintenance cost, and the external energy purchase cost. is the penalty factor for discarding the vth energy source, is the amount of the vth energy source discarded at time t, is the operation and maintenance coefficient of the i-th equipment, is the output of the i-th device at time t, is the price of the u-th purchased energy at time t, is the interaction amount of the u-th purchased energy at time t, T is the simulation period, V, I, and U are the energy source, equipment, and purchased energy sets, respectively; Step 32: Based on the operating cost of the energy supply side of the system and the total cost of the ladder-type carbon trading, the total operating cost of the system is calculated using the following formula: ， Where C is the total cost of operating the system, is the total cost of the ladder-type carbon trading.

6. The method according to claim 1, characterized in that In step 3, the environmental optimization goal is to minimize the system carbon emissions. The specific formula is as follows: ， in: ， In the formula, , , , They are the actual carbon emissions of the integrated energy system, power grid purchases, gas turbines, and gas boilers. is the carbon capture capacity of the carbon capture system, The carbon emissions caused by purchasing a unit of electricity from the upper power grid, is the power purchased at time t, The carbon emissions per unit of electricity produced by gas turbines, The carbon emissions caused by the production of unit heat by gas boilers, , are the powers of the gas turbine and gas boiler at time t respectively.

7. The method according to claim 1, characterized in that In step 4, the supply-demand balance constraint formula is as follows: ， In the formula, , are the output of photovoltaic power and wind power at time t, , are the charge and discharge amounts of the energy storage device at time t, , are the power consumption and heating capacity of the heat pump at time t, , are the outputs of the gas boiler and waste heat boiler at time t, , are the output of the gas turbine and the power grid at time t, , They are the output and heating capacity of the hydrogen fuel cell at time t, , are the power consumption of the carbon capture power plant and electrolyzer at time t, , are the electric and thermal loads of the integrated energy system at time t respectively; The energy storage device constraint formula is as follows: ， In the formula, is the capacity of the energy storage device at time t, , are the minimum and maximum capacities of the energy storage device, respectively. , are the charging and discharging efficiency of the energy storage device, is the self-loss coefficient, , are the upper limits of the charging and discharging power of the energy storage device at time t, , are the charging and discharging power states of the energy storage device at time t, respectively, which are Boolean variables; The upper and lower limit constraint formulas for equipment operating power are as follows: ， In the formula, , are the minimum and maximum operating power of the i-th device, is the power of the i-th device at time t; The equipment running climbing constraint formula is as follows: ， In the formula, , are the power of the i-th device at time t+1 and time t, respectively. is the climbing upper limit of the i-th device.

8. The method according to claim 1, characterized in that The step 5 comprises: Step 51, read system information, including the current operating status of each unit and equipment, the coal consumption information of the unit, and the forecast information of wind power and photovoltaic power in the next 5 minutes; Step 52, forming multiple non-dominated layers by fast non-dominated sorting, and placing the non-dominated layers F into a new population layer by layer, so that the population reaches a preset size; Step 53: Solve the objective function based on the compromise solution selection of fuzzy logic.

9. The method according to claim 8, characterized in that The step 53 comprises: Step 531, using the fuzzy set theory compromise solution, the membership function is: ， In the formula, is the membership function of the i-th solution and the k-th objective function, , is the maximum and minimum value of k objective functions, is the value of the kth objective function. For minimizing the objective function, the minimum value is the optimal solution; Step 532: The normalized membership function calculation formula of each solution is: ， Where I and K are the sets of non-dominated solutions and objective functions, respectively; Step 533: define the minimum normalized membership function as the optimal solution.

10. An integrated energy system optimization system based on power-to-gas and carbon capture equipment, used to implement the method according to any one of claims 1 to 9, characterized in that: include: System building module, model building module, objective function building module, constraint set building module and solution module; The system building module is used to build a comprehensive energy system including a two-stage power-to-gas device, a carbon capture device, a wind and solar power generation device, a cogeneration device, a gas boiler, an electric boiler, and an electric energy storage device; The model building module is used to introduce a ladder-type carbon trading mechanism according to the coupling relationship and regulation characteristics between load energy sources, and calculate the system carbon quota and actual carbon emissions to model the ladder-type carbon trading mechanism; The objective function building module is used to establish an objective function, taking into account the electricity purchase cost, gas purchase cost, carbon trading cost and operation and maintenance cost, and taking the minimum total cost and the minimum carbon emission as the optimization goals in the integrated energy system; The constraint set building module is used to establish a constraint set according to the thermal power balance, electrical power balance, system capacity constraints and carbon capture constraints in the integrated energy system; The solution module is used to solve the objective function in MATLAB according to a multi-objective evolutionary algorithm based on NSGA-III, taking into account various objective constraints.

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