Comprehensive energy low-carbon optimization system based on multi-energy coupling and supply and demand flexible response

Abstract

The invention relates to the technical field of energy scheduling, in particular to a comprehensive energy low-carbon optimization system based on multi-energy coupling and supply and demand flexible response, which comprises an information acquisition port, an information analysis port and an information feedback port, the basic data information comprises combined heat and power generation equipment basic data, gas boiler basic data, carbon capture system basic data, electricity-to-gas basic data and economic basic data; the information analysis port is used for storing and analyzing the acquired basic data information and outputting an analysis result; and the information feedback port is used for feeding back the analysis result output by the information analysis port to the display unit.

Description

Technical Field

[0001] The present invention relates to the technical field of energy dispatching, and in particular to a comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible response to supply and demand. Background Art

[0002] With the increasing severity of environmental problems such as global warming and the gradual depletion of traditional energy, the development of new energy and the promotion of energy conservation and emission reduction have become a global consensus. Hydrogen has obvious advantages among various energy sources, with characteristics such as high calorific value and low pollution. By integrating the gas hydrogen blending device and participating in the methanation process into the integrated energy system (IES), the goal of low-carbon development of the system can be achieved.

[0003] At present, the optimization of hydrogen energy participation in IES mainly focuses on modeling and coupling, and focuses on the economic benefits brought by hydrogen energy participation in the system, but ignores the effect of emission reduction. Under the background of "dual carbon", the carbon trading mechanism and demand response have good benefits for the low-carbon economic operation of the system, and the economic and low-carbon nature of the carbon trading mechanism and DR are considered, but the positive role of DR on the user side is ignored.

[0004] Therefore, it is necessary to propose a comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible response to supply and demand. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible response to supply and demand. The present invention realizes multi-energy coupling of cogeneration through a gas hydrogen blending device, and uses flexible response strategies on the supply side and the demand side to reduce the system operating cost; finally, a low-carbon optimization model is established with the minimum sum of energy purchase cost, carbon trading cost, carbon storage cost, wind abandonment cost, response compensation cost and grid interaction cost as the objective function, which effectively solves the shortcomings of the background technology.

[0006] To achieve the above object, the technical solution of the present invention is as follows:

[0007] An integrated energy low-carbon optimization system based on multi-energy coupling and flexible response to supply and demand, the system comprising an information collection port, an information analysis port and an information feedback port, the information collection port being used to collect basic data information, the basic data information comprising basic data of cogeneration equipment, basic data of gas boilers, basic data of carbon capture systems, basic data of power-to-gas conversion, and basic economic data; the information analysis port being used to store, analyze and output analysis results of the collected basic data information; the information feedback port being used to feed back the analysis results output by the information analysis port to a display unit.

[0008] A further improvement of the present invention is that the information analysis port includes an information storage module, an optimization model building module, a model conversion module, and a solution module. The information storage module is used to store the basic data information collected by the information collection port; the optimization model building module is used to establish a comprehensive energy low-carbon optimization scheduling model and construct an objective function; the model conversion module is used to convert the objective function into a mixed function linear model; the solution module is used to call the CPLEX solver to solve the mixed function linear model.

[0009] A further improvement of the present invention is that the optimization model building module includes a free carbon emission quota model building unit, a carbon emission cost model building unit, a step-by-step carbon emission trading model building unit, an energy coupling unit, a demand response model building unit and a low-carbon economic dispatch objective function building unit; the free carbon emission quota model building unit runs a free carbon emission quota calculation strategy, and the specific formula is:

[0010]

[0011] Among them, E E Free carbon emission quota for IES; e，b To purchase electricity quota from the power grid when there is a power shortage; E GT Free quota for gas turbines; E GB Free quota for gas boilers; g，l Free carbon emission quota for demand-side load; g，l is the free carbon emission quota coefficient of gas load; e , χ h P is the free quota coefficient of electricity and heat; GT，e (t) and P GT，h (t) is the electrical and thermal power of the gas turbine; P g，Load (t) is the gas consumption during time t.

[0012] A further improvement of the present invention is that the carbon emission cost model construction unit runs a carbon emission cost calculation strategy, and the specific formula of the carbon emission cost calculation strategy is:

[0013]

[0014] Among them, E Es，a is the actual carbon emissions of the entire IES; E TB，a is the actual carbon emissions of gas turbines and gas boilers; E e，b，a The amount of additional electricity that IES needs to purchase; R TB (t) is the power of the gas boiler and gas turbine at time t; δ is the carbon emission parameter of gas load consumption; E g，l，a is the actual carbon emission of the upper gas; E P，ais the actual carbon emissions reduced in the power-to-gas process; P p，g (t) is the power value of natural gas in the system at time t, θ is the output parameter; a, b, c are the parameters in the coal-fired power generation process; d, e, f are the natural gas consumption coefficients.

[0015] A further improvement of the present invention is that the step-by-step carbon emission trading model construction unit runs a carbon emission trading calculation strategy, and the specific formula of the carbon emission trading calculation strategy is:

[0016] E E，c =E E，a -E E

[0017]

[0018] F c is the carbon trading cost of the system; d is the length of the step interval; λ is the benchmark price of carbon trading; α is the increase in the step interval.

[0019] A further improvement of the present invention is that the energy coupling unit operates a cogeneration hydrogen-doped modeling strategy, and the specific formula is:

[0020]

[0021] Among them, P chp，e (t) is the electrical power of the gas turbine; P chp，h (t) is the thermal power of the gas turbine; and are the power corresponding to the natural gas and hydrogen consumed by the gas turbine at time t; and are the electrical and thermal efficiencies of the gas turbine respectively; Y H2，chp (t) is the hydrogen doping ratio at time t.

[0022] A further improvement of the present invention is that the demand response model building unit runs a demand response strategy, and the specific formula is:

[0023]

[0024] Where: n is the type of different response loads on the demand side; when n = c, is the power value of the mth type of load after participating in the transferable demand side response at time t, is the power value of the mth type of transferable load participating in the demand side response at time t; when n = d, is the power value of the mth type of load after participating in the alternative demand-side response at time t, is the power value of the mth type of replaceable load participating in the demand side response at time t; and is a binary parameter, indicating the parameters of the m-th load transfer in and out; and The lower and upper power limits for participating in demand-side response.

[0025] A further improvement of the present invention is that the low-carbon economic dispatch objective function construction unit runs an objective function construction strategy, and the specific formula is:

[0026] F=min(F buy +F c +F f +F w +F d +F ex );

[0027] Among them, F buy is the energy purchase cost, F c is the carbon trading cost, F f is the carbon sequestration cost, F w is the wind curtailment cost, F d is the DR compensation cost, F ex is the grid interaction cost.

[0028] A further improvement of the present invention is that the low-carbon economic dispatch objective function construction unit also includes an operation objective function constraint strategy, and the objective function constraint strategy includes a wind power output constraint sub-strategy, a power balance constraint sub-strategy, a carbon capture constraint sub-strategy, an energy storage constraint sub-strategy, a gas unit climbing and output constraint sub-strategy, a gas boiler climbing and output constraint sub-strategy, an improved P2G energy consumption upper and lower limits and climbing constraint sub-strategy and a tie line power constraint sub-strategy.

[0029] A further improvement of the present invention is that the model conversion module runs a function linearization processing strategy, and the function linearization processing strategy performs piecewise linearization processing on the square term in the formula of the carbon emission cost calculation strategy, including the following specific steps:

[0030] Step 1: Divide the function into Q intervals and select Q+1 segmentation points [r1, r2, …, r Q+1 ].

[0031] Step 2: Add Q binary auxiliary variables [z1, z2, .., z Q ] and Q+1 continuous auxiliary variables [w1, w2, …, w Q+1 ], where the variables satisfy:

[0032]

[0033] Step 3: Convert the nonlinear function into a linear expression:

[0034]

[0035] The technical effects of the present invention are as follows:

[0036] The present invention realizes multi-energy coupling of cogeneration through a gas hydrogen blending device, and uses flexible response strategies on the supply side and the demand side to reduce the system operating cost; finally, a low-carbon optimization model is established with the minimum sum of energy purchase cost, carbon trading cost, carbon storage cost, wind abandonment cost, response compensation cost and grid interaction cost as the objective function, which effectively solves the shortcomings of the background technology, effectively reduces the system operating cost and actual carbon emissions, and has the characteristics of economy, low carbon and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0038] Figure 1 It is a structural schematic diagram of the comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible response to supply and demand of the present invention. DETAILED DESCRIPTION

[0039] Example 1

[0040] This embodiment proposes a comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible response to supply and demand, realizes cogeneration multi-energy coupling through a gas hydrogen blending device, and uses flexible response strategies on the supply side and the demand side to reduce the system operating cost; finally, a low-carbon optimization model is established with the minimum sum of energy purchase cost, carbon trading cost, carbon storage cost, wind abandonment cost, response compensation cost and grid interaction cost as the objective function, which effectively solves the shortcomings of the background technology, effectively reduces the system operating cost and actual carbon emissions, and has the characteristics of economy, low carbon and flexibility.

[0041] like Figure 1 As shown, a comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible response to supply and demand, the system includes an information collection port, an information analysis port and an information feedback port, the information collection port is used to collect basic data information, the basic data information includes basic data of cogeneration equipment, basic data of gas boilers, basic data of carbon capture systems, basic data of power-to-gas, and basic economic data; the information analysis port is used to store, analyze and output the analysis results of the collected basic data information; the information feedback port is used to feed back the analysis results output by the information analysis port to the display unit.

[0042] In this embodiment, the information analysis port includes an information storage module, an optimization model building module, a model conversion module, and a solution module. The information storage module is used to store the basic data information collected by the information collection port; the optimization model building module is used to establish a comprehensive energy low-carbon optimization scheduling model and construct an objective function; the model conversion module is used to convert the objective function into a mixed function linear model; the solution module is used to call the CPLEX solver to solve the mixed function linear model.

[0043] In this embodiment, the optimization model building module includes a free carbon emission quota model building unit, a carbon emission cost model building unit, a step-by-step carbon emission trading model building unit, an energy coupling unit, a demand response model building unit and a low-carbon economic scheduling objective function building unit; the free carbon emission quota model building unit runs the free carbon emission quota calculation strategy, and the specific formula is:

[0044]

[0045] Among them, E E Free carbon emission quota for IES; e，b To purchase electricity quota from the power grid when there is a power shortage; E GT Free quota for gas turbines; E GB Free quota for gas boilers; g，l Free carbon emission quota for demand-side load; g，l is the free carbon emission quota coefficient of gas load; e , χ h The coefficient of free quota for electricity and heat: P GT，e (t) and P GT，h (t) is the electrical and thermal power of the gas turbine; P g，Load (t) is the gas consumption during time t.

[0046] In this embodiment, the carbon emission cost model construction unit runs a carbon emission cost calculation strategy, and the specific formula of the carbon emission cost calculation strategy is:

[0047]

[0048] Among them, E ES，a is the actual carbon emissions of the entire IES; E TB，a is the actual carbon emissions of gas turbines and gas boilers; E e，b，a The amount of additional electricity that IES needs to purchase; R TB (t) is the power of the gas boiler and gas turbine at time t; δ is the carbon emission parameter of gas load consumption; E g，l，a is the actual carbon emission of the upper gas; E P，ais the actual carbon emissions reduced in the power-to-gas process; P p，g (t) is the power value of natural gas in the system at time t, θ is the output parameter; a, b, c are the parameters in the coal-fired power generation process; d, e, f are the natural gas consumption coefficients.

[0049] In this embodiment, the step-by-step carbon emission trading model construction unit runs a carbon emission trading calculation strategy, and the specific formula of the carbon emission trading calculation strategy is:

[0050] E E，c =E E，a -E E

[0051]

[0052] F c is the carbon trading cost of the system; d is the length of the step interval; λ is the benchmark price of carbon trading; α is the increase in the step interval.

[0053] In this embodiment, the energy coupling unit operates a cogeneration hydrogen-doped modeling strategy, and the specific formula is:

[0054]

[0055] Among them, P chp，e (t) is the electrical power of the gas turbine; P chp，h (t) is the thermal power of the gas turbine; and are the power corresponding to the natural gas and hydrogen consumed by the gas turbine at time t; and are the electrical and thermal efficiencies of the gas turbine respectively; Y H2，chp (t) is the hydrogen doping ratio at time t.

[0056] In this embodiment, the demand response model building unit runs the demand response strategy, and the specific formula is:

[0057]

[0058] Where: n is the type of different response loads on the demand side; when n = c, is the power value of the mth type of load after participating in the transferable demand side response at time t, is the power value of the mth type of transferable load participating in the demand side response at time t; when n = d, is the power value of the mth type of load after participating in the alternative demand-side response at time t, is the power value of the mth type of replaceable load participating in the demand side response at time t; and is a binary parameter, indicating the parameters of the m-th load transfer in and out; and The lower and upper power limits for participating in demand-side response.

[0059] In this embodiment, the low-carbon economic dispatch objective function construction unit runs the objective function construction strategy, and the specific formula is:

[0060] F=min(F buy +F c +F f +F w +F d +F ex );

[0061] Among them, F buy is the energy purchase cost, F c is the carbon trading cost, F f is the carbon sequestration cost, F w is the wind curtailment cost, F d is the DR compensation cost, F ex is the grid interaction cost.

[0062] 1) Energy purchase cost

[0063]

[0064] F buy =F th +F buy，g

[0065] Where a th , b th and c th is the coal consumption cost coefficient of the coal-fired unit; P th (t) is the power generated by the thermal power unit at time t; c buyg is the unit price of natural gas; P buy，g (t) is the natural gas power purchased by the system at time t; F th is the coal cost; F buy，g The cost of purchasing gas.

[0066] 2) Carbon storage costs

[0067]

[0068] Where: c f E is the cost of storing unit mass of CO2; storage (t) is the mass of stored CO2 at time t; T is the scheduling period.

[0069] 3) Wind curtailment costs

[0070]

[0071] Where: DC P is the penalty coefficient for wind abandonment; DG，cut (t) is the wind power abandoned at time t.

[0072] 4) DR compensation costs

[0073]

[0074] Where: p and δ c are the compensation coefficients for transferable load and replaceable load respectively.

[0075] 5) Grid interaction costs

[0076]

[0077] Where: C rs (t) and C rb (t) are the electricity sales and purchase prices respectively; P ex (t) is the interaction power between IES and the large power grid.

[0078] 6) Carbon trading costs F c

[0079] In this embodiment, the low-carbon economic dispatch objective function construction unit also includes an operation objective function constraint strategy, and the objective function constraint strategy includes a wind power output constraint sub-strategy, a power balance constraint sub-strategy, a carbon capture constraint sub-strategy, an energy storage constraint sub-strategy, a gas unit climbing and output constraint sub-strategy, a gas boiler climbing and output constraint sub-strategy, an improved P2G energy consumption upper and lower limits and climbing constraint sub-strategy, and a tie line power constraint sub-strategy.

[0080] 1) Wind power output constraints

[0081]

[0082] Where: P DG (t) is the wind power output power at time t: The maximum output power of wind power.

[0083] 2) Power balance constraints

[0084]

[0085] Where: P Dc (t) is the wind power supply power at time t; P g，GB (t) is the power corresponding to the natural gas consumed by the gas boiler at time t; P GB，h (t) is the thermal power output of the gas boiler at time t; and are the heat storage and heat release power of the heat storage tank respectively;

[0086] 3) Carbon capture constraints

[0087]

[0088] Where: is the maximum operating energy consumption; P CCS (t) is the carbon capture output at time t; and They are the upper and lower limits of the rich liquid tank capacity respectively; and They are the upper and lower limits of the lean liquid tank capacity respectively; and is the initial capacity of the liquid storage device; and This is the capacity of the liquid storage device at the end.

[0089] 4) Energy storage constraints

[0090]

[0091] Where: and are the upper and lower limits of heat storage capacity respectively; S HS (t) is the capacity of the heat storage tank at time t; and They are respectively the charging and discharging status of the heat storage tank at time t; and are the maximum charging and discharging power of the heat storage tank respectively; is the initial capacity of the heat storage tank.

[0092] 5) Gas unit ramp-up and output constraints

[0093]

[0094] Where: P HE (t) is the total output of the gas generator set at time t; and They are the upper and lower limits of gas turbine thermal output respectively; and They are the upper and lower limits of gas turbine electrical output respectively; and They are the upper and lower limits of gas turbine climbing grade, respectively.

[0095] 6) Gas boiler ramp-up and output constraints

[0096]

[0097] Where: η GB The energy conversion efficiency of the gas boiler; and They are the upper and lower limits of gas boiler input power respectively; and They are the upper and lower limits of the gas boiler climbing slope respectively.

[0098] 7) Improve P2G energy consumption upper and lower limits and climbing constraints

[0099] 8) Tie line power constraints

[0100] P linemin ≤P ex (t)≤P linemax

[0101] Where: P linemax and P linemin are the maximum and minimum power of the tie line.

[0102] In this embodiment, the model conversion module runs a function linearization processing strategy, which performs piecewise linearization processing on the square term in the formula of the carbon emission cost calculation strategy, including the following specific steps:

[0103] Step 1: Divide the function into Q intervals and select Q+1 segmentation points [r1, r2, …, r Q+1 ].

[0104] Step 2: Add Q binary auxiliary variables [z1, z2, .., z Q ] and Q+1 continuous auxiliary variables [w1, w2, …, w Q+1 ], where the variables satisfy:

[0105]

[0106] Step 3: Convert the nonlinear function into a linear expression:

[0107]

[0108] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0109] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0110] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0111] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0112] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0113] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0114] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0116] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0117] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible response to supply and demand, characterized by: The system includes an information collection port, an information analysis port and an information feedback port. The information collection port is used to collect basic data information, and the basic data information includes basic data of cogeneration equipment, basic data of gas boilers, basic data of carbon capture systems, basic data of power-to-gas conversion, and basic economic data; The information analysis port is used to store and analyze the collected basic data information and output the analysis results; The information feedback port is used to feed back the analysis result output by the information analysis port to the display unit.

2. The comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible supply and demand response according to claim 1 is characterized in that: The information analysis port includes an information storage module, an optimization model building module, a model conversion module, and a solution module. The information storage module is used to store the basic data information collected by the information collection port; the optimization model building module is used to establish a comprehensive energy low-carbon optimization scheduling model and construct an objective function; the model conversion module is used to convert the objective function into a mixed function linear model; the solution module is used to call the CPLEX solver to solve the mixed function linear model.

3. The comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible supply and demand response according to claim 2 is characterized in that: The optimization model building module includes a free carbon emission quota model building unit, a carbon emission cost model building unit, a step-by-step carbon emission trading model building unit, an energy coupling unit, a demand response model building unit and a low-carbon economic dispatch objective function building unit; the free carbon emission quota model building unit runs the free carbon emission quota calculation strategy, and the specific formula is: Among them, E E Free carbon emission quota for IES; e，b To purchase electricity quota from the power grid when there is a power shortage; E GT Free quota for gas turbines; E GB Free quota for gas boilers; g，l Free carbon emission quota for demand-side load; g，l is the free carbon emission quota coefficient of gas load; e , χ h P is the free quota coefficient of electricity and heat; GT，e (t) and P GT，h (t) is the electrical and thermal power of the gas turbine; P g，Load (t) is the gas consumption during time t.

4. The comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible supply and demand response according to claim 3 is characterized in that: The carbon emission cost model building unit runs a carbon emission cost calculation strategy, and the specific formula of the carbon emission cost calculation strategy is: Among them, E ES，a is the actual carbon emissions of the entire IES; E TB，a is the actual carbon emissions of gas turbines and gas boilers; E e，b，a P is the amount of additional electricity that IES needs to purchase; TB (t) is the power of the gas boiler and gas turbine at time t; δ is the carbon emission parameter of gas load consumption; E g，l，a is the actual carbon emission of the upper gas; E P，a is the actual carbon emissions reduced in the power-to-gas process; P p，g (t) is the power value of natural gas in the system at time t, θ is the output parameter; a, b, c are the parameters in the coal-fired power generation process; d, e, f are the natural gas consumption coefficients.

5. The comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible supply and demand response according to claim 4 is characterized in that: The step-by-step carbon emission trading model construction unit runs a carbon emission trading calculation strategy, and the specific formula of the carbon emission trading calculation strategy is: AND E，c =And E，a -AND E F c is the carbon trading cost of the system; d is the length of the step interval; λ is the benchmark price of carbon trading; α is the increase in the step interval.

6. The comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible supply and demand response according to claim 5 is characterized in that: The energy coupling unit operates the cogeneration hydrogen modeling strategy, and the specific formula is: Among them, P chp,e (t) is the electrical power of the gas turbine; P chp,h (t) is the thermal power of the gas turbine; and are the power corresponding to the natural gas and hydrogen consumed by the gas turbine at time t; and are the electrical and thermal efficiencies of the gas turbine respectively; Y H2,chp (t) is the hydrogen doping ratio at time t.

7. The comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible supply and demand response according to claim 6 is characterized in that: The demand response model building unit runs the demand response strategy, and the specific formula is: Where: n is the type of different response loads on the demand side; when n = c, is the power value of the mth type of load after participating in the transferable demand side response at time t, is the power value of the mth type of transferable load participating in the demand side response at time t; when n = d, is the power value of the mth type of load after participating in the alternative demand-side response at time t, is the power value of the mth type of replaceable load participating in the demand side response at time t; and is a binary parameter, indicating the parameters of the m-th load transfer in and out; and The lower and upper power limits for participating in demand-side response.

8. The comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible supply and demand response according to claim 7 is characterized in that: The low-carbon economic dispatch objective function construction unit runs the objective function construction strategy, and the specific formula is: F=min(F buy +F c +F f +F w +F d +F ex ); Among them, F buy is the energy purchase cost, F c is the carbon trading cost, F f is the carbon sequestration cost, F w is the wind curtailment cost, F d is the DR compensation cost, F ex is the grid interaction cost.

9. The comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible supply and demand response according to claim 8 is characterized in that: The low-carbon economic dispatch objective function construction unit also includes an operation objective function constraint strategy, which includes a wind power output constraint sub-strategy, a power balance constraint sub-strategy, a carbon capture constraint sub-strategy, an energy storage constraint sub-strategy, a gas unit climbing and output constraint sub-strategy, a gas boiler climbing and output constraint sub-strategy, an improved P2G energy consumption upper and lower limits and climbing constraint sub-strategy, and a tie line power constraint sub-strategy.

10. The comprehensive energy low-carbon optimization system based on multi-energy coupling and flexible supply and demand response according to claim 9 is characterized in that: The model conversion module runs a function linearization processing strategy, which performs piecewise linearization processing on the square term in the formula of the carbon emission cost calculation strategy, including the following specific steps: Step 1: Divide the function into Q intervals and select Q+1 segmentation points [r1, r2, …, r Q+1 ]. Step 2: Add Q binary auxiliary variables [z1, z2, ··, z Q ] and Q+1 continuous auxiliary variables [w1, w2, …, w Q+1 ], where the variables satisfy: Step 3: Convert the nonlinear function into a linear expression:

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