Power grid low-carbon scheduling method considering operation behavior of carbon-containing capture gas turbine

Abstract

The invention discloses a power grid low-carbon scheduling method considering the operation behavior of a carbon capture gas turbine, and relates to the field of power grid low-carbon scheduling, comprising the following steps: constructing an operation energy consumption mathematical model of the carbon capture gas turbine; the operation mode of the carbon-containing capture gas turbine comprises a flue gas diversion mode and a liquid storage mode. Based on the operation energy consumption mathematical model, constructing a power grid low-carbon dispatching model; comprising the steps of constructing a power grid operation pre-scheduling model and power grid operation constraint conditions; an uncertain scene scheduling model considering wind and light output prediction errors; and solving the power grid low-carbon scheduling model, and generating a power grid low-carbon scheduling strategy. According to the method, the adjustment flexibility of the gas turbine is improved through a flue gas flow division and liquid storage type operation mode, and economic, safe and low-carbon target collaboration is achieved by combining a mathematical model and two-stage scheduling optimization; and a wind and light output model is constructed, uncertainty is quantified to reduce the wind and light abandoning rate, and new energy consumption and efficient operation of a low-carbon power grid are supported.

Description

Technical Field

[0001] The present invention relates to the field of low-carbon dispatching of power grids, and more particularly to a low-carbon dispatching method for power grids that takes into account the operating behavior of carbon-capture gas turbines. Background Art

[0002] As the global energy structure transitions toward a low-carbon future, power systems urgently need to achieve carbon emission reduction targets through optimized scheduling. Coordinated scheduling of carbon capture gas turbines and renewable energy sources is a key technical direction. However, existing technologies still have the following shortcomings:

[0003] Existing carbon capture gas turbines mostly use a fixed operating mode, relying solely on a single carbon capture process and lacking the coordinated design of flue gas diversion and liquid storage operation. Flue gas diversion can adjust the carbon capture rate through a bypass valve, and liquid storage operation can dynamically balance the regeneration load through a liquid storage tank. However, existing technologies do not combine the two, resulting in limited flexibility of carbon capture devices. For example, during peak load periods, carbon capture energy consumption cannot be quickly reduced to increase power generation, and during low load periods, it is difficult to improve carbon capture efficiency to reduce carbon emissions. This results in energy waste and economic losses, and seriously restricts the system's ability to respond to fluctuations in renewable energy and changes in load peaks and valleys.

[0004] Existing energy consumption models for gas turbines with carbon capture ignore the dynamic characteristics of multiple operating modes, such as the impact of flue gas split ratios and changes in the liquid level in the liquid storage tank on carbon capture energy consumption, and simply describe complex coupling relationships using linearized or static parameters. Furthermore, the models fail to incorporate multi-dimensional boundary conditions such as branch power flow constraints and safe operation constraints, resulting in significant deviations between scheduling strategies and actual operations. For example, the dynamic relationship between the volume and molar mass of the liquid in the liquid storage tank is not considered, making it impossible to accurately calculate regeneration energy consumption, ultimately affecting the economic efficiency and carbon emission reduction effectiveness of the scheduling plan.

[0005] Existing low-carbon grid dispatch methods lack robust optimization frameworks and quantitative models when dealing with wind and solar output forecast errors. They rely solely on deterministic scenarios or simple probability distributions, making them incapable of coping with extreme fluctuations. Furthermore, the objective function focuses solely on a single economic cost or carbon emission reduction indicator, failing to fully incorporate multiple factors such as the penalty costs for wind and solar turbine curtailment, the main grid's electricity purchase costs, network loss costs, load revenue, and carbon trading profits. This results in partially optimized dispatch strategies. For example, ignoring the penalty costs for curtailment can lead to over-reliance on renewable energy at the expense of system stability. Ignoring carbon trading profits makes it difficult to incentivize the application of low-carbon technologies through market mechanisms, hindering the achievement of overall optimization goals.

[0006] Therefore, how to design a low-carbon dispatching method for the power grid that takes into account the operating behavior of carbon capture gas turbines, and can take into account carbon capture flexibility, model accuracy, wind and solar uncertainty quantification capabilities, and multi-objective collaborative optimization is an issue that technical personnel in this field urgently need to solve. Summary of the Invention

[0007] In view of this, the present invention provides a low-carbon dispatching method for power grids that takes into account the operating behavior of carbon-capture gas turbines. The flexible adjustment capability of carbon-capture gas turbines is improved through flue gas diversion and liquid storage operation modes. The refined mathematical model and phased dispatching strategy are combined to collaboratively optimize the economy, safety and low-carbon goals of the power grid. At the same time, a wind and solar output model is constructed to quantify the uncertainty of new energy and optimize the dispatching of extreme scenarios, supporting the high proportion of renewable energy consumption and the efficient and stable operation of the low-carbon power grid.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A low-carbon dispatch method for a power grid considering the operating behavior of a gas turbine containing carbon capture includes the following steps:

[0010] S1. Constructing a mathematical model of energy consumption for operation of a carbon capture gas turbine; the carbon capture gas turbine operation modes include flue gas splitting and liquid storage;

[0011] S2. Constructing a low-carbon dispatch model for the power grid based on the operation energy consumption mathematical model; including:

[0012] S21. Constructing a grid operation pre-dispatch model and grid operation constraints;

[0013] S22. Construct an uncertain scenario scheduling model considering wind and solar power output prediction errors;

[0014] S3. Solve the low-carbon dispatch model of the power grid and generate a low-carbon dispatch strategy for the power grid.

[0015] Preferably, in S1, the flue gas diversion operation mode is achieved by providing a flue gas bypass on the pipeline between the gas turbine and the carbon capture device, the output end of the flue gas bypass leads to the atmosphere, and the amount of flue gas entering the carbon capture device is controlled by adjusting the opening of the flue gas bypass valve.

[0016] Preferably, in S1, the liquid storage operation mode is achieved by arranging a liquid storage tank between the absorption tower and the regeneration tower of the carbon capture device, wherein the liquid storage tank includes a rich liquid tank and a lean liquid tank, and the regeneration load of the regeneration tower is controlled by adjusting the liquid levels of the rich liquid tank and the lean liquid tank.

[0017] Preferably, in S1, the operation energy consumption mathematical model is expressed as:

[0018]

[0019] Among them, P N 、P total 、P C and P DThey represent the net output power of the gas turbine with carbon capture, the total power generation, the operating loss of the carbon capture device, and the fixed energy consumption respectively; λ represents the energy consumption of the carbon capture device per unit CO2 processed; and E CG They represent the total mass of CO2 processed by the carbon capture device and the mass of CO2 supplied to the carbon capture device by the liquid storage tank; β represents the carbon capture efficiency; δ represents the flue gas split ratio; e g represents the amount of CO2 released per unit of electricity produced; η represents the upper limit working state coefficient of the carbon capture device; P total,max Represents the upper limit of the total power generation capacity of the gas turbine with carbon capture.

[0020] Preferably, in S21, the objective function of the grid operation pre-dispatch model is to minimize the total operating cost:

[0021]

[0022] Among them, C DS represents the total cost of distribution network operation, C DG represents the operating cost of wind and solar power units, C MT represents the operating cost of the gas turbine unit with carbon capture, C M represents the main grid interactive power purchase cost, C loss represents the network loss cost, C L represents the load electricity revenue, Indicates that the system carbon trading is profitable.

[0023] Preferably, in S21, the power grid operation constraint condition includes a branch power flow constraint:

[0024]

[0025]

[0026] in, and Respectively represent the square of the voltage of node i and node j during period t; r ij and x ij Represent the resistance and reactance of branch ij respectively; P ij,t and Q ij,t They represent the active power and reactive power flowing into branch ij during period t respectively; represents the square of the current flowing through branch ij during period t; p j,t and q j,t They represent the active load and reactive load of node j in period t respectively; P jk,t and Q jk,t They represent the active power and reactive power flowing from node j to the connected node k during period t.

[0027] Preferably, in said S21, the grid operation constraint condition includes the carbon capture gas turbine operation constraint:

[0028]

[0029] in, They represent the net output power, total power generation and fixed energy consumption of the hth carbon capture gas turbine in period t respectively; They represent the total mass of CO2 processed by the hth carbon capture device during period t and the mass of CO2 supplied from the liquid storage tank to the carbon capture device; δ h,t represents the flue gas split ratio of the hth carbon capture device during period t; e g,h represents the amount of CO2 released corresponding to h units of electricity produced; M represents the volume of solution corresponding to the CO2 produced by the h-th liquid storage tank during time period t; MEA and Represent the molar masses of ethanolamine and carbon dioxide respectively; θ, μ l and ρ l They represent the CO2 desorption amount from the rich liquid tank to the lean liquid tank, the liquid concentration of the storage tank, and the liquid density of the storage tank respectively; and They represent the storage volume of the hth rich liquid tank and lean liquid tank in time period t respectively; and They represent the storage volume of the hth rich liquid tank and the lean liquid tank in time period t-1 respectively; represents the storage upper limit of the h-th liquid storage tank.

[0030] Preferably, in S21, the power grid operation constraint condition includes a safe operation constraint:

[0031]

[0032] Among them, U i,max and U i,min Respectively represent the upper and lower limits of the voltage at node i; I ij,max and I ij,min They represent the upper and lower limits of the current allowed to flow through branch ij respectively.

[0033] Preferably, the S22 includes: constructing a wind and solar power output operation mathematical model taking into account wind and solar power output prediction errors:

[0034]

[0035] in, and They represent the photovoltaic grid-connected power, predicted output and maximum prediction error at node i during period t respectively; and They represent the wind turbine grid-connected power, predicted output and maximum prediction error at node i during period t respectively; and is a 0-1 variable, representing the auxiliary variable of the photovoltaic output error at node i during period t; and is a 0-1 variable, representing the auxiliary variable of the wind turbine output error at node i during period t; Γ PV and Γ WT Respectively represent the adjustable error parameters of photovoltaic and wind turbine output.

[0036] Preferably, the S22 further includes: setting an uncertain scene scheduling objective function:

[0037]

[0038] Where, ΔC DG , ΔC MT , ΔC M , ΔC loss , ΔC L and They respectively represent the operating costs of wind and solar units, the operating costs of carbon capture gas turbine units, the main grid interactive power purchase costs, network loss costs, load electricity revenue and system carbon trading profits when the output values ​​of photovoltaic and wind turbines are minimum and the load electricity consumption is maximum.

[0039] It can be seen from the above technical solution that compared with the prior art, the technical solution of the present invention has the following advantages:

[0040] Beneficial effects:

[0041] 1. This method significantly enhances the flexible regulation capability of the carbon capture gas turbine by introducing two operating modes: flue gas diversion and liquid storage. Flue gas diversion controls the carbon capture rate by adjusting the bypass valve opening, while liquid storage operation adjusts the regeneration load by adjusting the liquid level in the storage tank. This enables the system to dynamically adjust carbon capture energy consumption and power generation according to grid load peaks and valleys, effectively responding to load fluctuations and changes in renewable energy output. This addresses the limited regulation capability of existing technologies due to a single operating mode.

[0042] 2. By constructing a mathematical model that comprehensively considers parameters such as the gas turbine's net output power, the carbon capture unit's energy consumption, and the dynamic characteristics of the liquid storage tank, the operating behavior of the gas turbine with carbon capture is accurately characterized. Incorporating multi-dimensional constraints such as branch power flow constraints and safe operation constraints, along with a phased dispatch model, this approach achieves coordinated optimization of the grid's economic, environmental, and safety performance, overcoming the inaccuracy of traditional models that ignore the energy consumption characteristics of multiple operating modes.

[0043] 3. By establishing a scheduling model for uncertain scenarios that considers wind and solar output forecast errors, introducing 0-1 auxiliary variables and adjustable error parameters, quantifying the randomness and volatility of photovoltaic and wind turbine output, and constructing a robust optimization objective function, this method optimizes scheduling strategies under extreme wind and solar output fluctuations, reduces wind and solar curtailment rates, and improves the grid's adaptability to renewable energy consumption. This addresses the existing problem of a lack of mathematical models that effectively address wind and solar forecast errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0045] Figure 1 A flow chart of a low-carbon dispatch method for a power grid that takes into account the operating behavior of a carbon capture gas turbine provided by an embodiment of the present invention;

[0046] Figure 2 Schematic diagram of the flexible and adjustable operation mode of a carbon capture gas turbine provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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 efforts are within the scope of protection of the present invention.

[0048] like Figure 1 As shown, this embodiment provides a low-carbon dispatch method for a power grid considering the operating behavior of a carbon-capture gas turbine, comprising the following steps:

[0049] S1. Constructing a mathematical model of energy consumption for operation of a carbon capture gas turbine; the carbon capture gas turbine operation modes include flue gas splitting and liquid storage;

[0050] S2. Constructing a low-carbon dispatch model for the power grid based on the operation energy consumption mathematical model; including:

[0051] S21. Constructing a grid operation pre-dispatch model and grid operation constraints;

[0052] S22. Construct an uncertain scenario scheduling model considering wind and solar power output prediction errors;

[0053] S3. Solve the low-carbon dispatch model of the power grid and generate a low-carbon dispatch strategy for the power grid.

[0054] This method significantly improves the flexible adjustment capability of the carbon capture gas turbine by introducing flue gas diversion and liquid storage operation mode, enabling it to dynamically balance carbon capture energy consumption and power generation demand; based on refined mathematical models and phased scheduling strategies, it takes into account the economy, safety and low-carbon goals of power grid operation, effectively improving the scheduling accuracy and multi-constraint collaborative optimization capabilities; at the same time, by constructing a robust model of wind and solar output prediction errors, quantifying the randomness of new energy and optimizing the scheduling strategy under extreme scenarios, it enhances the system's adaptability to wind and solar fluctuations, reduces the wind and solar curtailment rate, and provides technical support for the high-proportion new energy consumption and low-carbon power grid construction.

[0055] The following further describes each step in the above method in detail;

[0056] In this embodiment S1, a mathematical model of energy consumption of a carbon capture gas turbine is constructed;

[0057] like Figure 2 As shown, the carbon capture gas turbine operation modes include flue gas splitting and liquid storage;

[0058] The flue gas diversion mode is achieved by installing a flue gas bypass in the pipeline between the gas turbine and the carbon capture unit. The output of the flue gas bypass is connected to the atmosphere, and the amount of flue gas entering the carbon capture unit is controlled by adjusting the opening of the flue gas bypass valve. During peak grid load periods, the flue gas bypass valve opening can be increased to reduce the amount of flue gas entering the carbon capture unit, reducing its energy consumption and increasing the gas turbine's output power. During off-peak grid load periods, the flue gas bypass valve opening can be decreased to increase the amount of flue gas entering the carbon capture unit and improve the carbon capture rate.

[0059] Liquid storage operation is achieved by installing a liquid storage tank between the carbon capture unit's absorption tower and regeneration tower. The storage tank consists of a rich liquid tank and a lean liquid tank. The regeneration load of the regeneration tower is controlled by adjusting the liquid levels in the rich and lean liquid tanks. The rich liquid tank stores the carbon dioxide-rich solution flowing out of the bottom of the absorption tower, while the lean liquid tank stores the carbon dioxide-lean solution flowing out of the bottom of the regeneration tower. By controlling the liquid levels in the rich and lean liquid tanks, the regeneration load of the regeneration tower can be adjusted, thereby adjusting the energy consumption of the carbon capture unit. During peak grid load periods, the flow of rich liquid into the regeneration tower can be reduced, reducing the regeneration tower's energy consumption and increasing the gas turbine's output power. During off-peak grid load periods, the flow of rich liquid into the regeneration tower can be increased, improving the carbon capture rate.

[0060] Furthermore, the mathematical model of operating energy consumption is expressed as:

[0061]

[0062] Among them, P N 、P total 、P C and P D They represent the net output power of the gas turbine with carbon capture, the total power generation, the operating loss of the carbon capture device, and the fixed energy consumption respectively; λ represents the energy consumption of the carbon capture device per unit CO2 processed; and E CG They represent the total mass of CO2 processed by the carbon capture device and the mass of CO2 supplied to the carbon capture device by the liquid storage tank; β represents the carbon capture efficiency; δ represents the flue gas split ratio; e g represents the amount of CO2 released per unit of electricity produced; η represents the upper limit working state coefficient of the carbon capture device; P total,max Represents the upper limit of the total power generation capacity of the gas turbine with carbon capture.

[0063] In this embodiment S2, based on the operation energy consumption mathematical model, a low-carbon dispatch model for the power grid is constructed, which includes:

[0064] S21. Constructing a grid operation pre-dispatch model and grid operation constraints;

[0065] S22. Construct an uncertain scenario scheduling model considering wind and solar power output prediction errors;

[0066] Among them, the objective function of the grid operation pre-dispatch model is to minimize the total operating cost:

[0067]

[0068] Among them, C DS represents the total cost of distribution network operation; C DG 、C MT 、C M 、C loss 、C L and They are the operating cost of wind and solar units, the operating cost of gas turbine units with carbon capture, the main grid interactive power purchase cost, network loss cost, load electricity income and system carbon trading profit; p WT and p PV are the unit output cost of wind turbine and the unit output cost of photovoltaic power respectively; c WT and c PV They are the penalty unit price for wind curtailment and the penalty unit price for solar curtailment respectively; and are the amount of wind and solar power abandoned during the period t and t, respectively; c MT is the output cost coefficient of the gas turbine with carbon capture, is the total power generation of the h-th liquid storage tank during period t; G CYis the price of the liquid storage tank in the carbon capture gas turbine; ω and N C are the depreciation rate and depreciation period of the liquid storage tank respectively; c md Real-time electricity price for the main distribution network; and are the power purchased and sold by the main distribution network during period t; c loss is the unit cost of network loss, r ij represents the resistance of branch ij, represents the square of the current in branch ij during period t; c ds Real-time electricity price for distribution network load; is the load power of the i-th node in period t, and are the charging price and discharging price set by the aggregator during period t, and are the charging power and discharging power of the kth charging station in period t respectively; σ cp is the carbon trading price; and are the carbon quota and carbon emissions of the hth gas turbine with carbon capture, respectively.

[0069] Furthermore, the grid operation constraints include branch power flow constraints:

[0070]

[0071] in, and Respectively represent the square of the voltage of node i and node j during period t; r ij and x ij Represent the resistance and reactance of branch ij respectively; P ij,t and Q ij,t They represent the active power and reactive power flowing into branch ij during period t respectively; represents the square of the current flowing through branch ij during period t; p j,t and q j,t They represent the active load and reactive load of node j in period t respectively; P jk,t and Q jk,t They represent the active power and reactive power flowing from node j to the connected node k during period t.

[0072] Furthermore, the grid operation constraints include the operation constraints of the gas turbine with carbon capture:

[0073]

[0074] in, They represent the net output power, total power generation and fixed energy consumption of the hth carbon capture gas turbine in period t respectively; They represent the total mass of CO2 processed by the hth carbon capture device during period t and the mass of CO2 supplied from the liquid storage tank to the carbon capture device; δ h,t represents the flue gas split ratio of the hth carbon capture device during period t; e g,h represents the amount of CO2 released corresponding to h units of electricity produced; M represents the volume of solution corresponding to the CO2 produced by the h-th liquid storage tank during time period t; MEA and Represent the molar masses of ethanolamine and carbon dioxide respectively; θ, μ l and ρ l They represent the CO2 desorption amount from the rich liquid tank to the lean liquid tank, the liquid concentration of the storage tank, and the liquid density of the storage tank respectively; and They represent the storage volume of the hth rich liquid tank and lean liquid tank in time period t respectively; and They represent the storage volume of the hth rich liquid tank and the lean liquid tank in time period t-1 respectively; represents the storage upper limit of the h-th liquid storage tank.

[0075] Furthermore, the grid operation constraints include safe operation constraints:

[0076]

[0077] Among them, U i,max and U i,min Respectively represent the upper and lower limits of the voltage at node i; I ij,max and I ij,min They represent the upper and lower limits of the current allowed to flow through branch ij respectively.

[0078] Furthermore, S22 includes constructing a wind and solar power output operation mathematical model taking into account wind and solar power output prediction errors:

[0079]

[0080] in, and They represent the photovoltaic grid-connected power, predicted output and maximum prediction error at node i during period t respectively; and They represent the wind turbine grid-connected power, predicted output and maximum prediction error at node i during period t respectively; and is a 0-1 variable, representing the auxiliary variable of the photovoltaic output error at node i during period t; and is a 0-1 variable, representing the auxiliary variable of the wind turbine output error at node i during period t; Γ PV and Γ WT Respectively represent the adjustable error parameters of photovoltaic and wind turbine output.

[0081] Furthermore, S22 also includes setting an uncertain scenario scheduling objective function:

[0082]

[0083] Where, ΔC DG , ΔC MT , ΔC M , ΔC loss , ΔC L and They respectively represent the operating costs of wind and solar units, the operating costs of carbon capture gas turbine units, the main grid interactive power purchase costs, network loss costs, load electricity revenue and system carbon trading profits when the output values ​​of photovoltaic and wind turbines are minimum and the load electricity consumption is maximum.

[0084] In this embodiment, S3, solving the low-carbon dispatch model of the power grid and generating a low-carbon dispatch strategy for the power grid;

[0085] Specifically, a two-stage robust optimization framework is used to solve the low-carbon dispatch model of the power grid. The first stage is based on the pre-dispatch model, with the goal of minimizing the total operating cost, and combines the carbon capture gas turbine operating energy consumption model, branch flow constraints, safety constraints, etc. to construct a deterministic optimization problem. The initial dispatch plan is solved by nonlinear programming methods (such as the interior point method), including decision variables such as gas turbine output, liquid level in the storage tank, and main grid power purchase. In the second stage, based on the wind and solar output forecast error, an uncertain scenario set is constructed, and extreme fluctuation scenarios (such as minimum output and maximum load) are incorporated into the robust optimization model. The KKT condition and duality theory are used to transform the original problem into a mixed integer second-order cone programming (MISOCP) model, and the column constraint generation algorithm is used to iteratively solve and dynamically correct the pre-dispatch plan to ensure the feasibility and economy of the strategy in extreme scenarios.

[0086] After the solution is complete, the two-stage optimization results are combined to generate the final scheduling strategy, including time-based gas turbine operating modes (such as flue gas split ratios and liquid storage / release instructions), wind and solar power generation plans, and power purchase and sales from the main grid. The effectiveness of the strategy is verified through simulations: by comparing traditional methods, indicators such as the carbon capture energy consumption reduction rate and wind and solar power curtailment rates are verified. Sensitivity analysis is used to evaluate the model's robustness to parameter changes, ensuring its engineering practicality under multi-constraint coupling, ultimately achieving the coordinated optimization of economic efficiency, safety, and low-carbon goals.

[0087] This embodiment significantly improves the flexibility and scheduling accuracy of carbon capture gas turbines through a dynamic adjustment mechanism for flue gas diversion and liquid storage operation, combined with a two-stage robust optimization framework and a multi-dimensional constraint coupling model. This effectively balances carbon capture energy consumption with power generation demand. Furthermore, through quantitative modeling of wind and solar output uncertainty and optimization for extreme scenarios, it significantly reduces the wind and solar curtailment rate and enhances the grid's ability to accommodate a high proportion of renewable energy. Simulation verification and sensitivity analysis confirm that this method can achieve coordinated optimization of economic efficiency, safety, and low-carbon goals, providing reliable technical support for the low-carbon transformation of smart grids.

[0088] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. References to the same or similar parts between the various embodiments are sufficient. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For relevant parts, refer to the method description.

[0089] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A low-carbon dispatching method for a power grid considering the operating behavior of a carbon capture gas turbine, characterized in that: The following steps are involved: S1. Construct a mathematical model of energy consumption for the operation of a gas turbine with carbon capture; The carbon capture gas turbine operation modes include flue gas splitting and liquid storage; S2. Constructing a low-carbon dispatch model for the power grid based on the operation energy consumption mathematical model; including: S21. Constructing a grid operation pre-dispatch model and grid operation constraints; S22. Construct an uncertain scenario scheduling model considering wind and solar power output prediction errors; S3. Solve the low-carbon dispatch model of the power grid and generate a low-carbon dispatch strategy for the power grid.

2. A low-carbon dispatching method for a power grid considering the operating behavior of a carbon capture gas turbine according to claim 1, characterized in that: In S1, the flue gas diversion operation mode is achieved by providing a flue gas bypass on the pipeline between the gas turbine and the carbon capture device, the output end of the flue gas bypass is connected to the atmosphere, and the amount of flue gas entering the carbon capture device is controlled by adjusting the opening of the flue gas bypass valve.

3. A low-carbon dispatching method for a power grid considering the operating behavior of a carbon capture gas turbine according to claim 1, characterized in that: In S1, the liquid storage operation mode is achieved by arranging a liquid storage tank between the absorption tower and the regeneration tower of the carbon capture device. The liquid storage tank includes a rich liquid tank and a lean liquid tank. The regeneration load of the regeneration tower is controlled by adjusting the liquid levels of the rich liquid tank and the lean liquid tank.

4. A low-carbon dispatching method for a power grid considering the operating behavior of a carbon capture gas turbine according to claim 1, characterized in that: In S1, the mathematical model of operating energy consumption is expressed as: Among them, P N 、P total 、P C and P D They represent the net output power of the gas turbine with carbon capture, the total power generation, the operating loss of the carbon capture device, and the fixed energy consumption respectively; λ represents the energy consumption of the carbon capture device per unit CO2 processed; and E CG They represent the total mass of CO2 processed by the carbon capture device and the mass of CO2 supplied to the carbon capture device by the liquid storage tank; β represents the carbon capture efficiency; δ represents the flue gas split ratio; e g represents the amount of CO2 released per unit of electricity produced; η represents the upper limit working state coefficient of the carbon capture device; P total,max Represents the upper limit of the total power generation capacity of the gas turbine with carbon capture.

5. The low-carbon dispatching method for a power grid considering the operating behavior of a carbon capture gas turbine according to claim 1, characterized in that: In S21, the objective function of the grid operation pre-dispatch model is to minimize the total operating cost: Among them, C DS represents the total cost of distribution network operation, C DG represents the operating cost of wind and solar power units, C MT represents the operating cost of the gas turbine unit with carbon capture, C M represents the main grid interactive power purchase cost, C loss represents the network loss cost, C L represents the load electricity revenue, Indicates that the system carbon trading is profitable.

6. A low-carbon dispatching method for a power grid considering the operation behavior of a carbon capture gas turbine according to claim 1, characterized in that: In S21, the grid operation constraints include branch power flow constraints: in, and Respectively represent the square of the voltage of node i and node j during period t; r ij and x ij Represent the resistance and reactance of branch ij respectively; P ij,t and Q ij,t They represent the active power and reactive power flowing into branch ij during period t respectively; represents the square of the current flowing through branch ij during period t; p j,t and q j,t They represent the active load and reactive load of node j in period t respectively; P jk,t and Q jk,t They represent the active power and reactive power flowing from node j to the connected node k during period t.

7. A low-carbon dispatching method for a power grid considering the operating behavior of a carbon capture gas turbine according to claim 1, characterized in that: In S21, the grid operation constraints include the carbon capture gas turbine operation constraints: in, They represent the net output power, total power generation and fixed energy consumption of the hth carbon capture gas turbine in period t respectively; They represent the total mass of CO2 processed by the hth carbon capture device during period t and the mass of CO2 supplied from the liquid storage tank to the carbon capture device; δ h,t represents the flue gas split ratio of the hth carbon capture device during period t; e g,h represents the amount of CO2 released corresponding to h units of electricity produced; M represents the volume of solution corresponding to the CO2 produced by the h-th liquid storage tank during time period t; MEA and Represent the molar masses of ethanolamine and carbon dioxide respectively; θ, μ l and ρ l They represent the CO2 desorption amount from the rich liquid tank to the lean liquid tank, the liquid concentration of the storage tank, and the liquid density of the storage tank respectively; and They represent the storage volume of the hth rich liquid tank and lean liquid tank in time period t respectively; and They represent the storage volume of the hth rich liquid tank and the lean liquid tank in time period t-1 respectively; represents the storage upper limit of the h-th liquid storage tank.

8. The low-carbon dispatching method for a power grid considering the operation behavior of a carbon capture gas turbine according to claim 1, characterized in that: In S21, the grid operation constraints include safe operation constraints: Among them, U i,max and U i,min Respectively represent the upper and lower limits of the voltage at node i; I ij,max and I ij,min They represent the upper and lower limits of the current allowed to flow through branch ij respectively.

9. The low-carbon dispatch method for power grid considering the operation behavior of carbon capture gas turbines according to claim 1, characterized in that: The S22 includes: constructing a wind and solar power output operation mathematical model taking into account wind and solar power output prediction errors: in, and They represent the photovoltaic grid-connected power, predicted output and maximum prediction error at node i during period t respectively; and They represent the wind turbine grid-connected power, predicted output and maximum prediction error at node i during period t respectively; and is a 0-1 variable, representing the auxiliary variable of the photovoltaic output error at node i during period t; and is a 0-1 variable, representing the auxiliary variable of the wind turbine output error at node i during period t; Γ PV and Γ WT Respectively represent the adjustable error parameters of photovoltaic and wind turbine output.

10. The low-carbon dispatching method for a power grid considering the operation behavior of a carbon capture gas turbine according to claim 1, characterized in that: The step S22 further includes: setting an uncertain scene scheduling objective function: Where, ΔC DG , ΔC MT , ΔC M , ΔC loss , ΔC L and They respectively represent the operating costs of wind and solar units, the operating costs of carbon capture gas turbine units, the main grid interactive power purchase costs, network loss costs, load electricity revenue and system carbon trading profits when the output values ​​of photovoltaic and wind turbines are minimum and the load electricity consumption is maximum.

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