Method for planning of electric-gas integrated energy system based on cooperative game

Abstract

The application discloses a power-gas integrated energy system planning method based on cooperative game, comprising the following steps: S1, establishing a multi-agent power-gas integrated energy system model comprising a carbon capture power plant, a renewable energy source and a carbon utilization unit; S2, constructing a distributed optimization objective function and operation constraints of each agent model; S3, based on a cooperative game alliance, constructing a cooperative game model through distributed optimization according to the distributed optimization objective function and operation constraints; S4, decomposing and cooperatively solving the cooperative game model by using an alternating direction multiplier method, and generating power-carbon bilateral transaction volume and dynamic negotiation price. The application can process distributed decision-making behavior among multiple agents, truly depict the physical interaction relationship among multiple energy flows in the power-gas-carbon system, improve the physical reliability and engineering applicability of the model, maximize the benefits of each agent, take into account the privacy protection demand and cooperative residual value distribution, and improve the system operation efficiency and fairness.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system planning, in particular to a power-gas integrated energy system planning method based on cooperative game. BACKGROUND

[0002] Carbon dioxide (CO2) is one of the main greenhouse gases leading to global warming. Carbon capture, utilization and storage (CCUS) is a key technology for reducing and removing post-emission CO2, and is also a key link to achieve the zero-carbon goal. In addition, the coupling technology of proton exchange membrane electrolysis cell (EC) and Sabatier reactor (SR) into the power-gas integrated energy system provides a promising solution for deep decarbonization through the capture and subsequent utilization of carbon dioxide, which has gradually become a new direction worthy of research.

[0003] However, the existing power-gas integrated energy system planning rarely simulates the physical and operating processes of CCUS in detail, and is mostly based on a centralized framework. The centralized framework is difficult to coordinate the conflicts of interest of multiple subjects, ignores the different ownership and independent decision-making behaviors between entities, and thus limits its applicability in actual scenarios, and the system operation efficiency and fairness are low. SUMMARY

[0004] The purpose of the present application is to solve the technical problem of low applicability, operation efficiency and fairness of the existing power-gas integrated energy system, and to propose a power-gas integrated energy system planning method based on cooperative game.

[0005] In order to achieve the above purpose, the present application adopts the following technical scheme:

[0006] A power-gas integrated energy system planning method based on cooperative game, comprising the following steps: S1, establishing a multi-agent power-gas integrated energy system model including carbon capture power plants, renewable energy and carbon utilization units; S2, constructing distributed optimization objective functions and operating constraints of carbon capture power plant models, renewable energy models and carbon utilization unit models; S3, based on cooperative game alliance, according to the distributed optimization objective functions and operating constraints, constructing a cooperative game model through distributed optimization; S4, using the alternating direction multiplier method to decompose and cooperatively solve the cooperative game model, generating electricity-carbon bilateral trading volume and dynamic negotiation price through iterative updating of Lagrange multipliers and penalty factors, for cooperative planning of multi-agent power-gas integrated energy system.

[0007] In some embodiments, in step S2, the operation constraints of the carbon capture power plant model include unit start-stop constraints, material flow dynamic equations in the absorption-regeneration process, and carbon storage pressure balance equations; the operation constraints of the renewable energy model include output prediction constraints, local load supply-demand balance equations, and power trading revenue equations; and the operation constraints of the carbon utilization unit model include electrolysis / synthesis reaction efficiency constraints, carbon resource procurement path optimization equations, and power-gas coupled trading equations.

[0008] In some embodiments, the material flow dynamic equations in the absorption-regeneration process include material flow dynamic equations in an absorption tower and material flow dynamic equations in a regeneration tower, the material flow dynamic equations in the absorption tower adopt one-dimensional convection-diffusion partial differential equations, and specifically include the spatiotemporal evolution relationship between solvent flow rate and CO2 concentration; the material flow dynamic equations in the regeneration tower are associated with the CO2 concentration gradients of rich liquid and lean liquid through a desorption coefficient; and the carbon storage pressure balance equation calculates real-time storage mass based on a gas state equation and a fixed storage tank volume.

[0009] In some embodiments, in step S3, the conditions that the cooperative game alliance needs to meet include group rationality and individual rationality; the group rationality is that the cooperative revenue of the alliance as a whole should not be lower than the sum of the respective revenues of each subject in a non-cooperative situation; and the individual rationality is that the revenue obtained by each participating subject in the alliance should not be lower than its independent revenue when it does not participate in the alliance.

[0010] In some embodiments, the construction of the cooperative game model through distributed optimization includes constructing the cooperative game model by maximizing Nash product, and the Nash product formula is as follows:

[0011]

[0012] wherein, N represents the total number of agents participating in the game, Pi represents the i-th agent, the actual revenue in the game, Pi represents the i-th agent, the optimal revenue obtained by the i-th agent in a non-cooperative situation, i.e., the game interruption point of the i-th agent. The difference represents the revenue increment obtained by the i-th agent through participating in the cooperative game, i.e., the profit surplus of the i-th agent.

[0013] In some embodiments, step S4 includes: S41, according to the geometric mean inequality, equivalently transforming the cooperative game model into a coalition revenue maximization sub-problem; S42, decomposing the coalition revenue maximization sub-problem into distributed optimization sub-models of each subject and cooperatively solving them by using an alternating direction multiplier method, while introducing Lagrange multipliers and penalty factors for iterative updating; and S43, outputting a global optimal solution that satisfies the fairness of coalition revenue distribution.

[0014] In some embodiments, the absorption tower employs a carbon capture method following the combustion of monoethanolamine, and the one-dimensional convection-diffusion partial differential equation is as follows:

[0015] in, This indicates the molar concentration of CO2 in the solution of the absorption tower, which is related to the distance from the top. x and time t Related, This indicates the time-varying flow rate of the solution in the absorption tower. Indicates the absorption coefficient at the A-end. This indicates the mass of CO2 input at end A.

[0016] The dynamic equation for the material flow within the regeneration tower, relating the CO2 concentration gradient between the rich and lean solutions via the desorption coefficient, is as follows:

[0017] in, This indicates the molar concentration of CO2 in the solution of the regeneration tower, which is related to the distance from the top. x and time t Related, This indicates the time-varying flow rate of the solution in the regeneration tower. The desorption coefficient is . This indicates the time step in the absorption tower. Regeneration tower i China E-end t CO2 mass at any time Regeneration tower i Mid-F end t CO2 mass at any time This indicates the density of the MEA solution. This represents the cross-sectional area of ​​the regeneration tower. Indicates the liquid loss rate of the regeneration tower. Regeneration tower i mid-G end t CO2 mass at any given time.

[0018] The carbon storage pressure balance equation is based on the gas state equation and the formula for calculating real-time storage mass with a fixed tank volume, as follows:

[0019] in, Indicates the volume of a fixed storage tank. The molar mass of CO2 The gas constant for CO2 is... t The volume of CO2 gas at any given time Indicates the interior of the storage tank t-1 The volume of CO2 gas at any given time Indicates storage tanki internal t pressure at time t, denotes the temperature inside the storage tank i internal t derivative of pressure at time t with respect to time, denotes the temperature inside the storage tank denotes the regeneration column i at height H t CO2 mass at time t, denotes the quadratic coefficient with respect to pressure, denotes the linear coefficient with respect to pressure, denotes the constant with respect to pressure, denotes the quadratic coefficient with respect to pressure, denotes the linear coefficient with respect to pressure, denotes the constant with respect to pressure.

[0020] In some embodiments, the one-dimensional convection-diffusion partial differential equation is discretized using finite difference method, and the formula is as follows:

[0021] wherein, denotes the height step in the absorption column.

[0022] In some embodiments, in step S2, the objective function of the carbon capture power plant model includes: power generation cost of a conventional power generation unit, investment cost of a carbon capture and storage system, carbon trading income obtained at a carbon wholesale price, and carbon trading income obtained at a carbon game price by a carbon utilization unit; the objective function of the renewable energy model includes: power sales revenue obtained at an on-grid electricity price, power trading revenue obtained at a trading electricity price, and grid access fee charged by a power company; and the objective function of the carbon utilization unit model includes: gas revenue obtained at a gas price, on-grid power purchase cost, game power purchase cost, CO2 wholesale purchase cost, CO2 game purchase cost, and investment cost of the carbon utilization unit itself.

[0023] In some embodiments, the objective function of the carbon capture power plant model is as follows:

[0024] wherein, denotes carbon trading income obtained at a carbon wholesale price, denotes carbon trading income obtained at a carbon game price, denotes carbon trading income obtained at a carbon wholesale price, denotes carbon trading income obtained at a carbon game price, represents a quadratic cost function of the generated power, represents the discount rate, represents the life cycle of the device , represents the discount factor of the device , represents the power of the carbon capture power plant i at t time, represents the abbreviation of the absorption tower, represents the abbreviation of the regeneration tower, represents the abbreviation of the storage tank.

[0025] The objective function of the renewable energy model is as follows:

[0026] wherein, represents the electricity trading revenue obtained according to the trading electricity price , represents the electricity sales revenue obtained according to the on-grid electricity price , is a quadratic cost function of the grid access power, represents the surplus electricity.

[0027] The objective function of the carbon utilization unit model is as follows:

[0028] wherein, represents the gas revenue obtained according to the gas price , represents the on-grid purchase cost of electricity, represents the electricity trading cost obtained according to the trading electricity price , represents the wholesale purchase cost of CO2, represents the game purchase cost of CO2, represents the unit investment cost of the device , represents the unit investment capacity of the device , represents the abbreviation of the proton exchange membrane electrolysis cell, represents the abbreviation of the Sabatier reactor.

[0029] In some embodiments, in step S4, the generation logic of the dynamic negotiation price comprises: when the industrial electricity price is lower than the on-grid electricity price, the carbon utilization unit preferentially purchases external grid power; when the real-time retail carbon price is lower than the wholesale carbon price, the carbon utilization unit preferentially purchases external carbon resources; during the electricity price / carbon price peak period, trigger the alliance internal game equilibrium transaction, and the dynamic negotiation price is between the subject supply cost and the demand marginal utility.

[0030] The beneficial effects of the present application compared with the prior art include:

[0031] The electric-gas integrated energy system planning method based on cooperative game provided by the present application can process the distributed decision-making behavior among multiple subjects including carbon capture power plants, renewable energy and carbon utilization units by establishing a carbon capture power plant model, a renewable energy model and a carbon utilization unit model, and constructing a distributed optimization objective function and operation constraints of each subject model. At the same time, a cooperative game model is constructed in combination with distributed optimization, and a two-stage distributed optimization solving algorithm based on the alternating direction multiplier method (ADMM) is constructed. In the first stage, the benefits of each subject are maximized, and in the second stage, the electricity and carbon trading prices are jointly determined, taking into account the privacy protection requirements and the cooperative residual value distribution, realizing the fair distribution of the multi-subject cooperative residual, and improving the system operation efficiency and fairness.

[0032] In some embodiments, the present application also has the following beneficial effects:

[0033] By introducing a material flow process model of post-combustion carbon capture based on monoethanolamine (MEA) absorption method, replacing the traditional linear approximation modeling method, the physical interaction between multiple energy flows in the electric-gas-carbon system can be truly depicted, further improving the physical reliability and engineering applicability of the model.

[0034] Other beneficial effects of the embodiments of the present application will be further described below. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 is a flow chart of the electric-gas integrated energy system planning method based on cooperative game of the embodiments of the present application;

[0036] Figure 2 is a multi-subject electric-gas integrated energy system park schematic diagram of the embodiments of the present application;

[0037] Figure 3 is a carbon capture process schematic diagram of the multi-subject electric-gas integrated energy system park of the embodiments of the present application;

[0038] Figure 4 is a CO2 concentration calculation schematic diagram inside the absorption tower of the embodiments of the present application;

[0039] Figure 5 is an intraday trading price schematic diagram of the embodiments of the present application;

[0040] Figure 6 is an intraday electricity trading volume schematic diagram of the embodiments of the present application;

[0041] Figure 7 is an intraday carbon trading volume schematic diagram of the embodiments of the present application;

[0042] Figure 8 A schematic diagram of a distributed algorithm for the profit maximization subproblem based on ADMM of an embodiment of the present application is shown in Figure 1.

[0043] Figure 9 A schematic diagram of a distributed algorithm for the transaction game subproblem based on ADMM of an embodiment of the present application is shown in Figure 2. DETAILED DESCRIPTION

[0044] The present application will be further described below with reference to the drawings in conjunction with the preferred embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0045] It should be noted that the left, right, up, down, top, bottom and other orientation terms in the embodiments are only relative concepts or are referenced to the normal use state of the product, and should not be considered as limiting.

[0046] An electric-gas integrated energy system planning method based on collaborative game is provided in an embodiment of the present application, which considers CCUS technology and can handle distributed decision-making behaviors among multiple subjects including carbon capture power plants (CCPP), renewable energy (RE) and carbon utilization units (CU). In addition, the planning framework introduces a material flow process model of post-combustion carbon capture based on monoethanolamine (MEA) absorption method, which replaces the traditional linear approximation modeling method, can truly depict the physical interaction between multi-energy flows in the electric-gas-carbon system, and improve the physical reliability and engineering applicability of the model. Finally, an alternating direction multiplier method (ADMM) based two-stage distributed optimization solution algorithm is constructed in an embodiment of the present application, the first stage maximizes the revenue of each subject respectively, and the second stage jointly determines the electricity and carbon trading prices, taking into account the privacy protection demand and the cooperative residual value distribution, improving the system operation efficiency and fairness. The method can guide the planning and design of multi-energy complementary electric-gas integrated energy system parks, and can provide reference for the decision-making of power generation companies, renewable energy companies, carbon utilization companies and third-party investment institutions, and has important reference significance for accelerating the construction of new power systems.

[0047] The electric-gas integrated energy system planning method based on collaborative game proposed in an embodiment of the present application is as shown in Figure 1, which includes the following steps: Figure 1

[0048] S1, a multi-subject electric-gas integrated energy system model including carbon capture power plants, renewable energy and carbon utilization units is established.

[0049] S2, a distributed optimization objective function and operation constraint of the carbon capture power plant model, the renewable energy model and the carbon utilization unit model are constructed.

[0050] ​The operation constraints of the carbon capture power plant model include unit start-stop constraints, material flow dynamic equations in the absorption regeneration process, and carbon storage pressure balance equations; the operation constraints of the renewable energy model include output prediction constraints, local load supply-demand balance equations, and power trading revenue equations; the operation constraints of the carbon utilization unit model include electrolysis / synthesis reaction efficiency constraints, carbon resource procurement path optimization equations, and power-gas coupled trading equations.

[0051] The material flow dynamic equations in the absorption regeneration process include material flow dynamic equations in the absorption tower and material flow dynamic equations in the regeneration tower. The material flow dynamic equations in the absorption tower use one-dimensional convection-diffusion partial differential equations, which specifically include the time-space evolution relationship between solvent flow rate and CO2 concentration. The material flow dynamic equations in the regeneration tower are associated with the CO2 concentration gradient of rich liquid and lean liquid through a desorption coefficient. The carbon storage pressure balance equation calculates the real-time storage mass based on the gas state equation and fixed storage tank volume.

[0052] The absorption tower uses the single ethanol amine combustion post capture method for carbon capture, and the one-dimensional convection-diffusion partial differential equation is as follows:

[0053]

[0054] wherein, represents the CO2 molar concentration of the solution in the absorption tower, which is related to the distance from the top end x and time t , represents the time-varying flow rate of the solution in the absorption tower, represents the absorption coefficient of A end, represents the input CO2 mass of A end.

[0055] The material flow dynamic equation in the regeneration tower is associated with the CO2 concentration gradient of rich liquid and lean liquid through a desorption coefficient, and the formula is as follows:

[0056]

[0057] wherein, represents the CO2 molar concentration of the solution in the regeneration tower, which is related to the distance from the top end x and time t , represents the time-varying flow rate of the solution in the regeneration tower, is the desorption coefficient, represents the time step in the absorption tower, represents the CO2 mass at E end i in the regeneration tower at time t , represents the CO2 mass at F end i in the regeneration tower at time t , Density of MEA solution, Cross-sectional area of regenerator, Liquid loss rate of regenerator, Regenerator i G end t CO2 mass at time t.

[0058] The carbon storage pressure balance equation is based on the gas state equation and the formula for calculating the real-time storage mass with a fixed storage volume as follows:

[0059] Where, V is the fixed storage volume, M is the molar mass of CO2, R is the gas constant of CO2, P is the pressure inside the storage t VCO2 is the CO2 gas volume at time t, P is the pressure inside the storage t-1 VCO2 is the CO2 gas volume at time t, P is the pressure inside the storage i at time t, t P is the pressure inside the storage dP / dt is the differential of pressure inside the storage i at time t, t T is the temperature inside the storage P is the pressure inside the regenerator H end i CO2 mass at time t, t a2 is the quadratic coefficient with respect to pressure, a1 is the linear coefficient with respect to pressure, a0 is the constant with respect to pressure, a2 is the quadratic coefficient with respect to pressure, a1 is the linear coefficient with respect to pressure, a0 is the constant with respect to pressure. a2 is the quadratic coefficient with respect to pressure, a1 is the linear coefficient with respect to pressure, a0 is the constant with respect to pressure. a2 is the quadratic coefficient with respect to pressure, a1 is the linear coefficient with respect to pressure, a0 is the constant with respect to pressure.

[0060] The one-dimensional convection-diffusion partial differential equation is discretized using the finite difference method, and the formula is as follows:

[0061] Where, Δz is the height step in the absorption tower.

[0062] ​The objective functions of the carbon capture power plant model include: the power generation cost of the conventional power generation unit, the investment cost of the carbon capture and storage system, the carbon trading revenue obtained at the wholesale carbon price, and the carbon trading revenue obtained by the carbon utilization unit at the game price; the objective functions of the renewable energy model include: the electricity sales revenue obtained at the feed-in tariff, the electricity trading revenue obtained at the trading tariff, and the grid connection fee collected by the power company; the objective functions of the carbon utilization unit model include: the gas revenue obtained at the gas price, the grid purchase cost of electricity, the game purchase cost of electricity, the wholesale purchase cost of CO2, the game purchase cost of CO2, and the investment cost of the carbon utilization unit itself.

[0063] The objective function of the carbon capture power plant model is as follows:

[0064]

[0065] in, Indicated by carbon wholesale price The carbon trading revenue obtained, This indicates that carbon utilization units are used to negotiate prices. The carbon trading revenue obtained, This represents a quadratic cost function in terms of power generation. Indicates the discount rate. Indicates device The life cycle, Indicates device The discount factor, Indicates carbon capture power plant i exist t Power at any moment It is an abbreviation for absorption tower. It is an abbreviation for regeneration tower. An abbreviation for storage tank.

[0066] The objective function of the renewable energy model is as follows:

[0067]

[0068] in, Indicates according to the transaction electricity price The revenue obtained from electricity trading, Indicates according to the grid connection price Electricity sales revenue obtained, Let the cost function be a quadratic function of the grid access power. This indicates excess power.

[0069] The objective function of the carbon utilization unit model is as follows:

[0070] in, represents the gas price obtained gas revenue, represents the on-grid purchase cost of electricity, represents the transaction electricity price obtained electricity transaction cost (income for renewable energy and cost for carbon utilization unit under different subjects), represents the wholesale purchase cost of CO2, represents the game purchase cost of CO2, represents the unit investment cost of equipment , represents the unit investment capacity of equipment , represents the abbreviation of proton exchange membrane electrolysis cell, represents the abbreviation of Sabatier reactor.

[0071] S3, based on the cooperative game alliance, a collaborative game model is constructed by distributed optimization according to the distributed optimization objective function and operation constraints.

[0072] The conditions that the cooperative game alliance needs to meet include group rationality and individual rationality; the group rationality is that the cooperative revenue of the alliance as a whole should not be lower than the sum of the respective revenues of each subject in a non-cooperative situation; the individual rationality is that the revenue obtained by each participating subject in the alliance should not be lower than its independent income when it does not participate in the alliance.

[0073] The construction of the collaborative game model by distributed optimization includes: constructing the collaborative game model by maximizing the Nash product, and the Nash product formula is as follows:

[0074]

[0075] wherein, represents the total number of agents participating in the game, represents the actual revenue of the agent in the game, represents the optimal revenue obtained by the agent in a non-cooperative situation, i.e. the game interruption point. The difference represents the revenue increment obtained by participating in the cooperative game, i.e. the profit surplus.

[0076] S4, the alternating direction multiplier method is used to decompose and collaboratively solve the collaborative game model, and through iterative updating of the Lagrange multiplier and the penalty factor, the electricity-carbon bilateral transaction volume and the dynamic negotiation price are generated, which are used for collaborative planning of the multi-agent electricity-gas integrated energy system. Specifically, it includes:

[0077] S41, according to the geometric mean inequality, the collaborative game model is equivalent to a sub-problem of maximizing the alliance revenue;

[0078] S42, the coalition benefit maximization sub-problem is decomposed into distributed optimization sub-models of each subject by using the alternating direction multiplier method, and is solved cooperatively, while introducing Lagrange multipliers and penalty factors for iterative updating;

[0079] S43, output the global optimal solution satisfying the coalition benefit allocation fairness.

[0080] The specific embodiments and experimental verification of the present application are further described below.

[0081] The method for planning an electricity-gas integrated energy system based on collaborative game proposed in this embodiment accurately depicts the electricity-gas-carbon coupling physical process through the MEA material flow model, realizes the fair distribution of multi-agent cooperation surplus by combining Nash product game, and designs an ADMM two-stage algorithm to solve the non-convex problem. The method includes the following steps:

[0082] S1, establish a multi-agent electricity-gas integrated energy system park planning framework.

[0083] The planning framework of the multi-agent electricity-gas integrated energy system park of this embodiment is shown in Figure 2 The planning framework can handle the identification of different subject ownerships on the basis of considering the CCUS technology to transform coal-fired power plants, thereby optimizing the distributed decision-making behavior between CCPP, RE and CU. Figure 2 The letters A-J in the middle represent the specific port numbers in the planning framework. The coal-fired power plant is retrofitted with carbon capture and storage devices to become a carbon capture power plant (CCPP), and the flue gas generated during power generation is guided to the absorption tower of the carbon capture equipment (CC) for capture and processing. Then, the pure carbon dioxide is extracted from the top of the stripping tower and stored in the carbon storage equipment (CS). The proton exchange membrane electrolysis cell (EC) is used as a renewable energy (RE) consumption and hydrogen production device due to its high adaptability to renewable energy and high energy conversion capacity, and the EC and Sabatier reactor together constitute a carbon utilization unit (CU) device. The stored carbon dioxide is transported to the CU to generate methane, and is transported to the nearest natural gas node. The planning framework can handle the identification of different subject ownerships on the basis of considering the CCUS technology to transform coal-fired power plants, thereby optimizing the distributed decision-making behavior between CCPP, RE and CU.

[0084] In the park of integrated electricity-gas energy system, the roles of CCPP, RE and CU are different, but they are interdependent. In the market environment without bargaining game, CCPP and RE devices sell electricity to the grid at the current on-grid price. The electricity generated by CCPP meets the energy demand of the internal carbon capture and storage equipment, and the captured carbon dioxide is sold to the carbon market at the wholesale price. On the other hand, the CU device purchases electricity from the grid at the industrial electricity price, and purchases carbon dioxide from the carbon market at the retail price for chemical production.

[0085] According to relevant regulations, renewable energy suppliers can directly trade electricity with users, and power companies are responsible for managing power transmission. This embodiment is based on the existing framework and proposes an integrated electricity-gas energy system planning model based on cooperative game considering CCUS. Renewable energy suppliers directly trade electricity with CUs through the grid by paying grid access fees; CCPP sells captured carbon dioxide directly to CUs. The price and quantity of electricity and carbon are determined through a game process, and each participant strives to maximize their own profits.

[0086] S2, construct the distributed optimization objective function and operation constraints of the carbon capture power plant model, the renewable energy model and the carbon utilization unit model.

[0087] (1) CCPP model:

[0088] The maturity of the monoethanolamine (MEA) combustion capture technology makes it possible to decarbonize large-scale coal-fired power plants. The carbon capture process is shown in FIG. 1. Flue gas emitted by the coal-fired power plant enters the bottom of the absorption tower (A end), and CO2 is dissolved and captured by low-temperature lean MEA solvent (blue). The unabsorbed CO2 (purified gas) is discharged from the D end, and the CO2-rich solvent is discharged from the bottom of the absorption tower and heated in the heat exchanger (B end). Subsequently, the hot solvent enters the top of the regeneration tower (E end), and the absorbed CO2 is released under high temperature and reduced pressure (G end). The high-temperature lean solvent is discharged from the bottom of the stripping tower (F end), cooled by the heat exchanger, and circulated to the top of the absorption tower to repeat the capture process (green, C end). Figure 3

[0089]

[0090]

[0091]

[0092]

[0093] To accurately describe the output characteristics of the carbon capture power plant, the CCPP model introduces unit start-stop constraints, as shown in formulas (PP1) to (PP6), where, represents the carbon capture power plant​i exist t The running status at any given moment, Indicates carbon capture power plant i power, Indicates carbon capture power plant i exist t Power at any moment Indicates carbon capture power plant i Shutdown power, Indicates carbon capture power plant i exist t- Power at time 1 Indicates carbon capture power plant i climbing power, Indicates carbon capture power plant i The shortest running time, Indicates carbon capture power plant i The shortest shutdown time, Indicates carbon capture power plant i exist t The starting action at any moment, Indicates carbon capture power plant i exist t The shutdown process is time-sensitive. The initial carbon emissions of a power plant are determined by emission factors. (Unit: kg / MWh) Calculation, see formula (CC1) for details. Indicates that end A is at t The CO2 mass is input at all times. To simulate the spatiotemporal variation of carbon dioxide concentration within the absorber, the model employs a one-dimensional convection-diffusion partial differential equation, detailed in formula (CC2). This indicates the molar concentration of CO2 in the solution of the absorption tower (unit: mol / m³). 3 ), its distance from the top x and time t Related, This indicates the time-varying flow rate of the solution in the absorption tower. Indicates the absorption coefficient at the A-end. This indicates the mass of CO2 input at point A. Time-varying flow rate. (Unit: m / s) is calculated using formula (CC3), where Indicates solvent density, This represents the cross-sectional area of ​​the absorption tower. Indicates absorption tower i Middle B t CO2 mass at any time Indicates absorption tower i C t The mass of CO2 at time. The mass of the purified gas after absorption has a time delay relationship with the mass of the original flue gas, as detailed in formula (CC4), where... The emission factor representing the purified gas, Indicates absorption tower i The molar concentration of CO2 in the solution is related to the distance from the top. x and time t Related, Indicates absorption tower i C t-1 CO2 mass at any time This represents the time step in the absorber. The MEA solvent volume constraint in the absorber is detailed in formula (CC5), where... Indicates the liquid loss rate. Indicates solvent volume, Indicates absorption tower i medium solvent t-1 Volume at time. The solvent is delivered within the carbon capture device via an electric pump; mass loss is generally ignored, i.e., it is assumed that... , Regeneration tower i China E-end t CO2 mass at any time Regeneration tower i Mid-terminal t The CO2 mass at any given time. The CO2 concentration at the top of the regeneration tower and the bottom of the absorption tower is the same, as shown in formula (CC6). Regeneration tower i Mid-top t The molar concentration of CO2 in the solution at time [time]. The partial differential equations for the change in CO2 concentration in the stripper are detailed in equations (CC7) to (CC9), where, This is the desorption coefficient; the meanings of the other letters are the same as in the absorption tower. This indicates the molar concentration of CO2 in the solution of the regeneration tower, which is related to the distance from the top. x and time t Related, This indicates the time-varying flow rate of the solution in the regeneration tower. This indicates the time step in the absorption tower. Regeneration tower i China E-end t CO2 mass at any time Regeneration tower i Mid-terminal t CO2 mass at any time This indicates the density of the MEA solution. This represents the cross-sectional area of ​​the regeneration tower. Indicates the liquid loss rate of the regeneration tower. Regeneration tower i Mid-G end tThe CO2 mass at any given time. The liquid volume constraint of the regeneration tower is given in formula (CC10). Simultaneously, to ensure separation efficiency, the CO2 concentration in the rich / lean solutions must be below a set threshold, detailed in formulas (CC11) and (CC12), where... Indicates absorption tower i The maximum molar concentration of CO2 in the solution. Regeneration tower i The maximum molar concentration of CO2 in the solution. The investment capacity of both the absorption tower and the regeneration tower is limited by their maximum volume, as shown in formulas (CC13) and (CC14), respectively. Indicates absorption tower i Investment capacity, Regeneration tower i The investment capacity. For the carbon sequestration process, the model accurately simulates the CO2 pressure and mass flow rate inside the storage tank, as detailed in formulas (CS1)-(CS3), where... Indicates the volume of a fixed storage tank. The molar mass of CO2 The gas constant for CO2 is... Indicates the interior of the storage tank t The volume of CO2 gas at any given time Indicates the interior of the storage tank t-1 The volume of CO2 gas at any given time Indicates storage tank i internal t Pressure at any moment Indicates storage tank i internal t The differential of pressure with respect to time at any given moment. Indicates the internal temperature of the storage tank. Regeneration tower i Mid-H end t CO2 mass at any time express Regarding the quadratic coefficient of pressure, express Regarding the first-order coefficient of pressure, express Regarding the constant of pressure, express Regarding the quadratic coefficient of pressure, express Regarding the first-order coefficient of pressure, express A constant related to pressure. The investment capacity constraint for carbon sequestration equipment is given in formula (CS4). Indicates storage tank i internal t The volume of CO2 gas at any given time Regeneration tower iThe investment capacity of the CCPP. Equation (CS5) defines the power balance relationship within the CCPP, where, represents the gas tank i H end t CO2 mass at time represents the carbon capture power consumption efficiency (unit: MWh / ton), is the real-time load of the system. The captured carbon can be sold in two ways (CS6): one is to sell to the carbon market at the wholesale price , and the other is to trade with the carbon user (CU) at the retail price . This CCPP model introduces a material flow process model of post-combustion carbon capture based on the monoethanolamine (MEA) absorption method, replacing the traditional linear approximation modeling method, which can truly depict the physical interaction between multi-energy flows in the electricity-gas-carbon system, and improve the physical reliability and engineering applicability of the model.

[0094] (2) RE model:

[0095]

[0096] Local renewable energy RE is used to meet the electricity demand of CU first, and its output power needs to meet the constraint that it does not exceed the predicted maximum value , as shown in equation (RE1). The operation power of the electricity-to-gas equipment and the surplus electricity do not exceed the RE output power , as shown in equations (RE2)-(RE3). In addition, on the basis of meeting the local electricity-to-gas equipment operation power , the surplus electricity can be sold to the upper grid at the grid electricity price, and the related transaction behavior is described by equation (RE4).

[0097] (3) CU model:

[0098]

[0099] Under steady-state operating conditions, the gas production rates of EC and SR have an approximately linear relationship with their power consumption, as shown in equations (CU1) and (CU2), respectively, where, represents the CO2 mass at time t at I, represents the operation efficiency of the equipment, represents the operation power of the equipment, represents the low heat value of the substance, represents the EC equipment efficiency, represents the power of the EC equipment at time t , and represents the low heat value of hydrogen, represents the SR equipment efficiency at time t represents the CO2 mass at time represents the SR equipment efficiency at time represents the SR equipment t represents the power at time represents the lower heating value of methane. The hydrogen produced in the electrolyzer is delivered to the synthesis reactor, where it reacts with compressed CO2 through the Power to Gas (PtG) process to produce methane, according to the reaction equation: The molar conservation relation in the synthesis reactor is given by equation (CU3), represents the molar mass of hydrogen t represents the CO2 molar mass at time represents the molar mass of hydrogen represents the CO2 molar mass at time represents the molar mass of methane. In addition, the investment capacity of the electrolyzer and the reactor equipment should satisfy the maximum allowed power limit, as given by equations (CU4) and (CU5), respectively, represents the EC equipment investment capacity represents the CU equipment t represents the power at time represents the CU equipment investment capacity. The CO2 required by the CU can be obtained in two ways: either through a negotiated transaction with a carbon capture power plant (CCPP) or by purchasing it from the carbon market at the retail price , represents the CO2 mass at time t represents the CO2 mass at time represents the CO2 mass at time t represents the CO2 mass at time represents the power balance equation for the CU t represents the power at time represents the power at time t represents the power at time

[0100] (4) Objective function:

[0101] From the perspective of the carbon capture power plant CCPP, its objective function is mainly composed of the following four parts: the generation cost of the conventional power unit, the investment cost of the carbon capture and storage system, the carbon trading revenue obtained at the carbon wholesale price , and the carbon trading revenue obtained at the carbon utilization unit's game price . This optimization model can be expressed as , where ​​This represents a quadratic cost function with respect to power generation, used to characterize the nonlinear variation of power generation costs. Indicates carbon capture power plant i exist t The power at time (O2). In O2, dr represents the discount rate, which is 0.05 in this embodiment. Indicates device The life cycle, Indicates device The discount factor, It is an abbreviation for absorption tower. It is an abbreviation for regeneration tower. An abbreviation for storage tank.

[0102]

[0103] From the perspective of renewable energy (RE), its objective function mainly consists of the following three parts: based on the feed-in tariff. Electricity sales revenue According to the transaction electricity price Electricity trading revenue And the grid connection fees collected by the power company. Let be a quadratic cost function with respect to the grid-connected power, used to describe the changing characteristics of grid connection costs. This optimization model can be expressed as: ,in For the objective function of renewable energy, the equation is... The constraints it follows.

[0104]

[0105] From the perspective of the carbon utilization unit (CU), its objective function mainly consists of the following three parts: based on gas prices Gas revenue obtained Electricity grid connection procurement cost The bargaining power procurement cost Wholesale procurement cost of CO2 The bargaining power of CO2 procurement costs And the investment cost of the CU itself. This optimization model can be expressed as... , where the formula Let CU be the objective function. Indicates device The unit investment cost Indicates device Unit investment capacity and style These are the constraints that the model follows. It is an abbreviation for proton exchange membrane electrolyzer. This is an abbreviation for the Sabadir reactor.

[0106]

[0107] S3. Based on the cooperative game alliance, and according to the distributed optimization objective function and operating constraints, a cooperative game model is constructed by maximizing the Nash product.

[0108] Cooperative game model:

[0109] For the nonlinear term introduced by the partial derivative in equation (CC2), the difference approximation of equations (R1) to (R2) is used for discretization. Wherein, and Let R1 and R2 represent the time step and height step, respectively, in the absorption tower. The differential multiplier satisfies the product relationship between the flow rate and the time multiplier. Substituting equations (R1) to (R3) into equation (CC2), the original partial differential equation can be transformed into a nonlinear algebraic form, as shown in equation (R4).

[0110] Figure 4 This is a schematic diagram illustrating the process of calculating CO2 concentration inside the absorption tower. Figure 4 It can be seen that the current position MEA concentration at the location The calculation depends not only on the previous spatial segment The concentration value also depends on the previous time period. Historical concentration information. Considering the continuous replenishment of MEA solvent in the system, the inlet concentration... Treat as a constant Thus, the continuous variables to be optimized in the system The total number can be expressed as: (number of segments - 1) × number of time steps. In the example, the time step is set. The number of spatial segments is 10. The linearization method for the partial derivative terms in equation (CC9) will not be elaborated here.

[0111]

[0112] The cooperative game framework proposed in this embodiment involves three independent subjects: CCPP, RE and CU. Among them, the CCPP meets its own carbon capture system and the power demand of the load, and obtains income by selling the captured carbon dioxide to the methane synthesis device SR; the RE can sell electricity to the grid at the grid price, or directly supply power to the CU unit at a negotiated price, and needs to bear the corresponding grid access fee; the CU can obtain power from the local RE or the external grid, and purchase carbon dioxide from the CCPP or the external carbon market, and then synthesize methane through the SR device and sell it to obtain income. Without considering the cooperative game, there is no transaction of electricity or carbon between the three subjects.

[0113] Generally, each participating subject independently formulates a strategy based on the information it has to maximize its benefits. Only when the following two conditions are met, a cooperative game alliance can be formed among the three parties:

[0114] 1) Group Rationality: the cooperative income of the alliance as a whole should not be lower than the sum of the respective incomes of the three parties in the non-cooperative case;

[0115] 2) Individual Rationality: the income of each participating party in the alliance should not be lower than its independent income when it does not participate in the alliance.

[0116] According to the Nash game theory, the cooperative game model is constructed by maximizing the Nash product in this embodiment. Among them, N represents the total number of agents participating in the game, Y represents the actual income of agent i in the game, Y represents the optimal income of agent i in the case of not participating in cooperation (i.e. negotiation failure), i.e. the game interruption point. The difference represents the income increment (i.e. profit surplus) obtained by participating in the cooperative game. In summary, the cooperative negotiation model proposed in the electric-gas integrated energy system in this embodiment is defined as follows, denoted as model .

[0117]

[0118] S4, the alternating direction multiplier method is used to decompose and cooperatively solve the cooperative game model, and the electric-carbon bilateral transaction volume and dynamic negotiation price are generated by iteratively updating the Lagrange multiplier and the penalty factor, which are used for cooperative planning of the multi-agent electric-gas integrated energy system.

[0119] Cooperative game solving method:​​​

[0120] Due to the model is essentially a non-convex nonlinear optimization problem, it is difficult to solve directly. Therefore, the embodiment first converts it into an alliance revenue maximization sub-problem model , that is, in the alliance of CCPP, RE and CU, the cooperative optimal solution is solved jointly. Subsequently, the model is further decomposed into three distributed optimization sub-models , which correspond to the local optimization problems of CCPP, RE and CU respectively. Compared with the centralized method, ADMM allows each agent to solve its sub-problem locally, and realizes global collaboration through limited information exchange, thereby effectively protecting privacy. To realize collaborative solving among agents, Lagrange multipliers and , and penalty factors and are introduced, and the corresponding augmented Lagrangian function is constructed, wherein represents the carbon / electricity trading volume expected to be purchased by the CU unit. Figure 8 is a schematic diagram of the profit maximization sub-problem distributed algorithm based on ADMM.

[0121]

[0122]

[0123]

[0124] After obtaining the collaborative game trading volume , considering the monotonicity of the natural logarithm function, the model is further converted into an equivalent form . To realize variable decoupling of the trading price, the embodiment introduces auxiliary variables and , which are used to describe the carbon price and electricity price between CCPP-CU and RE-CU respectively, so that the three-party model has distributed solving ability while retaining coordination. Figure 9 is a schematic diagram of the trading game sub-problem distributed algorithm based on ADMM.

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131] Experimental example:

[0132] The simulation experiment related to this embodiment is implemented in an electricity-gas integrated energy system park as shown in FIG. 1. The key parameters are set as shown in Table 1. The simulation is realized by modeling on a MATLAB platform, and is run on a computer environment of a 2.6 GHz dual-core processor and 16 GB memory using a Gurobi 10.0 optimization solver. Figure 1

[0133] Table 1 Key parameters

[0134]

[0135] As shown in Tables 2 and 3, the income of the three types of subjects CCPP, RE and CU before and after the cooperative game is listed, wherein a positive value represents income, a negative value represents cost, and N / A represents not applicable. After implementing the cooperative game mechanism proposed in this embodiment, the profits of the subjects CCPP, RE and CU are increased by 5081.98, 5081.14 and 5081.57 thousand yuan respectively, which are increased by about 6.68%, 14.72% and 6.42% respectively compared with the original profit level. At the same time, the total profit of the overall alliance is increased by 15244.69 thousand yuan, and the income increment of each participant is basically equal to about one third of the total gain, which reflects the fairness and balance of the cooperative game method in income distribution.

[0136] Table 2 Comparison of income changes of CCPP and RE before and after cooperative game (unit: 10 4 yuan RMB)

[0137]

[0138] Specifically, after the introduction of the carbon trading mechanism with the CU, the power generation of the CCPP and the corresponding carbon capture retrofit investment cost will increase accordingly; after the power trading with the CU, the power generation of the RE also increases, although the grid access fee needs to be paid, most of the power is still sold to the CU. For the CU, through the internal trading mechanism of the alliance, the procurement cost of carbon and electricity is significantly reduced, and the gas sales revenue is increased. Through the three-party cooperative game mechanism of the embodiment, the individual income of the three types of subjects is significantly increased, and the residual value generated by cooperation is equally and fairly distributed among the parties (obtained by solving the optimization problem), which reflects the incentive effectiveness and fairness of the income distribution in the multi-subject cooperation.

[0139] ​Table 3 Comparison of CU revenue variation before and after cooperative game (unit: 10 million RMB) 4

[0140]

[0141] Figure 5 The figure shows the real-time transaction price variation of CCPP, RE and CU within a day. It can be seen that when the industrial electricity price is lower than the on-grid electricity price (i.e. 01:00-07:00 and 23:00-24:00), no power transaction occurs between RE and CU, and CU chooses to purchase all power from external power grid. When the real-time retail carbon price is lower than the wholesale carbon price (i.e. 01:00-11:00 and 18:00-24:00), no carbon transaction occurs between CCPP and CU. In the period of relatively high electricity or carbon price, the game behavior is triggered among the three parties to reach a negotiated transaction. The actual transaction price at this time is between the original selling price of RE or CCPP and the purchase price of CU, so as to achieve game equilibrium. Figure 4

[0142] The figure shows the variation of power transaction volume of CCPP-RE-CU within a day. It can be seen from the figure that in the period of low electricity price (i.e. 01:00-07:00 and 23:00-24:00), CU tends to directly purchase power from the upper power grid rather than from the local RE, because the on-grid electricity price is higher than the industrial electricity price. In other periods, CU mainly purchases power from RE to meet the production electricity demand. In particular, in the peak period of electricity price, all electricity of CU is independently supplied by RE; and in the flat period (15:00-17:00), due to the limited power generation capacity of RE, part of the electricity demand of CU still needs to be supplemented by external power grid. Through reasonable utilization of peak-valley electricity price mechanism and game strategy, CU can effectively reduce the overall power procurement cost and realize economic optimization of energy procurement. Figure 6

[0143] The figure shows the variation trend of carbon transaction volume within a day, which is consistent with the carbon price variation in the figure. In the peak period of carbon price, CU purchases all required carbon resources from CCPP at the negotiated price; and in the remaining period, CU purchases carbon resources from external market at the retail price. The scatter variation and trend curve shown in the figure further reflect that, due to the influence of CU equipment operation law, the transaction volume of power and carbon resources within a day presents consistent fluctuation in time dimension, which verifies the timeliness, responsiveness and practicality of the embodiment of the present application in the coordinated transaction of multiple energy categories. Figure 7 Figure 5

[0144] ​​​The above is further detailed description of the present application in combination with specific preferred embodiments, and cannot be deemed as limitation of the specific implementation of the present application to these descriptions. For those skilled in the art to which the present application belongs, without departing from the concept of the present application, a number of equivalent substitutions or obvious variations can be made, and the performance or use is the same, which should be deemed as falling within the protection scope of the present application.

Claims

1. A planning method for an integrated electricity-gas energy system based on collaborative game theory, characterized in that, Includes the following steps: S1. Establish a multi-entity integrated electricity-gas energy system model that includes carbon capture power plants, renewable energy and carbon utilization units; S2. Construct the distributed optimization objective function and operational constraints for the carbon capture power plant model, renewable energy model, and carbon utilization unit model; S3. Based on the cooperative game alliance, a cooperative game model is constructed through distributed optimization according to the distributed optimization objective function and operating constraints. S4. The cooperative game model is decomposed and solved collaboratively using the alternating direction multiplier method. By iteratively updating the Lagrange multipliers and penalty factors, the bilateral electricity-carbon trading volume and dynamic negotiated price are generated for the collaborative planning of a multi-entity integrated electricity-gas energy system. In step S2, the operational constraints of the carbon capture power plant model include unit start-up and shutdown constraints, material flow dynamic equations in the absorption and regeneration process, and carbon storage pressure balance equations; the operational constraints of the renewable energy model include output prediction constraints, local load supply and demand balance equations, and electricity trading revenue equations; the operational constraints of the carbon utilization unit model include electrolysis / synthesis reaction efficiency constraints, carbon resource procurement path optimization equations, and electricity-gas coupling trading equations. The material flow dynamic equations in the absorption and regeneration process include: the material flow dynamic equations in the absorption tower and the material flow dynamic equations in the regeneration tower. The material flow dynamic equations in the absorption tower adopt one-dimensional convection-diffusion partial differential equations. The absorption tower employs a carbon capture method following the combustion of monoethanolamine. The one-dimensional convection-diffusion partial differential equation is as follows: in, Indicates carbon capture power plant i The molar concentration of CO2 in the solution of the absorption tower, which is related to the distance from the top. x and time t Related, Indicates carbon capture power plant i The time-varying flow rate of the solution in the absorption tower, This represents the absorption coefficient at end A in the absorption tower. Indicates carbon capture power plant i The mass of CO2 input at end A in the absorption tower; The dynamic equation for the material flow within the regeneration tower, relating the CO2 concentration gradient between the rich and lean solutions via the desorption coefficient, is as follows: in, Indicates carbon capture power plant i The molar concentration of CO2 in the solution of the regeneration tower, which is related to the distance from the top. x and time t Related, Indicates carbon capture power plant i The time-varying flow rate of the solution in the regeneration tower, The desorption coefficient is . This indicates the time step in the absorption tower. Indicates carbon capture power plant i E end of the regeneration tower t CO2 mass at any time Indicates carbon capture power plant i F end of the regeneration tower t CO2 mass at any time This indicates the density of the MEA solution. This represents the cross-sectional area of ​​the regeneration tower. Indicates the liquid loss rate of the regeneration tower. Indicates carbon capture power plant i G-end of the regeneration tower t CO2 mass at any time Indicates carbon capture power plant i Top solution in the regeneration tower t The molar concentration of CO2 at the time preceding time. Indicates carbon capture power plant i E end of the regeneration tower t CO2 mass at the previous moment; The carbon storage pressure balance equation is based on the gas state equation and the formula for calculating real-time storage mass with a fixed tank volume, as follows: in, Indicates the volume of a fixed storage tank. The molar mass of CO2 The gas constant for CO2 is... Indicates the interior of the storage tank t The volume of CO2 gas at any given time Indicates the interior of the storage tank t The volume of CO2 gas at the previous moment. Indicates carbon capture power plant i Inside the storage tank t Pressure at any moment Indicates carbon capture power plant i Inside the storage tank t The differential of pressure with respect to time at any given moment. Indicates the internal temperature of the storage tank. Indicates carbon capture power plant i H end of the regeneration tower t CO2 mass at any time express Regarding the quadratic coefficient of pressure, express Regarding the first-order coefficient of pressure, express Regarding the constant of pressure, express Regarding the quadratic coefficient of pressure, express Regarding the first-order coefficient of pressure, express A constant related to pressure; The objective function of the carbon capture power plant model is as follows: in, Indicated by carbon wholesale price The carbon trading revenue obtained, This indicates that carbon utilization units are used to negotiate prices. The carbon trading revenue obtained, This represents a quadratic cost function in terms of power generation. Indicates the discount rate. Indicates equipment The life cycle, Indicates equipment The discount factor, Indicates carbon capture power plant i exist t Power at any moment It is an abbreviation for absorption tower. It is an abbreviation for regeneration tower. Abbreviation for storage tank; The objective function of the renewable energy model is as follows: in, Indicates according to the transaction electricity price The revenue obtained from electricity trading, Indicates according to the grid connection price Electricity sales revenue obtained, Let the cost function be a quadratic function of the grid access power. Indicates surplus electricity; The objective function of the carbon utilization unit model is as follows: in, Indicates according to gas price The gas revenue obtained, This represents the cost of purchasing electricity from the grid. Indicates according to the transaction electricity price The cost of obtaining electricity through transactions, This represents the wholesale purchase cost of CO2. This represents the game-theoretic procurement cost of CO2. Indicates equipment The unit investment cost Indicates equipment Unit investment capacity It is an abbreviation for proton exchange membrane electrolyzer. This is an abbreviation for the Sabadir reactor.

2. The method for planning an integrated electricity-gas energy system based on collaborative game theory according to claim 1, characterized in that, The material flow dynamic equation in the absorption tower specifically includes the spatiotemporal evolution relationship between solvent flow rate and CO2 concentration; the material flow dynamic equation in the regeneration tower is related to the CO2 concentration gradient between rich and lean solutions through the desorption coefficient; the carbon storage pressure balance equation is based on the gas state equation and a fixed tank volume to calculate the real-time storage mass.

3. The method for planning an integrated electricity-gas energy system based on collaborative game theory according to claim 1, characterized in that, In step S3, the conditions that the cooperative game alliance must meet include group rationality and individual rationality; the group rationality is that the overall cooperative benefit of the alliance must not be less than the sum of the individual benefits of each entity in the non-cooperative situation; the individual rationality is that the benefit of each participating entity in the alliance must not be less than its independent benefit when it does not participate in the alliance.

4. The method for planning an integrated electricity-gas energy system based on collaborative game theory according to claim 3, characterized in that, The construction of the collaborative game model through distributed optimization includes: constructing the collaborative game model by maximizing the Nash product, the formula for which is as follows: ； in, This represents the total number of agents participating in the game. Indicates the agent The actual gains in the game, Indicates the agent The optimal payoff obtained without participating in cooperation, i.e., the point at which the game ends, is the difference. This represents the incremental gains obtained through participation in cooperative games, i.e., the profit surplus.

5. The method for planning an integrated electricity-gas energy system based on collaborative game theory according to claim 1, characterized in that, Step S4 includes: S41. Based on the geometric mean inequality, the cooperative game model is equivalently transformed into a subproblem of maximizing alliance revenue. S42. The alternating direction multiplier method is used to decompose the subproblem of maximizing the alliance revenue into distributed optimization sub-models of each subject and solve them collaboratively. At the same time, Lagrange multipliers and penalty factors are introduced for iterative updates. S43. Output the globally optimal solution that satisfies the fairness of the distribution of alliance benefits.

6. The method for planning an integrated electricity-gas energy system based on collaborative game theory according to claim 1, characterized in that, The one-dimensional convection-diffusion partial differential equation is discretized using the finite difference method, as shown in the following formula: in, This indicates the height step size in the absorption tower. Indicates carbon capture power plant i The absorption tower is located at a distance from the top. x Place, t Time before The molar concentration of CO2 at time t.

7. The method for planning an integrated electricity-gas energy system based on collaborative game theory according to claim 1, characterized in that, In step S2, the objective function of the carbon capture power plant model includes: the power generation cost of the conventional power generation unit, the investment cost of the carbon capture and storage system, the carbon trading revenue obtained at the wholesale carbon price, and the carbon trading revenue obtained by the carbon utilization unit at the game price; the objective function of the renewable energy model includes: the electricity sales revenue obtained at the grid connection price, the electricity trading revenue obtained at the trading price, and the grid connection fee collected by the power company; the objective function of the carbon utilization unit model includes: the gas revenue obtained at the gas price, the grid connection procurement cost of electricity, the game-theoretic procurement cost of electricity, the wholesale procurement cost of CO2, the game-theoretic procurement cost of CO2, and the investment cost of the carbon utilization unit itself.

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