Electricity-gas integrated energy system planning method based on cooperative game

Abstract

The invention discloses an electricity-gas comprehensive energy system planning method based on a cooperative game. The method comprises the steps that S1, a multi-main-body electricity-gas comprehensive energy system model comprising a carbon capture power plant, renewable energy sources and a carbon utilization unit is established; s2, constructing a distributed optimization objective function and an operation constraint of each main body model; s3, based on the cooperative game alliance, according to the distributed optimization objective function and the operation constraint, constructing a cooperative game model through distributed optimization; and S4, carrying out decomposition and collaborative solution on the collaborative game model by adopting an alternating direction multiplier method, and generating an electricity-carbon bilateral transaction volume and a dynamic negotiation price. According to the method, distributed decision behaviors among multiple subjects can be processed, the physical interaction relationship among multiple energy flows in an electricity-gas-carbon system can be truly described, the physical reliability and engineering applicability of the model are improved, the income of each subject can be maximized, the privacy protection demand and cooperation surplus value distribution are considered, and the method is suitable for popularization and application. And the operation efficiency and fairness of the system are improved.

Description

Technical Field

[0001] The present invention relates to the field of power system planning, and in particular to a collaborative game-based electricity-gas integrated energy system planning method. Background Art

[0002] Carbon dioxide (CO2) is one of the main greenhouse gases contributing to global warming. Carbon capture, utilization, and storage (CCUS) is a key technology for reducing and removing post-emission CO2 and a crucial step in achieving zero-carbon emissions. Furthermore, integrating proton exchange membrane electrolyzers (EC) and Sabatier reactors (SR) into integrated electricity-gas energy systems offers a promising solution for deep decarbonization through CO2 capture and subsequent utilization, which has gradually become a worthy new research direction.

[0003] However, existing integrated electricity-gas energy system planning rarely simulates the physical and operational processes of CCUS in detail, and most are based on a centralized framework. This framework has difficulty coordinating conflicts of interest among multiple entities and ignores the different ownership and independent decision-making behaviors among entities, thus limiting its applicability in actual scenarios and resulting in low system operation efficiency and fairness. Summary of the Invention

[0004] The purpose of the present invention is to solve the technical problems of low applicability, operation efficiency and fairness of the existing electricity-gas integrated energy system, and to propose a planning method for the electricity-gas integrated energy system based on collaborative game.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A planning method for an electric-gas integrated energy system based on collaborative game includes the following steps: S1. Establishing a multi-agent electric-gas integrated energy system model including a carbon capture power plant, renewable energy and a carbon utilization unit; S2. Constructing distributed optimization objective functions and operating constraints of the carbon capture power plant model, the renewable energy model and the carbon utilization unit model; S3. Based on a cooperative game alliance, constructing a collaborative game model through distributed optimization according to the distributed optimization objective function and operating constraints; S4. Using the alternating direction multiplier method to decompose and collaboratively solve the collaborative game model, and by iteratively updating the Lagrange multiplier and the penalty factor, generating the electric-carbon bilateral transaction volume and the dynamic negotiated price for the collaborative planning of the multi-agent electric-gas integrated energy system.

[0006] In some embodiments, in step S2, the operating constraints of the carbon capture power plant model include unit start and stop constraints, material flow dynamic equations during the absorption and regeneration process, and carbon storage pressure balance equations; the operating constraints of the renewable energy model include output forecast constraints, local load supply and demand balance equations, and electricity trading revenue equations; the operating 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.

[0007] In some embodiments, 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 a one-dimensional convection-diffusion partial differential equation, specifically including the spatiotemporal evolution relationship between the solvent flow rate and the CO2 concentration; the material flow dynamic equations in the regeneration tower associate the CO2 concentration gradients of the rich liquid and the lean liquid through the desorption coefficient; the carbon storage pressure balance equation calculates the real-time storage mass based on the gas state equation and the fixed storage tank volume.

[0008] In some embodiments, 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 benefits of the alliance shall not be lower than the sum of the individual benefits of each entity under non-cooperative circumstances; the individual rationality is that the benefits obtained by each participating entity in the alliance shall not be lower than its independent benefits when it does not participate in the alliance.

[0009] In some embodiments, constructing the collaborative game model through distributed optimization includes constructing the collaborative game model by maximizing the Nash product, where the Nash product formula is as follows:

[0010] in, represents the total number of agents participating in the game, Indicates the agent The actual gain in the game, Indicates the agent The optimal benefit obtained without cooperation is the game interruption point. It represents the incremental income obtained by participating in the cooperative game, that is, the profit surplus.

[0011] In some embodiments, step S4 includes: S41, based on the geometric mean inequality, converting the collaborative game model into an equivalent sub-problem of alliance profit maximization; S42, using the alternating direction multiplier method to decompose the alliance profit maximization sub-problem into distributed optimization sub-models of each entity and collaboratively solve them, while introducing Lagrange multipliers and penalty factors for iterative updating; S43, outputting the global optimal solution that satisfies the fairness of alliance profit distribution.

[0012] In some embodiments, the absorption tower uses a monoethanolamine post-combustion capture method to capture carbon, and the one-dimensional convection-diffusion partial differential equation is as follows:

[0013] in, Indicates the molar concentration of CO2 in the solution in the absorption tower, which is related to the distance from the top x and time t Related, represents the time-varying flow rate of the solution in the absorption tower, represents the absorption coefficient of end A, Indicates the quality of CO2 input at end A.

[0014] The material flow dynamic equation in the regeneration tower is related to the CO2 concentration gradient of the rich liquid and the lean liquid through the desorption coefficient as follows:

[0015] in, Indicates the molar concentration of CO2 in the regeneration tower, which is related to the distance from the top x and time t Related, 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, Regeneration tower i Middle E-end t CO2 mass at the moment, Regeneration tower i Middle F end t CO2 mass at the moment, represents the density of MEA solution, represents the cross-sectional area of the regeneration tower, represents the liquid loss rate of the regeneration tower, Regeneration tower i Mid-G end t CO2 mass at each moment.

[0016] The carbon storage pressure balance equation is based on the gas state equation and the fixed storage tank volume to calculate the real-time storage mass as follows:

[0017] in, represents the fixed tank volume, is the molar mass of CO2, is the CO2 gas constant, t The volume of CO2 gas at the moment, Indicates the inside of the tank t-1 The volume of CO2 gas at the moment, Indicates storage tank iinternal t The pressure of time, Indicates storage tank i internal t The differential of pressure with respect to time, Indicates the internal temperature of the tank, Regeneration tower i Middle H t CO2 mass at the moment, 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 about pressure.

[0018] In some embodiments, the one-dimensional convection-diffusion partial differential equation is discretized using a finite difference method, and the formula is as follows:

[0019] in, Indicates the height step in the absorption tower.

[0020] In some embodiments, 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 income obtained at the wholesale carbon price, and the carbon trading income obtained by the carbon utilization unit at the game price; the objective function of the renewable energy model includes: the electricity sales revenue obtained according to the on-grid electricity price, the electricity trading income obtained according to the trading electricity price, and the grid access fee charged by the power company; the objective function of the carbon utilization unit model includes: the gas income obtained according to the gas price, the on-grid procurement cost of electricity, the game procurement cost of electricity, the wholesale procurement cost of CO2, the game procurement cost of CO2, and the investment cost of the carbon utilization unit itself.

[0021] In some embodiments, the objective function of the carbon capture power plant model is as follows:

[0022] in, Indicates the wholesale price of carbon Carbon trading income obtained, Represents the carbon utilization unit at a game price Carbon trading income obtained, represents the quadratic cost function with respect to the generated power, represents the discount rate, Representation device life cycle, Representation device The discount factor, Carbon Capture Power Plant i exist t The power of the moment, Abbreviation for absorption tower. Abbreviation for regeneration tower. Abbreviation for storage tank.

[0023] The objective function of the renewable energy model is as follows:

[0024] in, Indicates that according to the transaction electricity price The income from electricity trading, Indicates that the on-grid electricity price Revenue from electricity sales, is the quadratic cost function for grid access power, Indicates surplus power.

[0025] The objective function of the carbon utilization unit model is as follows:

[0026] in, Indicates that according to gas price The gas revenue obtained, represents the cost of purchasing electricity on the grid, Indicates that according to the transaction electricity price The transaction cost of electricity obtained, represents the wholesale purchase cost of CO2, represents the gaming purchase cost of CO2, Representation device The unit investment cost, Representation device Unit investment capacity, Abbreviation for proton exchange membrane electrolyzer, Abbreviation for Sabatier reactor.

[0027] In some embodiments, in step S4, the logic for generating the dynamic negotiated price includes: when the industrial electricity price is lower than the on-grid electricity price, the carbon utilization unit gives priority to purchasing external grid electricity; when the real-time retail carbon price is lower than the wholesale carbon price, the carbon utilization unit gives priority to purchasing external carbon resources; during the peak electricity / carbon price period, the internal game equilibrium transaction of the alliance is triggered, and the dynamic negotiated price is between the supply cost of the subject and the marginal utility of demand.

[0028] The beneficial effects of the present invention compared with the prior art include: The collaborative game-based planning method for an integrated electricity-gas energy system proposed in this paper establishes models for a carbon capture power plant, renewable energy, and a carbon utilization unit, and constructs distributed optimization objective functions and operational constraints for each agent model. This method can handle distributed decision-making among multiple agents, including carbon capture power plants, renewable energy, and carbon utilization units. Furthermore, the method combines distributed optimization with a cooperative game model. A two-stage distributed optimization solution algorithm based on the alternating direction method of multipliers (ADMM) is constructed. The first stage maximizes the benefits of each agent, while the second stage jointly determines the electricity and carbon trading prices. This method balances privacy protection requirements with the distribution of cooperative surplus value, achieving fair distribution of the surplus value across multiple agents and improving system operational efficiency and fairness.

[0029] In some embodiments, the present invention also has the following beneficial effects: By introducing a material flow process model for post-combustion carbon capture based on the monoethanolamine (MEA) absorption method, replacing the traditional linear approximate modeling method, it is possible to truly characterize the physical interaction between multiple energy flows in the electricity-gas-carbon system, further improving the physical reliability and engineering applicability of the model.

[0030] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of a method for planning an electricity-gas integrated energy system based on collaborative game according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a multi-agent electric-gas integrated energy system park according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the carbon capture process of a multi-agent electricity-gas integrated energy system park according to an embodiment of the present invention; Figure 4 2 is a schematic diagram of calculating the CO2 concentration inside the absorption tower according to an embodiment of the present invention; Figure 5 This is a schematic diagram of intraday trading prices according to an embodiment of the present invention; Figure 6 This is a schematic diagram of intraday power trading volume according to an embodiment of the present invention; Figure 7 This is a schematic diagram of intraday carbon trading volume in an embodiment of the present invention; Figure 8 Schematic diagram of a distributed algorithm for the profit maximization subproblem based on ADMM according to an embodiment of the present invention; Figure 9 Schematic diagram of a distributed algorithm for a transaction game sub-problem based on ADMM according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The present invention will be further described below with reference to the accompanying drawings and in combination with preferred embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0033] It should be noted that the directional terms such as left, right, up, down, top, and bottom in this embodiment are merely relative concepts, or are based on the normal use status of the product, and should not be considered as restrictive.

[0034] This embodiment of the present invention provides a collaborative game-based planning method for an integrated electricity-gas energy system. This method considers CCUS technology and can handle distributed decision-making among multiple entities, including carbon capture power plants (CCPPs), renewable energy sources (RE), and carbon utilization units (CUs). Furthermore, this planning framework introduces a material flow process model for post-combustion carbon capture based on the monoethanolamine (MEA) absorption method, replacing traditional linear approximation modeling. This model realistically depicts the physical interactions between multiple energy flows in the electricity-gas-carbon system, improving the model's physical reliability and engineering applicability. Finally, this embodiment of the present invention constructs a two-stage distributed optimization algorithm based on the alternating direction multiplier method (ADMM). The first stage maximizes the benefits of each entity separately, while the second stage jointly determines the electricity and carbon trading prices, balancing privacy protection requirements with the distribution of cooperative surplus value, thereby improving system operational efficiency and fairness. This method can guide the planning and design of integrated electricity-gas energy system parks with multiple energy complementarities, providing a reference for decision-making by power generation companies, renewable energy companies, carbon utilization companies, and third-party investment institutions. It holds important implications for accelerating the construction of new power systems.

[0035] The electric-gas integrated energy system planning method based on collaborative game proposed in the embodiment of the present invention is as follows Figure 1 As shown, the following steps are included: S1. Establish a multi-agent electric-gas integrated energy system model including carbon capture power plants, renewable energy and carbon utilization units.

[0036] S2. Construct the distributed optimization objective functions and operation constraints of the carbon capture power plant model, renewable energy model and carbon utilization unit model.

[0037] The operating constraints of the carbon capture power plant model include unit start-up and shutdown constraints, material flow dynamic equations during the absorption and regeneration process, and carbon storage pressure balance equations; the operating constraints of the renewable energy model include output forecast constraints, local load supply and demand balance equations, and electricity trading revenue equations; the operating 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.

[0038] The material flow dynamic equations in the absorption and regeneration process include: the material flow dynamic equation in the absorption tower and the material flow dynamic equation in the regeneration tower. The material flow dynamic equation in the absorption tower adopts a one-dimensional convection-diffusion partial differential equation, specifically including the spatiotemporal evolution relationship between the solvent flow rate and the CO2 concentration; the material flow dynamic equation in the regeneration tower associates the CO2 concentration gradients of the rich liquid and the lean liquid through the desorption coefficient; the carbon storage pressure balance equation calculates the real-time storage mass based on the gas state equation and the fixed storage tank volume.

[0039] The absorption tower uses the monoethanolamine post-combustion capture method to capture carbon. The one-dimensional convection-diffusion partial differential equation is as follows:

[0040] in, Indicates the molar concentration of CO2 in the solution in the absorption tower, which is related to the distance from the top x and time t Related, represents the time-varying flow rate of the solution in the absorption tower, represents the absorption coefficient of end A, Indicates the quality of CO2 input at end A.

[0041] The material flow dynamic equation in the regeneration tower is related to the CO2 concentration gradient of the rich liquid and the lean liquid through the desorption coefficient as follows:

[0042] in, Indicates the molar concentration of CO2 in the regeneration tower, which is related to the distance from the top x and time t Related, 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, Regeneration tower i Middle E-end t CO2 mass at the moment, Regeneration tower i Middle F end t CO2 mass at the moment, represents the density of MEA solution, represents the cross-sectional area of the regeneration tower, represents the liquid loss rate of the regeneration tower, Regeneration tower i Mid-G end t CO2 mass at each moment.

[0043] The carbon storage pressure balance equation is based on the gas state equation and the fixed tank volume to calculate the real-time storage mass formula as follows:

[0044] in, represents the fixed tank volume, is the molar mass of CO2, is the CO2 gas constant, Indicates the inside of the tank t The volume of CO2 gas at the moment, Indicates the inside of the tank t-1 The volume of CO2 gas at the moment, Indicates storage tank i internal t The pressure of time, Indicates storage tank i internal t The differential of pressure with respect to time, Indicates the internal temperature of the tank, Regeneration tower i Middle H end t CO2 mass at the moment, 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 about pressure.

[0045] The finite difference method is used to discretize the one-dimensional convection-diffusion partial differential equation, and the formula is as follows:

[0046] in, Indicates the height step in the absorption tower.

[0047] 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 income obtained at the wholesale carbon price, and the carbon trading income obtained by the carbon utilization unit at the game price; the objective function of the renewable energy model includes: the electricity sales revenue obtained according to the on-grid electricity price, the electricity trading income obtained according to the trading electricity price, and the grid access fee charged by the power company; the objective function of the carbon utilization unit model includes: the gas income obtained according to the gas price, the on-grid procurement cost of electricity, the game procurement cost of electricity, the wholesale procurement cost of CO2, the game procurement cost of CO2, and the investment cost of the carbon utilization unit itself.

[0048] The objective function of the carbon capture power plant model is as follows:

[0049] in, Indicates the wholesale price of carbon Carbon trading income obtained, Represents the carbon utilization unit at a game price Carbon trading income obtained, represents the quadratic cost function with respect to the generated power, represents the discount rate, Representation device life cycle, Representation device The discount factor, Carbon Capture Power Plant i exist t The power of the moment, Abbreviation for absorption tower. Abbreviation for regeneration tower. Abbreviation for storage tank.

[0050] The objective function of the renewable energy model is as follows:

[0051] in, Indicates that according to the transaction electricity price The income from electricity trading, Indicates that the on-grid electricity price Revenue from electricity sales, is the quadratic cost function for grid access power, Indicates surplus power.

[0052] The objective function of the carbon utilization unit model is as follows:

[0053] in, Indicates that according to gas price The gas revenue obtained, represents the cost of purchasing electricity on the grid, Indicates that according to the transaction electricity price The transaction cost of electricity obtained (income for renewable energy and cost for carbon utilization units under different entities), represents the wholesale purchase cost of CO2, represents the gaming purchase cost of CO2, Representation device The unit investment cost, Representation device Unit investment capacity, Abbreviation for proton exchange membrane electrolyzer, Abbreviation for Sabatier reactor.

[0054] S3. Based on the cooperative game alliance, according to the distributed optimization objective function and operation constraints, a collaborative game model is constructed through distributed optimization.

[0055] The conditions that a cooperative game alliance must meet include group rationality and individual rationality; group rationality means that the overall cooperative benefits of the alliance must not be lower than the sum of the individual benefits of each entity under non-cooperative circumstances; individual rationality means that the benefits obtained by each participating entity in the alliance must not be lower than its independent benefits when it did not participate in the alliance.

[0056] Constructing a cooperative game model through distributed optimization includes: constructing a cooperative game model by maximizing the Nash product. The Nash product formula is as follows:

[0057] in, represents the total number of agents participating in the game, Indicates the agent The actual gain in the game, Indicates the agent The optimal benefit obtained without cooperation is the game interruption point. It represents the incremental income obtained by participating in the cooperative game, that is, the profit surplus.

[0058] S4. Use the alternating direction multiplier method to decompose and collaboratively solve the collaborative game model. By iteratively updating the Lagrange multiplier and penalty factor, generate electricity-carbon bilateral transaction volume and dynamic negotiated price for collaborative planning of multi-agent electricity-gas integrated energy systems. Specifically, it includes: S41. Based on the geometric mean inequality, the collaborative game model is equivalently transformed into a sub-problem of maximizing the alliance's benefits; S42. Decompose the alliance revenue maximization subproblem into distributed optimization submodels of each agent using the alternating direction multiplier method and solve them collaboratively. At the same time, introduce Lagrange multipliers and penalty factors for iterative updating. S43. Output the global optimal solution that satisfies the fairness of alliance profit distribution.

[0059] The specific embodiments and experimental verification of the present invention are further described below.

[0060] This example proposes a collaborative game-based integrated electricity-gas energy system planning method. This method uses the MEA material flow model to accurately characterize the electricity-gas-carbon coupling physical process, combines it with the Nash product game to achieve fair distribution of the multi-agent cooperative surplus, and designs an ADMM two-stage algorithm to solve non-convex problems. The method includes the following steps: S1. Establish a multi-agent electricity-gas integrated energy system park planning framework.

[0061] The planning framework of the multi-agent electric-gas integrated energy system park in this embodiment is as follows: Figure 2 As shown in Figure 3, the planning framework, based on the consideration of CCUS technology transformation of coal-fired power plants, can handle and identify the ownership of different entities, thereby optimizing the distributed decision-making behavior among CCPP, RE and CU. Figure 2 The letters AJ in the middle represent the specific port numbers in the planning framework. Coal-fired power plants are retrofitted with carbon capture and storage devices to become carbon capture power plants (CCPPs). The flue gas generated during the power generation process is directed to the absorption tower of the carbon capture equipment (CC) for capture and treatment. Pure carbon dioxide is then extracted from the top of the stripping tower and stored in the carbon storage equipment (CS). Proton exchange membrane electrolyzers (ECs) are used as renewable energy consumption and hydrogen production equipment due to their high applicability to renewable energy (RE) and efficient energy conversion capabilities. EC and Sabatier reactors together form a carbon utilization unit (CU) device. The stored carbon dioxide is transported to the CU to react and generate methane, which is then transported to the nearest natural gas node. Based on the consideration of CCUS technology to transform coal-fired power plants, this planning framework can handle and identify the ownership of different entities, thereby optimizing the distributed decision-making behavior between CCPP, RE and CU.

[0062] In a park with an integrated electricity-gas energy system, the CCPP, RE, and CU each play distinct yet interdependent roles. In a market environment free of price negotiation, both CCPP and RE units sell electricity directly to the grid at the prevailing feed-in tariff. The electricity generated by the CCPP meets the energy needs of the internal carbon capture and storage facility, while the captured CO2 is sold to the carbon market at wholesale prices. Meanwhile, the CU unit purchases electricity from the grid at industrial prices and purchases CO2 from the carbon market at retail prices for chemical production.

[0063] According to relevant regulations, renewable energy suppliers can trade electricity directly with consumers, with power companies responsible for managing transmission. This example builds on the existing framework and proposes a collaborative game-based integrated electricity-gas energy system planning model that considers CCUS. Renewable energy suppliers pay grid access fees and trade electricity directly with consumers through the grid. CCPPs sell captured CO2 directly to consumers. The price and quantity of electricity and carbon are determined through a game-based process, with each participant striving to maximize their profits.

[0064] S2. Construct the distributed optimization objective functions and operation constraints of the carbon capture power plant model, renewable energy model and carbon utilization unit model.

[0065] (1) CCPP model: The maturity of post-combustion capture technology of monoethanolamine (MEA) makes decarbonization of large-scale coal-fired power plants feasible. Figure 3 As shown in the figure, flue gas from a coal-fired power plant enters the bottom of the absorber (end A), where CO2 is dissolved and captured by the cool, lean MEA solvent (blue). Unabsorbed CO2 (purified gas) exits end D. The CO2-rich solvent exits the bottom of the absorber and is heated in a heat exchanger (end B). The hot solvent then enters the top of the regeneration tower (end E), where the absorbed CO2 is released at high temperature and reduced pressure (end G). The hot lean solvent exits the bottom of the stripping tower (end F), is cooled in a heat exchanger, and circulates to the top of the absorber to repeat the capture process (green, end C).

[0066]

[0067]

[0068]

[0069]

[0070] In order to accurately describe the output characteristics of the carbon capture power plant, the CCPP model introduces unit start-stop constraints, as detailed in formulas (PP1) to (PP6), where: Carbon Capture Power Plant i exist t The operating status at all times, Carbon Capture Power Plant i The power, Carbon Capture Power Plant i exist t The power of the moment, Carbon Capture Power Plant i The shutdown power, Carbon Capture Power Plant i exist t- Power at 1 moment, Carbon Capture Power Plant i The climbing power, Carbon Capture Power Plant i The shortest running time, Carbon Capture Power Plant i The shortest shutdown time, Carbon Capture Power Plant i exist t The starting action of the moment, Carbon Capture Power Plant i exist t The initial carbon emissions of the power plant are calculated by the emission factor (Unit: kg / MWh) For calculation, see formula (CC1). Indicates that end A is t Input the CO2 mass at all times. To simulate the spatiotemporal variation of the carbon dioxide concentration in the absorber, the model uses a one-dimensional convection-diffusion partial differential equation, see formula (CC2) for details. Indicates the molar concentration of CO2 in the absorption tower (unit: mol / m 3 ), which is the distance from the top x and time t Related, represents the time-varying flow rate of the solution in the absorption tower, represents the absorption coefficient of end A, Indicates the mass of CO2 input at end A. Time-varying flow rate (Unit: m / s) is calculated by formula (CC3), where represents the density of the solvent, represents the cross-sectional area of the absorption tower, Indicates absorption tower i Middle B t CO2 mass at the moment, Indicates absorption tower i Center C t The quality of CO2 at the moment. The quality of the purified gas after absorption has a time delay relationship with the original flue gas, see formula (CC4) for details, where represents the emission factor of the scrubbed 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 Center C t-1 CO2 mass at the moment, The volume constraint of MEA solvent in the absorption tower is detailed in formula (CC5), where represents the liquid loss rate, represents the volume of solvent, Indicates absorption tower i Medium solvent t-1 The solvent is transported by an electric pump in the carbon capture device, and the mass loss is generally ignored, that is, it is assumed that , Regeneration tower i Middle E-end t CO2 mass at the moment, Regeneration tower i Middle F end tThe CO2 concentration at the top of the regeneration tower is the same as that at the bottom of the absorption tower, as shown in formula (CC6). Regeneration tower i Middle top t The CO2 molar concentration of the solution at the moment. The partial differential equation for the change of CO2 concentration in the regeneration tower (Stripper) is detailed in formulas (CC7) to (CC9), where, is the desorption coefficient, and the meanings of the other letters are the same as those in the absorption tower. Indicates the molar concentration of CO2 in the regeneration tower, which is related to the distance from the top x and time t Related, represents the time-varying flow rate of the solution in the regeneration tower, represents the time step in the absorption tower, Regeneration tower i Middle E-end t CO2 mass at the moment, Regeneration tower i Middle F end t CO2 mass at the moment, represents the density of MEA solution, represents the cross-sectional area of the regeneration tower, represents the liquid loss rate of the regeneration tower, Regeneration tower i Mid-G end t The CO2 mass at the moment. The liquid volume constraint of the regeneration tower is shown in formula (CC10). At the same time, to ensure the separation efficiency, the CO2 concentration in the rich / lean liquid must be lower than the set threshold, as shown in formulas (CC11) and (CC12). 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 the absorption tower and the regeneration tower is also limited by their maximum volume, see formula (CC13) and (CC14) respectively, where, Indicates absorption tower i investment capacity, Regeneration tower i For the carbon storage process, the model accurately simulates the CO2 pressure and mass flow rate in the storage tank, as shown in formulas (CS1)-(CS3), where represents the fixed tank volume, is the molar mass of CO2, is the CO2 gas constant, Indicates the inside of the tank t The volume of CO2 gas at the moment, Indicates the inside of the tankt-1 The volume of CO2 gas at the moment, Indicates storage tank i internal t The pressure of time, Indicates storage tank i internal t The differential of pressure with respect to time, Indicates the internal temperature of the tank, Regeneration tower i Middle H end t CO2 mass at the moment, 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 The constant about pressure. The investment capacity constraint of carbon storage equipment is shown in formula (CS4). Indicates storage tank i internal t The volume of CO2 gas at the moment, Regeneration tower i The investment capacity. Formula (CS5) defines the power balance relationship within the CCPP, where Indicates gas tank i Middle H end t CO2 mass at the moment, (Unit: MWh / ton) represents the carbon capture electricity consumption efficiency, The captured carbon can be sold in two ways (CS6): Sell to the carbon market, or at retail price The CCPP model introduces a material flow process model for post-combustion carbon capture based on the monoethanolamine (MEA) absorption method, replacing traditional linear approximate modeling. This model can realistically depict the physical interactions between multiple energy flows in the electricity-gas-carbon system, improving the model's physical reliability and engineering applicability.

[0071] (2) RE model:

[0072] Local renewable energy RE is used first to meet the electricity demand of CU, and its output power Must not exceed the predicted maximum value For details, see formula (RE1). Power-to-gas equipment operating power and surplus power Do not exceed RE output power , see formula (RE2)-(RE3) for details. In addition, RE meets the operating power of local power-to-gas equipment. On the basis of The electricity is sold to the upper grid at the above grid price, and the relevant transaction behavior is described by formula (RE4).

[0073] (3) CU model:

[0074] Under steady-state operating conditions, the gas production rates of EC and SR are approximately linearly related to their power consumption, as shown in formulas (CU1) and (CU2), respectively, where: Indicates I t CO2 mass at the moment, Indicates the operating efficiency of the equipment. Indicates the operating power of the device. Indicates the lower heating value of a substance. represents the EC equipment efficiency, Indicates EC device t Moment power, Indicates the lower calorific value of hydrogen. Indicates J t CO2 mass at the moment, represents the SR equipment efficiency, Indicates SR device t Moment power, Represents the lower heating value of methane. The hydrogen generated by the electrolyzer will be transported to the synthesis reactor, where it will react with compressed CO2 to produce methane through the "Power to Gas" (PtG) process. The reaction equation is: The molar conservation relationship in the synthesis reactor is given by formula (CU3), express t Pure CO2 mass at any moment, represents the molar mass of hydrogen, represents the molar mass of CO2, In addition, the investment capacity of the electrolyzer and reactor equipment should meet the maximum allowable power limit, see formula (CU4) and (CU5) respectively, Indicates the investment capacity of EC equipment, Indicates CU device t Moment power, Indicates the investment capacity of CU equipment. The CO2 required by CU can be obtained in two ways: one is to negotiate a transaction with a carbon capture power plant (CCPP); , and secondly, purchase from the carbon market at retail prices , express t Always negotiate with CCPP to trade CO2 quality, express t The CO2 mass is purchased from the carbon market at the retail price at all times, as shown in formula (CU6). The electricity supply and demand balance of CU is described by formula (CU7). express t Always negotiate with RE to trade electric power, express t Purchase electrical power from the grid at all times.

[0075] (4) Objective function: From the perspective of the CCPP, its objective function is mainly composed of the following four parts: the power generation cost of the conventional power generation unit, the investment cost of the carbon capture and storage system, the wholesale carbon price, and the Carbon trading income , bargaining price with carbon utilization units Carbon trading income The optimization model can be expressed as ,in Represents the quadratic cost function of power generation, which is used to describe the nonlinear variation characteristics of power generation cost. Carbon Capture Power Plant i exist t The power at the moment. In (O2), dr represents the discount rate, which is 0.05 in this embodiment. Representation device life cycle, Representation device The discount factor, Abbreviation for absorption tower. Abbreviation for regeneration tower. Abbreviation for storage tank.

[0076]

[0077] From the perspective of renewable energy RE, its objective function mainly consists of the following three parts: Revenue from electricity sales , according to the transaction price Revenue from electricity trading , and grid access fees charged by power companies. is a quadratic cost function related to grid access power, which is used to describe the changing characteristics of grid access cost. The optimization model can be expressed as ,in is the objective function of renewable energy, The constraints it follows.

[0078]

[0079] From the perspective of the carbon utilization unit CU, its objective function mainly consists of the following three parts: Gas revenue obtained , the cost of purchasing electricity on the grid , the gaming procurement cost of electricity , wholesale purchase cost of CO2 , the gaming procurement cost of CO2 , and the investment cost of CU itself. The optimization model can be expressed as , its Chinese is the objective function of CU, Representation device The unit investment cost, Representation device Unit investment capacity, As well as are the constraints followed by the model, Abbreviation for proton exchange membrane electrolyzer, Abbreviation for Sabatier reactor.

[0080]

[0081] S3. Based on the cooperative game alliance, according to the distributed optimization objective function and operation constraints, a collaborative game model is constructed by maximizing the Nash product.

[0082] Collaborative game model: For the nonlinear term introduced by the partial derivative in equation (CC2), the differential approximation from equation (R1) to (R2) is used for discretization. and represent the time step and height step in the absorption tower, respectively. 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).

[0083] Figure 4 Schematic diagram of the CO2 concentration calculation process inside the absorption tower. Figure 4 It can be seen that the current location MEA concentration at The calculation depends not only on the previous space segment The concentration value also depends on the previous time period Considering the continuous supply of MEA solvent in the system, the inlet concentration Treated as a constant , so that 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, set the time step The number of spatial segments is 10. The linearization method of the partial derivative term in Equation (CC9) will not be described here.

[0084]

[0085] The collaborative game framework proposed in this embodiment involves three independent entities: the CCPP, the RE, and the CU. The CCPP meets the electricity needs of its own carbon capture system and loads and generates revenue by selling captured CO2 to the methane synthesis unit (SR). The RE can sell electricity to the grid at the on-grid price or directly supply the CU unit at a negotiated price, bearing the corresponding grid access fees. The CU can obtain electricity from the local RE or the external grid, while purchasing CO2 from the CCPP or the external carbon market, synthesizing methane in the SR unit and selling it externally for revenue. Without considering the collaborative game, no electricity or carbon transactions occur between the three entities.

[0086] Typically, each participant independently formulates strategies to maximize their interests based on the information they possess. A cooperative game alliance can only be formed between the three parties when the following two conditions are met simultaneously: 1) Group Rationality: The overall cooperative benefit of the alliance must not be less than the sum of the individual benefits of the three parties in a non-cooperative situation; 2) Individual Rationality: The benefits obtained by each participant in the alliance shall not be lower than their independent benefits without participating in the alliance.

[0087] According to Nash game theory, this embodiment adopts Maximize the Nash product to build a cooperative game model. represents the total number of agents participating in the game, Indicates the agent The actual gain in the game, Indicates the agent The optimal benefit obtained without cooperation (i.e., negotiation failure) is the game interruption point. It represents the incremental benefits (i.e., profit surplus) gained by participating in the cooperative game. In summary, the cooperative negotiation model proposed in this embodiment in the electricity-gas integrated energy system is defined as follows, denoted as model .

[0088]

[0089]

[0090] S4. The alternating direction multiplier method is used to decompose and collaboratively solve the collaborative game model. By iteratively updating the Lagrange multiplier and penalty factor, the electricity-carbon bilateral transaction volume and dynamic negotiated price are generated for the collaborative planning of multi-agent electricity-gas integrated energy systems.

[0091] Cooperative game solution method: Because the model This is essentially a non-convex nonlinear optimization problem, and it is difficult to solve directly. Therefore, this embodiment first converts it into an equivalent alliance benefit maximization sub-problem model based on the geometric mean inequality. , that is, within the CCPP, RE and CU alliance, jointly solve the optimal solution for cooperation. It is further decomposed into three distributed optimization sub-models , corresponding to the local optimization problems of CCPP, RE, and CU, respectively. Compared with the centralized approach, ADMM allows each agent to solve its subproblem only locally, achieving global collaboration through limited information exchange, thereby effectively protecting privacy. To achieve collaborative solutions among agents, the Lagrange multiplier is introduced. and , and the penalty factor and , construct the corresponding augmented Lagrangian function, where Indicates the carbon / electricity trading volume that the CU unit expects to purchase. Figure 8 Schematic diagram of the distributed algorithm for the profit maximization subproblem based on ADMM.

[0092]

[0093]

[0094]

[0095] In order to obtain the transaction volume of the coordinated game Finally, considering the monotonicity of the natural logarithm function, the model Applying logarithmic transformation, it can be further converted into an equivalent form To achieve variable decoupling of transaction price, this embodiment introduces auxiliary variables and , which are used to characterize the carbon price and electricity price in the game between CCPP-CU and RE-CU respectively, so that the three-party model has distributed solution capabilities while retaining coordination. Figure 9 Schematic diagram of the distributed algorithm for the transaction game sub-problem based on ADMM.

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102] Experimental example: The simulation experiment involved in this embodiment is as follows Figure 1 The integrated power-gas energy system shown in the figure is implemented in a park. Key parameter settings are listed in Table 1. The simulation was modeled on the MATLAB platform and run using the Gurobi 10.0 optimization solver on a computer with a 2.6 GHz dual-core processor and 16 GB of memory.

[0103] Table 1 Key parameters

[0104] Tables 2 and 3 show the revenues of the CCPP, RE, and CU before and after the cooperative game. Positive values represent revenue, negative values represent costs, and N / A indicates not applicable. After implementing the cooperative game mechanism proposed in this example, the profits of the CCPP, RE, and CU increased by 50.8198 million, 50.8114 million, and 50.8157 million yuan, respectively, representing increases of approximately 6.68%, 14.72%, and 6.42% compared to their original profit levels. At the same time, the total profit of the alliance as a whole increased by 152.4469 million yuan. The incremental revenue for each participant was essentially equal, accounting for approximately one-third of the total gain, demonstrating the fairness and balance of this cooperative game approach in profit distribution.

[0105] Table 2 Comparison of CCPP and RE income changes before and after the cooperative game (unit: 10 4 RMB)

[0106] Specifically, when the carbon trading mechanism with CU is introduced, the power generation of CCPP and its corresponding carbon capture transformation investment cost will increase accordingly; after RE conducts power trading with CU, its power generation will also increase. Although it needs to pay grid access fees, most of the electricity is still sold to CU first. For CU, through the internal trading mechanism of the alliance, its carbon and electricity procurement costs are significantly reduced, while gas sales revenue is increased. Through the three-party cooperative game mechanism of the embodiment of the present invention, the individual benefits of the three types of entities are significantly increased, and the surplus value generated by the cooperation is distributed equally and fairly among the parties (obtained by solving the optimization problem), reflecting the incentive effectiveness and benefit distribution fairness of this mechanism in multi-agent collaboration.

[0107] Table 3 Comparison of CU income changes before and after the cooperative game (unit: 10 4 RMB)

[0108] Figure 5 It shows the real-time transaction price changes of CCPP, RE and CU. Figure 4 As can be seen, when the industrial electricity price is lower than the on-grid electricity price (i.e., between 01:00–07:00 and 23:00–24:00), no electricity trading occurs between the RE and the CU, and the CU chooses to purchase all its electricity from the external grid. When the real-time retail carbon price is lower than the wholesale carbon price (i.e., between 01:00–11:00 and 18:00–24:00), no carbon trading occurs between the CCPP and the CU. However, during periods of relatively high electricity or carbon prices, bargaining occurs between the three parties, leading to negotiated transactions. The actual transaction price at this time falls between the original selling price of the RE or CCPP and the purchase price of the CU, thus achieving a bargaining equilibrium.

[0109] Figure 6 The figure shows the daily changes in electricity trading volume among the CCPP, RE, and CU. As can be seen, during periods of low electricity prices (i.e., 01:00–07:00 and 23:00–24:00), CUs prefer to purchase electricity directly from the upstream grid rather than from local REs, as the on-grid tariff is higher than the industrial price. During other periods, CUs primarily purchase electricity from REs to meet their production needs. During peak electricity price periods, REs independently supply all of CUs' electricity needs. During flat periods (15:00–17:00), due to the limited generation capacity of REs, CUs still rely on external grids for some of their electricity needs. By effectively utilizing peak and valley electricity pricing mechanisms and game-playing strategies, CUs can effectively reduce their overall electricity procurement costs and achieve economic optimization of energy procurement.

[0110] Figure 7The trend of carbon trading volume changes within the day is shown. Figure 5 The carbon price changes in the CU are consistent. During peak carbon price periods, the CU purchases all required carbon resources from the CCPP at negotiated prices; during other periods, the CU purchases carbon resources from the external market at retail prices. The scatter plot changes and trend curves shown in the figure further demonstrate that, influenced by the operating patterns of the CU equipment, its daily electricity and carbon resource trading volumes exhibit consistent fluctuations over time, verifying the timeliness, responsiveness, and practicality of the embodiments of the present invention in coordinating multi-energy category trading.

[0111] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. Those skilled in the art will recognize that several equivalent substitutions or obvious variations can be made without departing from the scope of the present invention, and that any equivalent performance or application should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for planning an electricity-gas integrated energy system based on collaborative game, characterized in that: The following steps are involved: S1. Establish a multi-agent electric-gas integrated energy system model including a carbon capture power plant, renewable energy, and carbon utilization units; S2. Construct the distributed optimization objective functions and operation constraints of the carbon capture power plant model, renewable energy model, and carbon utilization unit model; S3. Based on the cooperative game alliance, construct a collaborative game model through distributed optimization according to the distributed optimization objective function and operation constraints; S4. The alternating direction multiplier method is used to decompose and collaboratively solve the collaborative game model. By iteratively updating the Lagrange multiplier and penalty factor, the electricity-carbon bilateral transaction volume and dynamic negotiated price are generated for the collaborative planning of multi-agent electricity-gas integrated energy systems.

2. The method for planning an integrated electricity-gas energy system based on collaborative game according to claim 1, characterized in that: In step S2, the operating constraints of the carbon capture power plant model include unit start-up and shutdown constraints, material flow dynamic equations during the absorption and regeneration process, and carbon storage pressure balance equations; the operating constraints of the renewable energy model include output forecast constraints, local load supply and demand balance equations, and electricity trading revenue equations; the operating 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.

3. The method for planning an electricity-gas integrated energy system based on collaborative game according to claim 2, characterized in that: 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 a one-dimensional convection-diffusion partial differential equation, specifically including the spatiotemporal evolution relationship between the solvent flow rate and the CO2 concentration; the material flow dynamic equations in the regeneration tower associate the CO2 concentration gradients of the rich liquid and the lean liquid through the desorption coefficient; the carbon storage pressure balance equation calculates the real-time storage mass based on the gas state equation and the fixed storage tank volume.

4. The method for planning an electricity-gas integrated energy system based on collaborative game 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 benefits of the alliance must not be lower than the sum of the individual benefits of each entity under non-cooperative circumstances; the individual rationality is that the benefits obtained by each participating entity in the alliance must not be lower than its independent benefits when it does not participate in the alliance.

5. The method for planning an electricity-gas integrated energy system based on collaborative game according to claim 4, characterized in that: The collaborative game model constructed by distributed optimization includes: constructing the collaborative game model by maximizing the Nash product. The Nash product formula is as follows: ； in, represents the total number of agents participating in the game, Indicates the agent The actual gain in the game, Indicates the agent The optimal benefit obtained without cooperation, that is, the game interruption point, the difference It represents the incremental income obtained by participating in the cooperative game, that is, the profit surplus.

6. The method for planning an electricity-gas integrated energy system based on collaborative game according to claim 1, characterized in that: Step S4 includes: S41. Based on the geometric mean inequality, the collaborative game model is equivalently transformed into a sub-problem of maximizing the alliance's benefits; S42. Decompose the alliance revenue maximization subproblem into distributed optimization submodels of each agent using the alternating direction multiplier method and solve them collaboratively. At the same time, introduce Lagrange multipliers and penalty factors for iterative updating. S43. Output the global optimal solution that satisfies the fairness of alliance profit distribution.

7. The method for planning an electricity-gas integrated energy system based on collaborative game according to claim 3, characterized in that: The absorption tower uses a monoethanolamine post-combustion capture method to capture carbon, and the one-dimensional convection-diffusion partial differential equation is as follows: ; in, Indicates the molar concentration of CO2 in the solution in the absorption tower, which is related to the distance from the top x and time t Related, represents the time-varying flow rate of the solution in the absorption tower, represents the absorption coefficient of end A, Indicates the quality of CO2 input at end A; The material flow dynamic equation in the regeneration tower is related to the CO2 concentration gradient of the rich liquid and the lean liquid through the desorption coefficient as follows: ; in, Indicates the molar concentration of CO2 in the regeneration tower, which is related to the distance from the top x and time t Related, 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, Regeneration Tower i Middle E-end t CO2 mass at the moment, Regeneration Tower i Middle F end t CO2 mass at the moment, represents the density of MEA solution, represents the cross-sectional area of the regeneration tower, represents the liquid loss rate of the regeneration tower, Regeneration Tower i Mid-G end t CO2 mass at the moment; The carbon storage pressure balance equation is based on the gas state equation and the fixed storage tank volume to calculate the real-time storage mass as follows: ; in, represents the fixed tank volume, is the molar mass of CO2, is the CO2 gas constant, Indicates the inside of the tank t The volume of CO2 gas at the moment, Indicates the inside of the tank t-1 The volume of CO2 gas at the moment, Indicates storage tank i internal t The pressure of time, Indicates storage tank i internal t The differential of pressure with respect to time, Indicates the internal temperature of the tank, Regeneration tower i Middle H end t CO2 mass at the moment, 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 about pressure.

8. The method for planning an electricity-gas integrated energy system based on collaborative game according to claim 7, characterized in that: The finite difference method is used to discretize the one-dimensional convection-diffusion partial differential equation, and the formula is as follows: ; in, Indicates the height step in the absorption tower.

9. The method for planning an electricity-gas integrated energy system based on collaborative game 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 income obtained at the wholesale carbon price, and the carbon trading income obtained by the carbon utilization unit at the game price; the objective function of the renewable energy model includes: the electricity sales income obtained according to the on-grid electricity price, the electricity trading income obtained according to the trading electricity price, and the grid access fee charged by the power company; the objective function of the carbon utilization unit model includes: the gas income obtained according to the gas price, the on-grid procurement cost of electricity, the game procurement cost of electricity, the wholesale procurement cost of CO2, the game procurement cost of CO2, and the investment cost of the carbon utilization unit itself.

10. The method for planning an electricity-gas integrated energy system based on collaborative game according to claim 9, characterized in that: The objective function of the carbon capture power plant model is as follows: ； in, Indicates the wholesale price of carbon Carbon trading income obtained, Represents the carbon utilization unit at a game price Carbon trading income obtained, represents the quadratic cost function with respect to the generated power, represents the discount rate, Representation device life cycle, Representation device The discount factor, Carbon Capture Power Plant i exist t The power of the moment, Abbreviation for absorption tower. Abbreviation for regeneration tower. Abbreviation for storage tank; The objective function of the renewable energy model is as follows: ； in, Indicates that according to the transaction electricity price The income from electricity trading, Indicates that the on-grid electricity price Revenue from electricity sales, is the quadratic cost function for grid access power, Indicates surplus power; The objective function of the carbon utilization unit model is as follows: ； in, Indicates that according to gas price The gas revenue obtained, represents the cost of purchasing electricity on the grid, Indicates that according to the transaction electricity price The transaction cost of electricity obtained, represents the wholesale purchase cost of CO2, represents the gaming purchase cost of CO2, Representation device The unit investment cost, Representation device Unit investment capacity, Abbreviation for proton exchange membrane electrolyzer, Abbreviation for Sabatier reactor.

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