Method for optimal operation of integrated energy system under electricity-gas-carbon combined market based on VCG mechanism

Abstract

The application discloses a kind of based on VCG mechanism's electricity-gas-carbon combined market under comprehensive energy system optimization operation method, constructs integrated power, natural gas, carbon cycle and green certificate transaction comprehensive energy system coupling model, maximizes social welfare by distribution robust optimization model, adopts Wasserstein fuzzy uncertainty set to process carbon price and green certificate price uncertainty, calculates each unit externality payment value using VCG mechanism and endogenously determines electricity price and gas price, finally outputs optimal scheduling scheme satisfying multiple constraints.The method can solve the problems of low efficiency of resource allocation and insufficient carbon emission reduction incentives in existing single market mechanism, improve the reliability of multi-market data correlation analysis by exploiting energy conversion and carbon cycle, enhance the robustness of data under constraints using Wasserstein uncertainty set, and significantly improve the reliability of data analysis and the accuracy of optimization decision-making in complex energy systems by avoiding strategic pricing distortion using VCG mechanism, providing support for the construction of new power systems.

Description

Technical Field

[0001] This invention relates to multi-energy coupling and carbon emission reduction technologies, and in particular to a method for optimizing the operation of an integrated energy system under a VCG-based electricity-gas-carbon joint market. Background Technology

[0002] With the large-scale deployment of renewable energy (RES), the coupling between power and natural gas systems is becoming increasingly close, driving their evolution towards an Integrated Electricity-Gas System (IEGS). Under the IEGS architecture, power-to-gas (PtG) units can convert excess renewable electricity into synthetic methane, thereby improving energy efficiency and enhancing system flexibility and dispatchability. Gas-fired power generation (GPG), as a thermoelectric power unit with regulating capabilities, connects the power and natural gas networks, playing a crucial role in system reliability and load regulation. To further reduce carbon emissions from fossil fuel power generation, carbon capture and storage (CCS) technology has been widely deployed and is gradually becoming an important emission reduction pathway under IEGS.

[0003] The current energy structure demands diversification and decarbonization. The integration of multi-energy coupling with carbon reduction technologies such as PtG, GPG, and CCS is considered a key measure for building efficient and clean energy systems. However, existing technologies mostly focus on single operating mechanisms for power systems, natural gas systems, or carbon markets, lacking a unified market design scheme to coordinate the energy and carbon emission interactions among these three systems. This separate market structure easily leads to problems such as inefficient resource allocation, slow system dispatch response, and a lack of carbon reduction incentives in actual operation. There is an urgent need to build a unified market coordination mechanism to achieve multi-energy synergy, internalization of externalities, and overall system optimization, thereby supporting the flexible, resilient, and decarbonized development goals of the integrated electricity-gas-carbon system. Meanwhile, how to accurately mine the intrinsic correlations between data involving energy conversion, carbon cycle and physical constraints of energy flow when processing multi-dimensional operational data such as power output, natural gas flow, carbon capture, and green certificate trading, so as to improve the reliability of multi-energy flow and multi-market data correlation analysis, the accuracy of responding to external price uncertainties, and the precision of multi-market collaborative optimization, and avoid data distortion caused by strategic pricing, is an urgent problem to be solved by existing technologies.

[0004] It should be noted that the information disclosed in the background section above is only for understanding the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The main objective of this invention is to overcome the deficiencies in the aforementioned background technology and provide a method for optimizing the operation of an integrated energy system under a VCG-based electricity-gas-carbon joint market.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for optimizing the operation of an integrated energy system under a VCG-based electricity-gas-carbon joint market includes the following steps:

[0008] S1. Construct a coupled model of an integrated energy system (IEGS) that includes a carbon capture power plant (PP-CCS), a gas generator set (GS-GPG), and a power-to-gas conversion device (RES-PtG). The coupled model integrates the power grid, the natural gas grid, the carbon cycle system, and the green certificate trading mechanism.

[0009] S2. Establish a bibliometric optimization model with the goal of maximizing social welfare, which includes electricity trading revenue, natural gas trading revenue, carbon trading revenue, green certificate trading revenue, and system operating costs; for the uncertainty of carbon price and green certificate price, construct a fuzzy uncertainty set based on Wasserstein distance to minimize the objective function under the worst-case expectation.

[0010] S3. Market clearing using the VCG mechanism: Calculate the optimal social welfare when each participating unit (PP-CCS, GS-GPG, RES-PtG) is present; Virtually remove each participating unit one by one and recalculate the maximum social welfare; Determine the externality payment value of each unit based on the difference in social welfare before and after removal; Determine the electricity and gas prices based on the payment value and the endogenous output of the unit.

[0011] S4. Solve the bibliophile optimization model and output the optimal scheduling scheme that satisfies the electric-gas network constraints and the multi-market balance condition.

[0012] Furthermore, in step S1:

[0013] The power network is modeled using DC power flow equations, which include generator output constraints, ramp rate constraints, and node phase angle constraints.

[0014] The natural gas network is modeled using the Weymouth equation, which introduces binary variables to represent the direction of gas flow in the pipeline. McCormick relaxation is used to handle the bilinear terms of the pressure square difference and flow direction, and flow and pressure constraints are applied.

[0015] The carbon cycle system captures CO2 in the PP-CCS unit and supplies it to the RES-PtG unit to synthesize methane, forming a closed-loop carbon cycle.

[0016] The green certificate trading requires fossil fuel units to purchase green certificate allowances of no less than a predetermined percentage of their electricity generation.

[0017] Furthermore, the PP-CCS unit modeling includes:

[0018] The generating capacity of the unit is divided into the grid output power and the power consumed by the carbon capture system;

[0019] The capture capacity is calculated based on the capture power consumption conversion factor and power consumption, and is constrained by the capture efficiency and the unit's carbon emission factor;

[0020] Binary variables are introduced to represent the start-up and shutdown states of the unit, and constraints on minimum continuous operating time and minimum downtime are applied.

[0021] Furthermore, the GS-GPG unit modeling includes:

[0022] Natural gas supply is divided into gas consumption for gas-fired power generation and gas supply from external pipeline networks.

[0023] The output of the gas turbine unit is constrained by the maximum / minimum technical output and the ramp rate, while the start-stop time constraint is ignored.

[0024] The gas consumption for power generation is calculated by converting the unit's energy efficiency coefficient and the lower heating value of methane.

[0025] Furthermore, the RES-PtG unit modeling includes:

[0026] The output of renewable energy is constrained by the forecast range, and surplus electricity is supplied to power-to-gas conversion plants.

[0027] The electro-to-gas process is divided into two stages: electrolysis for hydrogen production and methanation. The hydrogen and methane production are calculated using efficiency coefficient conversion, respectively.

[0028] The mass of CO2 required for methane synthesis and the mass of CH4 produced satisfy a fixed stoichiometric ratio.

[0029] Furthermore, the electrical network balance constraints include:

[0030] The power node balance equation relates to the power of renewable energy units, PP-CCS units, GS-GPG units, and transmission line power;

[0031] The natural gas node balance equation relates the flow rates of natural gas sources, PtG production units, and gas transmission pipelines;

[0032] The power-phase relationship of the power network is described using DC power flow equations;

[0033] The linearized Weymouth equation is used to describe the relationship between gas flow rate and the squared difference of nodal pressure.

[0034] Furthermore, the social welfare objective function in step S2 is specifically as follows:

[0035] Electricity trading revenue = electricity price × (PP-CCS grid output power + GPG output power + RES surplus power);

[0036] Natural gas trading revenue = gas price × (natural gas supply from outside sources + PtG methane production);

[0037] Green certificate revenue = Green certificate price × (RES surplus electricity - projected percentage × total fossil fuel power generation);

[0038] Carbon trading revenue = carbon price × (PP - CCS capture volume - PtG CO2 consumption);

[0039] Operating costs = coal-fired unit costs + gas source costs + gas-fired unit costs + renewable energy costs. The first three costs are calculated using a quadratic cost function, while renewable energy costs include maintenance costs and curtailment penalty costs.

[0040] Furthermore, the construction of the Wasserstein fuzzy uncertainty set includes:

[0041] Based on historical carbon prices and green certificate price samples, an empirical distribution is defined.

[0042] The uncertainty set is defined with the empirical distribution as the center and the Wasserstein distance as the radius. The radius parameter is calculated by the maximum sample bias and the preset confidence level.

[0043] A rectangular boundary set is introduced to shrink the feasible region, the boundary of which is determined by the sample mean and a preset tolerance parameter.

[0044] Furthermore, the implementation process of the VCG mechanism in step S3 includes:

[0045] S3.1 Solve for the optimal scheduling scheme that maximizes social welfare when all units are present;

[0046] S3.2 Perform a virtual removal operation on each unit and re-solve for the optimal social welfare after removal;

[0047] S3.3, Calculate the unit's pay value = social welfare after removal - welfare of other units under the current optimal solution after removal;

[0048] S3.4 Calculate the individual energy price based on the unit payment value and output, and publish the unified price of the system.

[0049] Furthermore, in step S4, when solving the sub-Bruker optimization model, it is transformed into a mixed integer programming problem, a simulation model is constructed, and the Gurobi solver is used for optimization calculation; the optimal scheduling scheme satisfies the following constraints:

[0050] Power network constraints include upper and lower limits for generator output power, ramp rate constraints, upper and lower limits for transmission line power flow, and node phase angle range constraints.

[0051] Natural gas network constraints include upper and lower limits for natural gas pipeline flow, range constraints for the squared value of node pressure, constraints related to pipeline gas flow direction, and natural gas node balance constraints.

[0052] Carbon cycle system constraints include the relationship between carbon capture and power consumption in the PP-CCS unit, the performance constraints of capture capacity, and the mass ratio constraints of CO2 and CH4 in the methanation process of the PtG unit.

[0053] Multi-market balance constraints: supply and demand balance in the electricity market, supply and demand balance in the natural gas market, trading balance in the carbon market, and trading balance in the green certificate market. Among these, electricity and natural gas market prices are endogenously determined by the VCG mechanism, while carbon prices and green certificate prices are exogenously set.

[0054] The present invention has the following beneficial effects:

[0055] This invention proposes an optimized operation method for integrated energy systems under a VCG-based electricity-gas-carbon joint market. Addressing the lack of a joint electricity-gas-carbon trading mechanism in current multi-energy systems, this method proposes a novel integrated energy system (IEGS) optimization operation scheme that integrates carbon capture and storage (CCS), power-to-gas (PtG) devices, and gas-fired generators (GPG). It also establishes a unified scheduling and market settlement framework, improving incentive compatibility and market efficiency in the electricity-gas-carbon emission joint market. This effectively solves the problem of insufficient reliability and accuracy in existing mechanisms when processing multi-dimensional data such as power output, natural gas flow, carbon capture, and price signals in the coupled operation of multiple electricity-gas-carbon systems. By constructing a unified framework, it accurately captures the intrinsic correlation between multi-energy flows and multi-market data. This optimized operation method simultaneously considers multi-market transactions in electricity, natural gas, carbon emissions, and green certificate markets. By introducing a VCG mechanism to construct an IEGS market clearing model to determine endogenous electricity and natural gas prices, it ensures that participants' payments match their externalities, thereby incentivizing participants' genuine bidding behavior. To address the uncertainties surrounding external carbon emissions and green certificate prices, this invention constructs a Distributed Robust Optimization (DRO) framework based on the Wasserstein uncertainty set and embeds it into the operational model. This ensures the robustness of the solution under data constraints, addresses disturbances in external carbon prices and green certificate prices, and guarantees the reliability of system operation.

[0056] This invention helps to improve the incentive mechanism design of the low-carbon integrated energy market, provides technical support for improving the overall operating efficiency and fairness of the system, and enhances the reliability and accuracy of the analysis of complex energy system operating data, making system optimization decisions more in line with actual physical constraints and market laws, ensuring the stability of multi-energy coordinated operation and the achievement of low-carbon goals. It has important engineering value and application prospects for promoting the construction of new power systems and achieving low-carbon goals.

[0057] Other beneficial effects of the embodiments of the present invention will be further described below. Attached Figure Description

[0058] Figure 1 This is a diagram illustrating the operational and trading framework of IEGS under the unified market.

[0059] Figure 2 This is a topology diagram of the IEEE 24-node power system and the Belgian 20-node natural gas system.

[0060] Figure 3 This is a comparison chart of the VCG mechanism and the LMP mechanism.

[0061] Figure 4 Intraday price comparison chart under different mechanisms.

[0062] Figure 5 The utility changes of different gas source nodes under false pricing.

[0063] Figure 6 This is the overall flowchart of the method for optimizing the operation of an integrated energy system under a VCG-based electricity-gas-carbon joint market, as described in this invention. Detailed Implementation

[0064] The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary and not intended to limit the scope and application of the present invention.

[0065] See Figure 6 This invention provides a method for optimizing the operation of an integrated energy system under a VCG-based electricity-gas-carbon joint market, comprising the following steps:

[0066] Step S1: Construct an integrated energy system (IEGS) coupled model that includes a carbon capture power plant (PP-CCS), a gas generator set (GS-GPG), and an electricity-to-gas conversion unit (RES-PtG). The coupled model integrates the power grid, the natural gas grid, the carbon cycle system, and the green certificate trading mechanism.

[0067] In some embodiments, in step S1: the power grid is modeled using DC power flow equations, including generator output constraints, ramp rate constraints, and node phase angle constraints; the natural gas grid is modeled using the Weymouth equations, introducing binary variables to characterize the pipeline airflow direction, employing McCormick relaxation to handle the bilinear terms of pressure square difference and flow direction, and applying flow and pressure constraints; the carbon cycle system captures CO2 through the PP-CCS unit and supplies it to the RES-PtG unit for methane synthesis, forming a closed-loop carbon cycle; the green certificate trading requires fossil energy units to purchase green certificate quotas of no less than a predetermined percentage (e.g., 15%) of their power generation.

[0068] In some embodiments, the PP-CCS unit modeling includes: dividing the unit's power generation into grid output power and carbon capture system power consumption; calculating the capture amount based on the capture power conversion coefficient and power consumption, and subject to capture efficiency and unit carbon emission factor; introducing binary variables to characterize the unit's start-up and shutdown states, and applying minimum continuous operating time and minimum downtime constraints.

[0069] In some embodiments, the GS-GPG unit modeling includes: dividing the natural gas source supply into gas consumption for gas power generation and gas supply from the external pipeline network; the output of the gas turbine unit is constrained by the maximum / minimum technical output and the ramp rate, while ignoring start-up and shutdown time constraints; the gas consumption for power generation is calculated by converting the unit's energy efficiency coefficient and the lower heating value of methane.

[0070] In some embodiments, the RES-PtG unit modeling includes: renewable energy output is constrained by the prediction range, and surplus electricity is supplied to the power-to-gas conversion device; the power-to-gas conversion process is divided into two stages: electrolysis for hydrogen production and methanation, and the hydrogen production and methane production are calculated by efficiency coefficient conversion respectively; the mass of CO2 required for methane synthesis and the mass of CH4 produced satisfy a fixed stoichiometric ratio.

[0071] In some embodiments, the power-gas network balance constraints include: power node balance equations relating renewable energy units, PP-CCS units, GS-GPG units, and transmission line power; natural gas node balance equations relating natural gas sources, PtG production units, and gas pipeline flow; DC power flow equations describing the power-phase angle relationship of the power network; and linearized Weymouth equations describing the relationship between gas flow and the squared difference of node pressure.

[0072] Step S2: Establish a bibliometric optimization model with the goal of maximizing social welfare, which includes electricity trading revenue, natural gas trading revenue, carbon trading revenue, green certificate trading revenue, and system operating costs; for the uncertainty of carbon price and green certificate price, construct a fuzzy uncertainty set based on Wasserstein distance to minimize the objective function under the worst-case expectation.

[0073] In some embodiments, the social welfare objective function in step S2 is specifically:

[0074] Electricity trading revenue = electricity price × (PP-CCS grid output power + GPG output power + RES surplus power);

[0075] Natural gas trading revenue = gas price × (natural gas supply from outside sources + PtG methane production);

[0076] Green certificate revenue = Green certificate price × (RES surplus electricity - predetermined percentage (e.g., 15%) × total fossil fuel power generation);

[0077] Carbon trading revenue = carbon price × (PP - CCS capture volume - PtG CO2 consumption);

[0078] Operating costs = coal-fired unit costs + gas source costs + gas-fired unit costs + renewable energy costs. The first three costs are calculated using a quadratic cost function, while renewable energy costs include maintenance costs and curtailment penalty costs.

[0079] In some embodiments, the construction of the Wasserstein fuzzy uncertainty set includes: defining an empirical distribution based on historical carbon price and green certificate price samples; defining an uncertainty set with the empirical distribution as the center and the Wasserstein distance as the radius, the radius parameter being calculated by the maximum sample deviation and a preset confidence level; and introducing a rectangular boundary set to shrink the feasible region, the boundary of which is determined by the sample mean and a preset tolerance parameter.

[0080] Step S3: Market clearing using the VCG mechanism: Calculate the optimal social welfare when each participating unit (PP-CCS, GS-GPG, RES-PtG) is present; Virtually remove each participating unit one by one and recalculate the maximum social welfare; Determine the externality payment value of each unit based on the difference in social welfare before and after removal; Determine the electricity price and gas price based on the payment value and the endogenous output of the unit.

[0081] In some embodiments, the VCG mechanism implementation process in step S3 includes:

[0082] S3.1 Solve for the optimal scheduling scheme that maximizes social welfare when all units are present;

[0083] S3.2 Perform a virtual removal operation on each unit and re-solve for the optimal social welfare after removal;

[0084] S3.3, Calculate the unit's pay value = social welfare after removal - welfare of other units under the current optimal solution after removal;

[0085] S3.4 Calculate the individual energy price based on the unit payment value and output, and publish the unified price of the system.

[0086] Step S4: Solve the sub-Bluerg optimization model and output the optimal scheduling scheme that satisfies the electric-gas network constraints and the multi-market balance condition.

[0087] In some embodiments, when solving the sub-Bruker optimization model in step S4, it is transformed into a mixed integer programming problem, a simulation model is constructed, and the Gurobi solver is used for optimization calculation; the optimal scheduling scheme satisfies the following constraints:

[0088] Power network constraints include upper and lower limits for generator output power, ramp rate constraints, upper and lower limits for transmission line power flow, and node phase angle range constraints.

[0089] Natural gas network constraints include upper and lower limits for natural gas pipeline flow, range constraints for the squared value of node pressure, constraints related to pipeline gas flow direction, and natural gas node balance constraints.

[0090] Carbon cycle system constraints include the relationship between carbon capture and power consumption in the PP-CCS unit, the performance constraints of capture capacity, and the mass ratio constraints of CO2 and CH4 in the methanation process of the PtG unit.

[0091] Multi-market balance constraints: supply and demand balance in the electricity market, supply and demand balance in the natural gas market, trading balance in the carbon market, and trading balance in the green certificate market. Among these, electricity and natural gas market prices are endogenously determined by the VCG mechanism, while carbon prices and green certificate prices are exogenously set.

[0092] The technical advantage of this invention lies in its application to integrated energy systems under the combined electricity-gas-carbon market. It processes multi-dimensional operational data covering electricity output, natural gas flow, carbon capture, and green certificate trading. Through the Bruker bar optimization algorithm (based on Wasserstein uncertainty set) and related solution algorithms of the VCG mechanism, it uncovers the intrinsic correlations among data such as the physical laws of electricity and natural gas energy conversion (e.g., the stoichiometric relationship between PtG electrolysis for hydrogen production and methanation, and the energy conversion efficiency constraints of GPG power generation), the material conservation laws of the carbon cycle (e.g., the physical conversion between PP-CCS capture and energy consumption, and the mass ratio of PtG CO2 consumption to methanation), and the physical constraints of energy flow characterized by the DC power flow of the power grid and the Weymouth equation of the natural gas network. This solution addresses the technical problems of low resource allocation efficiency and insufficient response to external price uncertainties under traditional single-market mechanisms. By accurately capturing the intrinsic correlations between physical quantities and economic parameters in the energy system through algorithms, it improves the reliability and accuracy of multi-market collaborative optimization. This ensures the satisfaction of physical constraints in the electricity-gas network (such as power flow, pressure, and ramp rate) while addressing carbon price and green certificate price disturbances through a robust optimization framework. The final optimal scheduling scheme achieves a balance between maximizing social welfare and ensuring system robustness. The correlations captured by these algorithms are based on the operational patterns of the energy system, providing precise technical support for system optimization and effectively achieving the aforementioned technical effects.

[0093] The following further describes specific embodiments, algorithm examples, and experimental verifications of the present invention.

[0094] A method for optimizing the operation of an integrated energy system under a VCG-based electricity-gas-carbon joint market, comprising the following:

[0095] (1) The IEGS operation and trading framework under the joint market

[0096] Figure 1This invention presents an IEGS (Integrated Energy Storage and Control System) operation and trading framework under a combined electricity-gas-carbon market. The IEGS of this invention includes coal-fired power plants (PP), renewable energy sources (RES), natural gas sources (RES), CCS (Carbon Capture and Renewable Energy), GPG (Gas Propellant Gas), and PtG (PotG) units, collectively forming a multi-energy system coupling infrastructure. PP units, after being modified and integrated with CCS units, form carbon capture power plants (PP-CCS), and utilize their own power generation to meet the electricity demand of the carbon capture process. PtG units utilize the excess electricity generated by RES to produce hydrogen through electrolysis, and then combine the hydrogen with captured carbon dioxide (CO2) to synthesize methane. Simultaneously, GPG units can convert natural gas back into electricity during peak electricity demand periods, providing flexible regulation capabilities for the power grid.

[0097] The IEGS described in this invention operates collaboratively across four interconnected markets: electricity, natural gas, carbon, and green certificates (GC). These markets are coupled through economic mechanisms to achieve coordinated, low-carbon operation of the entire system. In the electricity market, PP-CCS, GS-GPG, and RES-PtG units submit bids based on their respective marginal generation costs. The market settlement mechanism determines the corresponding electricity prices and dispatch schemes to achieve a balance between grid supply and demand. In the natural gas market, the synthetic methane produced by GS and PtG participates in the process, further strengthening the coupling relationship of the electrical system through market price competition. In the carbon market, PP-CCS units obtain carbon credits by capturing CO2, while PtG units purchase industrially sourced CO2 as feedstock for methane synthesis, forming a closed-loop carbon cycle system that promotes emission reduction and synthetic fuel production. In the green certificate market, RES-PtG units obtain green certificates based on clean energy generation, while fossil fuel units (such as PP-CCS and GPG) must purchase green certificates to meet regulatory requirements. Electricity and gas prices are determined endogenously by market clearing mechanisms, while carbon prices and green certificate prices are exogenously set by relevant regulations. To effectively address the uncertainty of carbon prices and green certificate prices, this invention employs the Distributed Bruker Optimization (DRO) method based on Wasserstein uncertainty sets for modeling and optimization.

[0098] (2) PP-CCS model

[0099] The PP-CCS coupling device refers to the modification of a traditional coal-fired power unit (PP) with a carbon capture system (CCS) to achieve centralized capture of carbon emitted during its operation, thereby controlling carbon emissions. Formula (1) indicates that the electrical energy generated by the PP unit during operation can be divided into two parts: one is the output power supplied to the external power grid, denoted as... The second is the power supply for the operation of the CCS device, denoted as... The generator set's output power is limited by the following constraints: one of which is the binary operating state variable. Indicates the start / stop status (when) t When the generator unit is in operation ,otherwise Secondly, the upper and lower limits of output power, i.e., minimum output power. With maximum output See formula (2) for details. Furthermore, the unit's output variation is limited by the ramp rate; its ramp and descent power should not exceed the maximum ramp power. and maximum power drop For details, please refer to formula (3). To accurately characterize the start-up and shutdown behavior of the generator set, this invention introduces binary variables. and , respectively indicating that the unit is in t Start-up and shutdown behavior at specific times (if) This indicates that the unit itself t-1 Start at any time; if This indicates that the unit itself t-1 (Shut down immediately; otherwise, the value is 0). Startup behavior must meet the unit's minimum continuous operating time. Constraints are detailed in Formula (6). Shutdown actions must meet the unit's minimum downtime. Constraints are detailed in Formula (7).

[0100]

[0101]

[0102] Post-combustion carbon capture technology has been widely used in numerous commercial projects due to its excellent system compatibility and scalability. In this invention, the amount of CO2 captured is based on a capture power consumption conversion factor. (Unit: MWh / t) and power consumption calculation of the CCS device are detailed in formula (8). To ensure the physical feasibility of the capture process, the capture volume must meet the following two performance constraints: firstly, the energy efficiency level of the carbon capture system. Secondly, the unit carbon emission factor of the thermal power units in service. The corresponding constraints are shown in formula (9). Meanwhile, to avoid CCS overload operation, its power consumption should also be subject to the rated minimum power. and maximum power The constraints are shown in formula (10).

[0103]

[0104] (3) GS-GPG model

[0105]

[0106] Gas-fired generator sets (GPGs) do not emit sulfur dioxide or particulate matter during operation, and their flue gas CO2 concentration is significantly lower than that of traditional coal-fired units, demonstrating good environmental friendliness. Formula (11) describes the gas volume of a natural gas source with a GPG unit deployed. Supply constraints This indicates the rated maximum output. (GPG unit output) Its climbing performance is constrained by formulas (12) and (13), respectively, where Indicates maximum climb power. This represents the maximum power reduction. Compared to renewable energy (RE) generators and traditional coal-fired generators (PP), gas-fired generators have higher energy conversion efficiency and faster dynamic response speed. Therefore, for this type of generator, time-related start-stop constraints such as minimum continuous operating time and minimum downtime can be ignored. In addition, the gas supplied by the natural gas source GS shown in formula (14) can be divided into two parts, of which... This refers to the part used to support the power generation of the GPG generator unit. Indicates the coefficient of performance (COP) of a GPG device. This indicates the lower heating value of methane gas; the remainder is supplied to the external natural gas pipeline network. .

[0107] (4) RES-PtG model

[0108]

[0109]

[0110] PtG (Pollution-to-Gas) technology serves as an important supplementary pathway for natural gas production, effectively alleviating supply pressure on natural gas networks. The entire process of this technology consists of two stages: electrolysis for hydrogen production and methanation, corresponding to equations (16) and (17), respectively. This indicates the minimum / maximum operating power of the PtG. Indicates hydrogen conversion efficiency. express t Hydrogen production rate at any given time. The energy efficiency coefficient of PtG is... express t The amount of methane produced at any given time. In the methanation process, there is a fixed quantitative relationship between the required mass of CO2 and the amount of CH4 produced, based on the chemical reaction equation. The corresponding mass ratio is shown in equation (18). This indicates the molar mass of CH4 / CO2.

[0111]

[0112] Renewable energy sources such as wind and solar power exhibit significant volatility, and their increasing penetration rate poses a challenge to the stability of power grid operation. To ensure the reliability of system operation, this invention imposes a predictive range constraint on the output of renewable energy sources, ensuring their actual output power. Controlling the predicted output The relevant constraints are detailed in equation (19). Under the premise of satisfying the prediction boundary constraints, Surplus electricity available for external trading is defined as The calculation method is shown in equation (20).

[0113] (5) Energy balance constraint

[0114]

[0115] To achieve coupling between the IEGS and external power grid nodes and natural gas nodes, this invention establishes an energy balance constraint model for a multi-energy system. Let... Let represent the power nodes in the external power grid, and their power balance relationship is shown in equation (21). In this equation: Represents power nodes With renewable energy units The elements of the correlation matrix between them; Represents a node PP-CCS with carbon capture coal-fired units The elements of the correlation matrix between them; Represents a node With gas-fired power plants GS-GPG The elements of the correlation matrix between them; Represents a node With transmission lines The topological connections between them; For nodes At any moment The power load demand. This invention uses the DC power flow equation (22) to estimate the steady-state operation of the system during the planning period, where the reactance value is... Phase angle with node The relationship is based on the per-unit power reference. (Value taken as 100 MW) is used to characterize it. To ensure the operational safety of the power system, equation (23) constrains the maximum allowable power flow of the transmission line, and equation (24) limits the range of phase angle values ​​of the power nodes.

[0116]

[0117]

[0118]

[0119] set up Let represent the gas nodes in the natural gas network, and their gas balance relationship is shown in equation (25). Where: Indicates gas nodes With gas source The elements of the connection matrix between them; Indicates gas nodes With electro-gas PtG device The connection relationship between them; Indicates gas nodes With natural gas pipelines The connection relationship between them; For nodes At any moment The natural gas load. The values ​​of the above matrix elements follow the following rules: if there is an actual connection, the value is 1; if there is no connection, the value is 0. This invention uses the Weymouth equation to model the natural gas flow, the expression of which is: .in, For gas in the pipeline From the starting node Flow to the terminal node Instantaneous flow rate. Left-hand item. Used to characterize bidirectional gas flow, right-hand term This represents the squared difference in nodal pressure. To simplify the model structure, variables are introduced in equations (26)–(27). The pressure square term replacing the gas node and set binary variables and , respectively representing pipes The direction of gas flow, either forward or reverse. Meanwhile, to ensure system safety, equations (28) and (29) impose upper and lower limits on the gas flow rate and the squared value of the node pressure, respectively. To eliminate the bilinear form of the right-hand side of the constraint equation (26). This invention introduces the McCormick Relaxation method to linearize this constraint. Specifically, the bivariate product... By an auxiliary continuous variable Substitution. This auxiliary variable is constrained by a set of inequalities, where and They represent the time intervals. Output node and input node The square value of the pressure. Based on this, in constraint (30) Indicates pipeline Pipeline coefficient, product term It can be further equivalently represented as a convex quadratic set, as shown in Equation (31), thereby improving the computational solvability and convergence performance of the model.

[0120] (6) Objective function

[0121] The optimization objective function of this invention aims to maximize the total social welfare of the system, taking into account the following five components: 1) Electricity trading revenue 2) Natural gas trading revenue 3) Profits from green certificate trading 4) Carbon trading revenue 5) System operating costs .

[0122] Among them, green certificate trading revenue Through the market price of green certificates The green certificate is calculated by multiplying it by the corresponding traded electricity volume. Each green certificate corresponds to 1 MWh of renewable energy generation. All fossil fuel power generation units must purchase green certificates for at least 15% of their generation quota to comply with the green quota system.

[0123]

[0124] In this invention, the power generation cost of coal-fired power units and gas-fired equipment is expressed as a quadratic function, as shown in formulas (32)–(35). Wherein, and These are the primary, secondary, and constant coefficients for the operating costs of PP, GS, and GPG, respectively. For the renewable energy unit RES, its cost... The components include equipment maintenance costs. and the cost of penalties for power curtailment As shown in equation (35).

[0125]

[0126] (7) Wasserstein's uncertain set

[0127] In the electricity-gas-carbon joint market constructed in this invention, the electricity price ( ) and gas price ( Carbon prices are determined endogenously within the system through market clearing mechanisms and are therefore not included in the scope of external uncertainty modeling. In contrast, carbon prices... ) and Green Certificate Price ( The conditions are exogenously determined by regulations or external market mechanisms, and therefore involve significant uncertainty.

[0128] To characterize the uncertainty of the aforementioned price variables, this invention constructs a fuzzy uncertainty set based on Wasserstein distance. Let the historical observation samples be represented as... Based on this, the empirical distribution is defined. The specific form is shown in equation (36), where Indicates a sample The Dirac measure centered on this empirical distribution. The radius, measured by the Wasserstein distance, is... A corresponding set of uncertainties can be defined. See equation (37) for details. The distance calculation formula is shown in equation (38). Indicates all and The set of joint distributions of marginal distributions. This represents the Euclidean norm. The radius parameter of the uncertain set. Based on confidence level The setting is shown in equation (39).

[0129]

[0130] To further narrow the feasible region, this invention introduces a rectangular boundary box based on the Wasserstein uncertainty set. This enhances the practical constraint capability of the uncertain set. Let the sample mean vector be defined as... ,in , These represent the sample mean values ​​of carbon price and green certificate price, respectively. This is achieved through a preset relative tolerance parameter. and This allows for the further determination of the upper and lower bounds of each variable, specifically: Based on this, a fuzzy uncertain set with rectangular support constraints can be defined, and its set form is shown in Equation (40) and Equation (41), respectively.

[0131]

[0132] Best practice

[0133] (1) IEGS split-bar optimization operation model

[0134] The IEGS running model constructed in this invention is formulated as a mixed integer programming problem. In this model, electricity price... With gas prices As an endogenous variable, carbon price is dynamically determined through market clearing mechanisms; while carbon price Price of Green Certificate As an exogenous parameter, its volatility is affected by uncertainty through the introduction of the Wasserstein fuzzy uncertainty set. Modeling. Based on this, the objective function is optimized according to the "worst-case expectation" criterion, that is, across all possible distributions. To maximize the minimum expected value of total social welfare, the following IEGGS sub-bar optimization model is constructed:

[0135]

[0136] For the uncertain terms in the objective function ,in , According to the closed-form expression of Wasserstein's uncertain set, the expected value term in the plural bar optimization problem can be equivalently reconstructed into a deterministic form.

[0137] This makes the original problem solvable.

[0138] (2) VCG clearing mechanism

[0139] In the combined electricity-gas-carbon market, the traditional Locational Marginal Pricing (LMP) mechanism easily triggers strategic bidding behavior from participants. Fossil fuel generators typically raise their bids to pass on carbon capture or fuel costs; renewable energy producers tend to lower their bids to secure dispatch rights and green certificate revenue; and PtG and CO2 utilization units, as purchasers, also lower their bids to reduce input costs. To prevent market participants from inflating costs, maintain market fairness and efficiency, and maximize system social welfare, this invention introduces the VCG mechanism proposed by William Vickrey, Edward H. Clarke, and Theodore Groves. The core idea of ​​this mechanism is that the system operator "virtually removes" each participant and recalculates the optimal social welfare value when that participant is absent. Subsequently, based on the change in the total welfare of other participants caused by the participant's absence, the fee that participant must pay is determined. This payment value is the externality cost that participant inflicts on the system. Because the VCG mechanism is incentive compatible, participants are motivated to truthfully declare their utility functions under this mechanism, thereby ensuring the authenticity and reliability of the optimization process and the global optimum.

[0140] The implementation of this mechanism involves four steps: maximizing social welfare, calculating payment amounts, calculating utility, and determining prices. Let... The set of all feasible operating states in the joint market. For the set of bidder IDs, Indicates the bidder Operational plan The actual utility function. For a set of real utilities The problem of maximizing social welfare can be expressed as ,in, Including participants The set of all other participants besides [the group].

[0141] If a participant is removed Then solve for the corresponding optimal running scheme. Then the participant's VCG payout value is This value measures the impact of other participants' factors. The total social welfare lost due to participation is equivalent to The negative externalities imposed on the system. During market settlement, each type of VCG participant is bound to the operating unit corresponding to its function: carbon capture participants correspond to PP-CCS, PtG participants correspond to RES-PtG, and GPG participants correspond to GS-GPG.

[0142] In energy markets, prices are typically determined by supply and demand, reflecting the system's current marginal cost—the minimum incremental cost required to increase output by one unit. In this invention, based on the aforementioned VCG mechanism, all types of operating units first submit their actual power generation cost bids to the Independent System Operator (ISO). The ISO uses this as a basis to solve the problem of maximizing the system's total social welfare and determines the optimal resource allocation scheme accordingly. Subsequently, the ISO performs a "virtual removal" of each participant, recalculating the optimal social welfare value in their absence to assess their marginal externality to the system. This difference in externality is the VCG compensation (VCGPayment) that participant should pay. Ultimately, the market prices for electricity and natural gas are no longer obtained through the dual variables of traditional constraints but are directly derived from the VCG payment mechanism. This approach not only preserves incentive compatibility but also maintains compatibility with the structure of existing market models, effectively promoting deep coupling between the electricity-gas-carbon multi-energy system. At each time point, the system's uniform price can be calculated by dividing the total payment amount of all participants by their total output. The individual energy price for each participant is determined by dividing their VCG payment by their output. It should be noted that this price is only used for information transparency dissemination and not for market settlement. The optimal operation solution process for IEGS based on the VCG mechanism proposed in this invention is described in the following algorithm.

[0143]

[0144]

[0145]

[0146] (3) Basic Overview of the Implementation Location

[0147] The simulation model of this invention was built in the MATLAB environment and optimized using the Gurobi 12.0.1 solver. All simulations were performed on a computer equipped with a 2.6 GHz Intel Core i7 processor and 16 GB of memory. The example is based on a joint modeling of the improved IEEE 24-node power system and the Belgian 20-node natural gas system, covering a typical 24-hour operating cycle. Key parameter settings are detailed in Table \ref{parameter}. In the constructed IEGS, six PtG (Power to Gas) units were set up and deployed at grid nodes [3, 5, 7, 16, 21, 23], and connected one-to-one with gas grid nodes [15, 10, 7, 16, 1, 3]. At the same time, five GPG (Gas-to-Gas) units were configured, located at gas grid nodes [1, 2, 5, 8, 13], and connected to grid nodes [21, 22, 20, 18, 13]. Figure 2 The topology of the IEEE 24-node power system and the Belgian 20-node natural gas system is shown.

[0148] The IEGS system constructed in this invention operates simultaneously in the following four types of markets, where the carbon price and green certificate price are exogenously set: in the carbon quota trading market, the carbon price is set at $60 / ton; in the green certificate market, the certificate price is $40 / MWh, and fossil fuel power generation units must meet at least 15% renewable energy quota requirements; the prices in the electricity and natural gas markets are determined internally through the VCG mechanism, with initial reference prices set at $50 / MWh and $0.3 / MWh respectively. The external uncertainties of carbon prices and green certificate prices are addressed through a biblical optimization model based on Wasserstein distance, with a confidence level set at 95%.

[0149] Table 1 Key Parameters

[0150]

[0151] (4) Analysis of running results

[0152] To verify the effectiveness of the constructed IEGS model, four scenarios were designed for comparative analysis. Case 1 served as the baseline scenario, while Cases 2–4 were run under conditions excluding CCS, PtG, and GPG, respectively, to quantify the marginal contribution of each component to the system's social welfare. The specific scenarios are shown below:

[0153] Case 1: Complete IEGS system operation, including CCS, PtG and GPG units;

[0154] Case 2: Removal of Carbon Capture and Storage (CCS);

[0155] Case 3: Removal of PtG from electro-gas converter;

[0156] Case 4: Removal of gas-fired power generation GPG.

[0157] Table 2 shows the system operation indicators for each case study. The integration of different technologies significantly impacted the system's performance and revenue structure. In all scenarios, the trading revenue from electricity and natural gas remained stable at USD 488.4 million and USD 11890.2 million, respectively, indicating that the system load was consistently met. The differences between the schemes mainly lie in carbon reduction performance and the degree of renewable energy integration. In Case 2 (removal of CCS), the RES utilization rate decreased from 96.2% to 87.4%, green certificate revenue decreased from USD 100.4 million to USD 53.8 million, and carbon trading revenue was zero. Due to the absence of CCS, the electricity originally consumed was supplied to the load by conventional units, limiting the grid connection space for renewable energy, thus leading to a significant decrease in RES utilization and green certificate revenue. In Case 3 (removal of PtG), the RES utilization rate further decreased to 76.8%, green certificate revenue decreased to USD 93.8 million, carbon trading revenue was USD 165.3 million, and there was no longer any synthetic methane production. This indicates that PtG plays a crucial role in promoting renewable energy consumption and building a closed-loop carbon cycle. In Case 4 (removal of GPG), RES utilization actually increased to 98.4%, and green certificate revenue rose to $102.6 million, but carbon trading revenue decreased to $125.0 million. Due to the loss of the regulation capacity provided by GPG, the system became more reliant on renewable energy output, thereby reducing the power supply from CCS and thus decreasing carbon capture capacity. These results demonstrate that CCS, PtG, and GPG play irreplaceable roles in carbon emission reduction, multi-energy complementarity, and system flexibility assurance, respectively, and their absence will weaken the overall operating efficiency and profitability of the system from different dimensions.

[0158] Table 2 Results of different scenarios

[0159]

[0160] From a technological contribution perspective, the removal of various units under different scenarios significantly altered the system's operational trade-offs and resource allocation methods. In Case 2, despite the removal of the carbon capture unit (CCS), methane production increased. This was because the absence of the CCS freed up some electrical resources, allowing the PtG unit to obtain more electricity for methane synthesis. Although carbon emission reduction capacity was limited, the power-gas synergy was enhanced. In Case 3, carbon capture decreased to 30.4 kt. At this point, to avoid penalties for wind curtailment, the system prioritized wind power absorption, thus compressing the output space of dispatchable units (PP and GPG), resulting in insufficient available power for the CCS, operational constraints, and a significant decrease in carbon capture levels. In Case 4, carbon capture further decreased to a minimum of only 26.8 kt, but the RES utilization rate reached 98.4%, the highest among all scenarios, while the synthesized methane also decreased to 3.9 Mm. 3 The results indicate that when the system lacks flexible adjustment resources such as GPGs, scheduling priorities will favor renewable energy to ensure supply and demand balance, thus sacrificing some carbon reduction and synthesis capabilities. Overall, IEGS achieves a dynamic balance between carbon reduction, system flexibility, and renewable energy utilization during operation. The removal of different units significantly affects the system's optimal scheduling strategy, and this model effectively characterizes and quantifies the marginal contribution of each key technological unit to the system's social welfare.

[0161] (5) Analysis of the superiority of the VCG mechanism

[0162] Figure 3The diagram illustrates a comparison between the VCG mechanism based on externality pricing proposed in this invention and the traditional LMP mechanism (Lower Marginal Price Mechanism) in market clearing. In the LMP mechanism, marginal generating units set their market clearing price based on their marginal generation cost, resulting in higher producer surplus for non-marginal generating units. This clearing method easily leads to market participants manipulating market prices by submitting artificially high or low bids, resulting in strategic bidding behavior and undermining market fairness and efficiency. In contrast, the VCG mechanism calculates the marginal impact on overall social welfare by "virtually eliminating" each market participant and determines the payment amount each participant should receive. Specifically, each participant's payment equals the reduction in system social welfare after their exit, i.e., the externality cost they incur. Based on this design, the VCG mechanism achieves incentive compatibility, suppresses strategic bidding behavior, and ensures optimal system resource allocation. As shown in the shaded area on the right side of the diagram, this mechanism significantly reduces unreasonable producer surplus, links payments to actual system contributions, and achieves more fair and transparent settlement rules.

[0163] Figure 3 This study compares the hourly electricity price trends of the No. 21 power bus and the gas price trends of the No. 1 gas node under the VCG and LMP mechanisms. The No. 21 power bus has a complex network topology, integrating CCS, PtG, and renewable energy devices, connecting multiple transmission lines, and coupling with the natural gas system via pipelines. The No. 1 gas node is a key gas supply point with bidirectional coupling capabilities. Under the LMP mechanism, the electricity price during the evening peak period is pushed up to $112 / MWh, and the gas price reaches $0.48 / m³; while under the VCG mechanism, the clearing prices for the same period are only about $90 / MWh and $0.34 / m³, respectively. VCG restructures the payment mechanism, rather than the spot electricity price itself. Because the VCG payment mechanism adopts a "pay first, then deduct" approach, each participant determines its payment amount based on its marginal impact (i.e., externality) on the system's social welfare. With the coordinated operation of CCS, PtG, and RES, the price obtained by VCG is based on utility calculations, thus the price curve remains stable and generally low. LMP directly reflects grid congestion in node prices, while VCG endogenizes physical constraints as externalities, resulting in smoother and more coordinated price changes, avoiding the drastic price fluctuations that occur when LMP is triggered by network constraints.

[0164] Figure 4This study compares the hourly electricity price changes at node 21 and the hourly gas price changes at node 1 under the VCG and LMP mechanisms. Node 21 has a complex structure, integrating CCS, PtG, and renewable energy units, and features multiple transmission lines and electricity-gas coupling pipelines; while node 1 is a key gas supply hub, supporting bidirectional gas flow. Under the LMP mechanism, the electricity price climbs to $112 / MWh during peak evening hours, while the gas price reaches $0.48 / m³; under the VCG mechanism, the electricity price during the same period is only about $90 / MWh, and the gas price also decreases to $0.34 / m³. VCG does not directly change spot prices but restructures payment rules. This mechanism first determines the total payment amount based on social welfare and then settles through externality compensation. With the coordinated participation of CCS, PtG, and renewable energy, the node price generated by VCG reflects the marginal utility of each entity, resulting in a smoother and more stable price curve. In contrast, LMP directly transmits the congestion effect to node prices; while VCG internalizes physical constraints as externality costs, forming a more systematic and continuous price response, thus effectively avoiding the drastic price jumps that occur with LMP when there are network bottlenecks.

[0165] Table 3 Comparison of LMP and VCG Mechanisms

[0166]

[0167] STD: Standard deviation, --: Not applicable

[0168] Table 3 further compares the VCG and LMP mechanisms. Under the VCG mechanism, VCG social welfare represents the overall social welfare resulting from a participant's inclusion in the system; VCG compensation is the loss of social welfare for other subjects when the participant is removed, reflecting its externality; VCG utility is the actual retained benefit of the participant, equal to VCG social welfare minus VCG compensation. In contrast, the LMP mechanism only reflects marginal costs and does not consider externalities. Furthermore, VCG significantly reduces price volatility. The standard deviation of electricity prices decreased from 28.4 to 7.7, 15.5, and 6.7 (corresponding to RES / GPG / CCS); the standard deviation of gas prices decreased from 7.9 to 3.7 and 1.8 (corresponding to GS / PtG). These results validate the significant advantages of VCG in improving system stability and participant utility.

[0169] To further verify the nature of the VCG mechanism, Figure 5The utility changes of different gas source nodes (GS) under false bidding were compared. Under the VCG mechanism, participants' utility is maximized when they truthfully declare their costs. Whether raising or lowering bids, the final net profit decreases, thus verifying the known incentive compatibility of the VCG mechanism. Conversely, under the LMP mechanism, some participants can increase their own profits by falsely declaring prices. For example, raising bids may push up the system LMP at certain times, thereby obtaining a higher settlement price. This result indicates that VCG encourages truthful declarations and effectively curbs strategic bidding behavior, while LMP has the incentive risk of being manipulated.

[0170] In summary, this invention addresses the operational scenarios of integrated energy systems within a combined electricity-gas-carbon market. It effectively processes multi-dimensional operational data encompassing electricity output, natural gas flow, carbon capture, and green certificate trading. Through the Bruker bar optimization algorithm (based on Wasserstein uncertainty set) and the VCG mechanism solution algorithm, it accurately uncovers the intrinsic relationships between data—including the physical laws governing the energy conversion between electricity and natural gas (such as the stoichiometric relationship between PtG electrolysis for hydrogen production and methanation, and the energy conversion efficiency constraints of GPG power generation), the material conservation laws of the carbon cycle (such as the physical conversion between PP-CCS capture and energy consumption, and the mass ratio of PtG CO2 consumption to methanogen production), and the physical constraints of energy flow characterized by DC power flow in the power grid and the Weymouth equation in the natural gas network. This scheme effectively addresses the technical problems of insufficient reliability in multi-energy flow and multi-market data correlation analysis and low accuracy in responding to external price uncertainties under traditional single-market mechanisms. By deeply integrating physical constraints and economic parameters through algorithms, it improves the accuracy of multi-market collaborative optimization—the Wasserstein uncertainty set accurately quantifies the uncertainties of carbon prices and green certificate prices, ensuring the robustness of the model under data constraints. The VCG mechanism calculates externalities through "virtual removal," ensuring a precise match between market-clearing prices and physical contributions, avoiding data distortion caused by strategic bidding. Ultimately, the optimal scheduling scheme output by the system can achieve a balance between maximizing social welfare and operational robustness while satisfying the physical constraints of the electricity-gas network, significantly improving the reliability of big data analysis and the accuracy of optimization decisions for complex energy systems.

[0171] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various equivalent substitutions or obvious modifications can be made without departing from the concept of the present invention, and all such modifications, achieving the same performance or application, should be considered within the scope of protection of the present invention.

Claims

1. A method for optimal operation of an integrated energy system under a VCG mechanism-based electricity-gas-carbon combined market, characterized in that, The method comprises the following steps: S1, a comprehensive energy system IEGS coupling model is constructed, which comprises a carbon capture power plant PP-CCS, a gas generator set GS-GPG and an electricity-to-gas device RES-PtG, and the coupling model integrates an electricity network, a natural gas network, a carbon cycle system and a green certificate trading mechanism; wherein the electricity network is modeled by a direct current flow equation, and comprises generator set output constraints, ramp rate constraints and node phase angle constraints; the natural gas network is modeled by a Weymouth equation, a binary variable is introduced to represent the direction of pipeline gas flow, McCormick relaxation is used to process the bilinear term of pressure square difference and flow direction, and flow and pressure constraints are applied; the carbon cycle system is modeled by a PP-CCS unit to capture C O2, and supply RES-PtG unit to synthesize methane, forming a closed-loop carbon cycle; the green certificate trading requires fossil energy units to purchase no less than a predetermined percentage of green certificate quotas of their power generation; the RES-PtG unit modeling includes: renewable energy output is constrained by a prediction range, and surplus electricity is supplied to the electricity-to-gas device; the electricity-to-gas process is divided into two stages of hydrogen production by electrolysis and methanation, and hydrogen production and methane production are calculated by efficiency coefficient conversion respectively; C O2 required for methane synthesis is obtained from the PP-CCS unit, and the mass of generated C O2 meets the fixed stoichiometric ratio; S2, a distribution robust optimization model is established to maximize social welfare, which includes power trading revenue, natural gas trading revenue, carbon trading revenue, green certificate trading revenue and system operation cost; a fuzzy uncertainty set based on Wasserstein distance is constructed to minimize the objective function under the worst-case scenario; the construction of the Wasserstein fuzzy uncertainty set comprises: defining an empirical distribution based on historical carbon price and green certificate price samples; defining an uncertainty set with the empirical distribution as the center and the Wasserstein distance as the radius, and calculating the radius parameter through the maximum deviation of the samples and the preset confidence level; introducing a rectangular boundary set to shrink the feasible region, and the boundary is determined by the sample mean and the preset tolerance parameter; S3, market clearing is performed by using the VCG mechanism: the optimal social welfare of each participating unit in the field is calculated; each participating unit is virtually removed one by one, and the maximum social welfare is recalculated; the externality payment value of each unit is determined according to the difference in social welfare before and after removal; the electricity price and the gas price are determined based on the payment value and the unit output; wherein, the implementation process of the VCG mechanism comprises: solving the optimal dispatching scheme of the maximum social welfare of all units in the field; performing a virtual removal operation on each unit, and recalculating the optimal social welfare after removal; calculating the unit payment value = the social welfare after removal - the welfare of other units under the current optimal scheme; calculating the individual energy price according to the unit payment value and the output, and publishing the unified price of the system; S4, the distribution robust optimization model is solved, and an optimal dispatching scheme that meets the power-gas network constraints and multi-market balance conditions is output.

2. The method of claim 1, wherein, The PP-CCS unit modeling comprises: The power generation of the unit is divided into grid output power and carbon capture system power consumption; The capture amount is calculated according to the capture power consumption conversion coefficient and the power consumption, and is constrained by the capture efficiency and the unit carbon emission factor; Binary variables are introduced to represent the start-stop state of the unit, and minimum continuous operation time and minimum shutdown time constraints are applied.

3. The method of claim 1, wherein, The GS-GPG unit modeling comprises: The gas supply of the natural gas source is divided into gas consumption of the gas-fired unit and external pipeline gas supply; The output of the gas-fired unit is constrained by the maximum / minimum technical output and the ramp rate, and the start-stop time constraint is ignored; The gas consumption for power generation is calculated through the unit energy efficiency coefficient and the low heat value of methane.

4. The method of claim 1, wherein, The power-gas network balance constraints comprise: The power node balance equation is related to the renewable energy unit, the PP-CCS unit, the GS-GPG unit and the power transmission line power; The natural gas node balance equation is related to the natural gas source, the PtG gas production unit and the gas pipeline flow; The direct current flow equation is used to describe the power-angle relationship of the power network; The linearized Weymouth equation is used to describe the relationship between gas flow and the square difference of node pressure.

5. The method of claim 1, wherein, The social welfare objective function in step S2 is specifically: Power trading revenue = electricity price × (PP-CCS grid output power + GPG output + RES surplus power); Natural gas trading revenue = gas price × (natural gas source external supply + PtG methane production); Green certificate revenue = green certificate price × (RES surplus power - scheduled percentage × total fossil energy power generation); Carbon trading revenue = carbon price x (PP - CCS capture - PtG consumed C quantity); Operating cost = coal-fired unit cost + gas source cost + gas-fired unit cost + renewable energy cost, wherein the first three are quadratic cost functions, and the renewable energy cost includes maintenance cost and penalty cost of abandoned electricity.

6. The method of Claim 1, wherein, In step S4, the distribution robust optimization model is solved by converting it into a mixed integer programming problem, constructing a simulation model and using a Gurobi solver for optimization calculation; the optimal scheduling scheme meets the following constraints: Power network constraints: including upper and lower limits of generator output power, climbing rate constraints, upper and lower limits of power flow of transmission lines, and node phase angle range constraints; Natural gas network constraints: including upper and lower limits of natural gas pipeline flow, range of node pressure square value, pipeline gas flow direction related constraints, and natural gas node balance constraints; Carbon cycle system constraints: the constraints of the relationship between the carbon capture amount and the power consumption of the PP-CCS unit, the performance constraints of the capture amount, and the mass ratio constraints of C and C in the methanation link of the PtG device; Multi-market balance constraints: power market supply and demand balance, natural gas market supply and demand balance, carbon market transaction balance, and green certificate market transaction balance, wherein the prices of the power and natural gas markets are endogenously determined by the VCG mechanism, and the carbon price and green certificate price are exogenously set.

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