A method for collaborative planning of multi-energy systems and CCUS based on nash game

Abstract

The application provides a multi-energy system and CCUS collaborative planning method based on Nash game, and the method comprises the following steps: establishing a multi-energy system containing CCUS, establishing accurate mathematical models of each part of the system corresponding to the system framework, including a power transmission network model, a natural gas network model, an energy hub model, a CCUS process model and a CCUS source-sink matching model, establishing a multi-energy system and CCUS collaborative planning model based on Nash game in combination with the accurate mathematical models of each part of the system, and obtaining the best cooperation scheme of system joint investment operation and cost sharing; the application has the beneficial effects that the advantages of the multi-energy system and the CCUS technology are combined, the advantages of the energy hub and the CCUS energy saving and emission reduction are fully exerted, and the low carbon property and the economy of the multi-energy system are better realized.

Description

Technical Field

[0001] This invention relates to the field of energy route planning, and in particular to a multi-energy system and CCUS collaborative planning method based on Nash game theory. Background Technology

[0002] Multi-energy systems containing CCUS take electricity and natural gas systems as their core, and through their numerous internal energy conversion and storage devices, they achieve coordinated management and mutual support among various energy systems. This is of great significance for increasing the proportion of renewable energy and improving the overall efficiency of energy utilization. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides a multi-energy system and CCUS collaborative planning method, device, and storage medium based on Nash game theory. The multi-energy system and CCUS collaborative planning method based on Nash game theory mainly includes the following steps:

[0004] S1. Establish a multi-energy system framework including CCUS;

[0005] S2. Establish accurate mathematical models for the corresponding system framework, including transmission network model, natural gas network model, energy hub model, CCUS process model, and CCUS source-sink matching model;

[0006] S3. Based on the precise mathematical models of each part of the system, a multi-energy system and CCUS collaborative planning model is established using Nash game theory to obtain the optimal cooperation scheme for joint investment, operation and cost sharing of the system.

[0007] Furthermore, the multi-energy system framework with CCUS is based on electro-gas-thermal coupling and CCUS technology. The system framework includes generator sets, CHP equipment, gas boilers, carbon capture equipment, carbon dioxide storage tanks, P2G equipment, and natural gas storage tanks.

[0008] Furthermore, the transmission network model uses DC power flow to establish a linear relationship between line power flow and bus phase angle to achieve power grid planning:

[0009] P gen,n,t +P CHP,n,t -P ud,n,t -P pump,n,t -P P2G,n,t =K1·PL el,mn,t (1a)

[0010]

[0011]

[0012] -π≤θ n,t ≤π (1d)

[0013] |PL el,mn,t |≤PL el,mn,max (1e)

[0014] |PL el,mn,t |≤PL el,mn,max ·x bl，mn (1f)

[0015] θ ref =0 (1g)

[0016] Where m and n are line nodes, including pipelines and energy hubs; P gen,n,t P pump,n,t P CHP,n,t and P P2G,n,t PL represents the power of the generator set, carbon capture device, CHP device, and P2G device at node n at time t, respectively; el,mn,t P is the active power transmitted by line mn at time t; ud,n,t The power demand of node n at time t represents the power required by the user; K1 is the transmission matrix of the transmission line; x mn It is the line reactance; θ m,t and θ n,t θ represents the phase angle of the transmission network bus at time t at energy hub nodes m and n; ref Indicates the reference phase angle of the transmission network bus; x bl，mn Indicates the investment status of newly constructed lines; P Lel,mn,max This represents the maximum transmission capacity of line mn, where M is an extremely large number.

[0017] Furthermore, the natural gas network model uses the pressure difference between pipeline nodes to drive the flow of gas within the pipeline, thereby realizing the transportation of natural gas through the pipeline. The pressure difference is expressed as a nonlinear function of node pressure and natural gas pipeline flow rate.

[0018]

[0019]

[0020] -GT mn,max ≤GT mn,t ≤GT mn,max (2c)

[0021] P n,min ≤P n,t ≤P n,max (2d)

[0022] Where sgn() is the sign function; P m,t and P n,t GT represents the air pressure at nodes m and n at time t; mn,tG represents the amount of natural gas transported by line mn at time t; fur,n,t and G CHP,n,t V represents the natural gas consumption of the gas-fired boiler and combined heat and power equipment at time t. gass,n,t U represents the natural gas supply from the natural gas supplier at node n at time t; CH4,n,t K2 represents the amount of natural gas supplied by the natural gas storage tank at node n at time t; K2 is the natural gas pipeline transportation matrix; GT mn,max Indicates the maximum transmission capacity of line mn; C mn，t It is the pipeline coupling coefficient; P n,min and P n,max This represents the minimum and maximum pressure limits at node n.

[0023] Furthermore, the relationship between the electricity and heat output of each energy hub node is described by mathematical formula (3a):

[0024]

[0025] Among them, L e,n,t and L h,n,t η represents the electricity and natural gas generated at energy hub n at time t, respectively; gen,n and η fur,n η represents the efficiency of the generator set and gas boiler at node n; CHP,gen,n and η CHP,fur,n This represents the efficiency of the CHP device at node n.

[0026] 0≤P gen,n,t ≤Q gen,n (3b)

[0027] 0≤P fur,n,t ≤P fur,n,max (3c)

[0028] P fur,n,t =η fur,n ·H gas,fur ·G fur,n,t (3d)

[0029] 0≤P CHP,n,t ≤P CHP,n,max (3e)

[0030] P CHP,gen,n,t =η CHP,gen,n ·H gas,CHP ·G CHP,n,t (3f)

[0031] P CHP,h,n,t =η CHP,h,n ·H gas,CHP ·G CHP,n,t (3g)

[0032] Among them, Q gen,n P represents the planned capacity of the generator at node n; fur,n,max and P CHP,n,max Indicates the maximum capacity of gas-fired boilers and CHP equipment; H gas,fur and H gas,CHP It is the calorific value of natural gas used in gas-fired boilers and CHP equipment; P CHP,gen,n,t and P CHP,h,n,t These represent the electrical power and thermal power of the CHP device, respectively.

[0033] Furthermore, the CCUS process model includes a carbon emission model, a carbon capture model, a carbon storage model, a P2G model, and a natural gas storage model. The specific steps are as follows:

[0034] S241. During the operation of a multi-energy system, the equipment generates a large amount of carbon dioxide; a carbon emission model is established based on the relationship between carbon emissions and equipment power and carbon emission coefficient.

[0035] The carbon balance equation is shown in equation (4a).

[0036] P gen,n,t ·k gen +P fur,n,t ·k fur +P CHP,n,t ·k CHP =Q cap,n,t +Q emi,n,t (4a)

[0037] In the formula k gen k fur and k CHP These are the carbon emission coefficients for generator sets, gas-fired boilers, and combined heat and power (CHP) equipment, respectively; Q cap,n,t and Q emi,n,t This represents the amount of carbon dioxide captured and emitted at node n at time t.

[0038] S242. Carbon dioxide is cooled to 40°C and guided to the absorber. Considering the relationship between the carbon dioxide absorption amount and the concentration of MEA solvent at the inlet and outlet, as well as the constraints of the inlet and outlet flow rates of the absorber, a carbon capture model is established using the carbon dioxide absorption coefficient, the cross-sectional area of ​​the absorber, the inflow and outflow of carbon dioxide from the absorber, the concentration, density, and flow rate of the MEA solution.

[0039] Considering the effects of the mass flow rate from inlet to outlet and the amount of carbon dioxide captured in the absorber, the partial differential equation is:

[0040]

[0041] r1[Q cap,n,t-1 ]=η Ab ·Qcap,n,t-1 ·ρ MEA (5b)

[0042]

[0043] In the formula, C Ab,MEA,n (x,t) represents the MEA solution concentration at position x of the absorber at node n at time t; v Ab,n,t Let be the flow rate of the solution in the absorber at node n at time t; r1[] is the carbon dioxide absorption function, and the carbon dioxide absorption rate is related to the flue gas mass flow rate; η Ab ρ is the carbon dioxide absorption coefficient. MEA Q represents the density of the MEA solution. cap,n,t Q represents the amount of carbon captured at node n at time t; Ab,ch,n,t and Q Ab,disch,n,t S represents the inflow and outflow of the absorber at node n at time t; Ab The cross-sectional area of ​​the absorber is represented; the steady-state model in the partial differential equation can be approximated as follows:

[0044]

[0045] in, and This represents the average concentration of the MEA solution in the absorber at time t and time t-1.

[0046]

[0047] C MEA,min ≤C Ab,MEA,n (x,t)≤C MEA,max (5f)

[0048] Among them, L Ab C represents the height of the absorber. MEA,min and C MEA,max To ensure absorption efficiency, the lower and upper limits of the solubility of the MEA solvent in the absorber are defined; considering the complement of the newly added MEA solvent, C Ab,MEA,n (0,t) remains constant at the original concentration C. 0,MEA .

[0049] The amount of carbon dioxide absorbed in the absorber is related to the concentration of MEA solvent at the inlet and outlet as follows:

[0050] (C Ab,MEA,n (L Ab ,t)·Q Ab,disch,n,t -C Ab,MEA,n (0,t)·Q Ab,ch,n,t-1 )·Δt=Q ab,in,n,t-1 • The inlet and outlet flow rates of the absorber must meet the following constraints: Δt (6a)

[0051] -ΔQ Ab,n,max ≤Q Ab,ch,n,t -Q Ab,disch,n,t ≤ΔQ Ab,n,max (6b)

[0052] Q Ab,ch,n,min ≤Q Ab,ch,n,t ≤Q Ab,ch,n,max (6c)

[0053] Q Ab,disch,n,min ≤Q Ab,disch,n,t ≤Q Ab,disch,n,max (6d)

[0054] Among them, Q ab,in,n,t-1 ΔQ represents the amount of carbon dioxide absorbed by the absorber at node n at time t-1. Ab,n,max This is the maximum limit on the difference between import and export flows; Q Ab,ch,n,max and Q Ab,ch,n,min Q represents the minimum and maximum limits for the absorber inlet flow rate; Ab,disch,n,max and Q Ab,disch,n,min These are the minimum and maximum limits for the absorber outlet flow rate.

[0055] At the same time, the absorber should meet the mass flow rate constraint:

[0056] W Ab,n,t =(1-μ Ab W Ab,n,t-Δt +(Q Ab,ch,n,t -Q Ab,disch,n,t )·Δt (7a)

[0057] W Ab,n,min ≤W Ab,n,t ≤W Ab,n,max (7b)

[0058] Among them, W Ab,n,t Let μ be the volume of the solution inside the absorber at time t. Ab W is the loss coefficient during the absorber flow process; Ab,n,min and W Ab,n,max This is due to the capacity limitation of the absorber.

[0059] Assume the carbon dioxide after distillation flows at a mass flow rate Q. cap,out,n,t Leaving from the top of the stripping tower, the partial differential equation is expressed as:

[0060]

[0061] r2[C St,MEA,n [x,t)]=η St ·C St,MEA,n (x,t)·ρ MEA(8b)

[0062] In the formula, C St,MEA,n (x,t) represents the MEA solution concentration at position x in the stripper at node n at time t; v St,n,t Let be the flow rate of the solution in the stripper at node n at time t; r2[] is the carbon dioxide extraction function, and the carbon dioxide extraction efficiency is proportional to the concentration of the MEA solvent; η St The extraction coefficient for carbon dioxide is denoted as .

[0063] To calculate the steady-state model in the partial differential equations, we adopt the following approximation scheme:

[0064]

[0065] in, and Q represents the average concentration of the MEA solution in the stripper at time t and time t-1; where Q is the average concentration of the MEA solution at time t-1. St,ch,n,t and Q St,disch,n,t S represents the inflow and outflow of the stripper at node n at time t; St L is the cross-sectional area of ​​the stripper. St This refers to the height of the stripper.

[0066] C St,MEA,n (0,t)=C Ab,MEA,n (L Ab ,t) (9b)

[0067]

[0068] C MEA,min ≤C St,MEA,n (x,t)≤C MEA,max (9d)

[0069] For the outlet of pure carbon dioxide, considering the time aspect of the extraction process, the balance constraints are as follows:

[0070] (C St,MEA,n (0,t-1)·Q St,ch,n,t-1 -C St,MEA,n (L St ,t)·Q St,disch,n,t )·(1-μ St )=Q cap,out,n,t (10a)

[0071] The restrictions on the inlet and outlet flow rates of the stripping tower are as follows:

[0072] -ΔQ St,max ≤Q St,ch,n,t -Q St,disch,n,t ≤ΔQ St,max (10b)

[0073] Q St,ch,min ≤Q St,ch,n,t ≤Q St,ch,max (10c)

[0074] Q St,disch,min ≤Q St,disch,n,t ≤Q St,disch,max (10d)

[0075] Where, ΔQ St,n,max This is the maximum limit on the difference between import and export flows; Q St,ch,n,max and Q St,ch,n,min Q represents the minimum and maximum limits for the inlet flow rate of the stripper; St,disch,n,max and Q St,disch,n,min Minimum and maximum limits for the extractor outlet flow rate; μ St This represents the loss coefficient during the flow process in the stripper.

[0076] Considering the principle of mass conservation during the capture process, we have:

[0077] Q St,ch,n,t =Q Ab,disch,n,t (11a)

[0078] Q St,disch,n,t =Q Ab,ch,n,t (11b)

[0079] Similarly, strippers must also adhere to mass flow rate limits:

[0080] W St,n,t =(1-μ St W St,n,t-Δt +(Q St,ch,n,t -Q St,disch,n,t )·Δt (12a)

[0081] W St,n,min ≤W St,n,t ≤W St,n,max (12b)

[0082] Among them, W St,n,t W represents the volume of the solution inside the stripper. St,n,min and W St,n,max This is due to the capacity limitation of the stripper.

[0083] S243. The absorbed carbon dioxide is stored in a carbon dioxide storage tank. Considering the constraints of pressure and temperature, inlet and outlet flow rates and mass flow rate balance in the carbon dioxide storage tank, a carbon storage model is established based on the pressure and mass flow rate inside the tank.

[0084] The dynamic function expressions for the pressure and mass flow rate inside the tank are as follows:

[0085]

[0086] In the formula, and T represents the filling and releasing volume of the gas storage tank at node n at time t; C,n,t U represents the temperature inside the gas storage tank at node n at time t. C,tank This refers to the volume of the storage tank. It is the molar mass of carbon dioxide. p is the gas constant of carbon dioxide; C,tank,n Let n be the pressure inside the tank at node n.

[0087] The steady-state model formula is as follows:

[0088]

[0089] In the formula, p C,tank,n,t Let n be the pressure inside the tank at time t.

[0090] The following constraints must be met for the inflation and deflation rates, as well as the pressure and temperature inside the gas tank:

[0091]

[0092] Among them, VC SCO2,n,max and VD SCO2,n,max p represents the maximum limit for filling and releasing CO2 gas in the storage tank at node n; C,tank,min and p C,tank,max The minimum and maximum pressure limits within the gas storage tank; T C,n,min and T C,n,max These are the minimum and maximum temperature limits inside the gas storage tank.

[0093] The carbon dioxide storage tank satisfies the following mass flow balance constraints:

[0094]

[0095] Among them, W C,n,t Let t be the amount of carbon dioxide stored in the gas storage tank at node n at time t.

[0096] In the S244 and P2G equipment, the hydrogen produced by water electrolysis reacts with the absorbed carbon dioxide to generate natural gas. The volume ratio of hydrogen to natural gas during the reaction is 1:4. Considering the ramp power and start-stop constraints of the P2G equipment, a P2G model is established based on the power of the P2G equipment, the hydrogen conversion rate and calorific value, and the natural gas conversion rate.

[0097] The generated H2 is calculated as follows:

[0098]

[0099] In the formula, P represents the amount of hydrogen produced by node n at time t; P2G,n,t This represents the power of the P2G device at node n at time t; This refers to the calorific value of hydrogen. The conversion rate of hydrogen is denoted as .

[0100] According to the P2G equation, the ratio of natural gas volume to hydrogen volume is 1:4.

[0101]

[0102] In the formula, This represents the amount of natural gas generated at time n. This represents the natural gas conversion rate at node n.

[0103] Constraints (14c) and (14d) limit the ramp power and start / stop of P2G devices.

[0104] μ P2G,n ·P P2G,min ≤P P2G,n,t ≤μ P2G,n ·P P2G,max (14c)

[0105] |P P2G,n,t -P P2G,n,t-1 |≤P P2G,n,ramp (14d)

[0106] Where, μ P2G,n For P2G efficiency; P P2G,min and P P2G,max Capacity limitations for P2G devices; P P2G,n,ramp This represents the maximum ramping power of the P2G device.

[0107] The natural gas produced by the S245 and P2G equipment is stored in natural gas storage tanks and simultaneously supplied to equipment in the multi-energy system. Considering the constraints of pressure and temperature, inlet and outlet flow rates and mass flow rate balance in the natural gas storage tanks, a natural gas storage model is established based on the utilization, temperature, pressure and volume of the natural gas storage tanks.

[0108]

[0109] In the formula, T represents the amount of natural gas utilized at node n at time t; G,n,t Let p be the temperature inside the gas storage tank at node n at time t; G,tank,n U represents the pressure inside the gas storage tank at node n. G,tank For the volume of the storage tank; Here is the molar mass of CH4; is the gas constant of CH4.

[0110] Similarly, the constraints on the inlet and outlet flow rates, the temperature and pressure inside the gas storage tank are expressed as follows:

[0111]

[0112] Among them, V CH4,n,max and U CH4,n,max p represents the maximum limit for filling and releasing gas into the natural gas storage tank at node n; G,tank,min and p G,tank,max The minimum and maximum pressure limits within the gas storage tank; T G,n,min and T G,n,max These are the minimum and maximum temperature limits inside the gas storage tank.

[0113] At the same time, natural gas storage tanks should meet mass flow constraints:

[0114]

[0115] Among them, W G,n,t Let t be the amount of CH4 stored in the gas storage tank at node n at time t.

[0116] Furthermore, the CCUS source-sink matching model employs an improved CCUS source-sink matching method for planning, considering capture capacity, storage capacity, and route cost to solve for the optimal carbon transport route. The improved CCUS source-sink matching steps are as follows:

[0117] S251, Satisfies the mass balance equation; the sum of carbon dioxide inflow and capture at node n is equal to the sum of carbon dioxide outflow and storage / utilization at node n:

[0118]

[0119] In the formula, m and n are line nodes, including pipelines and energy hubs; G represents all nodes of CCUS, and F m,n,t c represents the amount of carbon dioxide that node m transmits to node n at time t. m,t u represents the amount of carbon dioxide captured by node m at time t. m,t This represents the amount of carbon dioxide stored or utilized by node m at time t.

[0120] S252. Consider carbon capture capacity, carbon storage capacity, and emission reduction targets;

[0121]

[0122]

[0123] In the formula, Q m,c Q represents the total capture capacity of node m; n,c u is the total storage capacity of node n; n,tT1 represents the amount of carbon dioxide stored or utilized by node n at time t; T2 represents the target value of the total capture amount; S represents all carbon capture nodes; R represents all storage and utilization nodes.

[0124] S253. The capacity constraints of the carbon dioxide transport route must be met, and the route construction status must satisfy the following formula:

[0125]

[0126] Among them, Q CL,mn,t Q represents the CO2 transport volume of route mn; CL,mn,min and Q CL,mn,max For minimum line capacity limits and maximum line capacity limits; Q CL,mn,t ,x PBC,mn This indicates the construction status of the line.

[0127] S254. Other non-negativity constraints must be met:

[0128]

[0129] Furthermore, the interaction between the multi-energy system and the CCUS system in the collaborative planning is modeled as a Nash bargaining game, and a collaborative planning model of the multi-energy system and CCUS based on the Nash game is established. During the collaborative planning, each module acts as the operator of the system, jointly determining the system equipment capacity and route planning, sharing investment costs through bargaining, and optimizing its own interests. The total cost under the cooperative state is not less than the total cost under the non-cooperative state, and the cooperation is carried out through incentive constraints.

[0130] The expected total cost of the system (i.e., investment plus operating costs) under both cooperative and non-cooperative scenarios is expressed as:

[0131]

[0132]

[0133]

[0134]

[0135]

[0136]

[0137] In the formula, TC coop TIC represents the total cost of the system in a cooperative scenario. coop and TIC non TOC represents the total investment cost of the system under cooperative and non-cooperative scenarios. coop,t and TOCnon,t IC represents the total operating cost of the system at time t under both cooperative and non-cooperative scenarios; eb,mn and IC pip,mn This represents the investment cost of the power transmission line and gas pipeline connecting m and n; IC gen,n IC cap,n IC cs,n IC P2G,n IC gs,n Let fg() be the investment cost of the generator set, carbon capture equipment, carbon storage tank, P2G equipment, and natural gas storage tank at node n; gen,n,t A convex quadratic function; P gen,n,t C represents the power (MW) of the generator unit at node n at time t; gas,n Let OC be the unit price of natural gas at node n; P2G,n and OC pump,n This represents the unit operating cost of the P2G equipment and pump at node n; P pump,n,t OC represents the power of the pump at node n at time t during the carbon capture process; cs,n and OC gs,n OM represents the storage cost per cubic meter of carbon dioxide and CH4 at node n; pip,mn The maintenance factor for line mn is 4% in this invention; EC n and CR n Represents the carbon tax and incentive price for node n; CG n This represents the benefit of storing CH4 cell at node n.

[0138] During collaborative planning, the multi-energy system and the CCUS system jointly determine system equipment capacity and wiring plans, and share investment costs through negotiation. Let v be the cost-sharing vector between the multi-energy system and the CCUS system. The sum of all cost-sharing should equal the total investment cost of the collaborative system:

[0139]

[0140]

[0141] In the formula, Z represents the set of operators participating in the price negotiation.

[0142] Furthermore, the incentive mechanism formula is as follows:

[0143]

[0144] v i Let r be the cost-sharing vector for operator i, r be the discount rate, and x be the planning year; TC non,i and TOC coop,iLet Z represent the total cost of operator i under non-cooperation and the operating cost under cooperation, and let Z be the set of operators participating in the negotiation.

[0145] Furthermore, the multi-energy collaborative planning Nash program can be equivalently decomposed into two parts: joint investment and operation, and cost sharing.

[0146]

[0147] subject to (1)-(16)

[0148]

[0149] subject to (18)-(19)

[0150] Among them, TOC coopbest,i The operating cost of operator i in the solution of equation (21).

[0151] The beneficial effects of the technical solution provided by this invention are as follows: This invention establishes a multi-energy system containing CCUS, establishes accurate mathematical models of each part of the system, including a transmission network model, a natural gas network model, an energy hub model, a CCUS process model, and a CCUS source-sink matching model, and establishes a multi-energy system and CCUS collaborative planning model based on Nash game theory by combining the accurate mathematical models of each part of the system. This yields the optimal cooperation scheme for joint investment, operation, and cost sharing of the system, combining the advantages of multi-energy systems and CCUS technology, giving full play to the energy hub and CCUS energy conservation and emission reduction advantages, and better realizing the low-carbon and economical nature of multi-energy systems. Attached Figure Description

[0152] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0153] Figure 1 This is a flowchart of a multi-energy system and CCUS collaborative planning method based on Nash game in an embodiment of the present invention;

[0154] Figure 2 This is a schematic diagram of a multi-energy system framework containing CCUS in an embodiment of the present invention;

[0155] Figure 3 This is a schematic diagram of the natural gas supply source in a multi-energy system containing CCUS in an embodiment of the present invention; Detailed Implementation

[0156] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0157] Embodiments of the present invention provide a multi-energy system and CCUS collaborative planning method based on Nash game theory.

[0158] Please refer to Figure 1 , Figure 1 This is a flowchart of a multi-energy system and CCUS collaborative planning method based on Nash game theory in an embodiment of the present invention, which specifically includes the following steps:

[0159] The first step is to establish a multi-energy system framework with CCUS based on electro-gas-thermal coupling and CCUS technology. The system framework includes generator sets, CHP equipment, gas boilers, carbon capture equipment, carbon dioxide storage tanks, P2G equipment, and natural gas storage tanks.

[0160] The second step is to establish accurate mathematical models for each part of the system corresponding to the system framework, including the transmission network model, the natural gas network model, the energy hub model, the CCUS process model, and the CCUS source-sink matching model.

[0161] The power transmission network model uses DC power flow to establish a linear relationship between the phase angle of the line power flow and the bus to realize power grid planning.

[0162] The natural gas network model uses the pressure difference between pipeline nodes to drive the flow of gas within the pipeline, thereby enabling natural gas pipeline transportation.

[0163] The electricity and heat output relationships for each energy hub node in the energy hub model are as follows:

[0164]

[0165] Among them, L e,n,t and L h,n,t η represents the electricity and natural gas generated at energy hub n at time t, respectively; gen,n and η fur,n η represents the efficiency of the generator set and gas boiler at node n; CHP,gen,n and η CHP,fur,n P represents the efficiency of the CHP device at node n; gen,n,t P fur,n,t and P CHP,n,t These represent the power of the generator set, gas boiler, and CHP equipment at node n at time t, respectively.

[0166] The CCUS process model includes a carbon emission model, a carbon capture model, a carbon storage model, a P2G model, and a natural gas storage model. The specific steps are as follows:

[0167] Step 1: During the operation of a multi-energy system, the equipment generates a large amount of carbon dioxide; establish a carbon emission model based on the relationship between carbon emissions and equipment power and carbon emission coefficient.

[0168] Step 2: Cool the carbon dioxide to 40°C and guide it to the absorber. Considering the relationship between the carbon dioxide absorption and the concentration of MEA solvent at the inlet and outlet, as well as the constraints of the inlet and outlet flow rates of the absorber, establish a carbon capture model using the carbon dioxide absorption coefficient, the cross-sectional area of ​​the absorber, the inflow and outflow of carbon dioxide from the absorber, the concentration, density, and flow rate of the MEA solution.

[0169] Step 3: The absorbed carbon dioxide is stored in a carbon dioxide storage tank. Considering the constraints of pressure and temperature, inlet and outlet flow rates and mass flow rate balance inside the carbon dioxide storage tank, a carbon storage model is established using the pressure and mass flow rate inside the tank.

[0170] Step 4: Hydrogen produced by water electrolysis in the P2G device reacts chemically with absorbed carbon dioxide to generate natural gas. The volume ratio of hydrogen to natural gas during the reaction is 1:4. Considering the ramp power and start-stop constraints of the P2G device, a P2G model is established based on the power of the P2G device, hydrogen conversion rate and calorific value, and natural gas conversion rate.

[0171] Step 5: The natural gas produced by the P2G equipment is stored in a natural gas storage tank and simultaneously supplied to the gas boiler and CHP equipment. Considering the constraints of pressure and temperature, inlet and outlet flow rates and mass flow rate balance in the natural gas storage tank, a natural gas storage model is established based on the utilization, temperature, pressure and volume of the natural gas storage tank.

[0172] The CCUS source-sink matching model uses an improved CCUS source-sink matching method for planning. It considers capture capacity, storage capacity, and route cost to solve for the optimal carbon transport route. The improved CCUS source-sink matching steps are as follows:

[0173] Step 1: The mass balance equation is satisfied; the sum of the carbon dioxide inflow and capture at node n is equal to the sum of the carbon dioxide outflow and storage / utilization at node n.

[0174]

[0175] In the formula, m and n are line nodes, including pipelines and energy hubs; G represents all nodes of CCUS, and F m,n,t c represents the amount of carbon dioxide that node m transmits to node n at time t. m,t u represents the amount of carbon dioxide captured by node m at time t. m,t This represents the amount of carbon dioxide stored or utilized by node m at time t.

[0176] Step two, consider carbon capture capacity, carbon storage capacity, and emission reduction targets;

[0177]

[0178]

[0179] In the formula, Q m,c Q represents the total capture capacity of node m; n,c u is the total storage capacity of node n; n,t T1 represents the amount of carbon dioxide stored or utilized by node n at time t; T2 represents the target value of the total capture amount; S represents all carbon capture nodes; R represents all storage and utilization nodes.

[0180] Step 3: Meet the capacity constraints of the carbon dioxide transport route; the route construction status must satisfy the following formula:

[0181]

[0182] Where QCL,mn,t represents the CO2 transport volume of line mn; Q CL,mn,min and Q CL,mn,max Due to line capacity limitations; Q CL,mn,t Let x be the carbon dioxide transport amount of carbon dioxide line mn at time t; PBC,mn This indicates the construction status of the line.

[0183] Step four: Satisfy other non-negativity constraints:

[0184]

[0185] The third step is to establish a multi-energy system and CCUS collaborative planning model based on Nash game theory, combining the precise mathematical models of each part of the system.

[0186] The interaction between the multi-energy system and the CCUS system in the collaborative planning is modeled as a Nash bargaining game, and a collaborative planning model of the multi-energy system and CCUS based on the Nash game is established. During the collaborative planning, each module acts as the operator of the system, jointly determines the system equipment capacity and route planning, shares investment costs through bargaining, and optimizes its own interests. The total cost in the cooperative state is not less than the total cost in the non-cooperative state, and the cooperation is carried out through incentive constraints.

[0187] The incentive mechanism formula is:

[0188]

[0189] v i Let r be the cost-sharing vector for operator i, r be the discount rate, and x be the planning year; TC non,i and TOC coop,i Let Z represent the total cost of operator i under non-cooperation and the operating cost under cooperation, and let Z be the set of operators participating in the negotiation.

[0190] The mathematical expression for multi-energy collaborative programming (Nash programming) is:

[0191]

[0192] Among them, TC bestnon,i It represents the optimal total cost for operator i in the non-cooperative scenario.

[0193] Multi-energy collaborative planning Nash planning can be equivalently decomposed into two parts: joint investment and operation and cost sharing.

[0194] In this invention, the proposed Nash game-based multi-energy system and CCUS collaborative planning scheme were tested in a multi-energy system. The energy balance equations for cooperative and non-cooperative scenarios differed to some extent, making the scheme more realistic. A comparison of two schemes verified the effectiveness and superiority of this invention: Scheme 1 is the proposed Nash game-based multi-energy system and CCUS collaborative planning scheme; Scheme 2 is the non-cooperative multi-energy system and CCUS planning scheme.

[0195] Table 1 lists the total cost, total investment cost, total operating cost, total cost of the multi-energy system, total cost of the CCUS system, and equipment investment capacity for Schemes 1-2. In the cooperative scenario, the total cost of each system consists of its operating cost plus the shared cost. It is worth noting that the CCUS system cost is negative, indicating that this system is profitable. Comparing the two schemes, the following conclusions can be drawn: Under the cooperative planning scenario, the total cost of the entire system is reduced by 32.5% compared to the non-cooperative scenario, with little change in investment cost and a significant reduction in operating cost; in Scheme 1, the total cost of the multi-energy system is reduced by 8% compared to Scheme 2, with a 45.6% reduction in total operating cost, but it requires bearing a large amount of shared cost, resulting in a slight overall cost reduction; compared to the CCUS system in Scheme 2, the total cost of the CCUS system in Scheme 1 is reduced by 19.8%, with little change in investment cost and a decrease in operating cost profitability, but a large amount of shared funding is provided through Nash bargaining, resulting in an overall increase in profitability. In summary, compared with Scheme 2, the scheme proposed in this invention can optimally coordinate the invested energy equipment, demonstrating the superiority of the proposed Nash game-based multi-energy system and CCUS collaborative planning scheme.

[0196]

[0197] Table 1. Optimization Results of Scheme 1-2

[0198] Please refer to Figure 2 , Figure 2 This is a schematic diagram of a multi-energy system framework containing CCUS in an embodiment of the present invention.

[0199] Please refer to Figure 3 , Figure 3 This is a schematic diagram of the natural gas supply source in a multi-energy system containing CCUS in an embodiment of the present invention. It can be seen that, under cooperative conditions, the natural gas of the multi-energy system mainly comes from P2G production, which greatly reduces the fees paid to natural gas suppliers during the operation of the multi-energy system. This is also the reason why the multi-energy system needs to bear most of the costs in cost sharing.

[0200] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0201] The key technical point of this invention is:

[0202] 1. A multi-energy system and CCUS collaborative planning model based on Nash game theory is proposed. In this model, electricity, heating, natural gas networks, and CCUS are connected and managed through energy hubs. This fully leverages the energy hub and CCUS's advantages in energy conservation and emission reduction, better realizing the low-carbon and economical aspects of the multi-energy system.

[0203] 2. By accurately modeling a series of technological processes involving carbon emissions, carbon capture, carbon transport, carbon storage, and carbon utilization, the conversion relationship between carbon flow and energy in the CCUS system is revealed, improving the operational flexibility of the CCUS system. Furthermore, this paper proposes a source-sink matching method for optimizing carbon transport routes, solving for the optimal carbon transport route by considering factors such as capture capacity, storage capacity, and route cost.

[0204] 3. To encourage collaborative planning between multi-energy systems and CCUS systems, a collaborative planning and cost-sharing scheme based on Nash bargaining is proposed. In this scheme, the investment level and corresponding cost sharing of the system are jointly determined by the multi-energy system and the CCUS system.

[0205] The beneficial effects of this invention are as follows: This invention establishes a multi-energy system containing CCUS, establishes accurate mathematical models of each part of the system, including a transmission network model, a natural gas network model, an energy hub model, a CCUS process model, and a CCUS source-sink matching model, and establishes a multi-energy system and CCUS collaborative planning model based on Nash game theory by combining the accurate mathematical models of each part of the system. This yields the optimal cooperation scheme for joint investment, operation, and cost sharing of the system, combining the advantages of multi-energy systems with CCUS technology, giving full play to the energy hub and CCUS energy conservation and emission reduction advantages, and better realizing the low-carbon and economical nature of multi-energy systems.

[0206] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-energy system and CCUS collaborative planning method based on Nash game theory, characterized in that, The steps are as follows: S1. Establish a multi-energy system including CCUS; S2. Establish accurate mathematical models for each part of the system, including the transmission network model, natural gas network model, energy hub model, CCUS process model, and CCUS source-sink matching model; S3. Based on the precise mathematical models of each part of the system, a multi-energy system and CCUS collaborative planning model is established using Nash game theory to obtain the best cooperation scheme for joint investment, operation and cost sharing of the system. The multi-energy system containing CCUS is based on electro-gas-thermal coupling and CCUS technology. The system framework includes generator sets, CHP equipment, gas boilers, carbon capture equipment, carbon dioxide storage tanks, P2G equipment, and natural gas storage tanks. The CCUS source-sink matching model processing procedure is as follows: S251, Satisfies the mass balance equation; the sum of carbon dioxide inflow and capture at node n is equal to the sum of carbon dioxide outflow and storage / utilization at node n: (16a) In the formula, m and n are line nodes, including pipelines and energy hubs; G represents all nodes of CCUS, and F m,n,t c represents the amount of carbon dioxide that node m transmits to node n at time t. m,t u represents the amount of carbon dioxide captured by node m at time t. m,t This represents the amount of carbon dioxide stored or utilized by node m at time t; S252. Consider carbon capture capacity, carbon storage capacity, and emission reduction targets; (16b) (16c) In the formula, Q m,c Q represents the total capture capacity of node m; n,c u is the total storage capacity of node n; n,t T1 represents the amount of carbon dioxide stored or utilized by node n at time t; T2 represents the target value of the total captured amount; S represents all carbon capture nodes; R represents all storage and utilization nodes. S253. The capacity constraints of the carbon dioxide transport route must be met, and the route construction status must satisfy the following formula: (16d) Among them, Q CL,mn,t Q represents the CO2 transport volume of route mn; CL,mn,min and Q CL,mn,max For minimum and maximum line capacity limits; x PBC,mn The line is under construction. S254. Other non-negativity constraints must be met: (16e）; The interaction between the multi-energy system and the CCUS system in the collaborative planning is modeled as a Nash bargaining game, and a collaborative planning model of the multi-energy system and CCUS based on the Nash game is established. During the collaborative planning, each module acts as the operator of the system, jointly determines the system equipment capacity and route planning, shares investment costs through bargaining, and optimizes its own interests. The total cost under the cooperative state is not less than the total cost under the non-cooperative state, and the cooperation is carried out through incentive constraints. The incentive mechanism formula is: (19) v i Let r be the cost-sharing vector for operator i, r be the discount rate, and x be the planning year; TC non,i and TOC coop,i Let Z represent the total cost of operator i under non-cooperation and the operating cost under cooperation, and let Z be the set of operators participating in the negotiation.

2. The multi-energy system and CCUS collaborative planning method based on Nash game theory as described in claim 1, characterized in that, The power transmission network model uses DC power flow to establish a linear relationship between the phase angle of line power flow and bus to achieve power grid planning. (1a) (1b) (1c) (1d) (1e) (1f) (1g) Where m and n are line nodes, including pipelines and energy hubs; P gen,n,t、 P pump,n,t P CHP,n,t and P P2G,n,t PL represents the power of the generator set, carbon capture device, CHP device, and P2G device at node n at time t, respectively; el,mn,t P is the active power transmitted by line mn at time t; ud,n,t The power demand of node n at time t represents the power required by the user; K1 is the transmission matrix of the transmission line; x mn It is the line reactance; θ m,t and θ n,t θ represents the phase angle of the transmission network bus at time t at energy hub nodes m and n; ref Indicates the reference phase angle of the transmission network bus; Indicates the investment status of newly constructed lines; P Lel,mn,max This represents the maximum transmission capacity of line mn, where M is an extremely large number.

3. The multi-energy system and CCUS collaborative planning method based on Nash game as described in claim 2, characterized in that, The natural gas network model uses the pressure difference between pipeline nodes to drive the flow of gas in the pipeline, thereby realizing natural gas pipeline transportation. The pressure difference is expressed as a nonlinear function of node pressure and natural gas pipeline flow rate: (2a) (2b) (2c) (2d) Where sgn() is the sign function; P m,t and P n,t GT represents the air pressure at nodes m and n at time t; mn,t G represents the amount of natural gas transported by line mn at time t; fur,n,t and G CHP,n,t V represents the natural gas consumption of the gas-fired boiler and combined heat and power equipment at time t. gass,n,t U represents the natural gas supply from the natural gas supplier at node n at time t; CH4,n,t K2 represents the amount of natural gas supplied by the natural gas storage tank at node n at time t; K2 is the natural gas pipeline transportation matrix; GT mn,max Indicates the maximum transmission capacity of line mn; C mn，t It is the pipeline coupling coefficient; P n,min and P n,max This represents the minimum and maximum pressure limits at node n.

4. The multi-energy system and CCUS collaborative planning method based on Nash game as described in claim 3, characterized in that, The electricity and heat output relationships for each energy hub node in the energy hub model are as follows: (3a) Among them, L e,n,t and L h,n,t η represents the electricity and natural gas generated at energy hub n at time t, respectively; gen,n and η fur,n η represents the efficiency of the generator set and gas boiler at node n; CHP,gen,n and η CHP,fur,n Indicates the efficiency of the CHP device at node n; (3b) (3c) (3d) (3e) (3f) (3g) Among them, Q gen,n P represents the planned capacity of the generator at node n; fur,n,max and P CHP,n,max Indicates the maximum capacity of gas-fired boilers and CHP equipment; H gas , fur and H gas,CHP It is the calorific value of natural gas used in gas-fired boilers and CHP equipment; P CHP,gen,n,t and P CHP,h,n,t These represent the electrical power and thermal power of the CHP device, respectively.

5. The multi-energy system and CCUS collaborative planning method based on Nash game as described in claim 4, characterized in that, The CCUS process model includes a carbon emission model, a carbon capture model, a carbon storage model, a P2G model, and a natural gas storage model. The specific steps are as follows: S241. During the operation of a multi-energy system, the equipment generates a large amount of carbon dioxide; establish a carbon emission model based on the relationship between carbon emissions and equipment power and carbon emission coefficient. Carbon balance equation: (4a) In the formula k gen k fur and k CHP These are the carbon emission coefficients for generator sets, gas-fired boilers, and combined heat and power (CHP) equipment, respectively; Q cap,n,t and Q emi,n,t This represents the amount of carbon dioxide captured and emitted at node n at time t; S242. Carbon dioxide is cooled to 40°C and guided to the absorber. Considering the relationship between the carbon dioxide absorption amount and the concentration of MEA solvent at the inlet and outlet, as well as the constraints of the inlet and outlet flow rates of the absorber, a carbon capture model is established using the carbon dioxide absorption coefficient, the cross-sectional area of ​​the absorber, the inflow and outflow of carbon dioxide in the absorber, the concentration, density and flow rate of the MEA solution. Considering the effects of the mass flow rate from inlet to outlet and the amount of carbon dioxide captured in the absorber, the partial differential equation is: (5a) (5b) (5c) In the formula, C Ab,MEA,n (x,t) represents the MEA solution concentration at position x of the absorber at node n at time t; v Ab,n,t Let be the flow rate of the solution in the absorber at node n at time t; r1[] is the carbon dioxide absorption function, and the carbon dioxide absorption rate is related to the flue gas mass flow rate; η Ab ρ is the carbon dioxide absorption coefficient. MEA The density of the MEA solution is Q; cap,n,t Q represents the amount of carbon captured at node n at time t; Ab,ch,n,t and Q Ab,disch,n,t S represents the inflow and outflow of the absorber at node n at time t; Ab The cross-sectional area of ​​the absorber is represented; the steady-state model in the partial differential equation can be approximated as follows: (5d) in, and This represents the average concentration of the MEA solution in the absorber at time t and time t-1; (5e) (5f) Among them, L Ab C represents the height of the absorber. MEA,min and C MEA,max To ensure absorption efficiency, the lower and upper limits of the solubility of the MEA solvent in the absorber are defined; considering the complement of the newly added MEA solvent, C Ab,MEA,n (0,t) remains constant at the original concentration C. 0,MEA ; The amount of carbon dioxide absorbed in the absorber is related to the concentration of MEA solvent at the inlet and outlet as follows: (6a) The inlet and outlet flow rates of the absorber must meet the following constraints: (6b) (6c) (6d) in, ΔQ represents the amount of carbon dioxide absorbed by the absorber at node n at time t-1. Ab,n,max This is the maximum limit on the difference between import and export flows; Q Ab,ch,n,max and Q Ab,ch,n,min Q represents the minimum and maximum limits for the absorber inlet flow rate; Ab,disch,n,max and Q Ab,disch,n,min These are the minimum and maximum limits for the absorber outlet flow rate; At the same time, the absorber should meet the mass flow rate constraint: (7a) (7b) Among them, W Ab,n,t Let μ be the volume of the solution inside the absorber at time t. Ab W is the loss coefficient during the absorber flow process; Ab,n,min and W Ab,n,max Due to the capacity limitation of the absorber; Assume the carbon dioxide after distillation flows at a mass flow rate Q. cap,out,n,t Leaving from the top of the stripping tower, the partial differential equation is expressed as: (8a) (8b) In the formula, C St,MEA,n (x,t) represents the MEA solution concentration at position x in the stripper at node n at time t; v St,n,t Let be the flow rate of the solution in the stripper at node n at time t; r2[] is the carbon dioxide extraction function, and the carbon dioxide extraction efficiency is proportional to the concentration of the MEA solvent; η St The extraction coefficient for carbon dioxide; To calculate the steady-state model in the partial differential equations, we adopt the following approximation scheme: （9a） in, and Q represents the average concentration of the MEA solution in the stripper at time t and time t-1; where Q is the average concentration of the MEA solution at time t-1. St,ch,n,t and Q St,disch,n,t S represents the inflow and outflow of the stripper at node n at time t; St L is the cross-sectional area of ​​the stripper. St The height of the stripper; (9b) (9c) (9d) For the outlet of pure carbon dioxide, considering the time aspect of the extraction process, the balance constraints are as follows: (10a) The restrictions on the inlet and outlet flow rates of the stripping tower are as follows: (10b) (10c) (10d) Where, ΔQ St,n,max This is the maximum limit on the difference between import and export flows; Q St,ch,n,max and Q St,ch,n,min Q represents the minimum and maximum limits for the inlet flow rate of the stripper; St,disch,n,max and Q St,disch,n,min These are the minimum and maximum limits for the extractor outlet flow rate; This represents the loss coefficient during the flow process in the stripper. Considering the principle of mass conservation during the capture process, we have: (11a) (11b) Similarly, strippers must also adhere to mass flow rate limits: (12a) (12b) Among them, W St,n,t W represents the volume of the solution inside the stripper. St,n,min and W St,n,max Due to capacity limitations of the stripper; S243. The absorbed carbon dioxide is stored in a carbon dioxide storage tank. Considering the constraints of pressure and temperature, inlet and outlet flow rates and mass flow rate balance in the carbon dioxide storage tank, a carbon storage model is established based on the pressure and mass flow rate inside the pipe. The dynamic function expressions for the pressure and mass flow rate inside the tank are as follows: (13a) In the formula, and T represents the filling and releasing volume of the gas storage tank at node n at time t; C,n,t U represents the temperature inside the gas storage tank at node n at time t. C,tank This refers to the volume of the storage tank. It is the molar mass of carbon dioxide. p is the gas constant of carbon dioxide; C,tank,n Let n be the pressure inside the tank at node n; The steady-state model formula is as follows: (13b) In the formula, p C,tank,n,t Let n be the pressure inside the tank at time t. The following constraints must be met for the inflation and deflation rates, as well as the pressure and temperature inside the gas tank: (13c) Among them, VC SCO2,n,max and VD SCO2,n,max p represents the maximum limit for filling and releasing CO2 gas in the storage tank at node n; C,tank,min and p C,tank,max The minimum and maximum pressure limits within the gas storage tank; T C,n,min and T C,n,max These are the minimum and maximum temperature limits inside the gas storage tank. The carbon dioxide storage tank satisfies the following mass flow balance constraints: (13d) Among them, W C,n,t Let be the amount of carbon dioxide stored in the gas storage tank at node n at time t. In the S244 and P2G equipment, the hydrogen produced by water electrolysis reacts with the absorbed carbon dioxide to generate natural gas. The volume ratio of hydrogen to natural gas during the reaction is 1:

4. Considering the ramp power and start-stop constraints of the P2G equipment, a P2G model is established based on the power of the P2G equipment, hydrogen conversion rate and calorific value, and natural gas conversion rate. The generated H2 is calculated as follows: (14a) In the formula, P represents the amount of hydrogen produced by node n at time t; P2G,n,t This represents the power of the P2G device at node n at time t; This refers to the calorific value of hydrogen. The conversion rate of hydrogen; According to the P2G equation, the ratio of natural gas volume to hydrogen volume is 1:

4. (14b) In the formula, This represents the amount of natural gas generated at time n. This represents the natural gas conversion rate at node n; Constraints (14c) and (14d) limit the ramp power and start / stop of P2G devices; (14c) (14d) Where, μ P2G,n For P2G efficiency; P P2G,min and P P2G,max Capacity limitations for P2G devices; P P2G,n,ramp This represents the maximum ramp power for P2G devices. The natural gas produced by the S245 and P2G equipment is stored in natural gas storage tanks and simultaneously supplied to gas boilers and CHP equipment. Considering the constraints of pressure and temperature, inlet and outlet flow rates and mass flow rate balance in the natural gas storage tanks, a natural gas storage model is established based on the utilization, temperature, pressure and volume of the natural gas storage tanks. (15a) In the formula, T represents the amount of natural gas utilized at node n at time t; G,n,t Let p be the temperature inside the gas storage tank at node n at time t; G,tank,n U represents the pressure inside the gas storage tank at node n. G,tank For the volume of the storage tank; Here is the molar mass of CH4; The gas constant of CH4; Similarly, the constraints on the inlet and outlet flow rates, the temperature and pressure inside the gas storage tank are expressed as follows: (15b) Among them, V CH4,n,max and U CH4,n,max p represents the maximum limit for filling and releasing gas into the natural gas storage tank at node n; G,tank,min and p G,tank,max The minimum and maximum pressure limits within the gas storage tank; T G,n,min and T G,n,max These are the minimum and maximum temperature limits inside the gas storage tank. At the same time, natural gas storage tanks should meet mass flow constraints: (15c) Among them, W G,n,t Let t be the amount of CH4 stored in the gas storage tank at node n at time t.

6. The multi-energy system and CCUS collaborative planning method based on Nash game as described in claim 1, characterized in that, The expected total cost of the system under cooperative and non-cooperative scenarios in multi-energy collaborative planning Nash programming is expressed as follows: (17a) (17b) (17c) (17d) (17e) (17f) In the formula, TC coop TIC represents the total cost of the system in a cooperative scenario. coop and TIC non This represents the total investment cost of the system under both cooperative and non-cooperative scenarios. TOC coop,t and TOC non,t This represents the total operating cost of the system at time t, regardless of whether the system operates cooperatively or not. IC eb,mn and IC pip,mn This represents the investment cost of the power transmission line and gas pipeline connecting m and n; IC gen,n IC cap,n IC cs,n IC P2G,n IC gs,n Let fg() be the investment cost of the generator set, carbon capture equipment, carbon storage tank, P2G equipment, and natural gas storage tank at node n; gen,n,t A convex quadratic function; P gen,n,t C represents the power of the generator set at node n at time t; gas,n Let OC be the unit price of natural gas at node n; P2G,n and OC pump,n This represents the unit operating cost of the P2G equipment and pump at node n; P pump,n,t OC represents the power of the pump at node n at time t during the carbon capture process; cs,n and OC gs,n OM represents the storage cost per cubic meter of carbon dioxide and CH4 at node n; pip,mn The maintenance factor represents line mn; EC n and CR n Represents the carbon tax and incentive price for node n; CG n This represents the benefit of storing CH4 cell at node n; Let v be the cost-sharing vector between the multi-energy system and the CCUS system. The sum of all cost-sharing costs equals the total investment cost of the cooperative system. (18a) (18b) In the formula, Z represents the set of operators participating in the price negotiation.

7. The multi-energy system and CCUS collaborative planning method based on Nash game as described in claim 6, characterized in that, Multi-energy collaborative planning Nash planning can be equivalently decomposed into two parts: joint investment and operation, and cost sharing. (21) (22) Among them, TOC coopbest,i TC represents the operating cost of operator i under the cooperative state. nonbest,i It represents the optimal total cost for operator i in the non-cooperative scenario.

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