A low-carbon economic dispatching method for source-load interaction of an electricity-gas interconnection system based on carbon emission flow

Abstract

The present application relates to a kind of based on carbon emission flow electro-gas interconnection system source load interaction low carbon economic dispatching method, belong to energy dispatching field.The present application is based on carbon emission flow model of power system, deduces the carbon emission flow model of natural gas system considering the dynamic characteristics of gas network;Build the carbon storage system mainly with liquid storage tank device, with thermal power unit wind turbine generator, electric gas conversion equipment composition TU-CSS-P2G-WT joint operation mode;Establish source load interaction two-stage low carbon economic dispatching model.In day stage, source side with the minimum system daily operation total cost calculates the carbon potential space-time distribution of energy network, load side according to the carbon potential signal of source side, adjusts the total cost of energy use behavior minimization user purchase energy;In the day stage, source side is re-dispatched according to the load demand reported by the total cost of user purchase energy minimum time, and user obtains updated actual carbon potential signal, corrects total cost of purchase energy itself.The present application improves source side resource utilization, realizes the low carbon of load side energy use, and reduces the carbon emission of system.

Description

Technical Field

[0001] This invention relates to a low-carbon economic dispatch method for source-load interaction in an electric-gas interconnected system based on carbon emission flows, belonging to the field of energy dispatch technology. Background Technology

[0002] As a major player in my country's energy sector, the power industry accounts for over 40% of the energy sector's carbon emissions. Wind power, as a clean energy source with low marginal cost and no carbon emissions, often suffers from curtailment due to its intermittent and volatile nature. Therefore, reducing carbon emissions in the power industry and improving wind power utilization are urgent issues that need to be addressed.

[0003] Installing carbon capture devices on the source side of power generation units and improving wind power utilization are effective means to reduce carbon emissions. The paper "Distributed Optimal Scheduling of Integrated Energy Microgrids Considering Carbon Capture and Methanation" proposes a distributed optimal scheduling model for integrated energy microgrids that considers carbon capture and methanation, effectively reducing system carbon emissions while also considering economic efficiency. The paper "Two-Layer Optimal Scheduling of Electricity-Gas Integrated Energy System Considering Wind Power Consumption via Electricity-to-Gas Conversion" considers the rational utilization of wind curtailment via P2G, converting excess wind power into natural gas to promote wind power consumption. However, these papers do not fully consider the carbon storage performance of carbon capture devices, only focusing on low-carbon scheduling of P2G and carbon capture devices or wind turbines, failing to fully utilize the coupled electro-carbon characteristics of these three elements to achieve optimal resource utilization.

[0004] Load-side users have a reactive effect on low-carbon dispatching on the source side. The paper "Low-Carbon Economic Dispatch of Power Systems Considering Source-Load Uncertainty and User Response Behavior" proposes a system-level low-carbon robust optimization model and an event-driven user low-carbon response model, effectively quantifying and assessing the uncertainty level of low-carbon economic dispatching and achieving synergy between low-carbon goals on both the source and load sides. However, this paper still focuses on demand-side resource analysis from a purely theoretical perspective, neglecting another important incentive factor: the incentive signal for users to proactively reduce carbon emissions from a "carbon perspective"—carbon potential. Accurate calculation of carbon potential is crucial for guiding users towards low-carbon energy use. The paper "Modeling carbon emission flow in multiple energy systems" proposes a method for calculating carbon emission flow in multi-energy systems based on the power system carbon emission flow method, emphasizing carbon emission responsibility allocation. However, this paper does not consider the carbon emission flow of natural gas systems with dynamic gas grid characteristics. Calculating carbon emission flow based solely on the steady-state characteristics of the gas grid will lead to inaccurate results. Summary of the Invention

[0005] The purpose of this invention is to provide a low-carbon economic dispatch method for source-load interaction in an electricity-gas interconnected system based on carbon emission flows. This method leverages the coupling characteristics of P2G, carbon capture devices, and wind turbines to achieve optimal utilization of source-side resources and reduce carbon emissions from thermal power units. It also establishes a carbon emission flow calculation method that considers the dynamic characteristics of the gas grid, providing accurate carbon emission accounting for load-side users participating in incentive-based low-carbon demand response. Furthermore, it establishes an incentive-based low-carbon demand response model based on a low-carbon response mechanism to guide load-side users towards low-carbon and economical energy use.

[0006] The technical solution of this invention is: a low-carbon economic dispatch method for source-load interaction in an electricity-gas interconnected system based on carbon emission flows, the specific steps of which are as follows: Step 1: Based on the carbon emission flow calculation model of the power system, derive the carbon emission flow calculation model of the natural gas system that takes into account the dynamic characteristics of the gas grid; Step 2: Construct a carbon storage system (CSS) mainly composed of liquid storage tank devices, and form a joint operation mode of TU-CSS-P2G-WT with thermal power units (TU), wind turbines (WT), and power to gas (P2G) equipment. Analyze the joint operation mechanism based on the electro-carbon characteristics of the four and establish a joint operation model. Step 3: Based on the parameters, load, and wind power forecast of the power-gas interconnection system, with the optimization objective of minimizing the total daily operating cost of the system, and constrained by the power system constraints, natural gas system dynamic constraints, and the TU-CSS-P2G-WT joint operation model from Step 2, establish a low-carbon economic operation model for the power-gas interconnection system. Step 4: Establish an incentive-based low-carbon demand response model based on the low-carbon response mechanism. Its optimization objective is to minimize the total energy purchase cost for users, and it is constrained by transferable low-carbon load and substitutable low-carbon load. Step 5: Based on Step 1 and Step 3, calculate the spatiotemporal distribution of carbon potential in the energy network and establish a low-carbon economic dispatch model for the source-load interaction of the electricity-gas interconnection system.

[0007] The beneficial effects of this invention are: 1. The TU-CSS-P2G-WT joint operation mode provides a larger grid connection capacity for zero-carbon wind power. Excess wind power is absorbed through P2G, which effectively reduces the system curtailment rate and significantly reduces the total daily operating cost and system carbon emissions.

[0008] 2. By fully utilizing the energy storage characteristics of the dynamic pipeline storage in the gas network, the reliability and economy of system operation are effectively improved; the introduction of carbon potential in the pipeline storage reduces the carbon potential of the gas network, enabling the system to operate with low carbon emissions.

[0009] 3. Incentive-based low-carbon demand response has a significant peak-shaving and valley-regulating effect on load adjustment, which can effectively improve wind power utilization and thus reduce the carbon emissions of the system. In addition, this strategy can also reduce the total daily operating cost of the system and the total energy purchase cost for users. Attached Figure Description

[0010] Figure 1 This is a diagram illustrating the "pipe storage" characteristics of the natural gas pipeline of the present invention; Figure 2 This is the electrocarbon characteristic diagram of the TU-CSS-P2G-WT of the present invention; Figure 3 This is a schematic diagram of the carbon storage system structure of the present invention; Figure 4 This is a two-stage low-carbon economic dispatch overall framework diagram of the present invention; Figure 5 This is a topology diagram of the electrical-electrical interconnection system of the present invention; Figure 6 This is the load and wind power forecast diagram of the present invention; Figure 7 This is the carbon potential diagram of the power grid node of the present invention; Figure 8 This is the carbon potential diagram of the gas grid of the present invention; Figure 9 This is a comparison chart of the running results for scenarios 1 and 2 of this invention; Figure 10 This is a comparison chart of the running results for scenarios 1 and 3 of this invention; Figure 11 This is a comparison diagram of user energy consumption behavior in scenarios 4, 8, and 9 of this invention. Detailed Implementation

[0011] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0012] Example This embodiment provides a low-carbon economic dispatch method for source-load interaction in an electricity-gas interconnected system based on carbon emission flows. The specific process is as follows: Step 1: Based on the carbon emission flow calculation model of the power system, establish a carbon emission flow calculation model that considers the steady-state characteristics of the gas grid, and derive a carbon emission flow calculation model of the natural gas system that considers the dynamic characteristics of the gas grid.

[0013] Step 1.1: Calculation Model of Carbon Emission Flow in Power System Analysis based on the principle of proportional sharing shows that the carbon flux density of all outflowing currents from a node is equal to the carbon potential of that node, i.e. (1) in, For nodes nActive power flow outflow nodes in connected branches n The set of all branches; For power grid branch i carbon flux density; For power grid nodes n The carbon potential.

[0014] The carbon potential of each grid node is determined by the amount of active power injected from the generators and transmission lines connected to that node, denoted as... (2) in, For nodes n Active power flow flows into nodes in connected branches. n The set of all branches, For nodes n A collection of connected generator sets; For generator h carbon emission intensity; P h For generator sets h The output active power.

[0015] Assume the power grid has N There are nodes, among which there are K Each unit injection node and M Let the load node be... n ( n =1, 2, ..., N The carbon potential of each node is Then the nodal carbon potential vector can be expressed as: (3) The formula for calculating the nodal carbon potential is expressed as follows: (4) in, P N The active flux matrix of the nodes; P B This is the branch power flow distribution matrix; P G Inject the distribution matrix into the unit. E G This is the unit's carbon emission intensity vector.

[0016] Step 1.2: Calculation Model of Carbon Emission Flow in Natural Gas System Step 1.2.1: Calculation Model of Carbon Emission Flow of Natural Gas System Considering the Steady-State Characteristics of Gas Network According to the principle of proportional sharing, the carbon flux density of all gas flows exiting a gas network node is equal to the carbon potential of that node, i.e. (5) in, To connect with gas network nodes g Airflow outlet node in connected pipes g The collection of all pipes; For pipelines p carbon flux density; As a gas network node g The carbon potential.

[0017] Since the compressor operates at very low power, the carbon emissions from the compressor are negligible. In this case, the carbon potential of each gas network node is determined by the gas source and flow rate connected to that node, expressed as: (6) in, For nodes g Airflow into the node from the connected pipes g All pipes p A set; For nodes g Connected gas source w gather; gas source w carbon emission intensity; q p , q w Pipes p And the source of gas w Airflow rate.

[0018] Step 1.2.2: Model for calculating carbon emission flow in natural gas systems considering the dynamic characteristics of the gas network The aforementioned gas network carbon flow model only considers the steady-state characteristics of the gas network, i.e., the gas flow is almost instantaneous. However, in reality, the transmission of natural gas in pipelines has a time delay, which alters the distribution of carbon emission flows. This time delay in natural gas transmission refers to the fact that the varying inflow and outflow rates of natural gas into the pipeline allow the pipeline to store a certain amount of gas, exhibiting a "pipeline storage" characteristic, i.e., the dynamic characteristics of the gas network. Therefore, based on the above carbon flow model considering the steady-state characteristics of the gas network, this invention proposes a carbon emission flow calculation model that considers the dynamic characteristics of the gas network.

[0019] Figure 1 For a natural gas pipeline UV The characteristics of storage, and These represent the gas flow rates at the pipeline inlet and outlet, respectively. This invention treats the "pipeline storage" as a virtual gas network node. r The existing gas supply is processed as a virtual gas source. s Injection Node rThe gas flow rate. Compared to the steady-state characteristics of the gas network, the number of gas flows, the number of nodes, and the number of gas sources are all increased by 1 for a natural gas pipeline containing "pipe storage".

[0020] Therefore, equation (6) transforms into equation (7), where the carbon potential of each gas network node is determined by the gas source, storage tank, and gas flow injection rate connected to that node, expressed as follows: (7) in, For nodes g Connecting pipe storage s gather; For storage s carbon emission intensity; q s For storage s Airflow rate.

[0021] Assume the gas network has G There are nodes, among which there are W Each gas injection node S Each storage node and L There are several gas load nodes. Considering the dynamic characteristics of the gas network, the gas network at this time has... G + S Each node. To solve for the carbon potential at the nodes of a gas network, the following basic concepts need to be defined.

[0022] 1) The airflow distribution matrix of the pipeline is G + S A square matrix of order, using Q B =( q Buv ) (G+S)×(G+S) Representation. Matrix elements are specifically defined as: if a node u With nodes v ( u , v =1,2,…, G + S There is a natural gas pipeline connecting the nodes, and the gas flows through this pipeline from the node. u To the node v The forward airflow is q ,but q Buv = q , q Bvu = 0; if the airflow passing through this branch q If it is a reverse airflow, then q Buv = 0, q Bvu = q In other cases qBuv = q Bvu =0, q Buu = 0.

[0023] 2) The gas source injection distribution matrix is ​​as follows: W ×( G + S ) order matrix, using Q W =( q Wwv ) W×(G+S) Representation. The specific definition of a matrix element is: if the first element... w ( w =1,2,…, W ) gas source access nodes v And from the first w A node containing a gas source injects into the node. v The airflow is q ,but q Wwv = q ,otherwise q Wwv = 0.

[0024] 3) The pipe-based injection distribution matrix is ​​as follows: S ×( G + S ) order matrix, using Q S =( q Ssv ) S×(G+S) Representation. The specific definition of a matrix element is: if the first element... s ( s =1,2,…, S ) storage access nodes v And from the first s Inject a node containing a storage into the node. v The airflow is q ,but q Ssv = q ,otherwise q Ssv = 0.

[0025] 4) The nodal airflow matrix is ​​as follows: G + S A square matrix of order, using Q N =( q Nuv ) (G+S)×(G+S) This matrix is ​​defined to describe the airflow injection volume at the nodes of the air network. The matrix elements are specifically represented as follows: (8) All off-diagonal elements of the matrix q Nuv = 0, ( u ≠ v ).

[0026] Based on the definitions of the above four matrices, Q N The first of the matrix i The diagonal elements of the row are equal to Q B , Q W ,and Q S Matrix number i The sum of the column elements is expressed as: (9) in, ξ G+W+S For all elements are 1 G + W + S Rank row vector.

[0027] Different gas sources have different carbon emission characteristics, which are known conditions in carbon flow calculations. The carbon emission intensity vector of the gas source is... W Let the dimensional row vector be the first row vector. w ( w =1, 2, ..., W The carbon potential of each node is e W w , represented as: (10) The carbon emission intensity vector of the pipeline is S Let the dimensional row vector be the first row vector. s ( s =1, 2, ..., S The carbon potential of each node is e S s , represented as: (11) The carbon potential vector of the gas network node is G + S Let the dimensional row vector be the first row vector. g (g=1, 2, ..., G + S The carbon potential of each node is , represented as: (12) Based on the nature of the carbon emission stream, the carbon flux density of the pipeline airflow To be replaced by the carbon potential at the pipe head node, equation (7) can be rewritten in the following matrix form: (13) in, =(0,0,…,1,…,0) is G + S A unit row vector of dimension, where the first... g Each element is 1.

[0028] According to the definition of the nodal airflow matrix, we can obtain: (14) From equations (13) and (14), we can obtain: (15) Extending equation (15) to the entire system dimension and transforming it into matrix form, we get: (16) After simplification, the formula for calculating the nodal carbon potential considering the dynamic characteristics of the gas network is as follows: (17) Step 2: Construct a carbon storage system (CSS) with liquid storage tanks as the main component, and form a joint operation mode of TU-CSS-P2G-WT with thermal power units (TU), wind turbines (WT), and power to gas (P2G) equipment. Analyze the joint operation mechanism based on the electro-carbon characteristics of the four components and establish a joint operation model.

[0029] Step 2.1: TU-CSS-P2G-WT Joint Operation Mechanism The thermal power unit, combined with the carbon storage system, captures CO2 with relatively high purity. The captured CO2 is sent to the P2G (Polycarbon Gas Generator) for methanation to generate CH4, which is then fed into the gas grid. The energy required for the operation of the P2G is provided by the wind turbine. The entire process is a combined operation mode of TU-CSS-P2G-WT.

[0030] Wind power typically outputs more at night, while load is concentrated during the day, exhibiting an inverse characteristic to wind power. This results in a situation where "wind power output is high when demand is high and low when demand is high," leading to increased wind curtailment rates. Simultaneously, thermal power units generate high output and high carbon emissions during the day. Therefore, this invention proposes a TU-CSS-P2G-WT joint operation model. The CO2 in CSS can be stored and released long-term to P2G for gasification, making it possible to reduce carbon emissions from thermal power units and integrate wind power. The mechanism by which CSS-P2G assists in integrating wind power into WT and reducing carbon emissions from TU is as follows: Figure 2As shown. The electric and carbon coordinate diagrams are mapped to each other, for the purpose of absorption. t 1- t 2. t 3- t 4-period wind curtailment S 1. S 3. WT provides power to P2G and, in conjunction with CSS, releases CO2 to produce gas, consuming a carbon dioxide amount of [missing information]. , . t 2- t The total operating power of the TU during the 3 time periods is S 2. The carbon released by TU is absorbed and stored by CSS, such as... As shown.

[0031] Step 2.2: TU-CSS-P2G-WT Joint Operation Model Step 2.2.1: The CO2 emitted by the thermal power unit is stored by the carbon storage system, and the TU-CSS model is established.

[0032] In a liquid storage carbon capture power plant, the device used to store CO2 is a storage tank containing an ethanolamine aqueous solvent (MEA). CO2 is readily soluble in this solvent. Due to the large-scale production and CO2 storage capabilities of this solvent, from a carbon perspective, it can be considered a carbon storage device. Therefore, the liquid storage carbon capture device can be viewed as a carbon storage system, and its main structure is as follows: Figure 3 As shown. Among them, m XS This refers to the amount of CO2 in the treated flue gas. V ZS , V R-in , V R-out , V P-in , V P-out These represent the amount of solvent flowing into the regeneration tower and the amount of solvent flowing into / out of the rich / lean solution storage tank, respectively.

[0033] The general operation of the carbon storage system is as follows: the flue gas generated by the equipment flows into the absorption tower, where the MEA solvent washes and absorbs CO2, forming a rich solution with a high CO2 concentration. When CO2 is needed externally, the rich solution enters the regeneration tower, where high temperature causes CO2 to separate from the MEA solvent, enabling CO2 reuse. After separation, the rich solution forms a lean solution with a low CO2 concentration, which flows back to the lean solution storage tank and the absorption tower for recycling.

[0034] This invention considers adding a carbon storage system to a thermal power unit. In the CO2 regeneration process, the thermal power unit needs to extract hot steam from the cylinder to heat the regeneration tower, causing CO2 to separate from the solvent and achieving sufficient CO2 capture. The output power of the thermal power unit is: (18) in, P G,t For thermal power units in t Total output power during the time period; P N,t For thermal power units in t Net output power during the period P B This is caused by changing the generator set structure for a fixed energy consumption carbon storage system, and is unrelated to the operating conditions. P CSS,t For carbon storage systems in t The operating power during a given time period is related to the amount of CO2 separated, and is expressed as: (19) in, λ CO2 Energy consumption for processing one unit of CO2 in a carbon storage system; m ZS,t The amount of CO2 produced by the regeneration tower, used for P2G gasification and carbon sequestration, is expressed as: (20) in, m p2g,t , m fc,t They are respectively t The amount of CO2 required for P2G gasification and the amount of CO2 required for carbon sequestration during the time period.

[0035] The amount of CO2 flowing into the carbon storage system is related to the flue gas diversion device of the thermal power unit, and the amount of CO2 flowing into the rich liquid storage tank is related to the solvent capture rate of the MEA absorber, as expressed as: (twenty one) (twenty two) in, δ The flue gas split ratio of the thermal power unit. e G Carbon emission intensity of thermal power units. m R-in,t for t The mass of CO2 dissolved in the rich solution during a given period. η The CO2 capture rate of the absorption tower.

[0036] CO2 exists in the storage tank dissolved in MEA solvent. The relationship between the mass of CO2 and the volume of MEA solvent needs to be calculated, expressed as follows: (twenty three) in, V R-in,t For carbon storage systems int The volume of MEA solvent required to dissolve CO2 in flue gas during a given time period; M MEA and M CO2 These are the molar masses of the solvent and CO2, respectively. μ R This refers to the concentration of the alkanolamine solution. is the density of the alkanolamine solution.

[0037] The volume of solvent flowing into / out of a rich solution reservoir is equal to the volume of solvent flowing out / in of a poor solution reservoir, expressed as: (twenty four) in, V R-out,t For flooded storage t The volume of solvent flowing out during the time period; V P-in,t , V P-out,t These are the starved fluid storage devices. t The volume of solvent flowing in and out during a given time period.

[0038] The solvent flowing out of the rich solution storage tank is used in the regeneration tower and then returned to the carbon storage system as lean solution, as follows: (25) in, V ZS,t for t The volume of solvent flowing into the regeneration tower during the specified time period.

[0039] Because the carbon storage system has two liquid storage tanks, and the liquid flow is a unidirectional circulation, the rich and lean liquid storage tanks are located in... t Time slot capacity and t -1 time period capacity and t It is related to the inflow and outflow volume over a given period of time, i.e. (26) in, V R,t For flooded storage t Capacity of a time period; V P,t For the starved fluid storage t Capacity of a time period.

[0040] Rich and poor electrolyte storage devices also need to meet capacity constraints.

[18] , represented as: (27) (28) in, V R,cap, V P,cap The rated capacities of the rich / poor solutions are represented by , respectively. To enable solvent reuse, equation (28) indicates that the rich / poor solutions capacity remains constant within a scheduling cycle.

[0041] Step 2.2.2: The power-to-gas equipment uses CO2 from the carbon storage system to produce natural gas. The electricity required for production is supplied by wind turbines. A TU-CSS-P2G-WT joint operation model is established.

[0042] As can be seen from the methanation reaction, the volume of CO2 consumed by P2G and the volume of CH4 produced are equal. Therefore... (29) (30) in, CO2 density; Q CH4,t for t The volume of methane generated by P2G during the specified time period; Q LHV This refers to the calorific value of natural gas. η p2g For P2G gas production efficiency; P p2g,t for t The power consumption of P2G operation during a given time period, provided by WT, is expressed as follows: (31) in, P net,t This refers to the grid-connected power of wind power.

[0043] Step 3: Based on the parameters, load, and wind power forecast of the power-gas interconnection system, with the goal of minimizing the total daily operating cost of the system, and constrained by the power system constraints, natural gas system dynamic constraints, and the TU-CSS-P2G-WT joint operation model from Step 2, establish a low-carbon economic operation model for the power-gas interconnection system.

[0044] Step 3.1: Objective Function of Low-Carbon Economic Operation Model for Electrical-Pneumatic Interconnected Systems (32) in, C G , C S These are the power generation and start-up / shutdown costs of thermal power units, respectively. C T The cost of wind curtailment for wind turbines; C F , C RThese are the costs of carbon sequestration and MEA solution loss in the carbon storage system, respectively. C B For gas purchase costs; C p2g For P2G operating costs; C SP , C SG These are the costs of system power failure and gas load, respectively.

[0045] Each term in the above formula is specifically represented as (33) (34) in, Ω TU A collection of thermal power units; U i,t For thermal power units i exist t Start / stop status during a given time period; a i , b i , c i For thermal power units i Cost parameters; κ s,i For thermal power units i The start-stop cost coefficient.

[0046] (35) in, Ω WT A collection of wind turbine units; κ q This represents the cost coefficient for wind curtailment. 、P w,k,t Wind turbine k exist t The predicted output and actual output for a given period of time.

[0047] (36) (37) in, Ω CS A collection of carbon storage systems; κ r This is the cost factor for MEA solvents; ς The solvent loss factor for carbon-absorbing MEA is given.

[0048] (38) in, ΩWell For gas source collection; κ g,x gas source x Natural gas cost coefficient; Q x,t gas source x exist t The amount of gas produced during a given time period.

[0049] (39) in, Ω p2g For P2G sets; κ p2g This represents the operating cost coefficient for P2G.

[0050] (40) (41) in, Ω ele , Ω gas These are sets of nodes for the electricity and gas networks, respectively. κ sp , κ sg These are the cost coefficients for power outage and gas load, respectively. For the first n Node users t Power loss load during the period; For the first g Node users t Gas loss load during a given period.

[0051] Step 3.2: Constraints of the Low-Carbon Economic Operation Model for Electrical-Pneumatic Interconnected Systems Step 3.2.1: Power System Constraints This invention employs DC power flow constraints, including node power balance constraints (42), branch power flow and power flow capacity constraints (43)-(44), thermal power unit operation constraints (45)-(47), gas turbine operation constraints (48), P2G equipment operation constraints (49), and wind turbine operation constraints (50).

[0052] (42) (43) (44) (45) (46) (47) (48) (49) (50) in, Ω GT , Ω B These are respectively gas turbines and power transmission branch collections; For power grid nodes n exist t Load demand during different time periods; P g,j,t For gas turbine j exist t Efforts during a specific time period; f pq,t For power grid branch pq exist t Trends of different time periods; branch road pq Maximum current capacity; B pq branch road pq Susceptivity; θ The phase angle of the node; R G,i For thermal power units i Climbing rate; and The units i Minimum shutdown and startup time; and The units i to t -1 represents the time during which the machine has been continuously turned on and off; , , , , , thermal power units i ,gas turbine j and P2G devices m The minimum and maximum output.

[0053] Step 3.2.2: Natural Gas System Constraints Including node gas volume balance constraints (51), gas network dynamic characteristics constraints (52)-(54), Weymouth dynamic equation of pipeline airflow (55), pipeline capacity constraints (56), pressure relationship between compressor pipeline terminal nodes (57), node gas pressure constraints (58), and gas source output constraints (59).

[0054] (51) (52) (53) (54) (55) (56) (57) (58) (59) in, Ω G A collection of natural gas pipelines; As a gas network node g exist t Load demand during different time periods; η g For the conversion efficiency of the gas turbine; for t Time Pipeline UV The amount of pipe stock; Δ x uv and D uv These are the length and diameter of the pipe, respectively. The average pressure in the pipeline; R It is the gas constant; T g The temperature of the natural gas; Z The compressibility factor of natural gas; ρ 0 represents the density of natural gas under standard conditions; T This represents the total number of time periods in the scheduling cycle. F uv For pipelines UV The coefficient of friction; For pipelines UV Average airflow rate; For pipelines UV Maximum airflow; β c This refers to the compressor coefficient; and They are nodes u Minimum and maximum air pressure; 、 Gas source x The minimum and maximum air output.

[0055] Step 4: Establish an incentive-based low-carbon demand response model based on the low-carbon response mechanism, with the goal of minimizing the total energy purchase cost for users, and with constraints of transferable low-carbon load and replaceable low-carbon load.

[0056] Step 4.1: Low-carbon response mechanism In power system carbon reduction, the focus is usually on controlling carbon emissions from source-side generating units, with little reliance on user energy consumption behavior. While the carbon responsibility system designates generating units as the primary carbon bearers, the actual responsibility lies with users, based on the principles of "source follows load" and "whoever uses electricity is responsible." However, directly assigning carbon emission responsibility to users is currently difficult, necessitating a transitional mechanism. Therefore, this invention designs a low-carbon response incentive mechanism for residential users to guide them towards low-carbon energy use. The specific steps are as follows: Step 1: Obtain the user's dynamic node carbon potential; the energy network calculates energy flow based on the load forecast curve, and then calculates carbon flow based on the network energy flow to obtain the carbon potential of the user's node, and sends the information to the user through the carbon meter.

[0057] Step 2: Obtain day-ahead carbon emissions; derive the day-ahead load curve through load forecasting, multiply the energy consumption for each period by the nodal carbon potential for that period, and then sum them up to obtain the day-ahead carbon emissions.

[0058] Step 3: Optimize users' daily energy consumption behavior; Since reducing energy consumption carbon emissions can bring carbon reduction benefits, users obtain carbon potential information from the carbon table at each time period of the day. A high carbon potential means high energy consumption carbon emissions, and vice versa. Users adjust their energy consumption behavior according to the size of the carbon potential to reduce carbon emissions, thereby obtaining the load curve after incentive-based low-carbon demand response.

[0059] Step 4: Carbon Emission Reduction Calculation; Based on the intraday node carbon potential and the load curve after the response, the actual intraday carbon emissions from energy consumption can be obtained. By comparing the day-ahead carbon emissions in Step 2, the carbon emission reduction after the incentive-based low-carbon demand response can be calculated. Multiplying this by the low-carbon incentive price yields the carbon reduction incentive benefit.

[0060] Step 4.2: Objective Function of the Incentive-Based Low-Carbon Demand Response Model Minimizes the total cost of energy purchases for users: (60) in, C buy Energy purchase cost for users; C I Incentives for carbon reduction.

[0061] Each term in the above formula is specifically represented as (61) in, , They are respectively t Electricity prices and natural gas prices during specific time periods.

[0062] (62) in, κ c Low-carbon incentive prices; M tr , M re These refer to the carbon emissions reduced by incentive-driven low-carbon demand response load shifting and load substitution.

[0063] (63) in, for t Time period nodes n The carbon potential; for t Time period nodes s Carbon potential; For the first n Node users t Transferable electrical loads during specific time periods; For the first g Node users t Transferable gas load during a given time period.

[0064] (64) in, Ω e-g A set of nodes that simultaneously supply electricity and gas loads to users; For the first z Each node user t Alternative electrical loads for different time periods. Note that when Δ e z,t When the load is greater than 0, the user selects gas load as the alternative load. It must be less than 0. M re >0; when Δ e z,t When the value is less than 0, the user selects electrical load as the alternative load. It must be greater than 0. M re >0.

[0065] Step 4.3: Constraints of the Incentive-Based Low-Carbon Demand Response Model Incentive-driven low-carbon demand response is defined as users taking measures to respond to low-carbon economic demands, selecting low-carbon energy consumption methods based on different carbon potential distributions, thereby indirectly reducing the system's carbon emissions. This invention considers that both electricity and gas loads possess the capability for horizontal and vertical incentive-driven low-carbon demand response. Both loads can achieve transfer in their respective time dimensions. Simultaneously, the two loads can be substituted for each other within the same time period. Therefore, any load can be divided into three parts: the original load, the transferable low-carbon load, and the substitute low-carbon load, i.e. (65) in, To meet the electricity / gas load demand of users after the incentive-based low-carbon demand response, y As an energy type, y ∈{P,G}, where P represents electrical load and G represents gas load; The original load demand before the incentive-based low-carbon demand response; For transferable low-carbon loads; It can be used as an alternative to low-carbon loads.

[0066] Step 4.3.1: Transferable low-carbon loads: In terms of time distribution, the carbon potential is different in different periods. Loads with high carbon potential can be transferred to periods with low carbon potential. Such loads are called transferable loads.

[0067] The transferable low-carbon loads are transferred only over time, with the total transfer amount remaining constant throughout the entire cycle, as expressed below: (66) in, , for t The amount of low-carbon load that can be transferred in and out during a given period.

[0068] To meet the basic energy needs of the load and the security of the energy network system, transferable low-carbon loads must also meet the following constraints: (67) in, , A variable that is 0-1 indicates that in t The status of time-transferable low-carbon loads entering and leaving the system; τ The third line ensures that users can only transfer low-carbon load rates at any given time.

[0069] Step 4.3.2: Alternative Low-Carbon Loads Users can choose different energy supply methods to meet their energy needs at the same time. Different energy sources have different carbon potentials at the same time; users can choose low-carbon-potential loads to replace high-carbon-potential loads, achieving mutual substitution between different energy sources. Such loads are called substitutable low-carbon loads. The specific model is represented as follows: (68) (69) in, , for t The time period can replace the energy input and output of low-carbon loads; , A variable that is 0-1 indicates that in t The time period can replace the substitution and removal of low-carbon loads; ω The alternative low-carbon load rate; Equation (68) indicates that the energy of mutually substitutable energy sources is equal at any time.

[0070] Step 5: Based on Step 1 and Step 3, calculate the spatiotemporal distribution of carbon potential in the energy network, establish a low-carbon economic dispatch model for the source-load interaction of the power-gas interconnection system, and obtain a low-carbon economic dispatch scheme for the source-load interaction of the power-gas interconnection system that considers the joint operation mode of TU-CSS-P2G-WT and the dynamic characteristics of the gas network.

[0071] The electric-gas interconnection system integrates carbon storage systems, power-to-gas conversion equipment, thermal power units, wind power units, gas turbines, natural gas sources, and adjustable loads of users. With the energy network side as the link and the user side as the foundation, it achieves source-load synergy and interaction to realize efficient energy supply and low-carbon, economical energy use. The specific framework is as follows: Figure 4 As shown, the low-carbon economic operation mechanism proposed in this invention is a two-stage optimization problem. In the day-ahead phase, the low-carbon economic operation model of the power-gas interconnection system schedules the output of each unit according to the day-ahead predicted load to minimize the system's total daily operating cost. This is achieved by calculating the power flow in each branch and the airflow in the pipeline, and then calculating the original node carbon potential of each load based on carbon emission flow theory. The power-gas interconnection system then sends simulated node carbon potential signals to users. Upon receiving this signal, users adjust their energy consumption behavior to minimize their total energy purchase cost based on the low-carbon incentive price signal and through incentive-based low-carbon demand response. In the intraday phase, the low-carbon economic operation model of the power-gas interconnection system reschedules the load demand reported by users when their total energy purchase cost is minimized. Due to changes in unit and gas source output, the carbon potential of the system network nodes will change. After receiving the updated actual carbon potential signal, users adjust their own total energy purchase cost accordingly.

[0072] This invention uses a computational example to verify the interconnection system between an improved IEEE 39-node power grid and a Belgian 20-node gas grid. The specific node distribution is shown in [link to specific example]. Figure 5The system comprises 5 thermal power units, 3 wind turbine units, 2 gas turbines, and 2 P2G units, each with a capacity of 60MW. W, T, and G represent the gas source, thermal power, and gas turbine, respectively. Thermal power units at nodes 30 and 33 are included in the CSS (System-Specific Components). Load data and wind power forecast output are available in [link to relevant documentation]. Figure 6 The power grid data comes from the MATPOWER-39 power grid node data; thermal power unit parameters are shown in Table 1; gas turbine parameters are shown in Table 2; gas source parameters are shown in Table 3; other relevant data are shown in Table 4; the flue gas split ratio δ is 0.6. The gas storage of each pipeline in the gas network is determined by the steady-state pressure at 24 hours of the previous scheduling day, and the basic carbon potential is set at 0.2 t / (MW·h). After linearization, the source-load interaction low-carbon economic scheduling model of the power-gas interconnected system considering carbon emission flow in this invention becomes a mixed integer linear programming (MILP) model, which is solved using the GUROBI commercial solver.

[0073] Table 1

[0074] Table 2

[0075] Table 3

[0076] Table 4

[0077] To verify the effectiveness of the proposed source-load interaction low-carbon economic dispatch model, the proposed joint operation mode is compared with the traditional operation mode. Traditional operation mode: wind power and thermal power units operate independently; thermal power units are not equipped with CSS (Coalition for Gas-Grid Interaction), and there is no P2G (Power-to-Government) operation. Based on whether the joint operation mode is considered, the dynamic characteristics of the gas grid, and incentive-based low-carbon demand response, seven scenarios are set up, as shown in Table 5.

[0078] Table 5

[0079] Results Analysis This invention considers the system and user operation under the above seven scenarios, and analyzes the advantages of joint operation mode, gas network dynamic characteristics, and incentive-based low-carbon demand response. Specific results are shown in Table 6. Considering the joint operation mode, CSS increases the ability of thermal power units to operate downwards, providing grid connection space for wind power. The cost of wind curtailment in Scenario 2 is $24,033, a reduction of $107,421 compared to Scenario 1, but the total cost is only reduced by $43,348. This is because the joint operation mode introduces P2G and CSS operating costs. The increased cost of power outage is due to the fixed energy consumption of CSS reducing the ability of thermal power units to peak upwards, leading to power outage loads. System carbon emissions are reduced by 5,894 tons, demonstrating that the joint operation mode can reduce the system's wind curtailment rate and total cost. Considering the dynamic characteristics of the gas network, the gas storage characteristics of natural gas pipelines ensure sufficient gas load supply. In Scenario 3, the cost of power outage and gas is 0, a reduction of $95,608 in total cost compared to Scenario 1, a reduction of 1,316 tons in system carbon emissions, and an increase of $141,407 in user energy purchase costs. After considering incentive-based low-carbon demand response, the total daily operating cost of the system in Scenario 4 is reduced by $211,232 compared to Scenario 1. This reduction is mainly concentrated in wind curtailment costs and power and gas loss costs. This is because the system's high-carbon potential period coincides with peak energy consumption, allowing users to shift high-carbon-emission energy consumption to low-carbon potential periods, thereby improving wind power utilization and alleviating energy supply pressure. Considering incentive-based low-carbon demand response, and comparing the impact of combined operation on system and user outcomes, Scenario 5 has a wind curtailment rate of 0, resulting in a total system cost reduction of $16,082 and a $9,760 reduction in user low-carbon incentive benefits. This is because some wind power is absorbed by the grid through TU-CSS operation, reducing user wind power utilization. After optimizing using the three strategies, although the total energy purchase cost for users in Scenario 7 is not the minimum, the system's wind curtailment rate, total cost, and carbon emissions are all minimized, decreasing by 13.06%, 19.28%, and 18.26% respectively compared to Scenario 1, validating the necessity of optimizing using the three strategies.

[0080] Table 6

[0081] After optimization of Scene 7 Figure 7It can be seen that, spatially, the high carbon potential nodes are those directly supplied by conventional thermal power units, with an average carbon potential of approximately 0.88 t / (MW·h), such as nodes 5-9 and 28-29; medium carbon potential nodes appear at TU-CSS and gas turbine adjacent nodes, with an average carbon potential of approximately 0.398 t / (MW·h), such as nodes 1-4 and 10-19; low carbon potential nodes are mainly supplied by wind power, with an average carbon potential of approximately 0.113 t / (MW·h), such as nodes 20-25. Temporally, the high carbon potential of the grid mainly occurs during peak user electricity consumption periods from 9:00 to 21:00, exhibiting load curve characteristics. However, it is worth noting that the carbon potential of some nodes increases sharply at 4:00, because wind power output is insufficient at this time, requiring thermal power units to supplement the output. For example... Figure 8 As shown in (a), the carbon potential of the gas network is spatially distributed such that, due to the introduction of lower carbon potential from the pipeline storage, the carbon potential of each node is lower than that of nearby gas source nodes, but the difference in carbon potential is not significant, with the largest difference being only 0.12 t / (MW·h). Temporally, the carbon potential of the nodes shows an increasing trend. Nodes 19 and 20 have low user gas loads during the 1:00-6:00 period, and gas demand is supplied by the pipeline storage, resulting in low carbon potential. Figure 8 As shown in (b), in terms of spatial distribution, the carbon potential of the pipes is directly affected by the carbon emission intensity of gas source 3 in pipes 6, 7, and 8, and directly affected by the carbon emission intensity of gas sources 1 and 2 in pipes 1, 2, and 3. The remaining pipes are affected by all gas sources, and the carbon potential of the pipes is basically similar to that of the carbon potential of nearby gas sources. In terms of temporal distribution, the carbon potential of the pipes is affected by the carbon emission intensity of the gas sources and shows an increasing trend.

[0082] Effectiveness analysis of joint operation mode By comparing the CSS operation, unit output, and system electro-carbon coupling characteristics in scenarios 1 and 2, the effectiveness of the combined operation mode is analyzed. Figure 9As shown, during periods of low load and abundant wind power, in Scenario 2, generators 1 and 4 reduce net output and increase CSS energy consumption, thereby increasing the grid-connected wind power capacity. Simultaneously, the rich liquid storage system increases rich liquid outflow to meet the P2G equipment's need to produce natural gas from wind power, resulting in a significant improvement in wind power utilization. Scenario 2 shows a 10.7% reduction in wind curtailment rate compared to Scenario 1. During periods of high load and low wind power output, the rich liquid outflow from the CCS in Scenario 2 is zero. Generators 1 and 4 reduce carbon storage system energy consumption and increase net output to meet load demand. Simultaneously, the CSS increases rich liquid inflow to capture CO2 generated by high-carbon thermal power, preparing for the next period of abundant wind power. Scenario 2 shows a 0.144 tCO2 / (MW·h) decrease in the grid average carbon potential compared to Scenario 1. This is because the carbon emission intensity of generators equipped with CSS is reduced, and the lower limit of the net output of thermal power provides more grid-connected capacity for wind power. Overall, considering the combined operation mode, the use of the CO2 buffer station formed by the liquid storage tank of the thermal power unit increases the adjustment range of the net output of thermal power. At the same time, the combination with WT-P2G improves the flexibility and wind power utilization rate.

[0083] Effectiveness analysis of dynamic characteristics of air network The influence of gas network storage characteristics on system gas source output and carbon potential, such as Figure 10 As shown in the diagram. Scenario 1 experiences a power shortage in electrical load between 10:00 and 15:00. This is because the gas network storage capacity is not taken into account, and the gas turbine supply is constrained by the gas source output. A power shortage in gas load occurs at 10:00 because the gas source output at this time cannot meet the peak load of the gas network. Scenario 3 increases the gas source output between 1:00 and 8:00, improving the storage capacity margin. Therefore, during peak gas load periods, the storage capacity assists the gas source output in meeting the gas load and gas turbine supply, resulting in zero load loss. Due to the introduction of storage carbon potential, the average carbon potential of the gas network decreases by 0.014 t / (MW·h) in Scenario 3 compared to Scenario 1. Due to the influence of gas source carbon potential, the carbon potential of the gas network storage shows a gradual increasing trend. In summary, the dynamic storage characteristics of the gas network ensure a reliable natural gas supply, reduce the gas network carbon potential, and improve the reliability and low-carbon nature of system operation.

[0084] Analysis of the impact of energy prices on incentive-driven low-carbon demand response The difference between energy prices and carbon prices affects user energy consumption behavior. To analyze the impact of energy prices on incentive-based low-carbon demand response, two additional scenarios are added to Scenario 4: Scenario 8: Considering price-based demand response under time-of-use pricing; Scenario 9: Considering incentive-based low-carbon demand response under time-of-use pricing. Time-of-use pricing data is shown in Table 7. The following simulation uses a user at node 16 as an example. Table 8 shows the user cost details under the three scenarios. Figure 11 A comparison of user energy consumption behavior in three scenarios.

[0085] Table 7

[0086] Table 8

[0087] As shown in Table 8, after considering price-based demand response, users reduce their energy purchase cost by $14,063 compared to the fixed energy purchase cost in Scenario 4 by adjusting their energy consumption behavior. Under time-of-use pricing, after considering incentive-based low-carbon demand response, users choose energy consumption based on the difference between energy price and carbon price. Scenario 9 increases the user's energy purchase cost by $616 compared to Scenario 8, and reduces the incentive benefits by $1,032 compared to Scenario 4, but its total cost is the lowest, proving that incentive-based low-carbon demand response is also applicable under the influence of energy price. While ensuring economic efficiency, Scenario 9 reduces carbon emissions by 58 tons compared to Scenario 8, indicating that incentive-based low-carbon demand response can fully tap users' carbon reduction potential and ensure low-carbon energy consumption.

[0088] Depend on Figure 11 As can be seen, based on the carbon potential signal transmitted by the system, users in Scenario 4 shift their daytime high-carbon loads to low-carbon potential periods when wind power output is higher. By comparing the curve trends of the original loads, it can be concluded that the incentive-based low-carbon demand response has the effect of peak shaving and valley adjustment, solving the energy shortage during peak loads and improving the wind power absorption rate.

[0089] Under time-of-use pricing, the price difference across different time periods is higher than the low-carbon incentive price. In Scenario 9, users' horizontal electricity consumption behavior shifts from being guided by carbon prices to being guided by electricity prices. During peak-hour pricing periods (9:00-14:00 and 19:00-22:00), users shift their electricity load to off-peak pricing periods (0:00-7:00). Users' vertical electricity consumption behavior is guided by both electricity and carbon prices. During peak-hour pricing periods, users substitute gas for electricity, and during off-peak pricing periods, they substitute electricity for gas. Because the price difference between electricity and gas is smaller than the carbon price during normal times (15:00-18:00), users substitute low-carbon gas loads for high-carbon electricity loads. Since gas prices are fixed, users' horizontal gas consumption behavior is guided by carbon prices, shifting high-carbon gas loads to low-carbon periods, while their vertical gas consumption behavior aligns with their vertical electricity consumption behavior. This shows that the guiding influence of energy prices and carbon potential overlap, but also exist at an angle, leading users to consider a balance between the two when adjusting their energy consumption behavior. However, it should be noted that with the improvement of market trading mechanisms and the continuous development of new energy sources, it is possible that carbon potential trends and energy price trends will overlap, in which case users can obtain the dual benefits of reduced energy purchase costs and low-carbon incentives.

Claims

1. A low-carbon economic dispatch method for source-load interaction in an electricity-gas interconnected system based on carbon emission flows, characterized in that, Includes the following steps: Step 1: Based on the carbon emission flow calculation model of the power system, derive the carbon emission flow calculation model of the natural gas system that takes into account the dynamic characteristics of the gas grid; Step 2: Construct a carbon storage system with liquid storage tank as the main component, and form a joint operation mode of TU-CSS-P2G-WT with thermal power units, wind power units, and power-to-gas equipment. Analyze the joint operation mechanism based on the electric carbon characteristics of the four components and establish a joint operation model. Step 3: Based on the parameters, load, and wind power forecast of the power-gas interconnection system, with the optimization objective of minimizing the total daily operating cost of the system, and constrained by the power system constraints, natural gas system dynamic constraints, and the TU-CSS-P2G-WT joint operation model from Step 2, establish a low-carbon economic operation model for the power-gas interconnection system. Step 4: Establish an incentive-based low-carbon demand response model based on the low-carbon response mechanism. Its optimization objective is to minimize the total energy purchase cost for users, and it is constrained by transferable low-carbon load and substitutable low-carbon load. The specific process of Step 4 is as follows: Step 4.1: Construct a low-carbon response mechanism; Step 1: Obtain the user's dynamic node carbon potential; the energy network calculates the energy flow based on the load forecast curve, and then calculates the carbon flow based on the network energy flow to obtain the carbon potential of the user's node, and sends the information to the user through the carbon meter. Step 2: Obtain day-ahead carbon emissions; derive the day-ahead load curve through load forecasting, multiply the energy consumption for each period by the nodal carbon potential for that period, and then sum them up to obtain the day-ahead carbon emissions; Step 3: Optimize users' daily energy consumption behavior; Users obtain carbon potential information from the carbon table at each time of the day and adjust their energy consumption behavior according to the magnitude of the carbon potential to reduce carbon emissions, thereby obtaining the load curve after incentive-based low-carbon demand response. Step 4: Carbon emission reduction calculation; Based on the intraday node carbon potential and the load curve after response, the actual carbon emissions of energy consumption during the day can be obtained; By comparing the daytime carbon emissions in Step 2, the carbon emission reduction after the incentive-based low-carbon demand response is obtained, and multiplying it by the low-carbon incentive price yields the carbon reduction incentive benefit. Step 5: Based on Step 1 and Step 3, calculate the spatiotemporal distribution of carbon potential in the energy network and establish a low-carbon economic dispatch model for source-load interaction in the electricity-gas interconnection system. The specific process of Step 5 is as follows: In the day-ahead phase, the source side calculates the spatiotemporal distribution of carbon potential in the energy network with the minimum total daily operating cost of the system. The load side establishes an incentive-based low-carbon demand response model based on the carbon potential signal from the source side and the low-carbon response mechanism to reasonably adjust energy consumption behavior and minimize the total energy purchase cost for users. During the intraday phase, the source side reschedules the load demand reported when the user's total energy purchase cost is minimized. After receiving the updated actual carbon potential signal, the user adjusts their own total energy purchase cost.

2. The low-carbon economic dispatch method for source-load interaction in an electricity-gas interconnected system based on carbon emission flows according to claim 1, characterized in that, The specific process of Step 1 is as follows: Step 1.1: Construct a calculation model for carbon emission flows in the power system; Based on the principle of proportional sharing, the analysis shows that the carbon flux density of all outflowing currents from a node is equal to the carbon potential of that node, i.e.: ； in, For nodes n Active power flow outflow nodes in connected branches n The set of all branches; For power grid branch i carbon flux density; For power grid nodes n carbon potential; The carbon potential of each grid node is determined by the amount of active power injected from the generators and transmission lines connected to that node, expressed as: ； in, For nodes n Active power flow flows into nodes in connected branches. n The set of all branches, For nodes n A collection of connected generator sets; branch road i active power, For generator h carbon emission intensity; P h For generator sets h Output active power; Assume the power grid has N There are nodes, among which there are K Each unit injection node and M The load node, let the first... n The carbon potential of each node is , n =1, 2, ..., N The carbon potential of the grid nodes is then: ； in, P N The active flux matrix of the nodes; P B This is the branch power flow distribution matrix; P G Inject the distribution matrix into the unit. E G This represents the unit's carbon emission intensity vector. Step 1.2: Construct a calculation model for carbon emission flow of a natural gas system that considers the steady-state characteristics of the gas network; According to the principle of proportional sharing, the carbon flux density of all gas flows exiting a gas network node is equal to the carbon potential of that node, i.e. ； in, To connect with gas network nodes g Airflow outlet node in connected pipes g The collection of all pipes; For pipelines p carbon flux density; As a gas network node g carbon potential; The carbon potential of each gas network node is determined by the gas source and gas flow rate connected to that node, and is expressed as follows: ； in, For nodes g Airflow into the node from the connected pipes g The collection of all pipes; For nodes g Connected gas source w gather; gas source w carbon emission intensity; q p , q w Pipes p And the source of gas w airflow rate; Step 1.3: Construct a calculation model for carbon emission flow of a natural gas system that considers the dynamic characteristics of the gas network; Natural gas pipelines have pipeline storage characteristics, and pipeline storage is a virtual gas network node. r The existing gas supply is processed as a virtual gas source. s Injection Node r The carbon potential of each gas network node is determined by the gas flow rate, and therefore the amount of gas injected into the gas source, storage tank, and pipeline connected to that node. Transformed into: ； in, For nodes g Connected pipe storage s gather; For storage s carbon emission intensity; q s For storage s airflow rate; Suppose the gas network has G nodes, including W gas source injection nodes, S pipeline storage nodes, and L gas load nodes. Considering the dynamic characteristics of the gas network, the network now has G+S nodes, and the carbon potential of the gas network nodes is: ； Q B The airflow distribution matrix of the pipeline. Q W For the gas source injection distribution matrix, Q S Inject the distribution matrix into the storage tube. Q N For the nodal airflow matrix, This is the carbon emission intensity vector from the gas source. This is the carbon emission intensity vector for storage.

3. The low-carbon economic dispatch method for source-load interaction in an electricity-gas interconnected system based on carbon emission flows according to claim 1, characterized in that, The specific process of Step 2 is as follows: Step 2.1: The CO2 emitted by the thermal power unit is stored by the carbon storage system, and the TU-CSS model is established; By installing a carbon storage system in a thermal power unit, during the CO2 regeneration process, the unit needs to extract hot steam from its cylinder to heat the regeneration tower, thus separating CO2 from the solvent and achieving complete CO2 capture. The output power of the thermal power unit is: ； in, P G,t For thermal power units in t Total output power during the time period; P N,t For thermal power units in t Net output power during the period P B For fixed energy consumption; P CSS,t For carbon storage systems in t Operating power during specific time periods; ； in, λ CO2 Energy consumption for processing one unit of CO2 in a carbon storage system; m ZS,t This refers to the amount of CO2 produced by the regeneration tower. ； in, m p2g,t , m fc,t They are respectively t The amount of CO2 required for P2G gasification and the amount of CO2 stored in carbon during the time period; The amount of CO2 flowing into the carbon storage system is related to the flue gas diversion device of the thermal power unit, and the amount of CO2 flowing into the rich liquid storage tank is related to the solvent capture rate of the MEA absorber, as expressed as: ； ； in, δ The flue gas split ratio of the thermal power unit. e G Carbon emission intensity of thermal power units. m R-in,t for t The mass of CO2 dissolved in the rich solution during a given period. η CO2 capture rate of the absorption tower; CO2 exists in the storage tank dissolved in MEA solvent. The relationship between the mass of CO2 and the volume of MEA solvent needs to be calculated, expressed as follows: ； in, V R-in,t For carbon storage systems in t The volume of MEA solvent required to dissolve CO2 in flue gas during a given time period; M MEA and M CO2 These are the molar masses of the solvent and CO2, respectively. μ R This refers to the concentration of the alkanolamine solution. The density of the alkanolamine solution; The volume of solvent flowing into and out of a rich-liquid reservoir is equal to the volume of solvent flowing out and in, expressed as: ； in, V R-out,t For flooded storage t The volume of solvent flowing out during the time period; V P-in,t , V P-out,t These are the starved fluid storage devices. t The volume of solvent flowing in and out during a given time period; The solvent flowing out of the rich solution storage tank is used in the regeneration tower and then returned to the carbon storage system as lean solution, as follows: ； in, V ZS,t for t The volume of solvent flowing into the regeneration tower during a given time period; Because the carbon storage system has two liquid storage tanks, and the liquid flow is a unidirectional circulation, the rich and lean liquid storage tanks are located in... t Time slot capacity and t -1 time period capacity and t It is related to the inflow and outflow volume over a given period of time, i.e. ； in, V R,t For flooded storage t Capacity of a time period; V P,t For the starved fluid storage t Capacity of a time period; Rich-liquid and lean-liquid storage devices also need to meet capacity constraints, expressed as: ； ； in, V R,cap , V P,cap These represent the rated capacities of the rich and poor solution reservoirs, respectively; the solvent is reusable, and the capacities of the rich and poor solution remain constant within a scheduling cycle. Step 2.2: The power-to-gas equipment uses CO2 from the carbon storage system to produce natural gas, and the electricity required for production is supplied by wind turbines. A TU-CSS-P2G-WT joint operation model is established. As can be seen from the methanation reaction, the volume of CO2 consumed by P2G and the volume of CH4 produced are equal. Therefore... ； ； in, CO2 density; Q CH4,t for t The volume of methane generated by P2G during the specified time period; Q LHV This refers to the calorific value of natural gas. η p2g For P2G gas production efficiency; P p2g,t for t The power consumption of P2G operation during a given time period, provided by WT, is expressed as follows: ； in, P net,t This refers to the grid-connected power of wind power.

4. The low-carbon economic dispatch method for source-load interaction in an electricity-gas interconnected system based on carbon emission flows according to claim 1, characterized in that, The specific process of Step 3 is as follows: Step 3.1: Construct the objective function of the low-carbon economic operation model for the electrical-electric interconnection system: ； in, C G , C S These are the power generation and start-up / shutdown costs of thermal power units, respectively. C T The cost of wind curtailment for wind turbines; C F , C R These are the costs of carbon sequestration and MEA solution loss in the carbon storage system, respectively. C B For gas purchase costs; C p2g For P2G operating costs; C SP , C SG These are the costs associated with system power failure and gas load, respectively. Step 3.2: Constructing the constraints of a low-carbon economic operation model for an electrical-electric interconnection system; Step 3.2.1: Construct power system constraints; These include node power balance constraints, branch power flow and power flow capacity constraints, thermal power unit operation constraints, gas turbine operation constraints, P2G equipment operation constraints, and wind turbine operation constraints. ； ； ； ； ； ； ； ； ； in, Ω GT , Ω B These are respectively gas turbines and power transmission branch collections; For power grid nodes n exist t Load demand during different time periods; P g,j,t For gas turbine j exist t Efforts during a specific time period; f pq,t For power grid branch pq exist t Trends of different time periods; branch road pq Maximum current capacity; B pq branch road pq Susceptivity; θ The phase angle of the node; R G,i For thermal power units i Climbing rate; and The units i Minimum shutdown and startup time; and The units i to t -1 represents the time during which the machine has been continuously turned on and off; , thermal power units i The minimum and maximum output, , Gas turbine j The minimum and maximum output, , P2G devices m Minimum and maximum output; Step 3.2.2: Constructing natural gas system constraints; This includes node gas volume balance constraints, gas network dynamic characteristic constraints, Weymouth dynamic equation for pipeline airflow, pipeline capacity constraints, pressure relationship between compressor pipeline terminal nodes, node gas pressure constraints, and gas source output constraints. ； ； ； ； ； ； ； ； ； in, Ω G A collection of natural gas pipelines; As a gas network node g exist t Load demand during different time periods; η g For the conversion efficiency of the gas turbine; for t Time Pipeline UV The amount of pipe stock; Δ x uv and D uv These are the length and diameter of the pipe, respectively. The average pressure in the pipeline; R It is the gas constant; T g The temperature of the natural gas; Z The compressibility factor of natural gas; ρ 0 represents the density of natural gas under standard conditions; T This represents the total number of time periods in the scheduling cycle. F uv For pipelines UV The coefficient of friction; For pipelines UV Average airflow rate; For pipelines UV Maximum airflow; β c This refers to the compressor coefficient; and They are nodes u Minimum and maximum air pressure; 、 Gas source x The minimum and maximum air output.

5. The low-carbon economic dispatch method for source-load interaction in an electricity-gas interconnected system based on carbon emission flows according to claim 1, characterized in that, The specific process of Step 4 also includes: Step 4.2: Construct the objective function of the incentive-based low-carbon demand response model; Minimizes the total cost of energy purchases for users: ； in, C buy Energy purchase cost for users; C I Incentives for carbon reduction; ； in, , They are respectively t Electricity prices during specific hours and natural gas prices; ； in, κ c Low-carbon incentive prices; M tr , M re These represent the carbon emissions reduced by load shifting and load substitution in incentive-based low-carbon demand response; ； in, for t Time period nodes n The carbon potential; for t Time period nodes s Carbon potential; For the first n Node users t Transferable electrical loads during specific time periods; For the first g Node users t Transferable gas load during a given time period; ； in, Ω e-g A set of nodes that simultaneously supply electricity and gas loads to users; For the first z Each node user t Alternative electrical loads for different time periods; Step 4.3: Constructing incentive-based low-carbon demand response model constraints; Step 4.3.1: Transferable low-carbon loads. In terms of time distribution, the carbon potential is different in different periods. Loads with high carbon potential can be transferred to periods with low carbon potential. Such loads are called transferable loads. The transferable low-carbon loads are transferred only over time, with the total transfer amount remaining constant throughout the entire cycle, as expressed below: ； in, , for t The amount of low-carbon load that can be transferred in and out during a given period; To meet the basic energy needs of the load and the security of the energy network system, transferable low-carbon loads must also meet the following constraints: ； in, , A variable that is 0-1 indicates that in t The status of time-transferable low-carbon loads entering and leaving the system; τ For transferable low carbon load rate; Step 4.3.2: Alternative Low-Carbon Loads At the same time, different energy sources have different carbon potentials. Low-carbon-potential loads can be selected to replace high-carbon-potential loads, achieving mutual substitution between different energy sources. Such loads are called substitutable low-carbon loads, and the specific model is as follows: ； ； in, , for t The time period can replace the energy input and output of low-carbon loads; , A variable that is 0-1 indicates that in t The time period can replace the substitution and removal of low-carbon loads; ω It can be used as an alternative to low carbon load rates.

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