A low-carbon dispatching method for gas-electricity coupling distribution network based on carbon emission reduction contribution

Abstract

A kind of low-carbon scheduling method of gas-electricity coupling distribution network based on carbon emission reduction contribution, comprising the following steps: step 1: establishing the collaborative operation relationship of two-stage electric-gas conversion, hydrogen storage equipment, carbon capture equipment, carbon storage equipment and hydrogen-doped gas turbine;Step 2: establishing and solving the optimization scheduling model of energy supply side, obtaining the electric and gas flow and equipment state in each period;Step 3: calculating the carbon potential of electric and gas network nodes, obtaining the dynamic carbon emission factor of electric and gas network by load weighting;Step 4: determining the carbon emission reduction contribution according to the load carbon responsibility difference before and after response, and establishing the user side demand response model;Step 5: solving the user side demand response model, returning the electric load after response and the gas load after response to the energy supply side optimization scheduling model for alternating iterative solution until convergence, and outputting the low-carbon scheduling control strategy.The present application can realize collaborative low-carbon scheduling of energy supply side and user side, and reduce operating energy consumption.

Description

Technical Field

[0001] This invention relates to the fields of new energy and integrated energy system technology, and in particular to integrated energy system optimization operation technology, specifically to a low-carbon dispatching method for gas-electric coupled distribution networks based on carbon emission reduction contributions. Background Technology

[0002] Currently, low-carbon dispatching of integrated electrical-gas distribution systems (IEGDS) mainly relies on the coordinated operation of power-to-gas (P2G) and gas turbine (GT) equipment to achieve energy complementarity between the power system and the natural gas system, thereby reducing system operating energy consumption and carbon emissions. As the installed capacity and penetration rate of renewable energy sources such as wind power continue to increase, the randomness, volatility, and intermittency of their output place higher demands on the system's flexible adjustment capabilities. Existing technologies typically utilize P2G to convert surplus wind power into hydrogen or synthetic natural gas, and then use GT to convert natural gas or hydrogen into electricity, thereby promoting renewable energy consumption and multi-energy coordinated operation.

[0003] The paper "Hierarchical Distributed Scheduling of Multi-Microgrid Distribution Systems Considering Multi-Dimensional Coordinated Carbon Emission Reduction Strategies" proposes a hierarchical distributed scheduling model for multi-microgrid distribution systems considering multi-dimensional coordinated carbon emission reduction strategies. By configuring CO2 storage devices in the collaborative operation of P2G and carbon capture systems (CCS), the timing decoupling of carbon capture and methanation is achieved, thereby improving the scheduling flexibility of captured CO2. The paper "Two-Level Optimization Scheduling of Integrated Energy Systems Considering Two-Stage P2G and Hydrogen Blending in Gas Filtration" proposes a two-level optimization scheduling model for integrated energy systems containing two-stage P2G and hydrogen-blended gas fusion (GT). It constructs a collaborative operation framework for two-stage P2G, CCS, and hydrogen-blended GT, effectively improving the system's low-carbon operation level and renewable energy absorption capacity. However, the above papers mainly focus on the optimization of single links or the coordination between local devices, without fully considering the timing coordination relationships of carbon storage, hydrogen storage, methanation, and hydrogen-blended power generation under a unified scheduling framework. Therefore, it is difficult to achieve the coordinated allocation and full utilization of hydrogen and carbon resources.

[0004] Active adjustment of demand-side resources is also an important way to improve the low-carbon operation level of IEGDS (Internet Energy Response System). The paper "A Low-Carbon Energy Management Strategy for Active Distribution Networks Based on Dynamic Carbon Entropy" proposes a two-stage low-carbon energy management strategy for active distribution networks based on dynamic carbon entropy. It constructs a two-stage low-carbon optimal scheduling model guided by carbon price to guide flexible loads to participate in low-carbon demand response. However, this paper mainly focuses on the flexible load response of the power system. Research on the characterization of the difference in carbon emission intensity between electricity and gas consumption in IEGDS and how to use this difference to guide the time-series transfer and cross-energy substitution of flexible loads is still relatively limited. Summary of the Invention

[0005] The purpose of this invention is to address the problems in existing gas-electric coupled distribution network low-carbon dispatching methods, such as the timing mismatch between hydrogen electrolysis and hydrogen consumption processes, and between carbon capture and carbon consumption processes; insufficient coordinated operation among hydrogen storage equipment, carbon storage equipment, methane reactors, and hydrogen-blended gas turbines, leading to difficulties in flexibly allocating hydrogen and captured carbon dioxide among different utilization paths; and the lack of a stable and unified carbon responsibility accounting benchmark due to the influence of network node locations and load changes on existing carbon signals. These issues result in difficulties in accurately quantifying users' carbon emission reduction contributions and a lack of effective coordination between user-side low-carbon response and energy supply-side equipment operation. Therefore, this invention is proposed to address these problems.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A low-carbon dispatching method for gas-electric coupled distribution networks based on carbon emission reduction contributions includes the following steps: Step 1: Obtain the gas-electric coupling distribution network topology, equipment parameters, predicted wind power output, original electrical load, original gas load, upstream energy purchase conversion energy consumption parameters, and user time-of-use energy loss parameters; establish the coordinated operation relationship between the two-stage electricity-to-gas conversion link, hydrogen storage equipment, carbon capture equipment, carbon storage equipment, and hydrogen-blended gas turbine; by constructing a coordinated operation relationship including carbon and hydrogen storage equipment, realize the time decoupling of the electrolysis hydrogen production and hydrogen consumption processes, and the carbon capture and carbon consumption processes, thereby alleviating the time mismatch problem of hydrogen production, carbon capture, and methanation processes; Step 2: Based on the collaborative operation relationship established in Step 1 regarding the two-stage electricity-to-gas conversion process, hydrogen storage equipment, carbon capture equipment, carbon storage equipment, and hydrogen-blended gas turbine, establish an energy supply-side optimization scheduling model with the goal of minimizing the overall energy consumption of the gas-electric coupled distribution network system. Solve the energy supply-side optimization scheduling model to obtain the power flow, natural gas flow, multi-energy equipment output, hydrogen storage status, and carbon storage status for each time period. By adjusting the output of each equipment and the charging and discharging status of the hydrogen and carbon storage equipment, improve the energy utilization efficiency of the gas-electric coupled distribution network and reduce system carbon emissions. Step 3: Based on the power flow, natural gas flow, multi-energy equipment output, hydrogen storage status, and carbon storage status obtained in Step 2, track the flow of carbon emissions within the gas-electric coupling distribution network to calculate the carbon potential of the distribution network nodes and the carbon potential of the gas distribution network nodes respectively; using the power load and gas load at the start of the current iteration, spatially weight the carbon potential of the distribution network nodes and the carbon potential of the gas distribution network nodes respectively to form the dynamic carbon emission factor of the power grid and the dynamic carbon emission factor of the gas network, so as to reduce the impact of the difference in node location in the physical network on user incentives and provide a consistent accounting benchmark for the carbon responsibility before and after demand response; Step 4: Combine the dynamic carbon emission factors of the power grid and the dynamic carbon emission factors of the gas grid obtained in Step 3 with the original electrical load and the original gas load to calculate the difference in the corresponding load carbon responsibility after the response, and determine the user's carbon emission reduction contribution; comprehensively consider the user's time-of-use energy loss, user carbon emission reduction contribution and user satisfaction, and establish a user-side demand response model; use the difference in carbon responsibility to represent the emission reduction contribution, so that positive carbon emission reduction contribution forms an incentive and negative carbon emission reduction contribution forms additional energy consumption, effectively guiding the transferable load to low-carbon periods and the alternative load to low-carbon energy conversion; Step 5: Solve the user-side demand response model established in Step 4 to obtain the post-response electrical load and post-response gas load; return the post-response electrical load and post-response gas load to the energy supply-side optimization scheduling model established in Step 2 for iterative solving until the preset convergence threshold is met, and output the low-carbon scheduling control strategy; according to the low-carbon scheduling control strategy, issue power adjustment commands to the energy supply equipment, energy conversion equipment, hydrogen storage equipment, and carbon storage equipment in the gas-electric coupling distribution network to control physical operation; through iterative optimization between the energy supply side and the user side, while meeting network security constraints and user satisfaction requirements, the energy consumption of physical system operation and the overall carbon emission reduction effect are taken into account, and the emission reduction potential on the load side is deeply explored.

[0007] Step 1 specifically includes the following sub-steps: Step 1-1) As the starting point for establishing a collaborative operation relationship, construct the two-stage electro-gas conversion operation constraints; firstly, determine the correlation between the hydrogen production power of the electrolyzer (EL) and the input electrical power and deduce the hydrogen production of the electrolyzer, while imposing restrictions on the input electrical power range and the ramp-up power of hydrogen production; then, based on the hydrogen power allocated to the methane reactor (MR), determine the natural gas output power of the methane reactor and convert it into methane production, and finally limit the range of input hydrogen power to the methane reactor; Step 1-2) Based on the parameters established in Step 1-1), construct the operating constraints of the hydrogen-blended gas turbine; characterize the mixed state of natural gas and hydrogen consumed by the hydrogen-blended gas turbine, calculate the power generation of the hydrogen-blended gas turbine, and limit the range of hydrogen blending ratio and the output of the hydrogen-blended gas turbine. Steps 1-3) Based on the power generation of the hydrogen-blended gas turbine obtained in Steps 1-2), construct the operating constraints of the carbon capture equipment; extract the power consumed by the generator set for carbon capture, combine it with the fixed operating power to obtain the actual amount of carbon dioxide captured during the carbon capture process, and set the upper limit of the amount of carbon dioxide captured. Steps 1-4) Based on the actual carbon dioxide capture amount obtained in Steps 1-3), construct the operating constraints of the carbon storage equipment; update the carbon storage amount of the carbon storage equipment at each time point, and tighten the boundary of the mutual exclusion condition of the 0-1 variables of the carbon charging and discharging state; use the carbon storage constraints with tightened boundary to achieve time decoupling of carbon capture and carbon utilization process for methanation, and improve the utilization efficiency of captured carbon dioxide. Steps 1-5) Combining the hydrogen production of the electrolyzer derived in Step 1-1), construct operational constraints for the hydrogen storage equipment; track the dynamic changes in hydrogen storage capacity, and set mutual exclusion restrictions for hydrogen charging and discharging states, upper and lower limits of hydrogen storage capacity, extreme values ​​of hydrogen charging and discharging, and charging and discharging balance constraints for scheduling cycles; finally, summarize all constraints to establish the collaborative operation relationship between the two-stage power-to-gas conversion process, hydrogen storage equipment, carbon capture equipment, carbon storage equipment, and hydrogen-blended gas turbine; by adding dynamic constraints to the hydrogen storage equipment, hydrogen can be flexibly allocated between methanation, intertemporal storage, and hydrogen-blended power generation, thereby improving the comprehensive utilization efficiency of hydrogen energy.

[0008] Step 2 specifically includes the following sub-steps: Step 2-1) Construct the energy supply side optimization scheduling objective function; the objective function includes the energy consumption of electricity purchased from the upper-level power grid, the energy consumption of gas purchased from the upper-level gas grid, the energy consumption of wind curtailment penalty, the energy consumption of grid loss, the energy consumption of external carbon purchase, the energy consumption of power outage load penalty, and the energy consumption of gas outage load penalty, with the optimization objective being to minimize the comprehensive operating energy consumption of the gas-electric coupled distribution network system; Step 2-2) Apply equipment operation constraints and network operation constraints to the energy supply side optimization scheduling objective function constructed in Step 2-1); solve the energy supply side optimization scheduling model to obtain the power flow, natural gas flow, multi-energy equipment output, hydrogen storage status and carbon storage status for each time period.

[0009] Step 3 specifically includes the following sub-steps: Step 3-1) Quantify the carbon potential of distribution network nodes; using the power flow obtained in Step 2, calculate the carbon potential of distribution network nodes based on the power inflow and outflow balance relationship; Step 3-2) Quantify the carbon potential of the gas distribution network nodes; Calculate the carbon potential of the gas distribution network nodes based on the natural gas flow patterns obtained in Step 2 and the natural gas pipeline flow patterns. Step 3-3) Calculate the carbon emission flow transfer of the coupled equipment; combined with the energy flow transfer path of the equipment, update the carbon emission intensity from the input end to the output end of the electrolyzer, from the input end to the output end of the methane reactor, and from the input end to the output end of the gas turbine; through the above steps 3-1) to 3-3), the carbon responsibility transfer process between the distribution network, gas distribution network and various coupled equipment can be continuously and accurately tracked according to the carbon flow conservation relationship; Steps 3-4) Establish a hydrogen-carbon ratio model to track carbon emission transfer in hydrogen storage equipment; when the hydrogen storage equipment is filled with hydrogen, update the hydrogen-carbon ratio according to the carbon emission intensity of the input hydrogen and the current hydrogen storage status of the hydrogen storage equipment; when the hydrogen storage equipment is discharged from hydrogen, determine the carbon emission intensity of the output hydrogen according to the current hydrogen-carbon ratio, so as to track the carbon emission transfer during the time-series storage of hydrogen. Steps 3-5) Spatially weight the carbon potential of the distribution network nodes and the carbon potential of the gas distribution network nodes quantified in Steps 3-1) and 3-2) according to the load at the start of the current iteration. After the weighting operation is completed, the dynamic carbon emission factor of the power grid and the dynamic carbon emission factor of the gas distribution network are obtained.

[0010] Step 4 specifically includes the following sub-steps: Step 4-1) Assess the carbon emission reduction gain brought about by demand response behavior; compare the difference between the original load and the load after response, and multiply by the dynamic carbon emission factor obtained in Step 3 to calculate the user's carbon emission reduction contribution. Step 4-2) Integrate the user carbon emission reduction contribution obtained in Step 4-1) with time-of-use energy loss to establish the user-side demand response objective function; thereby combining the user-side low-carbon behavior of adjusting energy consumption with time-of-use energy consumption signals to drive flexible load adjustment. Step 4-3) Set the boundaries of transferable load, alternative load, and user satisfaction limit, and add the constraints to the user-side demand response objective function constructed in Step 4-2) to obtain the user-side demand response model.

[0011] Step 5 specifically includes the following sub-steps: Step 5-1) Initialize the system iteration state; use the acquired original electrical load and original gas load as the initial iteration load, and set the iteration number parameter k=0; Step 5-2) Under the load condition of the kth iteration, solve the energy supply side optimization scheduling model to obtain the power flow, natural gas flow, multi-energy equipment output, hydrogen storage status and carbon storage status corresponding to the kth iteration; Step 5-3) Based on the power flow, natural gas flow, multi-energy equipment output, hydrogen storage status, and carbon storage status corresponding to the k-th iteration obtained in Step 5-2), perform the carbon emission flow calculation described in Step 3 to obtain the carbon potential of the distribution network nodes and the carbon potential of the gas distribution network nodes; using the power load and gas load at the beginning of the k-th iteration, spatially weight the carbon potential of the distribution network nodes and the carbon potential of the gas distribution network nodes respectively to obtain the dynamic carbon emission factor of the power grid and the dynamic carbon emission factor of the gas network corresponding to the k-th iteration; Step 5-4) Input the dynamic carbon emission factor of the power grid and the dynamic carbon emission factor of the gas grid corresponding to the kth iteration into the user-side demand response model for solution, and output the updated post-response electricity load and post-response gas load. Step 5-5) Calculate the electrical load deviation and gas load difference before and after the update; determine whether the electrical load deviation and gas load difference are both less than the preset convergence threshold; if not, let k=k+1, and return the updated response electrical load and response gas load to step 5-2) as the starting load to continue iterating; if satisfied, exit the loop output low-carbon scheduling control strategy, and issue force regulation, energy conversion and charging / discharging control commands to the power supply equipment, energy conversion equipment, hydrogen storage equipment and carbon storage equipment in the gas-electric coupling distribution network to control the physical operation of the gas-electric coupling distribution network.

[0012] In step 1-1), the relationship between the hydrogen production power of the electrolyzer and the input electrical power is determined by the following formula: ; The formula used to derive the hydrogen production capacity of an electrolyzer from its hydrogen production power is as follows: ; The formula for limiting the range of input electrical power is as follows: ; The formula used to limit the ramp-up power of hydrogen production is: ; The formula for determining the output power of a methane reactor using natural gas is: ; The formula for calculating the methane production of a methane reactor is as follows: ; The formula for calculating the hydrogen consumption of a methane reactor is as follows: ; The formula used to limit the range of hydrogen power input to the methane reactor is: ; In the formula: , They are respectively The hydrogen production power and input electrical power of EL at any given time; The electro-hydrogen conversion efficiency of EL; for The hydrogen production flow rate of EL at any given time; , These represent the minimum and maximum power consumption of EL, respectively. , These are the upper and lower limits of the ramp-up power for EL hydrogen production; This refers to the calorific value of hydrogen. , They are respectively MR input hydrogen power and output natural gas power at any time; The methanation efficiency of MR; , These represent the maximum and minimum hydrogen consumption power of MR, respectively. , They are respectively MR input hydrogen flow rate and output methane flow rate at any time; The calorific value of methane; The energy conversion coefficient is taken as 3600 MJ / (MW·h); In steps 1-2), the formula used to characterize the mixture state of natural gas and hydrogen consumed by the hydrogen-blended gas turbine is: ; ; The total power of the mixed gas is: ; The formula for calculating the power generation capacity of a hydrogen-blended gas turbine is as follows: ; The formula used to limit the upper and lower limits of the ramp rate of hydrogen-blended gas turbines is: ; The formula for limiting the range of hydrogen doping ratio is: ; ; The formula used to limit the output boundary of hydrogen-blended gas turbines is: ; In the formula, , They are respectively The amount of natural gas and hydrogen consumed by the hydrogen-blended GT at all times; The power generation efficiency of hydrogen-doped GT; , They are respectively The input gas power and power generation of the hydrogen-doped GT mixture at all times; , These are the power outputs of the hydrogen-blended GT system via natural gas input and the power output via hydrogen input, respectively. , These are the upper and lower limits of the ramp power for hydrogen-doped GT, respectively. , These represent the maximum and minimum power generation of hydrogen-doped GT, respectively. for Time GT hydrogen doping ratio; , These are the upper and lower limits of the hydrogen doping ratio in GT; In steps 1-3), the power consumed by the generator set for carbon capture is calculated using the following formula: ; The actual carbon dioxide capture amount is obtained using the following formula: ; The formula for setting the upper limit of carbon dioxide capture is as follows: ; In the formula, for The power consumed by the generator set for carbon capture at any given time; This refers to the fixed operating power during the carbon capture process; for Net power injected into the grid by the generator set at any given moment; Energy consumption coefficient per unit of CO2 processed; for time The amount of capture; The carbon emissions per unit of natural gas consumed by GT; For carbon capture efficiency; In steps 1-4), the carbon storage capacity of the carbon storage device at each time point is updated using the following formula: ; The formulas used to constrain the upper and lower limits of carbon storage capacity are as follows: ; The formula for constraining the extreme values ​​of carbon charge and discharge is: ; ; The formula for the mutual exclusion condition of carbon charging and discharging states is as follows: ; The formula for the full-time storage release balance constraint during the scheduling cycle is as follows: ; In the formula, , These refer to the storage efficiency and release efficiency of the carbon storage device, respectively. for The carbon capacity of the carbon storage device at all times; , These are the upper and lower limits of the capacity of carbon storage equipment, respectively. , They are respectively Real-time CO2 injection and release rates of carbon storage equipment; , They are respectively The maximum and minimum carbon loading capacity of the carbon storage equipment at all times; , They are respectively The maximum and minimum carbon release rates of the carbon storage equipment at all times; , These are 0-1 variables representing whether the carbon storage device is in an input / output state; The scheduling period is 24 hours. In steps 1-5), the formula used to track the dynamic changes in hydrogen storage is: ; The upper and lower limits of hydrogen storage capacity are constrained by the following formulas: ; The formula for extreme hydrogen charge / discharge constraints is as follows: ; ; The formula for limiting the mutual exclusion of hydrogen charging and discharging states is as follows: ; The formula for the all-time charge-discharge balance constraint is: ; In the formula, , These refer to the storage efficiency and release efficiency of hydrogen storage devices, respectively. for The hydrogen capacity of the hydrogen storage device at all times; , These are the upper and lower limits of hydrogen storage equipment capacity, respectively. , They are respectively Hydrogen charging and discharging power at all times; , They are respectively The maximum and minimum hydrogen storage power at any given moment; , They are respectively The maximum and minimum hydrogen release power at any given time; , These are 0-1 variables representing whether the hydrogen storage device is in an input or output state, respectively.

[0013] In step 2-1), the objective function for optimizing the energy supply side scheduling is constructed using the following formula: ; in: ; ; ; ; ; ; ; In the formula: for Energy consumption is calculated based on electricity purchased from the upper-level power grid at all times. for Calculate energy consumption by purchasing gas from the superior gas network at all times; for Constantly curtailing wind power results in energy consumption penalties. for Real-time network loss and energy consumption; for Real-time external carbon purchase energy consumption; for Energy consumption is penalized by load during constant power outages; for Constant air loss load penalty energy consumption; , They are respectively The power distribution network system purchases electricity and gas from the upstream energy network at all times; for Purchase carbon credits at all times; , These are the energy consumption coefficients for purchasing electricity and gas from the upper-level energy network, respectively. , , These are the energy consumption coefficients for wind curtailment penalties and network loss penalties, respectively. Purchased energy consumption coefficient; , They are respectively time Predicted power output and actual grid-connected power output of wind turbine units; For distribution network branch collection; for Timetable The current flowing through the segment; for Timetable The resistance of the segment; , These are the energy consumption coefficients for power outage and gas load penalties, respectively. for Time load Power loss; for Time load Loss of gas power; In step 2-2), the formula for adding distribution network operation constraints is as follows: ; ; ; ; ; ; In the formula: In the distribution network This is the set of the first nodes of the terminal node of the branch; In the distribution network It is the set of the last nodes of the first node of the branch; for Injection nodes from upstream power grid The active power; for Injection nodes from upstream power grid reactive power; for Constantly on load reactive power; , They are respectively Timetable The active and reactive power of the segment; For distribution network Time load The load demand; for Time load The power factor; for Time Node The voltage; for Timetable The reactance of the segment; , They are respectively Time Node Maximum and minimum voltage values; , They are respectively Timetable The maximum and minimum values ​​of the segment current; The formula for incorporating gas distribution network operation constraints is as follows: ; ; ; ; ; In the formula: For This is the set of the first nodes of the terminal node of the branch; For It is the set of the last nodes of the first node of the branch; , for Time Pipeline The power and flow rate of the natural gas flowing through the section; for Injection nodes of the upper-level gas network at all times Natural gas power; For gas distribution network Time load The load demand; For pipelines Pipeline coefficient; For pipelines The maximum natural gas flow rate that the segment can transmit; for Time Node air pressure; , for Time Node Maximum and minimum air pressure limits; The formula for incorporating wind power output constraints is as follows: ; The formula for incorporating hydrogen power constraint is as follows: ; ; ; ; In the formula: for The hydrogen power that EL transmits to MR at any given moment; for The hydrogen power that EL transmits to GT at any given moment; for The hydrogen power transmitted from the hydrogen storage device to the MR at all times; for The hydrogen power transmitted from the hydrogen storage device to the GT at all times; The formula for incorporating carbon flow constraint is as follows: ; In the formula: MR generation unit The amount of carbon dioxide consumed.

[0014] In step 3-1), the carbon potential of the distribution network nodes is calculated using the following formula: ; In the formula: for Power system nodes at all times carbon potential; For nodes Active power flow flows into nodes in connected branches. The set of branch paths; For distribution network nodes A collection of connected devices; , They are respectively Time Branch Inflow node Power, branch carbon flux density; , They are respectively Time and Node Connected generator sets Power injected into the grid, generator sets carbon emission intensity; In step 3-2), the carbon potential of the gas distribution network nodes is calculated using the following formula: ; In the formula: for Gas pipeline network nodes carbon potential; For nodes Airflow into the node from the connected pipes A collection of pipes; For gas pipeline network nodes A collection of connected devices; , They are respectively Time Pipeline The corresponding power and carbon flux density of the airflow; , They are respectively The source of air at all times The airflow power and air source flowing into the gas network carbon emission intensity; In step 3-3), the carbon potential is updated using the following formula: ; ; ; In the formula: , They are respectively Carbon emission intensity at EL input and output terminals at any given time; for Carbon emission intensity at the MR output at any given time; for Carbon emission intensity at the output of hydrogen storage equipment at all times; for Carbon emission intensity at the GT output at any given time; for Carbon potential of the gas distribution network nodes constantly connected to GT; In steps 3-4), the formula used is: ; In the formula: Hydrogen-to-carbon ratio, characterization The carbon emissions corresponding to a unit of energy hydrogen in a hydrogen storage device are... Carbon emission intensity at the output of hydrogen storage equipment at all times; In steps 3-5), the spatial weighting is performed using the following formula: ; In the formula: for time Carbon emission factors of energy network systems ; for Time Node of Energy requirements; for time Energy network nodes carbon potential; for The set of nodes in an energy network system.

[0015] Step 4-1) The formula for calculating the user's carbon emission reduction contribution is as follows: ; ; In the formula: for Time period Carbon emission reduction contribution of energy network demand response; for Time period Energy network demand response afterload; for Time period Original load of the energy network; Step 4-2) Establish the objective function for user-side demand response using the following formula: ; in: ; ; In the formula: The energy loss on the user side during time period t; Incentives for user-side carbon emission reduction contributions during period t; For grid load exist Demand during a specific time period; For gas network load exist Demand during a specific time period; , These are the time-of-use electricity loss coefficient and time-of-use gas loss coefficient for the user side during time period t, respectively. Contribute incentive coefficient to carbon emission reduction; Step 4-3) Set the boundaries for transferable loads and alternative loads, as well as user satisfaction limits. The specific constraints are as follows: (1) Load update formula after electrical and gas response: ; ; (2) Transferable load boundary formula: ; ; (3) Alternative load boundary formula: ; ; ; (4) User satisfaction limits include satisfaction with energy usage methods and satisfaction with expenditure: ; ; ; ; In the formula: , They are respectively The transfer power of loads that can be transferred at any time using electricity or gas; , They are respectively The power that can be replaced by electricity or gas at any time can replace the load. for The proportion of network-transferable load to total load; for The proportion of network-substituteable load to total load; for The electrical load that has been replaced by the gas load at any given time; for The amount of gas load replaced by electrical load at any given time; The electrical-gas substitution coefficient; , These are user satisfaction with energy usage methods and user satisfaction with energy expenditure. , These are the lower limits of user satisfaction with energy usage methods and user satisfaction with energy expenditure, respectively. for The original electrical load at that moment; for The original gas load at any given time.

[0016] Compared with the prior art, the present invention has the following technical effects: 1) This invention utilizes hydrogen storage equipment to decouple the processes of hydrogen production by electrolysis, hydrogen production by methanation, and hydrogen production by blending with hydrogen for power generation in a timely manner. It also utilizes carbon storage equipment to decouple the processes of carbon capture and carbon production by methanation in a timely manner. This allows hydrogen to be flexibly distributed between methanation, time-series storage, and hydrogen production by blending with hydrogen for power generation, alleviating the time mismatch between hydrogen production and hydrogen use, and between carbon capture and methanation, thereby improving the utilization efficiency of hydrogen energy and carbon dioxide capture. 2) This invention continuously tracks the transfer process of carbon responsibility among the distribution network, gas distribution network, EL, MR, hydrogen storage equipment, and hydrogen-blended GT according to the carbon flow conservation relationship, and uses the hydrogen-carbon ratio to record the carbon responsibility transferred across time by the hydrogen storage equipment; using the electrical load and gas load at the beginning of the current iteration, the carbon potential of the distribution network node and the carbon potential of the gas distribution network node are spatially weighted to form the dynamic carbon emission factor of the power grid and the dynamic carbon emission factor of the gas network. This can reduce the impact of node location differences on user incentives and ensure that the carbon responsibility before and after demand response has a consistent accounting benchmark. 3) This invention uses dynamic carbon emission factors from the power grid and gas grid to calculate the carbon responsibility difference between the original electrical load and the response electrical load, and between the original gas load and the response gas load, respectively. The carbon responsibility difference is used to characterize the user's carbon emission reduction contribution, so that positive carbon emission reduction contribution forms an incentive and negative carbon emission reduction contribution forms additional energy consumption. Based on the time-of-use energy loss signal, it can guide the migration of transferable loads to low-carbon periods and the conversion of alternative loads to low-carbon energy. Through iterative cycles between the energy supply side and the user side, it can meet network security constraints and user satisfaction requirements while taking into account the system's operational efficiency and overall carbon emission reduction effect. Attached Figure Description

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the process of the present invention; Figure 2 This is a gas-electric coupling distribution network topology diagram composed of the IEEE 33-node distribution network and the Belgian 20-node gas distribution network in an embodiment of the present invention. Figure 3 This is a graph showing the load and wind power forecast data of the present invention; Figure 4 This is the hydrogen power balance diagram under scenario 2 of the present invention; Figure 5 This is a comparison chart of the demand response results for scenarios 5 and 6 of the present invention; Figure 6 This is a schematic diagram of different types of demand response loads in scenario 6 of the present invention. Detailed Implementation

[0018] like Figure 1 As shown, a low-carbon dispatching method for gas-electric coupled distribution networks based on carbon emission reduction contributions is described, with the following specific steps: Step 1: Obtain the topology, equipment parameters, wind power forecast output, original electrical load, original gas load, upstream energy purchase conversion energy consumption parameters and user time-of-use energy loss parameters of the gas-electric coupled distribution network, and establish a two-stage P2G, hydrogen storage equipment, CCS, carbon storage equipment and hydrogen-blended GT collaborative operation model. Step 2: Establish an energy supply-side optimization scheduling model with the goal of minimizing the comprehensive operating energy consumption of the gas-electric coupled distribution network system, and solve for the power flow, natural gas flow, multi-energy equipment output, hydrogen storage status, and carbon storage status at each time period; Step 3: Track the flow of carbon emissions between the distribution network, gas distribution network, and coupled equipment based on the energy supply-side scheduling results, calculate the carbon potential of the distribution network nodes and the carbon potential of the gas distribution network nodes, and form the dynamic carbon emission factors of the power grid and the gas network by weighting the power load and gas load at the start of the current iteration; Step 4: Use the dynamic carbon emission factor of the power grid and the dynamic carbon emission factor of the gas grid as the carbon responsibility accounting benchmarks before and after the demand response of electricity load and gas load, respectively. Calculate the user's carbon emission reduction contribution based on the difference between the original electricity load and the electricity load after the response, and between the original gas load and the gas load after the response. Establish a user-side demand response model that takes into account the user's time-of-use energy loss, carbon emission reduction contribution incentives and user satisfaction. Step 5: Return the post-response electrical load and post-response gas load to the energy supply side optimization scheduling model, and alternately execute energy supply side optimization, carbon emission flow calculation and user side demand response until the convergence condition is met, and output the low-carbon scheduling result of the gas-electric coupling distribution network.

[0019] In step S1, a collaborative operation model is established for two-stage P2G, hydrogen storage equipment, CCS, carbon storage, and hydrogen-doped GT, specifically including: S101, establish a two-stage P2G model. Its energy conversion constraints and power consumption constraints are as follows: ; ; ; ; ; ; ; ; In the formula: , They are respectively The hydrogen production power and input electrical power of EL at any given time; The electro-hydrogen conversion efficiency of EL; for The hydrogen production flow rate of EL at any given time; , These represent the minimum and maximum power consumption of EL, respectively. , These are the upper and lower limits of the ramp-up power for EL hydrogen production; This refers to the calorific value of hydrogen. , They are respectively MR input hydrogen power and output natural gas power at any time; The methanation efficiency of MR; , These represent the maximum and minimum hydrogen consumption power of MR, respectively. , They are respectively MR input hydrogen flow rate and output methane flow rate at any time; The calorific value of methane; The energy conversion coefficient is taken as 3600 MJ / (MW·h).

[0020] S102, a hydrogen-doped GT model is established. Its energy conversion constraints and power consumption constraints are as follows: ; ; ; ; ; ; ; ; In the formula: , They are respectively The amount of natural gas and hydrogen consumed by the hydrogen-blended GT at all times; The power generation efficiency of hydrogen-doped GT; , They are respectively The input gas power and power generation of the hydrogen-doped GT mixture at all times; , These are the power outputs of the hydrogen-blended GT system via natural gas input and the power output via hydrogen input, respectively. , These are the upper and lower limits of the ramp power for hydrogen-doped GT, respectively. , These represent the maximum and minimum power generation of hydrogen-doped GT, respectively. for Time GT hydrogen doping ratio; , These represent the upper and lower limits of the hydrogen doping ratio in GT.

[0021] S103, Establish a carbon capture model. Its energy conversion constraints and power consumption constraints are as follows: ; ; ; In the formula: for The power consumed by the generator set for carbon capture at any given time; This refers to the fixed operating power during the carbon capture process; for Net power injected into the grid by the generator set at any given moment; Energy consumption coefficient per unit of CO2 processed; for time The amount of capture; The carbon emissions per unit of natural gas consumed by GT; For carbon capture efficiency.

[0022] S104, Establish a carbon storage device model. Its energy conversion constraints and power consumption constraints are as follows: ; ; ; ; ; ; In the formula: , These refer to the storage efficiency and release efficiency of the carbon storage device, respectively. for The carbon capacity of the carbon storage device at all times; , These are the upper and lower limits of the capacity of carbon storage equipment, respectively. , They are respectively Real-time CO2 injection and release rates of carbon storage equipment; , They are respectively The maximum and minimum carbon loading capacity of the carbon storage equipment at all times; , They are respectively The maximum and minimum carbon release rates of the carbon storage device at all times; , These are 0-1 variables representing whether the carbon storage device is in an input / output state; The scheduling period is 24 hours.

[0023] S105, Establish a hydrogen storage device model. Its energy conversion constraints and power consumption constraints are as follows: ; ; ; ; ; ; In the formula: , These refer to the storage efficiency and release efficiency of hydrogen storage devices, respectively. for The hydrogen capacity of the hydrogen storage device at all times; , These are the upper and lower limits of hydrogen storage equipment capacity, respectively. , They are respectively Hydrogen charging and discharging power at all times; , They are respectively The maximum and minimum hydrogen storage power at any given moment; , They are respectively The maximum and minimum hydrogen release power at any given time; , These are 0-1 variables representing whether the hydrogen storage device is in an input or output state, respectively.

[0024] In step S2, the objective function of the power supply-side optimization scheduling model is as follows: ; in: ; ; ; ; ; ; ; In the formula: for Energy consumption is calculated based on electricity purchased from the upper-level power grid at all times. for Calculate energy consumption by purchasing gas from the superior gas network at all times; for Constantly curtailing wind power results in energy consumption penalties. for Real-time network loss and energy consumption; for Real-time external carbon purchase energy consumption; for Energy consumption is penalized by load during constant power outages; for Constant air loss load penalty energy consumption; , They are respectively The power distribution network system purchases electricity and gas from the upstream energy network at all times; for Purchase carbon credits at all times; , These are the energy consumption coefficients for purchasing electricity and gas from the upper-level energy network, respectively. , , These are the energy consumption coefficients for wind curtailment penalties and network loss penalties, respectively. Purchased energy consumption coefficient; , They are respectively time Predicted power output and actual grid-connected power output of wind turbine units; For distribution network branch collection; for Timetable The current flowing through the segment; for Timetable The resistance of the segment; , These are the energy consumption coefficients for power outage and gas load penalties, respectively. for Time load Power loss; for Time load Power loss due to gas depletion.

[0025] In step S2, the constraints of the power supply-side optimized scheduling model are as follows: (1) Distribution network operation constraints: ; ; ; ; ; ; In the formula: In the distribution network This is the set of the first nodes of the terminal node of the branch; In the distribution network It is the set of the last nodes of the first node of the branch; for Injection nodes from upstream power grid The active power; for Injection nodes from upstream power grid reactive power; for Constantly on load reactive power; , They are respectively Timetable The active and reactive power of the segment; For distribution network Time load The load demand; for Time load The power factor; for Time Node The voltage; for Timetable The reactance of the segment; , They are respectively Time Node Maximum and minimum voltage values; , They are respectively Timetable The maximum and minimum values ​​of the segment current.

[0026] (2) Gas distribution network flow constraints: ; ; ; ; ; In the formula: For This is the set of the first nodes of the terminal node of the branch; For It is the set of the last nodes of the first node of the branch; , for Time Pipeline The power and flow rate of the natural gas flowing through the section; for Injection nodes of the upper-level gas network at all times Natural gas power; For gas distribution network Time load The load demand; For pipelines Pipeline coefficient; For pipelines The maximum natural gas flow rate that the segment can transmit; for Time Node air pressure; , for Time Node Maximum and minimum air pressure limits.

[0027] (3) Wind power output constraints: ; (4) Hydrogen flow confinement: ; ; ; ; In the formula: for The hydrogen power that EL transmits to MR at any given moment; for The hydrogen power that EL transmits to GT at any given moment; for The hydrogen power transmitted from the hydrogen storage device to the MR at all times; for The hydrogen power transmitted from the hydrogen storage device to the GT at all times; (5) Carbon flow constraint: ; In the formula: MR generation unit The amount of carbon dioxide consumed.

[0028] In step S3, the carbon emission flow calculation specifically includes: S301, Establish a carbon emission flow model for the power distribution network: ; In the formula: for Power system nodes at all times carbon potential; For nodes Active power flow flows into nodes in connected branches. The set of branch paths; For distribution network nodes A collection of connected devices; , They are respectively Time Branch Inflow node Power, branch carbon flux density; , They are respectively Time and Node Connected generator sets Power injected into the grid, generator sets Carbon emission intensity.

[0029] S302, Establish a carbon emission flow model for the gas distribution network: ; In the formula: for Gas pipeline network nodes carbon potential; For nodes Airflow into the node from the connected pipes A collection of pipes; For gas pipeline network nodes A collection of connected devices; , They are respectively Time Pipeline The corresponding power and carbon flux density of the airflow; , They are respectively The source of air at all times The power and gas source of the airflow flowing into the gas network Carbon emission intensity.

[0030] S303, Establish a carbon emission flow model for the coupled equipment: ; ; ; In the formula: , They are respectively Carbon emission intensity at EL input and output terminals at any given time; for Carbon emission intensity at the MR output at any given time; for Carbon emission intensity at the output of hydrogen storage equipment at all times; for Carbon emission intensity at the GT output at any given time; for Carbon potential of the gas distribution network nodes that are always connected to GT.

[0031] S304, Establishing a hydrogen-to-carbon ratio model: ; In the formula: Hydrogen-to-carbon ratio, characterization The carbon emissions corresponding to a unit of energy hydrogen in a hydrogen storage device are... Carbon emission intensity at the output of hydrogen storage equipment at all times.

[0032] S305, load-weighted aggregation of system node carbon potentials is performed to obtain dynamic carbon emission factors for the power grid and gas grid, respectively. The specific model is as follows: ; In the formula: for time Carbon emission factors of energy network systems ; for Time Node of Energy requirements; for time Energy network nodes carbon potential; for The set of nodes in an energy network system.

[0033] In step S4, the specific model for the carbon emission reduction contribution is as follows: ; In the formula: for Time period Carbon emission reduction contribution of energy network demand response; for Time period Energy network demand response afterload; for Time period Original load of the energy network; In step S4, the objective function of the user-side demand response model is as follows: ; in: ; ; In the formula: The energy loss on the user side during time period t; Incentives for user-side carbon emission reduction contributions during period t; For grid load exist Demand during a specific time period; For gas network load exist Demand during a specific time period; , These are the time-of-use electricity loss coefficient and time-of-use gas loss coefficient for the user side during time period t, respectively. Incentive coefficient for contributing to carbon emission reduction.

[0034] In step S4, the constraints of the user-side demand response model are as follows: (1) Demand response constraints: ; ; ; ; ; ; ; In the formula: , They are respectively The transfer power of loads that can be transferred at any time using electricity or gas; , They are respectively The power that can be replaced by electricity or gas at any time can replace the load. for The proportion of network-transferable load to total load; for The proportion of network-substituteable load to total load; for The electrical load that has been replaced by the gas load at any given time; for The amount of gas load replaced by electrical load at any given time; The electrical-gas substitution coefficient.

[0035] (2) User satisfaction constraints: ; ; ; ; In the formula: , These are user satisfaction with energy usage methods and user satisfaction with energy expenditure. , These are the lower limits of user satisfaction with energy usage methods and user satisfaction with energy expenditure, respectively. for The original electrical load at that moment; for The initial gas load at any given moment; In step S5, the iterative process of energy supply-side optimization, carbon emission flow calculation, and user-side demand response specifically includes: S501 uses the original electrical load and the original gas load as the initial load and sets the number of iterations. ; S502, in the The energy supply side optimization scheduling model is solved under the next iteration load to obtain the power flow, natural gas flow, multi-energy equipment output, hydrogen storage status and carbon storage status for each time period. S503 calculates the carbon potential of distribution network nodes, carbon potential of gas distribution network nodes, and dynamic carbon emission factors based on the energy supply side dispatch results. S504, Input the dynamic carbon emission factor into the user-side demand response model and solve for the updated post-response electricity load and post-response gas load; S505, determine whether the difference in response load between two adjacent iterations is less than a preset convergence threshold; if not, then let Return to step S502; if satisfied, proceed to step S506. S506 outputs the load, system operating energy consumption, and output of each unit after the response.

[0036] Example: This embodiment uses an IEEE 33-node distribution network and a Belgian 20-node gas distribution network to form a gas-electric coupling distribution network, as shown in the topology below. Figure 2 As shown in the table. Distribution network nodes 5, 13, and 29 are connected to three wind turbine generators. Distribution network node 7 and gas distribution network node 3 are energy-coupled via EL, MR, and hydrogen-doped GT. Gas sources are connected to gas distribution network nodes 1 and 8. The dispatch cycle is 24 hours, with a time period length of 1 hour. Relevant equipment parameters are shown in Table 1. The electricity purchase price from the main grid is 0.3 yuan / (kW·h), and the gas purchase price is 2.2 yuan / m³. 3 The benchmark price for carbon trading is 90 yuan / ton, and the price of CO2 purchased from outside the system is 2.8 yuan / m³. 3 Time-of-use electricity prices for user loads are shown in Table 2, time-of-use gas prices are shown in Table 3, and load and wind power forecast data are shown in [Table 2]. Figure 3 Other relevant parameters are shown in Table 4.

[0037] Table 1: Equipment Parameters;

[0038] Table 2: Time-of-use electricity pricing;

[0039] Table 3: Time-of-use gas prices;

[0040] Table 4: Relevant parameters;

[0041] To verify the effectiveness of the researched collaborative optimization scheduling model in terms of economy and low carbon emissions in gas-electric coupled distribution network systems, the following six operating scenarios were set up for comparative analysis: Scenario 1 considers only traditional P2G and GT; Scenario 2 considers two-stage P2G with hydrogen storage equipment, CCS with carbon storage equipment, and hydrogen-blended GT; Scenario 3 considers a tiered carbon trading mechanism based on Scenario 2; Scenario 4 considers a reward-penalty tiered carbon trading mechanism based on Scenario 3; Scenario 5 considers demand response based on time-of-use energy pricing based on Scenario 4; Scenario 6 further introduces carbon emission reduction contribution incentives based on Scenario 5, forming a user-side demand response that takes into account both time-of-use energy pricing and carbon emission reduction contributions. The system scheduling results under the six scenarios are shown in Table 5.

[0042] Table 5: Scheduling results for each scenario;

[0043] Table 5 shows that compared to Scenario 1, Scenario 2 reduces the total system operating cost by 0.88%, reduces wind curtailment to zero, and reduces net carbon emissions by 1.96%, indicating that the synergistic operation of the two-stage P2G, CCS, hydrogen storage equipment, and hydrogen-blended GT can effectively improve the system's economic efficiency and low-carbon performance. Combined with... Figure 4 As can be seen, Scenario 2 achieves temporal decoupling between electrolytic hydrogen production and methanation processes through hydrogen storage, enabling the flexible allocation of hydrogen produced during surplus wind power periods among storage, methanation, and hydrogen-blended power generation, and its release and utilization when electricity and gas load demands are high. On the one hand, this approach improves the absorption capacity of surplus wind power; on the other hand, hydrogen, through methanation to generate synthetic natural gas or directly participating in hydrogen-blended combustion, can reduce the system's dependence on purchased natural gas and high-carbon fuels, thereby achieving a simultaneous reduction in operating costs and net carbon emissions.

[0044] Building upon Scenario 2, Scenario 3, with its tiered carbon trading mechanism, resulted in a 3.51% decrease in net carbon emissions. However, due to the inclusion of carbon trading costs, the total operating cost slightly increased, indicating that escalating carbon costs can curb high-carbon operating practices and strengthen the system's carbon emission constraints. Scenario 4, further incorporating a reward-and-penalty tiered carbon trading mechanism, saw a 2.78% decrease in net carbon emissions compared to Scenario 3, a 1.39% decrease in total operating costs, and a reduction in carbon trading costs from 67.31 yuan to 35.19 yuan. This is because the reward-and-penalty tiered carbon trading mechanism, while increasing penalties for exceeding emission limits, provides additional rewards for emission reductions below the carbon quota, making the system more inclined to adjust equipment output and adopt low-carbon operating methods, thereby promoting carbon emission reduction while alleviating carbon trading cost pressures.

[0045] Scenario 5, building upon Scenario 4, considers demand response based on time-of-use energy pricing, resulting in a 7.67% decrease in total system operating costs and a 5.36% decrease in gas purchase costs. Combined with... Figure 5As shown in the load curve of Scenario 5, time-of-use energy pricing can guide flexible loads to shift from high-price periods to low-price periods, reducing peak-hour loads and increasing off-peak loads, thereby reducing peak-hour energy supply pressure and lowering the system's external energy purchase costs. Building on this, Scenario 6 further introduces dynamic carbon emission factors and carbon reduction contribution incentives, resulting in a 3.73% decrease in net carbon emissions compared to Scenario 5, a reduction in external CO2 purchases from 0.36t to 0.18t, while total operating costs only increase by 0.30%. Figure 5 A comparison of the load curves of scenarios 5 and 6 shows that scenario 6, while retaining the peak-shaving and valley-filling effect of time-of-use pricing, further guides the electrical load to shift from the high-carbon period of 09:00-18:00 to the relatively low-carbon periods of 01:00-08:00 and 19:00-24:00, and guides the gas load to migrate from periods with higher carbon emission factors to relatively low-carbon periods, so that load adjustment changes from simply responding to energy prices to taking into account both energy prices and carbon emission intensity. Figure 6 The specific components of load adjustment in Scenario 6 are further revealed. Transferable loads migrate from high-price, high-carbon periods to low-price, low-carbon periods within the same energy system based on time-of-use energy prices and the temporal differences in dynamic carbon emission factors. Substitutable loads switch between electricity and gas energy based on differences in electricity and gas energy prices and carbon emission intensity. Therefore, Scenario 6 achieves low-carbon migration in the time dimension through transferable loads and low-carbon substitution in the energy type dimension through substitutable loads. This demonstrates that carbon emission reduction incentives can further tap the emission reduction potential on the load side and improve the overall low-carbon operation level of gas-electric coupled distribution networks while maintaining basic system economics.

Claims

1. A low-carbon dispatching method for gas-electric coupled distribution networks based on carbon emission reduction contributions, characterized in that, Includes the following steps: Step 1: Obtain the gas-electric coupling distribution network topology, equipment parameters, wind power forecast output, original electrical load, original gas load, upstream energy purchase conversion energy consumption parameters, and user time-of-use energy loss parameters; establish the coordinated operation relationship between the two-stage electricity-to-gas conversion link, hydrogen storage equipment, carbon capture equipment, carbon storage equipment, and hydrogen-blended gas turbine; Step 2: Based on the collaborative operation relationship established in Step 1 between the two-stage electricity-to-gas conversion link, hydrogen storage equipment, carbon capture equipment, carbon storage equipment, and hydrogen-blended gas turbine, establish an energy supply-side optimization scheduling model with the goal of minimizing the comprehensive operating energy consumption of the gas-electric coupled distribution network system; solve the energy supply-side optimization scheduling model to obtain the power flow, natural gas flow, multi-energy equipment output, hydrogen storage status, and carbon storage status for each time period; Step 3: Based on the power flow, natural gas flow, multi-energy equipment output, hydrogen storage status, and carbon storage status obtained in Step 2, track the flow process of carbon emissions in the gas-electric coupling distribution network to calculate the carbon potential of the distribution network nodes and the carbon potential of the gas distribution network nodes respectively; using the power load and gas load at the start of the current iteration, spatially weight the carbon potential of the distribution network nodes and the carbon potential of the gas distribution network nodes respectively to form the dynamic carbon emission factor of the power grid and the dynamic carbon emission factor of the gas network. Step 4: Combine the dynamic carbon emission factor of the power grid and the dynamic carbon emission factor of the gas grid obtained in Step 3 with the original electrical load and the original gas load to calculate the difference in the corresponding load carbon responsibility after the response, and determine the user's carbon emission reduction contribution. A user-side demand response model is established by comprehensively considering user time-of-use energy loss, user carbon emission reduction contribution, and user satisfaction. Step 5: Solve the user-side demand response model established in Step 4 to obtain the post-response electrical load and post-response gas load; return the post-response electrical load and post-response gas load to the energy supply-side optimization scheduling model established in Step 2 for iterative solving until the preset convergence threshold is met, and output the low-carbon scheduling control strategy; according to the low-carbon scheduling control strategy, issue power adjustment commands to the energy supply equipment, energy conversion equipment, hydrogen storage equipment and carbon storage equipment in the gas-electric coupling distribution network to control physical operation.

2. The method according to claim 1, characterized in that, Step 1 specifically includes the following sub-steps: Step 1-1) As the starting point for establishing a collaborative operation relationship, construct the two-stage electro-gas conversion operation constraints; firstly, determine the correlation between the hydrogen production power of the electrolyzer EL and the input electrical power and deduce the hydrogen production of the electrolyzer, while imposing restrictions on the input electrical power range and the ramp-up power of the hydrogen production power; then, based on the hydrogen power allocated to the methane reactor MR, determine the natural gas output power of the methane reactor and convert it into methane production, and finally limit the range of input hydrogen power to the methane reactor; Step 1-2) Based on the parameters established in Step 1-1), construct the operating constraints of the hydrogen-blended gas turbine; characterize the mixed state of natural gas and hydrogen consumed by the hydrogen-blended gas turbine, calculate the power generation of the hydrogen-blended gas turbine, and limit the range of hydrogen blending ratio and the output of the hydrogen-blended gas turbine. Steps 1-3) Based on the power generation of the hydrogen-blended gas turbine obtained in Steps 1-2), construct the operating constraints of the carbon capture equipment; extract the power consumed by the generator set for carbon capture, combine it with the fixed operating power to obtain the actual amount of carbon dioxide captured during the carbon capture process, and set the upper limit of the amount of carbon dioxide captured. Steps 1-4) Based on the actual carbon dioxide capture amount obtained in Steps 1-3), construct the operating constraints of the carbon storage equipment; update the carbon storage amount of the carbon storage equipment at each time point, and tighten the boundary of the mutual exclusion condition of the 0-1 variables of the carbon charging and discharging state; use the carbon storage constraints with tightened boundary to achieve time decoupling of carbon capture and carbon utilization process for methanation, and improve the utilization efficiency of captured carbon dioxide. Steps 1-5) Combining the hydrogen production of the electrolyzer derived in Step 1-1), construct operational constraints for the hydrogen storage equipment; track the dynamic changes in hydrogen storage capacity, and set mutual exclusion restrictions for hydrogen charging and discharging states, upper and lower limits of hydrogen storage capacity, extreme values ​​of hydrogen charging and discharging, and charging and discharging balance constraints for scheduling cycles; finally, summarize all constraints to establish the collaborative operation relationship between the two-stage power-to-gas conversion process, hydrogen storage equipment, carbon capture equipment, carbon storage equipment, and hydrogen-blended gas turbine; by adding dynamic constraints to the hydrogen storage equipment, hydrogen can be flexibly allocated between methanation, intertemporal storage, and hydrogen-blended power generation, thereby improving the comprehensive utilization efficiency of hydrogen energy.

3. The method according to claim 1, characterized in that, Step 2 specifically includes the following sub-steps: Step 2-1) Construct the energy supply side optimization scheduling objective function; the objective function includes the energy consumption of electricity purchased from the upper-level power grid, the energy consumption of gas purchased from the upper-level gas grid, the energy consumption of wind curtailment penalty, the energy consumption of grid loss, the energy consumption of external carbon purchase, the energy consumption of power outage load penalty, and the energy consumption of gas outage load penalty, with the optimization objective being to minimize the comprehensive operating energy consumption of the gas-electric coupled distribution network system; Step 2-2) Apply equipment operation constraints and network operation constraints to the energy supply side optimization scheduling objective function constructed in Step 2-1); solve the energy supply side optimization scheduling model to obtain the power flow, natural gas flow, multi-energy equipment output, hydrogen storage status and carbon storage status for each time period.

4. The method according to claim 1, characterized in that, Step 3 specifically includes the following sub-steps: Step 3-1) Quantify the carbon potential of distribution network nodes; using the power flow obtained in Step 2, calculate the carbon potential of distribution network nodes based on the power inflow and outflow balance relationship; Step 3-2) Quantify the carbon potential of the gas distribution network nodes; Calculate the carbon potential of the gas distribution network nodes based on the natural gas flow patterns obtained in Step 2 and the natural gas pipeline flow patterns. Step 3-3) Calculate the carbon emission flow transfer of the coupled equipment; combined with the energy flow transfer path of the equipment, update the carbon emission intensity from the input end to the output end of the electrolyzer, from the input end to the output end of the methane reactor, and from the input end to the output end of the gas turbine; through the above steps 3-1) to 3-3), the carbon responsibility transfer process between the distribution network, gas distribution network and various coupled equipment can be continuously and accurately tracked according to the carbon flow conservation relationship; Steps 3-4) Establish a hydrogen-carbon ratio model to track carbon emission transfer in hydrogen storage equipment; when the hydrogen storage equipment is filled with hydrogen, update the hydrogen-carbon ratio according to the carbon emission intensity of the input hydrogen and the current hydrogen storage status of the hydrogen storage equipment; when the hydrogen storage equipment is discharged from hydrogen, determine the carbon emission intensity of the output hydrogen according to the current hydrogen-carbon ratio, so as to track the carbon emission transfer during the time-series storage of hydrogen. Steps 3-5) Spatially weight the carbon potential of the distribution network nodes and the carbon potential of the gas distribution network nodes quantified in Steps 3-1) and 3-2) according to the load at the start of the current iteration. After the weighting operation is completed, the dynamic carbon emission factor of the power grid and the dynamic carbon emission factor of the gas distribution network are obtained.

5. The method according to claim 1, characterized in that, Step 4 specifically includes the following sub-steps: Step 4-1) Assess the carbon emission reduction gain brought about by demand response behavior; compare the difference between the original load and the load after response, and multiply by the dynamic carbon emission factor obtained in Step 3 to calculate the user's carbon emission reduction contribution. Step 4-2) Integrate the user carbon emission reduction contribution obtained in Step 4-1) with time-of-use energy loss to establish the user-side demand response objective function; thereby combining the user-side low-carbon behavior of adjusting energy consumption with time-of-use energy consumption signals to drive flexible load adjustment. Step 4-3) Set the boundaries of transferable load, alternative load, and user satisfaction limit, and add the constraints to the user-side demand response objective function constructed in Step 4-2) to obtain the user-side demand response model.

6. The method according to claim 1, characterized in that, Step 5 specifically includes the following sub-steps: Step 5-1) Initialize the system iteration state; use the acquired original electrical load and original gas load as the initial iteration load, and set the iteration number parameter k=0; Step 5-2) Under the load condition of the kth iteration, solve the energy supply side optimization scheduling model to obtain the power flow, natural gas flow, multi-energy equipment output, hydrogen storage status and carbon storage status corresponding to the kth iteration; Step 5-3) Based on the power flow, natural gas flow, multi-energy equipment output, hydrogen storage status, and carbon storage status corresponding to the k-th iteration obtained in Step 5-2), perform the carbon emission flow calculation described in Step 3 to obtain the carbon potential of the distribution network nodes and the carbon potential of the gas distribution network nodes; using the power load and gas load at the beginning of the k-th iteration, spatially weight the carbon potential of the distribution network nodes and the carbon potential of the gas distribution network nodes respectively to obtain the dynamic carbon emission factor of the power grid and the dynamic carbon emission factor of the gas network corresponding to the k-th iteration; Step 5-4) Input the dynamic carbon emission factor of the power grid and the dynamic carbon emission factor of the gas grid corresponding to the kth iteration into the user-side demand response model for solution, and output the updated post-response electricity load and post-response gas load. Step 5-5) Calculate the electrical load deviation and gas load difference before and after the update; Determine whether the electrical load deviation and gas load difference are both less than the preset convergence threshold. If not, set k=k+1 and return the updated response electrical load and response gas load to step 5-2) as the starting load to continue iterating. If satisfied, exit the loop output low-carbon scheduling control strategy and issue force regulation, energy conversion and charging / discharging control commands to the power supply equipment, energy conversion equipment, hydrogen storage equipment and carbon storage equipment in the gas-electric coupling distribution network to control the physical operation of the gas-electric coupling distribution network.

7. The method according to claim 2, characterized in that, In step 1-1), the relationship between the hydrogen production power of the electrolyzer and the input electrical power is determined by the following formula: ； The formula used to derive the hydrogen production capacity of an electrolyzer from its hydrogen production power is as follows: ； The formula for limiting the range of input electrical power is as follows: ； The formula used to limit the ramp-up power of hydrogen production is: ； The formula for determining the output power of a methane reactor using natural gas is: ； The formula for calculating the methane production of a methane reactor is as follows: ； The formula for calculating the hydrogen consumption of a methane reactor is as follows: ； The formula used to limit the range of hydrogen power input to the methane reactor is: ； In the formula: , They are respectively The hydrogen production power and input electrical power of EL at any given time; The electro-hydrogen conversion efficiency of EL; for The hydrogen production flow rate of EL at any given time; , These represent the minimum and maximum power consumption of EL, respectively. , These are the upper and lower limits of the ramp-up power for EL hydrogen production; This refers to the calorific value of hydrogen. , They are respectively MR input hydrogen power and output natural gas power at any time; The methanation efficiency of MR; , These represent the maximum and minimum hydrogen consumption power of MR, respectively. , They are respectively MR input hydrogen flow rate and output methane flow rate at any time; The calorific value of methane; Energy conversion coefficient; In steps 1-2), the formula used to characterize the mixture state of natural gas and hydrogen consumed by the hydrogen-blended gas turbine is: ； ； The total power of the mixed gas is: ； The formula for calculating the power generation capacity of a hydrogen-blended gas turbine is as follows: ； The formula used to limit the upper and lower limits of the ramp rate of hydrogen-blended gas turbines is: ； The formula for limiting the range of hydrogen doping ratio is: ； ； The formula used to limit the output boundary of hydrogen-blended gas turbines is: ； In the formula, , They are respectively The amount of natural gas and hydrogen consumed by the hydrogen-blended GT at all times; The power generation efficiency of hydrogen-doped GT; , They are respectively The input gas power and power generation of the hydrogen-doped GT mixture at all times; , These are the power outputs of the hydrogen-blended GT system via natural gas input and the power output via hydrogen input, respectively. , These are the upper and lower limits of the ramp power for hydrogen-doped GT, respectively. , These represent the maximum and minimum power generation of hydrogen-doped GT, respectively. for Time GT hydrogen doping ratio; , These are the upper and lower limits of the hydrogen doping ratio in GT; In steps 1-3), the power consumed by the generator set for carbon capture is calculated using the following formula: ； The actual carbon dioxide capture amount is obtained using the following formula: ； The formula for setting the upper limit of carbon dioxide capture is as follows: ； In the formula, for The power consumed by the generator set for carbon capture at any given time; This refers to the fixed operating power during the carbon capture process; for Net power injected into the grid by the generator set at any given moment; Energy consumption coefficient per unit of CO2 processed; for time The amount of capture; The carbon emissions per unit of natural gas consumed by GT; For carbon capture efficiency; In steps 1-4), the carbon storage capacity of the carbon storage device at each time point is updated using the following formula: ； The formulas used to constrain the upper and lower limits of carbon storage capacity are as follows: ； The formula for constraining the extreme values ​​of carbon charge and discharge is: ； ； The formula for the mutual exclusion condition of carbon charging and discharging states is as follows: ； The formula for the full-time storage release balance constraint during the scheduling cycle is as follows: ； In the formula, , These refer to the storage efficiency and release efficiency of the carbon storage device, respectively. for The carbon capacity of the carbon storage device at all times; , These are the upper and lower limits of the capacity of carbon storage equipment, respectively. , They are respectively Real-time CO2 injection and release rates of carbon storage equipment; , They are respectively The maximum and minimum carbon loading capacity of the carbon storage equipment at all times; , They are respectively The maximum and minimum carbon release rates of the carbon storage equipment at all times; , These are 0-1 variables representing whether the carbon storage device is in an input / output state; The scheduling period; In steps 1-5), the formula used to track the dynamic changes in hydrogen storage is: ； The upper and lower limits of hydrogen storage capacity are constrained by the following formulas: ； The formula for extreme hydrogen charge / discharge constraints is as follows: ； ； The formula for limiting the mutual exclusion of hydrogen charging and discharging states is as follows: ； The formula for the all-time charge-discharge balance constraint is: ； In the formula, , These refer to the storage efficiency and release efficiency of hydrogen storage devices, respectively. for The hydrogen capacity of the hydrogen storage device at all times; , These are the upper and lower limits of hydrogen storage equipment capacity, respectively. , They are respectively Hydrogen charging and discharging power at all times; , They are respectively The maximum and minimum hydrogen storage power at any given moment; , They are respectively The maximum and minimum hydrogen release power at any given time; , These are 0-1 variables representing whether the hydrogen storage device is in an input or output state, respectively.

8. The method according to claim 3, characterized in that, In step 2-1), the objective function for optimizing the energy supply side scheduling is constructed using the following formula: ； in: ； ； ； ； ； ； ； In the formula: for Energy consumption is calculated based on electricity purchased from the upper-level power grid at all times. for Calculate energy consumption by purchasing gas from the superior gas network at all times; for Constantly curtailing wind power results in energy consumption penalties. for Real-time network loss and energy consumption; for Real-time external carbon purchase energy consumption; for Energy consumption is penalized by load during constant power outages; for Constant air loss load penalty energy consumption; , They are respectively The power distribution network system purchases electricity and gas from the upstream energy network at all times; for Purchase carbon credits at all times; , These are the energy consumption coefficients for purchasing electricity and gas from the upper-level energy network, respectively. , , These are the energy consumption coefficients for wind curtailment penalties and network loss penalties, respectively. Purchased energy consumption coefficient; , They are respectively time Predicted power output and actual grid-connected power output of wind turbine units; For distribution network branch collection; for Timetable The current flowing through the segment; for Timetable The resistance of the segment; , These are the energy consumption coefficients for power outage and gas load penalties, respectively. for Time load Power loss; for Time load Loss of gas power; In step 2-2), the formula for adding distribution network operation constraints is as follows: ； ； ； ； ； ； In the formula: In the distribution network This is the set of the first nodes of the terminal node of the branch; In the distribution network It is the set of the last nodes of the first node of the branch; for Injection nodes from upstream power grid The active power; for Injection nodes from upstream power grid reactive power; for Constantly on load reactive power; , They are respectively Timetable The active and reactive power of the segment; For distribution network Time load The load demand; for Time load The power factor; for Time Node The voltage; for Timetable The reactance of the segment; , They are respectively Time Node Maximum and minimum voltage values; , They are respectively Timetable The maximum and minimum values ​​of the segment current; The formula for incorporating gas distribution network operation constraints is as follows: ； ； ； ； ； In the formula: For This is the set of the first nodes of the terminal node of the branch; For It is the set of the last nodes of the first node of the branch; , for Time Pipeline The power and flow rate of the natural gas flowing through the section; For gas distribution network Time load The load demand; For pipelines Pipeline coefficient; For pipelines The maximum natural gas flow rate that the segment can transmit; for Time Node air pressure; , for Time Node Maximum and minimum air pressure limits; The formula for incorporating wind power output constraints is as follows: ； The formula for incorporating hydrogen power constraint is as follows: ； ； ； ； In the formula: for The hydrogen power that EL transmits to MR at any given moment; for The hydrogen power that EL transmits to GT at any given moment; for The hydrogen power transmitted from the hydrogen storage device to the MR at all times; for The hydrogen power transmitted from the hydrogen storage device to the GT at all times; The formula for incorporating carbon flow constraint is as follows: ； In the formula: MR generation unit The amount of carbon dioxide consumed.

9. The method according to claim 4, characterized in that, In step 3-1), the carbon potential of the distribution network nodes is calculated using the following formula: ； In the formula: for Power system nodes at all times carbon potential; For nodes Active power flow flows into nodes in connected branches. The set of branch paths; For distribution network nodes A collection of connected devices; , They are respectively Time Branch Inflow node Power, branch carbon flux density; , They are respectively Time and Node Connected generator sets Power injected into the grid, generator sets carbon emission intensity; In step 3-2), the carbon potential of the gas distribution network nodes is calculated using the following formula: ； In the formula: for Gas pipeline network nodes carbon potential; For nodes Airflow into the node from the connected pipes A collection of pipes; For gas pipeline network nodes A collection of connected devices; , They are respectively Time Pipeline The corresponding power and carbon flux density of the airflow; , They are respectively The source of air at all times The airflow power and air source flowing into the gas network carbon emission intensity; In step 3-3), the carbon potential is updated using the following formula: ； ； ； In the formula: , They are respectively Carbon emission intensity at EL input and output terminals at any given time; for Carbon emission intensity at the MR output at any given time; for Carbon emission intensity at the output of hydrogen storage equipment at all times; for Carbon emission intensity at the GT output at any given time; for Carbon potential of the gas distribution network nodes constantly connected to GT; In steps 3-4), the formula used is: ； In the formula: Hydrogen-to-carbon ratio, characterization The carbon emissions corresponding to a unit of energy hydrogen in a hydrogen storage device are... Carbon emission intensity at the output of hydrogen storage equipment at all times; In steps 3-5), the spatial weighting is performed using the following formula: ； In the formula: for time Carbon emission factors of energy network systems ; for Time Node of Energy requirements; for time Energy network nodes carbon potential; for The set of nodes in an energy network system.

10. The method according to claim 5, characterized in that: Step 4-1) The formula for calculating the user's carbon emission reduction contribution is as follows: ； ; In the formula: for Time period Carbon emission reduction contribution of energy network demand response; for Time period Energy network demand response afterload; for Time period Original load of the energy network; Step 4-2) Establish the objective function for user-side demand response using the following formula: ； in: ； ； In the formula: The energy loss on the user side during time period t; Incentives for user-side carbon emission reduction contributions during period t; For grid load exist Demand during a specific time period; For gas network load exist Demand during a specific time period; , These are the time-of-use electricity loss coefficient and time-of-use gas loss coefficient for the user side during time period t, respectively. Contribute incentive coefficient to carbon emission reduction; Step 4-3) Set the boundaries for transferable loads and alternative loads, as well as user satisfaction limits. The specific constraints are as follows: (1) Load update formula after electrical and gas response: ； ； (2) Transferable load boundary formula: ； ； (3) Alternative load boundary formula: ； ； ； (4) User satisfaction limits include satisfaction with energy usage methods and satisfaction with expenditure: ； ； ； ； In the formula: , They are respectively The transfer power of loads that can be transferred at any time using electricity or gas; , They are respectively The power that can be replaced by electricity or gas at any time can replace the load. for The proportion of network-transferable load to total load; for The proportion of network-substituteable load to total load; for The electrical load that has been replaced by the gas load at any given time; for The amount of gas load replaced by electrical load at any given time; The electrical-gas substitution coefficient; , These are user satisfaction with energy usage methods and user satisfaction with energy expenditure. , These are the lower limits of user satisfaction with energy usage methods and user satisfaction with energy expenditure, respectively. for The original electrical load at that moment; for The original gas load at any given time.

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