P2G two-stage modeling gas-electricity-hydrogen coupling distribution network distribution robust low-carbon operation method

Abstract

The invention relates to the technical field of optimized operation of a comprehensive energy system, and discloses a P2G two-stage modeling gas-electricity-hydrogen coupling distribution network distribution robust low-carbon operation method. The method comprises the following steps: establishing a P2G refinement model in consideration of two-stage processes of water electrolysis hydrogen production and hydrogen methanation, constructing a target function which takes the minimum operation cost of a gas-electricity-hydrogen coupling distribution network system as a target function, and converting a low-carbon operation optimization model into a mixed integer second-order cone programming model for solving; uncertain scenes of wind power output and load fluctuation are obtained, an uncertain parameter distribution probability fuzzy set is constructed based on KL divergence, and a gas-electricity-hydrogen coupling distribution network distribution robust low-carbon operation model considering source load uncertainty is established; according to the method, the flexibility and the environmental protection property of system scheduling can be improved, and the optimal operation decision for balancing the economical efficiency and the safety is obtained on the premise of guaranteeing the safe operation of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of optimized operation of integrated energy systems, and specifically to a robust low-carbon operation method for a gas-electricity-hydrogen coupled distribution network using P2G two-stage modeling. Background Art

[0002] In recent years, the concept of green and low-carbon development has become a global consensus. The large-scale development and utilization of renewable energy, represented by wind and solar power, can effectively address energy crises and environmental pollution, and is gradually replacing fossil fuels. As a major source of emissions, the low-carbon operation of energy systems is crucial for achieving emission reduction targets. In the context of the Energy Internet and low-carbon electricity, integrated energy systems have become a key vehicle for the low-carbon transformation of the energy system. Gas-electricity-coupled integrated energy systems utilize energy conversion equipment to interconnect the power and natural gas systems, enabling bidirectional energy flow. Power-to-gas (P2G) technology, as an important energy conversion device, not only provides a new direction for power storage but also strengthens the coupling between the power grid and the natural gas grid. It is currently considered one of the most suitable methods for absorbing excess renewable energy. Furthermore, the introduction of carbon emission trading (CET) into integrated energy systems is an effective way to achieve their low-carbonization. Carbon capture technology can directly reduce carbon emissions from power generation sources while also providing raw materials for the P2G process, offering a practical solution for carbon emission reduction. Furthermore, as the proportion of renewable energy access increases annually and load demands become increasingly complex, optimizing the dispatch of power systems and integrated energy systems, taking into account source-load uncertainty, is crucial for system security. In recent years, distributionally robust optimization (DRO) methods, combining the advantages of both stochastic optimization (SO) and robust optimization (RO), have been widely applied to optimization problems with uncertain parameters.

[0003] Currently, most P2G modeling approaches simplify P2G as an energy conversion hub with controllable load power, emphasizing its power-to-gas coupling function while ignoring internal constraints such as start-stop and ramp-up. Furthermore, on the distribution network side, they rarely consider carbon flow, carbon cycling, and hydrogen utilization within the system. Therefore, based on the operational model of a gas-electricity coupled integrated energy system, further research is needed to investigate the impact of refined modeling of the P2G two-stage process on the operational optimization of gas-electricity coupled integrated energy systems, as well as the feasibility and adaptability of the DRO model in the field of multi-energy optimization and complementarity. Summary of the Invention

[0004] In response to the above problems, the purpose of the present invention is to provide a low-carbon operation method for gas-electricity-hydrogen coupled distribution network that considers P2G two-stage modeling. Taking into account the actual operating conditions of P2G, a P2G refined model that takes into account the two-stage process of water electrolysis hydrogen production and hydrogen methanation is proposed. On this basis, the hydrogen sales link and CO2 capture are introduced into the gas-electricity-hydrogen coupled distribution network system to improve the flexibility and low-carbon nature of the system operation; and the distributed blue stick method is used to deal with the uncertainty of the renewable energy system to ensure the safety and flexibility of the gas-electricity-hydrogen coupled distribution network. The technical solution is as follows:

[0005] A P2G two-stage modeling method for distributed low-carbon operation of a gas-electricity-hydrogen coupled distribution network includes the following steps:

[0006] Step 1: Establish a refined P2G model that takes into account the two-stage process of hydrogen production by water electrolysis and hydrogen methanation. This includes the state transition constraints and power consumption constraints of the electrolyzer during the alkaline water electrolysis process, and the state transition constraints and power consumption constraints of the methanation equipment during the methanation process.

[0007] Step 2: Construct a deterministic low-carbon operation optimization model with the objective function of minimizing the operating cost of the gas-electricity-hydrogen coupled distribution network, taking into account the operating constraints of the gas-electricity-hydrogen coupled distribution network, the constraints of various coupled equipment, and the constraints of the tiered carbon emission rights trading;

[0008] Step 3: Convert the low-carbon operation optimization model obtained in step 2 into a mixed integer second-order cone programming model for solution;

[0009] Step 4: Obtain the uncertainty scenarios of wind power output and load fluctuations, construct a probability fuzzy set of uncertainty parameter distribution based on KL divergence, and establish a robust low-carbon operation model for the gas-electricity-hydrogen coupled distribution network that considers source-load uncertainty;

[0010] The gas-electricity-hydrogen coupled distribution network robust low-carbon operation model considering source-load uncertainty is a two-stage model. The first stage minimizes the operating cost of the basic scenario; the second stage minimizes the expected load loss penalty cost considering the uncertain scenario.

[0011] Furthermore, the P2G refinement model of the two-stage process in step 1 is as follows:

[0012]

[0013] Where, P at is the active power consumed by the ath P2G device at time t; The electrical power consumed by the electrolytic cell; The electrical power consumed by the methanation equipment;

[0014] The state transition constraints and power consumption constraints of the electrolyzer in the alkaline water electrolysis hydrogen production process are as follows:

[0015]

[0016] Where: and are 0-1 variables representing whether the ath electrolytic cell is in shutdown, hot standby, and startup status; and are the time consumed in the electrolytic cell startup process and the minimum startup time of the electrolytic cell respectively; P is the power consumed by the electrolytic cell when it is turned on; re,P2H is the fixed insulation power of the electrolytic cell; is the hydrogen production power of the a-th electrolyzer at time t; The electrical power consumed by the device for one startup; The amount of hydrogen produced per unit electrical power consumed by the electrolyzer; is the amount of hydrogen produced by the electrolyzer; and They are the upper and lower limits of the power range for safe operation of the electrolyzer; and are the upper and lower limits of hydrogen production power respectively; and They are the upper and lower ramp rate limits of the electrolyzer respectively;

[0017] The state transition constraints and power consumption constraints of the methanation equipment during the methanation process are as follows:

[0018]

[0019] Where: A 0-1 variable representing whether the methanogen is in state k at time t, where k = 1, 2, ... 7; Determine whether the duration of state k reaches the duration at time t The restricted 0-1 variable; p(k) and q(k) are the state sets that the reaction can enter at the next moment before and after state k reaches the duration limit; and are the power consumption of the methanogenation device when it is turned on and off respectively; is the insulation power of the device; is the power consumption of the methanation reaction circulator compressor at time t; The amount of H2 that can be pressurized per unit of electrical power consumed by the circulator compressor during the methanation process; is the amount of hydrogen input to the methanation unit at any time; and The power consumed by the device when it is turned on and off once respectively; and are the ramp-up rate and ramp-down rate of the equipment respectively; Represents the electrical power consumed by the injection of raw gas in state k at time t; and They are the upper and lower limits of the electric power consumed by the injected raw gas respectively; is the material ratio of H2 and CH4 in the methanation reaction; is the amount of synthesized CH4.

[0020] Furthermore, the objective function in step 2 is as follows:

[0021]

[0022] in:

[0023]

[0024] Where: The cost of purchasing electricity from the upstream network for the system; The cost of purchasing natural gas from the upstream network for the system; Wind power operation and maintenance costs; is the cost generated by carbon trading; the cost of generating electricity for distributed generation units; Penalty for power loss load; Penalty for loss of air load; To earn revenue from selling hydrogen; GU Assemble for gas units; and are the unit prices for purchasing electricity and gas from the superior energy network; and They are the electricity power and gas power purchased by the distribution network system from the upper energy network at time t respectively; is the operation and maintenance cost per unit of wind power generation; P wt is the power generation power of the w-th wind turbine; and F g (·) are respectively the fuel cost and heat rate curves of the generator sets; P gt is the active power generated by the g-th generator set at time t; is the unit carbon trading price, Carbon emission rights that need to be traded in the carbon market; and are the unit penalty charges for power loss load and gas loss load respectively; and are the power of power loss load and gas loss load respectively; c Hd The unit price of hydrogen sold; is the hydrogen power input into the hydrogen storage tank m through the compressor.

[0025] Furthermore, the constraints of the low-carbon operation optimization model in step 2 include:

[0026] (1) Distribution network operation constraints

[0027]

[0028] Where: is the reactive power exchanged between the distribution network and the upper power grid; ele is the set of distribution network nodes; Ω(j) is the set of devices connected to distribution network node j; δ(j) is the set of branch end nodes with node j as the head node; is the power factor of load d at time t; and are the active power and reactive power of load d at time t respectively; P at is the active power consumed by the ath P2G device at time t; is the power consumption of the mth compressor; is the output power of the generator set; P jk,t and Q jk,t are the active power and reactive power of line jk section at time t respectively; P ij,t and Q ij,t are the active power and reactive power of line section ij at time t respectively; Q st is the reactive power generated by the g-th generator set at time t; I ij,t is the current flowing through the line ij at time t; V it and V jt are the voltages of nodes i and j at time t; R ij and X ij are the resistance and reactance of line segment ij respectively; The maximum current allowed to pass through the ij section of the line; They are the upper and lower limits of the distribution network bus voltage amplitude respectively; is the upper limit of the predicted power generation of the w-th wind turbine generator set;

[0029] (2) Gas distribution network operation constraints:

[0030]

[0031] Where: Purchase gas power from the upper energy network for the distribution network system; gas is the set of nodes in the gas distribution network; Ω(n) is the set of devices connected to node n in the gas distribution network; δ(n) is the set of end nodes in the gas distribution network with n as the head node; is the size of the natural gas load q at time t; G atand G gt are the power generated and consumed by the combination of the ath P2G device and the gth gas generator at time t; G nl The flow of the nl section of the gas distribution network transmission pipeline; is the load loss of natural gas load q at time t; G mn,t is the natural gas flowing through pipeline mn at time t; K is the maximum value of natural gas that the natural gas pipeline mn can transmit at time t; mn is the Weymouth characteristic parameter of the natural gas pipeline; π mt and π nt is the gas pressure at nodes m and n in the gas distribution network at time t; and are the minimum and maximum gas pressures allowed at node m in the gas distribution network, respectively;

[0032] (3) Power exchange constraints between the distribution network and gas distribution network and the upper network:

[0033]

[0034] Where: P in,min and P in,max are the lower and upper limits of the active power exchanged between the distribution network and the upper power grid; Q in,min and Q in,max are the lower limit and upper limit of reactive power exchanged between the distribution network and the upper grid respectively; G in,min and G in,max The lower and upper limits of natural gas power are exchanged between the gas distribution network and the upper energy network respectively;

[0035] (4) Hydrogen balance constraints:

[0036]

[0037] Where: is the hydrogen power input into the hydrogen storage tank m through the compressor;

[0038] (5) Carbon balance constraints:

[0039]

[0040] Where: is the carbon emissions of the distribution network system at time t; Carbon emissions generated by purchasing electricity from the upstream power grid; is the carbon emissions of the g-th distributed generator set at time t; The amount of CO2 captured by the carbon capture systems installed on distributed generation units; and are the amount of CO2 purchased and sold by the distribution network system at time t; Mat The amount of CO2 consumed by the P2G equipment; and are the release and storage capacity of the rth carbon storage device at time t; μ upper and are the carbon emission coefficients of electricity purchased from the upper power grid and distributed power generation units; is the material ratio of CH4 and CO2 in the methanation reaction; P is the power purchased by the distribution network system from the upper power grid at time t; gt is the active power generated by the g-th generator set at time t;

[0041] (6) Equipment operation constraints:

[0042] 1) Generator set

[0043]

[0044] Where: κ HHV High calorific value; I gt is the operating status of the g-th distributed generator set at time t; and are the minimum and maximum output respectively; and They are the start-up and shutdown time counters of the unit at time t-1 respectively; and are the minimum startup and shutdown time of the unit respectively; and are the heat consumption costs generated when the unit is started and shut down once; and The costs incurred for starting and stopping the unit respectively; and are the unit's ramp-up rate and ramp-down rate respectively; and are the minimum and maximum output values respectively; F(·) is the heat rate curve of the generator set; a g 、b g 、c g is the cost coefficient of the heat consumption curve; Ω GU For gas engine combination set;

[0045] 2) Carbon capture device

[0046]

[0047] Where: P gt The total power generated by the generator set; Power consumed by the generator set for carbon capture; is the fixed power consumption in the carbon capture process; is the operating status of the carbon capture equipment, where the start is 1 and the shutdown is 0; θ cap The energy consumption required to process a unit of CO2; is the capture capacity of the generator set; is the carbon emission intensity per unit of electricity; η g is the carbon capture rate; and are the up and down ramp rates of the carbon capture system operation, respectively;

[0048] 3) Carbon storage equipment constraints

[0049]

[0050] Where: and are the storage efficiency and release efficiency of carbon storage device r; E rt is the carbon capacity of carbon storage device r at time t; and are the upper and lower limits of the carbon storage equipment capacity, respectively; and are 0-1 variables representing whether the carbon storage device is in the input and output working states; and are the CO2 injection and release amounts of carbon storage device r at time t, respectively; and are the minimum and maximum injection amounts of CO2 into the carbon storage device r at time t, respectively; and are the minimum and maximum CO2 release of carbon storage device r at time t;

[0051] 4) Compressor constraints

[0052]

[0053] Where: The power consumed by compressing unit H2 is a constant coefficient C H is the constant-pressure specific heat capacity of H2; M H is the molar mass of H2; r H is the isentropic index of hydrogen; p in and p out are the input pressure and output pressure of the compressor respectively;

[0054] 5) Other constraints

[0055]

[0056] (7) Tiered carbon emission trading constraints include:

[0057]

[0058]

[0059] Where: The total amount of carbon emission allowances allocated to the distribution network system; Carbon emission quotas obtained from purchasing electricity from the upper power grid; Carbon emission quota obtained for the power generation unit; μ gri and They are the unit quota for electricity purchased from the upper power grid and the unit quota for power supply by gas generator sets; n tra is the carbon trading interval index; δ is the carbon trading base price; p is the carbon emission interval length of the tiered carbon trading; α is the carbon trading price growth rate for each additional interval length increase in the tiered carbon trading; The amount of carbon emission rights that can be sold profitably when the system's carbon emissions are less than the quota; is in the interval n tra The amount of carbon emission rights; N tra is the number of tiered carbon trading intervals.

[0060] Furthermore, in step 3, the low-carbon operation optimization model is converted into a mixed integer second-order cone programming model using a second-order cone relaxation method for solving the problem;

[0061]

[0062] Where: intermediate variable and V it The numerical values are respectively the current I flowing through the line ij at time t ij,t and the voltage V at node i at time t it The square of .

[0063] Furthermore, the uncertainty scenario acquisition method in step 4 is as follows: randomly generating a set of wind power output and load level scenarios through Monte Carlo simulation, and then performing scenario reduction to obtain a typical set S of wind power output and load level scenarios;

[0064] The method for constructing the uncertainty parameter fuzzy set based on KL divergence in step 4 is as follows:

[0065]

[0066] Ω P ={P|D KL (P||P0)≤η}

[0067] The size of the divergence tolerance η determines the conservatism of the fuzzy set, and the conservatism of the model can be adjusted by adjusting the divergence tolerance:

[0068]

[0069] Where: D KL (·) is the KL divergence, P and P0 are the distribution function and empirical distribution of the uncertainty variable, respectively; s and S are the index and set of typical wind power output and load level scenarios, respectively; is the initial distribution probability; ρ s is the probability value of the uncertain scene distribution; represents the confidence level of the dispersion tolerance; is a chi-square distribution with S-1 degrees of freedom Upper quantile.

[0070] Furthermore, in step 4, a distributed blue rod optimization method is used to establish a distributed blue rod low-carbon operation model of the gas-electricity-hydrogen coupled distribution network considering source-load uncertainty as follows:

[0071]

[0072] Where: δ s is the total load loss value of the system under scenario s taking into account the uncertainty of source and load; J is the weight coefficient of load loss; ε is the pre-set threshold value for allowing system load loss; x is the day-ahead robust decision variable in the first stage that does not change with the actual scenario, namely the unit start and stop state variable; y is the dispatch output of each device in the system based on the decision in the first stage, such as the output of the generator set and the storage of the energy storage device; H(x) is a model for obtaining the worst probability distribution that may occur in the system under the factors of operational uncertainty; a T x, b T y are the unit start-up and shutdown costs and the equipment operating costs respectively; A, C, D, K are the abstract coefficient matrices corresponding to the constraints respectively; b, f, h T are the constant vectors corresponding to the constraints; Ω P is the fuzzy set of uncertainty parameters; υ is the result of the first stage decision variable after the υth iteration;

[0073] The two-stage model is as follows:

[0074]

[0075]

[0076] Where: υ is the number of iterations; v is the total value of the iteration count, y υ is the result of the first stage decision variable after the υth iteration; is the probability of scenario s taking into account source-load uncertainty after the υth iteration.

[0077] The beneficial effects of the present invention are:

[0078] 1) The present invention establishes a refined model of the P2G process considering the actual operating conditions of P2G, which can improve the accuracy of scheduling. On this basis, the hydrogen sales link and CO2 capture are introduced into the gas-electricity-hydrogen coupled distribution network system, which can improve the flexibility and environmental friendliness of system scheduling.

[0079] 2) The present invention adopts a distributed robust method based on KL divergence to deal with uncertainty, and obtains the optimal operation decision that balances economy and safety while ensuring the safe operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 Schematic diagram of the process of the present invention.

[0081] Figure 2 This is a structural diagram of the gas-electricity-hydrogen coupled distribution network system formed by coupling the IEEE 33-node distribution network and the Belgian 20-node gas distribution network in an embodiment of the present invention.

[0082] Figure 3 Schematic diagram comparing the power consumption and wind curtailment of P2G in Schemes 1 and 2 of the embodiments of the present invention.

[0083] Figure 4 Schematic diagram of the impact of different divergence tolerance η values on system operating costs in an embodiment of the present invention. DETAILED DESCRIPTION

[0084] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0085] The present invention considers the two-stage refined modeling of P2G, and adopts the gas-electricity-hydrogen coupled distribution network distributed robust model based on KL divergence considering the uncertainty of source and load for operation optimization; the gas-electricity-hydrogen coupled distribution network data, equipment parameters, and operation parameters are input into the model, and the commercial solver gurobi is used to solve it to obtain the optimal scheduling result; based on the calculation results, the gas-electricity-hydrogen coupled distribution network operation strategy is proposed, and the flow diagram is shown as follows: Figure 1 The specific steps are as follows:

[0086] Step 1: Establish a refined P2G model that takes into account the two-stage process of water electrolysis and hydrogen methanation, including the state transition constraints and power consumption constraints of the electrolyzer and methanation equipment.

[0087] The P2G process can be divided into two parts: water electrolysis and hydrogen methanation. The power consumption can be divided into two parts: water electrolysis and methanation.

[0088]

[0089] Where: P atis the active power consumed by the ath P2G device at time t; The electrical power consumed by the electrolytic cell; The electrical power consumed by the methanation equipment.

[0090] The alkaline water electrolysis hydrogen production process is modeled in detail as follows, including the state transition constraints and power consumption constraints of the electrolyzer:

[0091]

[0092] Where: and are 0-1 variables representing whether the ath electrolytic cell is in shutdown, hot standby, and startup status; and are the time consumed in the electrolytic cell startup process and the minimum startup time of the electrolytic cell respectively; P is the power consumed by the electrolytic cell when it is turned on; re,P2H is the fixed insulation power of the electrolytic cell; is the hydrogen production power of the a-th electrolyzer at time t; The electrical power consumed by the device for one startup; The amount of hydrogen produced per unit electrical power consumed by the electrolyzer; is the amount of hydrogen produced by the electrolyzer; and They are the upper and lower limits of the power range for safe operation of the electrolyzer; and are the upper and lower limits of hydrogen production power respectively; and They are the upper and lower ramp rate limits of the electrolyzer respectively.

[0093] The methanation process is modeled in detail as follows, including the state transition constraints and power consumption constraints of the methanation equipment:

[0094]

[0095]

[0096] Where: A 0-1 variable representing whether the methanogen is in state k; To determine whether the duration of state k reaches the duration Restricted 0-1 variables; p(k) and q(k) are the state sets that the reaction can enter at the next moment before and after state k reaches the duration limit, where for state 3, The maximum duration of heat preservation and pressure holding, states 1 and 2 are fixed durations, and the rest are the shortest durations; They are the power consumption of methanation device startup and shutdown respectively; is the insulation power of the device; Power consumption for the methanation reaction circulator compressor; The amount of H2 that can be pressurized per unit of electrical power consumed by the circulator compressor during the methanation process; is the amount of hydrogen input to the methanation unit at any time; The power consumed by the device when it is turned on and off once; are the ramp-up rate and ramp-down rate of the equipment respectively; Represents the electrical power consumed by the injection of raw gas in state k at time t; They are the upper and lower limits of the electric power consumed by the injected raw gas respectively; is the material ratio of H2 and CH4 in the methanation reaction; is the amount of synthesized methane.

[0097] The methanation unit's operating process includes multiple transition states, including cold start, hot start, long-term shutdown, and short-term shutdown. In actual operation, each state lasts for hours. The reaction typically occurs under high temperature and pressure. A cold start involves heating the bed in State 1 and introducing feed gas in State 2. A hot start only involves State 2. A long-term shutdown requires cooling and depressurizing the reactor bed, which involves State 4 and State 5. A short-term shutdown only involves State 3, which maintains heat and pressure. Within a certain timeframe, the methanation bed temperature remains at the required level for catalyst activity, allowing the system to start hot. If this timeframe is exceeded, the system transitions to long-term shutdown via State 5. State 6 represents the unit's shutdown state, while State 7 represents its operational state.

[0098] Step 2: Construct a deterministic low-carbon operation optimization model with the minimization of the operating cost of the gas-electricity-hydrogen coupled distribution network system as the objective function, taking into account the operating constraints of the gas-electricity-hydrogen coupled distribution network, various coupling equipment constraints, and the tiered carbon emission trading constraints.

[0099] Objective function: The optimization operation model of the gas-electricity-hydrogen coupled distribution network system considering the refined modeling of the P2G process aims to minimize the total operating cost of the entire system.

[0100]

[0101] in:

[0102]

[0103] Where: The cost of purchasing electricity from the upstream network for the system; The cost of purchasing natural gas from the upstream network for the system; Wind power operation and maintenance costs; is the cost generated by carbon trading; The cost of power generation for distributed units; Penalty for power loss load; Penalty for loss of air load; To earn revenue from selling hydrogen; GU Assemble for gas units; are the unit prices for purchasing electricity and gas from the superior energy network; and They are the power of electricity and gas purchased by the distribution network system from the superior energy network at time t; is the operation and maintenance cost per unit of wind power generation; P wt is the power generation power of the w-th wind turbine; F g (·) are the fuel cost and heat rate curves of the generator sets respectively; Pgt is the active power generated by the g-th generator set at time t; is the unit carbon trading price, Carbon emission rights that need to be traded in the carbon market; These are the unit penalty fees for power and gas load loss, respectively; are the power of power loss and gas load respectively; c Hd The unit price of hydrogen.

[0104] The constraints include distribution network operation constraints, gas distribution network operation constraints, power exchange constraints between the distribution network and the gas distribution network and the upper-level network, hydrogen balance constraints, carbon balance constraints, equipment operation constraints and tiered carbon emission trading constraints.

[0105] (2) Distribution network operation constraints

[0106] Since the voltage level of the distribution line is relatively low, it can be simulated using the AC power flow method.

[0107]

[0108] Where: ele is the set of distribution network nodes; Ω(j) is the set of devices connected to distribution network node j; δ(j) is the set of branch end nodes with j as the head node; is the power factor of load d at time t; and are the active power and reactive power of load d at time t respectively; P at is the active power consumed by the ath P2G device at time t; is the power consumption of the mth compressor; is the output power of the generator set; P jk,t , Q jk,t are the active power and reactive power of line section jk at time t respectively; Q stis the reactive power generated by the g-th generator set at time t; I ij,t is the current flowing through the line ij at time t; V it is the voltage of node i at time t; R ij 、X ij are the resistance and reactance of line segment ij respectively; The maximum current allowed to pass through the ij section of the line; They are the upper and lower limits of the distribution network bus voltage amplitude respectively; is the upper limit of the predicted power generation of the w-th wind turbine.

[0109] (2) Gas distribution network operation constraints:

[0110] As an energy transmission network, the gas distribution network is similar to the power distribution network. Its operation constraints include:

[0111]

[0112] Where: Purchase gas power from the upper energy network for the distribution network system; gas is the set of nodes in the gas distribution network; Ω(n) is the set of devices connected to node n in the gas distribution network; δ(n) is the set of end nodes in the gas distribution network with n as the head node; is the size of the natural gas load q at time t; G at , G gt are the power generated and consumed by the combination of the ath P2G device and the gth gas generator at time t; G nl The flow of the nl section of the gas distribution network transmission pipeline; is the load loss size of natural gas load q at time t; G mn,t is the natural gas flowing through pipeline mn at time t; K is the maximum value of natural gas that the natural gas pipeline mn can transmit at time t; mn is the Weymouth characteristic parameter of the natural gas pipeline; π mt is the gas pressure at node m in the gas distribution network at time t; are the minimum and maximum gas pressures allowed at node m in the gas distribution network.

[0113] (3) Power exchange constraints between the distribution network and gas distribution network and the upper network:

[0114]

[0115] Where: P in,min and P in,max are the lower and upper limits of the active power exchanged between the distribution network and the upper power grid; Q in,min and Q in,maxare the lower limit and upper limit of reactive power exchanged between the distribution network and the upper grid respectively; G in,min and G in,max The lower and upper limits of natural gas power are exchanged between the gas distribution network and the upper energy network respectively.

[0116] (4) Hydrogen balance constraints and carbon balance constraints

[0117] The P2G process can be divided into two steps: water electrolysis to produce hydrogen and hydrogen methanation. The process needs to meet the hydrogen balance constraints and carbon balance constraints:

[0118]

[0119] Where: is the hydrogen power input into the hydrogen storage tank m through the compressor; is the carbon emissions of the distribution network system at time t; Carbon emissions generated by purchasing electricity from the upstream power grid; is the carbon emissions of the g-th distributed generator set at time t; The amount of CO2 captured by the carbon capture systems installed on distributed generation units; are the amount of carbon dioxide purchased and sold by the distribution network system at time t; M at The amount of carbon dioxide consumed by P2G equipment; are the release and storage capacity of the rth carbon storage device at time t; μ upper 、 are the carbon emission coefficients of electricity purchased from the upper power grid and distributed power generation units; is the material ratio of CH4 and CO2 in the methanation reaction; P is the power purchased by the distribution network system from the upper power grid at time t; gt is the active power generated by the g-th generator set.

[0120] (5) Equipment operation constraints include:

[0121] 1) Generator set

[0122] In the distribution network, the generator sets need to meet the normal operating constraints of the generator sets during operation.

[0123]

[0124] Where: κ HHV It is the high calorific value, and its value is 1.026KBtu / kcf; I gt is the operating status of the g-th distributed generator set at time t; and are the minimum and maximum output respectively; and They are the start-up and shutdown time counters of the unit respectively; and are the minimum startup and shutdown time of the unit respectively; and are the heat consumption costs generated when the unit is started and shut down once; and The costs incurred for starting and stopping the unit respectively; and are the unit's ramp-up rate and ramp-down rate respectively; and are the minimum and maximum output respectively; F(·) is the heat rate curve of the generator set; a g 、b g 、c g is the cost coefficient of the heat consumption curve; Ω GU For gas unit assembly.

[0125] 2) Carbon capture device

[0126] Distributed generators within the distribution network can be equipped with carbon capture equipment to reduce their own carbon emissions. The CO2 capture process consumes energy, and a portion of the output power will be allocated to the capture equipment, resulting in a relative decrease in power generation efficiency.

[0127]

[0128]

[0129] Where: P gt The total power generated by the generator set; Power consumed by the generator set for carbon capture; is the fixed power consumption in the carbon capture process; is the operating status of the carbon capture equipment, where the start is 1 and the shutdown is 0; θ cap The energy consumption required to process a unit of CO2; is the capture capacity of the generator set; is the carbon emission intensity per unit of electricity; η g is the carbon capture rate; are the up and down ramp rates of the carbon capture system operation, respectively.

[0130] 3) Carbon storage equipment constraints

[0131] Supplying the CO2 captured by carbon capture equipment to P2G for methanation reaction can reduce the system's carbon emissions while lowering the total operating cost, while carbon storage equipment can eliminate the imbalance in the operating time of carbon capture and P2G.

[0132]

[0133] Where: are the storage efficiency and release efficiency of carbon storage device r; E rt is the carbon capacity of carbon storage device r at time t; are the upper and lower limits of the carbon storage equipment capacity, respectively; are 0-1 variables representing whether the carbon storage device is in the input / output working state; are the minimum and maximum injection amounts of CO2 into the carbon storage device r at time t, respectively;

[0134] are the minimum and maximum carbon dioxide release of carbon storage device r at time t.

[0135] 4) Compressor constraints

[0136] Since the pressure of H2 produced by the existing electrolyzer cannot meet the H2 storage pressure, it needs to be compressed to 200 bar by a compressor before being stored in a hydrogen storage tank.

[0137]

[0138] Where: The power consumed by compressing unit H2 is a constant coefficient C H is the constant-pressure specific heat capacity of H2; M H is the molar mass of H2; r H is the isentropic index of hydrogen; p in 、p out are the input and output pressures of the compressor respectively.

[0139] 5) Other constraints

[0140] In addition, gas units and P2G devices connected to the same distribution network node are not allowed to operate at the same time:

[0141]

[0142] (6) Tiered carbon emission trading constraints include:

[0143] The constraints of carbon emission trading using the free allocation quota of the baseline method and the settlement method of tiered carbon trading include:

[0144]

[0145] Where: The total amount of carbon emission allowances allocated to the distribution network system; Carbon emission quotas obtained from purchasing electricity from the upper power grid; Carbon emission quota obtained for the power generation unit; μ gri 、 They are the unit quota for electricity purchased from the upper power grid and the unit quota for power supply by gas generator sets; n tra is the carbon trading interval index; δ is the carbon trading base price; p is the carbon emission interval length of the tiered carbon trading; α is the carbon trading price growth rate for each additional interval length increase in the tiered carbon trading; The amount of carbon emission rights that can be sold profitably when the system's carbon emissions are less than the quota; is in the interval n tra The amount of carbon emission rights; N tra is the interval number of the tiered carbon trading.

[0146] Step 3: Convert the low-carbon operation optimization model obtained in step 2 into a mixed integer second-order cone programming model for solution;

[0147] The low-carbon economic optimization model of the gas-electricity-hydrogen coupled distribution network system established in this invention is a mixed integer nonlinear programming model, in which the distribution network flow equation and the natural gas flow equation are both nonlinear, and are linearized using the second-order cone programming method:

[0148]

[0149]

[0150] Where: intermediate variable and V it , the numerical values are I ij,t and V it The square of .

[0151] Step 4: Obtain the uncertainty scenarios of wind power output and load fluctuations, construct the probability fuzzy set of uncertainty parameter distribution based on KL divergence, and establish a robust low-carbon operation model of the gas-electricity-hydrogen coupled distribution network considering source-load uncertainty; the robust low-carbon operation model of the gas-electricity-hydrogen coupled distribution network considering new energy uncertainty is a two-stage model. The first stage minimizes the operating cost of the basic scenario; the second stage minimizes the expected load loss penalty cost considering the uncertain scenario.

[0152] The uncertainty scenario acquisition method is as follows: a set of wind power output and load level scenarios is randomly generated through the Monte Carlo simulation method, and then the scenarios are reduced to obtain S typical wind power output and load level scenarios.

[0153] The method of constructing the uncertainty parameter fuzzy set based on KL divergence is as follows:

[0154]

[0155] Ω P ={P|D KL (P||P0)≤η}

[0156] The size of the divergence tolerance η determines the conservatism of the fuzzy set, and the conservatism of the model can be adjusted by adjusting the divergence tolerance:

[0157]

[0158] Where: D KL (·) is the KL divergence, P and P0 are the distribution function and empirical distribution of the uncertainty variable, respectively; s and S are the wind power output and load level scenario index and set, respectively; is the initial distribution probability; ρ s is the probability value of the uncertain scene distribution; represents the confidence level of the dispersion tolerance; is a chi-square distribution with S-1 degrees of freedom Upper quantile.

[0159] Using the distributed blue rod optimization method, a distributed blue rod low-carbon operation model of the gas-electricity-hydrogen coupled distribution network considering the uncertainty of source and load is established as follows:

[0160]

[0161] Where: δ s is the total load loss value of the system under scenario s taking into account the uncertainty of source and load; J is the weight coefficient of load loss; ε is the pre-set threshold value for allowing system load loss; x is the day-ahead robust decision variable in the first stage that does not change with the actual scenario, namely the unit start and stop state variable; y is the dispatch output of each device in the system based on the decision in the first stage, such as the output of the generator set and the storage of the energy storage device; H(x) is a model for obtaining the worst probability distribution that may occur in the system under the factors of operational uncertainty; a T x, b T y are the unit start-up and shutdown costs and the equipment operating costs respectively; A, C, D, K are the abstract coefficient matrices corresponding to the constraints respectively; b, f, h T are the constant vectors corresponding to the constraints; Ω P is the fuzzy set of uncertainty parameters; υ is the result of the first stage decision variable after the υth iteration.

[0162] The two-stage model is as follows:

[0163]

[0164] Where: υ is the number of iterations; v is the total value of the iteration count; y υ is the result of the first stage decision variable after the υth iteration; is the probability of scenario s taking into account source-load uncertainty after the υth iteration.

[0165] The low-carbon economic operation method of the gas-electricity-hydrogen coupled distribution network of the present invention takes into account the P2G two-stage refined modeling, and adopts the gas-electricity-hydrogen coupled distribution network distributed bluster model considering the source-load uncertainty obtained based on KL divergence for operation optimization; the gas-electricity-hydrogen coupled distribution network data, equipment parameters, and operating parameters are input into the model, and the commercial solver gurobi is used to solve it to obtain the optimal scheduling result; and the gas-electricity-hydrogen coupled distribution network operation strategy is proposed based on the calculation results.

[0166] Among them, the gas-electricity-hydrogen coupled distribution network data includes the topology and parameters of the distribution network; the equipment parameters include electrolyzers, methanation equipment, generator sets, energy storage equipment, carbon capture devices, and compressors; the operating parameters include load, wind and solar forecast values, power limits and prices exchanged with the upper-level network, and carbon trading parameters.

[0167] The present invention is further described below using specific examples.

[0168] The structure diagram of the gas-electricity-hydrogen coupled distribution network system, which is formed by coupling the IEEE 33-node distribution network and the Belgian 20-node gas distribution network, is shown in the attached figure. Figure 2 The example system includes two wind turbines, three diesel generators (denoted by G) equipped with CO2 capture and storage devices, one gas generator (denoted by GT), and one P2G generator, each equipped with a CO2 capture and storage device, an H2 compressor, and a hydrogen storage tank.

[0169] The voltage range at the distribution network nodes was set between 0.95 and 1.05 pu. The base price for the tiered carbon trading system was set at 50 yuan / ton, the interval length was set at 0.15 tons, and the price increase multiplier was set at 15%. The entire example test tool was solved using the commercial GUROBI solver on a computer equipped with a GenIntel(R) Core(TM) i9-13900K processor and 64GB of memory.

[0170] In order to study the improvement of the accuracy of scheduling results by taking into account the refined modeling of the P2G process, and the impact of the combined use of carbon cycle and carbon trading mechanism on reducing system carbon emissions, 6 schemes were set for comparative analysis. The setting information of each scheme is shown in Table 1.

[0171]

[0172] The comparison of operating costs and wind curtailment is shown in Table 2. Comparing Schemes 1 and 2, the total cost of Scheme 2 is slightly higher than that of Scheme 1, and wind curtailment occurs. This is because the P2G scheduling in Scheme 2 is significantly different under the effect of refined modeling. The power consumption and wind curtailment of P2G in Schemes 1 and 2 are shown in the attached figure. Figure 3As shown in the figure, in Option 2, P2G power consumption is significantly reduced, and the number of units operating is also reduced. Consequently, the amount of synthetic methane supplied to the system is reduced, which is reflected in the increased gas purchase cost. This is due to the fact that, on the one hand, the P2G refined modeling takes into account ramping constraints, so the P2G does not reach its output limit during the early morning hours, as in Option 1. On the other hand, considering the startup and shutdown costs of the P2G equipment, under the condition of optimal economic performance, the daytime wind power output is low or even insufficient to maintain P2G operation. During this time, purchasing electricity to synthesize natural gas is uneconomical. As a result, the P2G is not operated between 11:00 and 16:00, resulting in a small amount of wind curtailment. Option 3, by adding a hydrogen sales component compared to Option 2, reduces the amount of wind curtailment by 0.608 MWh and slightly reduces the total operating cost. Overall, the hydrogen sales component enables the system to better respond to the volatility of renewable energy output, achieving more economical operation.

[0173] Table 2 Comparison of operating costs and abandoned air volume of schemes 1 to 6

[0174]

[0175]

[0176] Scheme 4, based on Scheme 3, introduces improved tiered carbon trading constraints. The objective function takes carbon trading costs into account, optimizing the environmental performance of the distribution network. Table 2 shows that Scheme 4 increases total system dispatch costs compared to Scheme 3; Table 3 shows that Scheme 4 reduces net CO2 emissions by 0.66 tons per day compared to Scheme 3. In Scheme 5, CO2 capture devices are installed on each of the four generators. Using these carbon storage devices to supply the methanation process in the P2G system, this can achieve a certain degree of carbon recycling within the distribution network. Table 3 shows that Scheme 5 further reduces CO2 emissions. The reduction in total system operating costs is primarily due to lower carbon trading costs. Furthermore, based on Scheme 4, the system shifts from purchasing raw CO2 to recycling generator CO2 emissions and selling 4.31 tons of captured CO2. Overall, the carbon capture and tiered carbon trading settlement model combined can achieve carbon recycling within the distribution network and effectively incentivize emissions reductions.

[0177] Table 3 Comparison of carbon trading situations in Schemes 3-6

[0178]

[0179] Scheme 6 takes into account the uncertainty of wind power output and load. From Table 2, it can be seen that the total cost of system operation is significantly higher than that of Scheme 5, and wind curtailment occurs under the influence of uncertainty. In the distributed robust model, when constructing the fuzzy set of uncertainty parameters based on KL divergence, the impact of different divergence tolerance η values on system scheduling results is shown in the attached figure. Figure 4 As shown in Figure 2, as the divergence tolerance η increases, the total system operating cost increases and is between SO and RO. The conservativeness of the decision results of the DRO method based on KL divergence is adjustable and is between SO and RO.

Claims

1. A P2G two-stage modeling method for low-carbon operation of gas-electricity-hydrogen coupled distribution network, characterized in that: The following steps are involved: Step 1: Establish a refined P2G model that takes into account the two-stage process of hydrogen production by water electrolysis and hydrogen methanation. This includes the state transition constraints and power consumption constraints of the intermediate electrolyzer during alkaline water electrolysis, as well as the state transition constraints and power consumption constraints of the methanation equipment during the methanation process. Step 2: Construct a deterministic low-carbon operation optimization model with the objective function of minimizing the operating cost of the gas-electricity-hydrogen coupled distribution network, taking into account the operating constraints of the gas-electricity-hydrogen coupled distribution network, the constraints of various coupled equipment, and the constraints of the tiered carbon emission rights trading; Step 3: Convert the low-carbon operation optimization model obtained in step 2 into a mixed integer second-order cone programming model for solution; Step 4: Obtain the uncertainty scenarios of wind power output and load fluctuations, construct a probability fuzzy set of uncertainty parameter distribution based on KL divergence, and establish a robust low-carbon operation model for the gas-electricity-hydrogen coupled distribution network that considers source-load uncertainty; The gas-electricity-hydrogen coupled distribution network robust low-carbon operation model considering source-load uncertainty is a two-stage model. The first stage minimizes the operating cost of the basic scenario; the second stage minimizes the expected load loss penalty cost considering the uncertain scenario.

2. The P2G two-stage modeling method for low-carbon operation of gas-electricity-hydrogen coupled distribution network according to claim 1 is characterized in that: The P2G refinement model of the two-stage process in step 1 is as follows: Where, P at is the active power consumed by the ath P2G device at time t; The electrical power consumed by the electrolytic cell; The electrical power consumed by the methanation equipment; The state transition constraints and power consumption constraints of the electrolyzer in the alkaline water electrolysis hydrogen production process are as follows: Where: and are 0-1 variables representing whether the ath electrolytic cell is in shutdown, hot standby, and startup status; and are the time consumed in the electrolytic cell startup process and the minimum startup time of the electrolytic cell respectively; P is the power consumed by the electrolytic cell when it is turned on; re,P2H is the fixed insulation power of the electrolytic cell; is the hydrogen production power of the a-th electrolyzer at time t; The power consumed by the device when it is turned on once; α P2H,H2 The amount of hydrogen produced per unit electrical power consumed by the electrolyzer; is the amount of hydrogen produced by the electrolyzer; and They are the upper and lower limits of the power range for safe operation of the electrolyzer; and are the upper and lower limits of hydrogen production power respectively; and They are the upper and lower ramp rate limits of the electrolyzer respectively; The state transition constraints and power consumption constraints of the methanation equipment during the methanation process are as follows: Where: A 0-1 variable representing whether the methanogen is in state k at time t, where k = 1, 2, ... 7; Determine whether the duration of state k reaches the duration at time t The restricted 0-1 variable; p(k) and q(k) are the state sets that the reaction can enter at the next moment before and after state k reaches the duration limit; and are the power consumption of the methanogenation device when it is turned on and off respectively; is the insulation power of the device; is the power consumption of the methanation reaction circulator compressor at time t; The amount of H2 that can be pressurized per unit of electrical power consumed by the circulator compressor during the methanation process; is the amount of hydrogen input to the methanation unit at any time; and The power consumed by the device when it is turned on and off once respectively; and are the ramp-up rate and ramp-down rate of the equipment respectively; Represents the electrical power consumed by the injection of raw gas in state k at time t; and They are the upper and lower limits of the electric power consumed by the injected raw gas respectively; is the material ratio of H2 and CH4 in the methanation reaction; is the amount of synthesized CH4.

3. The P2G two-stage modeling method for low-carbon operation of gas-electricity-hydrogen coupled distribution network according to claim 1 is characterized in that: The objective function in step 2 is as follows: in: Where: The cost of purchasing electricity from the upstream network for the system; The cost of purchasing natural gas from the upstream network for the system; Wind power operation and maintenance costs; is the cost generated by carbon trading; The cost of power generation for distributed units; Penalty for power loss load; Penalty for loss of air load; To earn revenue from selling hydrogen; GU Assemble for gas units; and are the unit prices for purchasing electricity and gas from the superior energy network; and They are the electricity power and gas power purchased by the distribution network system from the upper energy network at time t respectively; is the operation and maintenance cost of unit wind power generation; P wt is the power generation power of the w-th wind turbine; and F g (·) are respectively the fuel cost and heat rate curves of the generator sets; P gt is the active power generated by the g-th generator set at time t; is the unit carbon trading price, is the carbon emission right that needs to be traded in the carbon market; t ele and φ t gas are the unit penalty charges for power loss load and gas loss load respectively; and are the power of power loss load and gas loss load respectively; c Hd The unit price of hydrogen sold; is the hydrogen power input into the hydrogen storage tank m through the compressor.

4. The P2G two-stage modeling method for low-carbon operation of gas-electricity-hydrogen coupled distribution network according to claim 3 is characterized in that: The constraints of the low-carbon operation optimization model in step 2 include: (1) Distribution network operation constraints: Where: is the reactive power exchanged between the distribution network and the upper power grid; ele is the set of distribution network nodes; Ω(j) is the set of devices connected to distribution network node j; δ(j) is the set of branch end nodes with node j as the head node; is the power factor of load d at time t; and are the active power and reactive power of load d at time t respectively; P at is the active power consumed by the ath P2G device at time t; is the power consumption of the mth compressor; is the output power of the generator set; P jk,t and Q jk,t are the active power and reactive power of line jk section at time t respectively; P ij,t and Q ij,t are the active power and reactive power of line section ij at time t respectively; Q st is the reactive power generated by the g-th generator set at time t; I ij,t is the current flowing through the line ij at time t; V it and V jt are the voltages of nodes i and j at time t; R ij and X ij are the resistance and reactance of line segment ij respectively; The maximum current allowed to pass through the ij section of the line; They are the upper and lower limits of the distribution network bus voltage amplitude respectively; is the upper limit of the predicted power generation of the w-th wind turbine generator set; (2) Gas distribution network operation constraints: Where: Purchase gas power from the upper energy network for the distribution network system; gas is the set of nodes in the gas distribution network; Ω(n) is the set of devices connected to node n in the gas distribution network; δ(n) is the set of end nodes in the gas distribution network with n as the head node; is the size of the natural gas load q at time t; G at and G gt are the power generated and consumed by the combination of the ath P2G device and the gth gas generator at time t; G nl The flow of the nl section of the gas distribution network transmission pipeline; is the load loss size of natural gas load q at time t; G mn,t is the natural gas flowing through pipeline mn at time t; K is the maximum value of natural gas that the natural gas pipeline mn can transmit at time t; mn is the Weymouth characteristic parameter of the natural gas pipeline; π mt and π nt is the gas pressure at nodes m and n in the gas distribution network at time t; and are the minimum and maximum gas pressures allowed at node m in the gas distribution network, respectively; (3) Power exchange constraints between the distribution network and gas distribution network and the upper network: Where: P in,min and P in,max are the lower and upper limits of the active power exchanged between the distribution network and the upper power grid; Q in,min and Q in,max are the lower limit and upper limit of reactive power exchanged between the distribution network and the upper grid respectively; G in,min and G in,max The lower and upper limits of natural gas power are exchanged between the gas distribution network and the upper energy network respectively; (4) Hydrogen balance constraints: Where: is the hydrogen power input into the hydrogen storage tank m through the compressor; (5) Carbon balance constraints: Where: is the carbon emissions of the distribution network system at time t; Carbon emissions generated by purchasing electricity from the upstream power grid; is the carbon emissions of the g-th distributed generator set at time t; The amount of CO2 captured by the carbon capture systems installed on distributed generation units; and are the amount of CO2 purchased and sold by the distribution network system at time t; M at The amount of CO2 consumed by the P2G equipment; and are the release and storage capacity of the rth carbon storage device at time t; μ upper and are the carbon emission coefficients of electricity purchased from the upper power grid and distributed power generation units; is the material ratio of CH4 and CO2 in the methanation reaction; P t in P is the power purchased by the distribution network system from the upper power grid at time t; gt is the active power generated by the g-th generator set at time t; (6) Equipment operation constraints: 1) Generator set Where: κ HHV High calorific value; I gt is the operating status of the g-th distributed generator set at time t; and are the minimum and maximum output respectively; and They are the start-up and shutdown time counters of the unit at time t-1 respectively; and are the minimum startup and shutdown time of the unit respectively; and are the heat consumption costs generated when the unit is started and shut down once; and The costs incurred for starting and stopping the unit respectively; and are the unit's ramp-up rate and ramp-down rate respectively; and are the minimum and maximum output respectively; F(·) is the heat rate curve of the generator set; a g 、b g 、c g is the cost coefficient of the heat consumption curve; Ω GU Assemble for gas units; 2) Carbon capture device Where: P gt The total power generated by the generator set; The power consumed by the generators for carbon capture; is the fixed power consumption in the carbon capture process; is the operating status of the carbon capture equipment, where the start is 1 and the shutdown is 0; θ cap The energy consumption required to process a unit of CO2; is the capture capacity of the generator set; is the carbon emission intensity per unit of electricity; η g is the carbon capture rate; and are the up and down ramp rates of the carbon capture system operation, respectively; 3) Carbon storage equipment constraints Where: and are the storage efficiency and release efficiency of carbon storage device r; E rt is the carbon capacity of carbon storage device r at time t; and are the upper and lower limits of the carbon storage equipment capacity, respectively; and are 0-1 variables representing whether the carbon storage device is in the input and output working states; and are the CO2 injection and release amounts of carbon storage device r at time t, respectively; and are the minimum and maximum injection amounts of CO2 into the carbon storage device r at time t, respectively; and are the minimum and maximum CO2 release of carbon storage device r at time t; 4) Compressor constraints Where: The power consumed by compressing unit H2 is a constant coefficient C H is the constant-pressure specific heat capacity of H2; M H is the molar mass of H2; r H is the isentropic index of hydrogen; p in and p out are the input pressure and output pressure of the compressor respectively; 5) Other constraints (7) Tiered carbon emission trading constraints include: Where: The total amount of carbon emission allowances allocated to the distribution network system; Carbon emission quotas obtained from purchasing electricity from the upper power grid; Carbon emission quota obtained for the power generation unit; μ gri and They are the unit quota for electricity purchased from the upper power grid and the unit quota for power supply by gas generator sets; n tra is the carbon trading interval index; δ is the carbon trading base price; p is the carbon emission interval length of the tiered carbon trading; α is the carbon trading price growth rate for each additional interval length increase in the tiered carbon trading; The amount of carbon emission rights that can be sold profitably when the system's carbon emissions are less than the quota; is in the interval n tra The amount of carbon emission rights; N tra is the interval number of the tiered carbon trading.

5. The P2G two-stage modeling method for low-carbon operation of gas-electricity-hydrogen coupled distribution network according to claim 4 is characterized in that: In step 3, the low-carbon operation optimization model is converted into a mixed integer second-order cone programming model using a second-order cone relaxation method for solving the problem; Where: intermediate variable and The numerical values are respectively the current I flowing through the line ij at time t ij,t and the voltage V at node i at time t it The square of .

6. The P2G two-stage modeling method for low-carbon operation of gas-electricity-hydrogen coupled distribution network according to claim 1 is characterized in that: The method for obtaining the uncertainty scenario in step 4 is as follows: randomly generating a set of wind power output and load level scenarios by Monte Carlo simulation, and then performing scenario reduction to obtain a set S of typical wind power output and load level scenarios; The method for constructing the uncertainty parameter fuzzy set based on KL divergence in step 4 is as follows: Oh P ={P|D KL (P||P0)≤η} The size of the divergence tolerance η determines the conservatism of the fuzzy set, and the conservatism of the model can be adjusted by adjusting the divergence tolerance: Where: D KL (·) is the KL divergence, P and P0 are the distribution function and empirical distribution of the uncertainty variable, respectively; s and S are the index and set of typical wind power output and load level scenarios, respectively; ρ s 0 is the initial distribution probability; ρ s is the probability value of the uncertain scene distribution; represents the confidence level of the dispersion tolerance; is a chi-square distribution with S-1 degrees of freedom Upper quantile.

7. The P2G two-stage modeling method for low-carbon operation of gas-electricity-hydrogen coupled distribution network according to claim 6 is characterized in that: In step 4, a distributed blue rod optimization method is used to establish a distributed blue rod low-carbon operation model of the gas-electricity-hydrogen coupled distribution network considering source-load uncertainty as follows: stAx-b≤0,x∈{0,1}; Cx+Dy-f≤0; ||Ky||≤h T yes Where: δ s is the total load loss value of the system under scenario s taking into account the uncertainty of source and load; J is the weight coefficient of load loss; ε is the pre-set threshold value for allowing system load loss; x is the day-ahead robust decision variable in the first stage that does not change with the actual scenario, namely the unit start and stop state variable; y is the dispatch output of each device in the system based on the decision in the first stage, such as the output of the generator set and the storage of the energy storage device; H(x) is a model for obtaining the worst probability distribution that may occur in the system under the factors of operational uncertainty; a T x and b T y are the unit start-up and shutdown costs and the equipment operating costs respectively; A, C, D, K are the abstract coefficient matrices corresponding to the constraints respectively; b, f, h T are the constant vectors corresponding to the constraints; Ω P is the fuzzy set of uncertainty parameters; υ is the result of the first stage decision variable after the υth iteration; The two-stage model is as follows: min a T x υ +b T y stAx-b≤0,x∈{0,1}; Cx+Dy-f≤0; Cx+Dy s -f+Jδ s ≤0; s.t.Cx+Dy s -f+Jδ s ≤0 Where: υ is the number of iterations; v is the total value of the iteration count, y υ is the result of the first stage decision variable after the vth iteration; is the probability of scenario s taking into account source-load uncertainty after the υth iteration.

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