Distributed optimization method of comprehensive energy microgrid access power distribution network considering electricity-carbon-hydrogen coupling

Abstract

The invention discloses a distributed optimization method for accessing a comprehensive energy micro-grid to a power distribution network by considering electricity-carbon-hydrogen coupling. The method comprises the following steps: step 1, modeling main equipment in the comprehensive energy micro-grid; 2, considering multi-type application of a carbon capture system and hydrogen energy, and modeling the comprehensive energy microgrid; 3, considering the access of the integrated energy microgrid, and modeling the power distribution network; 4, considering that the power distribution network is connected to the micro-grid, connecting the power distribution network and the micro-grid through a connecting line, and enabling the output power of the power distribution network to be equal to the input power of the micro-grid; step 5, constructing a comprehensive energy microgrid-power distribution network distributed optimization scheduling model considering electricity-carbon-hydrogen coupling; step 6, carrying out iterative solution on the optimized scheduling model established in the step 5 by adopting an alternating direction multiplier method algorithm; according to the method, the problem of optimal scheduling of the electricity-carbon-hydrogen coupled comprehensive energy microgrid connected to the power distribution network is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of optimized operation of a microgrid connected to a distribution network, and in particular to a distributed optimization method for connecting a comprehensive energy microgrid to a distribution network taking into account electricity-carbon-hydrogen coupling. Background Art

[0002] Integrated energy microgrids (IEMs) can effectively address the issue of large-scale grid integration of renewable energy sources, such as wind power, photovoltaics, and hydrogen. However, due to their inherent scale, microgrids cannot integrate large-scale renewable energy integration. Therefore, the concept of connecting multiple microgrids to the distribution network has been introduced to address this large-scale grid integration issue. Furthermore, my country's energy structure has long been dominated by coal, making research on the use of renewable energy urgent. Research is needed to improve the utilization rate of renewable energy. Therefore, multiple IEMs can be connected to the distribution network to form a multi-microgrid-distribution network system. This will reduce carbon emissions, improve economic efficiency, and promote the transformation and upgrading of traditional power systems towards clean and efficient ones.

[0003] The existing integrated energy microgrid scheduling method considering electricity-carbon-hydrogen coupling is as follows:

[0004] 1) Invention Patent 1, Chang Pengxia; Zhu Tian; Shi Jiakang; Li Chaoshun. An optimized scheduling method and system for an electric-hydrogen integrated energy system. Application number: CN202310273506.9. This patent provides an optimized scheduling method and system for an electric-hydrogen integrated energy system. Under constraints, this method solves an optimized scheduling model to obtain the optimal solution for the decision variables. This invention ensures low resource consumption, achieves peak load shifting in the power system, and promotes the integration of new energy sources.

[0005] 2) Invention Patent 2, Yang Junru; Fu Haijun; Xu Yueyang; Zhao Leyuan. A multi-level hierarchical control system for an electric-hydrogen coupled AC / DC hybrid microgrid. Application number: CN202311608547.5. This patent discloses a multi-level hierarchical control system for an electric-hydrogen coupled AC / DC hybrid microgrid, comprising a global optimization layer (EMS), a local control layer (MGCC), a micro-source control layer (MC), and a load control layer (LC). Each component is precisely controlled, and the microgrid is interconnected through the flow of electric and hydrogen energy.

[0006] 2) Invention Patent 3, Zhu Jizhong; Guo Taiheng; Li Shenglin; Zhu Haohao; Dong Hanjiang. A hierarchical coordination method for island microgrids based on electric-hydrogen hybrid energy storage. Application number: CN202410604972.5. This patent discloses a hierarchical coordination method for island microgrids based on electric-hydrogen hybrid energy storage. The hierarchical coordination method includes year-round daytime scheduling and daily day-ahead scheduling. The method first solves the year-round daytime scheduling model, then solves the daily day-ahead scheduling model. Hydrogen energy storage is used to compensate for prediction errors at different time scales to ensure power balance in the island microgrid.

[0007] None of the above existing technologies solves the problem of optimizing the scheduling of the integrated energy microgrid coupled with electricity, carbon and hydrogen by connecting it to the distribution network, and does not consider the scheduling problem of the integrated energy microgrid coupled with electricity, carbon and hydrogen and the mitigation of carbon emissions and the scheduling problem of the integrated energy microgrid. Summary of the Invention

[0008] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide a distributed optimization method for the access of an integrated energy microgrid to the distribution network considering the coupling of electricity, carbon and hydrogen, so as to consider the scheduling problem of the integrated energy microgrid with electricity, carbon and hydrogen, thereby alleviating carbon emissions and the scheduling problem of the integrated energy microgrid.

[0009] To solve the above technical problems, the technical solution adopted by the present invention is: a distributed optimization method for connecting an integrated energy microgrid to a distribution network considering electricity-carbon-hydrogen coupling, which includes the following steps:

[0010] Step 1: Model the main equipment in the integrated energy microgrid;

[0011] Step 2: Modeling the integrated energy microgrid, considering carbon capture systems and multiple types of hydrogen applications;

[0012] Step 3: Considering the integration of the integrated energy microgrid, model the distribution network;

[0013] Step 4: Consider connecting the distribution network to the microgrid. The two are connected through a tie line, and the output power of the distribution network must be equal to the input power of the microgrid.

[0014] Step 5: Construct a distributed optimization scheduling model for the integrated energy microgrid-distribution network considering electricity-carbon-hydrogen coupling;

[0015] Step 6: Use the alternating direction multiplier method to iteratively solve the optimization scheduling model established in step 5.

[0016] Preferably, in step 1, the main equipment in the integrated energy microgrid includes a cogeneration unit, a gas boiler, a carbon capture power-to-gas device, an electrolysis hydrogen production device, a fuel cell, an electric thermal hydrogen energy storage, and a hydrogen-doped cogeneration unit with CCS and P2G.

[0017] More preferably, the hydrogen-doped cogeneration unit with CCS and P2G specifically includes the following parts:

[0018] The mathematical model of CCS is as follows: The actual power of the carbon capture device includes operating power consumption and basic power consumption. The expression is as follows:

[0019]

[0020] Where, is the carbon capture power of the CCS device, is the basic power consumption, is the operating power consumption, μ ccs The electrical power required to capture a unit of carbon dioxide, The amount of carbon dioxide captured;

[0021] The P2G mathematical model is: a two-stage power-to-gas device including electrolysis to produce hydrogen and hydrogen to produce methane, expressed as follows:

[0022]

[0023] in, and The amount of H2 generated by electrolysis and the power consumption are and are respectively the methane production power and methanation hydrogen consumption, η e2H and η H2G are the efficiencies of hydrogen production by electrolysis and methanation, respectively;

[0024] At the same time, the production of methane also uses a certain amount of electrolyzer power, which is expressed as follows:

[0025]

[0026] are respectively the methane generation power and the electrolytic cell power consumption, η P2G is the conversion efficiency of P2G;

[0027] The amount of CO2 required for the methanation process comes from the actual CO2 captured by the carbon capture power plant, and its expression is as follows:

[0028]

[0029] is the CO2 power required for the microgrid to produce methane at time t, L CH4 is the calorific value of natural gas per unit volume; is the CO2 gas density, is the amount of CO2 stored; Equation (5) shows that the CO2 required for methanogenesis and the stored CO2 all come from CCS, completing the coupling of CCS and P2G;

[0030] The mathematical model of the hydrogen-doped cogeneration unit is as follows: In order to increase the application types of hydrogen energy, a hydrogen-doped cogeneration unit is added to improve the efficiency of hydrogen energy use and reduce system carbon emissions. Its expression is as follows:

[0031] Hydrogen doping ratio:

[0032]

[0033] Power generation and heat generation:

[0034]

[0035] Where, is the hydrogen doping ratio, is the hydrogen power consumed by the cogeneration unit at time t, is the natural gas power consumed by the cogeneration unit at time t, are the calorific values ​​of hydrogen and natural gas, respectively; are the electrical efficiency and thermal efficiency of the cogeneration unit, They are the electricity production efficiency and heat production efficiency of the cogeneration unit respectively.

[0036] Preferably, in step 2, the specific process of modeling the integrated energy microgrid is as follows:

[0037] 2.1、Objective function:

[0038] The objective function of the microgrid consists of four parts: gas purchase cost, operation and maintenance cost, penalty for curtailment of wind and solar power, and carbon trading cost;

[0039]

[0040] Where C gas,buy , C RE , They are gas purchase cost, operation and maintenance cost, penalty for curtailment of wind and solar power, and carbon trading cost;

[0041] 2.1.1 Gas purchase cost:

[0042]

[0043] Where c buy is the unit price of natural gas, Purchase natural gas power for time t;

[0044] 2.1.2 Operation and maintenance costs:

[0045]

[0046] Where c chpis the CHP unit operation and maintenance cost coefficient, δ storage is the carbon storage cost coefficient, c P2G is the P2G operation and maintenance cost coefficient, c CCS is the CCS operation and maintenance cost coefficient, c FC is the operation and maintenance cost coefficient;

[0047] 2.1.3 Penalties for curtailing wind and solar power:

[0048] C RE =c RE P RE (11)

[0049] Where c RE P is the penalty cost coefficient for curtailing wind and solar power RE The amount of wind and solar power curtailed;

[0050] 2.1.4 Carbon trading costs:

[0051]

[0052] Where, is the price of purchasing a unit of carbon emission credit, is the price of selling a unit of carbon emission, are the carbon allowances purchased and sold respectively;

[0053] 2.2 Constraints:

[0054] 2.2.1. Balance constraints:

[0055]

[0056] Where, are the electric and thermal hydrogen carbon balance of IEM, The power injected into the microgrid's interconnection line for the distribution network, Purchase power from the distribution network for IEM, The price at which IEM sells electricity to the distribution network; They represent the loads after the electricity and heat loads are responded to at time t; E0 is the initial carbon emission allocation of IEM; is the total carbon emissions of IEM; The total amount of carbon emission credits purchased; The total amount of carbon emission credits sold;

[0057] 2.2.2. Power limit of IEM transmission to distribution network:

[0058]

[0059] Where, Purchase power from the distribution grid for IEM, The price at which IEM sells electricity to the distribution grid;

[0060] 2.2.3. Restrictions on electrolytic hydrogen production, cogeneration units, and gas boilers;

[0061] 1) Maximum power and ramp constraints for hydrogen production by electrolysis:

[0062]

[0063] Where, The output limit of the electrolysis equipment are the lower and upper limits of the climbing constraint respectively;

[0064] 2) Maximum power and ramp constraints of cogeneration units:

[0065]

[0066] The hydrogen blending ratio of the cogeneration unit should be within 20% to ensure the safe and stable operation of the cogeneration unit; that is:

[0067]

[0068] Where, are the lower and upper limits of the power output of the cogeneration unit, are the lower and upper limits of the thermal output of the cogeneration unit, are the lower and upper limits of the climbing constraint respectively;

[0069] 3) Maximum power and ramp constraints of gas boilers:

[0070]

[0071] Where, The lower and upper limits of gas boiler output, are the lower and upper limits of the climbing constraint respectively;

[0072] 4) CCS, P2G maximum power constraints:

[0073]

[0074] Where, are the lower and upper limits of CCS output, The lower and upper limits of CCS output;

[0075] 5) Fuel cell maximum power and ramp constraints:

[0076]

[0077] Where, The lower and upper limits of fuel cell output, are the lower and upper limits of the climbing constraint respectively;

[0078] 2.2.4 Constraints on renewable energy generation:

[0079]

[0080] The predicted value of wind turbine or photovoltaic power generation output;

[0081] 2.2.5 Energy Storage Constraints:

[0082] Considering the source-load inconsistency problem, electricity and heat storage equipment are introduced to alleviate it. Since the various energy storage models are similar, they are modeled uniformly. The mathematical model of the hydrogen storage tank is as follows:

[0083]

[0084] Where, are the energy storage and release power, is the energy storage capacity, are the energy storage charging efficiency and release efficiency respectively, are 0-1 variables representing the charging and discharging of the energy storage device, respectively. are the maximum charging and discharging power of the storage device, Respectively represent the minimum and maximum energy storage capacity of the storage device, are the initial and final values ​​of the energy storage equipment in the entire scheduling period respectively; if the energy storage is hydrogen energy storage, X is H2.

[0085] Preferably, in step 3, the process of modeling the distribution network is as follows:

[0086] 3.1、Objective function:

[0087] The optimization goal of the distribution network is to minimize the total cost F, taking into account the power generation cost of the distribution network and the cost of purchasing electricity from the upper grid:

[0088]

[0089] Where: is the power generation cost of the distribution network, represents the active power of the micro-turbine at node i, a g , b g , c g are the cost coefficients of micro turbines, G A collection of micro-turbines connected to the distribution network; The cost of electricity purchased from the main grid by the distribution network, To purchase electricity from the main grid, The power purchased from the main grid at time t;

[0090] 3.2 Constraints:

[0091] 3.2.1. Power flow constraints:

[0092] The DistFlow model is used to represent the power flow of the distribution network and linearize it;

[0093]

[0094] Where, is the active power between nodes i and j at time t, is the load power of node j at time t, is the active power of the generator at node j at time t, is the power injected into the interconnection line of the distribution network by the microgrid at time t, is the active power of node j at time t; is the reactive power between nodes i and j at time t, is the load power of node j at time t, is the reactive power of the generator at node j at time t, is the node reactive power at time t; are the voltages of nodes i and j at time t, and r ij , x ij are the impedance and admittance of branch ij respectively;

[0095] 3.2.2 Microturbine Constraints:

[0096]

[0097] Where: represents the reactive power of the micro-turbine at node i at time t, Respectively represent the lower and upper limits of the active power, reactive power and ramp power of the micro-turbine;

[0098] 3.2.3 Power transmission constraints between distribution network and microgrid:

[0099] Tie line power constraints:

[0100]

[0101] Where, represents the transmission power of the tie line between the IEMs in the distribution network at time t, are the minimum and maximum transmission power of the tie line respectively;

[0102] 3.2.4. Node voltage constraints:

[0103]

[0104] Where V i t is the voltage amplitude of node i at time t, Vi min ,V i max are the lower and upper limits of the node voltage amplitude respectively;

[0105] 3.2.5. Power purchase constraints for distribution networks and main grids:

[0106]

[0107] Where, They are the lower and upper limits of power purchased from the main grid.

[0108] Preferably, the specific process of step 4 is as follows:

[0109] The IEM and the distribution network are connected by tie lines. The tie lines meet consistency constraints. The consistency constraints tend to ensure that the tie line power between the active distribution network and the microgrid remains consistent. When performing distributed management, the output power of the distribution network must be equal to the input power of the microgrid, that is:

[0110]

[0111] Preferably, in step 5, the integrated energy microgrid-distribution network distributed optimization scheduling model considering electricity-carbon-hydrogen coupling is constructed as follows:

[0112]

[0113] st G(x)≤0 (33)

[0114] H(x)=0 (34)

[0115]

[0116] Where: (32) represents the total objective function of the system, which includes the distribution network cost F DN and microgrid cost F MG,i G represents a multi-microgrid set; (33) represents an inequality constraint; (34) represents an equality constraint; (35) represents the consistency constraint of each sub-region, x i0 is the interaction between the various areas, that is, the active power on the connection line It is a global variable that is updated with iterations;

[0117] For the optimization model under the distributed framework, since each region is independent of each other and solved in parallel, the interaction power is finally consistent. Therefore, the consistency constraint is added to the objective function through the Lagrange multiplier and converted into an augmented Lagrangian function, whose expression is shown in (36), where represents the Lagrange multiplier of the mth iteration, ηm represents the penalty factor of the mth iteration; then solve the interaction between each microgrid and distribution network (x i ), providing a basis for updating consistent variables for the next ADMM iteration;

[0118]

[0119] Preferably, the specific process of step 6 is as follows:

[0120] Multiple microgrids are connected through distribution networks and tie lines, so the exchange variable in the optimization is the active power on the tie line. The global variables are updated as follows:

[0121]

[0122] The dual variables are updated as:

[0123]

[0124] Iteration termination principle:

[0125] During the algorithm calculation process, with continuous iterations, the original residual and the dual residual gradually converge, and eventually the optimal solution is obtained. The calculation formulas are:

[0126]

[0127] Where r m is the original residual, s m is the dual residual, and the iterative termination condition of the algorithm is r m ≤ε r And s m ≤ε s .

[0128] Beneficial effects of the present invention:

[0129] 1. Under the operating method of the carbon capture device and multi-type hydrogen energy application of the present invention, CCS can supply the captured CO2 to the power-to-gas device to generate natural gas, thus realizing carbon circulation, reducing the operating cost of the IEM, reducing the IEM carbon emissions, improving the economic benefits of the system and its ability to absorb renewable energy, and exploring the system scheduling potential.

[0130] 2. Compared with centralized optimization, the distributed optimization scheduling model proposed in this paper achieves almost identical results, with a cost error of only 0.08%. However, the distributed optimization scheduling can complete global optimization scheduling while exchanging only a small amount of boundary information, ensuring the privacy of each microgrid. When distribution network microgrids are managed by different operators, privacy leakage can be reduced, which facilitates independent decision-making for each microgrid.

[0131] 3. The present invention solves the problem of optimizing the scheduling of the integrated energy microgrid coupled with electricity, carbon and hydrogen by connecting it to the distribution network, considers the scheduling problem of the integrated energy microgrid coupled with electricity, carbon and hydrogen, and alleviates carbon emissions and the scheduling problem of the integrated energy microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0132] Figure 1 A diagram of the load and transaction price of each microgrid implemented in the present invention;

[0133] Figure 2 A diagram showing the power situation of the tie line between the microgrid and the distribution network provided by the present invention;

[0134] Figure 3 This is the system optimization scheduling diagram for scenario 5 provided by the present invention;

[0135] Figure 4 This is a diagram of the voltage status of the distribution network nodes provided by the present invention. DETAILED DESCRIPTION

[0136] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0137] A distributed optimization method for integrating an integrated energy microgrid into a distribution network considering electricity-carbon-hydrogen coupling is provided, comprising the following steps:

[0138] Step 1: Model the main equipment in the integrated energy microgrid;

[0139] Step 2: Modeling the integrated energy microgrid, considering carbon capture systems and multiple types of hydrogen applications;

[0140] Step 3: Considering the integration of the integrated energy microgrid, model the distribution network;

[0141] Step 4: Consider connecting the distribution network to the microgrid. The two are connected through a tie line, and the output power of the distribution network must be equal to the input power of the microgrid.

[0142] Step 5: Construct a distributed optimization scheduling model for the integrated energy microgrid-distribution network considering electricity-carbon-hydrogen coupling;

[0143] Step 6: Use the alternating direction multiplier method to iteratively solve the optimization scheduling model established in step 5.

[0144] Preferably, in step 1, the main equipment in the integrated energy microgrid includes a hydrogen-doped cogeneration unit with CCS and P2G, a gas boiler, a carbon capture power-to-gas device, an electrolysis hydrogen production device, a fuel cell, and an electric thermal hydrogen energy storage.

[0145] Preferably, the hydrogen-doped cogeneration unit with CCS and P2G specifically includes the following parts:

[0146] The mathematical model of CCS is as follows: The actual power of the carbon capture device includes operating power consumption and basic power consumption. The expression is as follows:

[0147]

[0148] Where, is the carbon capture power of the CCS device, is the basic power consumption, is the operating power consumption, μ ccs The electrical power required to capture a unit of carbon dioxide, The amount of carbon dioxide captured;

[0149] The P2G mathematical model is: a two-stage power-to-gas device including electrolysis to produce hydrogen and hydrogen to produce methane, expressed as follows:

[0150]

[0151] in, and The amount of H2 generated by electrolysis and the power consumption are and are respectively the methane production power and methanation hydrogen consumption, η e2H and η H2G are the efficiencies of hydrogen production by electrolysis and methanation, respectively;

[0152] At the same time, the production of methane also uses a certain amount of electrolyzer power, which is expressed as follows:

[0153]

[0154] are respectively the methane generation power and the electrolytic cell power consumption, η P2G is the conversion efficiency of P2G;

[0155] The amount of CO2 required for the methanation process comes from the actual CO2 captured by the carbon capture power plant, and its expression is as follows:

[0156]

[0157] is the CO2 power required for the microgrid to produce methane at time t, L CH4 is the calorific value of natural gas per unit volume; is the CO2 gas density, is the amount of CO2 stored; Equation (5) shows that the CO2 required for methanogenesis and the stored CO2 all come from CCS, completing the coupling of CCS and P2G;

[0158] The mathematical model of the hydrogen-doped cogeneration unit is as follows: In order to increase the application types of hydrogen energy, a hydrogen-doped cogeneration unit is added to improve the efficiency of hydrogen energy use and reduce system carbon emissions. Its expression is as follows:

[0159] Hydrogen doping ratio:

[0160]

[0161] Power generation and heat generation:

[0162]

[0163] Where, is the hydrogen doping ratio, is the hydrogen power consumed by the cogeneration unit at time t, is the natural gas power consumed by the cogeneration unit at time t, are the calorific values ​​of hydrogen and natural gas, respectively; are the electrical efficiency and thermal efficiency of the cogeneration unit, They are the electricity production efficiency and heat production efficiency of the cogeneration unit respectively.

[0164] 1.2 Gas boiler:

[0165] Gas boilers are mainly used to generate heat. When the combined heat and power generation cannot meet the heat load, the gas boilers will supplement the heat load. The expression is as follows:

[0166]

[0167] Where, is the thermal power η generated at time t GB For gas boiler hot spot conversion efficiency, is the natural gas power consumed by the gas boiler at time t.

[0168] 1.3 Fuel Cells

[0169] Fuel cells can convert hydrogen energy into electrical energy, which can be expressed as follows:

[0170]

[0171] Where, is the fuel cell output power η at time t FC is the hydrogen-to-electricity conversion efficiency at time t, is the hydrogen power consumed by the fuel cell at time t.

[0172] 1.4. Carbon quota and carbon emissions of integrated energy microgrids:

[0173] The initial carbon emission allocation consists of two parts: the carbon emission allocation of traditional units of CHP and GB and the carbon emission allocation of new energy units of PV and WT. The expression is as follows:

[0174]

[0175] Where, E 0, is the default carbon emission amount, μ c It is the carbon emission benchmark quota given per unit power generation / heating power.

[0176] The carbon emissions of the integrated energy microgrid are mainly from CHP units and gas boiler units. As CCS can capture some carbon dioxide, the actual carbon emissions of the IEM are:

[0177]

[0178] Where, is the carbon emissions of the CHP unit; is the carbon emission of GB unit; ε1 is the carbon emission coefficient of CHP unit; ε2 is the carbon emission coefficient of GB unit.

[0179] Preferably, in step 2, the specific process of modeling the integrated energy microgrid is as follows:

[0180] 2.1、Objective function:

[0181] The objective function of the microgrid consists of four parts: gas purchase cost, operation and maintenance cost, penalty for curtailment of wind and solar power, and carbon trading cost;

[0182]

[0183] 2.1.1 Gas purchase cost:

[0184]

[0185] Where c buy is the unit price of natural gas, Purchase natural gas power for time t;

[0186] 2.1.2 Operation and maintenance costs:

[0187]

[0188] Where c chp is the CHP unit operation and maintenance cost coefficient, δ storage is the carbon storage cost coefficient, c P2G is the P2G operation and maintenance cost coefficient, c CCS is the CCS operation and maintenance cost coefficient, c FC is the operation and maintenance cost coefficient;

[0189] 2.1.3 Penalties for curtailing wind and solar power:

[0190] C RE =c RE P RE (15)

[0191] Where c RE P is the penalty cost coefficient for curtailing wind and solar power RE The amount of wind and solar power curtailed;

[0192] 2.1.4 Carbon trading costs:

[0193]

[0194] Where, is the price of purchasing a unit of carbon emission credit, is the price of selling a unit of carbon emission, are the carbon allowances purchased and sold respectively;

[0195] 2.2 Constraints:

[0196] 2.2.1. Balance constraints:

[0197]

[0198] Where, are the electric and thermal hydrogen carbon balance of IEM, The power injected into the microgrid's interconnection line for the distribution network, Purchase power from the distribution network for IEM, The price at which IEM sells electricity to the distribution network; They represent the loads after the electricity and heat loads are responded to at time t; E0 is the initial carbon emission allocation of IEM; is the total carbon emissions of IEM; The total amount of carbon emission credits purchased; The total amount of carbon emission credits sold;

[0199] 2.2.2. Power limit of IEM transmission to distribution network:

[0200]

[0201] Where, Purchase power from the distribution grid for IEM, The price at which IEM sells electricity to the distribution grid;

[0202] 2.2.3. Restrictions on electrolytic hydrogen production, cogeneration units, and gas boilers;

[0203] 1) Maximum power and ramp constraints for hydrogen production by electrolysis:

[0204]

[0205] Where, The output limit of the electrolysis equipment are the lower and upper limits of the climbing constraint respectively.

[0206] 2) Maximum power and ramp constraints of cogeneration units:

[0207]

[0208] Where, are the lower and upper limits of the power output of the cogeneration unit, are the lower and upper limits of the thermal output of the cogeneration unit, are the lower and upper limits of the climbing constraint respectively.

[0209] The hydrogen blending ratio of the gas turbine should be within 20% to ensure the stable operation of the gas turbine; that is:

[0210]

[0211] Where, are the lower and upper limits of the power output of the cogeneration unit, are the lower and upper limits of the thermal output of the cogeneration unit, are the lower and upper limits of the climbing constraint respectively.

[0212] 3) Maximum power and ramp constraints of gas boilers:

[0213]

[0214] Where, The lower and upper limits of gas boiler output, are the lower and upper limits of the climbing constraint respectively.

[0215] 4) CCS, P2G maximum power constraints:

[0216]

[0217] Where, are the lower and upper limits of CCS output, The lower and upper limits of CCS output.

[0218] 5) Fuel cell maximum power and ramp constraints:

[0219]

[0220] Where, The lower and upper limits of fuel cell output, are the lower and upper limits of the climbing constraint respectively.

[0221] 2.2.4 Constraints on renewable energy generation:

[0222]

[0223] Where, The predicted value of wind turbine or photovoltaic power generation output;

[0224] 2.2.5 Energy Storage Constraints:

[0225] Considering the source-load inconsistency problem, electricity storage, heat storage and other equipment are introduced to alleviate it. Since the various energy storage models are similar, they are modeled uniformly. The mathematical model of the hydrogen storage tank is as follows:

[0226]

[0227] Where, are the energy storage and release power, is the energy storage capacity, are the energy storage charging efficiency and release efficiency respectively, are 0-1 variables representing the charging and discharging of the energy storage device, respectively. are the maximum charging and discharging power of the storage device, Respectively represent the minimum and maximum energy storage capacity of the storage device, The values ​​for the energy storage device during the entire dispatch cycle are X and H, respectively. For hydrogen storage, X is H2; for thermal storage, X is ESS and H, respectively. That is, for electrical storage, X is ESS; for thermal storage, X is H.

[0228] Preferably, in step 3, the process of modeling the distribution network is as follows:

[0229] 3.1、Objective function:

[0230] The optimization goal of the distribution network is to minimize the total cost F, taking into account the power generation cost of the distribution network and the cost of purchasing electricity from the upper grid:

[0231]

[0232] Where: is the power generation cost of the distribution network, represents the active power of the micro-turbine at node i, a g , b g , c g are the cost coefficients of micro turbines, G A collection of micro-turbines connected to the distribution network; The cost of electricity purchased from the main grid by the distribution network, To purchase electricity from the main grid, The power purchased from the main grid at time t;

[0233] 3.2 Constraints:

[0234] 3.2.1. Power flow constraints:

[0235] The DistFlow model is used to represent the power flow of the distribution network and linearize it;

[0236]

[0237] Where, is the active power between nodes i and j at time t, is the load power of node j at time t, is the active power of the generator at node j at time t, is the power injected into the interconnection line of the distribution network by the microgrid at time t, is the active power of node j at time t; is the reactive power between nodes i and j at time t, is the load power of node j at time t, is the reactive power of the generator at node j at time t, is the node reactive power at time t; are the voltages of nodes i and j at time t, and r ij , x ij are the impedance and admittance of branch ij respectively;

[0238] 3.2.2 Microturbine Constraints:

[0239]

[0240] Where: represents the reactive power of the micro-turbine at node i at time t, Respectively represent the lower and upper limits of the active power, reactive power and ramp power of the micro-turbine;

[0241] 3.2.3 Power transmission constraints between distribution network and microgrid:

[0242] Tie line power constraints:

[0243]

[0244] Where, represents the transmission power of the tie line between the IEMs in the distribution network at time t, are the minimum and maximum transmission power of the tie line respectively;

[0245] 3.2.4. Node voltage constraints:

[0246] V i min ≤V i t ≤Vi max (33)

[0247] Where V i t is the voltage amplitude of node i at time t, V i min ,V i max are the lower and upper limits of the node voltage amplitude respectively;

[0248] 3.2.5. Power purchase constraints for distribution networks and main grids:

[0249]

[0250] Where, They are the lower and upper limits of power purchased from the main grid.

[0251] Preferably, the specific process of step 4 is as follows:

[0252] The IEM and the distribution network are connected by tie lines. The tie lines meet consistency constraints. The consistency constraints tend to ensure that the tie line power between the active distribution network and the microgrid remains consistent. When performing distributed management, the output power of the distribution network must be equal to the input power of the microgrid, that is:

[0253]

[0254] Each IEM's decision-making is independent and does not involve information exchange with other IEMs. Only tie-line information is shared between the distribution network and the IEMs to ensure operational consistency. Therefore, the coordinated operation of multi-microgrid distribution systems can be addressed in a fully decentralized manner while maintaining independent decision-making by each subsystem operator.

[0255] Preferably, since the coupling consistency constraints between the regions are implemented through active power, the regions only need to have interactive boundary active power. In step 5, the integrated energy microgrid-distribution network distributed optimization scheduling model considering electricity-carbon-hydrogen coupling is constructed as follows:

[0256]

[0257] st G(x)≤0 (37)

[0258] H(x)=0 (38)

[0259]

[0260] Where: (36) represents the total objective function of the system, which includes the distribution network cost F DN and microgrid cost F MG,iG represents a multi-microgrid set; (37) represents an inequality constraint; (38) represents an equality constraint; (39) represents the consistency constraint of each sub-region, x i0 is the interaction between the various areas, that is, the active power on the connection line It is a global variable that is updated with iterations;

[0261] For the optimization model under the distributed framework, since each region is independent of each other and solved in parallel, the interaction power is finally consistent. Therefore, the consistency constraint is added to the objective function through the Lagrange multiplier and converted into an augmented Lagrangian function, whose expression is shown in (40), where represents the Lagrange multiplier of the mth iteration, η m represents the penalty factor of the mth iteration; then solve the interaction between each microgrid and distribution network (x i ), providing a basis for updating consistent variables for the next ADMM iteration;

[0262]

[0263] Preferably, the specific process of step 6 is as follows:

[0264] Multiple microgrids are connected through distribution networks and tie lines, so the exchange variable in the optimization is the active power on the tie line. The global variables are updated as follows:

[0265]

[0266] The dual variables are updated as:

[0267]

[0268] Iteration termination principle:

[0269] During the algorithm calculation process, with continuous iterations, the original residual and the dual residual gradually converge, and eventually the optimal solution is obtained. The calculation formulas are:

[0270]

[0271] Where r m is the original residual, s m is the dual residual, and the iterative termination condition of the algorithm is r m ≤ε r And s m ≤ε s .

[0272] Case analysis:

[0273] The present invention takes three integrated energy microgrids connected to the IEEE33 node distribution network system as an example to verify the effectiveness of the distributed optimization method after adding carbon capture systems and multi-type hydrogen energy applications. Each microgrid includes: a hydrogen-blended cogeneration unit with CCS and P2G, a gas boiler, a wind turbine, a photovoltaic device, a two-stage power-to-gas device, an electrolytic hydrogen production device, a fuel cell, and an electric thermal hydrogen storage device, while taking into account the response to the electric load demand. The equipment parameters are shown in Appendix Table A. The three integrated energy microgrids mentioned above are connected to nodes 10, 20, and 30 of the IEEE33 node distribution network, and nodes 3, 9, and 16 are connected to the generator. In the calculation of the optimal power flow, the voltage reference value is 12.66kV and the reference power is 100MVA. The voltage range is [0.95 1.05] and the initial values ​​of the pu coupling variable and the multiplier are all set to 0. The examples in this paper are solved by calling the Gurobi solver using YAMLIP in the matlabR2018b environment. The specific equipment parameters and load conditions are shown in the table, and the load conditions of each microgrid are shown in the appendix. Figure 1 shown.

[0274] To verify the effectiveness, economics, and environmental impact of a distributed optimization approach when integrating carbon capture systems and diverse hydrogen applications, this paper considers five scenarios for comparative analysis. These scenarios are centered around the operational status of carbon capture equipment, hydrogen-to-methane, fuel cells, and hydrogen-integrated cogeneration units with CCS and P2G. The specific scenarios are shown in Appendix Table B, where √ indicates operational status and × indicates non-operation.

[0275] Table A parameter settings

[0276]

[0277] Table B 5 different scene settings

[0278]

[0279] Economic analysis of various applications of carbon capture and hydrogen energy:

[0280] Table 1 Economic benefits of microgrid A in different scenarios

[0281]

[0282] Table 2 Economic benefits of microgrid B in different scenarios

[0283]

[0284] Table 3 Economic benefits of C microgrid in different scenarios

[0285]

[0286] Tables 1, 2, and 3 show that, comparing Scenario 1 and Scenario 2, the addition of carbon capture and storage (CCS) reduces carbon emissions by approximately 60%. Furthermore, carbon trading revenue increases by 2,443,810 yuan compared to Scenario 1. This is because Scenario 2 can sell excess carbon allowances to generate carbon trading revenue. As carbon emissions decrease, carbon trading revenue continues to increase, encouraging microgrids to reduce their carbon emissions. Scenario 1 also faces significant wind and solar power curtailment issues. However, due to the high energy consumption of the carbon capture equipment, it can absorb excess wind and solar power, reducing curtailment costs. This demonstrates its environmental and economic benefits.

[0287] Comparing Scenario 2, Scenario 3, and Scenario 4, Scenario 2 also considers carbon sequestration costs after capturing CO2, while Scenario 3 incorporates methanation. While Scenario 3 introduces methanation equipment, which increases maintenance costs, this is lower than the reduced carbon trading costs and wind and solar curtailment, resulting in improved overall returns. While Scenario 3 only considers hydrogen production as a single hydrogen energy application, it still utilizes previously unabsorbed renewable energy generation, improving energy efficiency. Scenario 4 introduces fuel cells, fully utilizing hydrogen energy. Furthermore, hydrogen fuel cells can replace some combined heat and power generation, further reducing system carbon emissions and highlighting its environmental benefits.

[0288] Compared to Scenario 4 and Scenario 5, the introduction of hydrogen-blended CHP units further expands hydrogen utilization, achieving thermal-hydrogen coupling and further reducing system carbon emissions. Since hydrogen is blended with natural gas, methane usage in cogeneration units and gas boilers is further reduced. Since hydrogen has a higher calorific value than methane, the system prioritizes green hydrogen-blended CHP units for power generation and heat generation, further improving power generation and heat generation efficiency, reducing methane purchases, and improving energy utilization, further emphasizing economic and environmental benefits.

[0289] Table 4 Total benefits in different scenarios

[0290]

[0291] Table 4 shows the overall benefits of the microgrid under different operating scenarios. Since the interaction costs between the distribution network and the microgrid are not considered, the distribution network costs remain unchanged as the microgrid scenario changes, as the connection between the microgrid and the distribution network is only the power on the interconnection line. This also indirectly demonstrates that the distributed optimization proposed in this paper can effectively protect the privacy of each sub-region, achieving relatively independent solutions and distributed solutions to the optimization scheduling problems of each microgrid, demonstrating the privacy protection of the proposed scenario. Furthermore, as the scenario progresses, the microgrid costs will gradually decrease, and carbon emissions will also decrease, demonstrating the economic and environmental benefits of the proposed scenario.

[0292] Optimal scheduling analysis considering multiple applications of carbon capture and hydrogen energy:

[0293] By the attached Figure 3 (a)(b)(c) First, analyze microgrid A. Since the load is low and the wind power output is high between 0:00-6:00 and 21:00-24:00, the energy storage is charged, and the interconnection line between the microgrid and the distribution network has power feedback. The PCCS and PP2G increase the power to absorb the excess wind power. At the same time, after 6:00, if Figure 2 As shown in the figure, as the load increases, the interconnection line power begins to flow in the positive direction. From 19:00 to 21:00, due to the excessive load, the source and load are balanced by discharging the energy storage. After 21:00, the load returns to normal and continues to charge the energy storage.

[0294] For microgrid B, the load power has been relatively stable. From 0:00 to 6:00, since the photovoltaic output is 0, the interconnection line power is increased to balance the source-load relationship. From 9:00 to 15:00, the photovoltaic power generation is large, and the corresponding interconnection line power is also relatively reduced to absorb the excess photovoltaic power.

[0295] For microgrid C, as the load increases from 0:00 to 9:00, the interconnection line power also increases, and the energy storage is also charged. From 9:00 to 18:00, although the photovoltaic output is large, the microgrid system can fully absorb it by increasing the PCCS power. From 19:00 to 21:00, there is no photovoltaic power, the equipment climbing power is limited, and the energy storage is discharged.

[0296] By the attached Figure 3 As shown in (d)(e)(f), the system's thermal power is primarily supplied by the hydrogen-blended CHP unit. This is because hydrogen-blended CHP units produce less carbon emissions per unit of heat energy than gas-fired boilers, resulting in lower overall costs. The heat storage tank primarily serves a regulatory role. When the heat load is high, the maximum heat output of the CHP unit is insufficient to meet the load, so the gas-fired boiler also generates heat to supply the heat load. When the heat load is low, the excess heat generated by the CHP unit can be stored in the heat storage tank and released when the heat load is high to balance the source and load.

[0297] By the attached Figure 3 (g)(h)(i) It can be seen that since all the hydrogen energy of the system is supplied by power-to-gas, that is, electrolysis hydrogen production, and the power of the power-to-gas device can be obtained from the system electric power diagram, it can be seen that the hydrogen production is roughly consistent with the power trend of the power-to-gas device in Figures (a)(b)(c); when the hydrogen production power in the system is large, the excess hydrogen will be stored in the hydrogen energy storage. When the hydrogen production power of the system is insufficient, the hydrogen storage tank will release the stored hydrogen.

[0298] Attachment Figure 4 It can be seen that the voltage conditions of the distribution network nodes are in line with the expectations of IEEE33 nodes, and no voltage exceeding the limit occurs after optimization. The node voltage conditions of centralized operation and distributed operation are consistent, verifying the effectiveness of distributed operation.

[0299] Convergence analysis:

[0300] In scenario 5, the system adopts centralized operation and ADMM algorithm for solution. The convergence accuracy is set to 2.5e -2 The table shows the cost, error, solution time, and number of iterations of the system under the centralized operation and ADMM algorithm.

[0301] Table 5 Comparison of different algorithms

[0302]

[0303] Table 5 shows the system cost convergence under the three aforementioned scenarios. It can be seen that the costs obtained by the centralized and distributed algorithms are essentially identical, with an error of only 0.08%. Although distributed optimization is slower, centralized operation requires the unified calculation of all data within the system, which cannot guarantee the privacy, independence, and security of microgrids, hindering independent decision-making by each microgrid operator. Distributed operation allows microgrids to achieve global optimization by exchanging only a small amount of boundary information, namely, the power of the tie line. This reduces the time-consuming communication burden in actual operation and protects information privacy. Furthermore, as the scale of subsystems increases, centralized operation becomes more challenging to coordinate across microgrids, as different subsystems in a multi-microgrid system correspond to different operators. Therefore, the advantages of distributed operation become more pronounced. Although the computation time of the distributed operation solution is higher than that of the centralized operation, the advantages of distributed operation extend beyond this. Compared with centralized operation, distributed operation achieves decision autonomy and data privacy. Furthermore, sub-microgrids can perform local computations and then upload relevant data to the distribution network, and computations across sub-microgrids can be performed synchronously.

[0304] The above embodiments are merely preferred technical solutions of the present invention and should not be construed as limiting the present invention. The scope of protection of the present invention shall be the technical solutions set forth in the claims, including equivalent alternatives to the technical features of the technical solutions set forth in the claims. In other words, equivalent alternatives and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A distributed optimization method for integrated energy microgrid access to distribution network considering electricity-carbon-hydrogen coupling, characterized by: It includes the following steps: Step 1: Model the main equipment in the integrated energy microgrid; Step 2: Modeling the integrated energy microgrid considering carbon capture systems and multi-type applications of hydrogen energy; Step 3: Considering the access of integrated energy microgrid, model the distribution network; Step 4: Consider the connection between the distribution network and the microgrid. The two are connected through a tie line, and the output power of the distribution network must be equal to the input power of the microgrid. Step 5: Construct a distributed optimization dispatching model of integrated energy microgrid-distribution network considering electricity-carbon-hydrogen coupling; Step 6: Use the alternating direction multiplier method algorithm to iteratively solve the optimization scheduling model established in step 5.

2. According to claim 1, a distributed optimization method for connecting a comprehensive energy microgrid to a distribution network taking into account electricity-carbon-hydrogen coupling is characterized in that: In step 1, the main equipment in the integrated energy microgrid includes a cogeneration unit, a gas boiler, a carbon capture power-to-gas device, an electrolysis hydrogen production device, a fuel cell, an electric thermal hydrogen energy storage, and a hydrogen-blended cogeneration unit with CCS and P2G.

3. The distributed optimization method for accessing a comprehensive energy microgrid to a distribution network considering electricity-carbon-hydrogen coupling according to claim 2 is characterized in that: The hydrogen-blended cogeneration unit with CCS and P2G specifically includes the following parts: The CCS mathematical model is as follows: The actual power of the carbon capture device includes the operating power consumption and the basic power consumption. The expression is as follows: In the formula, is the carbon capture power of the CCS device, is the basic power consumption, is the operating power consumption, μ ccs is the electrical power required to capture a unit of carbon dioxide, The amount of carbon dioxide captured; The P2G mathematical model is: the two-stage power-to-gas device includes electrolysis to produce hydrogen and hydrogen to produce methane, and the expression is as follows: in, and The amount of H2 generated by electrolysis and the power consumption are and are respectively the methane power produced and the hydrogen consumption for methanogenesis, η e2H and η H2G are the efficiencies of hydrogen production by electrolysis and methanogenesis, respectively; At the same time, the production of methane will also use a certain amount of electrolyzer power, expressed as follows: are respectively the methane generation power and the power consumption of the electrolyzer, η P2G is the conversion efficiency of P2G; The amount of CO2 required for the methanogenesis process comes from the actual CO2 captured by the carbon capture power plant, and its expression is as follows: is the CO2 power required for the microgrid to produce methane at time t, L CH4 is the calorific value of natural gas per unit volume; is the CO2 gas density, is the amount of CO2 stored; Formula (5) shows that the CO2 required for methanogenesis and the stored CO2 all come from CCS, completing the coupling of CCS and P2G; The mathematical model of the hydrogen-doped cogeneration unit is as follows: In order to increase the application types of hydrogen energy, a hydrogen-doped cogeneration unit is added to improve the efficiency of hydrogen energy use and reduce system carbon emissions. Its expression is as follows: Hydrogen doping ratio: Power generation and heat generation: In the formula, is the hydrogen doping ratio, is the hydrogen power consumed by the cogeneration unit at time t, is the natural gas power consumed by the cogeneration unit at time t, are the calorific values ​​of hydrogen and natural gas, respectively; are the electrical efficiency and thermal efficiency of the cogeneration unit, They are the electricity production efficiency and heat production efficiency of the cogeneration unit respectively.

4. The distributed optimization method for accessing a comprehensive energy microgrid to a distribution network considering electricity-carbon-hydrogen coupling according to claim 1 is characterized in that: In step 2, the specific process of modeling the integrated energy microgrid is as follows: 2.

1. Objective function: The objective function of the microgrid consists of four parts: gas purchase cost, operation and maintenance cost, wind and solar power abandonment penalty, and carbon trading cost; In the formula, C gas,buy , C RE , They are gas purchase cost, operation and maintenance cost, penalty for wind and solar power abandonment, and carbon trading cost; 2.1.1 Gas purchase cost: In the formula, c buy is the natural gas unit price, Purchase natural gas power for time t; 2.1.2 Operation and maintenance costs: In the formula, c chp is the CHP unit operation and maintenance cost coefficient, δ storage is the carbon storage cost coefficient, c P2G is the P2G operation and maintenance cost coefficient, c CCS is the CCS operation and maintenance cost coefficient, c FC is the operation and maintenance cost coefficient; 2.1.3 Penalties for abandoning wind and solar power: C RE =c RE P RE (11) In the formula, c RE P is the penalty cost coefficient for wind and solar power abandonment RE The amount of wind and solar power abandoned; 2.1.4 Carbon trading costs: In the formula, is the price of purchasing a unit of carbon emission quota, is the price of selling a unit of carbon emission, are the carbon allowances purchased and sold, respectively; 2.2 Constraints: 2.2.

1. Balance constraints: Where, are the electric heat hydrogen carbon balance of IEM, The power injected into the microgrid’s interconnection line is Purchase power from the distribution network for IEM, The price at which IEM sells electricity to the distribution network; They represent the load after the electricity and heat load demand response at time t respectively; E0 is the initial carbon emission allocation of IEM; is the total carbon emissions of IEM; The total amount of carbon emission credits purchased; The total amount of carbon emission credits sold; 2.2.

2. Power limit of IEM to distribution network transmission: In the formula, Purchase power from the distribution grid for IEM, The price at which IEM sells electricity to the distribution network; 2.2.

3. Restrictions on hydrogen production by electrolysis, cogeneration units, and gas boilers; 1) Maximum power and ramp constraints for hydrogen production by electrolysis: In the formula, The output limit of the electrolysis equipment are the lower and upper limits of the climbing constraint respectively; 2) Maximum power and ramp constraints of cogeneration units: The hydrogen blending ratio of the cogeneration unit should be within 20% to ensure the safe and stable operation of the cogeneration unit; that is: In the formula, are the lower and upper limits of the power output of the cogeneration unit, are the lower and upper limits of the thermal output of the cogeneration unit, are the lower and upper limits of the climbing constraint respectively; 3) Maximum power and ramp constraints of gas boilers: In the formula, The lower and upper limits of gas boiler output. are the lower and upper limits of the climbing constraint respectively; 4)CCS, P2G maximum power constraints: In the formula, are the lower and upper limits of CCS output, The lower and upper limits of CCS output; 5) Fuel cell maximum power and climbing constraints: In the formula, is the lower and upper limits of fuel cell output, are the lower and upper limits of the climbing constraint respectively; 2.2.4 Constraints on renewable energy generation: It is the predicted value of wind turbine or photovoltaic power generation output; 2.2.5 Energy storage constraints: Considering the source-load inconsistency problem, electricity storage and heat storage equipment are introduced to alleviate it; each energy storage model is similar, so a unified model is built for them. The mathematical model of the hydrogen storage tank is as follows: In the formula, are energy storage and release power, is the energy storage capacity, are energy storage charging efficiency and release efficiency respectively, are 0-1 variables representing the charging and discharging of the energy storage device, respectively. are the maximum charging and discharging power of the storage device, They represent the minimum and maximum energy storage capacity of the energy storage device, respectively. They are the initial and final values ​​of the energy storage equipment in the entire scheduling period respectively; if the energy storage is hydrogen energy storage, X is H2.

5. The distributed optimization method for accessing a comprehensive energy microgrid to a distribution network considering electricity-carbon-hydrogen coupling according to claim 1 is characterized in that: In step 3, the process of modeling the distribution network is as follows: 3.

1. Objective function: The optimization goal of the distribution network is to minimize the total cost F, taking into account the power generation cost of the distribution network and the cost of purchasing electricity from the upper-level power grid: Where: is the power generation cost of the distribution network, represents the active power of the micro-turbine at node i, a g , b g , c g They represent the cost coefficient of micro-turbine respectively, G A collection of micro gas turbines connected to the distribution network; The cost of purchasing electricity from the main grid to the distribution network, To purchase electricity from the main grid, The power purchased from the main grid at time t; 3.2 Constraints: 3.2.

1. Power flow constraints: The DistFlow model is used to represent the power flow of the distribution network and linearize it; In the formula, is the active power between nodes i and j at time t, is the load power of node j at time t, is the active power of the generator at node j at time t, is the power injected into the interconnection line of the distribution network by the microgrid at time t, is the active power of node j at time t; is the reactive power between nodes i and j at time t, is the load power of node j at time t, is the reactive power of the generator at node j at time t, is the node reactive power at time t; are the voltages of nodes i and j at time t, and r ij , x ij are the impedance and admittance of branch ij respectively; 3.2.2 Micro gas turbine constraints: Where: represents the reactive power of the micro-turbine at node i at time t, They represent the lower and upper limits of the active power, reactive power and ramp power of the micro-turbine respectively; 3.2.

3. Power transmission constraints between distribution network and microgrid: Tie line power constraints: In the formula, represents the transmission power of the tie line between the IEMs in the distribution network at time t, are the minimum and maximum transmission power of the tie line respectively; 3.2.

4. Node voltage constraints: In i min ≤V i t ≤V i max (29) Where V i t is the voltage amplitude of node i at time t, V i min ,V i max are the lower and upper limits of the node voltage amplitude respectively; 3.2.

5. Power purchase constraints for distribution network and main network: In the formula, They are respectively the lower and upper limits of the power purchased from the main grid.

6. The distributed optimization method for accessing a comprehensive energy microgrid to a distribution network considering electricity-carbon-hydrogen coupling according to claim 1 is characterized in that: The specific process of step 4 is as follows: The IEM and the distribution network are connected by tie lines. The tie lines satisfy the consistency constraint. The consistency constraint tends to ensure that the tie line power between the active distribution network and the microgrid is consistent. When performing distributed management, the output power of the distribution network must be equal to the input power of the microgrid, that is:

7. The distributed optimization method for connecting a comprehensive energy microgrid to a distribution network considering electricity-carbon-hydrogen coupling according to claim 1 is characterized in that: The integrated energy microgrid-distribution network distributed optimization scheduling model considering electricity-carbon-hydrogen coupling is constructed in step 5 as follows: stG(x)≤0 (33) H(x)=0 (34) Where: (32) represents the overall objective function of the system, which includes the distribution network cost F DN and microgrid cost F MG,i G represents a multi-microgrid set; (33) represents an inequality constraint; (34) represents an equality constraint; (35) represents a consistency constraint for each sub-region, x i0 is the interaction between the various areas, that is, the active power on the connection line is a global variable that is updated with iterations; For the optimization model under the distributed framework, since each region is independent of each other and solved in parallel, the interaction power is finally consistent. Therefore, the consistency constraint is added to the objective function through the Lagrangian multiplier and converted into an augmented Lagrangian function, whose expression is shown in (36), where represents the mth iteration Lagrange multiplier, η m represents the penalty factor for the mth iteration; then solve the interaction between each microgrid and distribution network (x i ), providing a basis for updating consistent variables for the next ADMM iteration; 8. The distributed optimization method for accessing a comprehensive energy microgrid to a distribution network considering electricity-carbon-hydrogen coupling according to claim 1 is characterized in that: The specific process of step 6 is as follows: Multiple microgrids are connected through distribution networks and tie lines, so the exchange variable in the optimization is the active power on the tie line. The global variables are updated as follows: The dual variables are updated as: Iteration termination principle: During the algorithm calculation process, with continuous iterations, the original residual and the dual residual gradually converge, and finally the optimal solution is obtained. The calculation formulas are: In the formula, r m is the original residual, s m is the dual residual, and the iterative termination condition of the algorithm is r m ≤ε r And s m ≤ε s .

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