IEM access power distribution network optimization method and system considering thermoelectric demand response

Abstract

The invention provides an IEM access power distribution network optimization method and system considering thermoelectric demand response, and relates to the technical field of microgrid access power distribution network optimization operation. The method comprises the following steps: modeling by considering an IEM corresponding to a thermoelectric demand; carrying out modeling on the power distribution network by considering the access of the IEM; the power distribution network is connected to the micro-grid through a tie line; constructing a distributed scheduling model of the IEM-power distribution network considering the demand response; the distributed scheduling model is iteratively solved by adopting an ADMM algorithm, an optimal thermoelectric scheduling strategy is obtained, the overall benefit of the system is maximized, the operation cost is reduced, the energy utilization efficiency is improved, the thermoelectric demand of the micro-grid and the power balance of the power distribution network are ensured through modeling of the IEM and the power distribution network, and the thermoelectric demand of the micro-grid and the power balance of the power distribution network are ensured through solving of the distributed scheduling model and the ADMM algorithm. And a thermoelectric scheduling strategy is optimized, so that the energy utilization efficiency and the overall performance of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optimized operation of microgrid access to distribution network, and in particular to an optimization method and system for IEM access to distribution network considering thermal power demand response. Background Art

[0002] At present, new low-carbon power systems based on renewable energy are being rapidly built to reduce carbon emissions. Integrated energy microgrids (IEMs) can effectively solve the problem of large-scale grid connection of renewable energy such as wind power, photovoltaics, and hydrogen energy. However, due to its own scale, microgrids cannot integrate new energy access on a large scale, so the concept of multi-microgrid access to the distribution network is introduced to deal with the problem of large-scale grid connection of new energy. In addition, my country has long been in an energy structure dominated by coal fuel, and the research on the use of renewable energy is imminent, and research is needed to improve the utilization rate of new energy. Demand response DR (Demand Response, DR) can enhance the two-way interaction between the source and the load, achieve peak shaving and valley filling, and make the operation of the power system more flexible.

[0003] Existing optimization scheduling methods that consider demand response include: 1. Microgrid optimization scheduling method based on carbon trading and demand response, which eliminates some scenarios with low probability density according to the probability characteristic information of renewable energy output and load power consumption itself, establishes a demand response model, reduces conservatism, and obtains a microgrid optimization scheduling strategy with the minimum operating cost under the condition of user energy load; 2. Electric vehicle charging and discharging microgrid optimization scheduling method based on demand response, which comprehensively considers the complementary characteristics of multiple energy sources and the impact of wind and solar uncertainty on system optimization scheduling, can describe the uncertainty of wind and solar renewable energy output, and analyzes the adverse effects of disordered charging of electric vehicles on microgrids based on the driving characteristics of electric vehicles; 3. Multi-energy microgrid energy optimization management method and system considering demand response, establishes a mathematical model of solar panels and energy storage systems; takes minimum energy cost and peak-to-average ratio as the goals, establishes the objective function of the hybrid power generation system of photovoltaics, energy storage systems, and electric vehicles; solves the objective function to obtain the optimal energy management strategy.

[0004] However, the prior art rarely involves comprehensive consideration of the optimization and scheduling of the energy of multiple devices. Summary of the invention

[0005] The main purpose of the present invention is to provide an optimization method for IEM access to distribution network considering thermal power demand response, and to perform distributed collaborative optimization of IEM-distribution network. First, an IEM-distribution network system model considering demand response is established, and then the accelerated ADMM algorithm is used to perform distributed solution of the model. Compared with the traditional ADMM algorithm, the solution speed is improved, and the optimal scheduling scheme of IEM-distribution network containing thermal power demand response is obtained, which verifies the feasibility of the scheduling scheme.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: an optimization method for IEM access to distribution network considering thermal power demand response, comprising the following steps: S1: Modeling IEM based on the principle of thermal power demand response; S2: Model the distribution network based on the principle of access to IEM; S3: Based on the principle that the output power of the distribution network must be equal to the input power of the microgrid, the distribution network is connected to the microgrid through the tie line; S4: Construct a distributed dispatch model of IEM-distribution network based on demand response; S5: Use the ADMM algorithm to iteratively solve the distributed scheduling model and obtain the optimal thermal power scheduling strategy.

[0007] In the preferred embodiment, the related equipment of the IEM includes: a gas turbine, a gas boiler, a carbon capture power-to-gas device, an electrolytic hydrogen production device, a fuel cell device and an electric thermal energy storage device; Carry out corresponding mathematical modeling for each relevant equipment, and connect them comprehensively to construct the mathematical model of IEM.

[0008] In the preferred solution, mathematical modeling of the gas turbine is performed, specifically including: S11: Construct a CCS mathematical model based on the actual power of the carbon capture device. The actual power of the carbon capture device includes operating power consumption and basic power consumption. The formula is: (1) is the carbon capture power of the CCS device, is the basic power consumption, For operating power consumption, is the electrical power required to capture a unit of carbon dioxide, The amount of carbon dioxide captured; S12: Based on the two-stage power-to-gas device, a P2G mathematical model is constructed; wherein P2G includes two stages: electrolysis to produce hydrogen and hydrogen to produce methane, and the formula is: (2) in, and The amount of H2 generated by electrolysis and the power consumption are and They are methane production power and methanogenic hydrogen consumption; At the same time, the production of methane will also use a certain amount of electrolyzer power, the formula is: (3) and are the methane production power and the power consumption of the electrolyzer, respectively; The amount of CO2 required for the methanogenesis process comes from the actual CO2 captured by the carbon capture power plant, and the formula is: (4) (5) is the CO2 power required for the microgrid to produce methane at time t, is the calorific value of natural gas per unit volume; is the CO2 gas density, is the CO2 storage capacity; S13: According to the power generation and heat generation, a mathematical model of the cogeneration unit is established, wherein the power generation and heat generation formulas are: (6) In the formula, 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, and are the calorific values ​​of hydrogen and natural gas, and are the electrical efficiency and thermal efficiency of the gas turbine, and are the electricity and heat generation efficiencies of the gas turbine, respectively.

[0009] In the preferred solution, a mathematical model is performed on the gas boiler, specifically including: when the heat and power cogeneration cannot meet the heat load, the gas boiler supplements the heat load, and the formula is: (7) In the formula, is the thermal power generated at time t For the hot spot conversion efficiency of gas boiler, The natural gas power consumed by the gas boiler at time t; Mathematical modeling of fuel cells includes: Fuel cells convert hydrogen energy into electrical energy, the formula is: (8) In the formula, is the output power of the fuel cell at time t, is the hydrogen-to-electricity conversion efficiency at time t, is the hydrogen power consumed by the fuel cell at time t.

[0010] In the preferred scheme, the initial carbon emission allocation consists of two aspects: the carbon emission allocation of traditional units of CHP and gas boilers and the carbon emission allocation of new energy units of photovoltaic power generation and wind power generation. The formula is: (9) In the formula, For a given default carbon emission amount, A carbon emission benchmark quota given for a unit of electricity or heat generation; The carbon emissions of IEM are mainly from CHP units and gas boiler units, so the actual carbon emissions of IEM are: (10) In the formula, is the carbon emission of the CHP unit, is the carbon emission of the gas boiler unit, is the carbon emission coefficient of the CHP unit, is the carbon emission coefficient of the gas boiler unit.

[0011] In the preferred solution, S2 also includes an objective function for establishing an energy microgrid. The objective function Including gas purchase cost , Operation and maintenance costs , Penalties for abandoning wind and solar power , demand response cost and carbon trading costs , the formula is: (11) in, The formula is: (12) In the formula, is the unit price of natural gas, Purchase natural gas power for time t; The formula is: (13) In the formula, is the CHP unit operation and maintenance cost coefficient, is the carbon storage cost coefficient, is the P2G operation and maintenance cost coefficient, is the CCS operation and maintenance cost coefficient, is the operation and maintenance cost coefficient; The formula is: (14) The formula is: (15) 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; The demand response cost is obtained , the formula is: (16) is the movable electric load, To reduce the electrical load, To reduce heat load; and are the compensation unit prices for transferable load and curtailable load, respectively. This is the compensation unit price for reducing the heat load.

[0012] In the preferred solution, when S3 is performed, a balance constraint condition is set, and the formula is: (17) In the formula, 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. and They represent the load after the electricity and heat load demand response at time t, is the initial carbon emission allocation for IEM, is the total carbon emissions of IEM, To purchase the total amount of carbon emission credits, The total amount of carbon emissions sold.

[0013] The preferred solution also includes a power limit for IEM to transmit to the distribution network, and the formula is: (18) In the formula, Purchase power from the distribution grid for IEM, The price at which IEM sells electricity to the distribution network.

[0014] In the preferred solution, constraints are set for hydrogen electrolysis, cogeneration units and gas boilers, specifically: 1) The maximum power and ramp constraints of hydrogen production by electrolysis are: (19) 2) The maximum power and ramp constraints of the cogeneration unit are: (20) 3) The hydrogen blending ratio of the gas turbine should be within 20% to ensure the stable operation of the gas turbine. The constraints are: (twenty one) 4) The maximum power constraints for CCS and P2G are: (twenty three) 5) Fuel cell maximum power and ramp constraints are: (twenty four).

[0015] The preferred solution also includes adding renewable energy generation constraints, energy storage constraints and demand response constraints, specifically: 1) Constraints on renewable energy generation are: (25) In the formula, It is the predicted value of wind turbine or photovoltaic power generation output; 2) In the energy storage constraint, considering the source-load inconsistency problem, electricity storage and heat storage equipment are introduced to construct the mathematical model of hydrogen storage tank. The formula is: (26) In the formula, X represents hydrogen electric and thermal energy storage. If it is hydrogen energy storage, X is , heat and electricity are ; 3) The demand response constraints are: (27) In the formula, is the initial load of the electric load at time t, is the initial heat load at time t, is the upper limit of the movable electrical load, To reduce the upper limit of electrical load, To reduce the upper limit of heat load.

[0016] In the preferred solution, in S2, an objective function and constraint conditions are established for the distribution network model, wherein: The objective function is to make the total cost The lowest, taking into account the power generation cost of the distribution network and the cost of purchasing electricity from the upper grid, the formula is: (28) (29) (30) Where: is the power generation cost of the distribution network, For Node The active power of the micro-turbine is , , They represent the cost coefficient of micro-turbine respectively, A collection of micro gas turbines connected to the distribution network. The cost of purchasing electricity from the main grid for the distribution network, To purchase electricity from the main grid, for Always purchase electricity from the main grid; The constraints include power flow constraints, micro gas turbine constraints, power transmission constraints between distribution network and microgrid, node voltage constraints and power purchase constraints between distribution network and main grid. Specifically: 1) The flow constraint uses the DistFlow model to represent the flow of the distribution network and performs linear processing. The formula is: (31) In the formula, for Time Node The active power between for Time Node Load power, for Time Node Generator active power, for The power injected into the interconnection line of the distribution network by the microgrid at any time. for Time Node Active power, for Time Node The reactive power between for Time Node Load power, for Time Node Generator reactive power, for Reactive power at the node at the moment, They are Time Node Voltage, and are the impedance and admittance of branch ij respectively; 2) The constraint formula of micro gas turbine is: (32) Where: For Node exist The reactive power of the micro-turbine at the moment, , , , , and They represent the lower and upper limits of the active and reactive power and the ramp power of the micro-turbine respectively; 3) In the power transmission constraints between distribution network and microgrid, the tie line power constraint is: (33) In the formula, For IEM The transmission power of the tie line at the time, and are the minimum and maximum transmission powers of the tie line, respectively; 4) The node voltage constraint is: (34) In the formula, For Node exist The voltage amplitude at the moment, and are the lower and upper limits of the node voltage amplitude respectively; 5) The power purchase constraints of the distribution network and the main network are as follows: (35) In the formula, and They are respectively the lower and upper limits of the power purchased from the main grid.

[0017] In the preferred solution, in S3, the coupling between the IEM and the distribution network is specifically as follows: The IEM and the distribution network are connected by tie lines. The tie lines meet the consistency constraints. The consistency constraints 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: (36) The decision of each IEM should be independent and does not involve information interaction with other IEMs. Only the interconnection line information is shared between the distribution network and IEM, solving the problem of coordinated operation of multi-microgrid distribution systems in a completely decentralized manner while maintaining independent decision-making of each subsystem operator.

[0018] In the preferred solution, in S4, an IEM-distribution network distributed optimization scheduling model considering thermal power demand response is constructed. Since each region is coupled with active power for consistency constraints, the regions need to interact with each other in boundary active power. The algorithm model is as follows: (37) (38) (39) (40) Where: Equation (37) represents the overall objective function of the system, which includes the distribution network cost and microgrid costs G represents a multi-microgrid set; Formula (38) represents an inequality constraint; Formula (39) represents an equality constraint; Formula (40) represents the consistency constraint of each sub-region. is the interaction quantity of each area, 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, the solution is performed in parallel to achieve interaction power consistency, and then the consistency constraint is added to the objective function through the Lagrangian multiplier to transform it into an augmented Lagrangian function, whose expression is shown in (41): (41) In the formula, is the Lagrange multiplier of the mth iteration, is the penalty factor for the mth iteration, and then the interaction between each microgrid and distribution network is solved ( ), which provides a basis for updating the consistent variables for the next ADMM iteration.

[0019] In the preferred embodiment, in S5: The distribution network and microgrid are linked by tie lines. All microgrids are connected to the active distribution network through tie lines. There is no connection between microgrids. When performing distributed optimization, the distribution network and microgrid are optimized in their respective areas, and then the tie line power is transmitted. Therefore, the objective function introduces new variables and is converted into an augmented Lagrangian function. Among them, multiple microgrids are connected through distribution networks and tie lines. The exchange variable in the optimization is the active power on the tie line ( ), the global variable update formula is: (42) The dual variable update formula is: (43) Adopting the iterative 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: (44) (45) In the formula, is the original residual, is the dual residual, and the iterative termination condition of the algorithm is ; The method of adaptively adjusting the penalty factor according to the residual improvement is used to improve the calculation speed of the algorithm. The step size change rule is as follows: (46) In the formula, is the original residual, is the dual residual, is the penalty factor for the mth iteration.

[0020] An IEM access distribution network optimization system considering thermal power demand response, comprising: IEM modeling module, used to model IEM based on the principle of thermal power demand response; The distribution network modeling module is used to model the distribution network based on the principle of access to IEM; The microgrid access module is used to connect the distribution network to the microgrid through the interconnection line based on the principle that the output power of the distribution network must be equal to the input power of the microgrid; The dispatch model module is used to build a distributed dispatch model of IEM-distribution network based on demand response; The scheduling solution module is used to iteratively solve the distributed scheduling model using the ADMM algorithm to obtain the optimal thermal power scheduling strategy.

[0021] The present invention provides an IEM access distribution network optimization method and system considering thermal power demand response, modeling the IEM corresponding to the thermal power demand; modeling the distribution network considering the access of the IEM; the distribution network is connected to a microgrid through a tie line; a distributed scheduling model of the IEM-distribution network considering demand response is constructed; an ADMM (Alternating Direction Method of Multipliers) algorithm is used to iteratively solve the distributed scheduling model to obtain an optimal thermal power scheduling strategy to maximize the overall benefit of the system, reduce operating costs, and improve energy utilization efficiency. Through the modeling of the IEM and the distribution network, the thermal power demand of the microgrid and the power balance of the distribution network are ensured. Through the solution of the distributed scheduling model and the ADMM algorithm, the thermal power scheduling strategy is optimized, thereby improving energy utilization efficiency and the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 is a flow chart of the method of the present invention; Figure 2 It is a diagram of the power situation of the tie line between the microgrid and the distribution network of the present invention; Figure 3 It is the electric power optimization dispatch diagram of the system of the present invention; Figure 4 It is the thermal power optimization scheduling diagram of the system of the present invention; Figure 5 is the electric load demand response diagram of the present invention; Figure 6 is the heat load demand corresponding diagram of the present invention; Figure 7 is a diagram of cost convergence of the present invention; Figure 8 It is the original residual comparison diagram of the present invention; Fig. 9 It is a comparison diagram of the dual residuals of the present invention; Fig.10 It is the microgrid load and electricity price diagram of the present invention. DETAILED DESCRIPTION

[0023] Example 1 like Figure 1-10 As shown, an optimization method for IEM access to a distribution network considering thermal power demand response includes the following steps: S1: Modeling IEM based on the principle of thermal power demand response.

[0024] S2: Model the distribution network based on the principle of access to IEM.

[0025] S3: Based on the principle that the output power of the distribution network must be equal to the input power of the microgrid, the distribution network is connected to the microgrid through the interconnection line.

[0026] S4: Construct a distributed dispatch model of IEM-distribution network based on demand response.

[0027] S5: Use the ADMM algorithm to iteratively solve the distributed scheduling model and obtain the optimal thermal power scheduling strategy.

[0028] In this embodiment, P2G is power to gas (P2G), and CCS is carbon capture system (CCS).

[0029] This embodiment first constructs an accurate microgrid model to analyze its thermal power demand and other energy demands, and then establishes a distribution network model to analyze its behavior and performance after connecting to the microgrid. The distribution network is connected to the microgrid through the interconnection line to ensure that the power balance requirements are met when the microgrid is connected to the distribution network. Finally, a distributed scheduling model that considers the thermal power demand response is constructed to optimize the scheduling strategy of thermal power resources, and ADMM is used for iterative solution to obtain the optimal thermal power scheduling strategy. This embodiment maximizes the overall benefits of the system, reduces operating costs, and improves energy utilization efficiency. Through the modeling of IEM and distribution network, the thermal power demand of the microgrid and the power balance of the distribution network are ensured. Through the solution of the distributed scheduling model and ADMM algorithm, the thermal power scheduling strategy can be optimized, thereby improving energy utilization efficiency and the overall performance of the system.

[0030] Table 1 Parameter settings

[0031] In the preferred embodiment, the related equipment of IEM includes: gas turbine, gas boiler, carbon capture power-to-gas device, electrolysis hydrogen production device, fuel cell device and electric thermal energy storage device.

[0032] Carry out corresponding mathematical modeling for each relevant device, and connect them comprehensively to construct the mathematical model of IEM.

[0033] This example uses three IEMs connected to the IEEE33 node distribution network system as an example to verify the effectiveness of the distributed optimization method after adding the carbon capture system and multi-type hydrogen energy applications. Each microgrid includes: gas turbines, gas boilers, wind turbines, photovoltaic equipment, two-stage power-to-gas devices, electrolytic hydrogen production devices, fuel cells, electric thermal energy storage, and considers the electrical load demand response.

[0034] The price of micro-net shopping is as follows Fig.10 As shown, the equipment parameters are shown in Table 1. The three IEMs mentioned above are connected to nodes 10, 20, and 30 of the IEEE33 node distribution network, and micro-turbines are connected to nodes 3, 9, and 16. 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 set to 0. In this embodiment, YAMLIP is used to call the Gurobi solver in the matlabR2018b environment. The specific equipment parameters are shown in Table 1, and CHP is combined heat and power (Cogeneration, Combined Heat and Power, CHP).

[0035] In the preferred solution, mathematical modeling of the gas turbine is performed, specifically including: S11: Construct a CCS mathematical model based on the actual power of the carbon capture device. The actual power of the carbon capture device includes operating power consumption and basic power consumption. The formula is: (1) is the carbon capture power of the CCS device, is the basic power consumption, For operating power consumption, is the electrical power required to capture a unit of carbon dioxide, is the amount of carbon dioxide captured.

[0036] S12: Based on the two-stage power-to-gas device, a P2G mathematical model is constructed; wherein P2G includes two stages: electrolysis to produce hydrogen and hydrogen to produce methane, and the formula is: (2) in, and The amount of H2 generated by electrolysis and the power consumption are and They are methane production power and methanogenesis hydrogen consumption.

[0037] At the same time, the production of methane will also use a certain amount of electrolyzer power, the formula is: (3) , They are the methane production power and the power consumption of the electrolyzer.

[0038] The amount of CO2 required for the methanogenesis process comes from the actual CO2 captured by the carbon capture power plant, and the formula is: (4) (5) is the CO2 power required for the microgrid to produce methane at time t, is the calorific value of natural gas per unit volume; is the CO2 gas density, is the amount of CO2 stored.

[0039] Equation (5) shows that the CO2 required for methanogenesis and the stored CO2 all come from CCS, completing the coupling of CCS and P2G.

[0040] S13: According to the power generation and heat generation, a mathematical model of the cogeneration unit is established, wherein the power generation and heat generation formulas are: (6) In the formula, 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, and are the calorific values ​​of hydrogen and natural gas, respectively; and are the electrical efficiency and thermal efficiency of the gas turbine, and are the electricity and heat generation efficiencies of the gas turbine, respectively.

[0041] In this embodiment, the gas boiler mainly produces heat. When the cogeneration cannot meet the heat load, the gas boiler supplements the heat load.

[0042] In the preferred solution, a mathematical model is performed on the gas boiler, specifically including: when the heat and power cogeneration cannot meet the heat load, the gas boiler supplements the heat load, and the expression is: (7) In the formula, is the thermal power generated at time t For the hot spot conversion efficiency of gas boiler, The natural gas power consumed by the gas boiler at time t.

[0043] Mathematical modeling of fuel cells includes: Fuel cells convert hydrogen energy into electrical energy, the formula is: (8) In the formula, is the output power of the fuel cell at time t, is the hydrogen-to-electricity conversion efficiency at time t, is the hydrogen power consumed by the fuel cell at time t.

[0044] In the preferred scheme, the initial carbon emission allocation consists of two aspects: the carbon emission allocation of traditional units of CHP and gas boilers and the carbon emission allocation of new energy units of photovoltaic power generation and wind power generation, and its expression is as follows: (9) In the formula, For a given default carbon emission amount, A carbon emission benchmark quota given for a unit of electricity generation / heat generation power.

[0045] IEM’s carbon emissions are mainly from CHP units and gas boiler units, and CCS can capture a portion of carbon dioxide. The actual carbon emissions of IEM are: (10) In the formula, is the carbon emission of the CHP unit; is the carbon emission of the gas boiler unit; is the carbon emission coefficient of the CHP unit; is the carbon emission coefficient of the gas boiler unit.

[0046] In the step 2, the access of the IEM is considered and the distribution network is modeled.

[0047] In the preferred solution, step S2 also includes establishing an objective function of the energy microgrid. The objective function Including gas purchase cost , Operation and maintenance costs , demand response costs , Penalties for abandoning wind and solar power and carbon trading costs , the formula is: (11) in, The formula is: (12) In the formula, is the unit price of natural gas, Purchase natural gas power for time t; The formula is: (13) In the formula, is the CHP unit operation and maintenance cost coefficient, is the carbon storage cost coefficient, is the P2G operation and maintenance cost coefficient, is the CCS operation and maintenance cost coefficient, is the operation and maintenance cost coefficient; The formula is: (14) The formula is: (15) In the formula, is the price of purchasing a unit of carbon emission quota, is the price of selling a unit of carbon emission, The carbon allowances purchased and sold respectively.

[0048] The demand response cost is obtained , the formula is: (16) is the movable electric load, To reduce the electrical load; To reduce heat load; and are the compensation unit prices for transferable load and curtailable load, respectively. This is the compensation unit price for reducing the heat load.

[0049] In the preferred solution, a balance constraint condition is set, and the formula is: (17) In the formula, 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. and They represent the load after the electricity and heat load demand response at time t, Allocate initial carbon emissions to IEM; is the total carbon emissions of IEM, To purchase the total amount of carbon emission credits, The total amount of carbon emissions sold.

[0050] Equation (17) is the electric thermal hydrogen carbon balance of IEM.

[0051] The preferred solution also includes a power limit for IEM to transmit to the distribution network, and the formula is: (18) In the formula, Purchase power from the distribution grid for IEM, The price at which IEM sells electricity to the distribution network.

[0052] In the preferred solution, constraints are set for hydrogen electrolysis, cogeneration units and gas boilers, specifically: 1) The maximum power and ramp constraints of hydrogen production by electrolysis are: (19) 2) The maximum power and ramp constraints of the cogeneration unit are: (20) 3) The hydrogen blending ratio of the gas turbine should be within 20% to ensure the stable operation of the gas turbine. The constraints are: (twenty one) 4) CCS, P2G maximum power constraints are: (twenty three) 5) Fuel cell maximum power and ramp constraints are: (twenty four).

[0053] The preferred solution also includes adding renewable energy generation constraints, energy storage constraints and demand response constraints, specifically: 1) Constraints on renewable energy generation are: (25) In the formula, It is the predicted value of wind turbine or photovoltaic power generation output; 2) In the energy storage constraint, considering the source-load inconsistency problem, electricity storage, heat storage and other equipment are introduced, and unified modeling is performed. The mathematical model formula of the hydrogen storage tank is: (26) In the formula, X represents hydrogen electric and thermal energy storage. If it is hydrogen energy storage, X is , and the thermoelectric .

[0054] 3) The demand response constraints are: (27) In the formula, is the initial load of the electric load at time t, is the initial heat load at time t, is the upper limit of the movable electrical load, To reduce the upper limit of electrical load, To reduce the upper limit of heat load.

[0055] In step 2 of this embodiment, the distribution network is modeled.

[0056] In the preferred solution, in step S2, the distribution network is modeled to set the objective function and constraints, where: (1) The objective function is to make the total cost The lowest, considering the power generation cost of the distribution network and the cost of purchasing electricity from the upper grid, the formula is: (28) (29) (30) Where: is the power generation cost of the distribution network, For Node The active power of the micro-turbine is , and are the micro turbine cost coefficients, A collection of micro gas turbines connected to the distribution network. The cost of purchasing electricity from the main grid for the distribution network, To purchase electricity from the main grid, for Purchase electricity from the main grid at all times.

[0057] (2) Constraints include power flow constraints, micro gas turbine constraints, power transmission constraints between distribution network and microgrid, node voltage constraints, and power purchase constraints between distribution network and main grid. Specifically: 1) The flow constraint uses the DistFlow model to represent the flow of the distribution network and performs linear processing. The formula is: (31) In the formula, for Time Node The active power between for Time Node Load power, for Time Node Generator active power, for The power injected into the interconnection line of the distribution network by the microgrid at any time. for Time Node Active power, for Time Node The reactive power between for Time Node Load power, for Time Node Generator reactive power, for Reactive power at the node at the moment, They are Time Node Voltage, and are the impedance and admittance of branch ij respectively.

[0058] 2) The constraint formula of micro gas turbine is: (32) Where: Representation Node exist The reactive power of the micro-turbine at the moment, , , , , and They respectively represent the lower and upper limits of the active, reactive and climbing power of the micro turbine.

[0059] 3) In the power transmission constraints between distribution network and microgrid, the tie line power constraint is: (33) In the formula, Indicates that the distribution network IEM The transmission power of the tie line at the time, , are the minimum and maximum transmission powers of the tie line respectively.

[0060] 4) The node voltage constraint is: (34) In the formula, For Node exist The voltage amplitude at the moment, , are the lower and upper limits of the node voltage amplitude respectively.

[0061] 5) The power purchase constraints of the distribution network and the main network are as follows: (35) In the formula, and They are respectively the lower and upper limits of the power purchased from the main grid.

[0062] In step 3 of this embodiment, the IEM and the distribution network are coupled.

[0063] In the preferred solution, in step S3, the coupling between the IEM and the distribution network is specifically 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: (36) The decision of each IEM should be independent and does not involve information exchange with other IEMs. Only the interconnection line information is shared between the distribution network and IEM to ensure the consistency of operation, solving the problem of coordinated operation of multi-microgrid distribution systems in a completely decentralized manner while maintaining independent decision-making of each subsystem operator.

[0064] In the preferred solution, in step S4, an IEM-distribution network distributed optimization scheduling model considering thermal power demand response is constructed. Since the regions are coupled with consistency constraints through active power, the regions need to interact with each other in boundary active power. The algorithm model is as follows: (37) (38) (39) (40) Where: Equation (37) represents the overall objective function of the system, which includes the distribution network cost and microgrid costs G represents a multi-microgrid set; Formula (38) represents an inequality constraint; Formula (39) represents an equality constraint; Formula (40) represents the consistency constraint of each sub-region. is the interaction quantity of each area, that is, the active power on the connection line ( ), is a global variable that is updated with the iteration.

[0065] For the optimization model under the distributed framework, the solution is performed in parallel to achieve interaction power consistency, and then the consistency constraint is added to the objective function through the Lagrangian multiplier to transform it into an augmented Lagrangian function, whose expression is shown in (41): (41) In the formula, is the Lagrange multiplier of the mth iteration, is the penalty factor for the mth iteration, and then the interaction between each microgrid and distribution network is solved ( ), which provides a basis for updating the consistent variables for the next ADMM iteration.

[0066] In the preferred embodiment, step S5 includes the following steps: S501: Initialize the iteration operator and tie line variable processing, the initial value of the iteration number m=0.

[0067] S502: Extract the reference value of the tie line coupling variable in each area, solve the sub-area scheduling model in parallel, obtain the tie line coupling variable result of the mth iteration, and calculate the original residual and dual residual of the mth iteration.

[0068] S503: Check whether the calculation results of each region meet the convergence criteria. If converged, the iteration ends and the scheduling result is output; otherwise, the calculation result of the mth iteration is used as the reference value of the m+1th iteration, the global variables and dual variables are updated, the number of iterations m=m+1 is set, and return to step S502.

[0069] S504: When the preset number of iterations is reached, an optimization management strategy is obtained.

[0070] It can be seen from the above process that the model solving, iterative operator calculation and residual calculation of each sub-area are all carried out in the local area, and only interact with the adjacent areas when extracting the reference value of the tie line variable and judging the residual convergence. There is little interactive information and low communication requirements, which protects the information privacy of each area and realizes distributed optimization with internal autonomy within the region and decentralized coordination between regions.

[0071] In the preferred solution, in step S5, the distribution network and the microgrid are linked by interconnection lines, all microgrids are connected to the active distribution network through interconnection lines, and there is no connection between the microgrids. When performing distributed optimization, the distribution network and the microgrid are optimized in their respective areas, and then the interconnection line power is transmitted. Therefore, the objective function introduces new variables and is converted into an augmented Lagrangian function.

[0072] Among them, multiple microgrids are connected through distribution networks and tie lines. The exchange variable in the optimization is the active power on the tie line ( ), the global variable update formula is: (42) The dual variable update formula is: (43) Adopting the iterative 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: (44) (45) In the formula, is the original residual, is the dual residual, and the iterative termination condition of the algorithm is .

[0073] The method of adaptively adjusting the penalty factor according to the residual improvement is used to improve the calculation speed of the algorithm. The step size change rule is as follows: (46) In the formula, is the original residual, is the dual residual, is the penalty factor for the mth iteration.

[0074] Combined with specific examples, the distributed optimization scheduling analysis of the system considering the thermal power demand is carried out 1. User demand response analysis Depend on Figure 5-6It can be found that during the 16:00-20:00 period, since the time-of-use electricity price is the highest at this time, the user's electricity consumption is reduced; in the low electricity price stage (22:00-05:00), the user's electricity load will increase. At this time, the user will shift some energy demand from the high electricity price period to the low electricity price period, thereby realizing the redistribution of peak and valley loads and making the load curve smoother. For example, at the peak electricity consumption time of 10:00, the user's electricity demand becomes 87.3% of the original; at the low electricity consumption time of 01:00, the user's electricity load increases by 12.5%. The changes before and after the cold and hot load demand response are similar. When the electricity price is high, the user will convert part of the electricity demand into heat load; when the electricity price is low, the user will convert the heat demand into electricity load. The convertible load in this embodiment only considers electricity-heat, and the hydrogen load does not participate in the demand response regulation.

[0075] 2. Scheduling Analysis Combined with the time-of-use electricity price, it can be seen that the electricity generated by the new energy units during the low-power consumption period can meet the user's electricity demand at most times, and when the electric boiler is running at full load, the P2G equipment will convert the excess electricity into gas energy, solving the problem of on-site consumption of excess electricity from the new energy units; during this period, the battery will be charged, and the electric boiler will consume more electricity to store energy in the heat storage tank; as can be seen from the figure, at 22:00, when the new energy units are generating insufficient electricity, it is most economical to purchase electricity from the power grid because the electricity price is the lowest at this time.

[0076] 3. Convergence Analysis of Adaptive Step Size ADMM Algorithm The above system is solved by centralized operation and distributed operation respectively, and the convergence accuracy is set to Table 2 shows the system cost, error, solution time, and number of iterations under centralized operation, traditional ADMM algorithm, and accelerated ADMM algorithm.

[0077] Table 2 Comparison of different algorithms

[0078] Fig.10 The microgrid load and electricity price diagram, combined with Table 2 and Figure 7 The convergence of system costs under the above three operating conditions is demonstrated. It can be concluded that the total costs obtained by the ADMM algorithm and the F-ADMM algorithm are basically the same, with errors within 0.1%. However, centralized operation requires unified calculation of all data in the system, which cannot guarantee the privacy, independence and security between microgrids, and is not conducive to independent decision-making by each microgrid operator.

[0079] Through distributed operation, microgrids can complete global optimization operation by exchanging only a small amount of boundary information, namely the power of the interconnection lines, which reduces the communication burden that takes up a lot of time in actual operation and protects information privacy.

[0080] As the scale of subsystems increases, since different subsystems correspond to different operators in a multi-microgrid system, centralized operation is difficult to coordinate various microgrids, so the advantages of distributed operation are more prominent. Although the calculation time of the two distributed operation schemes is higher than that of centralized operation, the advantages of distributed operation do not lie in this. Compared with centralized operation, distributed operation realizes decision-making autonomy and data privacy, and the sub-microgrid can upload relevant data to the distribution network after local calculation, and the calculation of each sub-microgrid can be performed synchronously. Moreover, compared with the traditional ADMM algorithm, the F-ADMM algorithm used in this article optimizes the number of iterations from 68 to 24 times through the adaptive step size method, and the calculation time is greatly reduced, which improves the calculation efficiency. Figure 8-9 shown.

[0081] Example 2 In combination with Example 1, an IEM access distribution network optimization system considering thermal power demand response includes: The IEM modeling module is used to model the IEM based on the principle of thermal power demand response.

[0082] The distribution network modeling module is used to model the distribution network based on the principle of access to IEM.

[0083] The microgrid access module is used to connect the distribution network to the microgrid through the interconnection line based on the principle that the output power of the distribution network must be equal to the input power of the microgrid.

[0084] The dispatch model module is used to build a distributed dispatch model of IEM-distribution network based on demand response.

[0085] The scheduling solution module is used to iteratively solve the distributed scheduling model using the ADMM algorithm to obtain the optimal thermal power scheduling strategy.

[0086] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. An optimization method for IEM access to distribution network considering thermal power demand response, characterized in that: The following steps are involved: S1: Modeling IEM based on the principle of thermal power demand response; S2: Model the distribution network based on the principle of access to IEM; S3: Based on the principle that the output power of the distribution network must be equal to the input power of the microgrid, the distribution network is connected to the microgrid through the tie line; S4: Construct a distributed dispatch model of IEM-distribution network based on demand response; S5: Use the ADMM algorithm to iteratively solve the distributed scheduling model and obtain the optimal thermal power scheduling strategy.

2. The IEM access distribution network optimization method considering thermal power demand response according to claim 1 is characterized in that: The related equipment of the IEM includes: gas turbine, gas boiler, carbon capture power-to-gas device, electrolysis hydrogen production device, fuel cell device and electric thermal energy storage device; Carry out corresponding mathematical modeling for each relevant equipment, and connect them comprehensively to construct the mathematical model of IEM.

3. The IEM access distribution network optimization method considering thermal power demand response according to claim 2 is characterized in that: Mathematical modeling of gas turbines, including: S11: Construct a CCS mathematical model based on the actual power of the carbon capture device. The actual power of the carbon capture device includes operating power consumption and basic power consumption. The formula is: (1) is the carbon capture power of the CCS device, is the basic power consumption, For operating power consumption, is the electrical power required to capture a unit of carbon dioxide, The amount of carbon dioxide captured; S12: Based on the two-stage power-to-gas device, a P2G mathematical model is constructed; wherein P2G includes two stages: electrolysis to produce hydrogen and hydrogen to produce methane, and the formula is: (2) in, and The amount of H2 generated by electrolysis and the power consumption are and They are methane production power and methanogenic hydrogen consumption; At the same time, the production of methane will also use a certain amount of electrolyzer power, the formula is: (3) and are the methane production power and the power consumption of the electrolyzer, respectively; The amount of CO2 required for the methanogenesis process comes from the actual CO2 captured by the carbon capture power plant, and the formula is: (4) (5) is the CO2 power required for the microgrid to produce methane at time t, is the calorific value of natural gas per unit volume; is the CO2 gas density, is the CO2 storage capacity; S13: According to the power generation and heat generation, a mathematical model of the cogeneration unit is established, wherein the power generation and heat generation formulas are: (6) In the formula, 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, and are the calorific values ​​of hydrogen and natural gas, and are the electrical efficiency and thermal efficiency of the gas turbine, and are the electricity and heat generation efficiencies of the gas turbine, respectively.

4. The IEM access distribution network optimization method considering thermal power demand response according to claim 2 is characterized in that: Mathematical modeling of gas boilers includes: when the combined heat and power generation cannot meet the heat load, the gas boiler supplements the heat load, the formula is: (7) In the formula, is the thermal power generated at time t For the hot spot conversion efficiency of gas boiler, The natural gas power consumed by the gas boiler at time t; Mathematical modeling of fuel cells includes: Fuel cells convert hydrogen energy into electrical energy, the formula is: (8) In the formula, is the output power of the fuel cell at time t, is the hydrogen-to-electricity conversion efficiency at time t, is the hydrogen power consumed by the fuel cell at time t.

5. The method for optimizing IEM access to distribution network considering thermal power demand response according to claim 2, characterized in that: The initial carbon emission allocation consists of two aspects: the carbon emission allocation of traditional units of CHP and gas boilers and the carbon emission allocation of new energy units of photovoltaic power generation and wind power generation. The formula is: (9) In the formula, For a given default carbon emission amount, A carbon emission benchmark quota given for a unit of electricity or heat generation; The carbon emissions of IEM are mainly from CHP units and gas boiler units, so the actual carbon emissions of IEM are: (10) In the formula, is the carbon emission of the CHP unit, is the carbon emission of the gas boiler unit, is the carbon emission coefficient of the CHP unit, is the carbon emission coefficient of the gas boiler unit.

6. The IEM access distribution network optimization method considering thermal power demand response according to claim 2 is characterized in that: S2 also includes the objective function of establishing an energy microgrid. Including gas purchase cost , Operation and maintenance costs , Penalties for abandoning wind and solar power , demand response cost and carbon trading costs , the formula is: (11) in, The formula is: (12) In the formula, is the natural gas unit price, Purchase natural gas power for time t; The formula is: (13) In the formula, is the CHP unit operation and maintenance cost coefficient, is the carbon storage cost coefficient, is the P2G operation and maintenance cost coefficient, is the CCS operation and maintenance cost coefficient, is the operation and maintenance cost coefficient; The formula is: (14) The formula is: (15) 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; The demand response cost is obtained , the formula is: (16) is the movable electric load, To reduce the electrical load, To reduce heat load; and are the compensation unit prices for transferable load and curtailable load, respectively. This is the compensation unit price for reducing the heat load.

7. The IEM access distribution network optimization method considering thermal power demand response according to claim 2 is characterized in that: When S3 is performed, the balance constraint condition is set, and the formula is: (17) In the formula, 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. and They represent the load after the electricity and heat load demand response at time t, is the initial carbon emission allocation for IEM, is the total carbon emissions of IEM, To purchase the total amount of carbon emission credits, The total amount of carbon emissions sold.

8. The method for optimizing IEM access to distribution network considering thermal power demand response according to claim 7, characterized in that: It also includes the power limit of IEM to the distribution network, the formula is: （18） In the formula, Purchase power from the distribution grid for IEM, The price at which IEM sells electricity to the distribution network.

9. The method for optimizing IEM access to distribution network considering thermal power demand response according to claim 2, characterized in that: Constraints are set for electrolysis hydrogen production, cogeneration units and gas boilers, specifically: 1) The maximum power and ramp constraints of hydrogen production by electrolysis are: (19) 2) The maximum power and ramp constraints of the cogeneration unit are: (20) 3) The hydrogen blending ratio of the gas turbine should be within 20% to ensure the stable operation of the gas turbine. The constraints are: (21) 4) The maximum power constraints for CCS and P2G are: (23) 5) Fuel cell maximum power and ramp constraints are: (24)。 10. The IEM access distribution network optimization method considering thermal power demand response according to claim 9, characterized in that: It also includes adding renewable energy generation constraints, energy storage constraints and demand response constraints, specifically: 1) Constraints on renewable energy generation are: (25) In the formula, It is the predicted value of wind turbine or photovoltaic power generation output; 2) In the energy storage constraint, considering the source-load inconsistency problem, electricity storage and heat storage equipment are introduced to construct the mathematical model of hydrogen storage tank. The formula is: (26) In the formula, X represents hydrogen electric and thermal energy storage. If it is hydrogen energy storage, X is , heat and electricity are ; 3) The demand response constraints are: （27） In the formula, is the initial load of the electric load at time t, is the initial heat load at time t, is the upper limit of the movable electrical load, To reduce the upper limit of electrical load, To reduce the upper limit of heat load.

11. The IEM access distribution network optimization method considering thermal power demand response according to claim 1, characterized in that: In S2, an objective function and constraint conditions are established for the distribution network model, where: The objective function is to make the total cost The lowest, taking into account the power generation cost of the distribution network and the cost of purchasing electricity from the upper grid, the formula is: （28） （29） （30） Where: is the power generation cost of the distribution network, For Node The active power of the micro-turbine is , , They represent the cost coefficient of micro-turbine respectively, A collection of micro gas turbines connected to the distribution network. The cost of purchasing electricity from the main grid for the distribution network, To purchase electricity from the main grid, for Always purchase power from the main grid; The constraints include power flow constraints, micro gas turbine constraints, power transmission constraints between distribution network and microgrid, node voltage constraints and power purchase constraints between distribution network and main grid. Specifically: 1) The flow constraint uses the DistFlow model to represent the flow of the distribution network and performs linear processing. The formula is: （31） In the formula, for Time Node The active power between for Time Node Load power, for Time Node Generator active power, for The power injected into the interconnection line of the distribution network by the microgrid at any time. for Time Node Active power, for Time Node The reactive power between for Time Node Load power, for Time Node Generator reactive power, for Reactive power at the node at the moment, They are Time Node Voltage, and are the impedance and admittance of branch ij respectively; 2) The constraint formula of micro gas turbine is: （32） Where: For Node exist The reactive power of the micro-turbine at the moment, , , , , and They represent the lower and upper limits of the active and reactive power and the ramp power of the micro-turbine respectively; 3) In the power transmission constraints between distribution network and microgrid, the tie line power constraint is: （33） In the formula, For the connection between the distribution network and IEM The transmission power of the tie line at the time, and are the minimum and maximum transmission powers of the tie line, respectively; 4) The node voltage constraint is: （34） In the formula, For Node exist The voltage amplitude at the moment, and are the lower and upper limits of the node voltage amplitude respectively; 5) The power purchase constraints of the distribution network and the main network are as follows: （35） In the formula, and They are respectively the lower and upper limits of the power purchased from the main grid.

12. The IEM access distribution network optimization method considering thermal power demand response according to claim 1, characterized in that: In S3, the coupling between the IEM and the distribution network is specifically as follows: The IEM and the distribution network are connected by tie lines. The tie lines meet the consistency constraints. The consistency constraints 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: （36） The decision of each IEM should be independent and does not involve information interaction with other IEMs. Only the interconnection line information is shared between the distribution network and IEM, solving the problem of coordinated operation of multi-microgrid distribution systems in a completely decentralized manner while maintaining independent decision-making of each subsystem operator.

13. The method for optimizing IEM access to distribution network considering thermal power demand response according to claim 1, characterized in that: In S4, an IEM-distribution network distributed optimization scheduling model considering thermal power demand response is constructed. Since the regions are coupled with active power for consistency constraints, the regions need to interact with each other in terms of boundary active power. The algorithm model is as follows: （37） （38） （39） （40） Where: Equation (37) represents the overall objective function of the system, which includes the distribution network cost and microgrid costs G represents a multi-microgrid set; Formula (38) represents an inequality constraint; Formula (39) represents an equality constraint; Formula (40) represents the consistency constraint of each sub-region. is the interaction quantity of each area, 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, the solution is performed in parallel to achieve interaction power consistency, and then the consistency constraint is added to the objective function through the Lagrangian multiplier to transform it into an augmented Lagrangian function, whose expression is shown in (41): （41） In the formula, is the Lagrange multiplier of the mth iteration, is the penalty factor for the mth iteration, and then the interaction between each microgrid and distribution network is solved ( ), which provides a basis for updating the consistent variables for the next ADMM iteration.

14. The method for optimizing IEM access to distribution network considering thermal power demand response according to claim 1, characterized in that: The S5 includes the following steps: S501: Initialize the iteration operator and tie line variable processing, the initial value of the iteration number m=0; S502: extracting the reference value of the tie line coupling variable in each area, solving the sub-area scheduling model in parallel, obtaining the tie line coupling variable result of the mth iteration, and calculating the original residual and dual residual of the mth iteration; S503: Check whether the calculation results of each region meet the convergence criterion. If converged, the iteration ends and the scheduling result is output; otherwise, the calculation result of the mth iteration is used as the reference value of the m+1th iteration, the global variable and the dual variable are updated, the number of iterations m=m+1 is set, and the process returns to step S502; S504: When the preset number of iterations is reached, an optimization management strategy is obtained.

15. The method for optimizing IEM access to distribution network considering thermal power demand response according to claim 14, characterized in that: In S5: The distribution network and microgrid are linked by tie lines. All microgrids are connected to the active distribution network through tie lines. There is no connection between microgrids. When performing distributed optimization, the distribution network and microgrid are optimized in their respective areas, and then the tie line power is transmitted. Therefore, the objective function introduces new variables and is converted into an augmented Lagrangian function. Among them, multiple microgrids are connected through distribution networks and tie lines. The exchange variable in the optimization is the active power on the tie line ( ), the global variable update formula is: （42） The dual variable update formula is: （43） Adopting the iterative 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: （44） （45） In the formula, is the original residual, is the dual residual, and the iterative termination condition of the algorithm is ; The method of adaptively adjusting the penalty factor according to the residual improvement is used to improve the calculation speed of the algorithm. The step size change rule is as follows: （46） In the formula, is the original residual, is the dual residual, is the penalty factor for the mth iteration.

16. An IEM access distribution network optimization system considering thermal power demand response, characterized in that: include: IEM modeling module, used to model IEM based on the principle of thermal power demand response; The distribution network modeling module is used to model the distribution network based on the principle of access to IEM; The microgrid access module is used to connect the distribution network to the microgrid through the interconnection line based on the principle that the output power of the distribution network must be equal to the input power of the microgrid; The dispatch model module is used to build a distributed dispatch model of IEM-distribution network based on demand response; The scheduling solution module is used to iteratively solve the distributed scheduling model using the ADMM algorithm to obtain the optimal thermal power scheduling strategy.

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