Distributed energy multi-microgrid optimization control method for electric energy transaction

Abstract

The invention discloses a distributed energy multi-microgrid optimization control method for electric energy transaction. The distributed energy multi-microgrid optimization control method for electric energy transaction comprises the following steps: constructing a distributed energy multi-microgrid optimization control constraint model for electric energy transaction; constructing a distributed energy multi-microgrid optimization control two-stage model oriented to electric energy transaction; and solving the distributed energy multi-microgrid optimization control constraint model facing the electric energy transaction, the distributed energy multi-microgrid optimization control two-stage model facing the electric energy transaction and a multi-microgrid cooperation cost minimization and income distribution scheme by using an ADMM algorithm. According to the method, the problem of distributed energy consumption and the problem of Nash cooperation benefit distribution in the prior art are solved, the overall benefit maximization of the alliance is realized, and each micro-grid can reduce and resist the operation cost increased due to uncertainty through a cooperation mode.

Description

Technical Field

[0001] The present invention relates to a distributed energy multi-microgrid optimization control method, and more particularly to a distributed energy multi-microgrid optimization control method for electric energy trading. Background Art

[0002] Distributed power sources have the characteristics of environmental protection and economy. They can meet the power supply needs of nearby areas and can also be connected to the grid to relieve the pressure of power supply and use. As an important hub connecting distributed power sources and large power grids, microgrids are built in suitable areas in combination with the characteristics of my country's rich wind and solar resources. It is of great significance to deeply develop renewable clean energy and solve the problems of distributed power grid connection and local consumption of clean energy. Microgrids can control power generation units in different regions, improve the flexibility and accessibility of distributed energy. As microgrids continue to develop towards scale, complexity and intelligence, they pose new challenges to their own economic and stable operation. Therefore, it is urgent to propose stable, efficient and economical power dispatching methods and strategies to maximize economic benefits while ensuring the coordinated and stable operation of multiple microgrids. The research on microgrid operation methods mainly aims at the economy of microgrid system operation and the improvement of clean energy consumption level. Considering the output characteristics and types of distributed power sources in microgrids, an optimization model that meets the operation requirements is established and solved according to supply and demand conditions. Reasonable operation methods can significantly improve the operating efficiency of microgrids, facilitate the maximum on-site consumption of clean energy, reduce wind and solar power abandonment, and ensure the efficient use of renewable energy.

[0003] Chinese and foreign scholars have studied the output model of distributed power sources in microgrids, the island operation method of microgrids, and the grid-connected operation method of microgrids from multiple aspects such as economy and environmental protection, and have achieved certain results and conclusions. However, there are few studies on the operation optimization of multiple microgrids in my country, and the optimization effect needs to be improved. It is necessary to analyze the distribution characteristics, types and output characteristics of distributed power sources in multiple microgrids, comprehensively consider factors such as grid interaction, equipment operation constraints, coordination and cooperation capabilities, grid output differences, power trading mechanisms, and carbon trading mechanisms, and optimize the electricity sales cost, gas purchase cost, carbon trading cost, and energy storage use cost of microgrids according to different scenarios to ensure the efficiency and economy of microgrid operation. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a distributed energy multi-microgrid optimization control method for electric energy trading that can reduce operating costs caused by uncertainty through cooperation in order to overcome the shortcomings of the prior art.

[0005] The technical solution adopted by the present invention is: a distributed energy multi-microgrid optimization control method for electric energy trading, comprising the following steps:

[0006] 1) According to the constraints of gas turbine electric heat output, gas boiler heating power, absorption chiller cooling power, electric chiller cooling power, electric energy storage charge state and charging and discharging power, thermal energy storage real-time heat storage and charging and discharging power, multi-microgrid electric and thermal energy interconnection line interactive power constraints, new energy unit wind and solar curtailment constraints, microgrid and external grid interaction constraints, power-to-gas-carbon capture coordinated operation constraints, and step-by-step carbon trading mechanism constraints, a distributed energy multi-microgrid optimization control constraint model for electric energy trading is constructed;

[0007] 2) A two-stage model for distributed energy multi-microgrid optimization control for power trading is constructed based on the cost of electricity purchase and sales, gas purchase cost, carbon trading cost, energy storage use cost, and penalty for wind and solar power abandonment;

[0008] 3) Use the ADMM algorithm to solve the distributed energy multi-microgrid optimization control constraint model for electricity trading and the two-stage model of distributed energy multi-microgrid optimization control for electricity trading, as well as the multi-microgrid cooperation cost minimization and profit distribution plan.

[0009] The invention discloses a distributed energy multi-microgrid optimization control method for electric energy trading, which solves the existing problems of distributed energy consumption and Nash cooperation benefit distribution in the prior art, and provides a benefit distribution model. The invention takes into account the coordinated operation mechanism of energy storage equipment of distributed power sources, power networks and thermal energy networks, and establishes an electric-heat joint scheduling model. The electricity sales cost, carbon trading cost and energy storage gas purchase cost are optimized in stages, and the interactive relationship between multiple different interest entities is considered through Nash bargaining game, so as to maximize the overall benefit of the alliance and complete the benefit distribution among the microgrids. Finally, the optimal solution of each microgrid is solved by the ADMM algorithm; the total cost of the distributed robust optimization adopted is between random optimization and robust optimization, taking into account the economy of random optimization and the conservatism of robust optimization. The cost of the microgrid after cooperation is lower than that of the non-cooperative mode, and each microgrid can reduce the operating cost due to uncertainty through cooperation. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a flow chart of a distributed energy multi-microgrid optimization control method for electric energy trading of the present invention;

[0011] Figure 2 It is a schematic diagram of the electric energy transaction between microgrids of the present invention;

[0012] Figure 3 is a schematic diagram of heat energy trading between microgrids of the present invention;

[0013] Figure 4 It is a schematic diagram of the transaction electricity price between microgrids of the present invention;

[0014] Figure 5 It is a schematic diagram of the heat price transaction between microgrids of the present invention. DETAILED DESCRIPTION

[0015] The following is a detailed description of a distributed energy multi-microgrid optimization control method for electric energy trading of the present invention in conjunction with the embodiments and drawings.

[0016] As shown in Section 1, a distributed energy multi-microgrid optimization control method for electric energy trading of the present invention comprises the following steps:

[0017] 1) According to the constraints of gas turbine electric heat output, gas boiler heating power, absorption chiller cooling power, electric chiller cooling power, electric energy storage charge state and charging and discharging power, thermal energy storage real-time heat storage and charging and discharging power, multi-microgrid electric and thermal energy interconnection line interactive power, new energy unit wind and solar curtailment constraints, microgrid and external grid interaction constraints, power to gas-carbon capture (P2G-CCS) collaborative operation constraints, and step-by-step carbon trading mechanism constraints, a distributed energy multi-microgrid optimization control constraint model for electric energy trading is constructed; specifically including:

[0018] (1) Gas turbine model

[0019] The gas turbine generates electricity and heat by burning natural gas. The gas turbine model is composed of the gas turbine's electrical and thermal output and constraints as follows:

[0020]

[0021]

[0022] In the formula, are respectively the power generation and heating power of the gas turbine at time t; are the power generation efficiency and heating efficiency of the gas turbine respectively; H LHV It is the low calorific value of natural gas; is the gas consumption of the gas turbine at time t; The upper limit of gas turbine power generation capacity;

[0023] (2) Gas boiler model

[0024] Gas boilers burn natural gas to generate heat energy. The gas boiler model is composed of heating power and constraints as follows:

[0025]

[0026] In the formula, is the heating power of the gas boiler at time t; is the heating efficiency of the gas boiler; is the gas consumption of the gas boiler at time t; It is the upper limit of heating power of gas boiler;

[0027] (3) Absorption Refrigeration Machine Model

[0028] The absorption chiller converts heat energy into cold energy. The absorption chiller model is composed of the cooling power and constraints as follows:

[0029]

[0030] In the formula, is the cooling power of the absorption chiller at time t; is the cooling efficiency of the absorption chiller; is the heat consumption of the absorption refrigerator at time t; The upper limit of cooling capacity of absorption chiller;

[0031] (4) Electric Refrigerator Model

[0032] The electric refrigerator uses electric energy for refrigeration. The electric refrigerator model is composed of the cooling power and constraints as follows:

[0033]

[0034] In the formula, is the cooling power of the electric refrigerator at time t; is the cooling efficiency of the electric chiller; is the power consumption of the electric refrigerator at time t; It is the upper limit of cooling power produced by electric refrigerator.

[0035] (5) Electric energy storage model

[0036] Electric energy storage can achieve efficient utilization of energy storage resources. The electric energy storage model is composed of the state of charge of the electric energy storage and the charging and discharging power constraints as follows:

[0037]

[0038] μ ech +μ edis ≤1 (16)

[0039] In the formula, is the storage capacity of the energy storage at time t; They are the charging efficiency and discharging efficiency of electric energy storage respectively;

[0040] are the charging power and discharging power of the energy storage at time t respectively; μ ech , μ edis They are the status bits for charging and discharging of the electric energy storage respectively; are the upper and lower limits of the electric energy storage capacity respectively; They are the upper limits of the charging power and discharging power of the energy storage respectively.

[0041] (6) Thermal energy storage model

[0042] Thermal energy storage can also achieve efficient utilization of energy storage resources. The thermal energy storage model is composed of the real-time heat storage capacity and the charging and discharging heat power constraints of thermal energy storage as follows:

[0043]

[0044] μ hch +μ hdis ≤1 (23)

[0045] In the formula, is the heat storage capacity of the thermal energy storage at time t, t = 0 ~ 24h; are the charging efficiency and releasing efficiency of thermal energy storage respectively; are the charging power and releasing power of the thermal energy storage at time t respectively; μ hch , μ hdis They are the state positions of thermal energy storage charging and releasing heat respectively; are the upper and lower limits of the thermal energy storage capacity, respectively; are the upper limits of the charging power and the discharging power of the thermal energy storage, respectively;

[0046] (7) Interaction model between microgrid and external grid

[0047] The microgrid interacts with the external grid for electric energy. The interaction model between the microgrid and the external grid is composed of the interaction model and the power sales and purchase constraints as follows:

[0048]

[0049] μ buy +μ sell ≤1 (26)

[0050] In the formula, are the amount of electricity purchased and sold by the microgrid to the external grid at time t; μ buy , μ sell They are the status bits of the microgrid purchasing and selling electricity to the external power grid; is the maximum power of the external power grid;

[0051] (8) Power-to-gas-carbon capture synergistic operation model

[0052] The power-to-gas and carbon capture are operated in synergy, and the CO2 captured by carbon capture is provided to the power-to-gas device as high-quality raw materials. The power-to-gas is used to absorb the abandoned wind and solar power to generate natural gas for combined heat and power (CHP) and gas. Power-to-gas-carbon capture can not only reduce the storage cost after capturing CO2, but also reduce the gas purchase cost of combined heat and power (CHP) and gas (GB). The power-to-gas-carbon capture synergistic operation model is composed of the power-to-gas consumption, carbon capture consumption and constraints as follows:

[0053]

[0054]

[0055] In the formula, is the volume of methane generated by power-to-gas conversion at time t; is the power consumed by the power-to-gas conversion at time t; q CO 2t is the amount of CO2 required for power-to-gas operation at time t, i.e., the amount of CO2 captured by carbon capture; is the electricity consumed by carbon capture at time t; is the actual carbon emission of combined heat and power after carbon capture at time t; η gas The efficiency of generating methane by consuming electrical power for power-to-gas conversion; η CO2 The efficiency of generating CO2 required for methane by consuming electrical power for power-to-gas conversion; η CCS The efficiency coefficient of capturing CO2 by consuming electrical power for carbon capture; η CHP The carbon dioxide emission coefficient for natural gas consumption in combined heat and power generation; are the upper limits of electrical power consumption for power-to-gas and carbon capture, respectively;

[0056] (9) Step-by-step carbon trading mechanism model

[0057] In order to further limit carbon emissions, a tiered carbon trading mechanism is adopted to divide the carbon price in different intervals according to the difference between the actual carbon emissions of the microgrid and the allocated carbon emission quota. The tiered carbon trading mechanism model is as follows:

[0058]

[0059] In the formula, E a is the actual carbon emissions of the microgrid; E c Free carbon quota for microgrids; CO2is the carbon emission rights trading amount of the microgrid; δ is the free carbon quota coefficient per unit power; a and b are the carbon emission characteristic coefficients; C CO2 is the step-by-step carbon trading cost; λ is the carbon trading base price; l is the length of the carbon emission interval; α is the price growth rate.

[0060] (10) Multi-microgrid interconnection model

[0061] Multiple microgrids are interconnected through tie lines to transmit electric energy and thermal energy. The multi-microgrid interconnection model is composed of the interactive power constraints of the electric energy and thermal energy tie lines as follows:

[0062]

[0063] In the formula, is the amount of electric power that the i-th microgrid expects to interact with the interconnected j-th microgrid; is the amount of thermal power that the i-th microgrid expects to interact with the interconnected j-th microgrid; are the transmission capacities of the electric energy and thermal energy interconnection lines between the i-th microgrid and the j-th microgrid respectively.

[0064] (11) New energy unit model

[0065] When wind power and photovoltaic power provide power to microgrids, it is inevitable that wind and photovoltaic power will be abandoned. The new energy unit model is composed of the unit power relationship and constraint conditions as follows:

[0066]

[0067]

[0068] In the formula, Predict the power of renewable energy at time t; is the actual output value of the renewable energy at time t; The power abandoned by the renewable energy at time t.

[0069] 2) A two-stage model for distributed energy multi-microgrid optimization control for power trading is constructed based on the cost of electricity purchase and sales, gas purchase cost, carbon trading cost, energy storage use cost, and penalty for wind and solar power abandonment;

[0070] The two-stage model of distributed energy multi-microgrid optimization control for electric energy trading is composed of the first-stage objective function and the second-stage objective function, which are as follows:

[0071] (1) Objective function of the first stage

[0072] The objective function of the first stage is to minimize the cost of electricity purchase and sale, and decide the electricity purchase and sale plan of the microgrid to the external power grid, as follows:

[0073]

[0074] In the formula, are the purchase and sale prices of electricity at time t respectively; are the amount of electricity purchased and sold by the microgrid to the external power grid at time t;

[0075] (2) Second stage objective function

[0076] The second-stage objective function of the two-stage distributed blue-robust optimization of the microgrid is composed of gas purchase cost, carbon trading cost, energy storage use cost and wind and solar power abandonment penalty. Considering the system adjustment cost and the minimum abandonment of solar power, the probability of the worst scenario, the charging and discharging plan of the energy storage equipment and the unit scheduling plan, the specific model is as follows:

[0077]

[0078] In the formula, is the gas consumption of the gas turbine at time t; is the gas consumption of the gas boiler at time t; is the volume of methane generated by power-to-gas conversion at time t; are respectively the power generation and heating power of the gas turbine at time t; is the heating power of the gas boiler at time t; is the cooling power of the absorption chiller at time t; is the cooling power of the electric refrigerator at time t; are the charging power and discharging power of the energy storage at time t respectively; are the charging power and releasing power of the thermal energy storage at time t respectively; are the amount of electricity purchased and sold by the microgrid to the external power grid at time t; is the electricity consumed by carbon capture at time t; is the actual output value of the renewable energy at time t; is the power abandoned by the new energy at time t; pro is the probability of the worst scenario; Ω is the uncertainty set of scenario probabilities; is the amount of electricity consumed by power-to-gas conversion at time t; is the heat consumption of the absorption refrigerator at time t; is the power consumption at time t; is the heat loss at time t; is the heat consumption of the absorption refrigerator at time t.

[0079] 3) Using the ADMM (Alternating Direction Multiplier Method) algorithm to solve the distributed energy multi-microgrid optimization control constraint model for power trading and the distributed energy multi-microgrid optimization control two-stage model for power trading, multi-microgrid cooperation cost minimization and benefit distribution scheme; including:

[0080] According to the two-stage model of distributed energy multi-microgrid optimization control for electric energy trading, the set X consisting of the first-stage decision variables and the set Y consisting of the second-stage decision variables are obtained, which can be expressed as:

[0081]

[0082] Where: are the amount of electricity purchased and sold by the microgrid to the external grid at time t; μ buy , μ sell are the status of the microgrid purchasing and selling electricity to the external power grid; P grid max is the maximum power of the external power grid; is the gas consumption of the gas turbine at time t; is the gas consumption of the gas boiler at time t; is the volume of methane generated by power-to-gas conversion at time t; X is the set of decision variables in the first stage; C CO2 For the tiered carbon trading costs; are the charging power and discharging power of the energy storage at time t respectively; are the charging power and discharging power of the thermal energy storage at time t, respectively; Y is the set of second-stage decision variables related to the s-th scenario; a, b, c, w, A, B, C, D are coefficient vectors or matrices corresponding to the set of decision variables; x is the first-stage decision variable; ξ s is the photovoltaic prediction information under the sth scenario; y s is the second-stage decision variable related to the sth scenario; ξ is the photovoltaic prediction information of the first stage; p s is the probability of the sth scene.

[0083] In order to make the feasible domain ψ of the scenario probability fit the actual operation situation and ensure the rationality of the scenario probability fluctuation range, the 1-norm constraint and the ∞-norm constraint are combined to construct a comprehensive norm constraint as follows:

[0084]

[0085] Where: p s0 is the initial probability value of the sth scene probability; θ1, θ ∞ is the probability allowable deviation limit under the 1-norm and ∞-norm constraints; S is the total number of scenarios;

[0086] where θ1 and θ ∞ for:

[0087]

[0088] Where: M is the number of actual running scenarios; α1, α ∞ is the confidence that the 1-norm and ∞-norm constraints hold;

[0089] The two-stage minimization problem-maximization problem-minimization problem is divided into a main problem and sub-problems, where:

[0090] The main problem J1 is described as:

[0091]

[0092] In the formula: z is the auxiliary variable; g is the number of iterations;

[0093] The unit output status in the real-time stage is determined by the day-ahead forecast stage, and the unit output value is adjusted according to the changes in the real-time photovoltaic output scenario, thereby achieving optimal scheduling in the real-time scenario;

[0094] Subproblem J2 is described as:

[0095]

[0096] The column and constraint generation algorithm is used to solve the main problem J1 and the subproblem J2.

[0097] The solution process of the column and constraint generation algorithm is as follows Figure 1 As shown, where b * is a 0-1 state variable; x * Provide power for the unit in the day-ahead phase; * s is the adjustment amount of each unit in the real-time stage scenario s.

[0098] The following selects three different microgrid source-load data in a certain area in northern my country to verify the effectiveness of the distributed energy multi-microgrid optimization control method for electric energy trading of the present invention.

[0099] like Figure 2 and Figure 3 The figure shows the electricity and heat energy transactions between microgrids. Microgrid 2 and Microgrid 3 sell electricity to Microgrid 1 during the period of 08:00-18:00. This is because the renewable energy of Microgrid 2 and Microgrid 3 is photovoltaic. After the photovoltaic output is absorbed by the load of the microgrid, there is still surplus electricity, so electricity is sold to Microgrid 1. During the periods of 01:00-07:00 and 19:00-24:00, Microgrid 2 and Microgrid 3 are in the night period, and the photovoltaic output is 0, so it is necessary to purchase electricity from Microgrid 1 to meet the load demand. For the heat energy interaction between microgrids, Microgrid 1 purchases heat energy from Microgrid 2 and Microgrid 3 during the period of 13:00-16:00. This is because the heat load of Microgrid 1 is at its peak during this period, and the unit output cost is reduced by purchasing heat energy from other microgrids.

[0100] Figure 4 It means that after Nash bargaining, the optimal transaction price between each microgrid is obtained, and the transaction price between microgrids is between the purchase and sale prices of the upper power grid during the entire dispatching period, which can effectively promote the electricity transaction between microgrids to reduce the operating cost of each microgrid system; Figure 5 It represents the optimal heat price of each microgrid after Nash bargaining. Figure 4 The analysis of is the same.

[0101] The cost and benefit distribution results of each microgrid are shown in Table 1.

[0102] Table 1 Cost and benefit analysis

[0103]

[0104] From Table 1, we can see that the final cooperation cost of each microgrid is 68178.88 yuan, 38090.03 yuan and 31385.28 yuan, and the breaking point costs are 70505.60 yuan, 40416.10 yuan and 33712.25 yuan respectively. In columns 4-6 of Table 1, the cooperation income of microgrid 1 is 2326.72 yuan, and the income increase accounts for 3.30%; the cooperation income of microgrid 2 is 2326.07 yuan, and the income increase accounts for 5.76%; the cooperation income of microgrid 3 is 2326.97 yuan, and the income increase accounts for 6.90%. From the perspective of the increased income of each microgrid, the microgrid can reduce the cost of each microgrid when it operates independently through cooperation, which means that each microgrid can reduce the operating cost increased by resisting uncertainty through cooperation.

Claims

1. A distributed energy multi-microgrid optimization control method for electric energy trading, characterized by: The following steps are involved: 1) According to the constraints of gas turbine electric heat output, gas boiler heating power, absorption chiller cooling power, electric chiller cooling power, electric energy storage charge state and charging and discharging power, thermal energy storage real-time heat storage and charging and discharging power, multi-microgrid electric and thermal energy interconnection line interactive power constraints, new energy unit wind and solar curtailment constraints, microgrid and external grid interaction constraints, power-to-gas-carbon capture coordinated operation constraints, and step-by-step carbon trading mechanism constraints, a distributed energy multi-microgrid optimization control constraint model for electric energy trading is constructed; 2) A two-stage model for distributed energy multi-microgrid optimization control for power trading is constructed based on the cost of electricity purchase and sales, gas purchase cost, carbon trading cost, energy storage use cost, and penalty for wind and solar power abandonment; 3) Use the ADMM algorithm to solve the distributed energy multi-microgrid optimization control constraint model for electricity trading and the two-stage model of distributed energy multi-microgrid optimization control for electricity trading, as well as the multi-microgrid cooperation cost minimization and profit distribution plan.

2. A distributed energy multi-microgrid optimization control method for electric energy trading according to claim 1, characterized in that: The step 1) of constructing a distributed energy multi-microgrid optimization control constraint model for electric energy trading specifically includes: (1) Gas turbine model The gas turbine generates electricity and heat by burning natural gas. The gas turbine model is composed of the gas turbine's electrical and thermal output and constraints as follows: In the formula, are respectively the power generation and heating power of the gas turbine at time t; are the power generation efficiency and heating efficiency of the gas turbine respectively; H LHV It is the low calorific value of natural gas; is the gas consumption of the gas turbine at time t; The upper limit of gas turbine power generation capacity; (2) Gas boiler model Gas boilers burn natural gas to generate heat energy. The gas boiler model is composed of heating power and constraints as follows: In the formula, is the heating power of the gas boiler at time t; is the heating efficiency of the gas boiler; is the gas consumption of the gas boiler at time t; It is the upper limit of heating power of gas boiler; (3) Absorption Refrigeration Machine Model The absorption chiller converts heat energy into cold energy. The absorption chiller model is composed of the cooling power and constraints as follows: In the formula, is the cooling power of the absorption chiller at time t; is the cooling efficiency of the absorption chiller; is the heat consumption of the absorption refrigerator at time t; The upper limit of cooling capacity of absorption chiller; (4) Electric Refrigerator Model The electric refrigerator uses electric energy for refrigeration. The electric refrigerator model is composed of the cooling power and constraints as follows: In the formula, is the cooling power of the electric refrigerator at time t; is the cooling efficiency of the electric chiller; is the power consumption of the electric refrigerator at time t; The upper limit of cooling power produced by the electric refrigerator; (5) Electric energy storage model Electric energy storage can achieve efficient utilization of energy storage resources. The electric energy storage model is composed of the state of charge of the electric energy storage and the charging and discharging power constraints as follows: m ech +m edis ≤1 (16) In the formula, is the storage capacity of the energy storage at time t; They are the charging efficiency and discharging efficiency of electric energy storage respectively; are the charging power and discharging power of the energy storage at time t respectively; μ ech , μ edis They are the status bits for charging and discharging of the electric energy storage respectively; are the upper and lower limits of the electric energy storage capacity respectively; They are the upper limits of the charging power and discharging power of the electric energy storage, respectively; (6) Thermal energy storage model Thermal energy storage can also achieve efficient utilization of energy storage resources. The thermal energy storage model is composed of the real-time heat storage capacity and the charging and discharging heat power constraints of thermal energy storage as follows: m hch +m hdis ≤1 (23) In the formula, is the heat storage capacity of the thermal energy storage at time t, t = 0 ~ 24h; are the charging efficiency and releasing efficiency of thermal energy storage respectively; are the charging power and releasing power of the thermal energy storage at time t respectively; μ hch , μ hdis They are the state positions of thermal energy storage charging and releasing heat respectively; are the upper and lower limits of the thermal energy storage capacity, respectively; are the upper limits of the charging power and the discharging power of the thermal energy storage, respectively; (7) Interaction model between microgrid and external grid The microgrid interacts with the external grid for electric energy. The interaction model between the microgrid and the external grid is composed of the interaction model and the power sales and purchase constraints as follows: m buy +m sell ≤1 (26) In the formula, are the amount of electricity purchased and sold by the microgrid to the external grid at time t; μ buy , μ sell They are the status bits of the microgrid purchasing and selling electricity to the external power grid; is the maximum power of the external power grid; (8) Power-to-gas-carbon capture synergistic operation model The power-to-gas and carbon capture are operated in coordination, and the CO2 captured by carbon capture is provided to the power-to-gas device as a high-quality raw material. The power-to-gas is used to absorb the abandoned wind and solar power to generate natural gas and provide it to combined heat and power (CHP) and gas. Power-to-gas-carbon capture can not only reduce the storage cost after capturing CO2, but also reduce the gas purchase cost of combined heat and power (CHP) and gas (GB). The power-to-gas-carbon capture coordinated operation model is composed of the power-to-gas consumption, carbon capture consumption and constraints as follows: In the formula, is the volume of methane generated by power-to-gas conversion at time t; is the power consumed by the power-to-gas conversion at time t; q is the amount of CO2 required for power-to-gas operation at time t, i.e., the amount of CO2 captured by carbon capture; is the electricity consumed by carbon capture at time t; is the actual carbon emission of combined heat and power after carbon capture at time t; η gas The efficiency of generating methane by consuming electrical power for power-to-gas conversion; η CO2 The efficiency of generating CO2 required for methane by consuming electrical power for power-to-gas conversion; η CCS The efficiency coefficient of capturing CO2 by consuming electrical power for carbon capture; η CHP The carbon dioxide emission coefficient for natural gas consumption in combined heat and power generation; are the upper limits of electrical power consumption for power-to-gas and carbon capture, respectively; (9) Step-by-step carbon trading mechanism model In order to further limit carbon emissions, a tiered carbon trading mechanism is adopted to divide the carbon price in different intervals according to the difference between the actual carbon emissions of the microgrid and the allocated carbon emission quota. The tiered carbon trading mechanism model is as follows: In the formula, E a is the actual carbon emissions of the microgrid; E c Free carbon quota for microgrids; CO2 is the carbon emission rights trading amount of the microgrid; δ is the free carbon quota coefficient per unit power; a and b are the carbon emission characteristic coefficients; C CO2 is the step-by-step carbon trading cost; λ is the carbon trading base price; l is the length of the carbon emission interval; α is the price growth rate; (10) Multi-microgrid interconnection model Multiple microgrids are interconnected through tie lines to transmit electric energy and thermal energy. The multi-microgrid interconnection model is composed of the interactive power constraints of the electric energy and thermal energy tie lines as follows: In the formula, is the amount of electric power that the i-th microgrid expects to interact with the interconnected j-th microgrid; is the amount of thermal power that the i-th microgrid expects to interact with the interconnected j-th microgrid; are the transmission capacities of the electric energy and thermal energy tie lines between the i-th microgrid and the j-th microgrid interconnected; (11) New energy unit model When wind power and photovoltaic power provide power to microgrids, it is inevitable that wind and photovoltaic power will be abandoned. The new energy unit model is composed of the unit power relationship and constraint conditions as follows: In the formula, Predict the power of renewable energy at time t; is the actual output value of the renewable energy at time t; The power abandoned by the renewable energy at time t.

3. The distributed energy multi-microgrid optimization control method for electric energy trading according to claim 1 is characterized in that: The two-stage model of distributed energy multi-microgrid optimization control for electric energy trading described in step 2) is composed of the first-stage objective function and the second-stage objective function, which are as follows: (1) Objective function of the first stage The objective function of the first stage is to minimize the cost of electricity purchase and sale, and decide the electricity purchase and sale plan of the microgrid to the external power grid, as follows: In the formula, are the purchase and sale prices of electricity at time t respectively; are the amount of electricity purchased and sold by the microgrid to the external power grid at time t; (2) Second stage objective function The second-stage objective function of the two-stage distributed blue-robust optimization of the microgrid is composed of gas purchase cost, carbon trading cost, energy storage use cost and wind and solar power abandonment penalty. Considering the system adjustment cost and the minimum abandonment of solar power, the probability of the worst scenario, the charging and discharging plan of the energy storage equipment and the unit scheduling plan, the specific model is as follows: In the formula, is the gas consumption of the gas turbine at time t; is the gas consumption of the gas boiler at time t; G is the volume of methane generated by power-to-gas conversion at time t; are respectively the power generation and heating power of the gas turbine at time t; is the heating power of the gas boiler at time t; is the cooling power of the absorption chiller at time t; is the cooling power of the electric refrigerator at time t; are the charging power and discharging power of the energy storage at time t respectively; are the charging power and releasing power of the thermal energy storage at time t respectively; are the amount of electricity purchased and sold by the microgrid to the external power grid at time t; is the electricity consumed by carbon capture at time t; is the actual output value of the renewable energy at time t; is the power abandoned by the new energy at time t; pro is the probability of the worst scenario; Ω is the uncertainty set of scenario probabilities; is the amount of electricity consumed by power-to-gas conversion at time t; is the heat consumption of the absorption refrigerator at time t; is the power consumption at time t; is the heat loss at time t; is the heat consumption of the absorption refrigerator at time t.

4. The distributed energy multi-microgrid optimization control method for electric energy trading according to claim 1 is characterized in that: Step 3) includes: According to the two-stage model of distributed energy multi-microgrid optimization control for electric energy trading, the set X consisting of the first-stage decision variables and the set Y consisting of the second-stage decision variables are obtained, which can be expressed as: Where: are the amount of electricity purchased and sold by the microgrid to the external grid at time t; μ buy , μ sell are the states of the microgrid purchasing and selling electricity to the external power grid; P gridmax is the maximum power of the external power grid; is the gas consumption of the gas turbine at time t; is the gas consumption of the gas boiler at time t; is the volume of methane generated by power-to-gas conversion at time t; X is the set of decision variables in the first stage; C CO2 For the tiered carbon trading costs; are the charging power and discharging power of the energy storage at time t respectively; are the charging power and discharging power of the thermal energy storage at time t, respectively; Y is the set of second-stage decision variables related to the s-th scenario; a, b, c, w, A, B, C, D are coefficient vectors or matrices corresponding to the set of decision variables; x is the first-stage decision variable; ξ s is the photovoltaic prediction information under the sth scenario; y s is the second-stage decision variable related to the sth scenario; ξ is the photovoltaic prediction information of the first stage; p s is the probability of the sth scene; In order to make the feasible domain ψ of the scenario probability fit the actual operation situation and ensure the rationality of the scenario probability fluctuation range, the 1-norm constraint and the ∞-norm constraint are combined to construct a comprehensive norm constraint as follows: Where: p s0 is the initial probability value of the sth scene probability; θ1, θ ∞ is the probability allowable deviation limit under the 1-norm and ∞-norm constraints; S is the total number of scenarios; where θ1 and θ ∞ for: Where: M is the number of actual running scenarios; α1, α ∞ is the confidence that the 1-norm and ∞-norm constraints hold; The two-stage minimization problem-maximization problem-minimization problem is divided into a main problem and sub-problems, where: The main problem J1 is described as: In the formula: z is the auxiliary variable; g is the number of iterations; The unit output status in the real-time stage is determined by the day-ahead forecast stage, and the unit output value is adjusted according to the changes in the real-time photovoltaic output scenario, thereby achieving optimal scheduling in the real-time scenario; Subproblem J2 is described as: The column and constraint generation algorithm is used to solve the main problem J1 and the subproblem J2.

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