Multi-microgrid operation optimization method considering supply and demand flexible double responses

Abstract

The invention discloses a multi-microgrid operation optimization method considering supply and demand flexible double responses. The method comprises the following steps: establishing a cost optimization model of multi-microgrid electric energy cooperation sharing; a gas turbine, a waste heat boiler and an organic Rankine cycle power generation device are introduced to construct a supply side response model for flexible output of electricity and heat energy of the cogeneration unit; a demand side establishes a demand side flexible response model by considering transferability and replaceability of the electric heating load; and a multi-microgrid electric energy sharing cooperative operation model is established based on the Nash negotiation theory, and then the model is decomposed into a microgrid alliance benefit maximization sub-problem and a benefit distribution sub-problem. And in the cooperative income distribution sub-problem, the micro-grids negotiate with each other by taking the electric energy contribution and the marginal contribution degree of the micro-grids in cooperation as bargaining capabilities, so that fair distribution of cooperative income is realized. According to the optimization method provided by the invention, the advantages of flexible supply and demand and resource coordination and complementation can be brought into full play, and the condition of unreasonable income distribution of each micro-grid in the multi-micro-grid cooperation system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation and control and relates to a multi-microgrid operation optimization method considering flexible dual response of supply and demand. Background Art

[0002] With the construction and development of a clean, low-carbon, safe and efficient energy system, renewable energy sources such as wind power and photovoltaic power are connected to the power grid on a large scale. Their intermittent and random output characteristics have brought challenges to the safe and stable operation of the power grid. Microgrid technology is an important means to achieve local renewable energy consumption. Limited by the shortcomings of a single microgrid in terms of adjustable capacity and risk resistance, the multi-microgrid system consisting of multiple microgrids has developed rapidly. The proportion of renewable energy access in the multi-microgrid system is high, and the uncertainty characteristics are significant. At the same time, each microgrid operator is often an independent entity. There are interest interactions and privacy protection issues between different entities, making the optimization and scheduling of the multi-microgrid system a difficult problem. Summary of the invention

[0003] 1. Technical problems to be solved:

[0004] How to improve the supply and demand flexibility of multi-microgrid cooperative operation and enhance the enthusiasm and stability of each microgrid in the multi-microgrid system to participate in cooperative operation

[0005] 2. Technical solution:

[0006] In order to solve the above problems, the present invention provides a multi-microgrid operation optimization method considering flexible dual response of supply and demand, comprising the following steps:

[0007] Step S1: Construct a multi-microgrid model that considers flexible dual response of supply and demand, wherein the multi-microgrid model includes a gas turbine, a waste heat boiler and an organic Rankine cycle power generation device to construct a supply-side response model for flexible output of electricity and thermal energy of the gas turbine unit; a demand-side flexible response model is established on the basis that the electricity and thermal loads have transferable and substitutable response characteristics; and a microgrid carbon emission and carbon trading model.

[0008] Step S2: Taking the minimization of the sum of microgrid external interaction costs, demand response costs, grid access fees, carbon trading costs, power interaction costs, energy storage loss costs and power transmission costs as the objective function, a multi-microgrid power sharing cooperative operation model is established based on the Nash negotiation theory; it is then decomposed into the microgrid alliance benefit maximization sub-problem and the benefit distribution sub-problem, and the alternating direction multiplier method is selected for distributed solution.

[0009] Step S3: In the cooperative benefit allocation sub-problem, each microgrid negotiates with each other using its power contribution and marginal contribution in the cooperation as bargaining power.

[0010] 3. Beneficial effects:

[0011] The present invention provides an optimization method for cooperative operation of multiple microgrids taking into account flexible dual responses of supply and demand. ORC and EB are introduced on the energy supply side to remove the limitation of thermal-electric coupling of CHP units, which can effectively expand the thermal-electric supply range of the units, improve the flexibility and efficiency of MMG energy supply, assist in grid connection and absorption of surplus wind power, optimize the economic and low-carbon operation of MMG, and quantify the bargaining power of each microgrid according to its contribution to electricity sharing and marginal contribution, effectively stimulating the enthusiasm of microgrid entities to participate in energy trading. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 Schematic diagram of the microgrid structure in the present invention.

[0013] Figure 2 The figure is a flow chart of the solution of the present invention.

[0014] Figure 3 is an electric power balance diagram of the microgrid MG1 in a specific embodiment.

[0015] Figure 4 It is a thermal power balance diagram of the microgrid MG1 in a specific embodiment. DETAILED DESCRIPTION

[0016] The present invention provides the following technical solution: a multi-microgrid operation optimization method considering flexible dual response of supply and demand, comprising the following steps: step S1: constructing a multi-microgrid model considering flexible dual response of supply and demand, the multi-microgrid (MMG) model includes introducing a gas turbine (GT), a waste heat boiler (WHB) and an organic Rankine cycle power generation device (ORC) to construct a supply-side response model for flexible output of electricity and heat energy of a gas turbine unit (CHP); establishing a demand-side flexible response model based on the transferable and substitutable response characteristics of electricity and heat loads; a microgrid carbon emission and carbon trading model; step S2: constructing a multi-microgrid model considering flexible dual response of supply and demand, the multi-microgrid (MMG) model includes introducing a gas turbine (GT), a waste heat boiler (WHB) and an organic Rankine cycle power generation device (ORC) to construct a supply-side response model for flexible output of electricity and heat energy of a gas turbine unit (CHP); establishing a demand-side flexible response model based on the transferable and substitutable response characteristics of electricity and heat loads; and establishing a microgrid carbon emission and carbon trading model. Step S2: Taking the minimization of the sum of microgrid external interaction costs, demand response costs, grid access fees, carbon trading costs, electricity interaction costs, energy storage loss costs and electricity transmission costs as the objective function and establishing a multi-microgrid electricity sharing cooperative operation model based on Nash negotiation theory; then decompose it into the microgrid alliance benefit maximization sub-problem and the profit distribution sub-problem, and select the alternating direction multiplier method (ADMM) for distributed solution; Step S3: In the cooperative profit distribution sub-problem, each microgrid negotiates with each other based on its electricity contribution and marginal contribution in the cooperation as bargaining power.

[0017] In one embodiment, in the step S1, a gas turbine (GT), a waste heat boiler (WHB) and an organic Rankine cycle power generation device (ORC) are introduced into the multi-microgrid (MMG) model to construct a supply-side response model for flexible output of electricity and heat energy of a gas turbine unit (CHP), and the expression thereof is as follows:

[0018] GT Unit

[0019]

[0020] in, is the natural gas power input to GT in the i-th microgrid during period t; is the electric power output by GT in the i-th microgrid during period t; They represent the gas-to-electricity and gas-to-heat efficiencies of GT respectively; Respectively represent the upper and lower limits of the GT input power; Respectively represent the upper and lower limits of GT climbing.

[0021] WHB and ORC units

[0022]

[0023] in, P i h,ORC (t) represents the thermal power input to WHB and ORC in the i-th microgrid during period t; P i ORC,e (t), P i WHB,h (t) are the output heat and electric power of WHB and ORC in the i-th microgrid during period t, respectively; h ORC represents the conversion efficiency of ORC; h WHB It represents the heat loss of WHB; Respectively represent the upper and lower limits of the input power of ORC; They represent the upper and lower limits of ORC climbing respectively.

[0024] CHP Units

[0025]

[0026] in, They represent the thermal and electrical output power of CHP in the i-th microgrid during period t.

[0027] In one embodiment, in step S1, a demand-side flexible response model is established on the basis that the electric and thermal loads introduced in the multi-microgrid (MMG) model have transfer and curtailment response characteristics, and its expression is as follows:

[0028]

[0029] Where: k represents the load type, k∈{e,h}; represents the value of the kth load in the i-th microgrid during period t; represents the fixed load of the kth load of the i-th microgrid during period t; represents the transferable load of the kth load in period t; Represents the alternative load of the kth load in period t.

[0030] Transferable loads, specifically:

[0031]

[0032] in, and The amount of the kth load transferable type after DR and the amount of load participating in DR in the i-th microgrid t period; are binary variables, representing the transfer-in and transfer-out parameters of the k-th load transferable type in the i-th microgrid during time period t; They represent the transfer-in and transfer-out power of the kth load transferable type in the i-th microgrid during time period t; They respectively represent the upper and lower limits of the DR amount of the kth type of transferable load in the ith microgrid in each period. This constraint is used to ensure the energy quality requirements of users.

[0033] Replaceable loads, specifically:

[0034]

[0035] in, They represent the kth load substitution type value after DR of the i-th microgrid in period t respectively; They represent the amount of the kth alternative load participating in DR in the i-th microgrid during period t; They represent the transfer-in and transfer-out parameters of the k-th load substitution type in the i-th microgrid during period t; They respectively represent the transfer-in and transfer-out power of the kth alternative load in the i-th microgrid in time period t.

[0036] In one embodiment, the step S1: carbon trading model in a multi-microgrid (MMG) model is expressed as follows:

[0037]

[0038] in, is the initial carbon emission allocation of the i-th microgrid; and is the electric power output by GT and GB at time t; is the electric power purchased by the i-th microgrid from the grid at time t; b 1 , b 2 and b 3 Carbon emissions allocated for GT, GB and external power purchase per unit power. is the actual carbon emissions; 1 、a 2and a 3 Carbon emissions generated per unit power for GT, GB and external electricity purchases; is the carbon emission right of the i-th microgrid; is the carbon emission cost of the i-th microgrid; x CET It is the basic price for carbon trading; ν is the price increase rate; △L is the length of the carbon emission interval.

[0039] In one embodiment, the objective function of step S2 is to minimize the sum of microgrid external interaction cost, demand response cost, grid fee cost, carbon trading cost, power interaction cost, energy storage loss cost and power transmission cost and establish a multi-microgrid power sharing cooperative operation model based on Nash negotiation theory. The specific expression is as follows:

[0040]

[0041] in, is the microgrid external interaction cost, is the demand response cost, is the carbon trading cost, is the electricity interaction cost, is the energy storage loss cost, The cost of power transmission. is the unit price coefficient of gas purchase in the system; is the price of electricity purchased and sold by MMG at time t. is the gas purchasing power of microgrid i at time t; are the compensation prices for transferable and curtailable electric and thermal load units, respectively; They are respectively capable of reducing electric heating load and capable of transferring electric heating load; is the electricity exchange unit price between microgrid i and microgrid j; is the electric energy interaction between microgrid i and microgrid j; is the unit loss cost of electrical energy storage and thermal energy storage; They are respectively the charging and discharging power of the electrothermal energy storage.

[0042] In one embodiment, the balance and constraints of the multi-microgrid cooperative operation optimization method considering flexible supply and demand dual response described in step S2 include: electric power balance constraints, thermal power balance constraints, gas balance constraints, and point-to-point P2P power trading constraints:

[0043]

[0044] in, Power generation for renewable energy; is the electric load after demand response.

[0045] The thermal power balance constraint is:

[0046]

[0047] in, The heating power of the gas boiler in the i-th microgrid during period t; Heat load after demand response.

[0048] The gas balance constraint is:

[0049]

[0050] in, Natural gas power consumption of gas boiler in the i-th microgrid during period t.

[0051]

[0052] in, is the maximum transmission power of the interconnection line between microgrids.

[0053] In one embodiment, the multi-microgrid cooperative operation optimization method considering flexible dual response of supply and demand described in step S2 establishes a Nash bargaining model, and the expression of the Nash bargaining model is:

[0054]

[0055] Among them, C i is the cost of the i-th negotiation participant; is the negotiation breakdown point of the i-th participant.

[0056] It can be broken down into two sub-problems:

[0057] 1) Sub-problem 1: Minimizing the cost of cooperative alliance

[0058]

[0059] Among them, F i C is the cost of the i-th MG participating in the cooperation.

[0060] 2) Sub-problem 2: Maximizing asymmetric benefit distribution

[0061]

[0062] in, is the optimal cost of subproblem 1; The cost of electricity transaction between MMGs; is the price of electricity transaction between MMG; S i is the bargaining power of microgrid i.

[0063] In one embodiment, the specific steps of distributing and solving the microgrid alliance cost minimization sub-problem of the multi-microgrid cooperative operation optimization method considering flexible supply and demand dual response described in step S2 based on the ADMM algorithm are as follows:

[0064] Step S281: Since the energy sharing balance constraint is multi-coupled among all microgrids, an auxiliary variable vector is introduced The cooperative alliance cost minimization is transformed into a double coupling model to be equivalent to multi-coupling constraints;

[0065] Step S282: The dual coupling model transformed into the microgrid cooperative alliance cost minimization sub-problem is as follows:

[0066]

[0067] in, represents the amount of electricity that microgrid i expects to trade with microgrid j. , it indicates that a consensus on power transaction has been reached between microgrid i and microgrid j.

[0068] Step S283: Establishing the augmented Lagrangian function of the cooperative alliance cost minimization sub-problem is as follows:

[0069]

[0070] in, is the Lagrange multiplier, setting the parameter ρ 1 =1×10 -4 .

[0071] Step S284: Each microgrid updates its own transaction power strategy through local calculation, and only the transaction power information is exchanged between microgrids; the calculation is repeated until each microgrid updates its transaction power strategy in the current iteration;

[0072] Step S285: After one round of iteration, the Lagrange multiplier and the number of iterations are updated according to the rules and the convergence of the algorithm is determined. If the convergence condition is met, the iteration is terminated, otherwise it enters the next round of iteration until the convergence condition is met or the set maximum number of iterations is reached.

[0073] In one embodiment, the asymmetric profit distribution maximization in step S2 is solved based on the distributed solution of the ADMM algorithm, and the specific steps are as follows:

[0074] Step S291: Introduce auxiliary variables for decoupling:

[0075]

[0076] in, represents the electricity price that microgrid i expects to trade with microgrid j. When , it indicates that a consensus on electricity price transaction has been reached between microgrid i and microgrid j.

[0077] Step S292: Substitute the cooperative alliance cost minimization result into the asymmetric benefit allocation maximization to establish the augmented Lagrangian function of the sub-asymmetric benefit allocation maximization as follows:

[0078]

[0079] in, is the Lagrange multiplier, setting the parameter ρ 2 =1×10 -4 .

[0080] Step S293: The microgrid updates the transaction electricity price decision through the local agent by exchanging electricity price information; each microgrid updates its transaction electricity price decision; other microgrids update their own transaction electricity price decisions after receiving the updated decision information.

[0081] Step S294: After one round of iteration, the Lagrange multiplier and the number of iterations are updated according to the rules and the convergence of the algorithm is determined. If the convergence condition is met, the iteration is terminated, otherwise the next round of iteration is entered until the convergence condition is met or the set maximum number of iterations is reached.

[0082] In one embodiment, in the cooperative benefit distribution sub-problem described in step S3, the nonlinear energy mapping function is used to quantify the contribution of each participant in the electric energy interaction and the asymmetric bargaining method considering the marginal contribution is: an exponential function based on the natural constant e is selected to form a nonlinear energy mapping function to quantify the bargaining power of each microgrid according to the contribution and marginal contribution of participating in electric energy sharing, where the bargaining power size S i The expression is as follows:

[0083]

[0084] in, Total electrical energy provided to MGi; is the total electrical energy obtained by MGi; is the maximum value of the electric energy supplied in each microgrid; is the maximum value of the electric energy received in each microgrid; e 1 、e 2 They are the proportion of electric energy contribution and marginal contribution respectively.

[0085] like Figure 1As shown in the figure, the internal system model of the microgrid, the access to the upper power grid, natural gas grid and renewable energy can effectively promote the consumption of renewable energy and alleviate the energy supply pressure of the system; under the coordination of the coupling equipment thermal decoupling CHP unit and gas boiler, the system flexibly transmits a variety of energy to ensure the energy demand on the demand side; among them, the thermal decoupling CHP unit is composed of a gas turbine, a waste heat boiler and an ORC; according to the price difference of energy in different periods, the system can achieve arbitrage by "low storage and high discharge" through batteries and heat storage tanks; multiple loads include electrical load and thermal load.

[0086] like Figure 2 As shown in the figure, the flowchart based on the ADMM algorithm is described as follows. First, due to the conflict of interest when MGs conduct energy transactions, auxiliary variables are introduced for decoupling; Lagrangian multipliers and penalty factors are introduced to establish the Lagrangian function of sub-problem 1: The solution steps of sub-problem 1 are as follows: ① Set the maximum number of iterations, convergence accuracy, and penalty factor; Parameter initialization: number of iterations = 1, initial interactive power of microgrid i Lagrange multiplier = 0; ② Each microgrid updates its transaction power decision through the local agent by exchanging power information; where: microgrid i updates its transaction power decision After receiving the updated decision information, other microgrids j update their transaction power decisions ③After one round of iteration, update the Lagrange multiplier; ④Update the number of iterations; ⑤Determine whether the algorithm meets the convergence conditions;

[0087] If the condition is satisfied, the iteration is terminated; otherwise, the calculation is repeated back to step 2 until the convergence condition is satisfied or the maximum number of iterations is reached.

[0088] After solving sub-problem 1, the optimal power of electricity trading between microgrids can be obtained at the same time, and then the bargaining power can be calculated. Substituting these values ​​into the revenue distribution problem can solve the price of electricity trading between microgrids. Using the decomposability of the objective function, ADMM can also be used to solve it to protect privacy. Due to similarity, it will not be repeated here.

[0089] Depend on Figure 3 It can be seen that: during the periods of 04:00-05:00 and 17:00-21:00, microgrid 1 has a large surplus of electricity, part of which is consumed by the energy storage battery, and the other part is exchanged with other microgrids through the interconnection line; during the period of 06:00-15:00, the power generation of the park's own equipment can no longer meet the gradually increasing load demand in the system. At this time, the system chooses to purchase electricity from other microgrids; during the period of 12:00-15:00, the electric load is at its peak, but the thermal load is at its low point. At this time, the system chooses the CHP unit equipped with ORC to bear the main power supply demand.

[0090] Depend on Figure 4It can be seen that: during the period of 00:00-06:00, the heat load demand is relatively low. At this time, the heat energy is mainly supplied by gas boilers and electric boilers. As the heat load demand gradually increases, the GB output thermal power also increases accordingly; during the periods of 07:00-10:00 and 12:00-21:00, the system heat load is at its peak. At this time, CHP supplements the output, and the system heat load is mainly borne by GB and CHP; during the period of 22:00-24:00, the heat load demand decreases, and the CHP output thermal power also decreases.

Claims

1. A multi-microgrid operation optimization method considering flexible dual response of supply and demand, comprising the following steps: Step S1: construct a multi-microgrid model considering flexible dual response of supply and demand, wherein the multi-microgrid model includes a gas turbine, a waste heat boiler and an organic Rankine cycle power generation device to construct a supply-side response model for flexible output of electricity and heat energy of the gas turbine unit; a demand-side flexible response model is established on the basis that the electricity and heat loads have transferable and substitutable response characteristics; and a microgrid carbon emission and carbon trading model; Step S2: Taking the minimization of the sum of microgrid external interaction costs, demand response costs, grid access costs, carbon trading costs, power interaction costs, energy storage loss costs and power transmission costs as the objective function and establishing a multi-microgrid power sharing cooperative operation model based on Nash negotiation theory; then decomposing it into the microgrid alliance benefit maximization sub-problem and the benefit distribution sub-problem, and selecting the alternating direction multiplier method for distributed solution; Step S3: In the cooperative benefit allocation sub-problem, each microgrid negotiates with each other using its power contribution and marginal contribution in the cooperation as bargaining power.

2. The multi-microgrid operation optimization method considering flexible dual response of supply and demand as claimed in claim 1, characterized in that: The expressions of the gas turbine, waste heat boiler and organic Rankine cycle power generation device in step S1 are as follows: GT unit: in, is the natural gas power input to GT in the i-th microgrid during period t; is the electric power output by GT in the i-th microgrid during period t; They represent the gas-to-electricity and gas-to-heat efficiencies of GT respectively; Respectively represent the upper and lower limits of the GT input power; Respectively represent the upper and lower limits of GT climbing; WHB and ORC units: in, They represent the thermal power input to WHB and ORC in the i-th microgrid during period t, respectively; are the output heat and electric power of WHB and ORC in the i-th microgrid during period t; h ORC represents the conversion efficiency of ORC; h WHB It represents the heat loss of WHB; Respectively represent the upper and lower limits of the input power of ORC; They represent the upper and lower limits of ORC climbing respectively. CHP Unit in, They represent the thermal and electrical output power of CHP in the i-th microgrid during period t.

3. The multi-microgrid operation optimization method considering flexible dual response of supply and demand as claimed in claim 1, characterized in that: The demand side flexible response model in step S1 is expressed as follows: Where: k represents the load type, k∈{e,h}; represents the value of the kth load in the i-th microgrid during period t; represents the fixed load of the kth load of the i-th microgrid during period t; represents the transferable load of the kth load in period t; Represents the alternative load of the kth load in period t.

4. The multi-microgrid operation optimization method considering flexible dual response of supply and demand as claimed in claim 3 is characterized by: The transferable load is specifically: in, and The amount of the kth load transferable type after DR and the amount of load participating in DR in the i-th microgrid t period; are binary variables, representing the transfer-in and transfer-out parameters of the k-th load transferable type in the i-th microgrid during time period t; They represent the transfer-in and transfer-out power of the kth load transferable type in the i-th microgrid during time period t; They respectively represent the upper and lower limits of the DR amount of the kth type of transferable load in the ith microgrid in each period. This constraint is used to ensure the energy quality requirements of users. The replaceable load is specifically: in, They represent the kth load substitution type value after DR of the i-th microgrid in period t respectively; They represent the amount of the kth alternative load participating in DR in the i-th microgrid during period t; They represent the transfer-in and transfer-out parameters of the k-th load substitution type in the i-th microgrid during period t; They respectively represent the transfer-in and transfer-out power of the kth alternative load in the i-th microgrid in time period t.

5. The multi-microgrid operation optimization method considering flexible dual response of supply and demand as claimed in claim 1, characterized in that: The carbon trading model described in step S1 is expressed as follows: in, is the initial carbon emission allocation of the i-th microgrid; and is the electric power output by GT and GB at time t; is the electric power purchased by the i-th microgrid from the grid at time t; b1, b2 and b3 are the carbon emissions allocated to GT, GB and external power purchase units. is the actual carbon emissions; a1, a2 and a3 are the carbon emissions generated by GT, GB and external power purchase per unit power; is the carbon emission right of the i-th microgrid; is the carbon emission cost of the i-th microgrid; x CET It is the basic price for carbon trading; ν is the price increase rate; △L is the length of the carbon emission interval.

6. The multi-microgrid operation optimization method considering flexible dual response of supply and demand as claimed in claim 1, characterized in that: The multi-microgrid power sharing cooperative operation model described in step S2 is specifically expressed as follows: in, is the microgrid external interaction cost, is the demand response cost, is the carbon trading cost, is the electricity interaction cost, is the energy storage loss cost, is the power transmission cost, is the unit price coefficient of gas purchase in the system; is the price of electricity purchased and sold by MMG at time t, is the gas purchasing power of microgrid i at time t; are the compensation prices for transferable and curtailable electric and thermal load units, respectively; They are respectively capable of reducing electric heating load and capable of transferring electric heating load; is the electricity exchange unit price between microgrid i and microgrid j; is the electric energy interaction between microgrid i and microgrid j; is the unit loss cost of electrical energy storage and thermal energy storage; They are respectively the charging and discharging power of the electrothermal energy storage.

7. The multi-microgrid operation optimization method considering flexible dual response of supply and demand as claimed in claim 1, characterized in that: In step S2, the expression of the Nash bargaining model is: Among them, C i is the cost of the i-th negotiation participant; is the negotiation breakdown point of the ith participant, It can be broken down into two sub-problems: Sub-problem 1: Minimizing the cost of cooperative alliances in, is the cost of the i-th MG to participate in the cooperation; Subproblem 2: Maximizing asymmetric revenue distribution in, is the optimal cost of subproblem 1; The cost of electricity transaction between MMGs; is the price of electricity transaction between MMG; S i is the bargaining power of microgrid i.

8. The multi-microgrid operation optimization method considering flexible dual response of supply and demand as claimed in claim 7, characterized in that: In step S2, the cost minimization subproblem is solved in a distributed manner based on the ADMM algorithm, and the specific steps are as follows: Step S281: Since the energy sharing balance constraint is multi-coupled among all microgrids, an auxiliary variable vector is introduced The cost minimization subproblem is transformed into a dual-coupled model to be equivalent to multi-coupled constraints; Step S282: The microgrid alliance cost minimization sub-question: The converted dual coupling model is as follows in, represents the amount of electricity that microgrid i expects to trade with microgrid j. When , it indicates that the microgrid i and microgrid j have reached a consensus on power transaction; Step S283: Establishing the augmented Lagrangian function of the cost minimization sub-problem as follows: in, is the Lagrange multiplier, setting the parameter ρ1 = 1 × 10 -4 . Step S284: Each microgrid updates its own transaction power strategy through local calculation, and only the transaction power information is exchanged between microgrids; the calculation is repeated until each microgrid updates its transaction power strategy in the current iteration; Step S285: After one round of iteration, the Lagrange multiplier and the number of iterations are updated according to the rules and the convergence of the algorithm is determined. If the convergence condition is met, the iteration is terminated, otherwise it enters the next round of iteration until the convergence condition is met or the set maximum number of iterations is reached.

9. The multi-microgrid operation optimization method considering flexible dual response of supply and demand as claimed in claim 7, characterized in that: The asymmetric profit distribution maximization in step S2 is solved based on the distributed solution of the ADMM algorithm, and the specific steps are as follows: Step S291: Introduce auxiliary variables for decoupling: in, represents the electricity price that microgrid i expects to trade with microgrid j. When , it indicates that microgrid i and microgrid j have reached a consensus on electricity price trading; Step S292: Substitute the result of the sub-cooperative alliance cost minimization into the asymmetric benefit allocation maximization to establish the augmented Lagrangian function of the sub-asymmetric benefit allocation maximization as follows: in, is the Lagrange multiplier, setting the parameter ρ2 = 1 × 10 -4 ; Step S293: The microgrid updates the transaction price decision through the local agent by exchanging electricity price information; each microgrid updates its transaction price decision; other microgrids update their own transaction price decisions after receiving the updated decision information; Step S294: After one round of iteration, the Lagrange multiplier and the number of iterations are updated according to the rules and the convergence of the algorithm is determined. If the convergence condition is met, the iteration is terminated, otherwise the next round of iteration is entered until the convergence condition is met or the set maximum number of iterations is reached.

10. The multi-microgrid operation optimization method considering flexible dual response of supply and demand as claimed in claim 1, characterized in that: In the step S3, in the cooperative benefit distribution sub-problem, a nonlinear energy mapping function is used to quantify the contribution of each participant in the electric energy interaction and an asymmetric bargaining method considering the marginal contribution is as follows: an exponential function based on the natural constant e is selected to form a nonlinear energy mapping function to quantify the bargaining power of each microgrid according to the contribution and marginal contribution of participating in electric energy sharing, where the bargaining power size S i The expression is as follows: in, Total electrical energy provided to MGi; is the total electrical energy obtained by MGi; is the maximum value of the electric energy supplied in each microgrid; is the maximum value of electric energy received in each microgrid; e1 and e2 are the proportions of electric energy contribution and marginal contribution respectively.

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