Multi-microgrid shared operation optimization method considering electricity-carbon-green certificate transaction

Abstract

The invention discloses a multi-microgrid shared operation optimization method considering an electricity-carbon-green certificate transaction. The method comprises the following steps: establishing a multi-microgrid point-to-point energy transaction framework; establishing a multi-micro-grid system model of a novel combined heat and power generation unit equipped with coupling electricity-to-gas-carbon capture in the micro-grid; based on the multi-micro-grid system model, establishing a multi-micro-grid low-carbon operation model considering a stepped carbon-green certificate combined transaction mechanism; based on the Nash negotiation theory, the original problem of the multi-microgrid low-carbon operation model is decomposed into a multi-microgrid cost minimization sub-problem and an asymmetric bargaining-based income distribution sub-problem, and an adaptive alternating term multiplier algorithm is adopted to solve the multi-microgrid cost minimization sub-problem and the asymmetric bargaining-based income distribution sub-problem in a distributed manner; fair distribution is realized according to the energy contribution of each micro-grid while the benefit maximization of the multiple micro-grids is realized, and the carbon emission in the micro-grid operation process is effectively reduced while the economic benefit is ensured.

Description

Technical Field

[0001] The present invention relates to a multi-microgrid shared operation optimization method, and belongs to the multidisciplinary cross-field of integrated energy systems, psychology, game theory, etc. Background Art

[0002] Faced with the dual pressures of energy demand and environmental pollution, my country is building a new power system with a high proportion of new energy supply and consumption system to help achieve the strategic goal of "carbon peak and carbon neutrality". In this context, building a multi-energy microgrid system that couples multiple energy forms of electricity, heat, and gas has become an important way to locally consume distributed new energy and reduce carbon emissions. Compared with the problems of limited configuration resources and energy supply stability in a single microgrid, the cooperative operation and resource complementarity of multiple microgrids are favored by many scholars. However, the coordinated operation of multiple microgrids involves the optimization and benefit distribution of multiple complex subsystems. Therefore, designing a reasonable multi-microgrid collaborative optimization strategy is very important for improving the energy supply stability of the system and promoting the low-carbon process of the power system.

[0003] At present, collaborative optimization methods and profit distribution methods have become hot topics in the optimization problems among multiple microgrids. Different from the difficulty of centralized dispatching in dealing with complex problems and the damage to privacy protection, distributed optimization dispatching allows each participant to make decisions locally, protecting their own privacy while collaborative optimization. Therefore, some scholars have achieved collaborative optimization among multiple microgrids through peer-to-peer (P2P) power trading, that is, a distributed optimization model of multiple microgrids has been established based on the overall alliance, but there is a lack of profit distribution for different entities within the alliance. Therefore, many scholars have relied on game theory to conduct research from two aspects: cooperative game and non-cooperative game. Compared with the non-cooperative game, the optimization operation problem of multiple microgrids mainly focuses on the interests of a single individual, while the cooperative game focuses on maximizing the overall interests of multiple microgrids, avoiding the selfish and antagonistic problems of microgrid individuals. As for the issue of benefit distribution after cooperation, some people have proposed using the Shapley value method to distribute the cooperation benefits according to the marginal benefits of each entity. However, as the number of cooperating agents increases, the Shapley value method may have a "combinatorial explosion" problem; others have established a standard payment model and used a symmetric Nash bargaining method to distribute cooperation income. For example, the invention with application number 202211359585.7 discloses a data-driven multi-microgrid cooperative operation method, and uses the Nash bargaining theory to establish a cooperative operation model of multi-microgrids under the carbon trading mechanism to explore the impact of the coordinated operation of multi-microgrids under the carbon trading mechanism on the environmental protection and economy of the system, but ignores the relationship between the contribution of each participating individual and income distribution.

[0004] In order to further enhance the low-carbon operation level of multi-microgrid systems, many scholars have conducted extensive research on low-carbon equipment and low-carbon policies. On the one hand, carbon capture systems (CCS) as a typical low-carbon technology can achieve carbon reduction targets by capturing and storing CO2 generated in the energy production process. Some scholars have coupled it with power-to-gas (P2G) equipment to effectively reduce carbon emissions in microgrid systems and achieve energy recycling. On the other hand, the imperfections of the carbon emission trading (CET) and green certificate trading (GCT) market mechanisms will restrict the green transformation of energy systems and the consumption of renewable energy. Therefore, some scholars have proposed a green certificate issuance evaluation system based on the entropy weight method from the perspective of renewable energy consumption assessment. In order to improve the green certificate trading mechanism, some scholars have introduced the CET mechanism in the multi-microgrid cooperative game. Furthermore, some scholars have studied the impact of the step-by-step CET mechanism on the economy and low carbonization in the multi-microgrid cooperative game. However, the above studies mainly focus on the impact of a single mechanism on the optimal operation of microgrids, or simply model or simplify the CET and GCT mechanisms. To this end, many scholars have studied the impact of the GCT-CET joint mechanism on carbon emissions and optimal scheduling costs in multi-microgrids, and proved that the GCT-CET joint mechanism can further stimulate the enthusiasm of renewable energy consumption in multi-microgrids and promote low-carbon transformation. However, there are interest disputes among various stakeholders in the CET-GCT joint market, which may lead to the enthusiasm of individual microgrids to participate in the market. At the same time, compared with the ordinary CET-GCT joint market, the stepped CET-GCT joint mechanism can better play an economic incentive and low-carbon transformation. At present, there are few literatures on the strategies of internal power allocation and external energy purchase of multi-microgrids under the stepped CET-GCT joint trading mechanism. Summary of the invention

[0005] The present invention proposes a multi-microgrid shared operation optimization method considering electricity-carbon-green certificate trading, aiming to ensure the maximization of the overall interests of the multi-microgrids while achieving a reasonable distribution of interests among the participating entities, and through the positive guidance of the stepped CET-GCT joint trading mechanism, effectively reduce carbon emissions during the operation of the microgrid while ensuring economic benefits, thereby achieving the low-carbon goal.

[0006] In order to achieve the above object, the technical solution of the present invention is achieved as follows:

[0007] A multi-microgrid shared operation optimization method considering electricity-carbon-green certificate trading includes the following steps:

[0008] Step 1: Establish a multi-microgrid point-to-point energy trading framework;

[0009] Step 2: Establish a multi-microgrid system model with a novel cogeneration unit coupled with power-to-gas-carbon capture in the microgrid;

[0010] Step 3: Based on the multi-microgrid system model, establish a multi-microgrid low-carbon operation model that considers the step-by-step carbon-green certificate joint trading mechanism;

[0011] Step 4. Based on the Nash negotiation theory, the original problem of the multi-microgrid low-carbon operation model is decomposed into the multi-microgrid cost minimization sub-problem and the profit allocation sub-problem based on asymmetric bargaining. The adaptive alternating term multiplier algorithm is used to distribute the multi-microgrid cost minimization sub-problem and the profit allocation sub-problem based on asymmetric bargaining.

[0012] Preferably, the multi-microgrid system model of the novel cogeneration unit coupled with power-to-gas-carbon capture in the microgrid includes models of each device in the microgrid and constraints of each device, a user demand response model and constraints of load power before and after demand response;

[0013] The device models and constraints of each device in the microgrid are:

[0014] The CHP unit model and the constraints of the CHP unit are:

[0015] The expression including the electrical output characteristics and constraints of the gas turbine is:

[0016]

[0017] In the formula, i is the microgrid number, t is the time period number, represents the amount of natural gas consumed by the gas turbine, η GT Represents the heat-to-electricity conversion efficiency of the gas turbine, represents the electrical power output of the gas turbine, is the calorific value of natural gas, represents the minimum output power of the gas turbine, Indicates the maximum output power of the gas turbine, ΔP i GT is the maximum climbing power;

[0018] The expression of the heat output characteristics of the waste heat boiler is:

[0019]

[0020] Where η WHB is the heating coefficient of the waste heat boiler;

[0021] The expression of the thermal power limitation condition of the CHP unit is:

[0022]

[0023] In the formula, C V1 is the electric-to-heat conversion coefficient of the CHP unit corresponding to the minimum output power, C V2 is the electric-to-heat conversion coefficient of the CHP unit corresponding to the maximum output power, h m is the linear supply slope of the CHP thermal power, It is the thermal power corresponding to the minimum power generation of the CHP unit;

[0024] The gas boiler model and the constraints of the gas boiler are:

[0025] The expressions of the thermal output characteristics and constraints of gas boilers are:

[0026]

[0027] Where η GB is the heating efficiency of GB, is the minimum output power of GB, is the maximum output power of GB, represents the amount of natural gas consumed by GB, is the maximum climbing power;

[0028] The battery model and the constraints of the battery are:

[0029] The expression of the battery output characteristics and constraints is:

[0030]

[0031] In the formula, represents the storage capacity of the battery of microgrid i at time t, η ch Represents the charging efficiency of the energy storage device, η dis Represents the discharge efficiency of the energy storage device, Represents the charging efficiency of the battery. Represents the discharge power of the battery, Represents the maximum battery charging power. Represents the maximum battery discharge power. A 0-1 variable representing the battery charge, A 0-1 variable representing the battery discharge, Represents the minimum value of the battery storage capacity. Represents the maximum value of the battery storage capacity. Represents the initial value of the battery in the entire scheduling cycle, Represents the final value of the battery during the entire dispatch cycle;

[0032] The model of the new combined heat and power unit coupled with power-to-gas-carbon capture and the constraints of the new combined heat and power unit are:

[0033] After the CCS equipment and P2G equipment are added to the CHP unit, the electric power generated by the gas turbine is supplied to the CCS equipment, P2G equipment and electric load. The power relationship is expressed as follows:

[0034]

[0035] In the formula, Represents the electrical power output of the CHP unit, Represents the electrical power supplied by the CHP unit to the P2G equipment, Represents the electrical power supplied by the CHP unit to the CCS equipment;

[0036] The operating characteristics and power constraints of P2G devices are expressed as follows:

[0037]

[0038] In the formula, represents the amount of natural gas consumed by the P2G device, α is the amount of natural gas produced by the P2G device per unit of electrical power consumed, represents the amount of CO2 consumed by the P2G device to generate natural gas, and β is the amount of CO2 that can be reacted by P2G per unit of electrical power. and They represent the minimum and maximum power consumed by the P2G device respectively;

[0039] The expressions of the operating characteristics and power constraints of CCS equipment are as follows:

[0040]

[0041] Where η CCS The electrical power consumed by the CCS equipment to capture a unit of CO2; represents the amount of captured CO2, and They are the lower and upper limits of the electric power consumed by the CCS equipment;

[0042] After adding CCS equipment and P2G equipment to the CHP unit, a new coupling relationship is generated. The heat-to-electricity constraint of the CHP unit is coupled with the electric power of the CCS equipment and the P2G equipment, and the expression of the constraint condition of the new CHP unit considering the electric power of the P2G and CCS equipment is obtained:

[0043]

[0044] The heat-to-electricity constraint of the CHP unit is coupled with the natural gas output of P2G, and the expression of the upper and lower limits of the gas production power of the new CHP unit is:

[0045]

[0046] Preferably, the user demand response model includes a demand response electric load model and a demand response thermal load model, and the demand response electric load includes a fixed electric load, a transferable electric load and a curtailable electric load;

[0047] The expression of the constraint condition of the transferable electric load is as follows:

[0048]

[0049] In the formula, ν1 is the proportion of transferable load to total load, T is the total number of time periods, represents the electric load before demand response, represents the transferable electrical load;

[0050] The expression of the constraint condition for reducing the electric load is:

[0051]

[0052] Where ν2 is the proportion of transferable load to total load, To reduce the electrical load;

[0053] The demand response heat load includes a fixed heat load and a curtailable heat load. The curtailable heat load constraint condition is expressed as follows:

[0054]

[0055] Where ν3 is the proportion of transferable load to total load, This means that the electrical load can be reduced. represents the heat load before demand response; the expression of the load power constraint before and after demand response is:

[0056]

[0057] In the formula, represents the electric load after demand response, and Represents the heat load after demand response.

[0058] Preferably, the multi-microgrid low-carbon operation model includes a cost optimization model for cooperative sharing of multi-microgrid electricity and constraints on electric power balance, thermal power balance, gas consumption balance and P2P electricity trading in the multi-microgrid system;

[0059] The cost optimization model of multi-microgrid power cooperative sharing is:

[0060]

[0061] In the formula, is the total operating cost of the i-th microgrid participating in power sharing, is the operation and maintenance cost of the new cogeneration unit, is the battery operation degradation cost, is the external interaction cost, is the demand response cost, is the CET cost of the microgrid, is the GCT cost, To cover the network cost, The operating cost of P2G;

[0062] The operation and maintenance costs of the new cogeneration unit include the operating costs of the gas turbine P2G operating costs Operating costs with CCS

[0063]

[0064] In the formula, a GT , b GT 、c GT are the operating cost coefficients of the gas turbine, c P2G With c CCS are the operating cost coefficients of P2G equipment and CCS equipment respectively.

[0065] The battery operation degradation cost is:

[0066]

[0067] In the formula, It represents the degradation cost of charging and discharging one unit of energy;

[0068] The external interaction costs include the natural gas purchase cost and the electricity purchase cost from the upper power grid:

[0069]

[0070] In the formula, Represents the price of natural gas purchased. Represents the amount of natural gas purchased, Represents the price of electricity purchased from the upper power grid, is the amount of electricity purchased from the upper power grid, and The price and quantity of electricity sold to the upper grid;

[0071] The demand response cost includes the economic compensation given to users for reducing and shifting electric load and reducing thermal load demand:

[0072]

[0073] In the formula, is the compensation unit price for transferable electric load, is the compensation unit price for reducing the electric load, This is the compensation unit price for reducing the heat load.

[0074] The network fee cost is:

[0075]

[0076] In the formula, a e is the grid-connection fee per unit of electricity, is the amount of electricity traded, and the set of multi-microgrids is defined as Γ, i, j∈Γ and j≠i;

[0077] The operating cost of P2G is:

[0078]

[0079] Preferably, the expression of the constraint condition of the electric power balance is as follows:

[0080]

[0081] They are the photovoltaic and wind power consumption at time t respectively;

[0082] The expression of the constraint condition of thermal power balance is as follows:

[0083]

[0084] The expression of the constraint condition of the gas consumption balance is as follows:

[0085]

[0086] The expression of the constraint condition of the P2P electric energy transaction is as follows:

[0087]

[0088] In the formula, represents the maximum transmission power of the interconnection line between microgrids, Represents the payment corresponding to the total amount of electricity traded by microgrid i in time period t.

[0089] Preferably, the method for obtaining the CET cost of the microgrid individual is:

[0090] Carbon emissions in individual microgrids come from new CHP units, gas turbines and external power purchases. Carbon quotas in microgrids The distribution is:

[0091]

[0092] In the formula, They represent the carbon quotas obtained by the microgrid from purchasing electricity from the upper power grid, the output of the new CHP unit and the output of the gas boiler. Represents the microgrid individual purchasing power from the upper grid, Represents the electricity demand supplied by the new CHP unit, and Respectively represent the heating power of the new CHP unit and the gas boiler in period t, ξ e,h represents the conversion factor from power generation to heat supply of the new CHP unit, ξ e With ξ h Represents the carbon emission quota obtained by generating unit electricity and unit heat respectively;

[0093] Actual carbon emissions in microgrids It is expressed as:

[0094]

[0095] In the formula, Represents the carbon emissions within the microgrid within a scheduling cycle; Represents the carbon emissions caused by external power purchases within a dispatch cycle; They are the CO2 emission coefficients of the new CHP units and gas boilers; represents the amount of carbon dioxide emissions from the system reduced after the addition of CCS equipment and P2G equipment; h1 is the electric-heat conversion coefficient of the new CHP unit corresponding to the minimum output power; ψ Grid is the carbon emission intensity coefficient of coal-fired units;

[0096] Calculate the emission rights of individual microgrids:

[0097]

[0098] The CET cost of an individual microgrid is:

[0099]

[0100] In the formula, λ is the carbon trading price, l is the length of the carbon emission interval, and α is the price growth rate.

[0101] Preferably, the method for obtaining the GCT cost is:

[0102] Green certificate quota index G that can be obtained by microgrid individuals d It is expressed as:

[0103]

[0104] In the formula, α Green is the green certificate quota coefficient, represents the electric load after demand response;

[0105] The actual number of green certificates held by microgrid individuals G s It is expressed as:

[0106]

[0107] In the formula, G s Represents the number of green certificates actually obtained by individual microgrids, They are the photovoltaic and wind power consumption at time t respectively;

[0108] The amount of green certificates that microgrid individuals participate in green certificate transactions is:

[0109]

[0110] By analogy with the tiered carbon trading mechanism, a tiered green certificate trading mechanism is proposed, and the GCT cost is:

[0111]

[0112] In the formula, γ is the price of green certificates; d is the length of the green certificate trading volume interval; β is the price growth rate.

[0113] Preferably, the CET cost of the individual microgrid is updated under the step-by-step GCT-CET joint transaction mechanism, specifically:

[0114] Green certificates can offset carbon emissions E reduce for:

[0115] E reduce =E coal -E res

[0116] In the formula, E coal and E res They are the carbon emission equivalents of coal-fired energy supply and new energy energy supply in the life cycle of their respective industrial chains;

[0117] Under the influence of the tiered GCT-CET joint trading mechanism, the carbon emission rights of microgrid individuals Updated to:

[0118]

[0119] The updated carbon emission rights of microgrid individuals Substitute it into the CET cost of the individual microgrid to obtain the CET cost of the individual microgrid under the stepped GCT-CET joint trading mechanism.

[0120] Preferably, the method of decomposing the original problem of the multi-microgrid low-carbon operation model into the multi-microgrid benefit maximization sub-problem and the benefit distribution sub-problem based on asymmetric bargaining is:

[0121] Establishing Nash negotiation theory model:

[0122]

[0123] In the formula, represents the operating cost of the microgrid before cooperation, represents the cost of microgrid i after cooperation, and satisfies the equation The solution under the condition is the Nash equilibrium solution; the Nash negotiation theory model is a non-convex nonlinear problem with multiple variable coupling, so the Nash negotiation theory model is decomposed into a multi-microgrid cost minimization sub-problem and a benefit allocation sub-problem based on asymmetric bargaining;

[0124] Multi-microgrid cost minimization sub-problem:

[0125]

[0126] The above formula is subject to the constraints in the multi-microgrid system model and the multi-microgrid low-carbon operation model. Due to the multiple coupling of the constraints of P2P power trading among all microgrids, auxiliary variables are introduced to solve the variable vector The multi-microgrid cost minimization subproblem is transformed into a dual coupling model as follows:

[0127]

[0128] In the formula, represents the amount of electricity that microgrid i expects to trade with microgrid j, It means the amount of electricity that microgrid j expects to trade with microgrid i. Once it meets This indicates that a consensus on power trading has been reached between microgrid i and microgrid j;

[0129] The sub-problem of profit allocation based on asymmetric bargaining:

[0130] A nonlinear energy mapping function based on natural logarithm is used to quantify the contribution of each microgrid in collaborative operations. The calculation formula is as follows:

[0131]

[0132] In the formula, ψ i is the contribution factor, P i-jrepresents the bargaining factor, P i s and P i r are the power provided and received by the microgrid individuals during the scheduling period, P i s,max and P i r,max are the maximum power provided and received in the individual microgrids, respectively;

[0133] According to Nash negotiation theory, the payment benefit maximization model based on asymmetric Nash negotiation is as follows:

[0134]

[0135] In the formula, Represents the optimal value of the microgrid individuals after participating in the cooperation, transforming the problem of solving the maximum value into the problem of solving the minimum value. The converted model is a model of the income distribution sub-problem based on asymmetric bargaining:

[0136]

[0137] Balancing multiple constraints on transactions The decoupling is converted to a double-coupled constraint as follows:

[0138]

[0139] in, represents the electricity price that microgrid i expects to trade with microgrid j, It means the amount of electricity that microgrid j expects to trade with microgrid i. Once it meets This indicates that a consensus on electricity trading has been reached between microgrid i and microgrid j.

[0140] Preferably, the method of using the adaptive alternating term multiplier algorithm to perform distributed solution to the multi-microgrid benefit maximization subproblem and the asymmetric bargaining-based benefit allocation subproblem is:

[0141] Multi-microgrid cost minimization sub-problem:

[0142] 6) Establish the augmented Lagrangian function of the multi-microgrid cost minimization subproblem

[0143]

[0144] In the formula, For the Lagrange multiplier, set the penalty parameter

[0145] 7) Each microgrid updates its own transaction power strategy through local calculation. Microgrids only exchange transaction power information. In each iteration, microgrid i updates the decision of microgrid i through the following formula:

[0146]

[0147] Other microgrids j receive updated decision information To update the decision of other microgrid j

[0148]

[0149] Repeat the calculation of the two equations in step 2) until each microgrid updates its own transaction power strategy in this iteration;

[0150] 8) After one round of iteration, the Lagrange multiplier is updated according to the rule:

[0151]

[0152] 9) Update the number of iterations v = v + 1;

[0153] 10) Determine the convergence situation:

[0154]

[0155] If the above equation is satisfied, the iteration is terminated, otherwise it returns to step 2) to enter the next round of iteration;

[0156] The sub-problem of profit allocation based on asymmetric bargaining:

[0157] ① Calculate the contribution factor ψ of each microgrid after participating in the power sharing cooperation i , construct the Lagrangian function of the objective function of the revenue allocation sub-problem of asymmetric bargaining

[0158]

[0159] In the formula, For the Lagrange multiplier, set the penalty parameter

[0160] ② Each microgrid calculates its own transaction price strategy locally, and only the updated price information is exchanged between microgrids. In each iteration, microgrid i updates the decision of microgrid i by the following formula:

[0161]

[0162] Other microgrids j receive updated decision information To update the decision of other microgrid j

[0163]

[0164] Repeat the calculation of the two equations in step ② until each microgrid updates its trading power strategy in this iteration;

[0165] ③After one round of iteration, the Lagrange multiplier is updated according to the rules:

[0166]

[0167] ④ Update the number of iterations v = v + 1;

[0168] ⑤Judge the convergence situation:

[0169]

[0170] If the above formula is satisfied, the iteration is terminated, otherwise it returns to step ② to enter the next round of iteration;

[0171] When solving the subproblem, the raw residual of P2P transaction volume and the dual residual The calculation formula is as follows:

[0172]

[0173] Adaptive parameters With adaptive parameters Adjust the step size according to the residual state. When the original residual is relatively large, increase the step size; when the original residual is relatively small, shorten the step size.

[0174]

[0175] In the formula, v represents the number of iterations, I represents the total number of specified convergences, represents the iteration step length, is the Lagrange multiplier corresponding to the subproblem;

[0176] The convergence criterion of the adaptive alternating term multiplier algorithm is as follows:

[0177]

[0178] Where ζ represents the convergence residual.

[0179] Compared with the prior art, the present invention has the following beneficial effects: the present invention ensures the fair distribution of the cooperative benefits of multiple microgrids by establishing a multi-microgrid point-to-point distributed energy trading framework; the low-carbonization level of the microgrid is reduced by equipping the microgrid with a new type of cogeneration unit coupled with power-to-gas-carbon capture; the low-carbonization level of the microgrid is further reduced by incorporating a stepped carbon-green certificate (GCT-CET) joint trading mechanism into the operating framework of the multi-microgrid system. The distributed solution of sub-problems is performed by using the adaptive alternating direction method of multiplier (ADMM) algorithm to improve the computational efficiency. The invention achieves fair distribution according to the energy contribution of each microgrid while maximizing the benefits of multiple microgrids, providing support for efficiently and stably solving the low-carbonization and economic problems of multiple microgrids. BRIEF DESCRIPTION OF THE DRAWINGS

[0180] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0181] Figure 1 This is the multi-microgrid point-to-point energy trading framework of the present invention.

[0182] Figure 2 It is a schematic diagram of the interaction principle between carbon trading and green certificate trading of the present invention.

[0183] Figure 3 The typical output scenarios and distribution probabilities of each microgrid new energy in one embodiment of the present invention. Figure 3 -(a) is a typical wind power output scenario of microgrid 1, Figure 3 -(b) is a typical photovoltaic output scenario of microgrid 2. Figure 3 -(c) is a typical photovoltaic output scenario of microgrid 3. Figure 3 -(d) is the distribution probability of each typical scenario.

[0184] Figure 4 The data of power load and heat load of each microgrid in one embodiment of the present invention.

[0185] Figure 5 This is the result of P2P transaction between microgrids in one embodiment of the present invention.

[0186] Figure 6 It is the transaction price after asymmetric bargaining in one embodiment of the present invention.

[0187] Figure 7This is the energy purchase situation in different scenarios in one embodiment of the present invention.

[0188] Figure 8 It is the iterative convergence result of solving the subproblem P1 of the total benefit of the microgrid alliance using the adaptive alternating term multiplier (AADMM) algorithm in one embodiment of the present invention; wherein, Figure 8 -(a) is the cost iteration of microgrid 1, Figure 8 -(b) is the cost iteration of microgrid 2, Figure 8 -(c) is the cost iteration of microgrid 3, Figure 8 -(d) Total benefits of multiple microgrids.

[0189] Fig. 9 It is the iterative convergence result of solving the subproblem P2 microgrid alliance bargaining by the adaptive alternating term multiplier (AADMM) algorithm in one embodiment of the present invention; wherein, Fig. 9 -(a) is the price negotiation iteration of microgrid 1, Fig. 9 -(b) is the price negotiation iteration of microgrid 2, Fig. 9 -(c) is the price negotiation iteration of microgrid 3, Fig. 9 -(d) is the distribution of iterative residuals. DETAILED DESCRIPTION

[0190] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0191] A multi-microgrid shared operation optimization method considering electricity-carbon-green certificate trading is proposed. First, in order to ensure the fair distribution of the cooperative benefits of multi-microgrids, a multi-microgrid peer-to-peer (P2P) energy trading framework is established. Secondly, in order to enhance the low-carbonization level of microgrids, a multi-microgrid system model equipped with a new combined heat and power (CHP) unit with coupled power-to-gas (P2G)-carbon capture systems (CCS) is established in the microgrid. Then, based on the multi-microgrid system model and Nash negotiation theory, a multi-microgrid low-carbon operation model considering the step-by-step carbon-green certificate (GCT-CET) joint trading mechanism is established, and the main problem is decomposed into the multi-microgrid benefit maximization subproblem and the cooperative benefit allocation subproblem. Finally, in order to improve the computational efficiency, the multi-microgrid benefit maximization subproblem and the cooperative benefit allocation subproblem are solved in a distributed manner based on the adaptive alternating direction method of multiplier (AADMM).

[0192] Step 1: To ensure fair distribution of the benefits of multi-microgrid cooperation, a multi-microgrid point-to-point energy trading framework is established.

[0193] like Figure 1 As shown in the figure, each microgrid integrates a variety of "source-grid-load-storage" components (i.e., power supply, grid, load and energy storage system) to achieve mutual complementarity between the three energy forms of electricity, heat and gas. Wind turbine (WT), photovoltaic (PV) and CHP units constitute the energy supply side of the microgrid body to meet the needs of electricity and heat loads. P2G equipment and gas boiler (GB) realize flexible conversion and coupling between different energy sources in the microgrid. Low-carbon technology equipment includes CCS equipment and P2G equipment. The energy storage equipment in the microgrid is supported by batteries to provide necessary energy regulation and backup support for the microgrid. The various entities in the multi-microgrid are located in the same area. Considering the long distance between the areas, the loss of cold and heat energy transmission is relatively large. Therefore, only power links are laid between the microgrids for P2P power trading.

[0194] Step 2: Establish a multi-microgrid system model equipped with a new type of cogeneration unit with coupled power-to-gas-carbon capture in the microgrid to increase the low-carbonization level of the multi-microgrid system.

[0195] 1. Establish models of each device in the microgrid and obtain the constraints of each device.

[0196] 1.1. Establish a CHP unit model and obtain the constraints of the CHP unit.

[0197] The CHP unit is mainly composed of a gas turbine (GT), a waste heat boiler (WHB) and other equipment. The gas turbine generates electricity by burning natural gas. The high-temperature waste heat gas generated when generating electricity is absorbed by the waste heat boiler and heated by a heat exchanger.

[0198] The expression of the gas turbine's electrical output characteristics and constraints is:

[0199]

[0200] In the formula, i is the microgrid number, t is the time period number, represents the amount of natural gas consumed by the gas turbine, η GT Represents the heat-to-electricity conversion efficiency of the gas turbine, represents the electrical power output of the gas turbine, is the calorific value of natural gas, which is 9.7MJ / m 3 , represents the minimum output power of the gas turbine, Indicates the maximum output power of the gas turbine, ΔP i GT is the maximum climbing power.

[0201] The heat output characteristic of the waste heat boiler is expressed as:

[0202]

[0203] Where η WHB is the heating coefficient of the waste heat boiler.

[0204] Referring to the constraint of “determining electricity by heat”, the expression of the thermal power limit condition of the CHP unit is:

[0205]

[0206] In the formula, C V1 is the electric-to-heat conversion coefficient of the CHP unit corresponding to the minimum output power, C V2 is the electric-to-heat conversion coefficient of the CHP unit corresponding to the maximum output power, h m is the linear supply slope of the CHP thermal power, It is the thermal power corresponding to the minimum power generation of the CHP unit.

[0207] 1.2. Establish a gas boiler model and obtain the constraints of the gas boiler.

[0208] The gas boiler is a heating device that converts chemical energy into thermal energy by burning natural gas and other fuels. When the thermal power output of the CHP unit cannot meet the thermal load demand, the thermal load shortage is supplemented by heating through the gas boiler. The thermal output characteristics and constraints of the gas boiler are expressed as follows:

[0209]

[0210] Where η GB is the heating efficiency of GB, is the minimum output power of GB, is the maximum output power of GB, represents the amount of natural gas consumed by GB, is the maximum climbing power.

[0211] 1.3. Establish a battery model and obtain the constraints of the battery.

[0212] The battery is used to store the excess power of each unit and release power to balance the power shortage when the unit output is insufficient. The present invention takes into account the charging and discharging cost of the battery, but does not consider the power loss of the battery. The output characteristics and constraints of the battery are expressed as follows:

[0213]

[0214] In the formula, represents the storage capacity of the battery of microgrid i at time t, η ch Represents the charging efficiency of the energy storage device, η dis Represents the discharge efficiency of the energy storage device, Represents the charging efficiency of the battery. Represents the discharge power of the battery, Represents the maximum battery charging power. Represents the maximum battery discharge power. A 0-1 variable representing the battery charge, A 0-1 variable representing the battery discharge, Represents the minimum value of the battery storage capacity. Represents the maximum value of the battery storage capacity. Represents the initial value of the battery in the entire scheduling cycle, Represents the final value of the battery during the entire dispatch cycle.

[0215] 1.4. Construct a new cogeneration unit model of coupled power-to-gas-carbon capture and obtain the constraints of the new cogeneration unit.

[0216] After the CCS equipment and P2G equipment are added to the CHP unit, the electric power generated by the gas turbine is supplied to the CCS equipment, P2G equipment and electric load. The power relationship is expressed as follows:

[0217]

[0218] In the formula, Represents the electrical power output of the CHP unit, Represents the electrical power supplied by the CHP unit to the P2G equipment, Represents the electrical power supplied by the CHP unit to the CCS equipment.

[0219] The operating characteristics and power constraints of P2G devices are expressed as follows:

[0220]

[0221] In the formula, represents the amount of natural gas consumed by the P2G device, α is the amount of natural gas produced by the P2G device per unit of electrical power consumed, represents the amount of CO2 consumed by the P2G device to generate natural gas, and β is the amount of CO2 that can be reacted by P2G per unit of electrical power. and They represent the minimum power and maximum power consumed by the P2G device respectively.

[0222] The expressions of the operating characteristics and power constraints of CCS equipment are as follows:

[0223]

[0224] Where η CCS The electrical power consumed by the CCS equipment to capture a unit of CO2; represents the amount of captured CO2, and They are respectively the lower and upper limits of the electric power consumed by CCS equipment.

[0225] After adding CCS equipment and P2G equipment to the CHP unit, a new coupling relationship will be generated. The heat-to-electricity constraint of the CHP unit is coupled with the electric power of the CCS equipment and the P2G equipment, and the constraint expression of the new CHP unit considering the electric power of the P2G and CCS equipment is obtained as follows:

[0226]

[0227] The heat-to-power constraint of the CHP unit is coupled with the natural gas output of P2G, and the expression of the upper and lower limit constraints of the gas production power of the new CHP unit is obtained:

[0228]

[0229] 2. Establish a user demand response model to obtain the constraints of load power before and after demand response. The user demand response model includes a demand response electric load model and a demand response thermal load model.

[0230] 2.1. The demand response electric load includes fixed electric load, transferable electric load and curtailable electric load.

[0231] The expression of the constraint condition of the transferable electric load is as follows:

[0232]

[0233] In the formula, ν1 is the proportion of transferable load to total load, T is the total number of time periods, represents the electric load before demand response, Represents transferable electrical load.

[0234] The expression of the constraint condition for reducing the electric load is as follows:

[0235]

[0236] Where ν2 is the proportion of transferable load to total load, To reduce the electrical load.

[0237] 2.2. The demand response heat load includes a fixed heat load and a curtailable heat load.

[0238] The expression of the constraint condition that can reduce the heat load is as follows:

[0239]

[0240] Where ν3 is the proportion of transferable load to total load, This means that the electrical load can be reduced. Represents the heat load before demand response.

[0241] 2.3. The expression of the load power constraint before and after the demand response is:

[0242]

[0243] In the formula, represents the electric load after demand response, and Represents the heat load after demand response.

[0244] Step 3. Based on the multi-microgrid system model, a multi-microgrid low-carbon operation model considering the stepped carbon-green certificate (GCT-CET) joint trading mechanism is established. The multi-microgrid low-carbon operation model includes a cost optimization model for multi-microgrid electricity cooperative sharing and constraints on electrical power balance, thermal power balance, gas consumption balance and P2P electricity trading in the multi-microgrid system.

[0245] The cost optimization model of multi-microgrid power cooperative sharing is:

[0246]

[0247] In the formula, is the total operating cost of the i-th microgrid participating in power sharing, is the operation and maintenance cost of the new cogeneration unit, is the battery operation degradation cost, is the external interaction cost, is the demand response cost, is the CET cost of the microgrid, is the GCT cost, To cover the network cost, The operating cost of P2G.

[0248] Furthermore, the operation and maintenance cost of the new cogeneration unit is Including gas turbine operating costs P2G operating costs Operating costs with CCS

[0249]

[0250] In the formula, a GT , b GT 、c GT are the operating cost coefficients of the gas turbine, c P2G With c CCS are the operating cost coefficients of P2G equipment and CCS equipment respectively.

[0251] Furthermore, the battery operation degradation cost is:

[0252]

[0253] In the formula, It represents the degradation cost of charging and discharging one unit of energy.

[0254] Furthermore, the external interaction cost Including the cost of purchasing natural gas and the cost of purchasing electricity from the upper-level power grid:

[0255]

[0256] In the formula, Represents the price of natural gas purchased. Represents the amount of natural gas purchased, Represents the price of electricity purchased from the upper power grid, is the amount of electricity purchased from the upper power grid, and The price and quantity of electricity sold to the superior power grid.

[0257] Furthermore, the demand response cost Including economic compensation to users for reducing and shifting electric load and reducing heat load demand:

[0258]

[0259] In the formula, is the compensation unit price for transferable electric load, is the compensation unit price for reducing the electric load, This is the compensation unit price for reducing the heat load.

[0260] Furthermore, the CET cost of the microgrid is GCT Cost See the introduction to the stepped GCT-CET joint trading mechanism below.

[0261] Furthermore, when transmitting electricity between microgrids, a transmission fee must be paid to the distribution network operator. Therefore, the transmission fee cost is for:

[0262]

[0263] In the formula, a e is the grid-connection fee per unit of electricity, is the amount of electricity traded, and the set of multi-microgrids is defined as Γ, i.e., i, j∈Γ and j≠i.

[0264] Furthermore, when microgrids transmit electricity, they need to pay a transaction price to each other. Therefore, the operating cost of P2G is:

[0265]

[0266] The expression of the constraint condition of the electric power balance is as follows:

[0267]

[0268] are the photovoltaic and wind power consumption at time t respectively.

[0269] The expression of the constraint condition of thermal power balance is as follows:

[0270]

[0271] The expression of the constraint condition of the gas consumption balance is as follows:

[0272]

[0273] The expression of the constraint condition of the P2P electric energy transaction is as follows:

[0274]

[0275] In the formula, represents the maximum transmission power of the interconnection line between microgrids, Represents the payment corresponding to the total amount of electricity traded by microgrid i in time period t.

[0276] In response to the low-carbon policy, CET and GCT trading mechanisms have been widely introduced into the power system. Both trading mechanisms play a guiding role in optimizing resource allocation through free trading. Figure 1 The multi-microgrid point-to-point energy trading framework shown in the figure can explore the low-carbon economic operation potential of multi-energy microgrids with the cooperation of multiple trading mechanisms. Compared with the single-price carbon-green certificate trading market, the ladder-type carbon-green certificate trading market can better enhance the enthusiasm of the carbon-green certificate trading market and play a positive guiding role in microgrid carbon emission reduction. To this end, the present invention couples the ladder-type GCT-CET joint trading mechanism to the operating framework of the multi-microgrid system, where the interaction principle of GCT and CET is as follows: Figure 2 As shown, the specific implementation process is as follows:

[0277] 1. Tiered carbon trading mechanism.

[0278] At present, my country's free carbon quota allocation mechanism mainly includes the baseline method, historical intensity method and historical emission method. In view of the fact that the carbon emission units in industrial parks have stable output all year round, the present invention adopts the baseline method to allocate the amount of carbon quota. The carbon emissions in individual microgrids mainly come from new CHP units, gas turbines and external power purchases, and it is believed that external power purchases come from coal-fired units. Therefore, the carbon quota in the microgrid The distribution is:

[0279]

[0280] In the formula, They represent the carbon quotas obtained by the microgrid from purchasing electricity from the upper power grid, the output of the new CHP unit and the output of the gas boiler. Represents the microgrid individual purchasing power from the upper grid, Represents the electricity demand supplied by the new CHP unit, and Respectively represent the heating power of the new CHP unit and the gas boiler in period t, ξ e,h represents the conversion factor from power generation to heat supply of the new CHP unit, ξ e With ξ h Represents the carbon emission quota obtained by generating unit electricity and unit heat respectively.

[0281] Considering that the CCS-P2G system will absorb part of the CO2 emitted in the microgrid, the actual carbon emissions in the microgrid It is expressed as:

[0282]

[0283] In the formula, Represents the carbon emissions within the microgrid within a scheduling cycle; Represents the carbon emissions caused by external power purchases within a dispatch cycle; They are the CO2 emission coefficients of the new CHP units and gas boilers; represents the amount of carbon dioxide emissions from the system reduced after the addition of CCS equipment and P2G equipment; h1 is the electric-heat conversion coefficient of the new CHP unit corresponding to the minimum output power; ψ Grid is the carbon emission intensity coefficient of coal-fired units.

[0284] The emission rights of individual microgrids are calculated by the following formula:

[0285]

[0286] Therefore, the CET cost of an individual microgrid is:

[0287]

[0288] In the formula: λ is the carbon trading price, l is the length of the carbon emission interval, α is the price growth rate, and the three parameter values ​​of λ, l, and α are 250 yuan / t, 2t, and 0.25 respectively.

[0289] 2. Tiered green certificate trading mechanism.

[0290] Similar to the CET trading mechanism, the GCT trading mechanism also combines the quota allocation system with the green certificate trading system. d It is expressed as:

[0291]

[0292] In the formula, α Green is the green certificate quota coefficient, Represents the electrical load after demand response.

[0293] Usually one green certificate represents 1MWh of renewable energy grid-connected electricity. Therefore, the actual number of green certificates held by a microgrid individual is G s It is expressed as:

[0294]

[0295] In the formula, G s Represents the number of green certificates actually obtained by individual microgrids, are the photovoltaic and wind power consumption at time t respectively.

[0296] The amount of green certificates that microgrid individuals participate in green certificate transactions:

[0297]

[0298] The relevant notice of the National Energy Administration stated: "For green certificates of projects enjoying central government subsidies, bilateral negotiation and listing will be mainly adopted in the initial stage." Among them, the bilateral negotiation plan includes negotiating transaction prices and transaction volumes. Therefore, in the early stage of trading, the government intends to delegate the power to set green certificate prices to each green certificate trading participant, which brings a certain application space for tiered green certificate trading. In addition, the notice also made it clear: "Study and promote the connection and coordination of green certificates with the national carbon emission trading mechanism and the voluntary greenhouse gas emission reduction trading mechanism." Therefore, with the connection and coordination of the GCT mechanism and the CET mechanism, the price of green certificates may be benchmarked against carbon prices, and corresponding price fluctuations will occur. Therefore, the present invention proposes a tiered green certificate trading mechanism by analogy with the tiered carbon trading mechanism, and the GCT cost is:

[0299]

[0300] In the formula: γ is the price of green certificate; d is the length of the green certificate trading volume interval; β is the price growth rate, and the three parameter values ​​of γ, d, and β are 80 yuan / piece, 100 pieces, and 0.25 respectively.

[0301] 3. Step-by-step GCT-CET joint trading mechanism.

[0302] Green certificates can realize the linkage between the CET market and the GCT market. When assessing the carbon emission rights of microgrids, the excess green certificates can be used to offset part of the carbon emissions to obtain more carbon emission rights. When the green certificate seller sells the green certificate, the renewable energy environmental attributes represented by the green certificate have been transferred from the seller to the buyer. At this time, attention should be paid to the ownership of green rights to avoid double counting of renewable energy environmental attributes.

[0303] Therefore, the carbon emissions that can be offset by green certificates are E reduce for:

[0304] Ereduce =E coal -E res (34)

[0305] In the formula, E coal and E res They are the carbon emission equivalents of coal-fired energy supply and new energy energy supply in the life cycle of their respective industrial chains.

[0306] Under the influence of the tiered GCT-CET joint trading mechanism, the carbon emission rights of microgrid individuals Updated to:

[0307]

[0308] The updated carbon emission rights of microgrid individuals Substituting into formula (29), we can get the CET cost of microgrid individuals under the step-by-step GCT-CET joint trading mechanism:

[0309] Step 4. Based on the Nash negotiation theory, the original problem of the multi-microgrid low-carbon operation model is decomposed into the multi-microgrid cost minimization sub-problem and the profit allocation sub-problem based on asymmetric bargaining. The adaptive alternating direction method of multiplier (ADMM) is used to distribute the multi-microgrid cost minimization sub-problem and the profit allocation sub-problem based on asymmetric bargaining to improve the computational efficiency.

[0310] 1. Nash negotiation model.

[0311] Nash negotiation is a cooperative game. After maximizing the interests of all participants, multiple participants distribute the benefits of cooperation through mutual negotiation, and satisfy the basic demands of the subjects for "rationality", "symmetry" and "social optimality".

[0312] The P2P power trading process is divided into two stages: energy trading volume negotiation and energy trading price negotiation. In the first stage, each microgrid first calculates its own power demand and communicates the expected power trading volume to other microgrids. With the continuous update of power trading volume information transmission and reception, the optimal power trading volume of each microgrid can be obtained. After the optimal power trading volume is determined in the first stage, the second stage needs to determine the power trading price between each microgrid to obtain the final benefits of each entity. In the second stage, each microgrid communicates the expected power trading price to other microgrids. As the power trading price is continuously updated, the optimal power trading price of each entity in the multi-microgrid system can be obtained. The specific implementation steps are as follows:

[0313] The Nash negotiation theory model is shown in formula (36):

[0314]

[0315] In the formula, represents the operating cost of the microgrid before cooperation, i.e., the Nash negotiation breaking point. represents the cost of microgrid i after cooperation, and satisfies the equation The solution under the condition is the Nash equilibrium solution. The Nash negotiation theory model is a non-convex nonlinear problem with multiple variables coupled. Therefore, the above Nash negotiation theory model is decomposed into two sub-problems: the multi-microgrid cost minimization sub-problem (P1) and the profit allocation sub-problem based on asymmetric bargaining (P2), which are solved in turn.

[0316] 2. Multi-microgrid cost minimization subproblem (P1).

[0317] The present invention assumes that each subject in the multi-microgrid is a rational cooperative subject and takes maximizing their own interests as the goal of cooperation. Therefore, the multi-microgrid benefit maximization problem is equivalent to the multi-microgrid cost minimization problem. The saved cost is the benefit achieved by cooperation. A model of the multi-microgrid cost minimization sub-problem (P1) based on the Nash negotiation theory model (36) is constructed, that is, the multi-microgrid power cooperation sharing model is as follows:

[0318]

[0319] The multi-microgrid power cooperative sharing model is limited by the constraints in the multi-microgrid system model and the constraints in the multi-microgrid low-carbon operation model. Since the constraints of P2P power trading are multiple coupled between all microgrids, auxiliary variables need to be introduced to solve the variable vector The multi-microgrid cost minimization subproblem (P1) is transformed into a dual coupling model as follows:

[0320]

[0321] In the formula, represents the amount of electricity that microgrid i expects to trade with microgrid j, It represents the amount of electricity that microgrid j expects to trade with microgrid i. Once formula (38) is satisfied, it indicates that a consensus on electricity trading has been reached between microgrid i and microgrid j.

[0322] The specific steps of distributed solution of multi-microgrid cost minimization subproblem (P1) based on ADMM algorithm are as follows:

[0323] 1) Establish the augmented Lagrangian function of subproblem P1

[0324]

[0325] In the formula, For the Lagrange multiplier, set the penalty parameter

[0326] 2) Each microgrid updates its own transaction power strategy through local calculation. Microgrids only exchange transaction power information. In each iteration, microgrid i updates the decision of microgrid i through equation (40):

[0327]

[0328] Other microgrids j receive updated decision information To update the decision of other microgrid j

[0329]

[0330] Repeat the calculation of equations (40) and (41) until each microgrid updates its own transaction power strategy in this iteration.

[0331] 3) After one round of iteration, the Lagrange multiplier is updated according to the rule:

[0332]

[0333] 4) Update the number of iterations v = v + 1.

[0334] 5) Determine the convergence situation:

[0335]

[0336] If equation (43) is satisfied, the iteration is terminated, otherwise, return to step 2) to enter the next round of iteration.

[0337] 3. The sub-problem of profit allocation based on asymmetric bargaining (P2).

[0338] Since the symmetric Nash bargaining payment benefit model will result in the same income distribution for each participant, it does not take into account the different contribution levels of all participants and cannot guarantee the fairness of benefit distribution. The asymmetric Nash bargaining model can give each participant different bargaining power according to the different contribution levels of each participant. Therefore, the present invention adopts a nonlinear energy mapping function based on natural logarithms to quantify the contribution level of each microgrid in collaborative operations, and the calculation formula is as follows:

[0339]

[0340] In the formula, ψ i is the contribution factor, P i-j represents the bargaining factor, P i s and Pi r are the power provided and received by the microgrid individuals during the scheduling period, P i s,max and P i r,max are the maximum power provided and received by the microgrid, respectively. When the contribution factor ψ i When it is larger, it means higher contribution of electric energy and more benefits.

[0341] According to Nash negotiation theory, the payment benefit maximization model based on asymmetric Nash negotiation is as follows:

[0342]

[0343] In the formula, Represents the optimal value of the microgrid individuals after participating in the cooperation. In order to facilitate the solution, the problem of solving the maximum value is transformed into the problem of solving the minimum value. The converted model is the model of the income distribution sub-problem (P2) based on asymmetric bargaining:

[0344]

[0345] Balancing multiple constraints on transactions The decoupling is converted into a double coupling constraint as shown in equation (47):

[0346]

[0347] same, represents the electricity price that microgrid i expects to trade with microgrid j, It represents the amount of electricity that microgrid j expects to trade with microgrid i. Once formula (47) is satisfied, it indicates that a consensus on electricity trading has been reached between microgrid i and microgrid j.

[0348] The specific steps of the distributed solution of the profit allocation sub-problem (P2) based on asymmetric bargaining based on the ADMM algorithm are as follows:

[0349] ① Calculate the contribution factor ψ of each microgrid after participating in the power sharing cooperation i .

[0350] Construct the Lagrangian function of the objective function of subproblem P2

[0351]

[0352] In the formula, For the Lagrange multiplier, set the penalty parameter

[0353] ② Each microgrid calculates its own transaction price strategy locally, and only the updated price information is exchanged between microgrids. In each iteration, microgrid i updates the decision of microgrid i through equation (49):

[0354]

[0355] Other microgrids j receive updated decision information To update the decision of other microgrid j

[0356]

[0357] Repeat the calculation of equations (49) and (50) until each microgrid updates its trading power strategy in this iteration.

[0358] ③After one round of iteration, the Lagrange multiplier is updated according to the rules:

[0359]

[0360] ④Update the number of iterations v=v+1.

[0361] ⑤Judge the convergence situation:

[0362]

[0363] If equation (52) is satisfied, the iteration is terminated; otherwise, the process returns to step ② and enters the next round of iteration.

[0364] 4. Solution of the adaptive ADMM algorithm.

[0365] When solving the subproblem, the raw residual of P2P transaction volume and the dual residual The calculation formula is as follows:

[0366]

[0367] In order to solve the problem of uneven residual convergence speed, the adaptive parameter k1 is used ADMM With adaptive parameter k2 ADMM , the step size is flexibly adjusted according to the residual state. When the original residual is relatively large, increasing the step size speeds up the convergence; when the original residual is relatively small, shortening the step size reduces the oscillation of the objective function.

[0368]

[0369] In the formula, v represents the number of iterations, I represents the total number of specified convergences, which is 100 in the present invention. represents the iteration step length, and the present invention takes 1×10 -4 , is the Lagrange multiplier corresponding to the subproblem, and the adaptive parameter With adaptive parameters Take 2.

[0370] The convergence criterion of the AADMM algorithm is shown in formula (56):

[0371]

[0372] Where ζ represents the convergence residual, and the present invention takes 1×10 -3 .

[0373] To verify the rationality of the present invention, a P2P power sharing transaction problem among three microgrids is adopted. Microgrid 1 and microgrid 3 are conventional CHP units, while microgrid 2 is coupled with a new CHP unit containing P2G-CCS equipment. The typical data scenarios and distribution probabilities of photovoltaic and wind power corresponding to the three microgrids are as follows: Figure 3 As shown, the load data is Figure 4 As shown, the system parameters and operating cost coefficients of each component of the microgrid are shown in Table 1 and Table 2. Physical network constraints such as the distance of power transmission between microgrids have little effect on the key conclusions of the present invention, so they are ignored. In the MATLAB 2022A environment, YALMIP is used to call the CPLEX 12.9 solver to solve subproblem P1, and YALMIP is used to call the MOSEK 10.1 solver to solve subproblem P2.

[0374] Table 1 Multi-microgrid system parameters

[0375]

[0376]

[0377] Table 2 Multi-microgrid operation cost coefficient table

[0378]

[0379] The results of P2P power interaction among microgrids are as follows: Figure 5 As shown in the figure, during the periods of 00:00-07:00 and 19:00-24:00, the wind power generation of microgrid 1 is in surplus, so the power is transmitted to microgrids 2 and 3, which are short of power due to the inability to generate power at night. During the period of 07:00-10:00, the power of microgrid 3 increases slowly and the photovoltaic power generation is in surplus, so the power is transmitted to microgrid 2 together with microgrid 1. During the period of 10:00-19:00, microgrid 1 has insufficient power supply due to the surge in power load during the day, and the photovoltaic power generation of microgrids 2 and 3 is at its peak at this time, so they are transmitted to microgrid 1 respectively.

[0380] The costs and benefits of each microgrid before and after the cooperation are shown in Table 3. It is calculated that the overall benefits of multiple microgrids are improved by 15,088.47 yuan. The asymmetric bargaining method is adopted to distribute benefits asymmetricly according to the amount of energy contributed by each microgrid in the process of power sharing, and the benefits are increased by 6,411.09, 5,115.53 and 3,561.85 yuan respectively; the benefit increase ratios are 12.85%, 19.58% and 26.13% respectively. This shows that through P2P power sharing, the interests of the participating entities are effectively improved, and each entity can obtain a fair amount of benefits according to the size of its power contribution.

[0381] Table 3 Cost and benefit analysis before and after cooperation

[0382]

[0383] In order to prove the effectiveness of the asymmetric bargaining method proposed in this study in terms of fair benefits, the standard Nash bargaining method was selected for comparison. When the benefits are distributed based on the standard Nash negotiation method, as shown in Table 4, the benefit improvement of each subject is basically equal, about 4638.72 yuan. When the energy contribution of each microgrid in the cooperation is different, this method is obviously unfair. When the asymmetric bargaining method proposed in this study is adopted, it can be seen from Table 5 that microgrid 1 has a larger energy contribution value, and accordingly, it obtains a larger asymmetric bargaining factor of 2.3504 after calculation, that is, its energy mapping function has a larger exponent, which tends to allocate more cooperation benefits to the microgrid; microgrids 2 and 3 have smaller new energy power generation, and most of them are receiving electric energy, so their asymmetric bargaining factors are also smaller, 2.1408 and 0.8481 respectively, and finally they obtain less benefit distribution from energy cooperation.

[0384] Table 4 Benefit distribution of standard model

[0385]

[0386] Table 5 Benefit distribution of asymmetric bargaining model

[0387]

[0388]

[0389] The final bargaining result is as follows Figure 6 As shown. Figure 6 It can be seen that multiple microgrids negotiate and set P2P transaction electricity prices based on the proposed asymmetric bargaining method, and the transaction electricity prices in each period are within the range of the upstream power grid's electricity sales and purchase prices. Therefore, P2P power trading can sell electricity at a price higher than the power grid's electricity purchase price, and purchase new energy electricity at a price lower than the power grid's electricity sales price, thus effectively increasing the revenue of each microgrid.

[0390] In order to verify the impact of the stepped GCT-CET joint trading mechanism proposed in the present invention on the cost and low-carbon performance of the microgrid, based on the scheme proposed in the present invention and referring to the existing research scenarios at home and abroad, multiple scenarios in Table 6 are set, and the optimization results are shown in Table 7.

[0391] As shown in Table 7, the cost of using scenario 4, which is the solution proposed in this paper, is the lowest. This is because the multi-microgrid system, after considering the step-by-step GCT-CET joint trading mechanism, ensures that the renewable energy within the multi-microgrid is fully absorbed, reduces the purchase of high-carbon emission energy from the upper coal-fired units, and uses the carbon trading market to sell carbon emission rights and the green certificate trading market to sell green certificates to obtain income and reduce system costs. Comparing carbon emissions, it can be seen that the carbon emissions of scenario 2 and scenario 4 are the smallest, but the total cost of scenario 4 is the lowest.

[0392] Table 6 Multi-microgrid scenario settings

[0393]

[0394] Table 7 Optimization results for different scenarios

[0395]

[0396] In each scenario, the upstream power purchase and natural gas consumption of multiple microgrids are as follows: Figure 7 As shown. Comparing the four scenarios, scenario 1 only considers the GCT mechanism, which promotes the consumption of new energy within the microgrid and reduces the amount of electricity purchased by the superior. Scenario 2 considers the stepped GCT mechanism, using the price incentives in the carbon trading market to suppress the purchase of high-carbon emission "grey electricity" from the superior power grid, and obtains carbon quotas by increasing the output of gas-fired units using natural gas as raw materials. Scenario 3 considers the ordinary GCT-CET joint trading mechanism. Although it can use the sale of green certificates to offset the carbon trading volume to ease the pressure on the carbon trading market, and at the same time, the "green electricity" obtained by consuming green certificates is supplied to the internal new CHP units for water electrolysis and gas recombination to reduce the demand for external natural gas, it ignores the price incentives and emission reduction motivation of the carbon market, thereby purchasing a large amount of electricity from the superior power grid, which cannot play a good incentive role in limiting the carbon emissions of thermal power generation. Scenario 4 considers a stepped GCT-CET joint trading mechanism, which maintains a high energy self-sufficiency rate and complete absorption of new energy, gets rid of dependence on the upper power grid, and uses price incentives from the carbon trading market and the green certificate trading market to reduce costs. Through transactions between microgrids and increased natural gas supply to microgrids equipped with traditional CHP units, the low-carbon characteristics of the microgrid are more obvious. Therefore, the stepped GCT-CET joint trading mechanism proposed in the present invention can take into account both environmental protection and economy.

[0397] The present invention adopts AADMM algorithm to solve subproblems P1 and P2 in a distributed manner. The iterative convergence is as follows: Figure 8 and Fig. 9 shown. Figure 8 The iterative convergence results of the microgrid costs in subproblem P1 are given. The proposed algorithm reaches 10 after 27 iterations. -3 Within, the calculation time is 251.4s; Fig. 9 This is the iterative convergence result of the transaction price in sub-problem P2. The residual converges to 10 after 7 iterations. -3 The calculation time is within 25.4s. Therefore, it can be seen that the distributed optimization algorithms proposed in this paper have good convergence performance and computational efficiency, taking into account the privacy protection of each subject and the needs of day-ahead optimization scheduling.

[0398] To illustrate the advantages of the adaptive ADMM (AADMM) algorithm proposed in this paper, the solution time of the two algorithms with different step sizes is listed in Table 8. From the analysis of Table 8, it can be seen that the adaptive ADMM algorithm reduces the dependence on the initial value selection by correcting the step size, smoothes the bargaining process at the beginning of the iteration, thereby reducing the number of iterations and solution time, and improving the solution efficiency.

[0399] Table 8 Comparison of solution time between standard ADMM and AADMM based on different step lengths

[0400]

[0401]

[0402] The above simulation results verify the effectiveness and practicality of the method of the present invention. In the context of a multi-microgrid with complementary electricity, heat and gas energy, the present invention establishes a multi-microgrid low-carbon operation model that couples a new CHP unit operation mode of CCS-P2G and considers a step-type GCT-CET joint trading mechanism. Based on the Nash negotiation theory, an asymmetric Nash bargaining strategy for multi-microgrids is established, and the adaptive ADMM algorithm is used to efficiently and stably solve the problem. While maximizing the benefits of multi-microgrids, fair distribution is achieved according to the energy contribution of each microgrid, effectively reducing carbon emissions during the operation of microgrids while ensuring economic benefits.

[0403] The above is a detailed description of the embodiments of the present invention in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in the field without departing from the purpose of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-microgrid shared operation optimization method considering electricity-carbon-green certificate trading, characterized in that: The following steps are involved: Step 1: Establish a multi-microgrid point-to-point energy trading framework; Step 2: Establish a multi-microgrid system model with a novel cogeneration unit coupled with power-to-gas-carbon capture in the microgrid; Step 3: Based on the multi-microgrid system model, establish a multi-microgrid low-carbon operation model that considers the step-by-step carbon-green certificate joint trading mechanism; Step 4. Based on the Nash negotiation theory, the original problem of the multi-microgrid low-carbon operation model is decomposed into the multi-microgrid cost minimization sub-problem and the profit allocation sub-problem based on asymmetric bargaining. The adaptive alternating term multiplier algorithm is used to distribute the multi-microgrid cost minimization sub-problem and the profit allocation sub-problem based on asymmetric bargaining.

2. The multi-microgrid shared operation optimization method considering electricity-carbon-green certificate trading according to claim 1 is characterized in that: The multi-microgrid system model equipped with a novel cogeneration unit coupled with power-to-gas-carbon capture in the microgrid includes models of each device in the microgrid and constraints of each device, a user demand response model and constraints of load power before and after demand response; The device models and constraints of each device in the microgrid are: The CHP unit model and the constraints of the CHP unit are: The expression including the electrical output characteristics and constraints of the gas turbine is: In the formula, i is the microgrid number, t is the time period number, represents the amount of natural gas consumed by the gas turbine, η GT Represents the heat-to-electricity conversion efficiency of the gas turbine, represents the electrical power output of the gas turbine, is the calorific value of natural gas, represents the minimum output power of the gas turbine, Indicates the maximum output power of the gas turbine, ΔP i GT is the maximum climbing power; The expression of the heat output characteristics of the waste heat boiler is: Where η WHB is the heating coefficient of the waste heat boiler; The expression of the thermal power limitation condition of the CHP unit is: In the formula, C V1 is the electric-to-heat conversion coefficient of the CHP unit corresponding to the minimum output power, C V2 is the electric-to-heat conversion coefficient of the CHP unit corresponding to the maximum output power, h m is the linear supply slope of the CHP thermal power, It is the thermal power corresponding to the minimum power generation of the CHP unit; The gas boiler model and the constraints of the gas boiler are: The expressions of the thermal output characteristics and constraints of gas boilers are: Where η GB is the heating efficiency of GB, is the minimum output power of GB, is the maximum output power of GB, represents the amount of natural gas consumed by GB, is the maximum climbing power; The battery model and the constraints of the battery are: The expression of the battery output characteristics and constraints is: In the formula, represents the storage capacity of the battery of microgrid i at time t, η ch Represents the charging efficiency of the energy storage device, η dis Represents the discharge efficiency of the energy storage device, Represents the charging efficiency of the battery. Represents the discharge power of the battery, Represents the maximum battery charging power. Represents the maximum battery discharge power. A 0-1 variable representing the battery charge, A 0-1 variable representing the battery discharge, Represents the minimum value of the battery storage capacity. Represents the maximum value of the battery storage capacity. Represents the initial value of the battery during the entire scheduling cycle, Represents the final value of the battery during the entire dispatch cycle; The model of the new combined heat and power unit coupled with power-to-gas-carbon capture and the constraints of the new combined heat and power unit are: After the CCS equipment and P2G equipment are added to the CHP unit, the electric power generated by the gas turbine is supplied to the CCS equipment, P2G equipment and electric load. The power relationship is expressed as follows: In the formula, Represents the electrical power output of the CHP unit, Represents the electrical power supplied by the CHP unit to the P2G equipment, Represents the electrical power supplied by the CHP unit to the CCS equipment; The operating characteristics and power constraints of P2G devices are expressed as follows: In the formula, represents the amount of natural gas consumed by the P2G device, α is the amount of natural gas produced by the P2G device per unit of electrical power consumed, represents the amount of CO2 consumed by the P2G device to generate natural gas, and β is the amount of CO2 that can be reacted by P2G per unit of electrical power consumed. and They represent the minimum and maximum power consumed by the P2G device respectively; The expressions of the operating characteristics and power constraints of CCS equipment are as follows: Where η CCS The electrical power consumed by the CCS equipment to capture a unit of CO2; represents the amount of captured CO2, and They are the lower and upper limits of the electric power consumed by the CCS equipment; After adding CCS equipment and P2G equipment to the CHP unit, a new coupling relationship is generated. The heat-to-electricity constraint of the CHP unit is coupled with the electric power of the CCS equipment and the P2G equipment, and the expression of the constraint condition of the new CHP unit considering the electric power of the P2G and CCS equipment is obtained: The heat-to-electricity constraint of the CHP unit is coupled with the natural gas output of P2G, and the expression of the upper and lower limits of the gas production power of the new CHP unit is:

3. The multi-microgrid shared operation optimization method considering electricity-carbon-green certificate trading according to claim 2 is characterized in that: The user demand response model includes a demand response electric load model and a demand response thermal load model, and the demand response electric load includes a fixed electric load, a transferable electric load and a curtailable electric load; The expression of the constraint condition of the transferable electric load is as follows: In the formula, ν1 is the proportion of transferable load to total load, T is the total number of time periods, represents the electric load before demand response, represents the transferable electrical load; The expression of the constraint condition for reducing the electric load is as follows: Where ν2 is the proportion of transferable load to total load. To reduce the electrical load; The demand response heat load includes a fixed heat load and a curtailable heat load. The curtailable heat load constraint condition is expressed as follows: Where ν3 is the proportion of transferable load to total load, This means that the electrical load can be reduced. represents the heat load before demand response; The expression of the load power constraint before and after the demand response is: In the formula, represents the electric load after demand response, and Represents the heat load after demand response.

4. The multi-microgrid shared operation optimization method considering electricity-carbon-green certificate trading according to claim 1 or 3 is characterized in that: The multi-microgrid low-carbon operation model includes a cost optimization model for cooperative sharing of multi-microgrid electricity and constraints on electric power balance, thermal power balance, gas consumption balance and P2P electricity trading in the multi-microgrid system; The cost optimization model of multi-microgrid power cooperative sharing is: In the formula, is the total operating cost of the i-th microgrid participating in power sharing, is the operation and maintenance cost of the new cogeneration unit, is the battery operation degradation cost, is the external interaction cost, is the demand response cost, is the CET cost of the microgrid, is the GCT cost, To cover the network cost, The operating cost of P2G; The operation and maintenance costs of the new cogeneration unit include the operating costs of the gas turbine P2G operating costs Operating costs with CCS In the formula, a GT 、b GT 、c GT are the operating cost coefficients of the gas turbine, c P2G With c CCS are the operating cost coefficients of P2G equipment and CCS equipment respectively. The battery operation degradation cost is: In the formula, It represents the degradation cost of charging and discharging one unit of energy; The external interaction costs include the natural gas purchase cost and the electricity purchase cost from the upper power grid: In the formula, Represents the price of natural gas purchased. Represents the amount of natural gas purchased, Represents the price of electricity purchased from the upper power grid, is the amount of electricity purchased from the upper power grid, and The price and quantity of electricity sold to the upper grid; The demand response cost includes the economic compensation given to users for reducing and shifting electric load and reducing thermal load demand: In the formula, is the compensation unit price for transferable electric load, The compensation unit price for reducing the electric load is This is the compensation unit price for reducing the heat load. The network fee cost is: In the formula, a e is the grid-connection fee per unit of electricity, is the amount of electricity traded, and the set of multi-microgrids is defined as Γ, i, j∈Γ and j≠i; The operating cost of P2G is:

5. The multi-microgrid shared operation optimization method considering electricity-carbon-green certificate trading according to claim 4 is characterized in that: The expression of the constraint condition of the electric power balance is as follows: They are the photovoltaic and wind power consumption at time t respectively; The expression of the constraint condition of thermal power balance is as follows: The expression of the constraint condition of the gas consumption balance is as follows: The expression of the constraint condition of the P2P electric energy transaction is as follows: In the formula, represents the maximum transmission power of the interconnection line between microgrids, Represents the payment corresponding to the total amount of electricity traded by microgrid i in time period t.

6. The multi-microgrid shared operation optimization method considering electricity-carbon-green certificate trading according to claim 5 is characterized in that: The method for obtaining the CET cost of the microgrid individual is: Carbon emissions in individual microgrids come from new CHP units, gas turbines and external power purchases. Carbon quotas in microgrids The distribution is: In the formula, They represent the carbon quotas obtained by the microgrid from purchasing electricity from the upper grid, the output of the new CHP unit and the output of the gas boiler. Represents the microgrid individual purchasing power from the upper grid, Represents the electricity demand supplied by the new CHP unit, and Respectively represent the heating power of the new CHP unit and the gas boiler in period t, ξ e,h represents the conversion factor from power generation to heat supply of the new CHP unit, ξ e With ξ h Represents the carbon emission quota obtained by generating unit electricity and unit heat respectively; Actual carbon emissions in microgrids It is expressed as: In the formula, Represents the carbon emissions within the microgrid within a scheduling cycle; Represents the carbon emissions caused by external power purchases within a dispatch cycle; They are the CO2 emission coefficients of the new CHP units and gas boilers; represents the amount of carbon dioxide emissions from the system reduced after the addition of CCS equipment and P2G equipment; h1 is the electric-heat conversion coefficient of the new CHP unit corresponding to the minimum output power; ψ Grid is the carbon emission intensity coefficient of coal-fired units; Calculate the emission rights of individual microgrids: The CET cost of an individual microgrid is: In the formula, λ is the carbon trading price, l is the length of the carbon emission interval, and α is the price growth rate.

7. The multi-microgrid shared operation optimization method considering electricity-carbon-green certificate trading according to claim 6 is characterized in that: The method for obtaining the GCT cost is: Green certificate quota index G that can be obtained by microgrid individuals d It is expressed as: In the formula, α Green is the green certificate quota coefficient, represents the electric load after demand response; The actual number of green certificates held by microgrid individuals G s It is expressed as: In the formula, G s Represents the number of green certificates actually obtained by individual microgrids, They are the photovoltaic and wind power consumption at time t respectively; The amount of green certificates that microgrid individuals participate in green certificate transactions is: By analogy with the tiered carbon trading mechanism, a tiered green certificate trading mechanism is proposed, and the GCT cost is: In the formula, γ is the price of green certificates; d is the length of the green certificate trading volume interval; β is the price growth rate.

8. The multi-microgrid shared operation optimization method considering electricity-carbon-green certificate trading according to claim 6 is characterized in that: The CET cost of the microgrid individual is updated under the tiered GCT-CET joint trading mechanism, specifically: Green certificates can offset carbon emissions E reduce for: AND reduce =And coal -AND res In the formula, E coal and E res They are the carbon emission equivalents of coal-fired energy supply and new energy energy supply in the life cycle of their respective industrial chains; Under the influence of the tiered GCT-CET joint trading mechanism, the carbon emission rights of microgrid individuals Updated to: The updated carbon emission rights of microgrid individuals Substitute it into the CET cost of the individual microgrid to obtain the CET cost of the individual microgrid under the stepped GCT-CET joint trading mechanism.

9. The multi-microgrid shared operation optimization method considering electricity-carbon-green certificate trading according to claim 1 is characterized in that: The method of decomposing the original problem of the multi-microgrid low-carbon operation model into the multi-microgrid benefit maximization sub-problem and the benefit allocation sub-problem based on asymmetric bargaining is as follows: Establishing Nash negotiation theory model: In the formula, represents the operating cost of the microgrid before cooperation, represents the cost of microgrid i after cooperation, and satisfies the equation The solution under the condition is the Nash equilibrium solution; the Nash negotiation theory model is a non-convex nonlinear problem with multiple variable coupling, so the Nash negotiation theory model is decomposed into a multi-microgrid cost minimization sub-problem and a benefit allocation sub-problem based on asymmetric bargaining; Multi-microgrid cost minimization sub-problem: The above formula is subject to the constraints in the multi-microgrid system model and the multi-microgrid low-carbon operation model. Due to the multiple coupling of the constraints of P2P power trading among all microgrids, auxiliary variables are introduced to solve the variable vector The multi-microgrid cost minimization subproblem is transformed into a dual coupling model as follows: In the formula, represents the amount of electricity that microgrid i expects to trade with microgrid j, It means the amount of electricity that microgrid j expects to trade with microgrid i. Once it meets This indicates that a consensus on power trading has been reached between microgrid i and microgrid j; The sub-problem of profit allocation based on asymmetric bargaining: A nonlinear energy mapping function based on natural logarithm is used to quantify the contribution of each microgrid in collaborative operations. The calculation formula is as follows: In the formula, ψ i is the contribution factor, P i-j represents the bargaining factor, P i s and P i r are the power provided and received by the microgrid individuals during the scheduling period, P i s,max and P i r,max are the maximum power provided and received in the individual microgrids, respectively; According to Nash negotiation theory, the payment benefit maximization model based on asymmetric Nash negotiation is as follows: In the formula, Represents the optimal value of the microgrid individuals after participating in the cooperation, transforming the problem of solving the maximum value into the problem of solving the minimum value. The converted model is a model of the income distribution sub-problem based on asymmetric bargaining: Balancing multiple constraints on transactions The decoupling is converted to a double-coupled constraint as follows: in, represents the electricity price that microgrid i expects to trade with microgrid j, It means the amount of electricity that microgrid j expects to trade with microgrid i. Once it meets This indicates that a consensus on electricity trading has been reached between microgrid i and microgrid j.

10. The multi-microgrid shared operation optimization method considering electricity-carbon-green certificate trading according to claim 9 is characterized in that: The method of using the adaptive alternating term multiplier algorithm to perform distributed solution to the multi-microgrid benefit maximization subproblem and the asymmetric bargaining-based benefit allocation subproblem is: Multi-microgrid cost minimization sub-problem: 1) Establish the augmented Lagrangian function of the multi-microgrid cost minimization subproblem In the formula, For the Lagrange multiplier, set the penalty parameter 2) Each microgrid updates its own transaction power strategy through local calculation. Microgrids only exchange transaction power information. In each iteration, microgrid i updates the decision of microgrid i through the following formula: Other microgrids j receive updated decision information To update the decision of other microgrid j Repeat the calculation of the two equations in step 2) until each microgrid updates its own transaction power strategy in this iteration; 3) After one round of iteration, the Lagrange multiplier is updated according to the rule: 4) Update the number of iterations v = v + 1; 5) Determine the convergence situation: If the above equation is satisfied, the iteration is terminated, otherwise it returns to step 2) to enter the next round of iteration; The sub-problem of profit allocation based on asymmetric bargaining: ① Calculate the contribution factor ψ of each microgrid after participating in the power sharing cooperation i , construct the Lagrangian function of the objective function of the revenue allocation sub-problem of asymmetric bargaining In the formula, For the Lagrange multiplier, set the penalty parameter ② Each microgrid calculates its own transaction price strategy locally, and only the updated price information is exchanged between microgrids. In each iteration, microgrid i updates the decision of microgrid i by the following formula: Other microgrids j receive updated decision information To update the decision of other microgrid j Repeat the calculation of the two equations in step ② until each microgrid updates its trading power strategy in this iteration; ③After one round of iteration, the Lagrange multiplier is updated according to the rules: ④ Update the number of iterations v = v + 1; ⑤Judge the convergence situation: If the above formula is satisfied, the iteration is terminated, otherwise it returns to step ② to enter the next round of iteration; When solving the subproblem, the raw residual of P2P transaction volume and the dual residual The calculation formula is as follows: Adaptive parameters With adaptive parameters Adjust the step size according to the residual state. When the original residual is relatively large, increase the step size; when the original residual is relatively small, shorten the step size. In the formula, v represents the number of iterations, I represents the total number of specified convergences, represents the iteration step length, is the Lagrange multiplier corresponding to the subproblem; The convergence criterion of the adaptive alternating term multiplier algorithm is as follows: Where ζ represents the convergence residual.

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