Multi-microgrid system distributed optimization scheduling method based on electric power-carbon market

Abstract

The invention discloses a multi-microgrid system distributed optimization scheduling method based on an electric power-carbon market, and the method comprises the steps: constructing an energy consumption equipment model and a carbon quota transaction model based on the energy flow and carbon quota transaction process in a microgrid; a dynamic collaborative pricing model is constructed based on the power and carbon quota market supply-demand relationship; constructing an operation cost optimization model of a single micro-grid system based on the above models, and constructing a multi-micro-grid collaborative optimization scheduling problem with the goal of minimizing the total operation cost of all micro-grids based on a Nash bargaining game framework; and solving by adopting an accelerated prediction-correction alternating direction multiplier method algorithm to obtain an optimal scheduling scheme based on power-carbon market coupling, thereby realizing energy operation scheduling of the multi-microgrid. Through power-carbon market coupling, dynamic pricing, game theory optimization and an efficient distributed algorithm, the operation cost of the micro-grid system is reduced, and the reduction of the operation cost assists in improving the operation income of the micro-grid system.

Description

Technical Field

[0001] This invention relates to the field of optimization scheduling technology for multi-microgrid systems, and specifically to a distributed optimization scheduling method for multi-microgrid systems based on the electricity-carbon market. Background Technology

[0002] Microgrids (MGs), as a crucial component of future power distribution systems, have garnered significant attention for their safe, efficient, clean, and low-carbon operation. Traditional microgrids typically employ centralized energy dispatch and trading schemes, balancing electricity supply and demand through transactions with upstream distribution networks. However, this approach forces microgrids to passively accept transaction prices allocated by the upstream grid, lacking flexibility and market responsiveness. Furthermore, existing research remains insufficient in the coordinated pricing and dispatching of electricity and carbon quota markets, particularly when considering microgrid privacy protection, distributed optimization, and dynamic market supply and demand relationships, making it difficult to balance economic and environmental benefits.

[0003] The concept of Multi-Microgrid Systems (MMGS) offers a novel solution for energy sharing among microgrids. Through flexible electricity and carbon quota trading, it not only improves the stability and reliability of system operation but also generates additional economic benefits. However, existing MMGS optimization strategies often rely on centralized optimization methods, posing risks of data privacy breaches and struggling to efficiently handle complex market coupling relationships. Furthermore, traditional pricing mechanisms fail to fully reflect real-time supply and demand changes in the electricity and carbon quota markets, resulting in inefficient resource allocation and hindering incentives for microgrids to actively participate in low-carbon dispatch.

[0004] Therefore, there is an urgent need to develop a distributed pricing and scheduling method that can both protect microgrid privacy and achieve efficient collaborative optimization. Summary of the Invention

[0005] To address the shortcomings of the existing technologies, this invention provides a distributed optimization scheduling method for multi-microgrid systems based on the electricity-carbon market. By coupling the electricity-carbon market, dynamic pricing, game theory optimization, and efficient distributed algorithms, the operating costs of microgrid systems are reduced, thereby helping to improve the operating revenue of microgrid systems through the reduction of operating costs.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] A distributed optimal scheduling method for multi-microgrid systems based on the electricity-carbon market includes the following steps:

[0008] S1. Based on the energy flow and carbon quota trading process within the microgrid, construct an energy-consuming equipment model and a carbon quota trading model;

[0009] S2. Based on the supply and demand relationship in the electricity and carbon quota markets, construct a dynamic collaborative pricing model;

[0010] S3. Based on the energy-consuming equipment model, carbon quota trading model, and dynamic collaborative pricing model, construct an operating cost optimization model for a single microgrid system;

[0011] S4. Based on the Nash bargaining game framework and combined with the operation cost optimization model of all microgrid systems, construct a multi-microgrid collaborative optimization scheduling problem with the goal of minimizing the total operation cost of all microgrids.

[0012] S5. The accelerated prediction-correction alternating direction multiplier method algorithm is used to solve the multi-microgrid collaborative optimization scheduling problem, and the optimal scheduling scheme based on the coupling of the power-carbon market is obtained; the energy operation scheduling of multi-microgrids is realized through the optimal scheduling scheme.

[0013] Preferably, in step S1, the energy-consuming equipment model includes a gas turbine model and a gas boiler model;

[0014] The gas turbine model is represented as follows:

[0015] P GT,t =L GHV η GT F GT,t ;

[0016] Q GT,t =P GT,t η r / η GT ;

[0017] 0≤P GT,t ≤P GT,max ;

[0018] In the formula, P GT,t L represents the output electrical power of the gas turbine during time period t; GHV Indicates the calorific value of the gas; η GT Indicates the power generation efficiency of a gas turbine; F GT,t Q represents the fuel consumption of the gas turbine during time period t; GT,t η represents the heat output of the gas turbine during time period t; r Indicates waste heat recovery efficiency; P GT,max This indicates the maximum output electrical power of the gas turbine;

[0019] The gas-fired boiler model is represented as follows:

[0020] Q GB,t =L GHV η GB F GB,t ;

[0021] 0≤Q GB,t ≤Q GB,max ;

[0022] In the formula, Q GB,t η represents the output thermal power of the gas-fired boiler during time period t; GB Indicates the power generation efficiency of a gas-fired boiler; F GB,t Q represents the fuel consumption of a gas-fired boiler during time period t; GB,max This indicates the maximum output electrical power of the gas-fired boiler;

[0023] The energy storage system model is represented as follows:

[0024]

[0025] In the formula, S ES,t η represents the remaining power of the energy storage system during time period t; cha Indicates energy storage charging efficiency; P cha,t P represents the charging power during time period t; rel,t η represents the discharge power during time period t; rel Indicates energy storage discharge efficiency; U ES Indicates the energy storage state; P ES,max Indicates the maximum charge / discharge power of the energy system; S ES,min and S ES,max These represent the minimum and maximum capacity of the energy storage system, respectively.

[0026] Preferably, in step S1, the carbon quota trading model includes an initial carbon quota model, an actual carbon emission model, and a carbon quota trading cost model.

[0027] The initial carbon quota model is expressed as follows:

[0028]

[0029] In the formula, This represents the initial carbon allowance for the microgrid during time period t; P represents the carbon allowance coefficient for renewable energy; PV,t and P WT,t These represent the power generation of photovoltaic and wind turbines respectively within time period t; and These represent the carbon quota coefficients for gas turbines and gas boilers, respectively.

[0030] The actual carbon emission model is represented as follows:

[0031]

[0032] In the formula, This represents the actual carbon emissions of the microgrid within time period t; and These represent the carbon emission coefficients of the gas turbine and the gas boiler, respectively.

[0033] The carbon quota trading cost model is as follows:

[0034]

[0035] In the formula, This represents the carbon quota trading cost model; This represents the unit price of carbon allowances purchased from the main grid within time period t; This represents the amount of carbon allowances purchased from the main grid during time period t. The unit price of carbon allowances sold from the main grid during the period t; The amount of carbon allowances sold from the main grid during period t; v M2M,t T represents the unit price for carbon quota trading with other microgrids within time period t; M2M,t This represents the amount of carbon allowances traded with other microgrids during time period t.

[0036] Preferably, in step S2, the dynamic collaborative pricing model includes a collaborative electricity pricing model and a collaborative carbon allocation pricing model;

[0037] 1) Collaborative electricity pricing model

[0038] When P t sup <P t de At that time, the collaborative electricity pricing model is expressed as:

[0039]

[0040] in,

[0041] When P t sup >P t de At that time, the collaborative electricity pricing model is expressed as:

[0042]

[0043] in,

[0044] When P t sup =P t de When ≠0, the collaborative electricity pricing model is expressed as:

[0045]

[0046] In the formula, P t sup P represents the total power supply during time period t. tde This represents the total electricity demand during time period t; This indicates the electricity supply-demand ratio when electricity demand exceeds supply. The electricity supply-demand ratio indicates when electricity supply exceeds demand.

[0047] 2) Cooperative carbon allocation pricing model

[0048] When T t sup <T t de At that time, the collaborative carbon quota model is expressed as:

[0049]

[0050] in,

[0051] When T t sup >T t de At that time, the collaborative carbon quota model is expressed as:

[0052]

[0053] in,

[0054] When T t sup =T t de When ≠0, the collaborative carbon quota model is expressed as:

[0055]

[0056] In the formula, T t sup T represents the total carbon allowance supply during time period t. t de This represents the total carbon allowance demand for time period t. This indicates the carbon allowance supply-demand ratio when demand exceeds supply. This indicates the carbon allowance supply-demand ratio when the supply of carbon allowances exceeds the demand.

[0057] Preferably, in step S3, the objective function of the operating cost optimization model for a single microgrid system is expressed as:

[0058]

[0059] In the formula, C MG C represents the total operating cost of a microgrid; M2G This indicates the cost of electricity transactions with the main power grid; This represents the unit price of electricity purchased from the main grid within time period t; This represents the electricity purchased from the main power grid during time period t; The unit price of electricity sold from the main grid within time period t; Electricity sold from the main grid during time period t; C M2M Indicates the cost of electricity transactions with other microgrids; λ M2M,t P represents the unit price of electricity trading between microgrids within time period t; M2M,t C represents the electricity traded between microgrids within time period t; Gas For gas cost; λ Gas,t C represents the unit price of gas within time period t; OP Indicates the operation and maintenance costs of other energy-consuming equipment; λ m P represents the operating cost of device m; m,t C represents the power output of device m during time period t; EM Indicates the cost of carbon emissions; λ n P represents the carbon emission penalty coefficient for device n; n,t C represents the carbon emissions of device n within time period t; SUB This indicates revenue from renewable energy generation subsidies; p G This refers to subsidies for photovoltaic and wind power generation; C DR This represents compensation income from demand response; This represents the increase in load during time period t; This indicates the load reduction during time period t.

[0060] Preferably, in step S3, the constraints of the operating cost optimization model include:

[0061] Energy balance constraints:

[0062]

[0063] In the formula, P L,t P represents the total electricity load demand during time period t; ES,t P represents the discharge power of the energy storage system during time period t; DR,t P represents the adjustment power of the demand response within time period t; t b and P t s Q represents the electricity purchased and sold to external parties during time period t, respectively; L,t This represents the total heat load demand for time period t;

[0064] Carbon quota balance constraints:

[0065]

[0066] In the formula, T t band T t s These represent the carbon allowances purchased and sold to external entities within time period t, respectively.

[0067] Electricity trading constraints:

[0068]

[0069] In the formula, This represents the electricity purchased from M2M or M2G during time period t; Indicate whether electricity is purchased from M2M or M2G; This indicates the maximum power limit for purchasing electricity from M2M or M2G; This represents the electricity sold from M2M or M2G during time period t; Indicates whether to sell electricity to M2M or M2G; This indicates the maximum power limit for electricity sold from M2M or M2G.

[0070] Carbon quota trading constraints:

[0071]

[0072] In the formula, This represents the amount of carbon allowances purchased from M2M or M2G during time period t; Indicate whether to purchase carbon allowances from M2M or M2G; This indicates the maximum allowable amount of carbon allowances that can be purchased in a single time period; This represents the amount of carbon allowances sold from M2M or M2G during time period t; Whether to sell carbon allowances from M2M or M2G; This indicates the maximum allowable amount of carbon allowances to be sold in a single time period;

[0073] Demand response constraints:

[0074]

[0075] In the formula, This represents the increase in load power due to demand response during time period t; Indicates whether to enable load increase; This indicates the upper limit of the load increase ratio; This represents the load power reduced through demand response during time period t; Indicates whether to enable load reduction; This indicates the upper limit of the load reduction ratio.

[0076] Preferably, in step S4, the objective function of the multi-microgrid collaborative optimization scheduling problem is expressed as:

[0077]

[0078] In the formula, This represents the independent operating cost of microgrid i when it does not participate in the collaboration; C MG,i This represents the actual operating cost of microgrid i after it participates in the cooperation.

[0079] Preferably, in step S4, the constraints of the multi-microgrid collaborative optimization scheduling problem are expressed as follows:

[0080]

[0081] In the formula, This represents the amount of electricity sold by microgrid i to microgrid j during time period t; This represents the amount of electricity sold by microgrid j to microgrid i during time period t; This represents the carbon allowance sold by microgrid i to microgrid j during time period t; This represents the carbon allowance that microgrid j sells to microgrid i during time period t.

[0082] Preferably, in step S5, the processing steps of the accelerated prediction-correction alternating direction multiplier method algorithm include:

[0083] S501: Based on the objective function and constraints of the multi-microgrid collaborative optimization scheduling problem, establish the enhanced Lagrangian function for each microgrid;

[0084] S502: Set the iteration number k to 1, set the initial value of the Lagrange multiplier, the penalty factor, the correction step size, and the initial energy trading volume of each microgrid;

[0085] S503: Substitute the enhanced Lagrangian function for each microgrid;

[0086] S504: Predictors and Lagrange multipliers;

[0087] S505: Correction variables and Lagrange multipliers;

[0088] S506: Determine if the algorithm has converged: if yes, obtain the local optimal scheduling scheme for a single microgrid; otherwise, update the penalty factor and execute k = k + 1, and return to step S503.

[0089] After obtaining the local optimal scheduling schemes for all microgrids through the above steps, update the electricity trading price and carbon quota trading price of each microgrid, and determine whether the global convergence condition is met (the variance of the electricity trading price and the variance of the carbon quota trading price are both less than 10-4): if so, then take the local optimal scheduling schemes of all microgrids as the optimal scheduling schemes; otherwise, return to step S501.

[0090] Preferably, in step S501, the enhanced Lagrangian function is expressed as:

[0091]

[0092] In the formula, L i Represents the enhanced Lagrangian function of each microgrid; ψ ij and ζ ij All represent Lagrange multipliers; ρ represents the multiplication factor.

[0093] Compared with the prior art, the present invention has the following technical effects:

[0094] 1. This invention significantly improves the precision of energy and carbon management by constructing an energy flow and carbon quota trading model within a microgrid. By accurately simulating the dynamic characteristics of distributed power sources, energy storage devices, and loads, it achieves real-time monitoring and optimized allocation of energy flow within the microgrid, effectively reducing energy waste. Simultaneously, the carbon quota trading model transforms carbon emission rights into quantifiable assets, and combined with dynamic carbon footprint calculation, helps operators accurately grasp carbon emission status and formulate targeted emission reduction strategies. Furthermore, by integrating the dual-dimensional constraints of energy cost and carbon cost, this model breaks through the single-objective limitations of economic or environmental considerations in traditional dispatching, achieving multi-objective synergistic optimization, which reduces total operating costs while meeting carbon emission quota requirements.

[0095] 2. The dynamic collaborative pricing model based on the supply-demand ratio of this invention can reflect market changes in real time. By flexibly adjusting the prices of electricity and carbon allowances, it incentivizes transactions between microgrids, thereby promoting the efficient allocation of resources. This not only optimizes the matching of electricity supply and demand, but also guides microgrids to reduce carbon emissions through the dynamic adjustment of carbon allowance prices, thereby reducing the operating costs of microgrid systems. The reduction in operating costs helps to improve the operating revenue of microgrid systems, thus balancing economic efficiency and low-carbon goals.

[0096] 3. This invention integrates energy-consuming equipment models, carbon quota trading models, and dynamic collaborative pricing models to construct a single microgrid operation cost optimization model that minimizes the entire chain of costs. This model covers the entire lifecycle of a microgrid from "electricity purchase - energy consumption - energy storage - carbon trading," accurately calculates the optimal operating strategy, and significantly reduces total operating costs. Furthermore, in terms of optimization strategies, the multi-microgrid collaborative optimization scheduling problem constructed based on the Nash bargaining game model achieves a balance between global optimality and fairness. By balancing the operating benefits of each participant, it avoids the uneven distribution of benefits that may occur in traditional centralized scheduling. Simultaneously, the application of the accelerated prediction-correction alternating direction multiplier method further improves the efficiency of distributed computing, reducing computational complexity while ensuring convergence speed and protecting the privacy data of each microgrid, ultimately obtaining an optimal scheduling scheme based on the coupling of the electricity and carbon markets. Attached Figure Description

[0097] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will now be described in further detail with reference to the accompanying drawings, wherein:

[0098] Figure 1 This is a flowchart of the distributed optimal scheduling method for multi-microgrid systems based on the electricity-carbon market, as described in this invention.

[0099] Figure 2 This is a schematic diagram of the PCB-ADMM method of the present invention;

[0100] Figure 3 This is a dynamic curve showing the market price of the present invention.

[0101] Figure 4 This is a schematic diagram of the basic structure of the multi-microgrid system of the present invention;

[0102] Figure 5 This is a diagram showing the results of the iterative convergence process in an embodiment of the present invention;

[0103] Figure 6 This is a graph showing the power trading results for each microgrid in an embodiment of the present invention;

[0104] Figure 7 This is a graph showing the carbon quota market trading results according to an embodiment of the present invention;

[0105] Figure 8 This is a graph showing the calculation results of electricity and carbon quota prices for each trading cycle in an embodiment of the present invention. Detailed Implementation

[0106] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but only to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0107] The present invention will now be described in further detail with reference to the accompanying drawings.

[0108] In traditional microgrid systems, centralized energy dispatch and trading schemes mean that microgrids can only passively accept trading prices allocated by the upstream distribution network, lacking flexibility. Meanwhile, existing research on pricing and dispatch strategies for the coupling of electricity and carbon quota markets has shortcomings, especially in considering microgrid privacy protection and distributed optimization solutions. These issues limit the economic and environmental benefits of multi-microgrid systems. To address these problems and shortcomings, this invention proposes a distributed optimization dispatch method for multi-microgrid systems based on the electricity-carbon market. This method organically combines single-microgrid optimization modeling, dynamic collaborative pricing, Nash bargaining game, and distributed solution to obtain an optimal dispatch scheme based on the coupling of the electricity-carbon market, achieving efficient collaborative dispatch of multi-microgrid systems. The final energy operation plan of the multi-microgrid system (MMGS) obtained through distributed algorithm iterative calculation includes not only dispatch instructions for equipment within each microgrid (MG) but also electricity and carbon quota trading plans across microgrids, achieving dual optimization of economics and environmental protection.

[0109] Specifically, such as Figure 1 As shown, the distributed optimal scheduling method for multi-microgrid systems based on the electricity-carbon market proposed in this invention specifically includes the following steps:

[0110] S1. Based on the energy flow and carbon quota trading process within the microgrid, construct energy-consuming equipment models and carbon quota trading models;

[0111] In practical implementation, the energy-consuming equipment model includes a gas turbine model and a gas boiler model. First, based on the structural characteristics of the microgrid, an energy conversion model is established for the energy-consuming equipment it contains. For power generation equipment, a gas turbine (GT) model is established as follows:

[0112] P GT,t =L GHV η GT F GT,t ;

[0113] Q GT,t =P GT,t η r / η GT ;

[0114] 0≤P GT,t ≤P GT,max ;

[0115] In the formula, P GT,t L represents the output electrical power of the gas turbine during time period t; GHV Indicates the calorific value of the gas; η GT Indicates the power generation efficiency of a gas turbine; F GT,t Q represents the fuel consumption of the gas turbine during time period t; GT,tη represents the heat output of the gas turbine during time period t; r Indicates waste heat recovery efficiency; P GT,max This indicates the maximum output electrical power of the gas turbine;

[0116] The gas-fired boiler (GB) model is represented as follows:

[0117] Q GB,t =L GHV η GB F GB,t ;

[0118] 0≤Q GB,t ≤Q GB,max ;

[0119] In the formula, Q GB,t η represents the output thermal power of the gas-fired boiler during time period t; GB Indicates the power generation efficiency of a gas-fired boiler; F GB,t Q represents the fuel consumption of a gas-fired boiler during time period t; GB,max This indicates the maximum output electrical power of the gas-fired boiler;

[0120] In this embodiment, the energy-consuming equipment model also includes an energy storage system model. For energy storage equipment, the energy storage system (ESS) model is represented as follows:

[0121]

[0122] In the formula, S ES,t η represents the remaining power of the energy storage system during time period t; cha Indicates energy storage charging efficiency; P cha,t P represents the charging power during time period t; rel,t η represents the discharge power during time period t; rel Indicates energy storage discharge efficiency; U ES Indicates the energy storage state; P ES,max Indicates the maximum charge / discharge power of the energy system; S ES,min and S ES,max These represent the minimum and maximum capacity of the energy storage system, respectively.

[0123] Secondly, a carbon allowance trading model is established, which typically includes three parts: the initial carbon allowance model, the actual carbon emission model, and the carbon allowance trading cost model.

[0124] The initial carbon quota model is expressed as follows:

[0125]

[0126] In the formula, This represents the initial carbon allowance for the microgrid during time period t; P represents the carbon allowance coefficient for renewable energy; PV,t and P WT,t These represent the power generation of photovoltaic and wind turbines respectively within time period t; and These represent the carbon quota coefficients for gas turbines and gas boilers, respectively.

[0127] The actual carbon emission model is represented as follows:

[0128]

[0129] In the formula, This represents the actual carbon emissions of the microgrid within time period t; and These represent the carbon emission coefficients of the gas turbine and the gas boiler, respectively.

[0130] The carbon quota trading cost model is as follows:

[0131]

[0132] In the formula, This represents the carbon quota trading cost model; This represents the unit price of carbon allowances purchased from the main grid within time period t; This represents the amount of carbon allowances purchased from the main grid during time period t. The unit price of carbon allowances sold from the main grid during the period t; The amount of carbon allowances sold from the main grid during period t; v M2M,t T represents the unit price for carbon quota trading with other microgrids within time period t; M2M,t This represents the amount of carbon allowances traded with other microgrids during time period t.

[0133] S2. Based on the supply and demand relationship in the electricity and carbon quota markets, construct a dynamic collaborative pricing model;

[0134] In practice, the dynamic collaborative pricing model includes a collaborative electricity pricing model and a collaborative carbon allocation pricing model.

[0135] 1) Collaborative electricity pricing model

[0136] When P t sup <P t de At that time, the collaborative electricity pricing model is expressed as:

[0137]

[0138] in,

[0139] When Pt sup >P t de At that time, the collaborative electricity pricing model is expressed as:

[0140]

[0141] in,

[0142] When P t sup =P t de When ≠0, the collaborative electricity pricing model is expressed as:

[0143]

[0144] In the formula, P t sup P represents the total power supply during time period t. t de This represents the total electricity demand during time period t; This indicates the electricity supply-demand ratio when electricity demand exceeds supply. The electricity supply-demand ratio indicates when electricity supply exceeds demand.

[0145] In other words, when electricity supply exceeds demand, the SDR (Special Price Ratio) is high, and a lower electricity price is set to encourage electricity consumption; conversely, when electricity demand exceeds supply, the SDR is low, and a higher electricity price is set to curb electricity consumption. This dynamic electricity price adjustment strategy reflects the real-time supply and demand situation in the electricity market, promoting the rational allocation and efficient utilization of electricity resources.

[0146] 2) Cooperative carbon allocation pricing model

[0147] When T t sup <T t de At that time, the collaborative carbon quota model is expressed as:

[0148]

[0149] in,

[0150] When T t sup >T t de At that time, the collaborative carbon quota model is expressed as:

[0151]

[0152] in,

[0153] When T t sup =T t de When ≠0, the collaborative carbon quota model is expressed as:

[0154]

[0155] In the formula, T t sup T represents the total carbon allowance supply during time period t. t de This represents the total carbon allowance demand for time period t. This indicates the carbon allowance supply-demand ratio when demand exceeds supply. This indicates the carbon allowance supply-demand ratio when the supply of carbon allowances exceeds the demand.

[0156] In other words, when carbon allowance supply exceeds demand, the SDR is high, and a lower carbon allowance price is set to encourage its use; conversely, when carbon allowance demand exceeds supply, the SDR is low, and a higher carbon allowance price is set to discourage its use. This dynamic carbon allowance pricing strategy reflects the real-time supply and demand situation in the carbon allowance market, promotes the rational allocation and efficient utilization of carbon allowance resources, and incentivizes microgrids to reduce carbon emissions and achieve low-carbon operation.

[0157] S3. Based on the energy-consuming equipment model, carbon quota trading model, and dynamic collaborative pricing model, construct an operating cost optimization model for a single microgrid system;

[0158] In practical implementation, an optimization model for a single microgrid is established, including an objective function and constraints. Each microgrid minimizes its total operating cost by optimizing the output of its internal controllable units and external electricity and carbon quota trading plans. Therefore, the objective function of the operating cost optimization model for a single microgrid system is expressed as:

[0159]

[0160] In the formula, C MG C represents the total operating cost of a microgrid; M2G This indicates the cost of electricity transactions with the main power grid; This represents the unit price of electricity purchased from the main grid within time period t; This represents the electricity purchased from the main power grid during time period t; The unit price of electricity sold from the main grid within time period t; Electricity sold from the main grid during time period t; C M2M Indicates the cost of electricity transactions with other microgrids; λ M2M,t P represents the unit price of electricity trading between microgrids within time period t; M2M,tC represents the electricity traded between microgrids within time period t; Gas For gas cost; λ Gas,t C represents the unit price of gas within time period t; OP Indicates the operation and maintenance costs of other energy-consuming equipment; λ m P represents the operating cost of device m; m,t C represents the power output of device m during time period t; EM Indicates the cost of carbon emissions; λ n P represents the carbon emission penalty coefficient for device n; n,t C represents the carbon emissions of device n within time period t; SUB This indicates revenue from renewable energy generation subsidies; p G This refers to subsidies for photovoltaic and wind power generation; C DR This represents compensation income from demand response; This represents the increase in load during time period t; This indicates the load reduction during time period t.

[0161] First, adopting a fully distributed design concept, each microgrid maintains independent operation capabilities while enabling point-to-point (P2P) electricity and carbon quota trading through the physical network. Second, through a dynamic power allocation mechanism, the system can automatically adjust energy flow paths based on real-time supply and demand. Finally, the innovative hierarchical control design organically combines centralized coordination with decentralized decision-making, ensuring that each local node retains full operational autonomy under the overall coordination of the "M31" main control unit.

[0162] Based on this, in order to ensure the stable operation of the microgrid, the following operating constraints must be met:

[0163] Energy balance constraints:

[0164]

[0165] In the formula, P L,t P represents the total electricity load demand during time period t; ES,t P represents the discharge power of the energy storage system during time period t; DR,t P represents the adjustment power of the demand response within time period t; t b and P t s Q represents the electricity purchased and sold to external parties during time period t, respectively; L,t This represents the total heat load demand for time period t;

[0166] Carbon quota balance constraints:

[0167]

[0168] In the formula, T tb and T t s These represent the carbon allowances purchased and sold to external entities within time period t, respectively.

[0169] Electricity trading constraints:

[0170]

[0171] In the formula, This represents the electricity purchased from M2M or M2G during time period t; Indicate whether electricity is purchased from M2M or M2G; This indicates the maximum power limit for purchasing electricity from M2M or M2G; This represents the electricity sold from M2M or M2G during time period t; Indicates whether to sell electricity to M2M or M2G; This indicates the maximum power limit for electricity sold from M2M or M2G.

[0172] Carbon quota trading constraints:

[0173]

[0174] In the formula, This represents the amount of carbon allowances purchased from M2M or M2G during time period t; Indicate whether to purchase carbon allowances from M2M or M2G; This indicates the maximum allowable amount of carbon allowances that can be purchased in a single time period; This represents the amount of carbon allowances sold from M2M or M2G during time period t; Whether to sell carbon allowances from M2M or M2G; This indicates the maximum allowable amount of carbon allowances to be sold in a single time period;

[0175] Demand response constraints:

[0176]

[0177] In the formula, This represents the increase in load power due to demand response during time period t; Indicates whether to enable load increase; This indicates the upper limit of the load increase ratio; This represents the load power reduced through demand response during time period t; Indicates whether to enable load reduction; This indicates the upper limit of the load reduction ratio.

[0178] Therefore, solving the optimization model can yield the optimal operating strategies of each microgrid in the microgrid system, including the output power of internal controllable units (such as the power generation of photovoltaic and wind turbines, the operating status of gas turbines, etc.) and external power and carbon quota trading plans (such as the trading volume and price with the upstream grid or carbon quota market, etc.), thereby providing a basis for microgrid operation decisions.

[0179] like Figure 3 As shown, Figure 3 This invention demonstrates the dynamic changes in market prices, using two curves to illustrate the dynamic adjustment mechanisms of electricity and carbon quota prices. Figure 3 (a) shows the price change curves under different supply-demand ratios, which intuitively reflects the nonlinear relationship between electricity or carbon quota prices and the supply-demand ratio (SDR). When the supply-demand ratio increases (supply exceeds demand), prices tend to decrease to stimulate consumption; when the supply-demand ratio decreases (supply falls short of demand), prices rise to suppress demand. This dynamic response mechanism ensures a rapid balance between market supply and demand. Figure 3 (b) The figure shows the price change process based on supply and demand, which shows the real-time price fluctuations during a typical scheduling cycle. The curve shape reflects the core feature of the dynamic collaborative pricing model of this invention: during peak renewable energy generation periods (such as midday when photovoltaic output is high), the increase in power supply leads to a decrease in price; while during peak energy consumption periods or periods of tight carbon quotas, the price automatically increases.

[0180] S4. Based on the Nash bargaining game framework and combined with the operation cost optimization model of all microgrid systems, construct a multi-microgrid collaborative optimization scheduling problem with the goal of minimizing the total operation cost of all microgrids.

[0181] In practical implementation, to achieve equilibrium in the complex game relationship among multiple microgrids, a Nash bargaining game model is adopted to develop the collaborative optimization of the multi-microgrid system. The reduction in operating costs after microgrid cooperative optimization represents the increase in operating revenue. The objective function of the multi-microgrid collaborative optimization scheduling problem is expressed as:

[0182]

[0183] In the formula, This represents the independent operating cost of microgrid i when it does not participate in the collaboration; C MG,i This represents the actual operating cost of microgrid i after it participates in the cooperation.

[0184] Since this is a non-convex and nonlinear optimization problem, a direct solution is difficult. Analysis shows that the non-convex and nonlinear properties of the problem stem from two aspects: the objective function of the Nash product and the binary variables in the constraints. To facilitate the solution, we take the negative logarithm of this optimization problem, transforming the original problem into minimizing the sum of a series of negative logarithmic functions:

[0185]

[0186] In this way, mathematical transformation simplifies the solution process for non-convex nonlinear problems and improves the feasibility of the algorithm.

[0187] The constraints of the multi-microgrid cooperative optimization scheduling problem are expressed as follows:

[0188]

[0189] In the formula, This represents the amount of electricity sold by microgrid i to microgrid j during time period t; This represents the amount of electricity sold by microgrid j to microgrid i during time period t; This represents the carbon allowance sold by microgrid i to microgrid j during time period t; This represents the carbon allowance that microgrid j sells to microgrid i during time period t.

[0190] S5. The accelerated prediction-correction alternating direction multiplier method algorithm is used to solve the multi-microgrid collaborative optimization scheduling problem, and the optimal scheduling scheme based on the coupling of the power-carbon market is obtained; the energy operation scheduling of multi-microgrids is realized through the optimal scheduling scheme.

[0191] In specific implementation, such as Figure 2 As shown, the processing steps of the accelerated prediction-correction alternating direction multiplier method algorithm include:

[0192] S501: Based on the objective function and constraints of the multi-microgrid collaborative optimization scheduling problem, establish the enhanced Lagrangian function for each microgrid;

[0193] S502: Set the iteration number k to 1, set the initial value of the Lagrange multiplier, the penalty factor, the correction step size, and the initial energy trading volume of each microgrid;

[0194] S503: Substitute the enhanced Lagrangian function for each microgrid;

[0195] S504: Predictors and Lagrange multipliers;

[0196] S505: Correction variables and Lagrange multipliers;

[0197] S506: Determine if the algorithm has converged: if yes, obtain the local optimal scheduling scheme for a single microgrid; otherwise, update the penalty factor and execute k = k + 1, and return to step S503.

[0198] After obtaining the local optimal scheduling schemes for all microgrids through the above steps, update the electricity trading price and carbon quota trading price of each microgrid, and determine whether the global convergence condition is met (the variance of the electricity trading price and the variance of the carbon quota trading price are both less than 10-4): if so, then take the local optimal scheduling schemes of all microgrids as the optimal scheduling schemes; otherwise, return to step S501.

[0199] In step S501, the enhanced Lagrangian function is expressed as:

[0200]

[0201] In the formula, L i Represents the enhanced Lagrangian function of each microgrid; ψ ij and ζ ij All represent Lagrange multipliers; ρ represents the multiplication factor.

[0202] In this embodiment, the local optimal scheduling scheme of a single microgrid is the output result after accelerating the convergence of the inner loop of the PCB-ADMM algorithm. Its specific components include: equipment operation plan, including the start-up and shutdown status and power output curves of key equipment such as gas turbines and energy storage systems, to ensure that local electricity and heat load demand is met; cross-grid transaction decision, clarifying the electricity purchase / sale volume and carbon quota trading plan with other microgrids or the upper-level grid, forming a preliminary transaction strategy; and cost control objective, to minimize the operating cost of a single microgrid under a fixed initial electricity price.

[0203] This invention first constructs a basic optimization model for a single microgrid, comprehensively considering the supply and demand relationship in the electricity-carbon market and the operational constraints of internal microgrid units to achieve overall system optimization and coordinated scheduling. Then, it proposes a dynamic coordinated pricing model that considers the supply and demand relationship in the electricity and carbon quota markets, dynamically adjusting prices to incentivize inter-microgrid transactions. Next, it establishes a multi-microgrid system optimization strategy based on the Nash bargaining game (NBG) model to maximize the operating revenue of each microgrid and ensure fair cooperation. Finally, it employs the Accelerated Predictive-Corrected Alternating Directional Multiplier Method (PCB-ADMM) algorithm to solve the multi-microgrid system optimization model, improving solution efficiency and convergence speed through distributed computing.

[0204] Figure 4The basic structure of a multi-microgrid system is demonstrated. This structure consists of multiple interconnected microgrid nodes, forming a distributed energy network. At the physical level, the system exchanges power with the upper-level grid through distribution network nodes, and simultaneously injects natural gas into the natural gas network through natural gas nodes to provide fuel for the gas turbines (GT) and gas boilers (GB) within each microgrid. The four microgrids (MG1-MG4) each integrate wind turbines (WT), photovoltaics (PV), gas turbines, gas boilers, and energy storage systems (ESS), forming an electric-gas complementary multi-energy microgrid. At the information level, the system adopts a "global-local" two-layer control architecture. The global microgrid controller (G-MGC) is interconnected with the local microgrid controllers (L-MGC1~4) corresponding to each microgrid through a high-speed information network to realize distributed optimization computing. Each L-MGC collects local power generation, energy storage, load and carbon emission data in real time, and only uploads the necessary boundary variables to the G-MGC. The G-MGC completes global collaborative decision-making based on the Nash bargaining game model and the accelerated prediction-correction alternating direction multiplier method (PCB-ADMM), and then issues scheduling and pricing instructions to each L-MGC, thereby achieving rapid convergence while protecting the privacy of each microgrid.

[0205] To better illustrate the effectiveness of this method, a specific example will be used to illustrate the method of this invention below.

[0206] To verify the effectiveness of the model, this embodiment studies a multi-microgrid system (MMGS) consisting of four independent microgrids (MG1, MG2, MG3, MG4). MG1, MG2, and MG3 are integrated energy systems, including wind turbines (WT), photovoltaics (PV), energy storage systems (ESS), and gas turbines (GT). MG4 is a solar energy storage plant, composed of photovoltaics (PV) and energy storage systems (ESS). The scheduling cycle is set to 24 hours, divided into 24 equal time periods. The purchase and sale prices of carbon allowances are ¥0.025 / kg and ¥0.05 / kg, respectively.

[0207] To verify the effectiveness and rationality of the proposed strategy, a comparative analysis was conducted on four different operational frameworks:

[0208] Framework 1: There is no peer-to-peer (P2P) electricity and carbon allowance trading within the MMGS. Each microgrid can only trade electricity and carbon allowances with its upstream distribution network.

[0209] Framework 2: P2P electricity trading exists within MMGS, but P2P carbon quota trading does not exist.

[0210] Framework 3: P2P electricity and carbon quota trading exists within MMGS. Pricing and scheduling are performed using the existing NBG model.

[0211] Framework 4: P2P electricity and carbon quota trading exists within MMGS. This embodiment employs the pricing strategy based on the supply-demand ratio (SDR) and the scheduling strategy based on the NBG.

[0212] like Figure 5 The image shows the results of the iterative convergence process. Figure 5 (a) and Figure 5 (b) shows the iterative curves for electricity prices and carbon allowance prices, respectively. It can be seen that electricity prices and carbon allowance prices tend to stabilize after 5 iterations. Figure 5 (d) The iterative residuals γ of different algorithms were compared. The results show that the accelerated PCB-ADMM algorithm significantly reduces the number of iterations and converges faster. Specifically, the ADMM and PCB-ADMM algorithms require 110 and 107 iterations to converge, respectively, while the accelerated PCB-ADMM algorithm requires only 42 iterations.

[0213] Figure 6 This demonstrates the electricity trading results for each microgrid (MG) within different time periods, including electricity purchase and sale, obtained by solving Framework 4. Figure 6 As can be seen, each microgrid maintains power balance in every time period. For example, MG1 operates its high-cost gas turbine (GT) during periods of high power demand (9-22 hours) and sells GT power during periods with higher renewable energy generation (23-24 hours and 1-8 hours). In the event of power shortages, each microgrid prioritizes meeting its power demand through P2P transactions within the MMGS. For example, MG1 purchases 1545.54 kW of power from the upstream distribution network. Each microgrid can store energy during periods of surplus generation or low electricity prices using energy storage devices and use it during periods of insufficient generation or high electricity prices. For example, MG1 stores an average of 120.3 kW of energy during 1-7 hours and releases an average of 160 kW of energy during 19-24 hours. Each microgrid can respond to peak shaving demands from the upstream distribution network by reducing load during high-demand periods to earn subsidy revenue. For example, MG1 reduces load by an average of 347.52 kW during 8-22 hours.

[0214] The results of carbon quota market transactions are as follows Figure 7 As shown, Figure 7 (a) shows the results of carbon allowance trading under Framework 4. Figure 7 (b) Results for total carbon emissions and total carbon cost in Frameworks 1-4. Key conclusions are as follows:

[0215] MG1 is the primary carbon allowance buyer, purchasing an average of 65.33 kg of carbon allowances per period. MG4 is the primary carbon allowance seller, selling an average of 80.14 kg of carbon allowances per period. MG3 has higher renewable energy generation and lower GT generation during hours 10-16, thus selling more carbon allowances during this period, averaging 46.37 kg. Without considering P2P carbon allowance trading (Framework 2), each microgrid reduces total carbon emissions by 1.39% by exchanging electricity through reduced GT generation. Frameworks 3 and 4 consider the coupling of electricity-carbon markets between microgrids, encouraging microgrids to retain carbon allowances for trading to gain more revenue. Therefore, total carbon emissions are reduced by 5.95% and 4.88% under Frameworks 3 and 4, respectively. Figure 7 (b) shows that high-carbon-emission microgrids reduce costs by purchasing carbon allowances from other microgrids with surplus carbon allowances. Therefore, total carbon emission costs are reduced by 32.47% and 31.88% in Frameworks 3 and 4, respectively.

[0216] In Framework 4, the electricity and carbon allowance settlement prices among multiple microgrids were calculated using a collaborative pricing model, and the results are as follows: Figure 8 As shown, Figure 8 (a) represents the inter-grid electricity price. Figure 8 (b) is the price of inter-network carbon quotas.

[0217] For most periods, the cooperative electricity price was close to the upstream distribution network's selling price. For example, during the 23-24 hour and 1-7 hour periods, the average cooperative electricity price was ¥0.37 / kWh. During peak electricity consumption periods, MG4's solar energy storage plant provided a large amount of electricity, resulting in a cooperative electricity price lower than the upstream distribution network's selling price, averaging a decrease of ¥0.4 / kWh. During the high-price periods of 12-15 hours and 19-22 hours, the total thermal unit output, without considering P2P carbon allowance trading, increased by 728.86 kW. This indicates that the microgrid tends to utilize its remaining carbon allowances for power generation to sell more electricity and increase revenue, leading to increased electricity sales and a corresponding decrease in electricity prices. However, during the low-price period of 7-10 hours, renewable energy generation was lower, and the microgrid's initial carbon allowances were limited, decreasing by an average of 14.5%. The total electricity sales, without considering P2P carbon allowance trading, decreased by an average of 182.9 kW. This indicates that when carbon allowances are insufficient, the microgrid prioritizes meeting its own electricity needs, resulting in decreased electricity sales and a corresponding increase in electricity prices. Figure 8(b) shows that all microgrids were carbon allowance buyers during the 1-6 hour and 19-24 hour periods. Therefore, the co-operated carbon allowance price reached the upstream distribution network's carbon allowance sales price, i.e., ¥0.05 / kg. However, with the activation of photovoltaic units, MG3 and MG4 allocated a large amount of carbon allowances, leading to an increase in the carbon allowance market supply, and the co-operated carbon allowance price decreased accordingly, reaching ¥0.0302 / kg.

[0218] This embodiment also compares the costs of different operating frameworks, with the following conclusions: Framework 1 has the worst economic performance due to the lack of cooperation between microgrids. Framework 2 introduces P2P electricity trading, which reduces costs somewhat. Framework 3 introduces P2P electricity and carbon quota trading, using the traditional NBG model, further reducing costs, but it is difficult to reflect actual market conditions. Framework 4, using the SDR-based pricing and NBG-based dispatching strategies proposed in this paper, has the lowest cost, more accurately reflects market dynamics, and its overall benefits are comparable to Framework 3. Furthermore, increasing demand response interaction with the upstream distribution network can improve the overall profitability of MMGS, but it should be noted that supplier microgrids may suffer revenue losses due to price fluctuations, requiring a reallocation of demand response revenue to ensure their profitability is not affected.

[0219] In summary, this invention achieves overall system optimization and coordinated scheduling by constructing a basic optimization model for a single microgrid, comprehensively considering the supply and demand relationship between the electricity and carbon markets, as well as the internal operating constraints of the microgrid. Secondly, the dynamic coordinated pricing model based on the supply-demand ratio can reflect market changes in real time. By flexibly adjusting the prices of electricity and carbon allowances, it incentivizes transactions between microgrids, thereby promoting the efficient allocation of resources. This not only optimizes the matching of electricity supply and demand but also guides the microgrid to reduce carbon emissions through the dynamic adjustment of carbon allowance prices, thus balancing economic efficiency and low-carbon goals.

[0220] In terms of optimization strategy, this invention addresses the multi-microgrid collaborative optimization scheduling problem based on the Nash bargaining game model. By maximizing the Nash product as the objective function, fairness in multi-microgrid cooperation is ensured. By balancing the operational benefits of each participant, the uneven distribution of benefits that may occur in traditional centralized scheduling is avoided. At the same time, the application of the accelerated prediction-correction alternating direction multiplier method further improves the efficiency of distributed computing. While ensuring convergence speed, it reduces computational complexity and protects the privacy data of each microgrid, ultimately obtaining the optimal scheduling scheme based on the coupling of the electricity-carbon market.

[0221] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described with reference to preferred embodiments, those skilled in the art should understand that various changes in form and detail can be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A distributed optimal scheduling method for multi-microgrid systems based on the electricity-carbon market, characterized in that, Includes the following steps: S1. Based on the energy flow and carbon quota trading process within the microgrid, construct an energy-consuming equipment model and a carbon quota trading model; S2. Based on the supply and demand relationship in the electricity and carbon quota markets, construct a dynamic collaborative pricing model; S3. Based on the energy-consuming equipment model, carbon quota trading model, and dynamic collaborative pricing model, construct an operating cost optimization model for a single microgrid system; S4. Based on the Nash bargaining game framework and combined with the operation cost optimization model of all microgrid systems, construct a multi-microgrid collaborative optimization scheduling problem with the goal of minimizing the total operation cost of all microgrids. S5. The accelerated prediction-correction alternating direction multiplier method algorithm is used to solve the multi-microgrid collaborative optimization scheduling problem, and the optimal scheduling scheme based on the coupling of the power-carbon market is obtained; the energy operation scheduling of multi-microgrids is realized through the optimal scheduling scheme.

2. The distributed optimal scheduling method for multi-microgrid systems based on the electricity-carbon market according to claim 1, characterized in that, In step S1, the energy-consuming equipment model includes a gas turbine model and a gas boiler model; The gas turbine model is represented as follows: P GT,t =L GHV or GT F GT,t ; Q GT,t =P GT,t or r / or GT ; 0≤P GT,t ≤P GT,max ; In the formula, P GT,t L represents the output electrical power of the gas turbine during time period t; GHV Indicates the calorific value of the gas; η GT Indicates the power generation efficiency of a gas turbine; F GT,t Q represents the fuel consumption of the gas turbine during time period t; GT,t η represents the heat output of the gas turbine during time period t; r Indicates waste heat recovery efficiency; P GT,max This indicates the maximum output electrical power of the gas turbine; The gas-fired boiler model is represented as follows: Q GB,t =L GHV the GB F GB,t ; 0≤Q GB,t ≤Q GB,max ; In the formula, Q GB,t This represents the output thermal power of the gas-fired boiler during time period t; η GB Indicates the power generation efficiency of a gas-fired boiler; F GB,t Q represents the fuel consumption of a gas-fired boiler during time period t; GB,max This indicates the maximum output electrical power of the gas-fired boiler.

3. The distributed optimal scheduling method for multi-microgrid systems based on the electricity-carbon market according to claim 1, characterized in that, In step S1, the carbon quota trading model includes an initial carbon quota model, an actual carbon emission model, and a carbon quota trading cost model. The initial carbon quota model is expressed as follows: In the formula, This represents the initial carbon allowance for the microgrid during time period t; P represents the carbon allowance coefficient for renewable energy; PV,t and P WT,t These represent the power generation of photovoltaic and wind turbines respectively within time period t; and These represent the carbon quota coefficients for gas turbines and gas boilers, respectively. The actual carbon emission model is represented as follows: In the formula, This represents the actual carbon emissions of the microgrid within time period t; and These represent the carbon emission coefficients of the gas turbine and the gas boiler, respectively. The carbon quota trading cost model is as follows: In the formula, This represents the carbon quota trading cost model; This represents the unit price of carbon allowances purchased from the main grid within time period t; This represents the amount of carbon allowances purchased from the main grid during time period t. The unit price of carbon allowances sold from the main grid during the period t; The amount of carbon allowances sold from the main grid during period t; v M2M,t T represents the unit price for carbon quota trading with other microgrids within time period t; M2M,t This represents the amount of carbon allowances traded with other microgrids during time period t.

4. The distributed optimal scheduling method for multi-microgrid systems based on the electricity-carbon market according to claim 1, characterized in that, In step S2, the dynamic collaborative pricing model includes a collaborative electricity pricing model and a collaborative carbon allocation pricing model. 1) Collaborative electricity pricing model When P t sup <P t de At that time, the collaborative electricity pricing model is expressed as: in, when At that time, the collaborative electricity pricing model is expressed as: in, when At that time, the collaborative electricity pricing model is expressed as: In the formula, This represents the total power supply during time period t. This represents the total electricity demand during time period t; This indicates the electricity supply-demand ratio when electricity demand exceeds supply. The electricity supply-demand ratio indicates when electricity supply exceeds demand. 2) Cooperative carbon allocation pricing model when At that time, the collaborative carbon quota model is expressed as: in, when At that time, the collaborative carbon quota model is expressed as: in, when At that time, the collaborative carbon quota model is expressed as: In the formula, This represents the total carbon allowance supply during time period t. This represents the total carbon allowance demand for time period t. This indicates the carbon allowance supply-demand ratio when demand exceeds supply. This indicates the carbon allowance supply-demand ratio when the supply of carbon allowances exceeds the demand.

5. The distributed optimal scheduling method for multi-microgrid systems based on the electricity-carbon market according to claim 1, characterized in that, In step S3, the objective function of the operating cost optimization model for a single microgrid system is expressed as: In the formula, C MG C represents the total operating cost of a microgrid; M2G This indicates the cost of electricity transactions with the main power grid; This represents the unit price of electricity purchased from the main grid within time period t; This represents the electricity purchased from the main power grid during time period t; The unit price of electricity sold from the main power grid within time period t; Electricity sold from the main grid during time period t; C M2M This indicates the cost of electricity transactions with other microgrids; λ M2M,t P represents the unit price of electricity trading between microgrids within time period t; M2M,t C represents the electricity traded between microgrids within time period t; Gas For gas cost; λ Gas,t C represents the unit price of gas within time period t; OP Indicates the operation and maintenance costs of other energy-consuming equipment; λ m P represents the operating cost of device m; m,t C represents the power output of device m during time period t; EM Indicates the cost of carbon emissions; λ n P represents the carbon emission penalty coefficient for device n; n,t C represents the carbon emissions of device n within time period t; SUB This indicates revenue from renewable energy generation subsidies; p G This refers to subsidies for photovoltaic and wind power generation; C DR This represents compensation income from demand response; This represents the increase in load during time period t; This indicates the load reduction during time period t.

6. The distributed optimal scheduling method for multi-microgrid systems based on the electricity-carbon market according to claim 5, characterized in that, In step S3, the constraints of the operating cost optimization model include: Energy balance constraints: In the formula, P L,t P represents the total electricity load demand during time period t; ES,t P represents the discharge power of the energy storage system during time period t; DR,t This indicates the adjustment power of the demand response within time period t; and Q represents the electricity purchased and sold to external parties during time period t, respectively; L,t This represents the total heat load demand for time period t; Carbon quota balance constraints: In the formula, and These represent the carbon allowances purchased and sold to external entities within time period t, respectively. Electricity trading constraints: In the formula, This represents the electricity purchased from M2M or M2G during time period t; Indicate whether electricity is purchased from M2M or M2G; This indicates the maximum power limit for purchasing electricity from M2M or M2G; This represents the electricity sold from M2M or M2G during time period t; Indicates whether to sell electricity to M2M or M2G; This indicates the maximum power limit for electricity sold from M2M or M2G. Carbon quota trading constraints: In the formula, This represents the amount of carbon allowances purchased from M2M or M2G during time period t; Indicate whether to purchase carbon allowances from M2M or M2G; This indicates the maximum allowable amount of carbon allowances that can be purchased in a single time period; This represents the amount of carbon allowances sold from M2M or M2G during time period t; Whether to sell carbon allowances from M2M or M2G; This indicates the maximum allowable amount of carbon allowances to be sold in a single time period; Demand response constraints: In the formula, This represents the increase in load power due to demand response during time period t; Indicates whether to enable load increase; This indicates the upper limit of the load increase ratio; This represents the load power reduced through demand response during time period t; Indicates whether load shedding is enabled; This indicates the upper limit of the load reduction ratio.

7. The distributed optimal scheduling method for multi-microgrid systems based on the electricity-carbon market according to claim 1, characterized in that, In step S4, the objective function of the multi-microgrid collaborative optimization scheduling problem is expressed as: In the formula, C represents the independent operating cost of microgrid i when it does not participate in the cooperation; MG,i This represents the actual operating cost of microgrid i after it participates in the cooperation.

8. The distributed optimal scheduling method for multi-microgrid systems based on the electricity-carbon market according to claim 7, characterized in that, In step S4, the constraints of the multi-microgrid collaborative optimization scheduling problem are expressed as follows: In the formula, This represents the amount of electricity sold by microgrid i to microgrid j during time period t; This represents the amount of electricity sold by microgrid j to microgrid i during time period t; This represents the carbon allowance sold by microgrid i to microgrid j during time period t; This represents the carbon allowance that microgrid j sells to microgrid i during time period t.

9. The distributed optimal scheduling method for multi-microgrid systems based on the electricity-carbon market according to claim 1, characterized in that, In step S5, the processing steps of the accelerated prediction-correction alternating direction multiplier method algorithm include: S501: Based on the objective function and constraints of the multi-microgrid collaborative optimization scheduling problem, establish the enhanced Lagrangian function for each microgrid; S502: Set the iteration number k to 1, set the initial value of the Lagrange multiplier, the penalty factor, the correction step size, and the initial energy trading volume of each microgrid; S503: Substitute the enhanced Lagrangian function for each microgrid; S504: Predictors and Lagrange multipliers; S505: Correction variables and Lagrange multipliers; S506: Determine if the algorithm has converged: if yes, obtain the local optimal scheduling scheme for a single microgrid; otherwise, update the penalty factor and execute k = k + 1, and return to step S503. After obtaining the local optimal scheduling schemes for all microgrids through the above steps, update the electricity trading price and carbon quota trading price of each microgrid, and determine whether the global convergence condition is met: if so, then take the local optimal scheduling schemes of all microgrids as the optimal scheduling schemes; otherwise, return to step S501.

10. The distributed optimal scheduling method for multi-microgrid systems based on the electricity-carbon market according to claim 9, characterized in that, In step S501, the enhanced Lagrangian function is expressed as: In the formula, L i Represents the enhanced Lagrangian function of each microgrid; ψ ij and ζ ij All represent Lagrange multipliers; ρ represents the multiplication factor.

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