Hierarchical distributed scheduling method and system for multi-microgrid distribution system based on carbon flow tracing

Abstract

The present invention discloses a hierarchical distributed scheduling method and system for a multi-microgrid distribution system under carbon flow tracing. The method comprises: establishing operating constraints for the multi-microgrid distribution system based on selected system parameters and scheduling parameters of the multi-microgrid distribution system; reasonably and fairly allocating the carbon responsibility on the source side of the distribution network to the load side based on the carbon emission flow theory, guiding the interactive low-carbon energy consumption behavior of the distribution-microgrid, and constructing carbon trading constraints for the distribution network system and multi-microgrid system under carbon flow tracing; establishing a hierarchical distributed scheduling model for the multi-microgrid distribution system under carbon flow tracing using the target cascade theory; and decoupling the interactive power through the target cascade method and performing iterative solution. Based on the hierarchical distributed optimization concept of the target cascade analysis method and the collaborative and complementary operation strategy of the multi-microgrid distribution system, the present invention uses a related solver to solve the mixed integer linear programming model in the optimization stage to obtain a system operation plan within the scheduling cycle.

Description

Technical Field

[0001] The present invention belongs to the field of hierarchical distributed scheduling, and in particular relates to a hierarchical distributed scheduling method and system for a multi-microgrid power distribution system under carbon flow tracing. Background Art

[0002] Energy is a key resource on which human society depends for survival and is vital to economic development.

[0003] As a small power generation and distribution system that can integrate various types of distributed generation, loads, energy storage devices, and energy conversion devices, microgrids have both "source" and "load" properties and a high degree of autonomy. Through reasonable scheduling, they can meet local electricity demand, improve energy utilization efficiency, empower the concept of low carbon, and better realize the local consumption of renewable energy. The decentralized access of multiple microgrids to the distribution network to form a multi-microgrid distribution system can achieve cascade utilization of energy through the production, transmission, and conversion of various types of energy, forming a system with overall coordinated optimization of comprehensive production capacity, energy supply, and energy consumption. It can fully utilize its advantages of multi-energy complementarity and energy conservation and emission reduction, reduce the power fluctuations of the interconnecting lines caused by the access of microgrids to the distribution network, and improve the safety and reliability of the microgrid and distribution network during operation. It has broad application prospects in optimizing the energy supply structure of power systems, improving energy utilization, and low-carbon economic operation.

[0004] However, the carbon emissions generated by energy interactions between distribution networks and microgrids remain unclear, and studying only source-side low-carbon technologies fails to maximize the system's carbon reduction potential. Furthermore, distribution networks and microgrids often belong to different operators, and data interactions pose privacy risks. Traditional centralized scheduling cannot meet the scheduling needs of node-level carbon potential. Summary of the Invention

[0005] Aiming at the low-carbon dispatch problem of multi-microgrid distribution systems, a hierarchical distributed low-carbon dispatch model for multi-microgrid and distribution network systems can be established that meets the carbon flow theory under privacy protection. Based on the optimization idea of ​​the target cascade analysis method, it comprehensively considers constraints such as carbon capture-power-to-gas coupling equipment, incentive-based demand response and carbon emission flow theory, and optimizes and coordinates the operation constraints of heterogeneous resources such as energy supply, energy storage and energy consumption devices.

[0006] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0007] The present invention provides a hierarchical distributed scheduling method for a multi-microgrid power distribution system under carbon flow tracing, the method comprising:

[0008] Establishing the operation constraints of the multi-microgrid distribution system based on the selected system parameters and dispatching parameters of the multi-microgrid distribution system;

[0009] Based on the carbon emission flow theory, the carbon responsibility of the distribution network source side is reasonably and evenly allocated to the load side, guiding the interactive low-carbon energy consumption behavior of distribution and microgrids, and constructing the carbon trading constraints of the distribution network system and the multi-microgrid system under carbon flow tracing;

[0010] The target cascade theory is used to establish a hierarchical distributed dispatching model for multi-microgrid distribution system under carbon flow tracing; the hierarchical distributed dispatching model for multi-microgrid distribution system is a two-layer structure, the upper layer is the distribution network system dispatching model, which mainly considers the distribution network system objective function and related constraints. The distribution network system takes the sum of the main network power purchase cost, distribution network loss penalty, distribution network system unit operation and maintenance cost and distribution network system carbon trading cost as the minimum within the dispatching period as the objective function. The distribution network system related constraints include distribution network system safe operation constraints, distribution network system unit ... System carbon trading constraints: The lower layer is a multi-microgrid system scheduling model, which mainly considers the multi-microgrid system objective function and related constraints. The multi-microgrid system takes the minimization of the sum of the multi-microgrid system gas purchase cost, the multi-microgrid system unit operation and maintenance cost, the multi-microgrid system demand response load compensation cost and the multi-microgrid system comprehensive carbon cost as the objective function. The multi-microgrid system related constraints include the microgrid system conventional unit operation constraint, the microgrid system flexible load constraint, the microgrid system electric load supply and demand balance constraint, and the multi-microgrid system carbon trading constraint. The two-layer scheduling system only exchanges power information and node carbon potential information.

[0011] Based on the hierarchical distributed scheduling model of a multi-microgrid distribution system, the interactive power is decoupled and iteratively solved through the target cascade method. The solution result is the scheduling plan within a scheduling cycle of the system, including gas purchase cost, carbon emissions, carbon trading cost, distribution network operation cost, multi-microgrid operation cost, each unit operating condition, carbon potential of each node in the distribution network, microgrid-distribution network interactive power, each microgrid's load supply and demand balance condition, demand response operation condition and hierarchical distributed iterative solution condition.

[0012] In one embodiment, the system parameters and scheduling parameters of the multi-microgrid power distribution system include: system line parameters, network topology connection relationship, operating voltage level, branch current limit, reference voltage and reference power, equipment composition, equipment operating parameters, type, access location, capacity and parameters of dispatchable distributed power sources, electric load, new energy output prediction value, setting the number of iterations of the distributed scheduling method, penalty coefficient, scheduling interval and scheduling initial time.

[0013] In one embodiment, the multi-microgrid distribution system operation constraints include: distribution network system safety operation constraints, distribution network system unit operation constraints, microgrid system conventional unit operation constraints, microgrid system flexible load constraints and microgrid system electric load supply and demand balance constraints.

[0014] In one embodiment, the carbon trading constraints of the distribution network system under the carbon flow traceability are:

[0015]

[0016]

[0017] Where, Carbon allowances obtained for the distribution network; C Trade,DN The cost of carbon trading in the distribution network; is the total carbon quota of the multi-microgrid distribution system area; c Trade is the carbon trading price; E t is the carbon potential matrix of each node in period t; It is the load energy consumption of distribution network.

[0018] In one embodiment, the carbon trading constraints of the multi-microgrid system under the carbon flow traceability are:

[0019]

[0020]

[0021]

[0022] Where C Trade，MMG Carbon trading costs for multiple microgrids; is the total carbon quota of the multi-microgrid distribution system area; c Trade is the carbon trading price; E t is the carbon potential matrix of each node in period t; is the energy consumption of microgrid load m; is the actual net CO2 emissions emitted to the atmosphere by microgrid m; E m,t is the node carbon potential of microgrid m; is the interaction power between the microgrid m and the distribution network. Since the carbon potential represents the carbon emission of energy consumption, the interaction power is only calculated when it is positive, that is, the distribution network transmits power to the microgrid; is the amount of CO2 emitted by the gas turbine in microgrid m during period t; is the carbon capture capacity of the carbon capture unit in microgrid m during period t; Carbon allowances obtained for microgrid m.

[0023] In one embodiment, the multi-microgrid power distribution system hierarchical distributed scheduling model is:

[0024] (1) Upper-level distribution network system dispatching model:

[0025]

[0026] Where C DNis the initial objective function of the distribution network system; is the expected interactive power value of the distribution network layer; is the expected interaction power value of the microgrid layer; is the expected interactive power value of the distribution network after the kth iteration; is the expected interactive power value of the microgrid after the k-1th iteration; α m,t , β m,t are the multipliers of the first and second terms of the Lagrange penalty function in the target cascade method; G DN (i, t) = 0 is the equality constraint related to the distribution network system; K DN (i, t) is the inequality constraint related to the distribution network system.

[0027] (2) Lower-layer multi-microgrid system scheduling model:

[0028]

[0029] Where C MMG is the initial objective function of the multi-microgrid system; G MMG (m, t) = 0 is the related equation constraint of multi-microgrid system; K MMG (m,t) is the inequality constraint related to the multi-microgrid system.

[0030] In one embodiment, the interactive power decoupling is:

[0031]

[0032] Where: is the interaction power between microgrid m and distribution network; is the expected interactive power value of the distribution network layer; is the expected interaction power value of the microgrid layer; is the maximum value of the interaction power between the microgrid and the distribution network.

[0033] In one embodiment, decoupling the interaction power by the target cascade method and performing an iterative solution includes:

[0034] Data initialization, setting the initial values ​​of coupling variables and penalty function multipliers, the initial iteration value k = 1;

[0035] The distribution network operator solves the low-carbon scheduling problem with the goal of minimizing the sum of operating costs and carbon trading costs under carbon flow theory. It then calculates the node carbon potential matrix at different times and transmits the obtained expected interactive power values ​​and node carbon potential results to the multi-microgrid operator.

[0036] Based on the obtained power demand information and node carbon potential information, the multi-microgrid operators solve the low-carbon scheduling problem with the goal of minimizing the sum of operating costs and comprehensive carbon trading costs under carbon flow theory, and pass the obtained expected interactive power value to the distribution network operator;

[0037] Calculate the total cost and coupling variable difference after each iteration;

[0038] The convergence condition is determined based on the difference in the expected interaction power between the two parties and the difference in the total cost of the multi-microgrid distribution system obtained from the two iterations.

[0039] In one embodiment, the step of determining the convergence condition based on the difference between the expected interaction powers of both parties and the difference between the total costs of the multi-microgrid power distribution system obtained from two iterations includes:

[0040] Convergence criterion 1: The difference between the expected interaction power of both parties should meet the convergence accuracy requirement:

[0041]

[0042] Convergence criterion 2: The difference between the total cost of the multi-microgrid distribution system obtained from the two iterations should meet the convergence accuracy requirement:

[0043]

[0044] Where, is the total cost of the multi-microgrid distribution system after the k+1th iteration; is the total cost of the multi-microgrid distribution system after the kth iteration; ε1 and ε2 are the convergence accuracy;

[0045] If both convergence criteria 1 and 2 are satisfied, the iteration terminates and the optimal scheduling result is output; otherwise, the penalty function multiplier is updated according to the following formula, k = k + 1, and the solution is continued;

[0046]

[0047] Where, are the coefficients of the linear and quadratic terms of the Lagrangian penalty function in the target cascade method after the kth iteration; is the expected interaction power value of the microgrid layer after the k-1th iteration; is the expected interactive power value of the distribution network layer after the k-1th iteration; ω is the update coefficient of the quadratic term of the penalty function multiplier, which is usually taken as 2≤ω≤3.

[0048] The present invention also provides a multi-microgrid power distribution system hierarchical distributed scheduling system under carbon flow tracing, the system comprising:

[0049] An operation constraint building module is used to establish the operation constraints of the multi-microgrid distribution system according to the selected system parameters and dispatch parameters of the multi-microgrid distribution system;

[0050] A trading constraint construction module is used to reasonably and evenly allocate the carbon responsibility of the distribution network source side to the load side based on carbon emission flow theory, guide the interactive low-carbon energy consumption behavior of distribution and microgrids, and construct carbon trading constraints for the distribution network system and multi-microgrid system under carbon flow tracing;

[0051] The objective function construction module is used to set the system dispatching targets for the multi-microgrid and distribution network. The objective function of the distribution network system is to minimize the sum of the main grid power purchase cost, distribution network loss penalty, unit operation and maintenance cost within the distribution network system, and carbon trading cost of the distribution network system within the dispatching period. The objective function of the multi-microgrid system is to minimize the sum of the multi-microgrid system gas purchase cost, unit operation and maintenance cost within the multi-microgrid system, demand response load compensation cost of the multi-microgrid system, and comprehensive carbon cost of the multi-microgrid system.

[0052] The hierarchical distributed scheduling model construction module is used to establish a hierarchical distributed scheduling model for multi-microgrid distribution system under carbon flow tracing using the target cascade theory; the hierarchical distributed scheduling model for multi-microgrid distribution system is a two-layer structure, the upper layer is the distribution network system scheduling model, which mainly considers the distribution network system objective function and related constraints. The distribution network system takes the sum of the main network power purchase cost, distribution network loss penalty, distribution network system unit operation and maintenance cost and distribution network system carbon trading cost as the minimum within the scheduling period as the objective function. The distribution network system related constraints include the distribution network system safe operation constraint, the distribution network system unit operation and maintenance constraint. The lower layer is a multi-microgrid system scheduling model, which mainly considers the objective function and related constraints of the multi-microgrid system. The multi-microgrid system takes the minimization of the sum of the multi-microgrid system gas purchase cost, the multi-microgrid system unit operation and maintenance cost, the multi-microgrid system demand response load compensation cost and the multi-microgrid system comprehensive carbon cost as the objective function. The multi-microgrid system related constraints include the microgrid system conventional unit operation constraint, the microgrid system flexible load constraint, the microgrid system electric load supply and demand balance constraint, and the multi-microgrid system carbon trading constraint. The two-layer scheduling system only exchanges power information and node carbon potential information.

[0053] The solution module is used to decouple the interactive power and perform iterative solution through the target cascade method based on the hierarchical distributed scheduling model of the multi-microgrid distribution system; the solution result is the scheduling plan within a scheduling cycle of the system, including gas purchase cost, carbon emissions, carbon trading cost, distribution network operation cost, multi-microgrid operation cost, each unit operating condition, carbon potential of each node in the distribution network, microgrid-distribution network interactive power, each microgrid electric load supply and demand balance condition, demand response operating condition and hierarchical distributed iterative solution condition.

[0054] Beneficial effects of the present invention:

[0055] The present invention is a hierarchical distributed scheduling method for multi-microgrid distribution systems under carbon flow tracing. It is based on solving the problem of coordinated and complementary low-carbon economic operation of multi-microgrid and distribution network systems under privacy protection, fully considering diversified carbon reduction measures such as carbon capture-power-to-gas coupling equipment, incentive-based demand response and carbon emission flow theory, and establishing a hierarchical distributed scheduling model for multi-microgrid distribution systems under carbon flow tracing. Based on the optimization idea of ​​the target cascade analysis method, through the linear transformation of some nonlinear constraints, the relevant mathematical solver is called for solution to obtain a low-carbon economic scheduling plan for the system. The present invention can meet the requirements of the coordinated low-carbon operation method of multi-microgrid and distribution network systems considering carbon flow theory under privacy protection. Based on the scheduling idea of ​​hierarchical distributed iterative solution of carbon emission flow theory and target cascade method, it coordinates the integration of heterogeneous resources such as energy supply, energy storage and energy consumption devices, and safely and reliably achieves the optimal economic and environmental benefits of multi-microgrid distribution systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings, as part of this disclosure, are intended to provide a further understanding of the disclosure. The exemplary embodiments of the disclosure and their descriptions are intended to explain the disclosure and do not constitute undue limitations thereon. Obviously, the drawings described below are merely examples, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0057] Figure 1 A flow chart of a hierarchical distributed scheduling method for a multi-microgrid power distribution system under carbon flow tracing provided by an embodiment of the present invention;

[0058] Figure 2 A structural diagram of an IEEE 33-node distribution network system including three microgrids provided in one embodiment;

[0059] Figure 3 This is an operating condition diagram of a carbon cycle unit under microgrid demand response provided in one embodiment;

[0060] Figure 4 A carbon potential diagram of each node in a distribution network provided in one embodiment;

[0061] Figure 5 This is a comparison diagram of interaction power in different scenarios provided in an embodiment;

[0062] Figure 6 A microgrid internal electric load supply and demand balance diagram provided in one embodiment;

[0063] Figure 7 A cost convergence graph is provided in one embodiment;

[0064] Figure 8 Iterative convergence diagram of the convergence criterion provided in one embodiment;

[0065] Figure 9 This is a tie line interaction power iteration diagram provided in one embodiment.

[0066] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but rather to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0067] The following describes in detail the method for optimizing the scheduling of a comprehensive mine energy system considering power-to-gas mixed with coalbed methane proposed by the present invention, in conjunction with the embodiments and drawings.

[0068] like Figure 1 As shown, the embodiment of the present disclosure provides a hierarchical distributed scheduling method for a multi-microgrid distribution system under carbon flow tracing, which specifically includes the following steps:

[0069] Step S100: Establishing operation constraints of the multi-microgrid power distribution system according to the selected system parameters and dispatching parameters of the multi-microgrid power distribution system.

[0070] Furthermore, according to the system parameters and dispatching parameters of the selected multi-microgrid distribution system, including input system line parameters, network topology connection relationship, operating voltage level, branch current limit, reference voltage and reference power, equipment composition, equipment operating parameters, type, access location, capacity and parameters of dispatchable distributed power sources, electric load, and new energy output forecast value, the number of iterations, penalty coefficient, dispatching interval and dispatching initial time of the distributed dispatching method are set.

[0071] In an embodiment of the present application, the operating constraints of the multi-microgrid distribution system include: distribution network system safety operation constraints, distribution network system unit operation constraints, microgrid system conventional unit operation constraints, microgrid system flexible load constraints and microgrid system electric load supply and demand balance constraints.

[0072] Furthermore, the safe operation constraints of the distribution network system are expressed as:

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080] For nonlinear constraints such as square terms in the constraints, let and Perform second-order cone relaxation on some constraints and update them as follows:

[0081]

[0082]

[0083]

[0084]

[0085] Where U i,t 、U j,t is the voltage amplitude of nodes i and j at time period t; U min 、U max are the lower and upper limits of the node voltage amplitude respectively; I max is the maximum value of the current amplitude of each branch; is the active and reactive load of node j in period t; P j,t , Q j,t is the net injected active and reactive power of node j during period t; is the interaction power between the microgrid and the distribution network connected to node j during period t; P is the active and reactive power output by the wind and solar power generator at node j during period t; jk,t , Q jk,t is the active and reactive power at the head end of branch jk during period t; P ij,t , Q ij,t is the active and reactive power at the head end of branch ij during period t; r ij is the resistance value of branch ij; x ij is the reactance value of branch ij.

[0086] Furthermore, the operating constraints of the distribution network system units can be expressed as:

[0087]

[0088]

[0089] Where, The minimum and maximum values ​​of active and reactive power output by the wind turbine; The minimum and maximum values ​​of active and reactive power output by photovoltaics; is the capacity of wind and solar turbines at node j.

[0090] Furthermore, the operating constraints of conventional units in the microgrid system can be expressed as:

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] Where, is the output power of the gas turbine in microgrid m during period t; is the amount of gas consumed by the gas turbine in microgrid m during period t; η GT is the power generation efficiency of the gas turbine; Q gas is the calorific value of natural gas; The upper and lower limits of gas turbine output; are the upper and lower limits of the gas turbine ramp power; Δt is the adjacent time interval; is the energy stored in the energy storage device in microgrid m during period t; u is the energy coefficient of energy storage device dissipation to the environment or self-consumption; η Ch ,η Dis are the charging and discharging efficiency of the energy storage device, respectively; are the charging and discharging power of the energy storage device in microgrid m during period t; The predicted maximum output of wind turbines and photovoltaics; is the total capacity of the energy storage equipment; They are the upper and lower limits of the state of charge of the energy storage equipment respectively; The upper limit of charging and discharging power of energy storage equipment; are 0-1 variables representing the charging and discharging states of the energy storage device in microgrid m during period t; is the amount of CO2 emitted by the gas turbine in microgrid m during period t; v1 is the carbon emission intensity of the gas turbine; is the carbon capture amount in microgrid m during period t; μ CCS for carbon capture efficiency; is the total energy consumption for carbon capture; Fixed energy consumption for carbon capture; is the energy consumption of carbon capture operation; CCS The energy consumption required for carbon capture and treatment of unit CO2; is the carbon storage capacity of the carbon storage device in microgrid m during period t; CS is the carbon storage loss coefficient of carbon storage; is the CO2 output of carbon storage in microgrid m during period t; is the amount of gas synthesized from electricity to gas in microgrid m during period t; er is the heat energy that can be converted into unit electrical energy; η P2G The efficiency of power-to-gas conversion; is the total power consumed by power-to-gas conversion in microgrid m during period t; The amount of CO2 required to generate natural gas per unit of power; The amount of CO2 consumed by power-to-gas conversion; is the maximum energy consumption of carbon capture and power-to-gas equipment; is the minimum and maximum carbon storage capacity of the carbon storage device.

[0104] Furthermore, the flexible load constraint of the microgrid system can be expressed as:

[0105]

[0106]

[0107]

[0108]

[0109] Where, is the initial load power that can be reduced in microgrid m during period t; The load power after microgrid m is reduced during period t; is the load power reduced by microgrid m during period t; is the power reduction threshold coefficient; μ cut,max is the maximum value of the reducible coefficient; is the initial transferable load power of microgrid m during period t; is the load power after the transfer of microgrid m during period t; are the transferable load in and out power of microgrid m during period t respectively; are the transferable load in and out states of the microgrid m during period t, which are 0-1 variables; is the transferable load power threshold coefficient, μ trans,maxis the corresponding maximum value; Φ trans Assemble for transferable work periods; is the initial interruptible load power of microgrid m during period t; is the load power after the interruption of microgrid m during period t; is the load power of microgrid m interrupted during period t; is the interruptible power threshold coefficient; μ inter,max is the maximum value of the interruptibility coefficient.

[0110] Furthermore, the microgrid system electric load supply and demand balance constraint is expressed as:

[0111]

[0112] Where, is the fixed electric load of microgrid m during period t.

[0113] Step S200: Based on the carbon emission flow theory, the carbon responsibility of the distribution network source side is reasonably and evenly allocated to the load side, guiding the interactive low-carbon energy consumption behavior of distribution and microgrids, and constructing the carbon trading constraints of the distribution network system and the multi-microgrid system under carbon flow tracing.

[0114] In this embodiment of the application, the carbon trading constraints of the distribution network system under carbon flow tracing can be expressed as follows: the overall carbon emissions of the distribution network system are calculated using node carbon potential theory, and carbon emission rights are reasonably allocated based on the distribution network load level. In this way, the load-side resources of the distribution network can directly participate in carbon market transactions, and the carbon emissions generated by the interactive energy consumption of distribution and microgrids are more clearly defined, which is conducive to improving the coordinated and complementary low-carbon operation efficiency of multi-microgrid distribution systems.

[0115]

[0116]

[0117] Where, Carbon allowances obtained for the distribution network; C Trade,DN The cost of carbon trading in the distribution network; is the total carbon quota of the multi-microgrid distribution system area; c Trade is the carbon trading price; E t is the carbon potential matrix of each node in period t; It is the load energy consumption of distribution network.

[0118] Furthermore, the carbon trading constraints of multi-microgrid systems under carbon flow tracing can be expressed as follows: calculate the carbon emissions within the microgrid through carbon capture technology, and use the node carbon potential theory to quantify the interactive carbon emissions between microgrids to obtain the overall carbon emissions, and then reasonably allocate carbon emission rights according to the load levels of multiple microgrids to realize carbon trading of the multi-microgrid system.

[0119]

[0120]

[0121]

[0122] Where C Trade，MMG Carbon trading costs for multiple microgrids; is the total carbon quota of the multi-microgrid distribution system area; c Trade is the carbon trading price; E t is the carbon potential matrix of each node in period t; is the energy consumption of microgrid load m; is the actual net CO2 emissions emitted to the atmosphere by microgrid m; E m,t is the node carbon potential of microgrid m; is the interaction power between the microgrid m and the distribution network. Since the carbon potential represents the carbon emission of energy consumption, the interaction power is only calculated when it is positive, that is, the distribution network transmits power to the microgrid; is the amount of CO2 emitted by the gas turbine in microgrid m during period t; is the carbon capture capacity of the carbon capture unit in microgrid m during period t; Carbon allowances obtained for microgrid m.

[0123] Step S300: Use the target cascade theory to establish a hierarchical distributed scheduling model for multi-microgrid distribution systems under carbon flow tracing; the hierarchical distributed scheduling model for multi-microgrid distribution systems is a two-layer structure, the upper layer is the distribution network system scheduling model, which mainly considers the distribution network system objective function and related constraints, and the lower layer is the multi-microgrid system scheduling model, which mainly considers the multi-microgrid system objective function and related constraints. The two-layer scheduling systems only exchange power information and node carbon potential information.

[0124] The embodiment of the present application sets up multiple microgrids and distribution networks in layers according to the idea of ​​hierarchical distributed scheduling, and sets the system scheduling targets of the multiple microgrids and distribution networks. The distribution network takes the minimization of the sum of the main network's electricity purchase cost, the distribution network loss penalty, the unit operation and maintenance cost within the distribution network system, and the carbon trading cost of the distribution network system within the scheduling period as its objective function, and the multi-microgrid system takes the minimization of the sum of the multi-microgrid system's gas purchase cost, the unit operation and maintenance cost within the multi-microgrid system, the multi-microgrid system's demand response load compensation cost, and the multi-microgrid system's comprehensive carbon cost as its objective function. The distribution network system-related constraints include the distribution network system's safe operation constraints, the distribution network system's unit operation constraints, and the distribution network system's carbon trading constraints. The multi-microgrid system-related constraints include the microgrid system's conventional unit operation constraints, the microgrid system's flexible load constraints, the microgrid system's electric load supply and demand balance constraints, and the multi-microgrid system's carbon trading constraints.

[0125] Furthermore, the hierarchical distributed scheduling model of the multi-microgrid distribution system is:

[0126] (1) Upper-level distribution network system dispatching model:

[0127]

[0128] Where C DN is the initial objective function of the distribution network system; is the expected interactive power value of the distribution network layer; is the expected interaction power value of the microgrid layer; is the expected interactive power value of the distribution network after the kth iteration; is the expected interactive power value of the microgrid after the k-1th iteration; α m,t , β m,t are the multipliers of the first and second terms of the Lagrange penalty function in the target cascade method; G DN (i, t) = 0 is the equality constraint related to the distribution network system; K DN (i, t) is the inequality constraint related to the distribution network system.

[0129] (2) Lower-layer multi-microgrid system scheduling model:

[0130]

[0131] Where C MMG is the initial objective function of the multi-microgrid system; G MMG (m, t) = 0 is the related equation constraint of multi-microgrid system; K MMG (m,t) is the inequality constraint related to the multi-microgrid system.

[0132] Step S400: According to the hierarchical distributed scheduling model of the multi-microgrid distribution system, the interactive power is decoupled and iteratively solved through the target cascade method.

[0133] The solution result is the scheduling plan within a scheduling cycle of the system, including gas purchase cost, carbon emissions, carbon trading cost, distribution network operation cost, multi-microgrid operation cost, operating conditions of each unit, carbon potential of each node in the distribution network, microgrid-distribution network interaction power, supply and demand balance conditions of each microgrid's electrical load, demand response operating conditions and hierarchical distributed iterative solution conditions.

[0134] Furthermore, the interaction power is decoupled as:

[0135]

[0136] Where: is the interaction power between microgrid m and distribution network; is the expected interactive power value of the distribution network layer; is the expected interaction power value of the microgrid layer; is the maximum value of the interaction power between the microgrid and the distribution network.

[0137] In the embodiment of the present application, the interactive power is decoupled and iteratively solved by the target cascade method, including:

[0138] (1) Data initialization: set the initial values ​​of coupling variables and penalty function multipliers, and the initial iteration value k = 1.

[0139] (2) The distribution network operator solves the low-carbon scheduling problem with the goal of minimizing the sum of the operating cost and the carbon trading cost under the carbon flow theory (Equation 39), and then calculates the carbon potential matrix of the nodes at different times. The obtained expected interactive power value and the node carbon potential results are transmitted to the multi-microgrid operator, giving full play to the low-carbon energy consumption behavior of the distribution-microgrid interaction under the guidance of carbon flow, thereby improving the energy flow distribution within the multi-microgrid and enhancing the clean operation effect of the multi-microgrid system.

[0140]

[0141] Where, is the expected interactive power value of the distribution network layer; is the expected interaction power value of the microgrid layer; is the expected interactive power value of the distribution network after the kth iteration; is the expected interactive power value of the microgrid after the k-1th iteration; α m,t , β m,t are the multipliers of the linear and quadratic terms of the Lagrange penalty function respectively.

[0142] (3) Based on the obtained electricity demand information and node carbon potential information, the multi-microgrid operators solve the low-carbon scheduling problem with the goal of minimizing the sum of the operating cost and the comprehensive carbon trading cost under the carbon flow theory (Equation (40)), and transmit the obtained expected interactive power value to the distribution network operator, thereby reasonably optimizing the output of the distribution network system units and improving the low-carbon economic operation effect of the distribution network system.

[0143]

[0144] (4) Calculate the total cost and coupling variable difference after each iteration.

[0145] (5) The convergence condition is determined based on the difference in the expected interaction power between the two parties and the difference in the total cost of the multi-microgrid distribution system obtained after two iterations.

[0146] Furthermore, the convergence conditions are determined based on the difference in the expected interaction power between the two parties and the difference in the total cost of the multi-microgrid distribution system obtained from the two iterations, including:

[0147] Convergence criterion 1: The difference between the expected interaction power of both parties should meet the convergence accuracy requirement.

[0148]

[0149] Convergence criterion 2: The difference between the total cost of the multi-microgrid distribution system obtained from the two iterations should meet the convergence accuracy requirements.

[0150]

[0151] Where, is the total cost of the multi-microgrid distribution system after the k+1th iteration; is the total cost of the multi-microgrid distribution system after the kth iteration; ε1 and ε2 are the convergence accuracy.

[0152] If both convergence criteria 1 and 2 are satisfied, the iteration terminates and the optimal scheduling result is output. Otherwise, the penalty function multiplier is updated according to the following formula, with k = k + 1, and the solution is continued.

[0153]

[0154] Where ω is the update coefficient of the quadratic term of the penalty function multiplier, which is usually taken as 2≤ω≤3.

[0155] The present invention establishes a hierarchical distributed solution strategy for multi-microgrid distribution systems under carbon flow tracing, comprehensively considering constraints such as carbon capture-power-to-gas cycle carbon reduction equipment, incentive-based demand response strategy, and carbon emission flow theory. Based on the hierarchical distributed optimization idea of ​​the target cascade analysis method and the collaborative and complementary operation strategy of the multi-microgrid distribution system, the mixed integer linear programming model in the optimization stage is solved using a related solver to obtain the system operation plan within the scheduling period.

[0156] In one embodiment, a hierarchical distributed dispatching system for a multi-microgrid distribution system under carbon flow tracing is proposed, the system comprising:

[0157] An operation constraint building module is used to establish the operation constraints of the multi-microgrid distribution system according to the selected system parameters and dispatch parameters of the multi-microgrid distribution system;

[0158] A trading constraint construction module is used to reasonably and evenly allocate the carbon responsibility of the distribution network source side to the load side based on carbon emission flow theory, guide the interactive low-carbon energy consumption behavior of distribution and microgrids, and construct carbon trading constraints for the distribution network system and multi-microgrid system under carbon flow tracing;

[0159] The objective function construction module is used to set the system dispatching targets for the multi-microgrid and distribution network. The objective function of the distribution network system is to minimize the sum of the main grid power purchase cost, distribution network loss penalty, unit operation and maintenance cost within the distribution network system, and carbon trading cost of the distribution network system within the dispatching period. The objective function of the multi-microgrid system is to minimize the sum of the multi-microgrid system gas purchase cost, unit operation and maintenance cost within the multi-microgrid system, demand response load compensation cost of the multi-microgrid system, and comprehensive carbon cost of the multi-microgrid system.

[0160] The hierarchical distributed dispatching model construction module is used to establish a hierarchical distributed dispatching model for multi-microgrid distribution system under carbon flow tracing using the target cascade theory; the hierarchical distributed dispatching model for multi-microgrid distribution system is a two-layer structure, the upper layer is the distribution network system dispatching model, which mainly considers the distribution network system objective function and related constraints. The distribution network system target number is to minimize the sum of the main network power purchase cost, distribution network loss penalty, distribution network system unit operation and maintenance cost and distribution network system carbon trading cost within the dispatching period as the objective function. The distribution network system related constraints include the distribution network system safe operation constraint, the distribution network system unit operation and maintenance cost ... The lower layer is a multi-microgrid system scheduling model, which mainly considers the multi-microgrid system objective function and related constraints. The multi-microgrid system takes the minimization of the sum of the multi-microgrid system gas purchase cost, the multi-microgrid system unit operation and maintenance cost, the multi-microgrid system demand response load compensation cost and the multi-microgrid system comprehensive carbon cost as the objective function. The multi-microgrid system related constraints include the microgrid system conventional unit operation constraints, the microgrid system flexible load constraints, the microgrid system electric load supply and demand balance constraints, and the multi-microgrid system carbon trading constraints. The two-layer scheduling system only exchanges power information and node carbon potential information.

[0161] The solution module is used to decouple the interactive power and perform iterative solution through the target cascade method based on the hierarchical distributed scheduling model of the multi-microgrid distribution system; the solution result is the scheduling plan within a scheduling cycle of the system, including gas purchase cost, carbon emissions, carbon trading cost, distribution network operation cost, multi-microgrid operation cost, each unit operating condition, carbon potential of each node in the distribution network, microgrid-distribution network interactive power, each microgrid electric load supply and demand balance condition, demand response operation condition and hierarchical distributed iterative solution condition.

[0162] It should be noted that the hierarchical distributed dispatching system for multi-microgrid distribution systems under carbon flow tracing provided in the above embodiment only uses the division of the above-mentioned functional modules as an example when executing the hierarchical distributed dispatching method for multi-microgrid distribution systems under carbon flow tracing. In actual applications, the above-mentioned functional distribution can be completed by different functional modules as needed, that is, the internal structure of the equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the hierarchical distributed dispatching system for multi-microgrid distribution systems under carbon flow tracing provided in the above embodiment and the embodiment of the hierarchical distributed dispatching method for multi-microgrid distribution systems under carbon flow tracing belong to the same concept. The implementation process thereof is detailed in the embodiment of the hierarchical distributed dispatching method for multi-microgrid distribution systems under carbon flow tracing, which will not be repeated here.

[0163] In a specific embodiment, the system line parameters, network topology connection relationship, operating voltage level, branch current limit, reference voltage and reference power, equipment composition, equipment operating parameters, type, access location, capacity and parameters of dispatchable distributed power supply, power load, new energy output forecast value are input, and the number of iterations of the distributed scheduling method, penalty coefficient, scheduling interval and scheduling initial time are set. In this system, an IEEE 33-node distribution network system connected to three microgrids is used for example analysis. The system structure is as follows: Figure 2 As shown, the rated voltage is 12.66 kV, and the voltage fluctuation range of each node is 0.95 to 1.05 times the rated voltage. Grid structural parameters such as the system line impedance are consistent with the standard IEEE 33-node system. Nodes 7 and 27 are connected to photovoltaic systems with capacities of 500 kVA and 200 kVA, respectively; nodes 10 and 20 are connected to wind turbines with capacities of 500 kVA and 400 kVA, respectively. The system contains three microgrids, connected to nodes 13, 24, and 30. Relevant operating parameters are shown in Table 1.

[0164] Table 1 Microgrid technical parameters

[0165]

[0166] For this embodiment, in order to verify the superiority of the proposed low-carbon scheduling model, four different scenarios are set for comparative analysis (see Table 2), and the system operation results are shown in Table 3. Figure 3 This is the operating condition diagram of the carbon cycle unit under demand response. Figure 4 It is the carbon potential diagram of each node in the distribution network. Figure 5 The following is a comparison chart of interaction power in different scenarios. Figure 6 This is the balance diagram of electric load supply and demand within the microgrid.

[0167] Figure 7 This is the cost convergence diagram based on the target cascade analysis method. Figure 8 Iterative convergence diagram of the convergence criterion based on the target cascade analysis method. Figure 9 It is the iterative diagram of the tie line interaction power based on the target cascade analysis method.

[0168] Table 2 Operation schemes under different scenarios

[0169]

[0170] Note: √ indicates that the scene contains the corresponding model, and × indicates the opposite.

[0171] Table 3 Comparison of optimization results for four scenarios

[0172]

[0173] The simulation software used for this example is MATLAB R2021a with the YALMIP toolbox, using the Gurobi 9.5 solver. The simulation platform used an Intel(R) Core(TM) i5-7300 HQ processor with 16GB of memory and a 64-bit Windows 10 operating system. The scheduling period is 24 hours, and the time interval is 1 hour.

[0174] Comparing the results of scenarios 1 and 2 in Table 3, we can see that the introduction of the carbon capture-power-to-gas coupled model increased operation and maintenance costs, while improving energy supply at the source, resulting in an 8.09% increase in natural gas costs and a 16.51% increase in total costs. However, the carbon capture-power-to-gas coupled units were able to leverage their carbon capture and utilization to reduce carbon emissions, reducing CO2 emissions in the multi-microgrid distribution system by 16.14% and carbon trading costs by 21.79%, thereby enhancing the benefits of low-carbon operation. This demonstrates that a low-carbon dispatch model based on the coordinated operation of carbon capture-power-to-gas can promote energy conservation and carbon reduction in the system while ensuring reasonable economic operation.

[0175] Combine Figure 3 Comparing the results of scenarios 2 and 3 in Table 3, we can see that after introducing the incentive-based demand response model, the flexible loads adjust energy usage timing and smooth the energy curve to increase the operating power of the carbon capture-power-to-gas coupled equipment, capture carbon emissions, and synthesize natural gas. This reduces natural gas costs by 18.53% and further reduces CO2 emissions by 3.03 tons, a year-on-year decrease of 9.39%. Within the dispatch cycle, fully leveraging the advantages of the demand response strategy can improve source-side production capacity, regulate load-side energy consumption, and maximize the promotion of source-load synergy in carbon reduction.

[0176] Combine Figure 4 and Figure 5 It can be seen that integrating renewable energy into the distribution network will produce a zero-carbon effect, and the carbon potential of nodes connected to new energy sources is reduced to varying degrees compared to the source end. At the same time, quantifying the carbon emission level of energy consumption at each node can guide microgrid energy consumption behavior under low-carbon goals, optimize the interactive power between microgrids and distribution networks, and ensure carbon emission reduction benefits while meeting the requirements of coordinated and complementary operation. Combined with Table 3, it can be seen that compared with Scenario 3, the improvement in the power interaction value between multiple microgrids and distribution networks at different times in Scenario 4 affects the proportion of energy supply from the source end within the multi-microgrid distribution system, further reducing natural gas costs by 12.32%, carbon emissions by 0.26 tons, and total operating costs by 2.55%, effectively improving the coordinated and complementary economic carbon reduction benefits of the multi-microgrid distribution system.

[0177] Combine Figure 6-Figure 9It can be seen that the hierarchical distributed optimization method based on the target cascade analysis method can effectively achieve internal balance among various operating entities and global coordinated optimization of the multi-microgrid distribution system under multi-dimensional linkage carbon constraints. It can also integrate various heterogeneous resources to achieve internal supply and demand balance within the microgrid. Comparing scenarios 1 and 4, it is shown that, guided by actual carbon quota allocation and carbon market policies, this system introduces a series of diversified carbon reduction measures, such as a carbon capture-power-to-gas coupled cycle carbon reduction model, incentive-based demand response, and carbon emission flow theory, to form a low-carbon operation strategy under a multi-dimensional linkage mode. This reduces the natural gas cost of the multi-microgrid distribution system by 22.79%, CO2 emissions by 24.69%, and carbon trading costs by 33.32%, with a total cost increase of only 7.86%. This shows that the proposed low-carbon scheduling model can achieve optimal environmental benefits while maintaining a certain economic efficiency, effectively promoting economic production capacity, efficient energy use, and clean emissions in the multi-microgrid distribution system, and verifying the superiority of the proposed low-carbon scheduling model.

[0178] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0179] Furthermore, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are also intended to fall within the scope of protection of the present invention and form different embodiments. For example, in the above embodiments, those skilled in the art will be able to use them in combination based on the known technical solutions and the technical problems to be solved by this application.

[0180] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any technician familiar with this patent can make slight changes or modifications to equivalent embodiments using the above-mentioned technical contents without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the solution of the present invention.

Claims

1. A hierarchical distributed scheduling method for multi-microgrid distribution systems under carbon flow tracing, characterized in that: The method comprises: Establishing the operation constraints of the multi-microgrid distribution system based on the selected system parameters and dispatching parameters of the multi-microgrid distribution system; Based on the carbon emission flow theory, the carbon responsibility of the distribution network source side is reasonably and evenly allocated to the load side, guiding the interactive low-carbon energy consumption behavior of distribution and microgrids, and constructing the carbon trading constraints of the distribution network system and the multi-microgrid system under carbon flow tracing; The target cascade theory is used to establish a hierarchical distributed dispatching model for multi-microgrid distribution system under carbon flow tracing; the hierarchical distributed dispatching model for multi-microgrid distribution system is a two-layer structure, the upper layer is the distribution network system dispatching model, including the distribution network system objective function and related constraints, the distribution network system takes the sum of the main network power purchase cost, distribution network loss penalty, distribution network system unit operation and maintenance cost and distribution network system carbon trading cost as the minimum within the dispatching period as the objective function, the distribution network system related constraints include the distribution network system safe operation constraint, distribution network system unit operation constraint, distribution network System carbon trading constraints; the lower layer is a multi-microgrid system scheduling model, including the multi-microgrid system objective function and related constraints. The multi-microgrid system takes the minimization of the sum of the multi-microgrid system gas purchase cost, the multi-microgrid system unit operation and maintenance cost, the multi-microgrid system demand response load compensation cost and the multi-microgrid system comprehensive carbon cost as the objective function. The multi-microgrid system related constraints include the microgrid system conventional unit operation constraint, the microgrid system flexible load constraint, the microgrid system electric load supply and demand balance constraint, and the multi-microgrid system carbon trading constraint. The two-layer scheduling system only exchanges power information and node carbon potential information. Based on the hierarchical distributed scheduling model of a multi-microgrid distribution system, the interactive power is decoupled and iteratively solved through the target cascade method. The solution result is the scheduling plan within a scheduling cycle of the system, including gas purchase cost, carbon emissions, carbon trading cost, distribution network operation cost, multi-microgrid operation cost, each unit operating condition, carbon potential of each node in the distribution network, microgrid-distribution network interactive power, each microgrid's load supply and demand balance condition, demand response operation condition and hierarchical distributed iterative solution condition.

2. The hierarchical distributed scheduling method for multi-microgrid power distribution systems under carbon flow tracing according to claim 1 is characterized in that: The system parameters and scheduling parameters of the multi-microgrid distribution system include: system line parameters, network topology connection relationship, operating voltage level, branch current limit, reference voltage and reference power, equipment composition, equipment operating parameters, type, access location, capacity and parameters of dispatchable distributed power sources, electric load, new energy output forecast value, setting the number of iterations of the distributed scheduling method, penalty coefficient, scheduling interval and scheduling initial time.

3. The hierarchical distributed scheduling method for multi-microgrid power distribution systems under carbon flow tracing according to claim 1 or 2 is characterized in that: The multi-microgrid distribution system operation constraints include: distribution network system safety operation constraints, distribution network system unit operation constraints, microgrid system conventional unit operation constraints, microgrid system flexible load constraints and microgrid system electric load supply and demand balance constraints.

4. The hierarchical distributed scheduling method for multi-microgrid power distribution systems under carbon flow tracing according to claim 1 is characterized in that: The carbon trading constraints of the distribution network system under the carbon flow traceability are: ， ， Where, The cost of carbon trading in the distribution network; Total carbon allowances for the multi-microgrid distribution system region; is the carbon trading price; for Carbon potential matrix of each node in the time period; It is the load energy consumption of distribution network.

5. The hierarchical distributed scheduling method for multi-microgrid power distribution systems under carbon flow tracing according to claim 1 is characterized in that: The carbon trading constraints of the multi-microgrid system under the carbon flow traceability are: ， ， ， Where, Carbon trading costs for multiple microgrids; Total carbon allowance for the multi-microgrid distribution system region; is the carbon trading price; for Carbon potential matrix of each node in the time period; For microgrids Load energy usage; For microgrids Actual net CO2 emissions to the atmosphere; For microgrids The nodal carbon potential of For microgrids The interaction power between the microgrid and the distribution network. Since the carbon potential represents the carbon emissions of energy consumption, the interaction power is only calculated when it is positive, that is, the distribution network transmits power to the microgrid. for Time period microgrid The amount of CO2 emitted by gas turbines; for Time period microgrid Carbon capture capacity of the medium carbon capture unit; For microgrids Carbon quotas obtained.

6. The hierarchical distributed scheduling method for multi-microgrid power distribution systems under carbon flow tracing according to claim 1 is characterized in that: The multi-microgrid distribution system hierarchical distributed scheduling model includes an upper-layer distribution network system scheduling model and a lower-layer multi-microgrid system scheduling model, wherein the upper-layer distribution network system scheduling model is: ， Where, is the initial objective function of the distribution network system; is the expected interactive power value of the distribution network layer; is the expected interaction power value of the microgrid layer; For the The expected interactive power value of the distribution network after the iteration; For the The expected interactive power value of the microgrid after the iteration; 、 They are the multipliers of the linear and quadratic terms of the Lagrange penalty function in the target cascade method respectively; Equality constraints related to the distribution network system; is the inequality constraint related to the distribution network system; The lower-level multi-microgrid system scheduling model is: ， Where, is the initial objective function of the multi-microgrid system; Constraints on equations related to multi-microgrid systems; are the inequality constraints related to the multi-microgrid system.

7. The hierarchical distributed scheduling method for multi-microgrid power distribution systems under carbon flow tracing according to claim 1 is characterized in that: The interactive power decoupling is: ， Where: is the expected interactive power value of the distribution network layer; is the expected interaction power value of the microgrid layer; is the maximum value of the interaction power between the microgrid and the distribution network.

8. The hierarchical distributed scheduling method for multi-microgrid power distribution systems under carbon flow tracing according to claim 7 is characterized in that: According to the hierarchical distributed scheduling model of the multi-microgrid distribution system, the interactive power is decoupled and iteratively solved through the target cascade method, including: Data initialization, setting the initial values ​​of coupling variables and penalty function multipliers, and the initial iteration value ; The distribution network operator solves the low-carbon scheduling problem with the goal of minimizing the sum of operating costs and carbon trading costs under carbon flow theory. It then calculates the node carbon potential matrix at different times and transmits the obtained expected interactive power values ​​and node carbon potential results to the multi-microgrid operator. Based on the obtained power demand information and node carbon potential information, the multi-microgrid operators solve the low-carbon scheduling problem with the goal of minimizing the sum of operating costs and comprehensive carbon trading costs under carbon flow theory, and pass the obtained expected interactive power value to the distribution network operator; Calculate the total cost and coupling variable difference after each iteration; The convergence condition is determined based on the difference in the expected interaction power between the two parties and the difference in the total cost of the multi-microgrid distribution system obtained from the two iterations.

9. The hierarchical distributed scheduling method for multi-microgrid power distribution systems under carbon flow tracing according to claim 8 is characterized in that: The convergence condition is determined based on the difference between the expected interaction power of both parties and the difference between the total cost of the multi-microgrid distribution system obtained from two iterations, including: Convergence criterion 1: The difference between the expected interaction power of both parties should meet the convergence accuracy requirement: ， Convergence criterion 2: The difference between the total cost of the multi-microgrid distribution system obtained from the two iterations should meet the convergence accuracy requirement: ， Where, For the Total cost of the multi-microgrid distribution system after iterations; For the Total cost of the multi-microgrid distribution system after iterations; 、 is the convergence accuracy; If convergence criterion 1 and convergence criterion 2 are satisfied at the same time, the iteration is terminated and the optimal scheduling result is output; otherwise, the penalty function multiplier is updated according to the following formula, and , continue to solve; ， Where, 、 Respectively The coefficients of the linear and quadratic terms of the Lagrangian penalty function in the target cascade method after iterations; For the The expected interactive power value of the microgrid layer after iteration; For the The expected interactive power value of the distribution network layer after iteration; is the update coefficient of the quadratic term of the penalty function multiplier, which is usually taken as .

10. A hierarchical distributed dispatching system for multi-microgrid power distribution systems under carbon flow tracing, characterized in that: A hierarchical distributed scheduling method for a multi-microgrid power distribution system under carbon flow tracing according to any one of claims 1 to 9 is implemented, the system comprising: An operation constraint building module is used to establish the operation constraints of the multi-microgrid distribution system according to the selected system parameters and dispatch parameters of the multi-microgrid distribution system; A trading constraint construction module is used to reasonably and evenly allocate the carbon responsibility of the distribution network source side to the load side based on carbon emission flow theory, guide the interactive low-carbon energy consumption behavior of distribution and microgrids, and construct carbon trading constraints for the distribution network system and multi-microgrid system under carbon flow tracing; The objective function construction module is used to set the system dispatching targets for the multi-microgrid and distribution network. The objective function of the distribution network system is to minimize the sum of the main grid power purchase cost, distribution network loss penalty, unit operation and maintenance cost within the distribution network system, and carbon trading cost of the distribution network system within the dispatching period. The objective function of the multi-microgrid system is to minimize the sum of the multi-microgrid system gas purchase cost, unit operation and maintenance cost within the multi-microgrid system, demand response load compensation cost of the multi-microgrid system, and comprehensive carbon cost of the multi-microgrid system. A hierarchical distributed scheduling model construction module is used to establish a hierarchical distributed scheduling model for multi-microgrid distribution systems under carbon flow tracing using the target cascade theory; the hierarchical distributed scheduling model for multi-microgrid distribution systems is a two-layer structure, the upper layer is a distribution network system scheduling model, including the distribution network system objective function and related constraints, the distribution network system takes the sum of the main network power purchase cost, distribution network loss penalty, distribution network system unit operation and maintenance cost and distribution network system carbon trading cost as the minimum within the scheduling period as the objective function, the distribution network system related constraints include the distribution network system safe operation constraint, the distribution network system unit Operation constraints, distribution network system carbon trading constraints, the lower layer is a multi-microgrid system scheduling model, including the multi-microgrid system objective function and related constraints. The multi-microgrid system takes the minimization of the sum of the multi-microgrid system gas purchase cost, the multi-microgrid system unit operation and maintenance cost, the multi-microgrid system demand response load compensation cost and the multi-microgrid system comprehensive carbon cost as the objective function. The multi-microgrid system related constraints include the microgrid system conventional unit operation constraints, the microgrid system flexible load constraints, the microgrid system electric load supply and demand balance constraints, and the multi-microgrid system carbon trading constraints. The two-layer scheduling system only exchanges power information and node carbon potential information. The solution module is used to decouple the interactive power and perform iterative solution through the target cascade method based on the hierarchical distributed scheduling model of the multi-microgrid distribution system; the solution result is the scheduling plan within a scheduling cycle of the system, including gas purchase cost, carbon emissions, carbon trading cost, distribution network operation cost, multi-microgrid operation cost, each unit operating condition, carbon potential of each node in the distribution network, microgrid-distribution network interactive power, each microgrid electric load supply and demand balance condition, demand response operating condition and hierarchical distributed iterative solution condition.

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