A Multi-Microgrid Cooperative Scheduling Method Based on Master-Slave Cooperative Game and Target Cascading Analysis

Abstract

The present invention belongs to the field of power system dispatching, and relates to a multi-microgrid collaborative dispatching method based on master-slave cooperative game and objective cascading analysis. By using the collaborative dispatching problem of power resources and carbon emissions in a multi-microgrid system, a two-layer master-slave cooperative game framework is constructed: the upper-layer grid operator coordinates the power dispatching and operation strategies of the microgrid alliance based on power flow analysis, voltage regulation, and carbon emission control; each microgrid in the lower-layer microgrid alliance realizes power trading and resource sharing through cooperative game; based on the objective cascading analysis algorithm, the Anderson acceleration method is introduced to dynamically adjust the tie-line power, and the augmented Lagrangian penalty function is combined to handle the constraints, optimizing the decoupled iterative process of the upper and lower layers and accelerating the convergence; effectively solving the problem of low efficiency of multi-agent collaborative optimization, ensuring the stability of system power and the reasonable allocation of electric energy resources, and providing technical support for the operation of the multi-microgrid system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching, and particularly relates to a multi-microgrid collaborative dispatching method based on master-slave cooperative game and objective cascade analysis. Background Art

[0002] The global energy structure is accelerating its transformation towards low-carbonization, and the carbon emission reduction policies of various countries are driving the power system to shift towards renewable energy. Microgrid (MG) technology, with its flexible and controllable characteristics, can integrate clean energy sources such as photovoltaic and wind power. However, due to the limited capacity of single MGs, it is difficult to achieve global optimization of resources. The multi-MG system has become a key path to improve energy efficiency and reduce carbon emissions through collaborative dispatching and resource sharing.

[0003] Driven by low-carbon policies, multi-MGs interact deeply with grid operators, forming a regional energy collaborative network. Through power complementarity and point-to-point (P2P) trading among MGs, power and carbon quotas are flexibly allocated, ensuring both stable energy supply and improved environmental benefits. For example, electricity is purchased for support during peak load periods, and excess renewable energy is fed back to the grid or neighboring MGs, reducing dependence on high-carbon units. This model significantly improves the clean energy consumption rate and provides new possibilities for building a low-carbon and efficient power system.

[0004] In the collaborative optimization of multi-MGs and grid operators, reasonable resource allocation and optimization efficiency are the keys to achieving economic and environmental benefits. However, there are differences in supply and demand objectives and cost allocation among MGs and grid operators, leading to challenges in resource sharing. In addition, the complexity of multi-agent game relationships and multi-dimensional constraints makes it difficult for traditional centralized optimization methods (such as those relying on KKT conditions or heuristic algorithms) to handle large-scale dynamic scenarios. Existing methods solve the problem by transforming the two-layer model into a single-layer programming or combining with state transition algorithms. Although effective in specific scenarios, they still have deficiencies such as low computational efficiency and slow convergence speed when facing real-time dispatching requirements, and cannot meet the fast response requirements of the system. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a multi-MG collaborative dispatching method based on master-slave cooperative game and objective cascade analysis.

[0006] To achieve the purpose of the present invention, the following technical solutions are adopted for implementation.

[0007] A multi-MG collaborative dispatching method based on master-slave cooperative game and objective cascade analysis includes the following steps:

[0008] S1. According to the power flow operation parameters of the multi - microgrid system, by constructing a microgrid alliance model, a grid operator model, and a carbon emission management model, and combining the power balance constraints, exchange power, and voltage constraints in the grid operator, the power balance constraints, power exchange constraints, charge - discharge constraints of energy storage devices, and load response constraints in the microgrid alliance, a two - layer master - slave cooperative game framework between the grid operator and the microgrid alliance is built;

[0009] The grid operator, as the leader of the master - slave cooperative game framework, coordinates the power resources and interactive power among the microgrids by adjusting the tie - line power between the grid operator and each microgrid in the microgrid alliance while satisfying the power balance constraints, exchange power, and voltage constraints;

[0010] The microgrid alliance, as the collaborator of the master - slave cooperative game framework, distributes the power resources among the microgrids by adjusting the interactive power among the microgrids while satisfying the power balance constraints, power exchange constraints, charge - discharge constraints of energy storage devices, and load response constraints. At the same time, the Nash bargaining game theory is used to distribute the benefits of each microgrid to encourage each microgrid to participate in coordinated scheduling;

[0011] Carbon emission management evaluates and controls the overall carbon emission level of the microgrid alliance during the coordinated scheduling process. By dynamically adjusting the carbon quota allocation of each microgrid, optimizing the unit operation mode, and power trading strategy, the total carbon emissions of the system are reduced, the low - carbon operation of the system is promoted, and finally the coordinated optimization of power resources and carbon emission targets is realized;

[0012] S2. Based on the idea of the objective - cascading analysis algorithm, the two - layer master - slave cooperative game problem between the grid operator and the microgrid alliance is decoupled into an upper - layer grid operator optimization problem and a lower - layer microgrid alliance problem that can be coupled through the tie - line power variable. By introducing the Anderson acceleration method to accelerate the update of the tie - line power, determining the Anderson acceleration coefficient of the tie - line power, solving the tie - line power coefficient to dynamically adjust the tie - line power variable, and combining the augmented Lagrangian penalty function to handle the constraints, the upper - and lower - layer decoupled iterative optimization is realized, and the convergence condition for terminating the iteration is set through the convergence criterion to accelerate the convergence;

[0013] S3. Based on the master - slave cooperative game framework built in step S1 and the objective - cascading analysis algorithm designed in step S2, under the conditions of satisfying voltage stability, power flow constraints, and power balance constraints, the coordinated optimization of the power resources and carbon emissions of the microgrid alliance is realized, and a scheduling plan that takes into account the fairness of each subject is output to adjust the interactive power within the microgrid alliance to ensure the low - carbon and stable operation of the microgrid alliance.

[0014] As a preferred solution of the present invention, the objective function of the grid operator is:

[0015]

[0016] N t is the total scheduling duration, N G , N MG are the numbers of the generating units and the microgrids respectively, ρ fuel is the operating coefficient of the diesel generating unit, a i,t and b i,t are the model coefficients of the generating units respectively, is the output power of the i-th generator, is the start-stop 0-1 variable of the generating unit i at time period t, and are the start-stop cost coefficients; are the electricity price and power of the transaction between the grid operator and the distribution network respectively; are the electricity price and power of the transaction between the grid operator and the microgrid i respectively; is the trading price of the carbon quota, are the CO2 emissions and carbon quota of the grid operator's generating unit i respectively.

[0017] As a preferred embodiment of the present invention, the constraints of the grid operator include:

[0018] (1) Power balance constraint: At each time period, the power exchange between the grid operator, the generators, the loads and the microgrids remains balanced:

[0019]

[0020] Among them, represents the power exchange between the grid operator and the superior distribution network, is the output power of the i-th generator, N G , N MG are the numbers of the generating units and the microgrids respectively, N LD , N cLD are the numbers of the fixed loads and the adjustable loads respectively, is the k-th fixed load, is the k-th adjustable load, is the exchange power between the microgrid i and the grid operator;

[0021] (2) Exchange power constraint: The power exchange between the microgrid alliance and the grid operator cannot exceed the upper limit of the tie line power and the transmission line capacity to avoid overload risks:

[0022]

[0023] Among them, is the exchange power between the grid operator and the superior distribution network, and are its upper and lower limits, is the power of the tie line between the grid operator and Microgrid i, and are its minimum and maximum power limits.

[0024] (3) Voltage constraint:

[0025]

[0026] wherein, v t = 0.95, v t represents the voltage at the grid operator at time t, v t2 and respectively represent the squares of the minimum and maximum values of the grid operator voltage at time t.

[0027] As a preferred solution of the present invention, the objective function of the microgrid alliance is:

[0028]

[0029] In the formula, N t and N MG are the total dispatching duration and the number of microgrids respectively; a1, b1, c1 are the fuel coefficients of the CHP unit, is the power generated by the CHP unit; is the degradation coefficient of the energy storage device, are the charging and discharging powers of the energy storage at time t respectively; are the tie line price and power respectively; is the price of carbon quota trading among microgrids, are the CO2 emissions and carbon quota of Microgrid i respectively; are the compensation price of the controllable load and the adjustable power of the adjustable load respectively; are the electricity price and the interaction power between Microgrid i and other microgrids respectively.

[0030] As a preferred solution of the present invention, the constraints of the microgrid alliance include:

[0031] (1), Power balance constraint:

[0032]

[0033] In the formula, is the power of the tie line between Microgrid i and the grid operator, is the power of the CHP unit, is the output of the photovoltaic in Microgrid i at time t, are the charging and discharging powers of the energy storage device of Microgrid i at time t respectively, is the interaction power between Microgrid i and other microgrids, are the powers for regulating the fixed load and adjustable load of microgrid i, respectively, N t 、N MG are the total scheduling duration and the number of microgrids, respectively;

[0034] (2) Power exchange constraint:

[0035]

[0036] In the formula, is the interactive power between microgrid i and other microgrids, are the upper and lower limits of the interactive power.

[0037] (3) Energy storage device charge and discharge constraint:

[0038]

[0039] Among them, are the charging and discharging powers of the energy storage at time t, respectively, and are the maximum values of the charging and discharging powers of the energy storage, respectively.

[0040] (4) Load response constraint:

[0041] The upper and lower limit constraints and total amount constraints of the transferable electrical load are as follows:

[0042]

[0043] Among them, v1 is the proportion of the transferable electrical load in the total electrical load, T is the total time, is the electrical load before demand response, is the transferable electrical load.

[0044] The constraint conditions of the reducible load are as follows:

[0045]

[0046] In the formula, v2 is the proportion of the reducible electrical load in the total electrical load, is the reducible electrical load, is the electrical load before demand response.

[0047] As a preferred solution of the present invention, the Nash bargaining game theory is used to describe the transaction interaction within the microgrid alliance, and the constructed model is as follows:

[0048]

[0049]

[0050] In the formula, N t 、NMG They are the total scheduling duration and the number of microgrids respectively; It represents the total operating cost of microgrid i; It is the cost of the combined heat and power (CHP) unit in microgrid i. a1, b1, and c1 are the fuel coefficients of the CHP unit, is the power generated by the CHP unit; It is the degradation cost of the energy storage device, is the degradation coefficient, They are the charging and discharging powers of the energy storage at time t respectively; It is the connection cost between microgrid i and the grid operator, They are the connection electricity price and power respectively; It is the carbon emission cost of the microgrid, is the price of carbon quota trading between microgrids, They are the CO2 emissions and carbon quota of microgrid i respectively; It is the demand response cost, They are the compensation electricity price of the controllable load and the regulation power of the adjustable load respectively; It is the cost of power exchange between microgrids, They are the electricity price and the interaction power between microgrid i and other microgrids respectively; It is the cost of microgrid i operating alone when not participating in the alliance, that is, the breakdown point of Nash negotiation. At this time, is the overall operating cost of microgrid i participating in cooperation; It is the payment income obtained by the microgrid participating in cooperation.

[0051] As a preferred solution of the present invention, the objective functions of the upper and lower layers after decoupling are respectively:

[0052]

[0053]

[0054] In the formula, N t is the total scheduling duration, N G , N MG are the numbers of units and microgrids respectively, ρ fuel is the operating coefficient of the diesel unit, a i,t and b i,t are the model coefficients of the units respectively, is the start-stop 0-1 variable of unit i at time t, and are the start-stop cost coefficients; They are the electricity price and power of the grid operator's transaction with the distribution network respectively; They are the electricity price and power of the grid operator's transaction with microgrid i respectively; is the trading price of carbon quotas, are respectively the CO2 emissions and carbon quotas of the grid operator's unit i; a1, b1, c1 are the fuel coefficients of the CHP unit, is the power generated by the CHP unit; is the degradation coefficient of the energy storage device, are respectively the charging and discharging powers of the energy storage at time t; are respectively the connection electricity price and power; is the price of carbon quota trading between microgrids, are respectively the CO2 emissions and carbon quotas of microgrid i; are respectively the compensation electricity price of the controllable load and the regulation power of the adjustable load; are respectively the electricity price and the interaction power between microgrid i and other microgrids; F UG and respectively represent the decoupled objective functions of the grid operator and the microgrid; x UG and respectively represent the decision variable vectors of the grid operator and the i-th microgrid; represents the multiplier of the first and second terms in the k-th iteration; the power traded between the grid operator and microgrid i; represents the optimization result feedback from the lower-level problem to the upper-level problem; is the tie-line power between microgrid i and the grid operator; represents the optimization result feedback from the upper-level problem to the lower-level problem, v i,t and ω i,t respectively represent the penalty factors of the first and second terms of the upper-level penalty function. The penalty term is used to constrain the unreasonable transactions between microgrids and ensure fair and reasonable transactions.

[0055] As a preferred solution of the present invention, the process of updating the tie-line power and interaction power by the Anderson acceleration method includes the following steps:

[0056] S81. Microgrid power acceleration update:

[0057]

[0058] Among them, is the power traded between the grid operator and the microgrid in the k-th iteration, N MG is the number of microgrids, is the power traded between the grid operator and the microgrid in the (k + 1)-th iteration, α i is the Anderson coefficient of the power traded between the grid operator and the microgrid;

[0059] S82. Tie-line power acceleration update:

[0060]

[0061] Among them, is the power of the connection line between the microgrid and the grid operator in the k-th iteration, N MG is the number of microgrids, is the power of the connection line between the microgrid and the grid operator in the (k + 1)-th iteration, β i is the Anderson coefficient of the power of the connection line between the microgrid and the grid operator;

[0062] S83. Accelerated update of the interactive power between microgrids:

[0063]

[0064] Among them, is the interactive power between microgrids in the k-th iteration, N MG is the number of microgrids, is the interactive power between microgrids in the (k + 1)-th iteration, γ i is the Anderson coefficient of the interactive power between microgrids;

[0065] S84. Determine the Anderson acceleration coefficient:

[0066]

[0067] Among them, represents the residual between the power of the transaction between the grid operator and the microgrid in the (k + 1)-th iteration and the result of the previous iteration, represents the residual between the power of the connection line between the microgrid and the grid operator in the (k + 1)-th iteration and the result of the previous iteration, represents the residual between the interactive power between microgrids in the (k + 1)-th iteration and the result of the previous iteration;

[0068] S85. Solve the coefficient:

[0069]

[0070] Among them, represents the residual between the power of the transaction between the grid operator and the microgrid in the k-th iteration and the result of the iteration i times before, represents the residual between the power of the connection line between the microgrid and the grid operator in the k-th iteration and the result of the iteration i times before, represents the residual between the interactive power between microgrids in the k-th iteration and the result of the iteration i times before.

[0071] As a preferred solution of the present invention, the convergence criterion is:

[0072]

[0073] Among them, is the power of the transaction between the power grid operator and the microgrid in the k-th iteration, is the power of the transaction between the power grid operator and the microgrid in the (k + 1)-th iteration; is the tie-line power between the microgrid and the power grid operator in the k-th iteration, is the tie-line power between the microgrid and the power grid operator in the (k + 1)-th iteration; The interactive power between microgrids in the k-th iteration, is the interactive power between microgrids in the (k + 1)-th iteration; ∈1 represents the convergence accuracy of the power of the transaction between the power grid operator and the microgrid, ∈2 represents the convergence accuracy of the tie-line power between the microgrid and the power grid operator, and ∈3 represents the convergence accuracy of the interactive power between microgrids.

[0074] Beneficial effects: The present invention proposes a multi-microgrid collaborative scheduling method based on master-slave cooperative game and objective cascading analysis, constructs a two-layer master-slave cooperative game between the power grid operator and the microgrid alliance to achieve efficient multi-agent collaboration; the upper-layer power grid operator coordinates the overall situation, dynamically formulates electricity price and carbon quota strategies, and optimizes system economy and carbon emission control; the lower-layer microgrid alliance realizes internal power trading and resource sharing through cooperative game, reducing the dependence on high-carbon units; aiming at the bottleneck of large-scale optimization efficiency, an improved objective cascading analysis algorithm is designed, introducing the Anderson acceleration method to dynamically update the tie-line power variable and combining with the augmented Lagrangian penalty function method to achieve decoupled iterative optimization of the upper and lower layers; experiments show that the improved objective cascading analysis algorithm improves the convergence speed, enhances the accuracy and efficiency of the results, and provides technical support for the low-carbon and efficient collaborative scheduling of multi-microgrid systems. Description of the Drawings

[0075] Figure 1 is the framework diagram of the multi-microgrid system;

[0076] Figure 2 is the flow chart of the improved ATC algorithm;

[0077] Figure 3 is the iterative convergence process of the improved ATC algorithm in different scenarios;

[0078] Figure 4 is the iterative process of the tie-line power between the microgrid and the power grid operator in different scenarios;

[0079] Figure 5 is the convergence characteristic of the penalty term of the improved ATC algorithm in different scenarios;

[0080] Figure 6 is the power balance diagram of each microgrid;

[0081] Figure 7 is the power interaction diagram between microgrids. Detailed Implementation Manner

[0082] The present invention will be further described in conjunction with the embodiments and the accompanying drawings.

[0083] As an embodiment of the present invention, as Figures 1 to 2 shown, a multi-microgrid collaborative scheduling method based on master-slave cooperative game and objective cascade analysis includes the following steps:

[0084] S1. According to the power flow operation parameters of the multi-microgrid system, by constructing a microgrid alliance model, a grid operator model, and a carbon emission management model, and combining the power balance constraints, exchange power, and voltage constraints in the grid operator, the power balance constraints, power exchange constraints, energy storage device charge and discharge constraints, and load response constraints in the microgrid alliance, a two-layer master-slave cooperative game framework between the grid operator and the microgrid alliance is built;

[0085] The grid operator, as the leader of the master-slave cooperative game framework, coordinates the power resources and interactive power between microgrids by adjusting the tie-line power between the grid operator and each microgrid in the microgrid alliance while satisfying the power balance constraints, exchange power, and voltage constraints;

[0086] The microgrid alliance, as the collaborator of the master-slave cooperative game framework, distributes the power resources between microgrids by adjusting the interactive power between microgrids while satisfying the power balance constraints, power exchange constraints, energy storage device charge and discharge constraints, and load response constraints. At the same time, the Nash bargaining game theory is used to distribute the benefits of each microgrid to encourage each microgrid to participate in collaborative scheduling;

[0087] Carbon emission management evaluates and controls the overall carbon emission level of the microgrid alliance during the collaborative scheduling process. By dynamically adjusting the carbon quota allocation of each microgrid, optimizing the unit operation mode, and the electricity trading strategy, the total carbon emissions of the system are reduced, the low-carbon operation of the system is promoted, and finally the collaborative optimization of power resources and carbon emission targets is realized;

[0088] S2. Based on the idea of the objective cascade analysis algorithm, the two-layer master-slave cooperative game problem between the grid operator and the microgrid alliance is decoupled into an upper-layer grid operator optimization problem and a lower-layer microgrid alliance problem that can be coupled through tie-line power variables. By introducing the Anderson acceleration method to accelerate the update of the tie-line power, determining the Anderson acceleration coefficient of the tie-line power, solving the tie-line power coefficient to dynamically adjust the tie-line power variable, and combining the augmented Lagrangian penalty function to handle the constraints, the upper and lower layer decoupled iterative optimization is realized, and the convergence condition for terminating the iteration is set through the convergence criterion to accelerate the convergence;

[0089] S3. Based on the master-slave cooperation game framework established in step S1 and the objective cascade analysis algorithm designed in step S2, under the conditions of meeting the voltage stability, power flow constraints, and power balance constraints, the collaborative optimization of the power resources and carbon emissions of the microgrid alliance is realized, and a scheduling scheme that takes into account the fairness of each subject is output to adjust the interactive power within the microgrid alliance to ensure the low-carbon and stable operation of the microgrid alliance.

[0090] As an embodiment of the present invention, the objective function of the grid operator is:

[0091]

[0092] N t is the total scheduling duration, N G 、N MG are the numbers of units and microgrids respectively, ρ fuel is the operation coefficient of the diesel generator set, a i,t and b i,t are the model coefficients of the unit respectively, is the output power of the i-th generator, is the start-stop 0-1 variable of unit i at time t, and are the start-stop cost coefficients; are the electricity price and power traded between the grid operator and the distribution network respectively; are the electricity price and power traded between the grid operator and microgrid i respectively; is the trading price of carbon quotas, are the CO2 emissions and carbon quotas of the grid operator's unit i respectively.

[0093] As an embodiment of the present invention, the constraints of the grid operator include:

[0094] (1) Power balance constraint: At each time period, the power exchange between the grid operator, generators, loads, and microgrids remains balanced:

[0095]

[0096] Among them, represents the power exchange between the grid operator and the superior distribution network, is the output power of the i-th generator, N G 、N MG are the numbers of units and microgrids respectively, N LD 、N cLD are the numbers of fixed loads and adjustable loads respectively, is the k-th fixed load, is the k-th adjustable load, is the exchanged power between microgrid i and the grid operator;

[0097] (2) Exchange power constraint: The power exchange between the microgrid alliance and the grid operator shall not exceed the tie-line power and the upper limit of the transmission line capacity to avoid overload risks:

[0098]

[0099] Among them, is the exchanged power between the grid operator and the superior distribution network, and are its upper and lower limits, is the tie-line power between the grid operator and microgrid i, and are its minimum and maximum power limits.

[0100] (3) Voltage constraint:

[0101]

[0102] Among them, v t = 0.95, v t represents the voltage at the grid operator at time t, v t2 and respectively represent the squares of the minimum and maximum voltages of the grid operator at time t.

[0103] As a preferred embodiment of the present invention, the objective function of the microgrid alliance is:

[0104]

[0105] In the formula, N t and N MG are the total dispatching duration and the number of microgrids respectively; a1, b1, c1 are the fuel coefficients of the CHP unit, is the power generated by the CHP unit; is the degradation coefficient of the energy storage device, are the charging and discharging powers of the energy storage at time t respectively; are the connection electricity price and power respectively; is the price of carbon quota trading among microgrids, are the CO2 emissions and carbon quota of microgrid i respectively; are the compensation electricity price of the controllable load and the regulation power of the adjustable load respectively; are the electricity price and the interaction power between microgrid i and other microgrids respectively.

[0106] As an embodiment of the present invention, the constraints of the microgrid alliance include:

[0107] (1) Power balance constraint:

[0108]

[0109] Wherein, is the power of the connection line between microgrid i and the grid operator, is the power of the CHP unit, is the output of the PV in microgrid i at time t, are the charging and discharging powers of the energy storage device in microgrid i at time t, respectively, is the interactive power between microgrid i and other microgrids, are the powers of the fixed load and adjustable load regulation in microgrid i, respectively. N t N MG are the total scheduling duration and the number of microgrids, respectively;

[0110] (2) Power exchange constraint:

[0111]

[0112] Wherein, is the interactive power between microgrid i and other microgrids, are the upper and lower limits of the interactive power.

[0113] (3) Energy storage device charge and discharge constraint:

[0114]

[0115] Among them, are the charging and discharging powers of the energy storage at time t, respectively, and are the maximum values of the energy storage charging and discharging powers, respectively.

[0116] (4) Load response constraint:

[0117] The upper and lower limit constraints and total amount constraints of the transferable electrical load are as follows:

[0118]

[0119]

[0120] Among them, v1 is the proportion of the transferable electrical load in the total electrical load, T is the total time, is the electrical load before demand response, is the transferable electrical load.

[0121] The constraints of the reducible load are as follows:

[0122]

[0123] In the formula, v2 is the proportion of the reducible electrical load in the total electrical load, is the reducible electrical load, is the electrical load before demand response.

[0124] As a preferred embodiment of the present invention, the Nash bargaining game theory is adopted to describe the transaction interaction within the microgrid alliance, and the constructed model is as follows:

[0125]

[0126] In the formula, N t , N MG are the total dispatching duration and the number of microgrids respectively; represents the total operating cost of microgrid i; is the cost of the combined heat and power (CHP) unit in microgrid i, and a1, b1, c1 are the fuel coefficients of the CHP unit, is the power generated by the CHP unit; is the degradation cost of the energy storage device, is the degradation coefficient, are the charging and discharging powers of the energy storage at time t respectively; is the connection cost between microgrid i and the grid operator, are the connection electricity price and power respectively; is the carbon emission cost of the microgrid, is the price of carbon quota trading between microgrids, are the CO2 emissions and carbon quota of microgrid i respectively; is the demand response cost, are the compensation electricity price of the controllable load and the regulation power of the adjustable load respectively; is the cost of power exchange between microgrids, are the electricity price and the interactive power between microgrid i and other microgrids respectively; is the cost of microgrid i operating alone when not participating in the alliance, that is, the breakdown point of Nash negotiation. At this time is the total operating cost of microgrid i participating in cooperation; is the payment income obtained by the microgrid participating in cooperation.

[0127] As an embodiment of the present invention, the objective functions of the upper and lower layers after decoupling are respectively:

[0128]

[0129] In the formula, N t is the total dispatching duration, N G , N MGThe number of units and microgrids respectively, ρ fuel is the operating coefficient of the diesel unit, a i,t and b i,t are the model coefficients representing the units respectively, is the start-stop 0-1 variable of unit i at time t, and is the start-stop cost coefficient; are the electricity price and power of the transaction between the grid operator and the distribution network respectively; are the electricity price and power of the transaction between the grid operator and microgrid i respectively; is the trading price of carbon quotas, are the CO2 emissions and carbon quotas of the grid operator's unit i respectively; a1, b1, c1 are the fuel coefficients of the CHP unit, is the power generated by the CHP unit; is the degradation coefficient of the energy storage device, are the charging and discharging powers of the energy storage at time t respectively; are the connection electricity price and power respectively; is the price of carbon quota trading between microgrids, are the CO2 emissions and carbon quotas of microgrid i respectively; are the compensation electricity price of the controllable load and the regulation power of the adjustable load respectively; are the electricity price and the interaction power between microgrid i and other microgrids respectively; F UG 、 represent the decoupled objective functions of the grid operator and the microgrid respectively; x UG and represent the decision variable vectors of the grid operator and the i-th microgrid respectively; represents the multiplier of the first and second terms in the k-th iteration; The power of the transaction between the grid operator and microgrid i; represents the optimization result of the feedback of the lower-level problem to the upper-level problem; is the tie-line power between microgrid i and the grid operator; represents the optimization result of the feedback of the upper-level problem to the lower-level problem, v i,t and ω i,t represent the penalty factors of the first and second terms of the upper-level penalty function respectively. The penalty term is used to constrain the unreasonable transactions between microgrids to ensure fair and reasonable transactions.

[0130] As an embodiment of the present invention, the process of updating the tie-line power and interaction power by the Anderson acceleration method includes the following steps:

[0131] S91. Microgrid power acceleration update:

[0132]

[0133] Among them, is the power of the transaction between the grid operator and the microgrid in the k-th iteration, N MG is the number of microgrids, is the power of the transaction between the grid operator and the microgrid in the (k + 1)-th iteration, α i is the Anderson coefficient of the power of the transaction between the grid operator and the microgrid;

[0134] S92, Accelerated update of tie-line power:

[0135]

[0136] Among them, is the tie-line power between the microgrid and the grid operator in the k-th iteration, is the tie-line power between the microgrid and the grid operator in the (k + 1)-th iteration, β i is the Anderson coefficient of the tie-line power between the microgrid and the grid operator;

[0137] S93, Accelerated update of the interactive power between microgrids:

[0138]

[0139] Among them, is the interactive power between microgrids in the k-th iteration, N MG is the number of microgrids, is the interactive power between microgrids in the (k + 1)-th iteration, γ i is the Anderson coefficient of the interactive power between microgrids;

[0140] S94, Determine the Anderson acceleration coefficient:

[0141]

[0142] Among them, represents the residual between the power of the transaction between the grid operator and the microgrid in the (k + 1)-th iteration and the result of the previous iteration, represents the residual between the tie-line power between the microgrid and the grid operator in the (k + 1)-th iteration and the result of the previous iteration, represents the residual between the interactive power between microgrids in the (k + 1)-th iteration and the result of the previous iteration;

[0143] S95, Solve the coefficient:

[0144]

[0145] Among them, represents the residual between the power of the transaction between the grid operator and the microgrid in the k-th iteration and the result of the iteration i times before, It represents the residual of the power of the connection line between the microgrid and the grid operator in the k-th iteration compared with the result of the previous i-th iteration. It represents the residual of the interactive power between microgrids in the k-th iteration compared with the result of the previous i-th iteration.

[0146] As an embodiment of the present invention, the convergence criterion is:

[0147]

[0148] Wherein, is the power of the transaction between the grid operator and the microgrid in the k-th iteration, is the power of the transaction between the grid operator and the microgrid in the (k + 1)-th iteration; is the power of the connection line between the microgrid and the grid operator in the k-th iteration, is the power of the connection line between the microgrid and the grid operator in the (k + 1)-th iteration; The interactive power between microgrids in the k-th iteration, is the interactive power between microgrids in the (k + 1)-th iteration; ∈1 represents the convergence accuracy of the power of the transaction between the grid operator and the microgrid, ∈2 represents the convergence accuracy of the power of the connection line between the microgrid and the grid operator, and ∈3 represents the convergence accuracy of the interactive power between microgrids;

[0149] As an embodiment of the present invention, as Figures 3 to 5 shown, first, the model parameters of the grid operator and each sub-microgrid of the test case are initialized. The grid operator includes three traditional diesel generator sets with powers of 350 kw, 300 kw, and 250 kw respectively. Microgrid 1 has a 200 kw CHP unit, a 250 kw photovoltaic unit, and a 200 kw energy storage device; Microgrid 2 has a 150 kw photovoltaic unit and a 200 kw energy storage device, and Microgrid 3 has a 400 kw CHP unit, a 100 kw photovoltaic unit, and a 100 kw energy storage device.

[0150] The following two scenarios are set for the test case to verify the practicability of the dispatching strategy: 1) Without considering the transactions between sub-microgrids, the sub-microgrids only conduct power transactions with the grid operator; 2) Considering the transactions between sub-microgrids, in the form of a cooperative game of the microgrid alliance; at the same time, the microgrid alliance can also conduct power transactions with the grid operator.

[0151] In the first scenario, that is, when the transactions between microgrids are not considered, the collaborative scheduling process of the entire multi - microgrid system is as follows: First, the main grid issues an initial scheduling plan to the grid operator according to the system operation conditions. Then, the grid operator determines the initial tie - line power plan with each microgrid based on the superior instructions and its own model. In this case, there is no direct electrical energy interaction between microgrids, and each microgrid only interacts with the grid operator in terms of electrical energy. Subsequently, the grid operator (upper - level problem) and each microgrid (lower - level problem) decouple the problem based on the ATC algorithm and independently optimize the model parameters. Next, the grid operator and each microgrid use the tie - line power variables to handle the constraint conditions through the augmented Lagrangian penalty function method, and use the Anderson acceleration method to dynamically update the tie - line power variables to improve the iteration efficiency. In the iterative solution stage, the grid operator and each microgrid repeatedly exchange optimization results and update variables until the tie - line power variables meet the convergence criterion of the algorithm. When the preset convergence accuracy is reached, the final tie - line power plan between the grid operator and the microgrid is output. At this time, a stable collaborative scheduling scheme is formed in the system, and each microgrid executes electrical energy interaction with the grid operator according to the obtained plan, realizing the stable and efficient operation of the overall system.

[0152] In the second scenario, that is, when electrical energy transactions between microgrids are allowed, the scheduling process of the system is different. At this time, the main grid also issues an initial overall scheduling plan to the grid operator, and the grid operator formulates an initial tie - line power strategy accordingly and issues the scheduling plan to the microgrid alliance. In this mode, there is also electrical energy interaction between sub - microgrids, that is, the transaction interaction within the microgrid alliance is carried out in the form of Nash bargaining game to achieve resource sharing and maximize the overall economic benefits. Therefore, the optimization problem of the lower - level microgrid alliance is more complex than that in the first scenario, and it is necessary to consider both the tie - line power transaction with the grid operator and the interactive power transaction between microgrids. In this case, the upper - level problem of the grid operator and the lower - level problem of the microgrid alliance are decoupled through the objective - cascaded analysis algorithm and further divided into multiple sub - problems. Each sub - microgrid simultaneously optimizes the local power generation and load scheduling and determines the electrical energy transaction strategy with other microgrids. Each microgrid feeds back the local optimization results to the grid operator, and the grid operator then dynamically adjusts the tie - line power strategy with the entire microgrid alliance. This process also uses the Anderson acceleration method to iteratively accelerate the update of the tie - line power between the grid operator and the microgrid and the interactive power between microgrids until the convergence criterion of the algorithm is reached and converges to the set accuracy. Finally, the system outputs the transaction plan between microgrids and the tie - line power allocation scheme between the entire microgrid alliance and the grid operator. Each microgrid and the grid operator execute the corresponding electrical energy interaction and resource allocation according to this scheme, realizing the low - carbon, economic, and efficient and stable operation under the condition of multi - subject participation.

[0153] Based on the Target Cascaded Analysis (ATC) algorithm, a distributed solution is carried out for the multi-microgrid collaborative scheduling model. The iterative convergence processes of Scenario 1 (without inter-microgrid transactions) and Scenario 2 (with inter-microgrid transactions) are as Figure 3 shown: In Scenario 1, the overall convergence occurs after 7 iterations. In Scenario 2, due to the increased complexity of inter-microgrid transactions, the convergence occurs after 9 iterations, and the residuals are all controlled within 10^-3. The iterative processes of the power of the connection lines between the microgrids and the grid operator under the two scenarios: In Scenario 1, when only the microgrids interact with the grid, the power of the connection lines tends to be consistent after 7 iterations; in Scenario 2, after introducing inter-microgrid transactions, the power of the connection lines requires 9 iterations to converge, indicating that the transaction complexity increases but has no essential impact on the system balance; as Figure 4 shown.

[0154] As Figure 5 shown, the convergence characteristics of the penalty terms under the two scenarios are further compared. In Scenario 1, due to no inter-microgrid interaction, the initial deviation of the penalty term is small and the change is gentle; in Scenario 2, due to the need to satisfy the inter-microgrid power balance constraint (P1 + P2 + P3 = 0) and the differences in the unit structures of each microgrid, the initial deviation of the penalty term is significant, but it gradually converges to the same order of magnitude through 9 iterations, verifying the adaptability of the algorithm to complex constraints.

[0155] After the cooperation of the microgrid alliance, the revenues of each microgrid are significantly improved: Microgrids 1, 2, and 3 are increased by 4.5%, 8.79%, and 1.85% respectively, indicating that the sharing of electric energy can optimize resource allocation and improve economic benefits; at the same time, the revenue of the grid operator decreases due to the reduced dependence on the microgrids, reflecting the characteristics of the redistribution of interests under the multi-agent game.

[0156] From the perspective of the energy structure, Microgrids 1 and 2 are mainly based on photovoltaic power, and redundant electric energy can be sold to Microgrid 3 during the low-load period; Microgrid 3 is centered on a gas turbine and can supply power in the reverse direction during the high-load period, forming a complementarity, as Figure 6 shown. For the power interaction between the microgrids, Microgrid 3 requires additional electric energy support during specific periods. At this time, Microgrids 1 and 2 provide clean power through P2P transactions, reducing the output of their high-carbon units. Although the inter-microgrid transaction volume is limited, through dynamic adjustment of the purchase and sale strategies, it realizes local balance in the regional network and reduces the grid scheduling pressure, as Figure 7 shown.

[0157] The above simulation results verify the effectiveness and practicality of the model and algorithm constructed in the present invention. Through the master-slave cooperation game framework and the improved ATC algorithm, the efficient cooperation of multiple agents is realized, the dynamic optimization efficiency is improved, and the real-time scheduling requirements of the system are ensured. This method can provide a reference for the low-carbon and efficient collaborative scheduling of multi-microgrid systems and has certain engineering practical value.

[0158] The preferred embodiments of the embodiments of the present application have been described above with reference to the accompanying drawings. This does not limit the scope of the rights of the embodiments of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall fall within the scope of the rights of the embodiments of the present application.

Claims

1. A multi - microgrid collaborative scheduling method based on master - slave cooperative game and target cascading analysis, characterized in that: It includes the following steps: S1. According to the power flow operation parameters of the multi - microgrid system, by constructing a microgrid alliance model, a grid operator model and a carbon emission management model, and combining the power balance constraints, interchange power and voltage constraints in the grid operator, the power balance constraints, power interchange constraints, charge - discharge constraints of energy storage devices, and load response constraints in the microgrid alliance, a two - layer master - slave cooperative game framework between the grid operator and the microgrid alliance is built; The grid operator, as the leader of the two - layer master - slave cooperative game framework, coordinates the electric power resources and interchange power among microgrids by adjusting the tie - line power between the grid operator and each microgrid in the microgrid alliance under the condition of satisfying the power balance constraints, interchange power and voltage constraints; The microgrid alliance, as the collaborator of the two - layer master - slave cooperative game framework, distributes the electric power resources among microgrids by adjusting the interchange power among microgrids under the condition of satisfying the power balance constraints, power interchange constraints, charge - discharge constraints of energy storage devices, and load response constraints. At the same time, the Nash bargaining game theory is used to distribute the benefits of each microgrid to encourage each microgrid to participate in coordinated scheduling; Carbon emission management evaluates and controls the overall carbon emission level of the microgrid alliance during the coordinated scheduling process. By dynamically adjusting the carbon quota allocation of each microgrid, optimizing the unit operation mode and electricity trading strategy, the total carbon emissions of the system are reduced, the low - carbon operation of the system is promoted, and finally the coordinated optimization of electric power resources and carbon emission targets is realized; S2. Based on the idea of the objective - cascading analysis algorithm, the two - layer master - slave cooperative game problem between the grid operator and the microgrid alliance is decoupled into an upper - layer grid operator optimization problem and a lower - layer microgrid alliance problem that can be coupled through tie - line power variables. The Anderson acceleration method is introduced to accelerate the update of tie - line power. By determining the Anderson acceleration coefficient and solving the tie - line power coefficient, the tie - line power variables are dynamically adjusted, and the augmented Lagrangian penalty function is combined to handle the constraints, realizing the decoupled iterative optimization of the upper and lower layers. And the convergence condition for terminating the iteration is set by the convergence criterion to accelerate the convergence; S3. Based on the master - slave cooperative game framework built in step S1 and the objective - cascading analysis algorithm designed in step S2, under the condition of satisfying the voltage stability, power flow constraints and power balance constraints, the coordinated optimization of the electric power resources and carbon emissions of the microgrid alliance is realized, and a scheduling scheme that takes into account the fairness of each subject is output to adjust the interchange power within the microgrid alliance to ensure the low - carbon and stable operation of the microgrid alliance.

2. The multi-microgrid collaborative scheduling method based on master-slave cooperative game and objective cascade analysis according to claim 1, characterized in that: The objective function of the grid operator is: N t is the total scheduling duration, N G , N MG are the numbers of the units and the microgrids respectively, ρ fuel is the operation coefficient of the diesel generator set, a i,t and b i,t are the model coefficients of the units respectively, is the output power of the i-th generator, is the start-stop 0-1 variable of unit i at time t, and are the start-stop cost coefficients; P t DN are the electricity price and power for the power grid operator's transaction with the distribution network respectively; are the electricity price and power for the power grid operator's transaction with the i-th microgrid respectively; is the trading price of carbon quotas, are the CO2 emissions and carbon quotas of the power grid operator's unit i respectively.

3. A multi - microgrid collaborative scheduling method based on master - slave cooperative game and objective cascade analysis according to claim 1, characterized in that: The constraints of the grid operator include: (1). Power balance constraint: At each time period, the power interchange among the grid operator, generators, loads and microgrids remains balanced: Among them, P t DN represents the power exchange between the power grid operator and the superior distribution network, is the output power of the i-th generator, N G and N MG are the numbers of the generating units and the microgrid respectively, N LD and N cLD are the numbers of the fixed load and the adjustable load respectively, is the k-th fixed load, is the k-th adjustable load, is the exchange power between the i-th microgrid and the power grid operator; (2). Interchange power constraint: The electric energy interchange between the microgrid alliance and the grid operator cannot exceed the tie - line power and the upper limit of the transmission line capacity to avoid overload risks: Among them, P t DN is the exchange power between the power grid operator and the superior distribution network, and are its upper and lower limits, is the tie-line power between the power grid operator and microgrid i, and are its minimum and maximum power limits; (3). Voltage constraint: where, v t = 0.95, v t represents the voltage at the grid operator at time t, and v t 2 and represent the squares of the minimum and maximum values of the grid operator's voltage at time t, respectively.

4. A multi-microgrid collaborative scheduling method based on master-slave cooperative game and objective cascading analysis according to claim 1, characterized in that: The objective function of the microgrid alliance is: where N t and N MG are the total scheduling duration and the number of microgrids respectively; a1, b1, c1 are the fuel coefficients of the CHP unit, is the power generated by the CHP unit; is the degradation coefficient of the energy storage device, are the charging and discharging powers of the energy storage at time t respectively; are the connection electricity price and power respectively; is the price of carbon quota trading among microgrids, are the CO2 emissions and carbon quota of microgrid i respectively; are the compensation electricity price of the controllable load and the regulation power of the adjustable load respectively; are the electricity price and the interaction power between microgrid i and other microgrids respectively.

5. A multi-microgrid collaborative scheduling method based on master-slave cooperative game and objective cascade analysis according to claim 1, characterized in that: The constraints of the microgrid alliance include: (1). Power balance constraint: wherein, is the power of the connection line between microgrid i and the grid operator, is the power of the CHP unit, is the output of the PV in microgrid i at time t, are the charging and discharging powers of the energy storage device in microgrid i at time t, respectively, is the interactive power between microgrid i and other microgrids, are the powers of the fixed load and the adjustable load regulation in microgrid i, respectively. N t and N MG are the total scheduling duration and the number of microgrids, respectively; (2). Power interchange constraint: wherein, is the interaction power between microgrid i and other microgrids, are the upper and lower limits of the interaction power; (3). Charge - discharge constraint of energy storage devices: wherein, are respectively the charging and discharging powers of the energy storage in the t period, and are respectively the maximum values of the charging and discharging powers of the energy storage; (4). Load response constraint: The upper and lower limits and total amount constraints of the transferable electric load are as follows: where v1 is the proportion of transferable electrical load in the total electrical load, T is the total time, is the electrical load before demand response, is the transferable electrical load; The load-shedding constraints are as follows: wherein, v2 is the proportion of the reducible electric load in the total electric load, is the reducible electric load, is the electric load before demand response.

6. The multi - microgrid collaborative scheduling method based on master - slave cooperative game and objective cascade analysis according to claim 1, characterized in that: The Nash bargaining game theory is used to describe the transaction interactions within the microgrid alliance, and the constructed model is as follows: Wherein, N t and N MG are the total scheduling duration and the number of microgrids respectively; represents the total operating cost of microgrid i; is the cost of the combined heat and power (CHP) unit in microgrid i, and a1, b1, c1 are the fuel coefficients of the CHP unit, is the power generated by the CHP unit; is the degradation cost of the energy storage device, is the degradation coefficient, are the charging and discharging powers of the energy storage at time t respectively; is the connection cost between microgrid i and the grid operator, are the connection electricity price and power respectively; is the carbon emission cost of the microgrid, is the price of carbon quota trading between microgrids, are the CO2 emissions and carbon quota of microgrid i respectively; is the demand response cost, are the compensation electricity price of the controllable load and the regulation power of the adjustable load respectively; is the cost of power exchange between microgrids, are the electricity price and the interaction power between microgrid i and other microgrids respectively; is the cost of microgrid i operating alone when not participating in the alliance, that is, the breakdown point of Nash negotiation. At this time, is the overall operating cost of microgrid i participating in cooperation; is the payment income obtained by the microgrid participating in cooperation.

7. A multi-microgrid collaborative scheduling method based on master-slave cooperative game and objective cascade analysis according to claim 1, characterized in that: The objective functions of the upper and lower layers after decoupling are respectively: Wherein, N t is the total scheduling duration, N G , N MG are the numbers of units and microgrids respectively, ρ fuel is the operating coefficient of the diesel generator set, a i,t and b i,t are the model coefficients of the units respectively, is the start-stop 0-1 variable of unit i at time t, and are the start-stop cost coefficients; P t DN are the electricity price and power of the transaction between the grid operator and the distribution network respectively; are the electricity price and power of the transaction between the grid operator and microgrid i respectively; is the trading price of carbon quotas, are the CO2 emissions and carbon quotas of unit i of the grid operator respectively; a1, b1, c1 are the fuel coefficients of the CHP unit, is the power generated by the CHP unit; is the degradation coefficient of the energy storage device, are the charging and discharging powers of the energy storage at time t respectively; are the connection electricity price and power respectively; is the price of carbon quota trading between microgrids, are the CO2 emissions and carbon quotas of microgrid i respectively; are the compensation electricity price of the controllable load and the adjustable power of the adjustable load respectively; are the electricity price and the interaction power between microgrid i and other microgrids respectively; F UG , represent the decoupled objective functions of the grid operator and the microgrid respectively; x UG and represent the decision variable vectors of the grid operator and the i-th microgrid respectively; represents the multiplier of the first and second terms in the k-th iteration; the power of the transaction between the grid operator and microgrid i; represents the optimization result of the feedback of the lower-level problem to the upper-level problem; is the tie-line power between microgrid i and the grid operator; represents the optimization result of the feedback of the upper-level problem to the lower-level problem, v i,t and ω i,t represent the penalty factors of the first and second terms of the upper-level penalty function respectively. The penalty term is used to constrain the unreasonable transactions between microgrids and ensure fair and reasonable transactions.

8. A multi-microgrid collaborative scheduling method based on master-slave cooperative game and objective cascade analysis according to claim 1, characterized in that: The process of updating the tie-line power and interactive power by the Anderson acceleration method includes the following steps: S81. Microgrid power acceleration update: Among them, is the power of the transaction between the grid operator and the microgrid in the k-th iteration, N MG is the number of microgrids, is the power of the transaction between the grid operator and the microgrid in the (k + 1)-th iteration, α i is the Anderson coefficient of the power of the transaction between the grid operator and the microgrid; S82. Tie-line power acceleration update: Among them, is the power of the connection line between the microgrid and the grid operator in the k-th iteration, N MG is the number of microgrids, is the power of the connection line between the microgrid and the grid operator in the (k + 1)-th iteration, β i is the Anderson coefficient of the power of the connection line between the microgrid and the grid operator; S83. Interactive power acceleration update between microgrids: Among them, is the interactive power between microgrids in the k-th iteration, N MG is the number of microgrids, is the interactive power between microgrids in the (k + 1)-th iteration, γ i is the Anderson coefficient of the interactive power between microgrids; S84. Determine the Anderson acceleration coefficient: wherein, represents the residual of the power traded between the grid operator and the microgrid in the (k + 1)-th iteration with respect to the result of the previous iteration, represents the residual of the tie-line power between the microgrid and the grid operator in the (k + 1)-th iteration with respect to the result of the previous iteration, represents the residual of the interactive power between microgrids in the (k + 1)-th iteration with respect to the result of the previous iteration; S85. Solve the coefficient: Among them, represents the residual of the power traded between the grid operator and the microgrid in the k-th iteration compared with the result of the previous i-th iteration. represents the residual of the power flow on the tie line between the microgrid and the grid operator in the k-th iteration compared with the result of the previous i-th iteration. represents the residual of the interactive power between microgrids in the k-th iteration compared with the result of the previous i-th iteration.

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