A multi-microgrid system optimization method, device and storage medium

Abstract

The application provides a multi-micro-grid system optimization method, device and storage medium, relates to the new energy power supply field, and the method comprises the following steps: determining the topological structure of the multi-micro-grid system in a shared energy storage mode, establishing an economic scheduling model based on the optimization scheduling process of the multi-micro-grid system, determining a source-load uncertainty model and a constraint condition, establishing a fuzzy opportunity constraint based on the source-load uncertainty model, constructing a game model to solve the economic scheduling model under the fuzzy opportunity constraint, and obtaining the optimization result of the multi-micro-grid system; and the device and the storage medium are used for realizing the method. The application has the beneficial effects that the source-load uncertainty in the multi-micro-grid system is considered, the scheduling precision is improved, and the application has important theoretical and application values for solving the multi-micro-grid system optimization problem.

Description

Technical Field

[0001] This invention relates to the field of new energy power supply, and in particular to a method, device and storage medium for optimizing multi-microgrid systems. Background Technology

[0002] A renewable energy power supply system is a power supply system that primarily uses renewable energy sources, supplemented by energy storage systems and power distribution networks. Renewable energy sources are often solar and wind power, using photovoltaic and wind turbine generators to convert energy into electricity for the load. During the power supply process, because single-source renewable energy supply is greatly affected by weather, multiple renewable energy sources are often used in a complementary manner. Furthermore, due to the volatility of renewable energy generation, energy storage systems are added to the renewable energy power supply system. When renewable energy generation meets load demand, excess energy can be stored. When renewable energy generation cannot meet load demand, the energy storage system discharges to satisfy the load. Additionally, when weather conditions are severe and the energy storage system's capacity is insufficient, electricity can be traded through the distribution network to meet load demand. In recent years, with increasingly acute energy and environmental issues, the development and utilization of renewable energy sources, represented by solar and wind power, have received increasing attention from many countries. Renewable energy power supply systems, due to their high renewable energy utilization rate and low environmental pollution, have become a comprehensive renewable energy utilization method with a huge market potential.

[0003] Existing technologies often employ individual energy storage within a single microgrid, but this form of energy storage has low energy utilization and low returns for both the microgrid and the user.

[0004] In existing technologies, uncertainties at the generation and user sides are rarely considered when modeling the entire microgrid system, resulting in models that lack the ability to accurately describe real energy exchange. Summary of the Invention

[0005] The purpose of this invention is to address the challenges of scheduling multiple microgrid systems under shared energy storage models, including high difficulty and insufficient accuracy due to uncertainties. This invention provides a method, equipment, and storage medium for optimizing multiple microgrid systems. The optimization method for multiple microgrid systems mainly includes the following steps:

[0006] S1. Determine the topology of the multi-microgrid system under the shared energy storage mode;

[0007] S2. Based on the optimization scheduling process of multi-microgrid systems, establish an economic scheduling model and determine the source-load uncertainty model and constraints.

[0008] S3. Establish fuzzy opportunity constraints based on the source-load uncertainty model;

[0009] S4. Construct a game theory model to solve the economic scheduling model under fuzzy chance constraints and obtain the optimization results of the multi-microgrid system.

[0010] Furthermore, the topology of a multi-microgrid system under the shared energy storage mode consists of three main components: active distribution network, shared energy storage, and multi-microgrid.

[0011] Furthermore, the scheduling process of a multi-microgrid system is divided into two stages, as follows:

[0012] The first stage of single-grid optimization dispatch: The charging and discharging power of a single microgrid is obtained with the goal of minimizing net load fluctuations and minimizing the cost of leasing shared energy storage;

[0013] The second phase of multi-grid integration dispatch: After microgrids form an alliance, their respective charging and discharging needs are combined to form the net charging and discharging needs of the multi-microgrid system. The charging and discharging needs of each microgrid are met by providing energy storage services to shared energy storage operators.

[0014] Furthermore, the economic dispatch model is established based on the objective function of the optimal dispatch process of a multi-microgrid system, specifically as follows:

[0015] The objective function for the first stage of scheduling is as follows:

[0016]

[0017]

[0018] in, For microgrids The cost of using energy storage; and microgrids The charging and discharging costs and capacity usage costs of leased energy storage; For microgrids Equivalent load variance after using energy storage services; For microgrids At any moment Net load; , microgrids The charging and discharging power at time t; For microgrids The equivalent load average after using energy storage services;

[0019] The objective function for the two-stage scheduling is as follows:

[0020]

[0021] In the formula, The total power generation cost of a multi-microgrid system; For revenue from the purchase and sale of electricity in multi-microgrid systems and active distribution networks; Cost of generating electricity from micro gas turbines in multi-microgrid systems;

[0022] The source-load uncertainty model represents the contradiction between two optimization objectives in the scheduling phase one.

[0023] Furthermore, the constraints include:

[0024] Constraints on the scheduling process of multi-microgrid systems, constraints on the participation of shared energy storage operators in the scheduling process, and constraints on the participation of active distribution networks in the optimization scheduling process.

[0025] Furthermore, the standard form of the fuzzy chance-constrained programming model for solving uncertain problems is as follows:

[0026]

[0027] In the formula, As decision variables, For fuzzy variables, Let be the objective function. As constraints, express Credibility of its establishment The confidence level to be set.

[0028] Furthermore, the game model adopts a one-master-multiple-slave game, with the game participants being the active distribution network, the shared energy storage system, and multiple microgrid systems;

[0029] The game strategy of an active distribution network is its peak-shaving power purchase and sale strategy and its frequency regulation power purchase strategy. The game strategy of a shared energy storage system is its peak-shaving power purchase and sale volume and its participation in frequency regulation services. The game strategy of a multi-microgrid system is its power purchase and sale volume for peak-shaving.

[0030] Furthermore, the working process of the game model is divided into two parts: the solution process for the charging and discharging power of a single microgrid and the solution process for the master-slave game model, specifically as follows:

[0031] S41. A non-dominated sorting genetic algorithm with an elite strategy is adopted, and a fuzzy membership function is constructed using the partial small fuzzy set decision theory to screen for compromise solutions and obtain the optimal charging and discharging strategy for each microgrid.

[0032] S42. The charging and discharging power under the optimal charging and discharging strategies of each microgrid is used as the initial condition and substituted into a master-slave game model for solution to obtain the optimal scheduling strategy of each master. A storage medium storing instructions and data for implementing a multi-microgrid system optimization method.

[0033] A computer device includes: a processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement a multi-microgrid system optimization method.

[0034] The beneficial effects of the technical solution provided by this invention are as follows: This invention establishes fuzzy chance constraints based on a source-load uncertainty model, considers the source-load uncertainty in multi-microgrid systems, improves scheduling accuracy, constructs a game theory model to solve the economic scheduling model under fuzzy chance constraints, obtains the optimization results of multi-microgrid systems, and coordinates the power interaction between microgrids, shared energy storage, and distribution networks, as well as between microgrids, while meeting the load demand of multi-microgrid systems. This has important theoretical and applied value for solving the optimization problem of multi-microgrid systems. Attached Figure Description

[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0036] Figure 1 This is a flowchart of a multi-microgrid system optimization method according to an embodiment of the present invention;

[0037] Figure 2 This is a flowchart illustrating the solution for the optimal charging and discharging strategy of a single microgrid in an embodiment of the present invention;

[0038] Figure 3 This is a flowchart of a master-slave game model for solving the optimal scheduling strategy of a system in an embodiment of the present invention;

[0039] Figure 4 This is a schematic diagram of the hardware device working in an embodiment of the present invention. Detailed Implementation

[0040] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0041] Embodiments of the present invention provide a method, device and storage medium for optimizing a multi-microgrid system.

[0042] Please refer to Figure 1 , Figure 1 This is a flowchart of a multi-microgrid system optimization method according to an embodiment of the present invention, which specifically includes the following steps:

[0043] The first step is to determine the topology of the multi-microgrid system under the shared energy storage mode.

[0044] The topology of a multi-microgrid system under the shared energy storage mode mainly consists of three parts: active distribution network, shared energy storage, and multi-microgrid.

[0045] The second step is to establish an economic dispatch model based on the optimization dispatch process of the multi-microgrid system, and to determine the source-load uncertainty model and constraints.

[0046] The optimal scheduling of multi-microgrid systems is divided into two stages:

[0047] Dispatch Phase 1: Individual microgrids optimize their charging and discharging power by minimizing net load fluctuations and minimizing the cost of leasing shared energy storage.

[0048] When a microgrid leases a large amount of energy storage services in order to minimize net load fluctuations, its leasing costs will increase significantly, creating a conflict between the two optimization objectives.

[0049] Based on the optimization scheduling process of multi-microgrid systems, a multi-objective optimization economic scheduling model for multi-microgrids is constructed. Weights are set for the two optimization objectives, and energy storage services are leased according to the preferences of the microgrids.

[0050] First, taking minimizing the cost of energy storage as the first optimization objective, the objective function is established as follows:

[0051]

[0052] In the formula, For microgrids The cost of using energy storage; and microgrids The charging and discharging costs and capacity utilization costs of leased energy storage are expressed as follows:

[0053]

[0054]

[0055] In the formula, Cost per unit of charge / discharge; , microgrids The charging and discharging power at time t; The cost of leasing unit power capacity for shared energy storage; The cost of leasing energy capacity for shared energy storage units; , microgrids The power and energy capacity requirements for microgrids The maximum charging and discharging power within a day are expressed as follows:

[0056]

[0057]

[0058] In the formula, This is the energy storage capacity margin coefficient. and For microgrids within a scheduling cycle The maximum and minimum values ​​of energy storage capacity are calculated using the following formulas:

[0059]

[0060]

[0061] in, The energy stored in the microgrid at time t is represented by the following formula:

[0062]

[0063] In the formula, , These are the charging and discharging efficiencies of the shared energy storage power station, respectively. , For charging and discharging, use 0-1 variables.

[0064] Secondly, minimizing the mean square error of the equivalent load after using energy storage is taken as the second optimization objective, and the objective function is as follows:

[0065]

[0066]

[0067]

[0068] In the formula, For microgrids Equivalent load variance after using energy storage services; For microgrids At any moment The net load, of which , , They represent microgrids In the moment ; For microgrids The equivalent load average after using energy storage services.

[0069] Phase 2 of dispatch: After multiple microgrids form an alliance, their respective charging and discharging needs are combined to form the net charging and discharging needs of the multi-microgrid system. The charging and discharging needs of each microgrid are met by providing energy storage services to shared energy storage operators.

[0070] After each microgrid receives charging and discharging services, it obtains its own equivalent load. At this point, the multi-microgrid system coordinates the power interaction between the microgrids. After power interaction, the power-deficient microgrids achieve power balance by responding to the peak-shaving dispatch of the active distribution network and controlling their own controllable units, thereby maximizing the overall operating efficiency of the multi-microgrid system. The objective function is:

[0071]

[0072] In the formula, For revenue from the purchase and sale of electricity in multi-microgrid systems and active distribution networks; Cost of generating electricity from micro gas turbines in multi-microgrid systems; Costs of using shared energy storage services in multi-microgrid systems.

[0073] in, The specific expression is as follows:

[0074]

[0075] In the formula, , These refer to the purchase and sale prices of electricity for peak shaving in active distribution networks; , microgrids exist It sells and purchases electricity to the active distribution network at all times.

[0076] also, The specific expression is as follows:

[0077]

[0078]

[0079]

[0080] In the formula, , These are the fuel costs of the gas turbine and the costs of environmental pollution control; , , For pollution parameters, For microgrids gas turbine at time The amount of electricity generated; , These are the unit pollution emission cost and the unit pollutant emission volume, respectively. Types of pollutants; This represents the total amount of pollutants.

[0081] at last, The expression is as follows:

[0082]

[0083]

[0084]

[0085] In the formula, , These are the net charging / discharging cost and net capacity cost of leased and shared energy storage for multi-microgrid systems. , For shared energy storage systems at all times Net charge and net discharge; This represents the maximum net charge and discharge power of a multi-microgrid system within a single day. The formulas for calculating the net charge and discharge capacity of a multi-microgrid system within one day are as follows:

[0086]

[0087]

[0088]

[0089]

[0090] In the formula, , These represent the maximum and minimum energy storage capacity of the microgrid system within a single scheduling cycle.

[0091] During the first phase of scheduling, the charging and discharging quantities of each microgrid have been obtained through calculation. Based on these quantities, the following can be calculated: , It is a constant. Therefore, in the second stage of scheduling, the final simplified objective function of the multi-microgrid system is as follows:

[0092]

[0093] Shared energy storage operators provide energy storage services to multi-microgrid systems through shared energy storage power stations. After meeting the energy storage needs of the multi-microgrid systems, the shared energy storage operators utilize the remaining capacity to participate in peak shaving and dispatching of the active distribution network, while also providing frequency regulation services to the active distribution network, thereby maximizing their own operational efficiency. Therefore, the shared energy storage operators' revenue... As shown in the following formula:

[0094]

[0095] In the formula, , These refer to the revenue generated by shared energy storage operators participating in active distribution network peak shaving and frequency regulation services; The charging, discharging, operation, and maintenance costs of a shared energy storage power station are shown in the following expressions for each parameter:

[0096]

[0097]

[0098]

[0099] In the formula, Time-of-use frequency regulation tariffs set for active distribution networks; The power declaration amount for shared energy storage systems participating in frequency regulation services; The unit charging and discharging operation and maintenance cost of energy storage power stations.

[0100] Active distribution networks incentivize shared energy storage systems and microgrids to actively participate in peak shaving and dispatching. Active distribution networks incentivize these systems to participate through time-of-use pricing. Due to power fluctuations, active distribution networks also implement time-of-use frequency regulation pricing to incentivize shared energy storage systems to participate in frequency regulation services, thereby meeting their own frequency regulation needs. The operational benefits are shown in the following formula:

[0101]

[0102] In the formula, For the power interaction benefits between active distribution networks and multiple microgrid systems, Benefits from peak-shaving scheduling of active distribution networks and shared energy storage systems; Cost of frequency regulation services for active distribution networks using shared energy storage systems; The cost of purchasing electricity from the main grid for an active distribution network; the expressions for each benefit are as follows:

[0103]

[0104]

[0105]

[0106]

[0107] In the formula, Main grid electricity sales price; This refers to the power that the active distribution network purchases from the main grid.

[0108] Based on the analysis of the above equation, the objective function of the active distribution network is: .

[0109] The constraints for multi-microgrid systems are as follows:

[0110] During the first phase of scheduling, the following constraints must be met:

[0111] microgrids The total charge and discharge capacity of energy storage must remain consistent throughout a scheduling cycle:

[0112]

[0113] microgrids at the same time The storage device can simultaneously perform charging and discharging services:

[0114]

[0115] microgrids The amount of energy stored must be within the upper and lower limits of the shared energy storage capacity.

[0116]

[0117] In the formula, , These represent the minimum and maximum states of charge of the shared energy storage power station, respectively.

[0118] Methods for calculating the state of charge of shared energy storage power stations, and microgrids Upper and lower limits of charging and discharging power constraints:

[0119]

[0120]

[0121] In the formula, To share the state of charge of the energy storage power station, The amount of electricity generated by the shared energy storage power station; The upper limit of the electricity capacity of a shared energy storage power station; This sets the upper limit for the charging and discharging power of shared energy storage power stations.

[0122] During the two-phase scheduling process, the following constraints must be met:

[0123] microgrids Power balance constraints to be satisfied:

[0124]

[0125]

[0126] In the formula, , microgrids At any moment The 0-1 variables of buying and selling electricity to the active distribution network For microgrids At any moment Transmitted to microgrid The power; microgrids exist Constantly transmit to the microgrid The power; For microgrids The equivalent power at any given moment.

[0127] microgrids At any moment You cannot simultaneously purchase or sell electricity to the active distribution network.

[0128]

[0129] microgrids Tie-line constraints that must be satisfied when purchasing and selling electricity to an ADN and interacting with other microgrids:

[0130]

[0131] microgrids Output constraints of micro gas turbines:

[0132]

[0133] microgrids The constraint that must be met when interacting with other microgrids is that the amount of electricity transferred from a surplus microgrid to a deficit microgrid cannot exceed its own surplus electricity.

[0134]

[0135] The following constraints must be met when shared energy storage operators participate in dispatching:

[0136] Charging and discharging power constraints of shared energy storage power stations:

[0137]

[0138] Constraints on the charging and discharging behavior of shared energy storage power stations:

[0139]

[0140] Power constraints of shared energy storage power stations:

[0141]

[0142] The energy calculation formulas for shared energy storage power stations and the energy loss calculation formulas for shared energy storage power stations participating in frequency regulation are as follows:

[0143]

[0144]

[0145] In the formula, Energy loss due to shared energy storage power stations participating in frequency regulation; For the duration of frequency modulation service.

[0146] Power balance constraints of shared energy storage power stations:

[0147]

[0148] Constraints on the purchase and sale of electricity by shared energy storage power stations:

[0149]

[0150] Power declaration limits for FM service:

[0151]

[0152] In the formula, This refers to the upper limit of power for shared energy storage power stations to participate in frequency regulation.

[0153] Frequency modulation (FM) requirement constraints:

[0154]

[0155] In the formula, The frequency regulation demand factor caused by net load. , , The active distribution network at time The load power, wind power generation and photovoltaic power generation;

[0156] The following constraints must be met when an active distribution network participates in optimized scheduling:

[0157] The active distribution network's electricity purchase price and sales price are constrained, meaning different prices correspond to different time periods:

[0158]

[0159]

[0160] In the formula, , , These are the off-peak electricity price, normal electricity price, and peak electricity price for active distribution networks; , , These are the off-peak hours, normal hours, and peak hours, respectively. , , These are the off-peak electricity price, normal electricity price, and peak electricity price for active distribution networks.

[0161] The power purchased by an active distribution network should be less than its power sold.

[0162]

[0163] Electricity sales price constraints are established to prevent active distribution networks from maliciously setting electricity prices to increase revenue.

[0164]

[0165] In the formula, Electricity sales price ceiling

[0166] Power balance constraints in active distribution networks:

[0167]

[0168] The third step is to establish fuzzy chance constraints based on the source-load uncertainty model.

[0169] In solving fuzzy chance-constrained programming problems, handling the chance constraints is crucial, and there are two main approaches: For simple problems, the fuzzy parameters in the constraints can be separated from the decision variables, or if there is a linear relationship between them, they can be transformed into clear equivalence classes for calculation using traditional methods; for more complex problems, stochastic simulation techniques are employed. However, simulation is an approximate process, resulting in imprecise results and difficulty in controlling the sample size. Therefore, this application adopts the first approach.

[0170] The standard form of a fuzzy chance-constrained programming model for solving uncertain problems can be described as follows:

[0171]

[0172] In the formula, As decision variables, For fuzzy variables, Let be the objective function. As constraints, express Credibility of its establishment The confidence level is set. As can be seen from the above formula, the fuzzy chance constraint strategy based on credibility theory not only effectively manages the uncertainty introduced by fuzzy variables, but also provides decision-makers with the ability to respond to potential extreme situations when constraints are violated, thereby improving the flexibility and adaptability of the decision-making process. When using the first method, the system operation has high security requirements; therefore, the confidence level should not be too low. When α ≥ 1 / 2, the chance constraint... The clear equivalence classes are:

[0173]

[0174] The expression for the trapezoidal fuzzy parameter is:

[0175]

[0176] The power balance constraint is transformed into a corresponding clear equivalence class using the trapezoidal parameter transformation method, as follows:

[0177]

[0178]

[0179] In the formula, , microgrids Membership function of the load; , microgrids Membership function of fan output; , microgrids Membership function of photovoltaic power output; , This is the membership function of the loads in an active distribution network; , The membership function of wind power output in an active distribution network; , This is the membership function of photovoltaic output in an active distribution network.

[0180] The fourth step is to construct a game theory model to solve the economic scheduling model under fuzzy chance constraints and obtain the optimization results of the multi-microgrid system.

[0181] The game theory model constructed in this paper consists of two steps. The first step is to solve the optimal charging and discharging problem of a single microgrid. This part involves two mutually exclusive objective functions, thus constituting a multi-objective optimization problem. A non-dominated sorting genetic algorithm with an elitist strategy is used to solve the optimal charging and discharging strategy for a single microgrid. A fuzzy membership function is constructed using partial small-scale fuzzy set decision theory for screening, thereby obtaining the optimal compromise solution. The fuzzy membership function is shown in the following equation:

[0182]

[0183] In the formula, , , For the first The objective function values, their maximum and minimum values, and the satisfaction function are shown below:

[0184]

[0185] In the formula, The number of objective functions. These are the weights of the objective function.

[0186] The flowchart for the first stage of the solution is as follows: Figure 2 As shown.

[0187] After obtaining the optimal charging and discharging power of each microgrid, it is used as an initial condition and substituted into a master-slave game model for solution. The master-slave game model can be expressed as follows:

[0188]

[0189]

[0190] In this solution phase, the time-of-use frequency regulation and peak-shaving electricity prices for the active distribution network are first generated using the particle swarm optimization algorithm. After receiving the pricing strategy, the shared energy storage system and the multi-microgrid system solve for their optimal peak-shaving power purchase and sale and frequency regulation power strategies using the CPLEX solver. The benefits of the active distribution network under this strategy are calculated and compared with previous benefits. The frequency regulation and peak-shaving electricity prices for the active distribution network, and the peak-shaving power purchase and sale and frequency regulation power strategies for the shared energy storage system and the multi-microgrid system under the optimal benefit are retained until the iteration limit is reached, thus obtaining the optimal scheduling strategy for the system. The flowchart of this process is as follows: Figure 3 As shown.

[0191] Please see Figure 4 , Figure 4 This is a schematic diagram of the hardware device operation according to an embodiment of the present invention. The hardware device specifically includes: a computer device 401, a processor 402, and a storage medium 403.

[0192] A computer device 401: The computer device 401 implements the multi-microgrid system optimization method.

[0193] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the multi-microgrid system optimization method.

[0194] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the multi-microgrid system optimization method.

[0195] The beneficial effects of this invention are as follows: By establishing fuzzy chance constraints based on a source-load uncertainty model, this invention considers the source-load uncertainty in multi-microgrid systems, improves scheduling accuracy, constructs a game theory model to solve the economic scheduling model under fuzzy chance constraints, obtains the optimization results of multi-microgrid systems, and coordinates the power interaction between microgrids, shared energy storage, and distribution networks, as well as between microgrids, while meeting the load demand of multi-microgrid systems. This has important theoretical and applied value for solving the optimization problem of multi-microgrid systems.

[0196] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for multi-microgrid system optimization, the method comprising: The specific steps include: S1, determining the topology structure of the multi-microgrid system in the shared energy storage mode; S2, establishing an economic dispatching model based on the optimization dispatching process of the multi-microgrid system, and determining a source-load uncertainty model and a constraint condition; S3, establishing a fuzzy chance constraint based on the source-load uncertainty model; S4, constructing a game model to solve the economic dispatching model under the fuzzy chance constraint, and obtaining the optimization result of the multi-microgrid system; The dispatching process of the multi-microgrid system is divided into two stages, which are as follows: The dispatching first stage of single grid optimization: a single microgrid obtains its charging and discharging power in order to minimize the net load fluctuation and the cost of renting the shared energy storage; The dispatching second stage of multi-grid fusion: after the microgrids ally, the charging and discharging demands of the microgrids are combined to form the net charging and discharging demand of the multi-microgrid system, and the charging and discharging demands of the microgrids are met by providing energy storage services to the shared energy storage operator; The standard form of the model for solving the uncertainty problem by the fuzzy chance constraint programming is as follows: In the formula, x is a decision variable, ξ is a fuzzy variable, f(x, ξ) is an objective function, g(x, ξ) is a constraint condition, Cr{g(x, ξ)≤0} represents the confidence level of g(x, ξ)≤0, and α is a set confidence level; The game model adopts a one-master multi-slave game, and the game participants are the active power grid, the shared energy storage system and the multi-microgrid system; The game strategy of the active power grid is its peak shaving and frequency modulation purchase and sale strategy, the game strategy of the shared energy storage system is its peak shaving and frequency modulation service power, and the game strategy of the multi-microgrid system is the purchase and sale power for participating in peak shaving; The working process of the game model is specifically as follows: S41, a non-dominated sorting genetic algorithm with an elitist strategy is adopted, a fuzzy membership function is constructed by using a small partial fuzzy set decision theory to screen compromise solutions, and the optimal charging and discharging strategy of each microgrid is obtained; S42, the charging and discharging power of each microgrid under the optimal charging and discharging strategy is taken as an initial condition to be brought into the one-master multi-slave game model for solving, and the optimal dispatching strategy of each subject is obtained.

2. The method of claim 1, wherein, The topology structure of the multi-microgrid system in the shared energy storage mode is composed of three subjects, which are the active power grid, the shared energy storage and the multi-microgrid.

3. The method of claim 2, wherein, The economic dispatching model is established based on the objective function of the optimization dispatching process of the multi-microgrid system, and specifically is: The objective function of the dispatching first stage is specifically as follows: C MGi,1 = C MGi,BAT + C MGi,CAP wherein C MGi,1 is the energy storage usage cost of microgrid i; C MGi,BAT is the energy storage usage cost of microgrid i; C MGi,CAP is the charging / discharging cost and capacity usage cost of microgrid i renting energy storage, respectively; C MGi,2 is the equivalent load mean square error of microgrid i after using energy storage service; is the net load of microgrid i at time t; is the charging / discharging power of microgrid i at time t; is the equivalent load mean value of microgrid i after using energy storage service; The objective function of the dispatching second stage is specifically as follows: C MMG =U MMG,ADN -C MMG,MT In the formula, C MMG is the total generation cost of the multi-microgrid system; U MMG,ADN is the electricity buying and selling revenue of the multi-microgrid system and the active distribution network; C MMG,MT is the micro gas turbine generation cost in the multi-microgrid system; The source-load uncertainty model is the contradiction existing in the two optimization objectives in the dispatching first stage.

4. The multi-microgrid system optimization method of claim 3, wherein, The constraint conditions include: The dispatching process constraint condition of the multi-microgrid system, the constraint condition of the shared energy storage operator participating in the dispatching process, and the constraint condition of the active power grid participating in the optimization dispatching process.

5. A storage medium characterized by: The storage medium stores instructions and data for realizing the multi-microgrid system optimization method in any one of claims 1-4.

6. A computer device, comprising: It includes: The processor loads and executes the instructions and data in the storage medium to realize the multi-microgrid system optimization method in any one of claims 1-4.

Images