A micro-grid bi-level planning method and system considering time-division group network mode

Abstract

This invention provides a two-layer planning method and system for microgrids considering time-grouping network modes. Utilizing Latin sampling and mean clustering, it generates and reduces uncertain power output from source loads under different scenarios, obtaining a description of renewable energy output in typical scenarios. A functional structure optimization model is constructed. Based on flexible load information in the microgrid, it determines the power supply structure on the output side of the agile power distributor by optimizing load aggregation and grouping. The structure obtained from each iteration is passed to the capacity-operation optimization layer to obtain the planning cost, which serves as the objective of this layer's structural optimization model. The model is iteratively solved using a genetic algorithm until convergence. Based on the iterative structural parameters, the annualized allocation cost of flexible resources and the annual operating cost of the system are used as optimization objectives, and the solution is obtained under the constraints of each unit's capacity and operation. This invention improves system flexibility and enhances the power balance between wind and solar power supply loads.

Description

Technical Field

[0001] This invention belongs to the field of microgrid planning, specifically relating to a two-layer planning method and system for microgrids that considers time-grouping network modes. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid growth of renewable energy installed capacity, its volatility and uncertainty pose severe challenges to the power balance and security of the power grid. In some power grids where the capacity to carry renewable energy is insufficient, many regions are striving to promote the integration of source-load-storage-microgrids, incorporating the functions of micro main grids into the local "transmission, distribution, storage and utilization" model, and prioritizing local consumption of renewable energy rather than wholesale to the grid, which brings serious challenges to microgrid planning.

[0004] Many existing studies focus on improving renewable energy utilization through renewable energy distribution and storage methods in microgrids. These studies aim to smooth renewable energy fluctuations and supply-demand imbalances, thereby enhancing microgrid efficiency, flexibility, and resilience. However, with a high proportion of random renewable energy integration, large-capacity energy storage units are required to ensure stable system operation while meeting wind and solar curtailment limits. This results in poor system security, and the high cost of energy storage configuration makes renewable energy utilization uneconomical. Besides configuring energy storage units, modifying the microgrid topology can enhance flexibility and scalability. With multiple buses receiving power, loads can select the most suitable bus for switching, improving system flexibility. However, complex power flow control issues are unavoidable, and backflow and coupling requirements are severe, posing significant challenges to complex microgrid planning. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a two-layer planning method and system for microgrids that considers time-group network mode. This invention constructs and solves a two-layer planning strategy that integrates energy supply structure search and capacity-operation optimization, thereby determining the capacity, operating parameters, and operating mode of flexible energy resources as well as the Clever powerdistributor (CPD).

[0006] According to some embodiments, the present invention adopts the following technical solution:

[0007] A two-level planning method for microgrids considering time-group network patterns includes the following steps:

[0008] By using Latin sampling and mean clustering methods, the generation and reduction of source load uncertainty in different scenarios are completed, and the description of new energy output in typical scenarios is obtained.

[0009] A functional structure optimization model is constructed. Based on the flexible load information in the microgrid, the power supply structure on the output side of the agile power distributor is determined by optimizing load aggregation and grouping. The structure solved in each iteration is passed to the capacity-operation optimization layer to obtain the planning cost, which is used as the target of the structure optimization model in this layer. The model is solved iteratively by a genetic algorithm until convergence.

[0010] The capacity-operation optimization layer, based on iterative structural parameters, takes the annualized configuration cost of flexible resources and the annual operating cost of the system as optimization objectives. Under the capacity and operating constraints of each unit, it solves for the configuration capacity of flexible resources, the operation plan, and the operation mode of the agile power distributor.

[0011] As an alternative implementation method, before generating and reducing uncertain power output from the source and load in different scenarios, the method also includes determining the number of energy sources supplied by the source side and the number of flexible loads in the grouped network mode when the load side participates, based on the actual microgrid structure, thereby determining the number of input paths and the number of loads to be grouped by the agile power distributor, and obtaining the power output coefficient of the source side and the original energy consumption data of the load side.

[0012] As an alternative implementation method, the process of generating and reducing uncertain power output of source loads under different scenarios using Latin sampling and mean clustering methods includes: for source load datasets with missing or insufficient data, supplementing the dataset using Latin hypercube, and determining the Euclidean distance between the current source load datasets using the mean clustering method to obtain the typical power output trend and load demand information of new energy under different weather types, thus obtaining a description of new energy power output under typical scenarios.

[0013] As an alternative implementation, the process of determining the power supply structure on the output side of the agile power distributor by optimizing load aggregation and grouping includes:

[0014] The aggregation number and grouping are represented as follows:

[0015]

[0016] in, Indicates load L i Grouping situation, L T N represents all loads to be grouped. T N represents the total number of aggregates. j This represents the j-th load cluster in the aggregation;

[0017] Based on the genetic algorithm, the optimization objectives for load aggregation and grouping are represented by an objective function, where S * ,C′ t * ,P t * D t* Based on the calculation results of the capacity-operation optimization layer, a nested approach is used to search for the optimal load aggregation number and grouping to determine the power supply structure on the output side of the agile power distributor. The objective function is:

[0018] Among them, S * D t * C′ represents the optimal flexible resource allocation capacity and the switching action of the agile power distributor, respectively. t * ,P t * These represent the optimal source-side output coefficient and output power, respectively.

[0019] As an alternative implementation, the optimization objective of the capacity-operation optimization layer is:

[0020] minC=C IC +Coc;

[0021] C represents the total planned cost, including construction cost C. IC and annualized operating cost C OC .

[0022] As a further implementation method, the construction cost C IC for:

[0023]

[0024] Among them, S i This represents the configured capacity of source-side unit i, with the unit capacity cost determined by c. i This means that Ω = {PV, WT, ESS}, where PV represents photovoltaic power, WT represents wind power, and ESS represents energy storage units. The capital recovery factor for CPD units. c is the capital recovery factor for laying busbars. CPD N represents the construction cost of the agile power distributor, η(·) represents the number of units in the set, and N represents the number of units in the set. a c represents the load aggregation number. l This indicates the unit cost of busbar installation. Y represents the capital recovery factor of unit i. i Indicates the discount rate. This indicates the service life of each unit.

[0025] As a further implementation method, the annualized operating cost C OC for:

[0026]

[0027] Among them, ce,t Let c represent the electricity price at time t. cur c represents the unit cost of wind and solar power curtailment penalty, β represents the penalty coefficient, and c represents the unit cost of wind and solar power curtailment penalty. w P represents the switching cost of the agile power distributor. cur,pv,t P cur,wt,t P represents the amount of wind and solar power curtailment at time t. buy,t The table shows the power purchased from the grid at time t, in w. nl,t Let n be the number of time intervals from time t to time t+1. l The number of times the load is switched.

[0028] As an alternative implementation, the constraints of the capacity-operation optimization layer include renewable energy installation capacity limits, grid purchase limits to improve power quality, energy storage configuration capacity constraints, total energy storage charging and discharging power constraints, and energy storage state of charge constraints.

[0029] A two-level planning system for microgrids considering time-grouping network patterns includes:

[0030] The typical scenario description module is configured to use Latin sampling and mean clustering methods to generate and reduce the uncertainty of source load output in different scenarios, so as to obtain the new energy output description in typical scenarios.

[0031] The upper-level optimization module is configured to build a functional structure optimization model. Based on the flexible load information in the microgrid, it determines the power supply structure on the output side of the agile power distributor by optimizing load aggregation and grouping. The structure solved in each iteration is passed to the capacity-operation optimization layer to obtain the planning cost, which is used as the target of the structure optimization model in this layer. The genetic algorithm is used to iteratively solve the problem until convergence.

[0032] The lower-level optimization module is configured to, based on the iterative structural parameters, use the annualized configuration cost of flexible resources and the annual operating cost of the system as optimization objectives, and solve for the configuration capacity of flexible resources, the operation plan, and the operation mode of the agile power distributor under the capacity and operation constraints of each unit.

[0033] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the steps in the method described above.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] This invention proposes a microgrid planning architecture based on agile power distributors (CPDs). Through intelligent selection of network modes with refined time-segmentation and flexible connection of renewable energy to accommodate loads, especially in microgrids dominated by flexible loads, it can solve the problems of flexible connection of fluctuating sources and loads and adaptation of multiple types of power sources and multi-granularity loads, thereby improving system flexibility and avoiding the complex power flow control problems of traditional point of common coupling (PCC). At the same time, based on the CPD's operating mode, it can realize the visualization of energy supply and load energy consumption paths, which is more conducive to the implementation of carbon metering and carbon tracking.

[0036] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0037] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0038] Figure 1 This is a schematic diagram of a microgrid system based on a time-grouping network mode, according to one embodiment.

[0039] Figure 2 This is a two-layer planning structure diagram for energy supply structure search and capacity-operation integration optimization in one embodiment;

[0040] Figure 3 This is an example of microgrid operation optimization results under a time-grouping network mode;

[0041] Figure 4 This is a power balance diagram of wind power and photovoltaic power supply load in one embodiment. Detailed Implementation

[0042] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0043] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0044] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0045] Where there is no conflict, the embodiments and features described in this application may be combined with each other.

[0046] Example 1

[0047] To address the challenges posed by high-penetration photovoltaic (PV) grid integration to grid capacity and distribution network safety and stability, a time-based grid-based microgrid planning method is proposed to promote local consumption of renewable energy. The method includes the following steps:

[0048] S1: Considering the differences in the impact of the same weather type on photovoltaic and wind power, Latin sampling and mean clustering methods are used to generate and reduce the uncertainty of source load output under different scenarios, so as to obtain the description of new energy output under typical scenarios.

[0049] S2: In the upper functional structure search layer: based on flexible load information in the microgrid, such as... Figure 1 As shown, by aggregating and grouping loads, flexible loads are bound together to ensure consistent power supply modes for load clusters of the same type, thus determining the CPD power supply side structure. The number of aggregations and groups can be expressed by the following formula:

[0050]

[0051] in, Indicates load L i Grouping situation, L T N represents all loads to be grouped. T N represents the total number of aggregates. j This represents the j-th load cluster of the aggregation.

[0052] Based on the genetic algorithm, the optimization objective for load aggregation and grouping can be expressed by the following formula, where S * ,C′ t * ,P t * D t * The calculation results for the capacity-run optimization layer, such as Figure 2 As shown, a nested approach is used to search for the optimal load aggregation number and grouping, thereby determining the CPD power supply side structure:

[0053]

[0054] Among them, S * D t * These represent the optimal flexible resource allocation capacity and the CPD on / off action, respectively. t ′ * ,P t * These represent the optimal source-side output coefficient and output power, respectively.

[0055] S3: At the lower capacity-operation optimization layer, microgrid planning based on time-grouping networks aims to obtain an economical system construction plan based on known renewable energy equipment parameters and typical clustering scenarios, energy prices, and user loads. This includes system structure and capacity configuration and operation plans for photovoltaic (PV), wind turbine (WT), and energy storage system (ESS) units. System structure is considered because the introduction of time-grouping network-based CPD units alters the CPD output structure and increases busbar laying costs. The optimization objective can be expressed as:

[0056] minC=C IC +Coc (3)

[0057] C represents the total planned cost, which includes the annualized construction and operation costs, respectively, and consists of C0... IC and C oc express.

[0058]

[0059] Among them, S i This represents the configured capacity of source-side unit i, with the unit capacity cost determined by c. i It is represented that Ω = {PV, WT, ESS}, c CPD N represents the construction cost of CPD, η(·) represents the number of units in the set, and N a c represents the load aggregation number. l This indicates the unit cost of busbar installation. γ represents the capital recovery factor. i Indicates the discount rate. This indicates the service life of each unit.

[0060]

[0061] Among them, c e,t Let c represent the electricity price at time t. cur c represents the unit cost of wind and solar power curtailment penalty, β represents the penalty coefficient, and c represents the unit cost of wind and solar power curtailment penalty. w This indicates the cost of switching CPDs.

[0062] The constraints of its planning model are:

[0063] The renewable energy installation capacity limit is shown in the following formula:

[0064]

[0065]

[0066] The total power generated by photovoltaic and wind turbines consists of the power supplied to n loads and the power generated by charging the batteries, which can be expressed by the following formula:

[0067]

[0068] Curtailment of wind and solar power originates from the surplus photovoltaic and wind power supplied to the load and channeled into energy storage units, and can be expressed by the following formula:

[0069]

[0070]

[0071] Among them, S pv S wt To install photovoltaic and wind power capacity in the system, P pv,t P wt,t Let P be the output power of photovoltaic and wind power at time t. pv,ln,t P wt,ln,t Supply l at time t respectively n Photovoltaic and wind power at load, P pv,ess,t P wt,ess,t To charge the output power of photovoltaic and wind turbines into the battery, S pv,min / max S wt,min / max To limit the minimum and maximum installed capacity of photovoltaic and wind power due to transformer capacity constraints, α pv,t α wt,t These are the output coefficients for photovoltaic and wind power, respectively. χ represents the system's allowable wind and solar curtailment rate, and P... cur,pv,t P cur,wt,t Let t be the amount of wind and solar power curtailment at time t.

[0072] Since renewable energy sources are being connected to the EFLM system off-grid, the power grid cannot be used as a supplement to the insufficient renewable energy supply. Therefore, it is necessary to consider increasing the lower limit of power purchases from the grid to improve power quality, with the following constraints:

[0073]

[0074] Among them, P buy,t The table shows the power purchased from the grid at time t, P. buy,ln,t Indicates the supply of l at time t n The power purchased by the grid at the load, Pgrid,min P grid,max This indicates the upper limit of power exchanged from the power grid.

[0075] In this system, the main function of battery energy storage is to mitigate fluctuations in renewable energy. To reduce losses caused by frequent charging and discharging of energy storage and to ensure the rationality of placing energy storage in the CPD matrix, the charging power only considers the surplus power generated by wind and solar power, without considering the interaction with the grid. During discharge, it serves as the source-side input of the CPD matrix, and the constraints are expressed as follows:

[0076] S ess,min ≤S ess ≤S ess,max (13)

[0077]

[0078]

[0079] P ess,max =σ ess ·S ess (16)

[0080]

[0081]

[0082] Among them, S ess To configure capacity for energy storage, S ess,max S ess,min For the maximum permissible installation capacity limited by geographical location and security constraints, P dis,t P ch,t P represents the total charging and discharging power of energy storage. ess,ln,t Represented as supply l n Energy storage discharge power at the load, μ ess,t This indicates the charging and discharging status of the energy storage battery, P. ess,max σ is the upper limit of charging and discharging power. ess The percentage of capacity occupied by charging and discharging power, SOC t λ represents the state of charge at time t. ch , λ dis For charge / discharge efficiency SOC max SOC min These represent the highest and lowest states of charge of the energy storage battery, respectively. Δt is the time step, and T is the charge / discharge cycle.

[0083] The real-time matching relationship between the source and load can be achieved by the following formula, where matrix D represents the switching action matrix of the CPD:

[0084]

[0085] Combination Figure 3 and Figure 4 It can be seen that this method, through the intelligent selection of the network mode by fine-grained time-segmentation and the flexible connection of new energy to adapt to the load absorption, especially in microgrids dominated by flexible loads, can solve the problems of flexible connection of fluctuating sources and loads and the adaptation of multiple types of power sources and multi-granularity loads, improve system flexibility, improve the power balance of wind power and photovoltaic supply loads, avoid the complex power flow control problems of traditional point of common coupling (PCC) mode, and realize the visualization of energy supply and load energy consumption path according to CPD operation mode, which is more conducive to the implementation of carbon metering and carbon tracking.

[0086] Example 2

[0087] A two-level planning system for microgrids considering time-grouping network patterns includes:

[0088] The typical scenario description module is configured to use Latin sampling and mean clustering methods to generate and reduce the uncertainty of source load output in different scenarios, so as to obtain the new energy output description in typical scenarios.

[0089] The upper-level optimization module is configured to build a functional structure optimization model. Based on the flexible load information in the microgrid, it determines the power supply structure on the output side of the agile power distributor by optimizing load aggregation and grouping. The structure solved in each iteration is passed to the capacity-operation optimization layer to obtain the planning cost, which is used as the target of the structure optimization model in this layer. The genetic algorithm is used to iteratively solve the problem until convergence.

[0090] The lower-level optimization module is configured to, based on the iterative structural parameters, use the annualized configuration cost of flexible resources and the annual operating cost of the system as optimization objectives, and solve for the configuration capacity of flexible resources, the operation plan, and the operation mode of the agile power distributor under the capacity and operation constraints of each unit.

[0091] Example 3

[0092] An electronic device includes a memory and a processor, and computer instructions stored in the memory and running on the processor, the computer instructions being executed by the processor to perform the following steps:

[0093] Step 1: Determine the number of energy sources supplied by the source side and the number of flexible loads in the grouped network mode when the load side participates based on the actual microgrid structure, thereby determining the number of input paths for the CPD and the number of loads to be grouped, and obtaining the source side output coefficient and the original energy consumption data of the load side;

[0094] Step 2: For source-load datasets with missing or insufficient data, use Latin hypercube to supplement the dataset, and determine the Euclidean distance between the current source-load datasets using the mean clustering method to obtain typical output trends and load demand information of new energy sources under different weather types.

[0095] Step 3: Build a functional structure optimization model based on genetic algorithm. Determine the power supply structure on the CPD output side by optimizing load aggregation and grouping. Pass the structure solved in each iteration to the capacity-run optimization layer to obtain the planning cost and use it as the target of the structure optimization model in this layer. Iterate through the genetic algorithm until convergence.

[0096] Step 4: Based on iterative structural parameters, with the annualized configuration cost of flexible resources and the annual operating cost of the system as optimization objectives, construct the capacity and operating constraints of each unit, establish the source-load supply-demand balance relationship equation under the time-group network mode, and solve it through the Gurobi solver to obtain the configuration capacity, operation plan and CPD action mode of flexible resources.

[0097] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0099] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0101] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art without creative effort within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A two-level planning method for microgrids considering time-grouping network modes, characterized in that, Includes the following steps: By using Latin sampling and mean clustering methods, the generation and reduction of source load uncertainty in different scenarios are completed, and the description of new energy output in typical scenarios is obtained. A functional structure optimization model is constructed. Based on the flexible load information in the microgrid, the power supply structure on the output side of the agile power distributor is determined by optimizing load aggregation and grouping. The structure solved in each iteration is passed to the capacity-operation optimization layer to obtain the planning cost, which is used as the target of the structure optimization model in this layer. The model is solved iteratively by a genetic algorithm until convergence. The process of determining the power supply structure on the output side of an agile power distributor by optimizing load aggregation and grouping includes: Aggregation and grouping are represented as follows: ； in, Indicates load L i Grouping situation, L T This represents all loads to be grouped. N T Indicates the total number of aggregates. N j The first term representing the aggregation j One load cluster; Based on the genetic algorithm, the optimization objectives for load aggregation and grouping are represented by an objective function, where... Based on the calculation results of the capacity-operation optimization layer, a nested approach is used to search for the optimal load aggregation number and grouping to determine the power supply structure on the output side of the agile power distributor. The objective function is: ; in, , These represent the load aggregation number and grouping status, respectively. These represent the optimal flexible resource allocation capacity and the switching action of the agile power distributor, respectively. These represent the optimal source-side output coefficient and output power, respectively. The capacity-operation optimization layer, based on iterative structural parameters, takes the annualized configuration cost of flexible resources and the annual operating cost of the system as optimization objectives. Under the capacity and operating constraints of each unit, it solves for the configuration capacity of flexible resources, the operation plan, and the operation mode of the agile power distributor.

2. The microgrid two-level planning method considering time-grouping network mode as described in claim 1, characterized in that, Before generating and reducing uncertain power output from sources and loads under different scenarios, the method of Latin sampling and mean clustering is used. It also includes determining the number of energy sources supplied by the source side and the number of flexible loads in the grouped network mode when the load side participates, based on the actual microgrid structure. This determines the number of input paths and the number of loads to be grouped by the agile power distributor, and obtains the power output coefficient of the source side and the original energy consumption data of the load side.

3. The microgrid two-level planning method considering time-grouping network mode as described in claim 1, characterized in that, The process of generating and reducing uncertain power output of source loads under different scenarios using Latin sampling and mean clustering methods includes: supplementing the source load dataset with missing or insufficient data using Latin hypercube, determining the Euclidean distance between the current source load datasets using mean clustering, obtaining typical power output trends and load demand information of new energy under different weather types, and obtaining a description of new energy power output under typical scenarios.

4. The microgrid two-level planning method considering time-grouping network mode as described in claim 1, characterized in that, The optimization objective of the capacity-operation optimization layer is: minC=C IC +Coc ； C This represents the total planned cost, including construction cost C. IC and annualized operating cost C OC .

5. The microgrid two-level planning method considering time-grouping network mode as described in claim 4, characterized in that, The construction cost C IC for: ； ； in, S i Indicates source-side unit i The configuration capacity, the unit capacity cost is determined by c i express, PV stands for photovoltaic, WT stands for wind power, and ESS stands for energy storage unit. The capital recovery factor for CPD units. The capital recovery factor for laying busbars. This indicates the construction cost of the agile power distributor. This indicates the number of units in the set. Indicates the number of load aggregations. This indicates the unit cost of busbar installation. Indicates the capital recovery factor. Indicates the discount rate. This indicates the service life of each unit.

6. The microgrid two-level planning method considering time-grouping network mode as described in claim 4, characterized in that, The annualized operating cost C OC for: ； in, c e,t express t Electricity price at any time c cur This indicates the penalty cost per unit for curtailing wind and solar power. Indicates the penalty coefficient. This indicates the switching cost of the agile power distributor. They are respectively t The amount of wind and solar energy abandoned at any given moment. Appearance in the scene t Power purchased from the grid at all times From t Time's up t +1 moment n l The number of times the load is switched.

7. The microgrid two-level planning method considering time-grouping network mode as described in claim 1, characterized in that, The constraints of the capacity-operation optimization layer include renewable energy installation capacity limits, grid purchase limits to improve power quality, energy storage configuration capacity constraints, total energy storage charging and discharging power constraints, and energy storage state of charge constraints.

8. A two-level planning system for microgrids considering time-grouping network mode, characterized in that, include: The typical scenario description module is configured to use Latin sampling and mean clustering methods to generate and reduce the uncertainty of source load output in different scenarios, so as to obtain the new energy output description in typical scenarios. The upper-level optimization module is configured to build a functional structure optimization model. Based on the flexible load information in the microgrid, it determines the power supply structure on the output side of the agile power distributor by optimizing load aggregation and grouping. The structure solved in each iteration is passed to the capacity-operation optimization layer to obtain the planning cost, which is used as the target of the structure optimization model in this layer. The genetic algorithm is used to iteratively solve the problem until convergence. The process of determining the power supply structure on the output side of an agile power distributor by optimizing load aggregation and grouping includes: Aggregation and grouping are represented as follows: ； in, Indicates load L i Grouping situation, L T This represents all loads to be grouped. N T Indicates the total number of aggregates. N j The first term representing the aggregation j One load cluster; Based on the genetic algorithm, the optimization objectives for load aggregation and grouping are represented by an objective function, where... Based on the calculation results of the capacity-operation optimization layer, a nested approach is used to search for the optimal load aggregation number and grouping to determine the power supply structure on the output side of the agile power distributor. The objective function is: ; in, , These represent the load aggregation number and grouping status, respectively. These represent the optimal flexible resource allocation capacity and the switching action of the agile power distributor, respectively. These represent the optimal source-side output coefficient and output power, respectively. The lower-level optimization module is configured to, based on the iterative structural parameters, use the annualized configuration cost of flexible resources and the annual operating cost of the system as optimization objectives, and solve for the configuration capacity of flexible resources, the operation plan, and the operation mode of the agile power distributor under the capacity and operation constraints of each unit.

9. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the steps of the method according to any one of claims 1-7.

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