Micro-grid double-layer planning method and system considering time-packet networking mode

Abstract

The invention provides a microgrid double-layer planning method and system considering a time-packet networking mode, and the method comprises the steps: completing the generation and reduction of uncertain output of a source load in different scenes through a Latin sampling and mean clustering method, and obtaining a new energy output description in a typical scene; a functional structure optimization model is constructed, an energy supply structure of an output side of an agile electric energy distributor is determined by optimizing load aggregation and grouping based on flexible load information in a micro-grid, a structure solved by iteration each time is transmitted to a capacity-operation optimization layer, and planning cost is obtained and serves as a target of the structure optimization model of the layer. Iteratively solving through a genetic algorithm until convergence; and solving under the capacity and operation constraints of each unit by taking the flexible resource annual configuration cost and the system annual operation cost as optimization targets based on the iterative structure parameters. According to the invention, the flexibility of the system is improved, and the power balance of wind power and photovoltaic supply loads is improved.

Description

Technical Field

[0001] The present invention belongs to the field of microgrid planning, and particularly relates to a two-layer planning method and system for a microgrid considering a time-sharing networking mode. Background Art

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

[0003] With the rapid growth of the installed capacity of renewable energy, its volatility and uncertainty have posed severe challenges to the power balance and security of the power grid. In some power grids with insufficient new energy carrying capacity, many regions are striving to promote the integration of source-load-storage-microgrid, incorporate the functions of the micro main grid into the local "transmission-distribution-storage-usage" mode, and prioritize the local consumption of new energy rather than wholesale to the power grid, bringing severe challenges to microgrid planning.

[0004] Many existing studies focus on improving the utilization rate of new energy by means of new energy energy storage configuration in microgrids, and improve the efficiency, flexibility and resilience of microgrids by smoothing the fluctuations of new energy and the imbalance between supply and demand. Facing the high proportion of random new energy access, under the red line of wind and light abandonment, it is necessary to configure a large-capacity energy storage unit to ensure the stable operation of the system. The system has poor safety, and its high energy storage configuration cost makes the utilization of new energy uneconomical. In addition to configuring an energy storage unit, changing the microgrid topology can enhance flexibility and expandability. The load receives power from multiple buses, and the load can select the most suitable bus to switch between buses, enhancing the flexibility of the system. However, complex power flow control problems are inevitable, the power flow reverse transmission and coupling requirements are serious, posing great challenges to complex microgrid planning problems. Summary of the Invention

[0005] In order to solve the above problems, the present invention proposes a two-layer planning method and system for a microgrid considering a time-sharing networking mode. The present invention constructs a two-layer planning strategy for energy supply structure search and capacity-operation integration optimization, and performs solution, which can determine the capacity, operation parameters of flexible energy supply resources, and the action mode of the Clever power distributor (CPD).

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

[0007] A two-layer planning method for a microgrid considering a time-sharing networking mode, comprising the following steps:

[0008] Using the Latin sampling and mean clustering methods, generate and reduce the uncertain output of the source and load under different scenarios, and obtain the description of the new energy output in typical scenarios;

[0009] Build an optimization model for the functional structure. Based on the flexible load information in the microgrid, determine the energy supply structure on the output side of the agile power distributor by optimizing load aggregation and grouping, and transfer the structure solved in each iteration to the capacity-operation optimization layer to obtain the planning cost, which is used as the objective of the structure optimization model in this layer. Iteratively solve using the genetic algorithm until convergence;

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

[0011] As an alternative implementation, before generating and reducing the uncertain output of the source and load under different scenarios using the Latin sampling and mean clustering methods, it also includes determining the number of energy supply sources on the source side and the number of flexible loads participating in the grouped network mode according to the actual microgrid structure, so as to determine the number of input paths of the agile power distributor and the number of loads to be grouped, and obtain the source side output coefficient and the original load energy consumption data.

[0012] As an alternative implementation, the process of generating and reducing the uncertain output of the source and load under different scenarios using the Latin sampling and mean clustering methods includes: for the source and load data sets with missing or insufficient data, use the Latin hypercube to supplement the data set, and determine the Euclidean distance between the current source and load data sets through the mean clustering method to obtain the typical output trends of new energy and load demand information under different weather types, and obtain the new energy output description under typical scenarios.

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

[0014] The representation of the aggregation number and grouping is as follows:

[0015]

[0016] Among them, represents the grouping situation of load L i , L T represents all the loads to be grouped, N T represents the total number of aggregations, N j represents the jth load cluster of the aggregation;

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

[0018] Among them, S * , D t * represent the optimal flexible resource allocation capacity and the switching actions of the agile power distributor respectively, and C′ t * , P t * 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 planning cost, including the construction cost C IC and the annualized operation cost C OC .

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

[0023]

[0024] Among them, S i represents the configuration capacity of the source - side unit i, and the unit - capacity cost is represented by c i represents, Ω = {PV, WT, ESS}, PV is photovoltaic, WT is wind power, and ESS is the energy storage unit, is the capital recovery factor of the CPD unit, is the capital recovery factor of laying the busbar, c CPD represents the construction cost of the agile power distributor, η(·) represents the number of units in the set, N a represents the load aggregation number, c l represents the unit cost of busbar laying, represents the capital recovery factor of unit i, Υ i represents the discount rate, represents the service life of each unit.

[0025] As a further implementation, the annualized operation cost C OC is:

[0026]

[0027] Among them, ce,t represents the electricity price at time t, c cur represents the penalty cost per unit of wind and PV curtailment, β represents the penalty coefficient, c w represents the switching cost of the agile power distributor switch, P cur,pv,t , P cur,wt,t are the amounts of wind and PV curtailment at time t respectively, P buy,t represents the power purchased from the grid at time t in the scenario, w nl,t is the number of switching times of the load at n l from time t to time t + 1.

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

[0029] A two - layer planning system for a micro - grid considering time - division networking mode, comprising:

[0030] A typical scenario description module, configured to use the Latin sampling and mean clustering methods to generate and reduce the uncertain output of the source and load under different scenarios, and obtain the new energy output description under typical scenarios;

[0031] An upper - layer optimization module, configured to construct a functional structure optimization model, based on the flexible load information in the micro - grid, determine the energy supply structure on the output side of the agile power distributor by optimizing load aggregation and grouping, and transfer the structure solved in each iteration to the capacity - operation optimization layer, obtain the planning cost and use it as the objective of the structure optimization model in this layer, and iteratively solve by the genetic algorithm until convergence;

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

[0033] An electronic device, comprising a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the steps in the above - mentioned method are completed.

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

[0035] The present invention proposes a microgrid planning architecture based on an agile power distributor (CPD). Through the mode of refined time-slice networking, it can intelligently select and flexibly connect new energy to the adaptable consumption load. Especially in a microgrid mainly composed of flexible loads, it can solve the problems of flexible connection between fluctuating power sources and loads, and the adaptation of multiple types of power sources to multiple granularity loads, improve the system flexibility, avoid the complex power flow control problems of the traditional point of common coupling (PCC) method, and at the same time, according to the CPD action mode, it can realize visual energy supply and load energy consumption paths, which is more conducive to the implementation of work such as carbon metering and carbon tracking.

[0036] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0038] Figure 1 It is a schematic diagram of a microgrid system based on a time-slice networking mode of an embodiment;

[0039] Figure 2 It is a double-layer planning structure diagram of the energy supply structure search and capacity-operation integration optimization of an embodiment;

[0040] Figure 3 It is the operation optimization result of a microgrid under the time-slice networking mode of an embodiment;

[0041] Figure 4 It is a power balance diagram of the wind power and photovoltaic power supply to the load of an embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The present invention will be further described below in conjunction with the drawings and embodiments.

[0043] It should be noted that the following detailed descriptions are all illustrative and are intended to provide a further description of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0044] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they specify the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0045] Without conflict, the embodiments in this application and the features in the embodiments can be combined with each other.

[0046] Embodiment 1

[0047] In view of the challenges brought by the access of high-penetration photovoltaic power to the grid carrying capacity and the safe and stable operation of the distribution network, a microgrid planning method for promoting the local consumption of new energy in a time-sharing networking mode is proposed, including the following steps:

[0048] S1: Considering the differences in the influence of photovoltaic and wind power by the same weather type, etc., using the Latin sampling and mean clustering methods, the generation and reduction of the uncertain output of the source and load are completed under different scenarios, and the description of the new energy output under typical scenarios is obtained;

[0049] S2: In the upper-layer functional structure search layer: Based on the flexible load information in the microgrid as Figure 1 shown, through the aggregation and grouping operations, the flexible loads are bound to ensure that the energy supply modes of the same load cluster are consistent, and then the CPD energy supply side structure is determined. The aggregation number and grouping can be expressed by the following formula:

[0050]

[0051] Among them, represents the grouping situation of load L i , L T represents all the loads to be grouped, N T represents the total number of aggregations, and N j represents the jth load cluster of the aggregation.

[0052] Based on the genetic algorithm, the optimization objective of load aggregation and grouping can be expressed by the following formula, where S * , C′ t * , P t * , D t * are the calculation results of the capacity-operation optimization layer. As Figure 2 shown, the nested form is used to search for the optimal load aggregation number and grouping, and then the CPD energy supply side structure is determined:

[0053]

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

[0055] S3: In the lower-layer capacity-operation optimization layer, the microgrid planning based on time-division networking aims to obtain an economic system construction plan according to the known parameters of renewable energy equipment, clustered typical scenarios, energy prices, and user loads, including the system structure and the capacity configuration and operation plan of photovoltaic (PV), wind turbine (WT), and energy storage system (ESS). Among them, considering the system structure is because the CPD unit of the time-division networking mode is introduced, and the load aggregation changes the CPD output structure, increasing the bus laying cost. The optimization objective can be expressed as:

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

[0057] C represents the total planning cost, including the annualized construction and operation costs, which are respectively represented by C IC and C oc respectively.

[0058]

[0059] Among them, S i represents the configuration capacity of the source-side unit i, and the unit capacity cost is represented by c i respectively. Ω = {PV, WT, ESS}, c CPD represents the CPD construction cost, η(·) represents the number of units in the set, N a represents the number of load aggregations, c l represents the unit bus laying cost, represents the capital recovery factor, γ i represents the discount rate, represents the service life of each unit.

[0060]

[0061] Among them, c e,t represents the electricity price at time t, c cur represents the unit penalty cost for wind and light abandonment, β represents the penalty coefficient, c w represents the CPD switch switching cost.

[0062] Its planning model constraints are as follows:

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

[0064]

[0065]

[0066] The total power generation of photovoltaic and wind turbines consists of the split power supplied to n loads and the power generation charged into the battery, and can be expressed by the following formula:

[0067]

[0068] The curtailment of wind and light comes from the remaining photovoltaic and wind power generation power supplied to the load and charged into the energy storage unit, and can be expressed by the following formula:

[0069]

[0070]

[0071] Among them, S pv , S wt are the installed photovoltaic and wind power capacities of the system, P pv,t , P wt,t are the output powers of photovoltaic and wind power at time t, P pv,ln,t , P wt,ln,t are the photovoltaic and wind power supplied to the load at location l n at time t respectively, P pv,ess,t , P wt,ess,t are the output powers of photovoltaic and wind turbines charged into the battery, S pv,min / max , S wt,min / max are the minimum and maximum installation capacity limits of photovoltaic and wind power under the transformer capacity limit, α pv,t , α wt,t are the output coefficients of photovoltaic and wind power respectively, χ represents the allowable curtailment rate of wind and light of the system, P cur,pv,t , P cur,wt,t are the curtailment amounts of wind and light at time t.

[0072] Since new energy is connected to the EFLM system off-grid and the power grid cannot be used as a means to supplement the shortage of new energy, considering increasing the lower limit of grid power purchase to improve power supply quality, the constraints are as follows:

[0073]

[0074] Among them, P buy,t represents the power purchased from the grid at time t in the scenario, P buy,ln,t represents the power purchased from the grid to supply the load at location l n at time t, Pgrid,min , P grid,max represents the upper limit of the power interacted with the power grid.

[0075] In the system, the main function of the battery energy storage is to smooth the fluctuations of new energy. To reduce the losses caused by the frequent charging and discharging of the energy storage and ensure the rationality of placing the energy storage in the CPD matrix, the charging power only considers the remaining power generation from wind and light, and does not consider the interaction with the power grid. When discharging, it is used as the source-side input of the CPD matrix, and the constraint is 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 is the configured capacity of the energy storage, S ess,max , S ess,min is the maximum allowable installation capacity restricted by geographical location and safety constraints, P dis,t , P ch,t represent the total charging and discharging power of the energy storage, P ess,ln,t represents the discharging power of the energy storage supplied to the load at location l n , μ ess,t represents the charging and discharging action state of the energy storage battery, P ess,max is the upper limit of the charging and discharging power, σ ess is the proportion of the charging and discharging power in the capacity, SOC t represents the state of charge at time t, λ ch , λ dis is the charging and discharging efficiency SOC max , SOC min represent the highest and lowest state of charge of the energy storage battery respectively, Δt is the time step, and T is the charging and discharging cycle.

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

[0084]

[0085] Combined Figure 3 and Figure 4 It can be seen that through the refined time-sliced networking mode, this method intelligently selects and flexibly connects new energy to the compatible consumption load. Especially in a microgrid dominated by flexible loads, it can solve the problems of flexible connection between fluctuating source and load, and the adaptation of multiple types of power sources to multiple granularity loads, improve the system flexibility, enhance the power balance of wind power and photovoltaic power supply to the load, avoid the complex power flow control problems of the traditional Point of Common Coupling (PCC) connection method, and at the same time, according to the CPD action mode, it can realize visual energy supply and load energy consumption paths, which is more conducive to the implementation of work such as carbon metering and carbon tracking.

[0086] Embodiment 2

[0087] A two-layer planning system for a microgrid considering time-sliced networking mode, comprising:

[0088] A typical scenario description module, configured to use Latin sampling and mean clustering methods to generate and reduce the uncertain output of the source and load under different scenarios, and obtain the new energy output description under typical scenarios;

[0089] An upper-layer optimization module, configured to construct an optimization model for the functional structure. Based on the flexible load information in the microgrid, determine the energy supply structure on the output side of the agile power distributor by optimizing load aggregation and grouping, and transfer the structure obtained by each iterative solution to the capacity-operation optimization layer to obtain the planning cost and use it as the objective of the structure optimization model in this layer, and iteratively solve through the genetic algorithm until convergence;

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

[0091] Embodiment 3

[0092] An electronic device, including a memory, a processor, and computer instructions stored on the memory and running on the processor. When the computer instructions are run by the processor, the following steps are completed:

[0093] Step 1: Determine the number of energy supply sources on the source side and the number of flexible loads participating in the time-sliced networking mode on the load side according to the actual microgrid structure, so as to determine the number of input paths of the CPD and the number of loads to be grouped, and obtain the source-side output coefficient and the original load-side energy consumption data;

[0094] Step 2: For the source-load data set with missing or insufficient data, use Latin hypercube to supplement the data set, and determine the Euclidean distance between the current source-load data sets through the mean clustering method to obtain the typical output trends of new energy and load demand information for different weather types;

[0095] Step 3: Build an optimization model for the functional structure based on the genetic algorithm. Determine the energy supply structure on the output side of the CPD by optimizing load aggregation and grouping, and transfer the structure solved in each iteration to the capacity-operation optimization layer to obtain the planning cost, which is used as the objective of the structure optimization model at this layer. Iteratively solve through the genetic algorithm until convergence;

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

[0097] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, system, or computer program product. Therefore, the present invention can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can adopt the form of a computer program product implemented 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] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0099] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including instruction means, and the instruction means realizes the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0100] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to generate a computer-implemented process, thereby providing instructions for implementing the functions specified in one process or multiple processes and / or blocks Figure 1 one process or multiple processes and / or blocks Figure 1 steps in one block or multiple blocks.

[0101] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made by those skilled in the art without creative efforts within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A two-layer planning method for microgrids considering a time-grouping network mode, characterized in that: The following steps are involved: By using Latin sampling and mean clustering methods, the generation and reduction of uncertain output of sources and loads are completed in different scenarios, and the description of new energy output in typical scenarios is obtained; Construct a functional structure optimization model. Based on the flexible load information in the microgrid, determine the energy supply structure on the output side of the agile power distributor by optimizing load aggregation and grouping. Pass the structure solved in each iteration to the capacity-operation optimization layer, and obtain the planning cost as the target of the structure optimization model at this layer. Iterate and solve it through the genetic algorithm until convergence. The capacity-operation optimization layer is based on iterative structural parameters, takes the annualized configuration cost of flexible resources and the annual operating cost of the system as optimization targets, and solves the configuration capacity, operation plan and action mode of the flexible resources under the capacity and operation constraints of each unit.

2. A two-layer planning method for a microgrid considering a time-group network mode as claimed in claim 1, characterized in that: Using Latin sampling and mean clustering methods, before completing the generation and reduction of source-load uncertainty output in different scenarios, it also includes determining the number of energy sources on the source side and the number of flexible loads in the grouping network mode when the load side participates according to the actual microgrid structure, thereby determining the number of input paths of the agile power distributor and the number of loads to be grouped, and obtaining the source side output coefficient and the original energy consumption data on the load side.

3. A two-layer planning method for a microgrid considering a time-group network mode as claimed in claim 1, characterized in that: Using Latin sampling and mean clustering methods, the process of generating and reducing source-load uncertainty output in different scenarios includes: for source-load data sets with missing or insufficient data, using Latin hypercube to supplement the data sets, and using the mean clustering method to determine the Euclidean distance between the current source-load data sets, obtaining the typical output trend and load demand information of new energy in different weather types, and obtaining the description of new energy output in typical scenarios.

4. A two-layer planning method for a microgrid considering a time-group network mode as claimed in claim 1, characterized in that: The process of determining the energy supply structure on the output side of the agile power distributor by optimizing load aggregation and grouping includes: The aggregation number and grouping are expressed as: in, Indicates load L i The grouping of L T Represents all loads to be grouped, N T Represents the total number of aggregations, N j represents the jth load cluster of the aggregation; Based on the genetic algorithm, the optimization goal of load aggregation and grouping is expressed by the objective function, where S * ,C t ' * ,P t * ,D t * The calculation result of the capacity-operation optimization layer is used to search for the optimal load aggregation number and grouping in a nested form to determine the energy supply structure on the output side of the agile power distributor. The objective function is: Among them, S * ,D t * They represent the optimal flexible resource configuration capacity and the switching action of the agile power distributor, C t ' * ,P t * represent the optimal source side output coefficient and output power respectively.

5. A two-layer planning method for a microgrid considering a time-group network mode as claimed in claim 1, characterized in that: The optimization objectives of the capacity-operation optimization layer are: minC=C IC +Coc; C represents the total planning cost, including the construction cost C IC and annualized operating cost C OC .

6. A two-layer planning method for a microgrid considering a time-group network mode as claimed in claim 5, characterized in that: The construction cost C IC for: Among them, S i represents the configuration capacity of source side unit i, and the unit capacity cost is given by c i Indicates that Ω = {PV, WT, ESS}, PV is photovoltaic, WT is wind power, ESS is energy storage unit, is the capital recovery factor of the CPD unit, is the capital recovery factor for laying busbars, c CPD represents the construction cost of the agile power distributor, η(·) represents the number of units in the set, N a represents the load aggregation number, c l represents the unit cost of busbar laying, represents the capital recovery factor, Υ i represents the discount rate, Indicates the service life of each unit.

7. A two-layer planning method for a microgrid considering a time-group network mode as claimed in claim 5, characterized in that: The annualized operating cost C OC for: Among them, c e,t represents the electricity price at time t, c cur represents the penalty cost per unit for wind and solar power abandonment, β represents the penalty coefficient, c w represents the switching cost of the agile power distributor switch, P cur,pv,t , P cur,wt,t are the amount of wind and solar power curtailment at time t, P buy,t The power purchased from the grid at time t in scenario w nl,t From time t to time t+1, n l The number of load switching.

8. A two-layer planning method for a microgrid considering a time-group network mode as claimed in claim 1, characterized in that: The constraints of the capacity-operation optimization layer include renewable energy installation capacity limitation constraints, power grid power purchase lower limit to improve power supply quality constraints, energy storage configuration capacity constraints, energy storage charging and discharging total power constraints and energy storage charge state constraints.

9. A two-layer planning system for microgrids considering a time-grouping network mode, characterized in that: include: The typical scenario description module is configured to use Latin sampling and mean clustering methods to complete the generation and reduction of source-load uncertainty output in different scenarios, and obtain the description of new energy output in typical scenarios; The upper optimization module is configured to build a functional structure optimization model. Based on the flexible load information in the microgrid, the energy 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 as the target of the structure optimization model of this layer, and the genetic algorithm is used to iteratively solve until convergence. The lower-level optimization module is configured as the capacity-operation optimization layer based on iterative structural parameters, with the annualized configuration cost of flexible resources and the annual operating cost of the system as optimization targets, and solves the configuration capacity, operation plan and action mode of the flexible resources under the capacity and operation constraints of each unit.

10. An electronic device, characterized in that: The method comprises a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps in the method according to any one of claims 1 to 8 are completed.

Images