A two-tier microgrid design method and system considering time-division networking mode.

Abstract

The present invention provides a two-tier microgrid planning method and system that takes into account time-division networking modes, the method comprising the steps of: generating and reducing uncertain power and load outputs in various scenarios using Latin hypersquare sampling and K-means method to obtain a description of new energy output in a typical scenario; determining the energy supply structure on the output side of smart power distributors by constructing a functional structure optimization model and optimizing load aggregation and grouping based on flexible load information within the microgrid, passing the structure solved in each iterative calculation to a capacity-operation optimization layer to obtain the target planning cost of the structure optimization model in this layer, and iteratively solving it using a genetic algorithm until convergence; and finding a solution under the constraints of the capacity and operation of each unit, with the annual configuration cost of flexible resources and the annual operating cost of the system as optimization targets based on iterative structure parameters. The present invention improves system flexibility and improves the balance of power supplied to the load from wind and solar power.

Description

Technical Field

[0001] (Cross - reference to related applications) This invention claims the priority of a Chinese patent application with an application number of 202510284901.6 and an invention title of "Two - layer planning method and system for a microgrid considering time - division networking mode", which was filed with the Chinese Patent Office on March 11, 2025. The entire content thereof is incorporated herein by reference and used for all purposes.

[0002] This invention belongs to the field of microgrid planning, and specifically relates to a two - layer planning method and system for a microgrid considering the time - division networking mode.

Background Art

[0003] The description in this part is only background technical information related to the present invention and does not necessarily constitute prior art.

[0004] As the installed capacity of renewable energy facilities increases rapidly, its variability and uncertainty pose serious challenges to the grid's power balance and safety. In some grids with insufficient new - energy transmission capacity, in many regions, efforts are being made to promote the integration of power sources, loads, storage, and microgrids, incorporate the functions of the micro - main grid into the regional "power generation, power distribution, storage, utilization" model, and prioritize local consumption rather than wholesale new energy to the grid, which poses serious challenges to microgrid planning.

[0005] Many existing studies focus on improving the utilization rate of new energy through the distribution and storage methods of new energy in microgrids, thereby smoothing the fluctuations and supply-demand imbalances of new energy, and enhancing the efficiency, flexibility, and resilience of microgrids. In a situation where the proportion of random introduction of new energy is high, in order to ensure the stable operation of the system while meeting the red line of output suppression for wind power and solar power generation, a large-capacity energy storage unit is required. However, this leads to a decrease in the security of the system and an increase in the installation cost of energy storage facilities, making the utilization of new energy uneconomical. In addition to the configuration of energy storage units, by changing the topology of the microgrid, flexibility and scalability can be improved. By supplying power to multiple buses, the load can select the optimal bus for switching between buses, improving the flexibility of the system. However, it is difficult to avoid the problem of complex power flow control, and the reverse power flow and coupling requirements of the power flow are strict, which becomes a major challenge for complex microgrid planning problems.

Summary of the Invention

Problems to be Solved by the Invention

[0006] To solve the above problems, the present invention proposes a two-layer planning method and system for a microgrid considering the time-division networking mode. The present invention constructs a two-layer planning strategy for exploring the energy supply structure and collaborative optimization of capacity-operation, and obtains solutions to determine the capacity of flexible energy supply resources, operation parameters, and the operation mode of a smart power distributor (CPD).

[0007] According to some embodiments, the present invention adopts the following technical solutions.

[0008] A two-layer planning method for a microgrid considering the time-division networking mode, comprising: The process involves using Latin Hypercube Sampling (LHS) and the K-Means Algorithm (K-Means) to generate and reduce uncertain power and load outputs under various scenarios, and describing the new energy output in a typical scenario. The process involves constructing a functional structure optimization model and optimizing load aggregation and grouping based on flexible load information within the microgrid to determine the energy supply structure on the output side of the smart power distributor. The structure solved in each iterative calculation is then passed to the capacity-operation optimization layer, where the target planning cost for the structural optimization model of this layer is obtained and iteratively solved using a genetic algorithm (GA) until convergence occurs. The capacity-operation optimization layer includes the steps of optimizing the annual configuration cost of flexible resources and the annual operating cost of the system based on iterative structural parameters, finding a solution under the capacity and operation constraints of each unit, and obtaining the configuration capacity of flexible resources, an operation plan, and an operating mode for the smart power distributor.

[0009] As an alternative embodiment, the process further includes using Latin hypersquare sampling and K-means to determine, based on the actual microgrid structure, the number of energy sources on the power supply side and the number of flexible loads participating in the time-division networking mode on the load side, before generating and reducing uncertain power supply and load outputs in different scenarios, thereby determining the number of input paths for the smart power distributor and the number of loads to be grouped, and obtaining power supply output coefficients and load-side original energy consumption data.

[0010] As an alternative embodiment, the process of generating and reducing uncertain power and load outputs under various scenarios using Latin hypersquare sampling and K-meansing involves supplementing data in power and load datasets with missing or incomplete data using Latin hypersquare sampling, determining the Euclidean distance between current power and load datasets using K-meansing, obtaining typical output trends and load demand information for new energy under various weather types, and describing new energy output under typical scenarios.

[0011] As an alternative embodiment, the process of determining the output-side energy supply structure of a smart power distributor by optimizing load aggregation and grouping includes the following: The aggregated number and grouping are expressed as follows:

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[0012] As an alternative embodiment, the optimization goal of the capacity - operation optimization layer is as follows. minC = C IC + C oc where C represents the total planned cost including the construction cost C IC and the annual operation cost C OC .

[0013] Furthermore, as another embodiment, the construction cost C IC is represented by the following formula.

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[0014] As a further embodiment, the annual operating cost C OC It can be expressed by the following formula:

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[0015] In an alternative embodiment, the constraints of the capacity-operation optimization layer include limitations on the installed capacity of renewable energy, a minimum purchase limit from the grid to improve power quality, an energy storage configuration capacity constraint, a total charge and discharge power constraint for energy storage, and a charge state constraint for energy storage.

[0016] A two-tiered microgrid design system considering time-division networking mode, A typical scenario description module is configured to generate and reduce uncertain power and load outputs in various scenarios using Latin hypersquare sampling and K-meansing, and to describe new energy outputs in typical scenarios. The upper layer optimization module is configured to construct a functional structure optimization model, determine the energy supply structure on the output side of a smart power distributor by optimizing load aggregation and grouping based on flexible load information within the microgrid, pass the structure solved in each iterative calculation to the capacity-operation optimization layer, obtain the target planning cost of the structural optimization model in this layer, and iteratively solve it using a genetic algorithm until convergence occurs. The capacity-operation optimization layer includes lower-layer optimization modules configured to optimize the annual configuration cost of flexible resources and the annual operating cost of the system based on iterative structural parameters, and to find solutions under the capacity and operation constraints of each unit to obtain flexible resource configuration capacity, operation plan, and smart power distributor operating mode.

[0017] An electronic device comprising memory, a processor, and computer instructions stored in memory and executed on the processor, wherein the steps of the above method are performed when the computer instructions are executed by the processor.

[0018] Compared to conventional technology, the beneficial effects of the present invention are as follows: This invention proposes a microgrid planning architecture based on CPD (Continuous Power Distribution). Through intelligent selection of networking modes based on sophisticated time division and flexible connection between new energy sources and the loads that consume them, it solves problems such as flexible connection of fluctuating power sources and loads, and adaptation to multiple types of power sources and multi-granularity loads, particularly in microgrids where flexible loads are dominant. This improves system flexibility and avoids the complex power flow control problems of conventional points of common coupling (PCCs). Simultaneously, based on the CPD operating modes, it enables visualization of energy supply and energy consumption paths of loads, contributing to carbon measurement and tracking.

[0019] To further clarify and facilitate understanding the above-mentioned objectives, features, and advantages of the present invention, preferred embodiments will be described in detail below with reference to the attached drawings. [Brief explanation of the drawing]

[0020] The drawings of the specification, which constitute part of the present invention, are provided for further understanding of the present invention, and the exemplary embodiments and descriptions of the present invention are used to illustrate the present invention and do not unduly limit the present invention.

[0021] [Figure 1] Figure 1 is a schematic diagram of a microgrid system based on a time-division networking model according to one embodiment. [Figure 2] Figure 2 is a two-tiered design structure diagram of energy supply structure exploration and capacity-operation collaborative optimization according to one embodiment. [Figure 3] Figure 3 shows the results of microgrid operation optimization based on a time-division networking model according to one embodiment. [Figure 4A] Figure 4A is a balance diagram of the power supplied from wind power generation to the load in one embodiment. [Figure 4B] Figure 4B is a balance diagram of the power supplied from solar power generation to the load in one embodiment. [Modes for carrying out the invention]

[0022] The present invention will be further described below in conjunction with the attached drawings and embodiments.

[0023] It should be noted that the following detailed description is illustrative and intended to further illustrate the invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art.

[0024] The terms used herein are for the sole purpose of describing specific embodiments and are not intended to limit the exemplary embodiments of the present invention. In this specification, unless otherwise explicitly stated in the context, singular nouns include plural nouns. Furthermore, where the terms “contains” and / or “includes” are used herein, they should be understood to indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0025] The embodiments and features of the embodiments of this application may be combined with each other, provided they do not contradict each other.

[0026] Example 1 To address the challenges that high-penetration solar power grid connections pose to grid capacity and the safety and stability of the distribution network, we propose a time-division networking mode microgrid planning method to promote local consumption of new energy. This method Step S1 involves considering the differences in the impact of the same weather conditions on solar and wind power generation, generating and reducing uncertain power output from the power source and load under various scenarios using the Latin hypersquare sampling method and the K-means method, and describing the new energy output under a typical scenario. In the upper functional structure exploration layer, based on the flexible load information within the microgrid shown in Figure 1, flexible loads are bound by aggregation and grouping, ensuring consistency of the energy supply mode for the same load cluster, and determining the CPD energy supply side structure in step S2, wherein the number of aggregations and groupings can be expressed by the following equations:

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[0027] The constraints of that planning model are as follows: The maximum installed capacity for renewable energy is expressed by the following formula:

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[0028] Because new energy sources are connected off-grid to efficient flexible load microgrid systems (EFLMs), the grid cannot be used to supplement any shortages of new energy. Therefore, in order to improve power quality, it is necessary to consider raising the minimum purchase threshold for electricity from the grid while meeting the following constraints.

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[0029] In this system, the primary function of energy storage by batteries is to mitigate fluctuations in new energy sources. To reduce losses due to frequent charging and discharging of energy storage and to ensure the rationale for placing the energy storage unit in the CPD matrix, the charging power considers only surplus power from wind and solar power generation, without considering interaction with the grid. During discharge, it functions as the power-side input to the CPD matrix, and the constraints are expressed as follows.

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[0030] The real-time matching relationship between the power supply and the load can be realized by the following equation, where matrix D represents the switching operation matrix of the CPD.

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[0031] As can be seen from Figures 3, 4A, and 4B, this method, through sophisticated time-division networking mode intelligent selection and flexible connection between new energy sources and the loads that consume them, solves problems such as flexible connection between fluctuating power sources and loads, and adaptation to multiple types of power sources and multi-granularity loads, particularly in microgrids where flexible loads are dominant. It improves system flexibility, strengthens the balance of power supplied from wind and solar power to the loads, and avoids the complex power flow control problems of conventional points of common coupling (PCCs). At the same time, based on the operating mode of the CPD, it enables visualization of energy supply and energy consumption paths of loads, contributing to carbon measurement and carbon tracking.

[0032] Example 2 A two-tiered microgrid design system considering time-division networking mode, A typical scenario description module is configured to generate and reduce uncertain power and load outputs in various scenarios using Latin hypersquare sampling and K-meansing, and to describe new energy outputs in typical scenarios. The upper layer optimization module is configured to construct a functional structure optimization model, determine the energy supply structure on the output side of a smart power distributor by optimizing load aggregation and grouping based on flexible load information within the microgrid, pass the structure solved in each iterative calculation to the capacity-operation optimization layer, obtain the target planning cost of the structural optimization model in this layer, and iteratively solve it using a genetic algorithm until convergence occurs. The capacity-operation optimization layer includes lower-layer optimization modules configured to optimize the annual configuration cost of flexible resources and the annual operating cost of the system based on iterative structural parameters, and to find solutions under the capacity and operation constraints of each unit to obtain flexible resource configuration capacity, operation plan, and smart power distributor operating mode.

[0033] Example 3 An electronic device comprising memory, a processor, and computer instructions stored in memory and executed on the processor, wherein when the computer instructions are executed by the processor, Step 1 involves determining the number of energy sources on the power supply side and the number of flexible loads participating in the time-division networking mode on the load side, thereby determining the number of input paths and the number of loads to be grouped in the CPD, and obtaining the power supply side output coefficient and the original energy consumption data on the load side. Step 2 involves, in the case of power / load datasets with missing or incomplete data, using Latin hypersquare sampling to supplement the dataset and using K-means to determine the Euclidean distance between current power / load datasets to obtain typical output trends and load demand information for new energy under various weather types. Step 3 involves constructing a functional structure optimization model based on a genetic algorithm, optimizing load aggregation and grouping to determine the energy supply structure on the output side of the CPD, passing the structure solved in each iterative calculation to a capacity-operation optimization layer, obtaining the target planning cost for the structural optimization model of this layer, and iteratively solving it using a genetic algorithm until convergence occurs. Step 4 is performed to obtain the configuration capacity, operational plan, and CPD operating mode of the flexible resources by constructing the capacity and operational constraints of each unit, with the annual configuration cost of the flexible resources and the annual operating cost of the system as the optimization targets based on iterative structural parameters, establishing the power / load supply demand balance relationship equation in time-division networking mode, and solving it using the Gurobi solver.

[0034] Those skilled in the art should 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 entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware. Furthermore, the present invention can also take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage devices, CD-ROMs, optical storage devices, etc.) containing computer-usable program code.

[0035] The present invention will be described with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be noted that each flow and / or block in a flowchart and / or block diagram, and combinations of flows and / or blocks within a flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a dedicated computer, an embedded processor, or other programmable data processing device, thereby enabling instructions executed via the processor of the computer or other programmable data processing device to generate means for realizing the functions specified in one or more flows and / or blocks of a flowchart and / or block diagram.

[0036] These computer program instructions can be stored in a computer-readable storage device, which can instruct a computer or other programmable data processing device to operate in a particular manner, and as a result, the instructions stored in the computer-readable storage device generate a product that includes instruction means to implement one or more flows in a flowchart and / or one or more blocks in a block diagram.

[0037] These computer program instructions can be loaded into a computer or other programmable data processing device, thereby executing a series of operational steps on the computer or other programmable device to generate a process realized by the computer, and consequently providing instructions to be executed on the computer or other programmable device to realize the steps of a function specified in one or more flows of a flowchart and / or one or more blocks of a block diagram.

[0038] The foregoing describes only preferred embodiments of the present invention and is not intended to limit the invention. Those skilled in the art can modify and alter the invention in various ways. Any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art without creative effort within the spirit and principles of the invention are all covered by the invention.

Claims

1. A two-tier microgrid planning method that takes into account time-division networking mode and is performed by a computer, The steps involve using Latin hypersquare sampling and K-meansing to generate and reduce uncertain power and load outputs under various scenarios, and obtaining an explanation of the new energy output under typical scenarios. This step involves constructing a functional structure optimization model, optimizing load aggregation and grouping based on flexible load information within the microgrid to determine the energy supply structure on the output side of the smart power distributor, passing the structure solved in each iterative calculation to the capacity-operation optimization layer, obtaining the target planning cost of the structure optimization model in this layer, and iteratively solving it using a genetic algorithm until convergence occurs. The process of determining the output-side energy supply structure of a smart power distributor by optimizing load aggregation and grouping includes the following: Aggregation and grouping can be expressed as follows: [Math 1] Here, Z ｇ，Ｌｉ Load L ｉ This represents the grouping status of L Ｔ This represents all loads that are grouped together, N Ｔ represents the total number of aggregates, N ｊ This represents the j-th load cluster of the aggregate, Based on the genetic algorithm, the optimization goal for load aggregation and grouping is expressed by the objective function, where S ＊ , [Math 2] This is the calculation result of the capacity-operation optimization layer, which uses a nested approach to search for the optimal load aggregation number and grouping, and determines the energy supply structure on the output side of the smart power distributor, with the objective function being as follows: [Math 3] Here, N ａ , Z ｇ represent the load aggregation number and the grouping status respectively, and S ＊ ， [Math 4] These represent the optimal flexible resource configuration capacity and the switching operation of the smart power distributor, respectively. [Math 5] These represent the optimal power supply output coefficient and output power, respectively, and A two-tier microgrid planning method considering a time-division networking mode, characterized in that the capacity-operation optimization layer optimizes the annual configuration cost of flexible resources and the annual operating cost of the system based on iterative structural parameters, seeks a solution under the capacity and operation constraints of each unit, and obtains the configuration capacity of flexible resources, an operation plan, and an operating mode for smart power distributors.

2. A two-tier microgrid planning method considering a time-division networking mode according to claim 1, further comprising the steps of determining, based on the actual microgrid structure, the number of energy sources on the power supply side and the number of flexible loads on the load side participating in the time-division networking mode, using Latin hypersquare sampling and K-means, before generating and reducing uncertain power and load outputs in various scenarios, thereby determining the number of input paths for smart power distributors and the number of loads to be grouped, and obtaining power supply output coefficients and original energy consumption data on the load side.

3. A two-tier microgrid planning method considering a time-division networking mode, according to claim 1, characterized in that, in the case of power and load datasets with missing or insufficient data, the process of generating and reducing uncertain power and load outputs in various scenarios is characterized by using Latin hypersquare sampling and K-means to supplement the dataset, determining the Euclidean distance between current power and load datasets using K-means to obtain typical output trends and load demand information for new energy under various weather types, and obtaining a description of new energy output in typical scenarios.

4. The optimization targets for the capacity-operation optimization layer are as follows: minC=C ＩＣ +C ｏｃ Here, C is the construction cost C ＩＣ and annual operating costs C ＯＣ A two-tier microgrid planning method considering the time-division networking mode according to claim 1, characterized in that it represents the total planning cost including the total planning cost.

5. The aforementioned construction cost C ＩＣ The following applies: [Math 6] 【Number 7】 Here, S ｉ This represents the configuration capacity of power supply unit i, and the unit capacity cost is c. ｉ It is expressed as Ω = {PV, WT, ESS}, where PV is solar power, WT is wind power, and ESS is an energy storage unit. [Number 8] This is the capital recovery factor for CPD units, [Number 9] This is the capital recovery factor for busbar construction, and C ＣＰＤ This is the construction cost of the smart power distributor. [Number 10] This is the number of units in the set, N ａ is the number of load aggregates, and Cι is the unit cost of busbar installation. [Math 11] This is the capital recovery factor, [Math 12] This is the discount rate, [Number 13] The method for planning a two-tier microgrid considering a time-division networking mode according to claim 4, characterized in that it represents the service life of each unit.

6. The aforementioned annual operating cost C ＯＣ The following applies: [Number 14] Here, c ｅ，ｔ c is the price of electricity at time t, ｃｕｒ is the unit cost of the penalty for curtailing wind and solar power output, β is the penalty coefficient, and c ｗ This is the switching cost of the smart power distributor, P ｃｕｒ，ｐｖ，ｔ and P ｃｕｒ，ｗｔ，ｔ This is the amount of wind and solar power output reduction at time t, and P ｂｕｙ，ｔ represents the power purchased from the grid at time t, and w ｎｌ，ｔ n from time t to time t+1 ｌ A two-tier microgrid planning method considering the time-division networking mode according to claim 4, characterized in that it represents the number of load switching cycles in the network.

7. The two-tier microgrid planning method considering a time-division networking mode according to claim 1, characterized in that the constraints of the capacity-operation optimization layer include a limit on the installed capacity of renewable energy, a lower limit on purchases from the grid to improve power quality, a constraint on the configuration capacity of energy storage, a constraint on the total charge and discharge power of energy storage, and a constraint on the charge state of energy storage.

8. A two-tiered microgrid design system that considers time-division networking mode, A typical scenario description module configured to generate and reduce uncertain power and load outputs in various scenarios using Latin hypersquare sampling and K-meansing, and to obtain a description of new energy output in a typical scenario, This upper-layer optimization module constructs a functional structure optimization model and determines the energy supply structure on the output side of a smart power distributor by optimizing load aggregation and grouping based on flexible load information within the microgrid. The structure solved in each iterative calculation is then passed to a capacity-operation optimization layer, where the target planning cost of the structural optimization model in this layer is obtained and iteratively solved using a genetic algorithm until convergence occurs. The process of determining the output-side energy supply structure of a smart power distributor by optimizing load aggregation and grouping includes the following: Aggregation and grouping can be expressed as follows: [Number 15] Here, Z ｇ，Ｌｉ Load L ｉ This represents the grouping status of L Ｔ This represents all loads that are grouped together, N Ｔ represents the total number of aggregates, N ｊ This represents the j-th load cluster of the aggregate, Based on the genetic algorithm, the optimization goals for load aggregation and grouping are expressed by the objective function, where S ＊ , [Number 16] This is the calculation result of the capacity-operation optimization layer, which uses a nested approach to search for the optimal load aggregation number and grouping, and to determine the energy supply structure on the output side of the smart power distributor, with the objective function being as follows: [Number 17] Here, N ａ Z ｇ These represent the number of load aggregated and the grouping status, respectively. ＊ ， [Number 18] These represent the optimal flexible resource configuration capacity and the switching operation of the smart power distributor, respectively. [Number 19] These are upper-layer optimization modules that represent the optimal power supply output coefficient and output power, respectively. A two-tier microgrid planning system considering time-division networking modes, characterized in that the capacity-operation optimization layer includes a lower-tier optimization module configured to optimize the annual configuration cost of flexible resources and the annual operating cost of the system based on iterative structural parameters, and to obtain the configuration capacity of flexible resources, the operational plan, and the operating mode of the smart power distributor by finding a solution under the capacity and operational constraints of each unit.

9. Electronic device comprising memory, a processor, and computer instructions stored in memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps of the method according to any one of claims 1 to 7 are performed.

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