Micro-grid group multi-source collaborative scheduling method and system based on flexible interconnection

Abstract

The application discloses a kind of microgrid group multi-source collaborative scheduling method and system based on flexible interconnection, which comprises the following steps: establishing resource-geographical coupling graph theory model, determining the optimal deployment position of flexible interconnection device using the double-target optimization function of minimizing construction cost and maximizing load accessibility, forming the physical interconnection topology structure of microgrid group;Real-time acquisition of wind, light, water and energy storage state data of each microgrid node, according to load reconstruction rule, the load of each microgrid node in physical interconnection topology structure is divided into rigid load and elastic load, the source load mismatch index and trend discriminant factor of microgrid group are calculated;Set trend discriminant factor threshold and source load mismatch index threshold, divide the operation state of microgrid group into different operation modes;According to operation mode, the corresponding energy scheduling strategy is executed;Through the method, geographical environmental factors can be considered, and flexible interconnection of key nodes can be realized with minimum cost, and the topology adaptability is strong.

Description

Technical Field

[0001] This invention relates to the field of microgrid energy scheduling technology, and in particular to a multi-source collaborative scheduling method and system for microgrid groups based on flexible interconnection. Background Technology

[0002] In remote mountainous areas, canyons, and other complex geographical environments, microgrids are an important means of solving power supply problems. However, the planning and operation of existing mountain microgrids face serious challenges: Topographical barriers lead to extreme resource unevenness: wind power is mostly located on mountain ridges, hydropower in valleys, and photovoltaic power on sunny slopes, while load centers are often located in flat areas. This geographical "source-load separation" makes it difficult for a single microgrid to self-balance; rigid interconnection is costly: the rigid interconnection lines used by traditional power grids are extremely expensive to lay in rugged mountainous areas and cannot flexibly adjust power flow, resulting in poor voltage quality at the end of the lines; dispatch strategies are relatively rigid: existing technologies are mostly based on simple power threshold judgments, lacking quantitative consideration of the cost of topographic constraints, and failing to fully utilize the differentiated requirements of different loads for power supply reliability (i.e., lacking rigidity and flexibility in classification).

[0003] Therefore, there is an urgent need for a microgrid group scheduling method that can adapt to complex terrain constraints, achieve multi-energy complementarity through flexible interconnection, and possess hierarchical collaborative optimization capabilities. Summary of the Invention

[0004] In view of the above-mentioned prior art, the present invention provides a method and system for multi-source collaborative scheduling of microgrid groups based on flexible interconnection, which mainly solves the technical problems existing in the background art.

[0005] To achieve the above objectives, the technical solution of the present invention is implemented as follows: In a first aspect, the present invention provides a multi-source collaborative scheduling method for microgrid groups based on flexible interconnection, comprising the following steps: Step S1: Establish a resource-geographic coupling graph theory model. Based on the graph theory model, use a dual-objective optimization function that minimizes construction costs and maximizes load accessibility to determine the optimal deployment location of the flexible interconnection device, forming the physical interconnection topology of the microgrid cluster. Step S2: Collect wind, solar, hydro and energy storage status data of each microgrid node in real time. According to the load reconfiguration rules, divide the load of each microgrid node in the physical interconnection topology into rigid load and elastic load. Calculate the source-load mismatch index and trend discrimination factor of the microgrid group based on the rigid load and the elastic load. Step S3: Set the trend discrimination factor threshold and the source-load mismatch index threshold. Based on the trend discrimination factor threshold and the source-load mismatch index threshold, divide the microgrid group's operating status into different operating modes. Step S4: Execute the corresponding energy scheduling strategy according to the operating mode.

[0006] Optionally, the construction of the graph theory model includes: Define nodes, each node including an attribute vector, expressed as:

[0007]

[0008]

[0009] in, It is a set of nodes, where each node represents a microgrid. This is a resource attribute vector for a node, used to describe the type of energy that the node possesses; To indicate whether a node possesses wind power generation resource attributes, a value of 1 is assigned when the node is in a wind energy capture zone, and a value of 0 is assigned otherwise. To indicate whether a node possesses photovoltaic power generation resource attributes, a value of 1 is assigned when the node is a light energy capture zone, and a value of 0 is assigned otherwise. To determine whether a node possesses hydropower resources, a value of 1 is assigned when the node is in a hydropower capture zone, and 0 otherwise. This indicates whether a node is a pure load or has no local generation capacity. It is set to 1 when the node only acts as a load buffer center, and 0 otherwise. Edges are defined based on the geographical distance between nodes, and edge weights are determined based on the equivalent electrical distance and construction costs.

[0010] Optionally, the expression for the bi-objective optimization function is:

[0011] in, It is a bi-objective optimization function. For decision variables; Cost per unit of installation; The equivalent electrical distance is subject to terrain constraints; The load importance weight of the node; This is an indicator of node reachability; These are the weighting coefficients; The total number of nodes is the number of elements in the node set V of the microgrid.

[0012] Optionally, the microgrid group load can be divided into rigid loads and resilient loads according to the load reconfiguration rules, including: Microgrid loads located in wind energy capture zones, solar energy capture zones, or hydro energy capture zones are defined as rigid loads. Loads that have demand response capabilities and are distributed in resource-idle areas or serve as supplementary loads are defined as elastic loads.

[0013] Optionally, the calculation formula for the source-load mismatch index and trend discrimination factor of the microgrid group based on the rigid load and the elastic load is as follows:

[0014]

[0015] in, The source-load mismatch index. For the first Real-time power of energy sources Confidence coefficient; Let t be the total active power demand of the rigid loads in the microgrid group at time t; Let t be the total active power demand of the resilient loads in the microgrid group at time t; Trend discriminant factor; For the real-time state of charge of the energy storage system, This serves as the reference state of charge threshold for the energy storage system. This is a correction factor.

[0016] Optionally, step S3 includes: The operating modes include island autonomy mode, interconnected support mode, and multi-level peak shaving and valley filling mode; when When the trend discriminant factor is close to zero, the operating mode is the island autonomous mode, where... The source-load mismatch index. The threshold for the source-load mismatch index; when And there exist adjacent nodes that satisfy At that time, the operating mode is the interconnection support mode, in which, Let be the source-load mismatch exponent of node i at time t. Let be the source-load mismatch index of adjacent node j at time t; when Furthermore, when the absolute value of the trend discrimination factor continues to increase, the operating mode is a multi-level peak shaving and valley filling mode.

[0017] Optionally, step S4 includes: When the operating mode is the island autonomous mode, only the local energy storage unit is used for power throughput to smooth out the fluctuation of the source-load mismatch index; When the operating mode is Interconnect Support Mode, the Flexible Interconnect Device is activated, and the transmission power is the minimum value among the source end surplus, the FID capacity limit, and the receiver end deficit. When the operating mode is multi-level peak shaving and valley filling mode, the hierarchical strategy is executed.

[0018] Optionally, when the operating mode is a multi-level peak shaving and valley filling mode, a hierarchical strategy is executed, including: The multi-level peak shaving and valley filling mode includes a power deficit mode and a power surplus mode; When the overall load change rate is greater than the overall power generation change rate, it is a power deficit mode. In this case, flexible loads are cut off first, followed by rigid loads in order of weight. When the overall power generation change rate is greater than the overall load change rate, it is a power surplus mode, in which case the dispatching flexible load will increase power consumption or limit power generation output.

[0019] Secondly, the present invention also provides a microgrid group multi-source collaborative scheduling system based on flexible interconnection, comprising: Resource allocation unit, used to divide mountainous areas into wind energy capture areas, solar energy capture areas, hydro energy capture areas and load buffer centers according to the terrain distribution of mountainous areas; Resource allocation unit, used to divide mountainous areas into wind energy capture areas, solar energy capture areas, hydro energy capture areas and load buffer centers according to the terrain distribution of mountainous areas; The model building unit is used to construct a resource-geographic coupling graph theory model based on the regions divided by the resource partitioning unit. Based on the graph theory model, the optimal deployment location of the flexible interconnection device is determined using a bi-objective optimization function, and the flexible interconnection device connects each resource partitioning unit. Edge collaborative controllers are configured in each microgrid node to collect wind, solar, hydro and energy storage status data of each microgrid node, calculate source-load mismatch index and trend discrimination factor, and classify the microgrid group operation status into different operation modes. The scheduling unit is used to execute the corresponding energy scheduling strategy according to the operating mode.

[0020] The beneficial effects of this invention are as follows: This invention provides a multi-source collaborative scheduling method for microgrids based on flexible interconnection. By establishing a resource-geographic coupling graph theory model, the nodes and edges in the graph theory model can describe the physical structure of the microgrid. Based on the graph theory model, a bi-objective optimization function is used to determine the optimal deployment location of the flexible interconnection device in the microgrid. Then, the state data of each microgrid node is collected, the source-load mismatch index and trend discrimination factor are calculated, and the operation mode of the microgrid is determined based on the source-load index and trend discrimination factor. The corresponding energy scheduling strategy is executed according to the operation mode. This method can consider geographical environmental factors and take into account the construction cost caused by terrain. It achieves flexible interconnection of key nodes at the lowest cost, has strong topology adaptability, and finally obtains a reliable power scheduling strategy. Energy scheduling is carried out according to the power scheduling strategy. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a microgrid group multi-source collaborative scheduling method based on flexible interconnection provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a microgrid group multi-source collaborative scheduling system based on flexible interconnection provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the physical interconnection topology provided in an embodiment of the present invention; Figure 4 The image shows the simulation results provided in this embodiment of the invention. Detailed Implementation

[0022] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Unless otherwise defined, 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. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. In the following description, the expression "some embodiments" refers to a subset of all possible embodiments; however, it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict.

[0023] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described in order to avoid obscuring the invention.

[0024] It should be understood that the present invention can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Furthermore, the terminology used herein is intended only to describe particular embodiments and is not intended to limit the invention. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “compose” and / or “comprising,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0025] It should also be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "inner," "outer," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0026] To fully understand this invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by this invention. Optional embodiments of the invention are described in detail below; however, in addition to these detailed descriptions, the invention may have other embodiments.

[0027] Example 1 Please refer to the attached document. Figure 1 This invention provides a multi-source collaborative scheduling method for microgrid groups based on flexible interconnection, comprising the following steps: Step S1: Establish a resource-geographic coupling graph theory model. Based on the graph theory model, use a dual-objective optimization function that minimizes construction costs and maximizes load accessibility to determine the optimal deployment location of the flexible interconnection device, forming the physical interconnection topology of the microgrid cluster. Step S2: Collect wind, solar, hydro and energy storage status data of each microgrid node in real time. According to the load reconfiguration rules, divide the load of each microgrid node in the physical interconnection topology into rigid load and elastic load. Calculate the source-load mismatch index and trend discrimination factor of the microgrid group based on the rigid load and the elastic load. Step S3: Set the trend discrimination factor threshold and the source-load mismatch index threshold. Based on the trend discrimination factor threshold and the source-load mismatch index threshold, divide the microgrid group's operating status into different operating modes. Step S4: Execute the corresponding energy scheduling strategy according to the operating mode.

[0028] Specifically, the microgrid cluster consists of multiple mountain microgrids and their flexible interconnection devices. Each microgrid acts as a sub-unit node, i.e., a microgrid node, which achieves energy sharing and coordinated scheduling through the flexible interconnection devices. Graph theory models are used to describe the physical interconnection structure of the microgrid cluster under complex terrain constraints. The bi-objective optimization function, based on the graph theory model, determines the optimal deployment location of the flexible interconnection devices by minimizing construction costs and maximizing load. The status data of wind, solar, hydro, and energy storage for each microgrid node includes real-time renewable energy output data (wind, solar, and hydropower), state-of-charge data of the energy storage system, node load power data, and operational status data of flexible interconnection devices. This data is used to calculate the source-load mismatch index and trend discrimination factor, which serve as input variables for operational mode classification and scheduling strategy triggering. Classifying the microgrid cluster's operational status based on the source-load mismatch index and trend discrimination factor allows for the determination of load power within each microgrid node according to different operational modes, leading to better energy scheduling.

[0029] As an optional implementation, the construction of the graph theory model includes: Define nodes, each node including an attribute vector, expressed as:

[0030]

[0031]

[0032] in, It is a set of nodes, where each node represents a microgrid. This is a resource attribute vector for a node, used to describe the type of energy that the node possesses; To indicate whether a node possesses wind power generation resource attributes, a value of 1 is assigned when the node is in a wind energy capture zone, and a value of 0 is assigned otherwise. To indicate whether a node possesses photovoltaic power generation resource attributes, a value of 1 is assigned when the node is a light energy capture zone, and a value of 0 is assigned otherwise. To determine whether a node possesses hydropower resources, a value of 1 is assigned when the node is in a hydropower capture zone, and 0 otherwise. This indicates whether a node is a pure load or has no local generation capacity. It is set to 1 when the node only acts as a load buffer center, and 0 otherwise. Edges are defined based on the geographical distance between nodes, and edge weights are determined based on the equivalent electrical distance and construction costs.

[0033] Specifically, nodes are defined based on geographical location and dominant energy function, including wind capture zone nodes, solar capture zone nodes, hydro capture zone nodes, and load buffer center nodes. Each node corresponds to a physical microgrid unit with independent energy output or load characteristics; that is, the microgrid group consists of multiple microgrids, and each microgrid corresponds to one node; the resource attributes in the attribute vector are binary variables, satisfying... Each node contains at least one resource attribute; for example, the same node may simultaneously possess both wind power generation resource attributes and photovoltaic power generation resource attributes. For example, please refer to the attached document. Figure 3The diagram shows four core nodes divided based on altitude and resource distribution: Nodes (The ridges at the top left and right corners of the image): marked as wind energy capture areas, attribute vectors Wind turbine generators are deployed; nodes (Sunny slope in the upper right corner of the image): Marked as the light energy capture area, attribute vector It is equipped with a photovoltaic array; nodes (Lower valley in the image): Marked as a hydropower capture area, attribute vector Small hydroelectric power stations are deployed; nodes (The flat area in the center of the diagram): marked as the load buffer center, attribute vector It is a residential area and a concentration of electric vehicle charging stations.

[0034] It should be noted that node categories are used to identify the dominant geographic functional zone of a node, while resource attribute vectors are used to describe the actual resource composition of the node; the two are complementary rather than mutually exclusive. Therefore, a single node can possess multiple resource attributes simultaneously, but its node category is labeled with its dominant resource or dominant function.

[0035] Edges represent candidate connection paths between nodes in geographic space where flexible interconnect devices can be deployed. Edge weights describe the equivalent electrical distance and construction cost under terrain constraints, supporting subsequent site selection optimization for flexible interconnect devices. Nodes and edges together constitute a resource-geographic coupling topology, used to describe the potential interconnection relationships between different energy nodes and their cost constraints.

[0036] As an optional implementation, the expression for the bi-objective optimization function is:

[0037] in, It is a bi-objective optimization function. For decision variables; Cost per unit of installation; The equivalent electrical distance is subject to terrain constraints; The load importance weight of the node; This is an indicator of node reachability; These are the weighting coefficients; The total number of nodes is the number of elements in the node set V of the microgrid.

[0038] Specifically, since the microgrid nodes are connected by flexible interconnection devices, the optimal deployment location of the flexible interconnection devices needs to be determined between the nodes. In this technical solution, by considering the two factors of construction cost and load accessibility, the most economical location of the flexible interconnection devices that ensures a stable power supply is obtained, thereby providing a basis for the subsequent formation of the physical interconnection topology of the microgrid group.

[0039] It should be noted that the optimal deployment location of flexible interconnection devices is determined using a dual-objective optimization function that minimizes construction costs and maximizes load accessibility. Maximizing load accessibility refers to the ability of each load node in the microgrid to obtain energy support through the physical interconnection topology. The accessibility index measures the connectivity between microgrid nodes and power generation resource nodes under the current topology and their potential ability to obtain power support. If only minimizing construction costs is the objective, flexible interconnection devices may be concentrated in a few low-cost areas, resulting in an unbalanced interconnection topology, leaving some critical load nodes isolated or under-supported during operation. Therefore, the load accessibility index is introduced when optimizing the deployment location of flexible interconnection devices to ensure that important load nodes can obtain external support through interconnection paths in the event of source-load mismatch, thereby improving the overall power supply reliability of the microgrid.

[0040] Flexible interconnect devices are not only used to form physical electrical connections, but also have bidirectional power regulation capabilities. They can realize cross-microgrid energy transmission in different operating modes. Their deployment location directly determines the power transmission path and capacity constraints in the interconnection support mode and multi-level peak shaving and valley filling mode in the subsequent operation phase. Therefore, the topology of flexible interconnect devices is the physical basis for the implementation of subsequent energy dispatch strategies.

[0041] For example, Figure 3 The solid lines represent candidate connection paths, while the smaller solid lines represent the paths of actual deployment of Flexible Interconnect Devices (FIDs) after algorithm optimization; this is achieved by solving the objective function. Taking into account the high construction costs of crossing the canyon Then, node was selected. A star-shaped connection architecture centered on key nodes maximizes the number of critical nodes while minimizing construction costs. Accessibility .

[0042] As an optional implementation, the microgrid group load is divided into rigid loads and flexible loads according to the load reconfiguration rules, including: Microgrid loads located in wind energy capture zones, solar energy capture zones, or hydro energy capture zones are defined as rigid loads. Loads that have demand response capabilities and are distributed in resource-idle areas or serve as supplementary loads are defined as elastic loads.

[0043] Specifically, since the nodes include wind energy capture zone nodes, solar energy capture zone nodes, hydro energy capture zone nodes and load buffer center nodes, and the loads in the wind energy capture zone nodes, solar energy capture zone nodes and hydro energy capture zone nodes have the characteristics of high power supply continuity requirements and strong coupling with local resource geographical location, the loads in the wind energy capture zone nodes, solar energy capture zone nodes and hydro energy capture zone nodes are defined as rigid loads.

[0044] As an optional implementation, the calculation formula for the source-load mismatch index and trend discrimination factor of the microgrid group based on the rigid load and the elastic load is as follows:

[0045]

[0046] in, The source-load mismatch index. For the first Real-time power of energy sources Confidence coefficient; Let t be the total active power demand of the rigid loads in the microgrid group at time t; Let t be the total active power demand of the resilient loads in the microgrid group at time t; Trend discriminant factor; For the real-time state of charge of the energy storage system, This serves as the reference state of charge threshold for the energy storage system. This is a correction factor.

[0047] Specifically, the reference state of charge threshold of the energy storage system is a pre-set fixed parameter that does not change over time; the trend discriminant factor represents the discriminant factor for the change trend of the source-load mismatch, which is used to describe the direction and rate of change of the source-load mismatch index. The first term in the trend discriminant factor calculation formula reflects the rate of change of the source-load imbalance, and the second term reflects the degree of deviation of the energy storage system's state of charge from the reference value.

[0048] As an optional implementation, step S3 includes: The operating modes include island autonomy mode, interconnected support mode, and multi-level peak shaving and valley filling mode; when ,and At that time, the operating mode is the isolated autonomous mode, in which, The source-load mismatch index. The threshold for the source-load mismatch index. As a trend discriminant factor, Threshold for trend discrimination factor; when And there exist adjacent nodes that satisfy At that time, the operating mode is the interconnection support mode, in which, Let be the source-load mismatch exponent of node i at time t. Let be the source-load mismatch index of adjacent node j at time t; when Furthermore, when the absolute value of the trend discrimination factor continues to increase, the operating mode is a multi-level peak shaving and valley filling mode.

[0049] Specifically, the operating mode is determined based on the combined result of the source-load mismatch index and the trend discriminant factor. The source-load mismatch index describes the current source-load imbalance of the microgrid, while the trend discriminant factor describes the changing trend of the source-load mismatch index. When the source-load mismatch index is small and the trend discriminant factor is close to zero, the microgrid is in a stable fluctuating state, and its operating mode is the island autonomous mode. When the source-load mismatch index is large and adjacent nodes satisfy... This indicates that the microgrid mismatch is intensifying, and the microgrid is operating in interconnected support mode. For example, if at a certain moment the total generating power of microgrid node i is 5 MW and the total load power is 8 MW, then the source-load mismatch power ΔP of that node is... i =-3MW; assuming the maximum adjustable power of the local energy storage unit at this node. =1.5 MW, then |ΔP i |=3 MW> This indicates that local energy storage cannot fully compensate for the power deficit; meanwhile, the total generating capacity of adjacent node j is 9 MW, and the total load capacity is 6 MW, then ΔP j =3 MW. At this time, ΔP i ·ΔP j =(-3)×(3)=-9<0, indicating that node i and node j have a power complementary relationship; therefore, it is determined that the interconnection support mode is entered, and node j transmits power to node i through the flexible interconnection device. When the source-load mismatch index is large and the absolute value of the trend discrimination factor continues to increase, it is determined that the system has entered a severe imbalance state. At this time, the operation mode of the microgrid group is the multi-level peak shaving and valley filling mode; for example, when the source-load mismatch index is large and the absolute value of the trend discrimination factor satisfies within k consecutive sampling periods When the absolute value of the trend discrimination factor continues to increase, the system enters a state of severe imbalance. At this time, the operating mode is a multi-level peak shaving and valley filling mode. Here, k is the preset number of judgment periods, which is used to filter out the influence of instantaneous disturbances. For example, when k=3 and the absolute value of the trend discrimination factor is 0.15, 0.22, 0.35 and 0.48 in 4 consecutive sampling periods, it can be determined that it continues to increase.

[0050] As an optional implementation, step S4 includes: When the operating mode is the island autonomous mode, only the local energy storage unit is used for power throughput to smooth out the fluctuation of the source-load mismatch index; When the operating mode is Interconnect Support Mode, the Flexible Interconnect Device is activated, and the transmission power is the minimum value among the source end surplus, the FID capacity limit, and the receiver end deficit. When the operating mode is multi-level peak shaving and valley filling mode, the hierarchical strategy is executed.

[0051] Specifically, local energy storage units refer to the energy storage units configured within a single microgrid node, and do not involve cross-microgrid energy transmission. When the operating mode is islanded autonomous mode, it indicates that the microgrid nodes within the microgrid cluster have internal regulation capabilities, and the microgrid is in a stable fluctuating state, capable of power regulation using only local energy storage units. When the operating mode is interconnected support mode, the microgrid itself cannot meet the demand, but the microgrid cluster can still maintain stability through flexible interconnection devices. Power regulation of microgrid nodes requiring energy dispatch is achieved through these flexible interconnection devices, thereby restoring the power of one or more microgrid nodes. When the operating mode is multi-level peak shaving and valley filling mode, it indicates that even with energy replenishment from microgrid nodes through flexible interconnection devices, energy stability of the microgrid cluster cannot be guaranteed. In this case, a tiered strategy is used for energy regulation.

[0052] As an optional implementation, when the operating mode is a multi-level peak shaving and valley filling mode, a hierarchical strategy is executed, including: The multi-level peak shaving and valley filling mode includes a power deficit mode and a power surplus mode; When the overall load change rate is greater than the overall power generation change rate, it is a power deficit mode. In this case, flexible loads are cut off first, followed by rigid loads in order of weight. When the overall power generation change rate is greater than the overall load change rate, it is a power surplus mode, in which case the dispatching flexible load will increase power consumption or limit power generation output.

[0053] Specifically, both the power deficit mode and the power surplus mode are derived by further subdividing the source-load change direction after the triggering conditions of the "multi-level peak shaving and valley filling mode" are met. In the power deficit mode, the load growth rate (or decrease) exceeds the generation adjustment capacity, resulting in generation falling behind the load change and thus creating a power gap. Flexible loads have the characteristics of being "interruptible and adjustable," so cutting off these loads has a smaller impact and a faster response time. Therefore, flexible loads are cut off first. If flexible loads are insufficient, some rigid loads need to be cut off. Rigid loads are usually sorted according to "importance weight," with priority given to ensuring critical loads to avoid system collapse. In the power surplus mode, the generation growth rate (or decrease) exceeds the load change, resulting in excess generation and an increase in system frequency. At this time, it is possible to guide flexible loads to increase electricity consumption or limit generation output.

[0054] For example, please refer to the attached document. Figure 4 , Figure 4 The simulation waveform diagram visually demonstrates the evolution of the microgrid group's operating status over time during a typical day (0:00-24:00).

[0055] Subgraph (a) Power balance analysis: During the period from 10:00 to 14:00, the solid green line (power generation) is significantly higher than the dashed red line (load demand), forming a green shaded area, indicating that the microgrid group is in a state of "power surplus"; During the period from 18:00 to 21:00, as the photovoltaic output disappears and the evening peak load arrives, the dashed red line is higher than the solid green line, forming a red shaded area, indicating that the microgrid group is in a state of "power deficit".

[0056] Subgraph (b) Source-load mismatch index and real-time state of charge analysis of energy storage system: The blue curve represents the source-load mismatch index ( At noon, Reaching the positive peak, the corresponding orange curve (representing the real-time state of charge (SOC) of the energy storage system) shows a rapid upward trend, indicating that the energy storage is absorbing excess photovoltaic power. When the value falls below the lower threshold (the lower dashed line in the figure), it indicates that local energy storage alone is no longer sufficient to maintain the balance.

[0057] Subgraph (c) mode switching logic: The microgrid automatically switches modes based on the states of subgraphs a and b: 06:00-16:00 (Mode I): Fluctuating within the threshold range, the microgrid cluster is in the light green zone (island autonomous mode), maintaining voltage stability solely through energy storage charging and discharging. 18:00-20:00 (Mode II): As photovoltaic output gradually disappears and evening peak load arrives, the source-load deficit widens. If the threshold is exceeded, the microgrid group enters the blue zone (interconnection support mode). At this time, the flexible interconnection path is activated, supporting the load center through nodes with generating surplus. 02:00-04:00 (Mode III): Simulating extreme weather, the microgrid group enters the red zone (multi-level peak shaving and valley filling mode), forcibly shedding flexible loads to protect core facilities.

[0058] Example 2 Please refer to the attached document. Figure 2 This invention provides a microgrid group multi-source collaborative scheduling system based on flexible interconnection, comprising: Resource allocation unit, used to divide mountainous areas into wind energy capture areas, solar energy capture areas, hydro energy capture areas and load buffer centers according to the terrain distribution of mountainous areas; The model building unit is used to construct a resource-geographic coupling graph theory model based on the regions divided by the resource partitioning unit. Based on the graph theory model, the optimal deployment location of the flexible interconnection device is determined using a bi-objective optimization function, and the flexible interconnection device connects each resource partitioning unit. Edge collaborative controllers are configured in each microgrid node to collect wind, solar, hydro and energy storage status data of each microgrid node, calculate source-load mismatch index and trend discrimination factor, and classify the microgrid group operation status into different operation modes. The scheduling unit is used to execute the corresponding energy scheduling strategy according to the operating mode.

[0059] It should be noted that the microgrid cluster consists of multiple microgrids, each corresponding to a resource allocation area, which is divided into wind energy capture area, solar energy capture area, hydro energy capture area or load buffer center according to the mountainous terrain distribution; each resource allocation area serves as a node in the graph theory model, and the microgrid cluster is physically interconnected through flexible interconnection devices.

[0060] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-source collaborative scheduling method for microgrid groups based on flexible interconnection, characterized in that, Includes the following steps: Step S1: Establish a resource-geographic coupling graph theory model. Based on the graph theory model, use a dual-objective optimization function that minimizes construction costs and maximizes load accessibility to determine the optimal deployment location of the flexible interconnection device, forming the physical interconnection topology of the microgrid cluster. Step S2: Collect wind, solar, hydro and energy storage status data of each microgrid node in real time. According to the load reconfiguration rules, divide the load of each microgrid node in the physical interconnection topology into rigid load and elastic load. Calculate the source-load mismatch index and trend discrimination factor of the microgrid group based on the rigid load and the elastic load. Step S3: Set the trend discrimination factor threshold and the source-load mismatch index threshold. Based on the trend discrimination factor threshold and the source-load mismatch index threshold, divide the microgrid group's operating status into different operating modes. Step S4: Execute the corresponding energy scheduling strategy according to the operating mode.

2. The microgrid group multi-source collaborative scheduling method based on flexible interconnection according to claim 1, characterized in that, The construction of the graph theory model includes: Define nodes, each node including an attribute vector, expressed as: in, It is a set of nodes, where each node represents a microgrid. This is a resource attribute vector for a node, used to describe the type of energy that the node possesses; To indicate whether a node possesses wind power generation resource attributes, a value of 1 is assigned when the node is in a wind energy capture zone, and a value of 0 is assigned otherwise. To indicate whether a node possesses photovoltaic power generation resource attributes, a value of 1 is assigned when the node is a light energy capture zone, and a value of 0 is assigned otherwise. To determine whether a node possesses hydropower resources, a value of 1 is assigned when the node is in a hydropower capture zone, and 0 otherwise. This indicates whether a node is a pure load or has no local generation capacity. It is set to 1 when the node only acts as a load buffer center, and 0 otherwise. Edges are defined based on the geographical distance between nodes, and edge weights are determined based on the equivalent electrical distance and construction costs.

3. The microgrid group multi-source collaborative scheduling method based on flexible interconnection according to claim 1, characterized in that, The expression for the bi-objective optimization function is: in, It is a bi-objective optimization function. For decision variables; Cost per unit of installation; The equivalent electrical distance is subject to terrain constraints; The load importance weight of the node; This is an indicator of node reachability; These are the weighting coefficients; The total number of nodes is the number of elements in the node set V of the microgrid.

4. The microgrid group multi-source collaborative scheduling method based on flexible interconnection according to claim 1, characterized in that, According to the load reconfiguration rules, microgrid loads are divided into rigid loads and resilient loads, including: Microgrid loads located in wind energy capture zones, solar energy capture zones, or hydro energy capture zones are defined as rigid loads. Loads that have demand response capabilities and are distributed in resource-idle areas or serve as supplementary loads are defined as elastic loads.

5. The microgrid group multi-source collaborative scheduling method based on flexible interconnection according to claim 4, characterized in that, The source-load mismatch index and trend discrimination factor of the microgrid group are calculated based on the rigid load and the elastic load, and the calculation formula is as follows: in, The source-load mismatch index. For the first Real-time power of energy sources Confidence coefficient; Let t be the total active power demand of the rigid loads in the microgrid group at time t; Let t be the total active power demand of the resilient loads in the microgrid group at time t; Trend discriminant factor; For the real-time state of charge of the energy storage system, This serves as the reference state of charge threshold for the energy storage system. This is a correction factor.

6. The microgrid group multi-source collaborative scheduling method based on flexible interconnection according to claim 1, characterized in that, Step S3 includes: The operating modes include island autonomy mode, interconnected support mode, and multi-level peak shaving and valley filling mode; when When the trend discriminant factor is close to zero, the operating mode is the island autonomous mode, where... The source-load mismatch index. The threshold for the source-load mismatch index; when And there exist adjacent nodes that satisfy At that time, the operating mode is the interconnection support mode, in which, Let be the source-load mismatch exponent of node i at time t. Let be the source-load mismatch index of adjacent node j at time t; when Furthermore, when the absolute value of the trend discrimination factor continues to increase, the operating mode is a multi-level peak shaving and valley filling mode.

7. The microgrid group multi-source collaborative scheduling method based on flexible interconnection according to claim 6, characterized in that, Step S4 includes: When the operating mode is the island autonomous mode, only the local energy storage unit is used for power throughput to smooth out the fluctuation of the source-load mismatch index; When the operating mode is Interconnect Support Mode, the Flexible Interconnect Device is activated, and the transmission power is the minimum value among the source end surplus, the FID capacity limit, and the receiver end deficit. When the operating mode is multi-level peak shaving and valley filling mode, the hierarchical strategy is executed.

8. The microgrid group multi-source collaborative scheduling method based on flexible interconnection according to claim 7, characterized in that, When the operating mode is a multi-level peak shaving and valley filling mode, a hierarchical strategy is executed, including: The multi-level peak shaving and valley filling mode includes a power deficit mode and a power surplus mode; When the overall load change rate is greater than the overall power generation change rate, it is a power deficit mode. In this case, flexible loads are cut off first, followed by rigid loads in order of weight. When the overall power generation change rate is greater than the overall load change rate, it is a power surplus mode, in which case the dispatching flexible load will increase power consumption or limit power generation output.

9. A microgrid group multi-source collaborative scheduling system based on flexible interconnection, characterized in that, include: Resource allocation unit, used to divide mountainous areas into wind energy capture areas, solar energy capture areas, hydro energy capture areas and load buffer centers according to the terrain distribution of mountainous areas; Resource allocation unit, used to divide mountainous areas into wind energy capture areas, solar energy capture areas, hydro energy capture areas and load buffer centers according to the terrain distribution of mountainous areas; The model building unit is used to construct a resource-geographic coupling graph theory model based on the regions divided by the resource partitioning unit. Based on the graph theory model, the optimal deployment location of the flexible interconnection device is determined using a bi-objective optimization function, and the flexible interconnection device connects each resource partitioning unit. Edge collaborative controllers are configured in each microgrid node to collect wind, solar, hydro and energy storage status data of each microgrid node, calculate source-load mismatch index and trend discrimination factor, and classify the microgrid group operation status into different operation modes. The scheduling unit is used to execute the corresponding energy scheduling strategy according to the operating mode.

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