Micro-grid hierarchical coordination control method, system and equipment oriented to source-load interaction

Abstract

The invention provides a source-load interaction-oriented microgrid hierarchical coordination control method, system and device, and relates to the technical field of power grids, and the method comprises the steps: carrying out the dynamic energy potential field analysis according to a source-load interaction scene, carrying out the dynamic reconstruction and interface interconnection of a microgrid, and determining a networking structure; for a potential field between grids of a networking structure, introducing a small-magnitude energy potential as a single coordination quantity, taking a potential field gradient direction as an energy direction, and executing multiple times of iterative scheduling analysis on defined pseudo grid nodes; aiming at an internal potential field of the grid; and determining a conversion relationship between an energy potential field and source load scheduling, converting a first-layer coordination strategy and a second-layer coordination strategy, and performing scheduling management and control in response to the micro-grid system as a hierarchical coordination scheme. According to the invention, the technical problem of poor electric energy distribution efficiency in the prior art can be solved, the technical target of constructing a hierarchical coordination control system for scheduling optimization is realized, and the technical effect of improving the electric energy distribution efficiency of the micro-grid is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid, and particularly relates to a micro-grid hierarchical coordination control method, system and equipment for source-load interaction. BACKGROUND

[0002] With the wide access of distributed power sources and diversified loads in the micro-grid, the dynamic interaction relationship between the source and the load is increasingly complex.

[0003] At present, most of the traditional micro-grid dispatching control technologies adopt centralized or hierarchical fixed strategies, which leads to technical bottlenecks. In the existing technology, under the dynamic interaction of the source and the load, the energy supply and demand state fluctuates frequently, and the centralized dispatching mode often lags in response, and it is difficult to capture the fine-grained energy change signal in time, which leads to problems such as untimely dispatching and unreasonable resource allocation when the load surges or the renewable power source fluctuates. Therefore, under the new application requirements of multi-source heterogeneous energy supply, dynamic load change, and frequent reconstruction of system structure, a new type of micro-grid dispatching control mechanism is urgently needed to improve the stability, flexibility and intelligent level of the system.

[0004] In summary, in the prior art, due to the rigidity of the dispatching architecture, there is a lack of modeling and execution ability of the real-time coordination mechanism of the source and the load, which leads to slow response and unbalanced coordination when facing the rapidly changing supply and demand state, further affecting the dynamic stability of the micro-grid, the efficiency of the power distribution, and the access ability of the high proportion of renewable energy. SUMMARY

[0005] The purpose of the present application is to provide a micro-grid hierarchical coordination control method, system and equipment for source-load interaction, to solve the technical problems in the prior art that due to the rigidity of the dispatching architecture, there is a lack of modeling and execution ability of the real-time coordination mechanism of the source and the load, which leads to slow response and unbalanced coordination when facing the rapidly changing supply and demand state, further affecting the dynamic stability of the micro-grid, the efficiency of the power distribution, and the access ability of the high proportion of renewable energy.

[0006] In view of the above problems, the present application provides a micro-grid hierarchical coordination control method, system and equipment for source-load interaction.

[0007] In a first aspect, the application provides a microgrid hierarchical coordination control method for source-load interaction, which is implemented by a microgrid hierarchical coordination control system for source-load interaction, and includes: performing dynamic energy potential field analysis according to a source-load interaction scenario, dynamically reconstructing and interconnecting a microgrid, and determining a networking structure; introducing a small-scale energy potential as a single coordination quantity for the potential field between grids of the networking structure, taking the gradient direction of the potential field as an energy direction, defining a pseudo-grid node, performing multiple iteration scheduling analyses, and determining a one-level coordination strategy; determining a two-level coordination strategy for the potential field inside the grid of the networking structure; determining a conversion relationship between the energy potential field and source-load scheduling, converting the one-level coordination strategy and the two-level coordination strategy, taking them as a hierarchical coordination scheme, and responding to the microgrid system for scheduling control.

[0008] Preferably, the microgrid hierarchical coordination control method for source-load interaction further includes: deploying a lightweight scheduling module in the microgrid system, wherein the lightweight scheduling module includes a potential field conversion layer, a potential field decision layer, and a potential field inverse conversion layer; triggering the potential field conversion layer to construct a dynamic energy potential field and reconstruct the networking according to the source-load interaction scenario, triggering the potential field decision layer to perform decision of the one-level coordination strategy and the two-level coordination strategy, and triggering the potential field inverse conversion layer to perform strategy conversion processing.

[0009] Preferably, the microgrid hierarchical coordination control method for source-load interaction further includes: determining source-end power distribution states and load-end power distribution states according to the source-load interaction scenario; constructing a first energy potential field for the source-end power distribution states; constructing a second energy potential field for the load-end power distribution states; mapping and subtracting the second energy potential field and the first energy potential field to determine the dynamic energy potential field.

[0010] Preferably, the microgrid hierarchical coordination control method for source-load interaction further includes: dividing the dynamic energy potential field according to energy potential magnitudes to determine energy potential field grids; introducing a potential barrier isolation based on preset energy potential magnitudes to calibrate survival grids in the energy potential field grids; performing interface temporary networking in grids as units according to the energy potential field grids and the survival grids to determine the networking structure.

[0011] Preferably, the microgrid hierarchical coordination control method for source-load interaction further includes: traversing the networking structure, performing mean value calculation on grid energy potentials, defining a pseudo-grid node, wherein the pseudo-grid node corresponds to a grid in the networking structure in a one-to-one manner; performing coordination scheduling in the gradient direction of the potential field according to the small-scale energy potential to determine a one-time scheduling result, wherein the one-time scheduling result includes updated pseudo-grid nodes; performing multiple iteration scheduling analyses based on the one-time scheduling result to determine the one-level coordination strategy.

[0012] Preferably, the microgrid hierarchical coordination control method for source-load interaction further includes: setting a potential baseline level, identifying the primary scheduling result, and virtualizing pseudo-grid nodes that meet the potential baseline level as updated pseudo-grid nodes; performing gradient coordination analysis based on a small potential level for the updated pseudo-grid nodes to determine the secondary scheduling result; and performing iterative analysis based on the secondary scheduling result until the potential baseline level is met.

[0013] Preferably, the microgrid hierarchical coordination control method for source-load interaction further includes: identifying the hierarchical coordination scheme, decoupling and determining the individual scheduling scheme, wherein the decoupling method is source-end decoupling or load-end decoupling; and delegating the individual scheduling scheme to each source-end device for device self-driven scheduling management, wherein the individual scheduling scheme corresponds one-to-one with the source-end device.

[0014] Preferably, the microgrid hierarchical coordination control method for source-load interaction further includes: coordinating, scheduling, monitoring, and tracking the microgrid to determine its coordination status; evaluating the microgrid's coordination status based on the stability and balance of the entire microgrid domain, tracing the secondary coordination point using a preset slack as a constraint; and performing feedback scheduling and control based on the imbalance causes for the secondary coordination point.

[0015] Secondly, this application also provides a microgrid hierarchical coordination control system for source-load interaction, used to execute the microgrid hierarchical coordination control method for source-load interaction as described in the first aspect, including: a network structure determination module, used to perform dynamic potential field analysis based on the source-load interaction scenario, dynamically reconstruct and interconnect the microgrid interfaces to determine the network structure; a first-level coordination strategy determination module, used to introduce a small amount of potential as a single coordination quantity for the inter-mesh potential field of the network structure, take the potential field gradient direction as the energy direction, and determine the first-level coordination strategy by performing multiple iterative scheduling analysis on the defined pseudo-mesh nodes; a second-level coordination strategy determination module, used to determine the second-level coordination strategy for the internal potential field of the network structure; and a scheduling and control module, used to determine the conversion relationship between the potential field and source-load scheduling, convert the first-level coordination strategy and the second-level coordination strategy as a hierarchical coordination scheme, and respond to the microgrid system for scheduling and control.

[0016] Thirdly, this application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the microgrid hierarchical coordinated control method for source-load interaction as described in any of the first aspects above.

[0017] The technical solution provided in this application has at least the following technical effects or advantages: by realizing the technical goals of constructing a hierarchical coordinated control system driven by a dynamic energy potential field, adaptive linkage between source and load resources, and multi-dimensional scheduling optimization, it achieves the technical effects of improving the response speed, operational flexibility, and resource utilization of microgrids under multi-source heterogeneous conditions, and enhancing system stability and economy.

[0018] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the hierarchical coordinated control method for microgrids based on source-load interaction proposed in this application.

[0021] Figure 2 This is a schematic diagram of the structure of the microgrid hierarchical coordination control system for source-load interaction in this application.

[0022] Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application.

[0023] Explanation of reference numerals in the attached diagram: Network structure determination module 11, Layer 1 coordination strategy determination module 12, Layer 2 coordination strategy determination module 13, Scheduling and control module 14, Bus 300, Receiver 301, Processor 302, Transmitter 303, Memory 304, Bus interface 305. Detailed Implementation

[0024] This application provides a hierarchical coordinated control method, system, and equipment for microgrids oriented towards source-load interaction. It addresses the technical problems in existing technologies, such as the rigid scheduling architecture and lack of modeling and execution capabilities for real-time source-load coordination mechanisms. These problems lead to slow response and coordination imbalances when facing rapidly changing supply and demand conditions, further impacting the dynamic stability, power distribution efficiency, and the ability to integrate high proportions of renewable energy into the microgrid. The application achieves the technical goals of constructing a hierarchical coordinated control system driven by a dynamic energy potential field, adaptive linkage between source and load resources, and multi-dimensional scheduling optimization. This results in improved response speed, operational flexibility, and resource utilization of the microgrid under multi-source heterogeneous conditions, enhancing system stability and economic efficiency.

[0025] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0026] Example 1, please refer to the appendix. Figure 1 This application provides a hierarchical coordinated control method for microgrids oriented towards source-load interaction, which is applied to a hierarchical coordinated control system for microgrids oriented towards source-load interaction. Specifically, it includes the following steps: S1: Based on the source-load interaction scenario, perform dynamic energy potential field analysis, dynamically reconfigure the microgrid and interconnect its interfaces to determine the network structure.

[0027] Specifically, this involves acquiring source-load interaction scenarios, i.e., scenarios of energy interaction between distributed energy sources (sources) and load demand (loads) through the volatility of energy supply, the temporal nature of load changes, and the influence of external conditions such as weather and prices on the supply-demand relationship. Based on these source-load interaction scenarios, dynamic energy potential field analysis is performed. The dynamic energy potential field is a quantitative description of the energy state of each region in the microgrid; high energy potential indicates sufficient energy or strong output capacity, while low energy potential indicates a large load or high energy demand. The dynamic nature of this potential field is reflected in its real-time changes over time, which can be estimated by collecting indicators such as voltage, current, and power from sensing nodes.

[0028] Subsequently, based on the distribution of the dynamic energy potential field, the microgrid is dynamically reconfigured and interconnected. Dynamic reconfiguration refers to adjusting electrical connections through control strategies without changing the infrastructure, temporarily altering energy flow paths to allow energy to flow more efficiently from surplus areas to strained areas. Interconnection refers to establishing control logic connections between different grid cells, enabling them to work collaboratively, such as using flexible switches to control whether a building is connected to photovoltaic power supply.

[0029] Ultimately, the network structure can be determined through dynamic analysis and connection adjustments. The network structure refers to the real-time connection status diagram of each power source, load, and control node in the microgrid, which is used for subsequent coordination and scheduling.

[0030] S2: For the inter-mesh potential field of the network structure, a small amount of energy potential is introduced as a single coordination quantity. With the potential field gradient direction as the energy direction, a coordination strategy is determined by performing multiple iterative scheduling analyses on the defined pseudo-mesh nodes.

[0031] Specifically, based on the microgrid network structure, the potential field distribution between different grid units is further analyzed. The potential field between grids reflects the energy differences between various regions, demonstrating the natural flow of energy from high-potential regions to low-potential regions. To achieve refined scheduling, a small-scale potential is introduced as a single coordination quantity in the scheduling strategy. That is, only a tiny energy adjustment unit is allowed in each coordination process to improve control accuracy and response sensitivity. For example, if the potential of a certain region is 15 units and that of a neighboring region is 12 units, then the single coordination quantity can be set to 1 unit to guide energy flow without drastically disturbing the system.

[0032] Next, the gradient direction of the potential field is used as the direction of energy flow to conduct scheduling analysis. The gradient direction of the potential field refers to the direction in which the energy potential value decreases the fastest, which is equivalent to the "spontaneous" transmission path of energy. For example, if the energy potential of a certain region is 20 units and the region to its south is 16 units, then the southward direction is the main gradient direction. Guiding energy flow along this direction helps to reduce scheduling losses and response delays.

[0033] To simplify the scheduling modeling process while maintaining accurate characterization of local mesh features, pseudo-mesh nodes are introduced. A pseudo-mesh node is a mathematical substitute for a real mesh node, defined by calculating the average potential of the mesh and its neighboring meshes. This method effectively eliminates the abrupt changes in individual nodes, thus smoothing the overall potential distribution of the system. For example, if the potentials of a mesh and its surrounding meshes are 14, 13, 15, and 16 units respectively, then its pseudo-mesh node value can be set to an average of 14.5 units.

[0034] Based on the pseudo-grid nodes and the potential field gradient direction, a preliminary coordination scheduling, or primary scheduling, is performed. Energy is then distributed or absorbed between grid nodes based on small-scale potential changes, resulting in a primary scheduling outcome. This outcome is reflected in the potential update of the pseudo-grid nodes. For example, if a pseudo-node's initial value is 15 units, its updated value becomes 16 units after receiving 1 unit of energy from a neighboring node. After obtaining the primary scheduling outcome, multiple iterative scheduling analyses are conducted to form a stable coordination strategy. Through continuous iteration, errors are corrected and coordination is strengthened, gradually bringing the potential towards equilibrium, thus determining a first-level coordination strategy. For example, if a node's potential is still significantly high in the first iteration, it can be further reduced in the second iteration. The iteration process typically sets stopping conditions, such as a potential difference of less than 1 unit or an energy change of less than 0.1 unit, as termination criteria.

[0035] S3: Determine the two-layer coordination strategy for the potential field inside the mesh of the network structure.

[0036] Specifically, after the microgrid forms a network structure, the energy potential distribution state within each specific grid cell—that is, the internal potential field reflecting the microscopic distribution, flow direction, and local load pressure of the grid cell—is analyzed. This involves energy potential analysis, small-scale energy potential introduction, potential field gradient direction determination, pseudo-node establishment, and multiple iterative scheduling analysis. The two-layer strategy operates on a more localized scale, thus achieving microscopic balance within each grid cell. For example, within a grid, if the terminal equipment in the upper left corner consumes more energy while the energy storage device in the lower right corner is underutilized, two-layer coordination can allocate energy potential, prioritizing the release of energy storage resources in the lower right corner, thereby bringing the overall grid energy potential towards local equilibrium.

[0037] S4: Determine the conversion relationship between the energy potential field and the source-load scheduling, convert the first-level coordination strategy and the second-level coordination strategy, and use them as a hierarchical coordination scheme to respond to the microgrid system for scheduling and control.

[0038] Specifically, the transformation relationship between the energy potential field and source-load scheduling is determined, and a mathematical or logical model is established that can map abstract energy potential field information into scheduling commands between actual source ends (such as generators, energy storage systems, etc.) and load ends (such as loads, electrical equipment, etc.). The energy potential field represents the energy state of each grid area in the microgrid, such as the degree of energy surplus or shortage, while source-load scheduling involves the control of specific energy flow, such as deciding which generating unit increases its power by how much or which energy storage device discharges. The establishment of the transformation relationship is modeled using methods such as combining historical operating data, power flow simulation, and boundary condition analysis to ensure the accurate transformation of the strategy from the potential field world to the physical world. After the transformation relationship is established, the first-level coordination strategy and the second-level coordination strategy are transformed from abstract energy potential control rules into specific executable scheduling commands, such as instructing a certain energy storage unit to discharge 3 kilowatt-hours within 10 minutes, while simultaneously shutting down a low-priority load, thereby actually regulating the flow of electrical energy.

[0039] The scheduling and management of the entire microgrid system is divided into different control levels, each with different coordination responsibilities. These include a first-level coordination strategy and a second-level coordination strategy. The first-level strategy focuses on the macroscopic balance of energy flow between grids, while the second-level strategy addresses local optimization within the grid. This hierarchical approach effectively addresses the complexity and local conflicts inherent in source-load interactions, thereby improving the sensitivity and efficiency of scheduling responses.

[0040] When the hierarchical coordination scheme is applied to the actual operation of a microgrid, for example, when the load of a node suddenly increases by 10 kilowatts or the power generation of a source decreases by 15 kilowatts, the hierarchical coordination scheme can automatically trigger a response mechanism to quickly rebalance the overall energy distribution through the first-level coordination, and at the same time, initiate the second-level coordination when necessary to optimize the operating status of local units.

[0041] Furthermore, this application also includes: deploying a lightweight scheduling module in the microgrid system, wherein the lightweight scheduling module includes a potential field conversion layer, a potential field decision layer, and a potential field inverse conversion layer; according to the source-load interaction scenario, the potential field conversion layer is triggered to construct a dynamic energy potential field and reconfigure the network; the potential field decision layer is triggered to execute the decision of the first-level coordination strategy and the second-level coordination strategy; and the potential field inverse conversion layer is triggered to perform strategy conversion processing.

[0042] Specifically, a lightweight scheduling module is deployed in the microgrid system, introducing scheduling units with low computational load and fast response speed to improve the real-time performance and efficiency of the scheduling system in resource-constrained or edge device environments. The lightweight scheduling module is designed to consist of three functional layers: a potential field conversion layer, a potential field decision layer, and a potential field inverse conversion layer. The potential field conversion layer converts traditional power information (such as voltage, current, and load) into an abstract energy potential field form for spatial distribution modeling; the potential field decision layer uses the energy potential representation to perform scheduling logic analysis and output coordination strategies; and the potential field inverse conversion layer is responsible for converting the formulated strategies back into specific control commands to act on the real physical network.

[0043] Subsequently, upon identifying source-load interaction scenarios, such as a sudden increase in load in a certain area or fluctuations in photovoltaic output, the potential field conversion layer is triggered. A dynamic energy potential field is constructed, which involves generating an energy distribution map based on the current state of each grid node. The network is then reconstructed, meaning the connections between nodes are reorganized according to the newly generated potential field to adapt to the new supply and demand conditions. Next, the potential field decision layer is triggered, executing a first-level coordination strategy—large-scale energy scheduling between grids—followed by a second-level coordination strategy—fine-grained adjustments between grids within the network—to improve overall supply and demand matching accuracy. Finally, the potential field inverse conversion layer is activated, transforming the content of the first and second-level coordination strategies into control commands that the microgrid control system can directly recognize and execute, such as inverter settings, switch adjustments, and charge / discharge regulation, completing the closed-loop conversion from virtual potential field to actual control.

[0044] Furthermore, this application also includes: determining the source-end power distribution state and the load-end power distribution state according to the source-load interaction scenario; constructing a first potential field for the source-end power distribution state; constructing a second potential field for the load-end power distribution state; and calculating the difference between the mapping of the second potential field and the first potential field to determine the dynamic potential field.

[0045] Specifically, the source side considers economic efficiency and the overall situation, while the load side focuses on the load's own energy supply needs. Simultaneously considering both sides presents significant analytical challenges. Furthermore, the diverse and complex energy structures of the power grid further complicate the decision-making process. Therefore, an energy potential field is introduced, transforming the analysis into one based on the energy potential field. Simultaneously, dispatch direction decisions are eliminated, as they are directly determined by the potential field gradient.

[0046] In source-load interaction scenarios, it is crucial to clearly define the current distribution status of both the energy supplier (source) and the energy consumer (load). Distribution status refers to specific information about how electricity is allocated, transmitted, and used within the distribution network, including data such as voltage, current, active power, reactive power, and load factor. This information allows us to obtain the energy flow under current operating conditions, providing foundational data for subsequent potential field modeling. For example, in a small microgrid, if the output of a source node is 500 kW, and the total load of the corresponding load node is 450 kW, it can be determined that this node has a surplus power supply capacity of 50 kW.

[0047] Next, based on the power distribution status at the source, a first potential field is constructed. The potential field is a mathematical model of the potential energy of electrical energy distribution, used to reflect the relative strengths and weaknesses of each node in the energy transmission process. The first potential field describes the power supply potential radiating from the source node to the entire power grid.

[0048] Next, a second potential field is constructed based on the power distribution status at the load end. In contrast to the first potential field, the second potential field characterizes the urgency of each load node's demand for electrical energy. If a region has high power density and frequently experiences undervoltage issues, its potential value will be lower, indicating a greater demand for energy. For example, during peak hours, if the load in a region surges to 900 kilowatts, while its local power supply capacity is only 600 kilowatts, its potential value will be significantly lower than normal.

[0049] Subsequently, by mapping and subtracting the second and first potential fields, a dynamic potential field is obtained, which reflects the real-time difference between supply and demand, revealing the direction and intensity of energy flow from high-supply areas to high-demand areas. The construction of the dynamic potential field not only captures the static distribution but also considers changes in the time dimension, thus enabling subsequent coordinated scheduling and hierarchical control.

[0050] Furthermore, this application also includes: dividing the dynamic potential field according to the potential level to determine the potential field grid; introducing a potential barrier isolation based on a preset potential level to mark a survival grid within the potential field grid; and performing temporary interface networking with grids as units based on the potential field grid and the survival grid to determine the networking structure.

[0051] Specifically, after the dynamic potential field is established, it is spatially divided according to the potential magnitude, thus forming a series of potential field grids. The potential magnitude refers to the quantitative value of the difference in power supply and demand exhibited by different regions in the potential field. For example, if the potential of one region is 10 units while that of another region is 5 units, the former has a higher energy surplus. Dividing the entire dynamic potential field into several grids, that is, dividing the power grid space into multiple small blocks with similar potential levels, each grid represents a local supply and demand characteristic region, thereby more clearly depicting the local energy flow trend and facilitating fine-grained regulation.

[0052] Subsequently, to further enhance the effectiveness of regulation, it is necessary to introduce potential barrier isolation into the already divided energy potential field grid and define the survivability grid. Potential barrier isolation is an energy potential threshold defined by those skilled in the art based on actual conditions, used to delineate regions where direct energy transfer is not possible, thus avoiding violent, disordered flows between high-energy and low-energy regions. The survivability grid refers to an effective grid that still possesses energy exchange capabilities after considering the influence of the potential barrier; it is the actual participant in subsequent network configuration and scheduling. For example, the survivability grid can prevent significant imbalances caused by extreme events.

[0053] Next, based on the relationship between the potential field grid and the survival grid, an interface temporary network is implemented, with the grid as the unit. The interface temporary network temporarily combines survival grids with energy interaction potential to form a scheduling network structure to support the flexible scheduling of electrical energy between local areas. It is then dynamically generated based on the real-time potential state, thereby ensuring that the network always maintains high responsiveness and determining the network structure.

[0054] Therefore, from the spatial partitioning of the dynamic energy potential field to the identification of surviving grids under potential barrier isolation, and then to the construction of temporary interface networks, the entire process constitutes a hierarchical modeling process from coarse to fine and from static to dynamic. This approach not only ensures the scheduling security between different areas of the power grid but also improves the overall response flexibility. For example, if only three surviving grids are initially identified, the system can build a small-scale network. As the energy potential changes, the number of surviving grids increases to seven, and the network structure can be dynamically expanded to support energy coordination over a wider range, thereby promoting the evolution of microgrid regulation from local optimization to global coordination.

[0055] Furthermore, this application also includes: traversing the network structure, calculating the mean value of the grid potential, and defining pseudo-grid nodes, wherein the pseudo-grid nodes correspond one-to-one with the grids in the network structure; coordinating and scheduling according to the small-scale potential in the direction of the potential field gradient to determine a scheduling result, wherein the scheduling result includes the updated pseudo-grid nodes; and performing multiple iterative scheduling analyses based on the scheduling result to determine the coordination strategy of the first layer.

[0056] Specifically, during the sequential access to the network structure, the potential value of each grid is comprehensively scanned and analyzed. The network structure refers to the microgrid regional network after being divided by potential fields and interconnected by interfaces. Grid potential is a numerical indicator reflecting the abundance of power resources in a certain area. Calculating the mean of the grid potential, and weighting the potential of the grid with that of its neighboring grids, can effectively smooth abnormal fluctuations and reflect the actual operating trend. Pseudo-grid nodes are defined, using the mean to represent the actual grid state, thereby simplifying the subsequent scheduling process. There is a one-to-one correspondence between pseudo-grid nodes and actual grids, ensuring the integrity and traceability of the model.

[0057] Subsequently, preliminary coordinated scheduling is conducted based on the small-scale energy potential and the potential field gradient direction. The small-scale energy potential refers to the minimum energy adjustment unit allowed in a single scheduling process, for example, it can be set to 0.5 units to prevent excessive adjustment from causing system instability. The potential field gradient direction is the direction in which the energy potential decreases the fastest, representing the most natural path for energy flow. Scheduling along the potential field gradient direction can minimize energy loss and response delay. Coordinated scheduling rationally distributes electrical energy from high-potential grids to low-potential grids, thereby optimizing the overall load balance. The scheduling result is reflected in the energy potential value update of the pseudo-grid nodes; for example, an initial value of 18 units decreases to 17 units after scheduling, indicating that it released 1 unit of electrical energy to the surrounding grids.

[0058] After obtaining a scheduling result, multiple iterative scheduling analyses are performed based on it. After the initial scheduling, there may still be energy potential imbalances or deviations from the target state. Multiple iterations refer to repeatedly executing the coordination process based on the previous round of scheduling, gradually approaching a network-wide optimal or stable coordination state. In each iteration, the energy potential of the pseudo-grid nodes is readjusted according to the new adjacency relationships and potential fields until termination conditions are met, such as the maximum potential difference across the entire network being less than 1 unit or the overall energy mobilization being less than 0.1 unit, ensuring the stability and reliability of the scheduling results.

[0059] Furthermore, this application also includes: setting an energy potential baseline level, identifying the first scheduling result, and blurring the pseudo-mesh nodes that conform to the energy potential baseline level as the updated pseudo-mesh nodes; performing gradient coordination analysis based on a small energy potential for the updated pseudo-mesh nodes to determine the second scheduling result; and performing iterative analysis based on the second scheduling result until the energy potential baseline level is met.

[0060] Specifically, the energy potential baseline level is a standard for measuring source-load consistency. It represents the electrical energy level that each pseudo-grid node should achieve under ideal conditions, and can be customized by those skilled in the art based on actual circumstances. The energy potential baseline level can be set; for example, it can be set to 12 units of electrical energy.

[0061] Then, by identifying the scheduling results, pseudo-grid nodes that have reached or are close to the baseline are selected and made virtual. Virtualization means that pseudo-grid nodes are considered to be nodes that have completed scheduling and no longer participate actively in subsequent scheduling, but participate in the overall potential field balance as a reference benchmark.

[0062] Next, for the updated pseudo-grid nodes—the unbalanced node group remaining after removing the virtual nodes—gradient coordination analysis is performed based on small-scale energy potentials. Small-scale energy potentials refer to minor energy differences relative to the baseline, such as adjusting only 1 or 2 units of electrical energy each time, thus maintaining scheduling stability and system asymptoticity. Gradient coordination analysis, based on the energy potential gradient direction between the current pseudo-grid nodes, finds the most reasonable path direction for power transmission and performs secondary scheduling accordingly, bringing the node states closer to the baseline.

[0063] Then, based on the results of the secondary scheduling, iterative analysis continues, which involves repeated identification, virtualization, scheduling, and updating. After each iteration, the state of the pseudo-grid nodes is updated, continuously determining whether the current node has met the potential baseline level. If all nodes have reached or are very close to the baseline, for example, the power state of most nodes is between 11 and 13 units, and the overall potential difference is less than 1 unit, the potential baseline condition is considered met, and the scheduling process terminates.

[0064] Furthermore, this application also includes: identifying the hierarchical coordination scheme, decoupling and determining the individual scheduling scheme, wherein the decoupling method is source-end decoupling or load-end decoupling; delegating the individual scheduling scheme to each source-end device for device self-driven scheduling management, wherein the individual scheduling scheme corresponds one-to-one with the source-end device.

[0065] Specifically, within the multi-level coordinated control system, hierarchical coordination schemes are identified, individual scheduling schemes are decoupled and determined, and the complete scheduling plan formed by the established first-level and second-level coordination strategies is identified. This plan is then further refined and broken down into independently executable sub-tasks. The decoupling and refinement process aims to ensure that each scheduling sub-task corresponds only to a specific source or load-side resource, enabling distributed execution. Decoupling methods include source-side decoupling and load-side decoupling. Source-side decoupling assigns the scheduling task to energy suppliers such as power generation equipment or energy storage equipment, while load-side decoupling assigns it to various electrical loads, i.e., the demand side. For example, in source-side decoupling, if the overall strategy requires releasing 30 kWh of energy, it might be decomposed into having three battery energy storage units each discharge 10 kWh; while in load-side decoupling, it might involve adjusting the operating power of several cold storage facilities, air conditioners, or charging piles to reduce the load.

[0066] The decoupled scheduling tasks are pushed to each specific energy device, which then autonomously executes the operations locally according to the task requirements. A single-device scheduling scheme refers to a scheduling task that a single device needs to perform, such as a storage device charging 2 kWh or discharging 1 kWh within 5 minutes. The single-device scheduling scheme corresponds one-to-one with the source device, meaning each task is dedicated to a specific device, avoiding task overlap or scheduling conflicts. Device self-driving refers to devices possessing a certain degree of autonomy, capable of adjusting their operating status locally based on received task instructions, without continuous intervention from the central system. This improves system response speed and reduces the pressure on the central scheduling system.

[0067] Furthermore, this application also includes: coordinating, scheduling, monitoring, and tracking the microgrid to determine its coordination status; assessing the microgrid's coordination status based on the stability and balance of the entire microgrid domain, tracing the secondary coordination point using a preset slack as a constraint; and performing feedback scheduling and control based on the cause of the imbalance at the secondary coordination point.

[0068] Specifically, this involves coordinating, scheduling, monitoring, and tracking the microgrid, continuously collecting operational data from its various energy units, load units, and interface nodes to determine the microgrid's coordination status, and dynamically monitoring and analyzing the effectiveness of scheduling commands. Coordinating scheduling refers to the rational control of power flow between the source and load ends based on first-level and second-level coordination strategies to avoid phenomena such as voltage instability and frequency anomalies. Monitoring and tracking includes real-time reading of key indicators such as voltage, current, frequency, and power, while comparing and analyzing the execution of scheduling commands. This allows for the determination of whether the microgrid is currently in the expected coordinated state, such as a basic balance between power generation and consumption, and fluctuations at key nodes being less than set thresholds.

[0069] Next, after understanding the current operating status of the microgrid, a quantitative assessment of the overall system's operational quality is conducted. The microgrid's overall stability and balance are evaluated to assess its coordination status. Stability refers to its self-recovery capability after disturbances, such as the time it takes for the system frequency to return to normal after a sudden load increase. Balance refers to the spatial equilibrium between load sources, such as whether some areas are overloaded or others are idle. Using a preset slack as a constraint, secondary coordination points are traced. Slack is a tolerance range, i.e., the acceptable deviation from the target, for example, setting an allowable frequency deviation of ±0.2 Hz. If any indicator exceeds this slack, the tracing mechanism is triggered to locate the key point causing the imbalance, i.e., the secondary coordination point. The secondary coordination point may be caused by sudden load changes, inverter response lag, communication anomalies, etc.

[0070] Finally, for secondary coordination points, feedback scheduling and control are implemented based on the causes of imbalance, i.e., rescheduling at the secondary level for refined intervention. Causes of imbalance may include a sudden 20 kW increase in load, insufficient discharge of an energy storage device, voltage dips, etc. Feedback scheduling and control strategies include temporarily activating backup energy storage, adjusting inverter operating parameters, and power curtailment of certain loads, with the aim of restoring the stable operation of the entire system as quickly as possible. For example, if a node experiences a sudden voltage drop of 0.4 volts and frequency deviation, a feedback instruction can be given to the nearest energy storage unit to immediately compensate with 10 kWh of energy, or a non-critical load can be instructed to suspend operation for 5 minutes, thereby returning to a balanced state. Table 1 shows a partial record of the most recent microgrid coordinated scheduling monitoring and feedback scheduling process.

[0071] Table 1: Partial Records of the Most Recent Microgrid Coordinated Dispatch Monitoring and Feedback Dispatch Process

[0072] In summary, the microgrid hierarchical coordinated control method for source-load interaction provided in this application has the following technical effects: by realizing the technical objectives of constructing a hierarchical coordinated control system driven by a dynamic energy potential field, adaptive linkage between source and load resources, and multi-dimensional scheduling optimization, it achieves the technical effects of improving the response speed, operational flexibility, and resource utilization of the microgrid under multi-source heterogeneous conditions, and enhancing system stability and economy.

[0073] Example 2: Based on the same inventive concept as the microgrid hierarchical coordination control method for source-load interaction in the foregoing examples, this application also provides a microgrid hierarchical coordination control system for source-load interaction. Please refer to the appendix. Figure 2 The system includes: a network structure determination module 11, used to perform dynamic energy potential field analysis based on the source-load interaction scenario, dynamically reconstruct and interconnect the microgrid interface, and determine the network structure; a first-level coordination strategy determination module 12, used to introduce a small amount of energy potential as a single coordination quantity for the inter-grid potential field of the network structure, take the potential field gradient direction as the energy direction, and determine the first-level coordination strategy by performing multiple iterative scheduling analysis on the defined pseudo-grid nodes; a second-level coordination strategy determination module 13, used to determine the second-level coordination strategy for the internal potential field of the grid of the network structure; and a scheduling and control module 14, used to determine the conversion relationship between the energy potential field and the source-load scheduling, convert the first-level coordination strategy and the second-level coordination strategy as a hierarchical coordination scheme, and respond to the microgrid system for scheduling and control.

[0074] Furthermore, the microgrid hierarchical coordination control system for source-load interaction is also used to: deploy a lightweight scheduling module in the microgrid system, wherein the lightweight scheduling module includes a potential field conversion layer, a potential field decision layer, and a potential field inverse conversion layer; according to the source-load interaction scenario, trigger the potential field conversion layer to construct a dynamic energy potential field and reconfigure the network; trigger the potential field decision layer to execute the decision of the first-level coordination strategy and the second-level coordination strategy; and trigger the potential field inverse conversion layer to perform strategy conversion processing.

[0075] Furthermore, the microgrid hierarchical coordination control system for source-load interaction is also used to: determine the source-end power distribution state and the load-end power distribution state according to the source-load interaction scenario; construct a first potential field for the source-end power distribution state; construct a second potential field for the load-end power distribution state; and determine the dynamic potential field by mapping the second potential field and the first potential field.

[0076] Furthermore, the microgrid hierarchical coordination control system for source-load interaction is also used to: divide the dynamic potential field according to the potential level to determine the potential field grid; introduce a potential barrier isolation based on a preset potential level to mark the survival grid within the potential field grid; and perform temporary interface networking with grids as units according to the potential field grid and the survival grid to determine the networking structure.

[0077] Furthermore, the microgrid hierarchical coordination control system for source-load interaction is also used for: traversing the network structure, calculating the mean value of the grid potential, defining pseudo-grid nodes, wherein the pseudo-grid nodes correspond one-to-one with the grids in the network structure; coordinating and scheduling according to the small-scale potential in the direction of the potential field gradient, determining a scheduling result, wherein the scheduling result includes the updated pseudo-grid nodes; and performing multiple iterative scheduling analyses based on the scheduling result to determine the first-layer coordination strategy.

[0078] Furthermore, the microgrid hierarchical coordination control system for source-load interaction is also used to: set a potential baseline level, identify the primary scheduling result, and virtualize pseudo-grid nodes that meet the potential baseline level as updated pseudo-grid nodes; perform gradient coordination analysis based on a small potential level for the updated pseudo-grid nodes to determine the secondary scheduling result; and perform iterative analysis based on the secondary scheduling result until the potential baseline level is met.

[0079] Furthermore, the microgrid hierarchical coordination control system for source-load interaction is also used to: identify the hierarchical coordination scheme, decouple and determine the individual scheduling scheme, wherein the decoupling method is source-end decoupling or load-end decoupling; and decentralize the individual scheduling scheme to each source-end device for self-driven scheduling management, wherein the individual scheduling scheme corresponds one-to-one with the source-end device.

[0080] Furthermore, the microgrid hierarchical coordination control system for source-load interaction is also used for: coordinating, scheduling, monitoring, and tracking the microgrid to determine its coordination status; evaluating the microgrid's coordination status based on the stability and balance of the entire microgrid domain, tracing the secondary coordination point using a preset slack as a constraint; and performing feedback scheduling and control based on the cause of the imbalance at the secondary coordination point.

[0081] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The microgrid hierarchical coordination control method and specific examples for source-load interaction in the foregoing embodiment 1 are also applicable to the microgrid hierarchical coordination control system for source-load interaction in this embodiment. Through the foregoing detailed description of the microgrid hierarchical coordination control method for source-load interaction, those skilled in the art can clearly understand the microgrid hierarchical coordination control system for source-load interaction in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0082] Example 3: Based on the inventive concept of the microgrid hierarchical coordinated control method for source-load interaction described in the foregoing embodiments, this application also provides an electronic device, including: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the microgrid hierarchical coordinated control method for source-load interaction described in any one of the above embodiments.

[0083] Appendix Figure 3 This is a schematic diagram of the structure of an exemplary electronic device of this application. Figure 3 In this document, the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges, and bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0084] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0085] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A hierarchical coordinated control method for microgrids oriented towards source-load interaction, characterized in that, The method includes: Based on the source-load interaction scenario, dynamic energy potential field analysis is performed to dynamically reconfigure and interconnect interfaces of the microgrid and determine the network structure. For the inter-mesh potential field of the network structure, a small-scale energy potential is introduced as a single coordination quantity, and the energy direction is taken as the gradient direction of the potential field. By performing multiple iterative scheduling analyses on the defined pseudo-mesh nodes, a coordination strategy is determined. For the potential field inside the mesh of the aforementioned network structure, a two-layer coordination strategy is determined; The conversion relationship between the energy potential field and the source-load scheduling is determined, and the first-level coordination strategy and the second-level coordination strategy are converted into a hierarchical coordination scheme to respond to the microgrid system for scheduling and control.

2. The hierarchical coordinated control method for microgrids oriented towards source-load interaction as described in claim 1, characterized in that, A lightweight scheduling module is deployed in a microgrid system, wherein the lightweight scheduling module includes a potential field transformation layer, a potential field decision layer, and a potential field inverse transformation layer; Based on the source-load interaction scenario, the potential field conversion layer is triggered to construct a dynamic energy potential field and reconstruct the network. The potential field decision layer is triggered to execute the decision of the first-level coordination strategy and the second-level coordination strategy. The potential field inverse conversion layer is triggered to perform strategy conversion processing.

3. The hierarchical coordinated control method for microgrids oriented towards source-load interaction as described in claim 1, characterized in that, Before performing dynamic potential field analysis, the construction of the dynamic potential field includes: Based on the source-load interaction scenario, determine the source-side power distribution status and the load-side power distribution status; Construct a first potential field for the power distribution state at the source end; A second potential field is constructed for the load-side power distribution state; The dynamic potential field is determined by taking the difference between the mapping between the second potential field and the first potential field.

4. The hierarchical coordinated control method for microgrids oriented towards source-load interaction as described in claim 3, characterized in that, Dynamic reconfiguration and interface interconnection of microgrids are performed to determine the network structure, including: The dynamic potential field is divided according to the potential level to determine the potential field grid. A potential barrier isolation based on a preset potential level is introduced, and a survival grid is calibrated within the potential field grid. Based on the energy potential field grid and the survival grid, a temporary interface network is formed with grids as units to determine the network structure.

5. The hierarchical coordinated control method for microgrids oriented towards source-load interaction as described in claim 1, characterized in that, Determine a coordination strategy at one level, including: Traverse the network structure, calculate the mean value of the grid potential, and define pseudo-grid nodes, wherein each pseudo-grid node corresponds one-to-one with a grid in the network structure. Based on the small-scale potential, coordinated scheduling is performed in the direction of the potential field gradient to determine a scheduling result, wherein the scheduling result includes the updated pseudo-mesh nodes; Based on the scheduling result, multiple iterative scheduling analyses are performed to determine the coordination strategy for the first layer.

6. The microgrid hierarchical coordinated control method for source-load interaction as described in claim 5, characterized in that, Perform multiple iterative scheduling analyses, including: Set an energy potential baseline level, identify the scheduling result, and blur the pseudo-mesh nodes that conform to the energy potential baseline level as the updated pseudo-mesh nodes; For the updated pseudo-mesh nodes, perform gradient coordination analysis based on a small energy potential to determine the secondary scheduling result; Perform iterative analysis based on the results of the secondary scheduling until the baseline potential level is met.

7. The hierarchical coordinated control method for microgrids oriented towards source-load interaction as described in claim 1, characterized in that, As a hierarchical coordination scheme, it responds to the scheduling and control of microgrid systems, including: Identify the hierarchical coordination scheme and decouple to determine the individual scheduling scheme, wherein the decoupling method is source-end decoupling or load-end decoupling; The individual scheduling scheme is deployed to each source device for self-driven scheduling management, wherein the individual scheduling scheme corresponds one-to-one with the source device.

8. The hierarchical coordinated control method for microgrids oriented towards source-load interaction as described in claim 1, characterized in that, After scheduling and control are implemented, the following are included: Coordinate, schedule, monitor, and track the microgrid to determine its coordination status; The coordination state of the microgrid is evaluated based on the stability and balance of the entire microgrid area, and the secondary coordination point is traced back to the source by using a preset relaxation degree as a constraint. For the aforementioned secondary coordination point, feedback scheduling and control are implemented based on the causes of the imbalance.

9. A hierarchical coordinated control system for microgrids oriented towards source-load interaction, characterized in that, The steps for implementing the microgrid hierarchical coordinated control method for source-load interaction as described in any one of claims 1 to 8 include: The network structure determination module is used to perform dynamic energy potential field analysis based on the source-load interaction scenario, dynamically reconfigure the microgrid and interconnect interfaces to determine the network structure; A first-layer coordination strategy determination module is used to introduce a small amount of energy potential as a single coordination quantity for the inter-mesh potential field of the network structure, take the potential field gradient direction as the energy direction, and determine a first-layer coordination strategy by performing multiple iterative scheduling analysis on the defined pseudo-mesh nodes. The two-layer coordination strategy determination module is used to determine the two-layer coordination strategy for the potential field inside the mesh of the network structure. The scheduling and control module is used to determine the conversion relationship between the energy potential field and the source load scheduling, and to convert the first-level coordination strategy and the second-level coordination strategy as a hierarchical coordination scheme to respond to the microgrid system for scheduling and control.

10. An electronic device, characterized in that, include: At least one processor; A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the steps of the microgrid hierarchical coordinated control method for source-load interaction as described in any one of claims 1 to 8.

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