Virtual power plant flexible resource hierarchical partitioning method, device and storage medium

Abstract

The application discloses a virtual power plant flexible resource hierarchical partitioning method and device and a storage medium, relates to the technical field of virtual power plants, and comprises the following steps: acquiring a response characteristic interval model constructed by response characteristic parameters of each flexible resource; based on the response characteristic interval model, evaluating the voltage influence of each flexible resource on each physical node through a voltage sensitivity analysis interval model to obtain each voltage change interval; based on each voltage change interval, clustering and dividing each flexible resource to obtain main resource characteristics of a plurality of aggregated nodes; and returning to execute the step of evaluating the voltage influence of each flexible resource on each physical node through the voltage sensitivity analysis interval model, iteratively optimizing each aggregated node, and determining the hierarchical partitioning result of each flexible resource when the change rate of an aggregated target function value is less than a preset threshold, so as to solve the problem that the static hierarchical partitioning of virtual power plant flexible resources leads to a safety risk, and match the hierarchical partitioning of flexible resources with the latest state.

Description

Technical Field

[0001] This application relates to the field of virtual power plant technology, and in particular to a method, device and storage medium for flexible resource hierarchical partitioning of a virtual power plant. Background Technology

[0002] With the large-scale integration of flexible resources such as distributed energy, energy storage devices, and electric vehicles into distribution networks, the form and operating characteristics of distribution networks have undergone profound changes. Currently, in the process of hierarchical partitioning of virtual power plants, offline partitioning strategies based on fixed rules or static topology are commonly adopted, dividing flexible resources into several physical areas according to geographical location or load type. When virtual power plants are hierarchically partitioned according to fixed rules or static topology and centrally optimized and dispatched based on the partitioning results, voltage over-limits or local power flow anomalies may occur due to the centralized adjustment of some highly sensitive resources, affecting the safe and stable operation of the distribution network. Summary of the Invention

[0003] The main objective of this application is to provide a method, device, and storage medium for flexible resource hierarchical partitioning of virtual power plants, which aims to solve the technical problem of distribution network security risks caused by existing static hierarchical partitioning methods for flexible resources in virtual power plants.

[0004] To achieve the above objectives, this application proposes a flexible resource hierarchical partitioning method for virtual power plants, which includes:

[0005] A response characteristic interval model is obtained by constructing a response characteristic parameter of each flexible resource in the distribution network. The response characteristic interval model is used to characterize the power of each flexible resource as it changes over time.

[0006] Based on the response characteristic interval model, the voltage sensitivity analysis interval model is used to evaluate the voltage impact of each flexible resource on each physical node, and various voltage change intervals of each physical node are obtained. The voltage sensitivity analysis interval model is constructed based on the power flow equation of the power system.

[0007] Based on the voltage variation ranges, the flexible resources are clustered to obtain the main resource characteristics of multiple aggregation nodes, and each aggregation node includes at least one of the flexible resources.

[0008] Return to the step of evaluating the impact of each flexible resource on the voltage of each physical node through the voltage sensitivity analysis interval model, to recalculate the various voltage variation intervals of each physical node, iteratively optimize each aggregate node, and obtain the aggregate objective function value for each iteration;

[0009] When the rate of change of the aggregate objective function value is less than a preset threshold, each aggregate node is determined as the hierarchical partitioning result of each flexible resource.

[0010] In one embodiment, before the step of obtaining the response characteristic range model constructed from the response characteristic parameters of each flexible resource in the distribution network, the method further includes:

[0011] Based on the response characteristic parameters of each of the flexible resources, the response capacity, response rate, and response duration of each of the flexible resources are determined.

[0012] Based on interval analysis theory, the response capacity, response rate, and response duration are modeled as dynamic functions that change with time, and the upper and lower bounds of the response capacity are used as the adjustable ranges of active power and reactive power, thus forming the interval model of the response characteristics.

[0013] In one embodiment, before the step of evaluating the voltage impact of each flexible resource on each physical node using a voltage sensitivity analysis interval model based on the response characteristic interval model to obtain various voltage variation intervals for each physical node, the method further includes:

[0014] Based on the power flow equations of the power system, the voltage sensitivity analysis interval model is constructed using the response characteristic interval model.

[0015] In one embodiment, the step of constructing the voltage sensitivity analysis interval model based on the power system flow equations and using the response characteristic interval model includes:

[0016] The power flow equations of the power system are linearized to obtain active power voltage correction equations and reactive power voltage correction equations. The active power voltage correction equations are used to represent the impact of changes in active power injection from flexible resources on the voltage of physical nodes, and the reactive power voltage correction equations are used to represent the impact of changes in reactive power injection from flexible resources on the voltage of physical nodes.

[0017] Using the response characteristic range model as input variables, the voltage change range of each physical node caused by the power range change of each flexible resource is calculated through the active power voltage correction equation and the reactive power voltage correction equation, thereby obtaining the voltage sensitivity analysis range model to quantify the impact of the power change of the flexible resource on the voltage of the physical node.

[0018] In one embodiment, the step of clustering and dividing the flexible resources based on each of the voltage variation intervals to obtain the main resource characteristics of multiple aggregation nodes includes:

[0019] The voltage variation ranges of each of the flexible resources are spliced ​​together to obtain the voltage variation range splicing result corresponding to each of the flexible resources.

[0020] Vector embedding is performed on the splicing results of each voltage change interval to obtain the voltage sensitivity vector corresponding to each flexible resource.

[0021] Calculate the electrical coupling distance between any two of the voltage sensitivity vectors, the electrical coupling distance being used to quantify the similarity of the voltage impact patterns of the two flexible resources on the physical node;

[0022] Based on the electrical coupling distances of each resource, a spectral clustering algorithm is used to cluster and group the flexible resources to obtain the main resource characteristics of each aggregation node.

[0023] In one embodiment, the step of clustering and dividing the flexible resources based on the voltage variation ranges to obtain the main resource characteristics of multiple aggregation nodes further includes:

[0024] The voltage variation ranges of each of the flexible resources are spliced ​​together to obtain the voltage variation range splicing result corresponding to each of the flexible resources.

[0025] The geographical locations of each flexible resource and the voltage variation ranges are spliced ​​together to obtain the multi-dimensional splicing results corresponding to each flexible resource.

[0026] Vector embedding is performed on each of the multidimensional splicing results to obtain the multidimensional feature vectors corresponding to each of the flexible resources;

[0027] Calculate the coupling distance between any two of the multidimensional feature vectors, and based on each coupling distance, use a spectral clustering algorithm to cluster and group each of the flexible resources to obtain the main resource features of each aggregation node.

[0028] In one embodiment, after the step of determining each of the aggregation nodes as the hierarchical partitioning result of each of the flexible resources, the method further includes:

[0029] Obtain the current voltage change, maximum voltage adjustment, and minimum voltage adjustment for each physical node;

[0030] Based on the current voltage change, the maximum voltage adjustment, and the minimum voltage adjustment, calculate the current correction factor for each physical node;

[0031] The flexible resource connected to the physical node is identified as the target flexible resource. When the current correction factor is less than a first preset threshold, the response capacity of the target flexible resource is corrected according to the current correction factor, so as to limit the power regulation capability of the target flexible resource through the corrected response capacity.

[0032] In one embodiment, before the step of correcting the response capacity of the target flexible resource according to the current correction factor, the method further includes:

[0033] When the current correction factor is less than the second preset threshold, the voltage sensitivity analysis interval model is used to re-evaluate the impact of the target flexible resource on the voltage of the physical node, and the updated voltage change interval of the target flexible resource is obtained, wherein the second preset threshold is less than the first preset threshold.

[0034] Based on the updated range of the voltage variation interval, high-impact flexible resources are identified from the target flexible resources, and these high-impact flexible resources are migrated from the original aggregation node to other aggregation nodes.

[0035] Furthermore, to achieve the above objectives, this application also proposes a flexible resource hierarchical partitioning device for a virtual power plant, the device comprising:

[0036] The acquisition module is used to acquire a response characteristic interval model constructed by the response characteristic parameters of each flexible resource in the distribution network. The response characteristic interval model is used to characterize the power of each flexible resource as it changes over time.

[0037] The voltage sensitivity analysis module is used to evaluate the voltage impact of each flexible resource on each physical node based on the response characteristic interval model, and to obtain various voltage change intervals of each physical node. The voltage sensitivity analysis interval model is constructed based on the power flow equations of the power system.

[0038] The clustering module is used to cluster and divide the flexible resources based on the voltage change ranges to obtain the main resource characteristics of multiple cluster nodes, and each cluster node includes at least one of the flexible resources.

[0039] The iterative optimization module is used to return to the step of evaluating the voltage impact of each flexible resource on each physical node through the voltage sensitivity analysis interval model, to recalculate the various voltage change intervals of each physical node, iteratively optimize each aggregate node, and obtain the aggregate objective function value for each iteration;

[0040] The determination module is used to determine each of the aggregation nodes as the hierarchical partitioning results of each of the flexible resources when the rate of change of the aggregation objective function value is less than a preset threshold.

[0041] In addition, to achieve the above objectives, this application also proposes a virtual power plant flexible resource hierarchical partitioning device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the virtual power plant flexible resource hierarchical partitioning method as described above.

[0042] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the virtual power plant flexible resource hierarchical partitioning method described above.

[0043] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the virtual power plant flexible resource hierarchical partitioning method described above.

[0044] One or more technical solutions proposed in this application have at least the following technical effects: Obtaining a response characteristic interval model constructed using the response characteristic parameters of each flexible resource in the distribution network. This model characterizes the power of each flexible resource as it changes over time, describing the power adjustment capability of the flexible resource in interval form, and covering its dynamic characteristics as it changes over time. Based on the response characteristic interval model, evaluating the voltage impact of each flexible resource on each physical node according to the voltage sensitivity analysis interval model, obtaining various voltage change intervals for each physical node, and obtaining the sensitivity intervals of the power increase and decrease intervals of each flexible resource to the voltage of each physical node. The voltage sensitivity analysis interval model is constructed based on the power flow equations of the power system. Based on each voltage change interval, clustering and classifying each flexible resource to obtain the main resource characteristics of multiple aggregate nodes, each aggregate node including at least one flexible resource. Returning to the step of evaluating the voltage impact of each flexible resource on each physical node according to the voltage sensitivity analysis interval model, repeating the voltage sensitivity evaluation and flexible resource evaluation steps. The clustering and adjustment of resources involves recalculating the voltage variation ranges of each physical node, iteratively optimizing each aggregate node, and obtaining the aggregation objective function value for each iteration. When the rate of change of the aggregation objective function value is less than a preset threshold, each aggregate node is determined as the hierarchical partitioning result of each flexible resource. This achieves dynamic hierarchical partitioning reconstruction of the live resources of the virtual power plant at each aggregate node, ensuring that the scheduling of flexible resources always conforms to the actual situation of the distribution network. This addresses the technical problem of distribution network security risks caused by the static hierarchical partitioning method of flexible resources in existing virtual power plants, ensuring that the hierarchical partitioning results of flexible resources match the latest state, thereby reducing security risks caused by partition mismatch. Based on the hierarchical partitioning method of flexible resources in virtual power plants disclosed in this application, the power regulation capability of each flexible resource is characterized as a dynamic range that changes over time through a response characteristic interval model, accurately characterizing the uncertainty of flexible resources. On this basis, a voltage sensitivity analysis interval model is used to evaluate the potential impact range on the voltage of all physical nodes in the distribution network when each flexible resource adjusts within its capability interval, i.e., the various voltage variation ranges of each physical node. Subsequently, based on the voltage variation ranges, flexible resources with similar electrical influence patterns are aggregated into an aggregation node. An iterative mechanism is introduced. After initial clustering, based on the newly formed aggregation nodes, the voltage influence range of each flexible resource on each physical node is recalculated. Accordingly, each aggregation node is dynamically adjusted and optimized. This makes the hierarchical partitioning of flexible resources in the virtual power plant no longer a static configuration, but rather a real-time sensing and adaptive adjustment of changes in the power grid operation status. This significantly improves the accuracy and robustness of the hierarchical partitioning results and avoids safety hazards such as voltage overruns and power flow overloads caused by mismatch in the hierarchical partitioning results. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0046] To more clearly illustrate the technical solutions in the embodiments of 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, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating an embodiment of the flexible resource hierarchical partitioning method for virtual power plants in this application.

[0048] Figure 2 A heatmap of the hierarchical partitioning results of a flexible resource provided in this application;

[0049] Figure 3 A flowchart illustrating another flexible resource hierarchical partitioning method for virtual power plants provided in this application;

[0050] Figure 4 A flowchart illustrating another flexible resource hierarchical partitioning method for virtual power plants provided in this application;

[0051] Figure 5 This is a schematic diagram of the module structure of the virtual power plant flexible resource hierarchical and partitioning device in an embodiment of this application;

[0052] Figure 6 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the flexible resource hierarchical partitioning method for virtual power plants in the embodiments of this application.

[0053] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0054] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0055] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0056] The main solution of this application embodiment is as follows: A response characteristic interval model is obtained by constructing a response characteristic parameter model of each flexible resource in the distribution network. This model characterizes the power of each flexible resource as it changes over time. Based on the response characteristic interval model, the voltage sensitivity analysis interval model is used to evaluate the voltage impact of each flexible resource on each physical node, resulting in various voltage variation intervals for each physical node. The voltage sensitivity analysis interval model is constructed based on the power flow equations of the power system. Based on each voltage variation interval, each flexible resource is clustered to obtain the main resource characteristics of multiple aggregate nodes, each aggregate node including at least one flexible resource. The process returns to the step of evaluating the voltage impact of each flexible resource on each physical node according to the voltage sensitivity analysis interval model, to recalculate various voltage variation intervals for each physical node, iteratively optimize each aggregate node, and obtain the aggregate objective function value for each iteration. When the rate of change of the aggregate objective function value is less than a preset threshold, each aggregate node is determined as a hierarchical partitioning result of each flexible resource.

[0057] In this embodiment, for ease of description, the following description will focus on the flexible resource hierarchical and zoning system of the proposed power plant.

[0058] Existing technologies generally employ offline partitioning strategies based on fixed rules or static topology in the process of hierarchical partitioning of virtual power plants. These strategies divide flexible resources into several physical regions according to geographical location or load type. However, this static partitioning model fails to fully consider the real-time dynamic characteristics of the power grid's operating status, such as the intermittent fluctuations in renewable energy output, the random changes in load demand, and the time-varying nature of equipment health. When virtual power plants are hierarchically partitioned according to fixed rules or static topology and then centrally optimized and dispatched based on the partitioning results, the centralized adjustment of some highly sensitive resources may trigger voltage exceedances or local power flow anomalies, affecting the safe and stable operation of the distribution network.

[0059] This application provides a solution that characterizes the power regulation capability of each flexible resource as a dynamic range that changes over time using a response characteristic range model, accurately depicting the uncertainty of flexible resources. Based on this, a voltage sensitivity analysis range model is used to assess the potential impact range of each flexible resource's regulation on the voltage of all physical nodes in the distribution network, i.e., the voltage change range. Subsequently, based on each voltage change range, flexible resources with similar electrical influence patterns are aggregated into an aggregate node. An iterative mechanism is introduced; after initial clustering, the voltage impact range of each flexible resource on each physical node is recalculated based on the newly formed aggregate nodes. The aggregate nodes are then dynamically adjusted and optimized accordingly. This ensures that the hierarchical partitioning of flexible resources in the virtual power plant is no longer a static configuration, but rather a real-time sensing and adaptive adjustment based on changes in the grid's operating status, significantly improving the accuracy and robustness of the hierarchical partitioning results and avoiding safety hazards such as voltage exceeding limits and power flow overload caused by mismatches in the hierarchical partitioning results.

[0060] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a virtual power plant flexible resource hierarchical partitioning device capable of achieving the above functions. The following description uses a virtual power plant flexible resource hierarchical partitioning system as an example to illustrate this embodiment and the subsequent embodiments.

[0061] Based on this, embodiments of this application provide a flexible resource hierarchical partitioning method for virtual power plants, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the virtual power plant flexible resource hierarchical partitioning method of this application.

[0062] In this embodiment, the virtual power plant flexible resource hierarchical partitioning method includes steps 101-104:

[0063] Step 101: Obtain the response characteristic interval model constructed by the response characteristic parameters of each flexible resource in the distribution network. The response characteristic interval model is used to characterize the power of each flexible resource as it changes over time.

[0064] Specifically, a virtual power plant is a power coordination and management system that uses advanced information and communication technologies and software systems to aggregate and coordinate energy storage systems, controllable loads, electric vehicles, etc., to participate in the electricity market and grid operation as a special power plant. The distribution network is the last link in the power system directly facing end users. Located between the transmission network (high voltage, long-distance transmission) and users (homes, factories, shops), it is responsible for safely and reliably distributing high-voltage electricity from the transmission network to households and various electrical devices at voltage levels suitable for user use (such as 220V / 380V) after being stepped down at each stage. Flexible resources refer to distributed units with adjustable capabilities in the distribution network, mainly including distributed energy sources, energy storage devices, and controllable loads. Distributed energy sources can be rooftop photovoltaics, small wind power, etc., whose output can be controlled (e.g., power-limited operation). Energy storage devices can be battery energy storage, which can be charged and discharged and regulated. Controllable loads can be interruptible loads, temperature-controlled loads (air conditioning), and electric vehicle charging loads. The power consumption or time of controllable loads can be adjusted. Response characteristic parameters are key parameters used to describe the flexibility of resource regulation capabilities. These include the response capacity, response rate, and response duration of the flexible resource. The response capacity of the flexible resource includes active power regulation range and reactive power regulation range. For example, the active power regulation range can be the maximum / minimum generating power or the maximum / minimum charging / discharging power, while the reactive power regulation range can be the inverter's reactive power output capability (Q). min Q max The response characteristic interval model is a mathematical model, for example, Output an interval. This indicates that at time t, the response capacity of the flexible resource may vary from g(t0) to c. The response characteristic interval model is dynamic, and its interval boundary changes with time t. Within a given time period, the response capacity of flexible resources varies with time and is described by the function g(t). By obtaining the response characteristic parameters of each flexible resource and constructing a response characteristic interval model, the active / reactive power regulation capability of each flexible resource is characterized as a dynamic interval that varies with time, rather than a fixed value. The response characteristic interval model comprehensively considers the response deviation of flexible resources and equipment operating constraints, which can truly reflect the uncertainty of resource capabilities. This provides an accurate input boundary for subsequent voltage impact assessment, enabling the analysis to cover the most unfavorable situation of resource regulation. This supports robust zoning decisions for distribution network safety and avoids distortion of zoning results and safety risks caused by ignoring uncertainties.

[0065] In some embodiments, before the step of obtaining the response characteristic range model constructed from the response characteristic parameters of each flexible resource in the distribution network, the method further includes:

[0066] Based on the response characteristic parameters of each flexible resource, the response capacity, response rate, and response duration of each flexible resource are determined.

[0067] Based on interval analysis theory, the response capacity, response rate, and response duration are modeled as dynamic functions that change with time. The upper and lower bounds of the response capacity are used as the adjustable ranges of active power and reactive power, forming a response characteristic interval model.

[0068] Specifically, interval analysis theory is a mathematical theory used to handle calculations involving uncertainty or error. Interval analysis theory does not calculate a single numerical result, but rather a range (interval) of results. In this application, parameters with uncertainty or requiring consideration of limiting cases, such as response capacity, response rate, and response duration, are represented as intervals using interval analysis theory, and these intervals are used to derive the final power interval.

[0069] As an example, before constructing a response characteristic range model, it is necessary to analyze and model the response characteristic parameters of each flexible resource. Specifically, based on the response characteristic parameters of each flexible resource, the response capacity, response rate, and response duration of each flexible resource are extracted and determined. Here, response capacity refers to the range of active / reactive power that the resource can adjust at a specific moment; response rate represents the maximum rate of power change, reflecting the resource's dynamic response capability; and response duration refers to the time the resource can operate sustainably under maximum adjustment, constrained by state of charge or user demand. Introducing range analysis theory, the response capacity, response rate, and response duration of the flexible resource are modeled as dynamic functions that change over time, i.e.

[0070] in, To provide flexible resource response capacity, For time variables, In response to the start time, When its maximum response capacity is reached, The dynamic function, which varies with time, reflects the change in the response capacity of flexible resources at different points in time, representing the final moment of the response duration. According to interval analysis theory, for a given set of real numbers in a bounded closed interval, denoted by the symbol [x], that is:

[0071]

[0072] in, This indicates the lower bound of the interval; This represents the upper bound of the interval. and That is, the lower and upper bounds of the response capacity. For any uncertain quantity x, this application does not use a definite value to represent it, but rather uses an interval. Therefore, the above dynamic function can be expressed in the following interval form:

[0073]

[0074] in, , , This represents the range of values ​​for the time variable t, from the start time t0 of the response to the end time t2 of the response. This represents the range of values ​​for the response capacity x, that is, from the initial response capacity g(t0) to the maximum response capacity c, which is the adjustable range of active power and reactive power.

[0075] By taking the upper and lower bounds of the response capacity as the adjustable range of active and reactive power, and assigning them reasonable upper and lower bound intervals to characterize the uncertainty of the parameters themselves, a response characteristic interval model that can dynamically reflect the change of resource regulation capacity over time is formed. The response characteristic interval model can provide an accurate and robust input basis for subsequent voltage sensitivity analysis.

[0076] The following is the expression for the response characteristic interval model:

[0077]

[0078] in, c , P , E , T These represent cost, active power, energy, and time, respectively. Superscripts indicate the type of aggregated resource: AGG represents load aggregator, DG represents distributed generation, and ESS represents energy storage system. Additionally, W represents wind power, PV represents photovoltaic power, CL represents controllable load, and DR represents demand response load. Subscripts... i It is the index of the network node, subscript t It is an index of time. f This represents the aggregate functions within AGG and each resource.

[0079] Step 102: Based on the response characteristic interval model, the voltage sensitivity analysis interval model is used to evaluate the voltage impact of each flexible resource on each physical node, and obtain various voltage change intervals for each physical node. The voltage sensitivity analysis interval model is constructed based on the power flow equation of the power system.

[0080] Specifically, the voltage variation range refers to the range within which the voltage amplitude of a physical node in the distribution network may change when the active and reactive power of a flexible resource varies within its response characteristic range. The response characteristic range model characterizes the dynamic range of the active and reactive power regulation capabilities of each flexible resource over time using an interval form, providing dynamic input for voltage sensitivity analysis and covering the uncertainty of resource regulation capabilities. The power flow equations are a set of nonlinear equations describing the relationship between node voltage, branch power, and injected power in a power system, forming the cornerstone of power system analysis. The power flow equations include active power flow equations and reactive power flow equations. The active power flow equation is:

[0081] The reactive power flow equation is:

[0082] Where i, j, and k are node numbers; For PQ and PV node sets, nP represents... The number of elements; Let PQ be the set of nodes, and nQ represent... The number of elements; Let nN be the set of all nodes, and nN represents... The number of elements; Inject active power into node i. Inject reactive power into node i; Let the electrical conductance be between node i and node j. The susceptance between node i and node j; Let be the voltage magnitude at node i. Let be the voltage amplitude at node j; This represents the phase angle difference between node i and node j; Let i be the electrical conductance between node i and node k. The susceptance between node i and node k; Let be the voltage amplitude at node k; This represents the phase angle difference between node i and node k.

[0083] In power systems, PV nodes (voltage control nodes) are nodes where active power (P) and voltage amplitude (V) remain constant during operation, while reactive power (Q) can vary. PV nodes typically correspond to nodes regulated by power electronic equipment, such as generator nodes. PV nodes are characterized by high power and voltage stability, making them crucial for grid voltage control. PQ nodes (load nodes) are nodes where active power (P) and reactive power (Q) are fixed during operation, but voltage amplitude and phase angle are variable. In power system analysis, most load nodes are typically considered PQ nodes. PQ nodes are characterized by their power output or consumption being predetermined, but their voltage level is affected by the power system's operating conditions.

[0084] The voltage sensitivity analysis interval model is a comprehensive model that integrates the response characteristic interval model and the power flow equations of the power system. It is used to perform conversion calculations from the power interval to the voltage interval. The voltage sensitivity analysis interval model can be expressed in the following interval form:

[0085]

[0086] In the formula,

[0087] in, This represents the response characteristic range of the input flexible resources, encompassing the active and reactive power regulation ranges of all flexible resources. This represents the range of phase angle changes in physical nodes caused by the active power adjustment of flexible resources. This represents the minimum phase angle change of a physical node due to the active power adjustment of flexible resources. This represents the maximum phase angle change of a physical node caused by the adjustment of active power by flexible resources.

[0088] This represents the range of voltage changes at a physical node caused by the active power regulation of flexible resources. This represents the minimum voltage change at a physical node caused by the active power regulation of flexible resources. This represents the maximum voltage change at a physical node caused by the active power regulation of flexible resources.

[0089] This represents the range of phase angle changes at physical nodes due to flexible resource reactive power regulation. This represents the minimum phase angle change of a physical node due to reactive power regulation by flexible resources. This represents the maximum phase angle change of a physical node caused by the reactive power adjustment of flexible resources.

[0090] This represents the range of voltage changes at physical nodes caused by reactive power regulation of flexible resources. This represents the minimum voltage change at a physical node caused by the active power regulation of flexible resources. This represents the maximum voltage change at a physical node caused by the active power regulation of flexible resources.

[0091] The above and That is, the range of voltage variation.

[0092] In some embodiments, the response characteristic range model is used as input to the voltage sensitivity analysis range model, and the variation range of active and reactive power of the flexible resources is defined by the following formula:

[0093]

[0094] in, This represents the range of active power variation at node j. This represents the range of reactive power variation at node k. , Let represent the maximum and minimum values ​​of the active power injected into node i, respectively. , These represent the maximum and minimum values ​​of reactive power injected at node i, respectively.

[0095] Furthermore, the impact of power adjustments by various flexible resources on the voltage of physical nodes is analyzed. Through linearization of the power flow equations, the correlation between power changes and voltage fluctuations is quantified. By combining the upper and lower limits of the power range with the sensitivity relationship of the power flow equations, the actual voltage variation range of each physical node is calculated, i.e., the voltage variation interval. The voltage variation interval can be determined by... and express

[0096] To accurately assess the impact of a single flexible resource, a disturbance analysis method can be used: based on the typical operating state of the system, the power of the target flexible resource is set at the lower and upper limits of its response range, while keeping the output of other flexible resources unchanged. Then, the interval power flow analysis is performed again to calculate the range of node voltage offset caused by the target flexible resource. Finally, the quantitative range of the impact of each flexible resource on the voltage of each physical node is obtained.

[0097] By obtaining the active / reactive power regulation range of each flexible resource through the response characteristic interval model, and constructing a voltage sensitivity analysis interval model based on the power flow equation of the power system, this model is used to evaluate the impact of each resource on the voltage of each physical node when the power range changes, and outputs the corresponding voltage change interval. This correlates the uncertainty of resource regulation with the grid voltage response, realizes the quantitative analysis of voltage fluctuation range, and helps to identify the electrical coupling strength of resources to grid nodes. This provides an accurate basis for subsequent dynamic clustering based on electrical characteristics, avoids partition distortion caused by ignoring uncertainty, and improves the robustness of virtual power plant resource aggregation and grid operation safety.

[0098] In some embodiments, before the step of evaluating the voltage impact of each flexible resource on each physical node using a voltage sensitivity analysis range model based on a response characteristic range model to obtain various voltage variation ranges for each physical node, the method further includes:

[0099] Based on the power flow equations of the power system, a voltage sensitivity analysis interval model is constructed using the response characteristic interval model.

[0100] Optionally, based on the distribution network topology, line parameters, and node information, a power flow equation describing the relationship between system power and voltage is established as the basic framework. Subsequently, key variables directly related to voltage in the power flow equation are extracted, clarifying the fundamental relationship between power flow and voltage distribution described by the power flow equation. Using a response characteristic interval model, the response characteristic intervals of flexible resources (such as energy storage, photovoltaics, and adjustable loads) are organized to characterize the active and reactive power of each flexible resource as they change over time. Based on this, a mapping relationship is established between the power regulation interval of flexible resources and the power flow equation. By analyzing the impact path of power injection changes on voltage in the power flow equation, the power regulation interval of flexible resources is transformed into a potential impact range on node voltage, constructing a voltage sensitivity analysis interval model. This voltage sensitivity analysis interval model, by combining uncertainty (power regulation interval) with the physical laws of the power grid (power flow equation), provides a quantitative basis for subsequently assessing the safety risks of virtual power plant dispatch to the grid voltage, helping to ensure that dispatch decisions do not exceed the grid voltage stability boundary.

[0101] In some embodiments, the step of constructing a voltage sensitivity analysis interval model based on the power flow equations and using a response characteristic interval model includes:

[0102] Linearizing the power flow equations of the power system yields the active power voltage correction equation and the reactive power voltage correction equation. The active power voltage correction equation is used to represent the impact of changes in active power injection from flexible resources on the voltage of physical nodes, while the reactive power voltage correction equation is used to represent the impact of changes in reactive power injection from flexible resources on the voltage of physical nodes.

[0103] Using the response characteristic range model as input variables, the voltage change range of each physical node caused by the power range change of each flexible resource is calculated through the active power voltage correction equation and the reactive power voltage correction equation. This yields the voltage sensitivity analysis range model, which quantifies the impact of power changes of flexible resources on the voltage of physical nodes.

[0104] Specifically, power flow equations in a power system include active power flow equations and reactive power flow equations. The active power flow equation is as follows:

[0105] The reactive power flow equation is:

[0106] Linearizing the power flow calculation equations of the power system near the operating point yields the corrected equations:

[0107] in, The active power voltage correction equation is as follows: The reactive power voltage correction equation is given, where, for The inverse of the Jacobian matrix during the calculation process, This represents the sensitivity of the voltage magnitude at node i to changes in the active power injected at node j, i.e., the amount of change in active power injected at node j. The effect on the voltage amplitude at node i; This represents the sensitivity of the voltage magnitude at node i to changes in the reactive power injected at node k, i.e., the amount of reactive power injection change at node k. The effect on the voltage amplitude at node i Inject changes into the active power of node j.

[0108] in, The active power phase angle correction equation, The reactive power phase angle correction equation is given, where, for The inverse of the Jacobian matrix during the calculation process, This represents the sensitivity of the phase angle of node i to changes in the active power injected into node j, i.e., the amount of change in active power injection into node j. The effect on the phase angle of node i; The phase angle of node i is sensitive to changes in reactive power injection at node k, and the amount of reactive power injection change at node k is the change in reactive power injection at node k. The effect on the phase angle of node i This involves injecting a change in reactive power at node k. By linearizing the power flow equations of the power system, the nonlinear problem is transformed into a linear relationship. Combined with the inverse of the Jacobian matrix and interval analysis, the impact of power (active and reactive power) changes of flexible resources on the voltage and phase of physical nodes is quantified. This effectively assesses the sensitivity of the voltage and phase angle of each node in the power system to changes in active and reactive power, thus providing important theoretical support and technical means for the safe and stable operation of the distribution network.

[0109] As an example, the nonlinear power flow equations of the power system are linearized, decomposing them into two simplified linear relationships: the active power voltage correction equation and the reactive power voltage correction equation. The active power voltage correction equation represents the impact of changes in active power injection from flexible resources on the voltage of physical nodes, while the reactive power voltage correction equation represents the impact of changes in reactive power injection from flexible resources on the voltage of physical nodes. Subsequently, using the response characteristic interval model as input, the active and reactive power regulation capabilities of each flexible resource are considered as interval quantities varying within a certain range. Using the linearized active power voltage correction equation and the reactive power voltage correction equation, the possible fluctuation range of the voltage phase angle of each physical node when the active power of all resources varies within its interval, and the possible variation range of the voltage amplitude of each node when the reactive power varies within its interval, are calculated respectively. This yields the upper and lower limits of voltage variation for each physical node, thus determining its overall voltage variation interval. Finally, the above calculation results are integrated to form a comprehensive model that can quantify the impact of resource power uncertainty on grid voltage, namely, the voltage sensitivity analysis interval model. The voltage sensitivity analysis interval model significantly improves computational efficiency while ensuring accuracy. By quantifying the dynamic impact of resource adjustments on voltage, it provides a basis for safety constraints on resource partitioning.

[0110] The voltage sensitivity analysis interval model is as follows:

[0111]

[0112] Step 103: Based on each voltage variation range, cluster each flexible resource to obtain the main resource characteristics of multiple aggregation nodes. Each aggregation node includes at least one flexible resource.

[0113] Specifically, flexible resources are individuals to be aggregated, such as a single photovoltaic inverter, energy storage unit, or adjustable load. Aggregation nodes are groups formed after clustering. Each aggregation node represents a set of flexible resources with similar electrical behavior, which can be regarded as a "virtual machine group" with greater adjustment capabilities.

[0114] In some embodiments, the set of voltage variation ranges for each flexible resource (i.e., its voltage impact range on all nodes) is constructed into a vector, which represents the unique electrical impact fingerprint of the flexible resource. Subsequently, a clustering algorithm (such as K-means or hierarchical clustering) is used to group resources based on the similarity between vectors, automatically classifying resources with similar electrical impact patterns into one category. Each category forms an aggregate node, resulting in the main resource characteristics of multiple aggregate nodes. Each aggregate node contains one or more flexible resources, realizing the homogenization and large-scale aggregation of dispersed heterogeneous resources according to their electrical coupling characteristics.

[0115] Step 104: Return to the step of evaluating the voltage impact of each flexible resource on each physical node through the voltage sensitivity analysis interval model, in order to recalculate the various voltage change intervals of each physical node, iteratively optimize each aggregate node, and obtain the aggregate objective function value for each iteration.

[0116] Step 105: When the rate of change of the aggregation objective function value is less than a preset threshold, each aggregation node is determined as the hierarchical partitioning result of each flexible resource.

[0117] refer to Figure 2 , Figure 2 A heatmap showing the hierarchical partitioning results of flexible resources in this application:

[0118] like Figure 2 As shown, if a column has more dark-colored squares, then that column has a higher voltage sensitivity. Taking column 21 as an example: when the power of bus 21 changes, it causes significant changes in the voltage of buses 19, 20, etc.; this indicates that bus 21 has a relatively high voltage sensitivity. Therefore, based on the aforementioned black, white, and gray squares, the buses can be sorted from highest to lowest voltage sensitivity, thus determining the voltage-sensitive area of ​​each bus, and subsequently realizing a virtual power plant with voltage sensitivity considered in its hierarchical zoning.

[0119] also, Figure 2 It also shows the correlation between certain bus voltages, which is determined by the "rows" of the heatmap. For example, row 1 shows that the voltage sensitivity of bus 1 is closely related to the power increments of buses 21, 24, and 25. The virtual power plant can regulate the voltage of bus 1 by aggregating the resources of buses 21, 24, and 25.

[0120] In addition, the aggregation objective function value is an indicator used to quantify the quality of the clustering results (each aggregation node) in each iteration, reflecting the degree of consistency of the voltage influence patterns of each flexible resource within the aggregation node. For example, the aggregation objective function value can be a graph cut value. The rate of change of the aggregation objective function value refers to the relative change of the objective function value in the current iteration compared to the value in the previous iteration. The preset threshold can be a positive number close to zero, such as 0.001, used to determine whether the rate of change of the aggregation objective function value is small enough.

[0121] Optionally, after initially completing the clustering and partitioning of flexible resources and forming multiple aggregate nodes, the voltage impact assessment step will be returned to and re-executed. Each currently formed aggregate node will be treated as a whole control unit, and its equivalent comprehensive response characteristic range will be calculated, i.e., the set boundary of the adjustable active / reactive power range of all flexible resources within that aggregate node. This will yield an updated response characteristic range. Based on the updated response characteristic range, the voltage sensitivity analysis range model will be reconstructed, and the impact of flexible resources in each aggregate node on the voltage of each physical node in the distribution network will be assessed to obtain new voltage variation ranges for each physical node. Based on the updated voltage variation ranges for each physical node, all flexible resources will be clustered again, merging flexible resources with highly similar electrical characteristics and splitting and reorganizing aggregate nodes with large differences in internal characteristics. By repeatedly evaluating the voltage impact of flexible resources on physical nodes and re-clustering, the aggregation structure (each aggregate node) will be continuously optimized. In each iteration, the aggregation objective function value for each iteration will be calculated, and the convergence of the partitioning results will be determined by the rate of change of the aggregation objective function value. When the preset convergence condition is met (the rate of change of the aggregate objective function value is less than a preset threshold), the iteration terminates, and the final determined aggregate node structure is the hierarchical partitioning result of each flexible resource. By using the initially partitioned aggregate nodes as new evaluation units, the voltage impact of each flexible resource on each physical node is evaluated. Flexible resources with highly similar electrical characteristics are merged, or aggregate nodes with large differences in internal characteristics are split and recombined, thereby continuously optimizing the aggregate structure. Through multiple iterations, the deviation of the initial partitioning is effectively eliminated, making the internal resources of each aggregate node tightly coupled and with highly consistent response characteristics, significantly improving the accuracy and robustness of the hierarchical partitioning of flexible resources in the virtual power plant.

[0122] Based on the virtual power plant flexible resource hierarchical partitioning method proposed in this application, a response characteristic interval model is obtained by constructing a response characteristic parameter of each flexible resource in the distribution network. This response characteristic interval model characterizes the active and reactive power of each flexible resource as it changes over time, describing the active / reactive power adjustment capability of the flexible resource in interval form, and covering its dynamic characteristics over time. Based on the response characteristic interval model, the voltage sensitivity analysis interval model is used to evaluate the voltage impact of each flexible resource on each physical node, obtaining various voltage change intervals for each physical node. The power increase and decrease intervals of each flexible resource on the voltage of each physical node are obtained. The voltage sensitivity analysis interval model is constructed based on the power flow equations of the power system. Based on each voltage change interval, each flexible resource is clustered to obtain the main resource characteristics of multiple aggregate nodes, each aggregate node including at least one flexible resource. The process then returns to the step of evaluating the voltage impact of each flexible resource on each physical node according to the voltage sensitivity analysis interval model, repeating the voltage sensitivity evaluation and flexible resource evaluation steps. The clustering and adjustment of resources involves recalculating the voltage variation ranges of each physical node, iteratively optimizing each aggregate node, and obtaining the aggregation objective function value for each iteration. When the rate of change of the aggregation objective function value is less than a preset threshold, each aggregate node is determined as the hierarchical partitioning result of each flexible resource. This achieves dynamic hierarchical partitioning reconstruction of the live resources of the virtual power plant at each aggregate node, ensuring that the scheduling of flexible resources always conforms to the actual situation of the distribution network. This addresses the technical problem of distribution network security risks caused by the static hierarchical partitioning method of flexible resources in existing virtual power plants, ensuring that the hierarchical partitioning results of flexible resources match the latest state, thereby reducing security risks caused by partition mismatch. Based on the hierarchical partitioning method of flexible resources in the virtual power plant disclosed in this application, the active / reactive power regulation capability of each flexible resource is characterized as a dynamic range that changes over time through a response characteristic interval model, accurately characterizing the uncertainty of flexible resources. On this basis, a voltage sensitivity analysis interval model is used to evaluate the potential impact range on the voltage of all physical nodes in the distribution network when each flexible resource adjusts within its capability interval, i.e., the various voltage variation ranges of each physical node. Subsequently, based on the voltage variation ranges, flexible resources with similar electrical influence patterns are aggregated into an aggregation node. An iterative mechanism is introduced. After initial clustering, based on the newly formed aggregation nodes, the voltage influence range of each flexible resource on each physical node is recalculated. Accordingly, each aggregation node is dynamically adjusted and optimized. This makes the hierarchical partitioning of flexible resources in the virtual power plant no longer a static configuration, but rather a real-time sensing and adaptive adjustment of changes in the power grid operation status. This significantly improves the accuracy and robustness of the hierarchical partitioning results and avoids safety hazards such as voltage overruns and power flow overloads caused by mismatch in the hierarchical partitioning results.

[0123] In some embodiments, reference Figure 3Step 103, which involves clustering and dividing each flexible resource based on its voltage variation range to obtain the main resource characteristics of multiple aggregation nodes, includes:

[0124] Step 301: Patch together the voltage change ranges of each physical node for each flexible resource to obtain the voltage change range patching result corresponding to each flexible resource.

[0125] Step 302: Perform vector embedding on the splicing results of each voltage change interval to obtain the voltage sensitivity vector corresponding to each flexible resource;

[0126] Step 303: Calculate the electrical coupling distance between any two voltage sensitivity vectors. The electrical coupling distance is used to quantify the similarity of the voltage impact patterns of two flexible resources on physical nodes.

[0127] Step 304: Based on each electrical coupling distance, use a spectral clustering algorithm to cluster and group each flexible resource to obtain the main resource characteristics of each aggregation node.

[0128] As an example, for each flexible resource, its voltage variation ranges across all physical nodes in the distribution network are sequentially concatenated according to node order to form a complete voltage variation range concatenation result. This concatenation result comprehensively characterizes the overall impact pattern of the flexible resource on the network voltage. Subsequently, to facilitate mathematical calculations and model processing, a vector embedding operation is performed on each voltage variation range concatenation result, converting each voltage variation range into a numerical pair (e.g., lower and upper limits). This transforms the entire concatenation sequence into a high-dimensional real-valued vector, namely the voltage sensitivity vector, which serves as the electrical characteristic representation of the flexible resource. Based on this, the distance between the voltage sensitivity vectors of any two flexible resources is calculated to obtain the electrical coupling distance. The smaller the electrical coupling distance, the more similar the voltage impact patterns of the two flexible resources on the nodes of the power grid, and the closer their electrical correlation. Finally, based on the electrical coupling distance between all flexible resources, a similarity relationship matrix is ​​constructed, and a spectral clustering algorithm is used for clustering. By analyzing the spectral characteristics of the similarity matrix, the inherent nonlinear structure and complex clustering patterns of the data are effectively identified, and flexible resources with highly similar electrical influence characteristics are automatically grouped into one category. Each category forms an aggregation node, realizing the clustering and division of all flexible resources and obtaining the main resource characteristics of multiple aggregation nodes.

[0129] In some embodiments, reference Figure 4 The step of clustering and dividing each flexible resource based on each voltage variation range to obtain the main resource characteristics of multiple aggregation nodes also includes:

[0130] Step 301: Patch together the voltage change ranges of each physical node for each flexible resource to obtain the voltage change range patching result corresponding to each flexible resource.

[0131] Step 402: The geographical locations of each flexible resource are spliced ​​with the splicing results of each voltage variation range to obtain the multi-dimensional splicing results corresponding to each flexible resource.

[0132] Step 403: Perform vector embedding on each multidimensional splicing result to obtain the multidimensional feature vector corresponding to each flexible resource;

[0133] Step 404: Calculate the coupling distance between any two multidimensional feature vectors, and based on each coupling distance, use the spectral clustering algorithm to cluster and group each flexible resource to obtain the main resource features of each aggregation node.

[0134] As an example, when obtaining the voltage variation range of each flexible resource for all physical nodes in the distribution network, all voltage variation ranges corresponding to a flexible resource are horizontally concatenated according to the order of the physical nodes to obtain the voltage variation range concatenation result for that flexible resource. The voltage variation range concatenation result can characterize the global pattern of its electrical influence. Subsequently, a geographic location dimension is introduced, and the geographic location information such as the latitude and longitude coordinates or regional codes of each flexible resource are concatenated again with the voltage variation range concatenation result to construct a multidimensional concatenation result, realizing the organic integration of electrical features and spatial features, so that subsequent clustering considers both the similarity of electrical response and the proximity of geographic distribution. Next, vector embedding processing is performed on each multidimensional concatenation result, converting all voltage variation ranges into numerical form (such as lower and upper limits of the range), and encoding the geographic location information into a numerical vector. Finally, the entire multidimensional concatenation structure is transformed into a high-dimensional real number vector, namely the multidimensional feature vector. The multidimensional feature vector contains both the electrical response fingerprint and spatial location information of the flexible resource, serving as the comprehensive input for cluster analysis. Based on this, the coupling distance between the multidimensional feature vectors of any two flexible resources is calculated. This coupling distance comprehensively measures the overall differences between any two flexible resources in terms of electrical influence patterns and geographical location. The smaller the coupling distance, the higher the similarity between the two flexible resources in both electrical and geographical aspects. Finally, a similarity matrix is ​​constructed based on the pairwise coupling distances of all flexible resources, and a spectral clustering algorithm is used for clustering. By analyzing the spectral structure of the similarity matrix, flexible resources that are closely related in both electrical and geographical dimensions are accurately identified. Each cluster group is a cluster node, resulting in each cluster node. By integrating voltage variation range and geographical location information for comprehensive clustering, not only is the electrical similarity of the flexible resources' impact on grid voltage considered, but also their geographical proximity. By constructing multidimensional feature vectors containing electrical and spatial characteristics and calculating the comprehensive coupling distance, the clustering results simultaneously satisfy the dual optimization objectives of electrical homogeneity and geographical proximity. The spectral clustering algorithm can effectively identify complex association patterns, and the resulting cluster nodes have both good electrical coordination and are easy to manage and communicate locally, thereby helping to improve the practicality, control efficiency, and engineering feasibility of the hierarchical and zoning of virtual power plants.

[0135] In some embodiments, after determining each aggregation node as a hierarchical partitioning result of each flexible resource, the method further includes:

[0136] Obtain the current voltage change, maximum voltage adjustment, and minimum voltage adjustment for each physical node;

[0137] Calculate the current correction factor for each physical node based on the current voltage change, maximum voltage adjustment, and minimum voltage adjustment.

[0138] The flexible resources connected to the physical nodes are identified as target flexible resources. When the current correction factor is less than the first preset threshold, the response capacity of the target flexible resources is corrected according to the current correction factor, so as to limit the power regulation capability of the target flexible resources through the corrected response capacity.

[0139] Specifically, a physical node refers to an actual electrical node in the distribution network, such as a busbar, feeder section, or load connection point. The current voltage change is the actual voltage deviation of that physical node relative to the system reference voltage at the current moment. The maximum voltage adjustment is the theoretical upper limit of the potential voltage rise of that physical node under current operating conditions, which can be obtained through power flow calculations or predictions. The minimum voltage adjustment is the theoretical lower limit of the potential voltage drop of that physical node under current operating conditions. The current correction factor is an indicator that quantifies the voltage safety margin of a physical node, and can be calculated using the following formula:

[0140] Where α is the current correction factor for the physical node. This is the maximum voltage regulation amount. This is the minimum voltage adjustment amount. This represents the current voltage change at the physical node. Response capacity is the active / reactive power regulation capability available from flexible resources; the corrected response capacity is the actual available regulation capability after being limited by voltage safety margin (current correction factor).

[0141] As an example, the current voltage value of each physical node is collected in real time, and its current voltage change relative to the reference voltage is calculated. Simultaneously, the maximum and minimum voltage adjustment amounts that each physical node's voltage may reach in the short term are determined, i.e., the upper and lower limits of voltage fluctuation. Then, combining the current voltage change, maximum voltage adjustment, and minimum voltage adjustment, the current correction factor for each physical node is calculated. The current correction factor reflects the safety margin of the node voltage and is defined as the normalized value of the distance between the current voltage and the over-limit boundary; the smaller the value, the closer it is to the voltage over-limit risk. When the current correction factor of a physical node is lower than a first preset threshold, the node is determined to have a voltage safety hazard. At this time, flexible resources that are electrically close to the physical node and have a significant voltage impact are identified as target flexible resources. Based on the magnitude of the current correction factor, the response capacity (such as the adjustable range of active / reactive power) of the target resource is dynamically corrected, for example, by proportionally reducing its adjustable capacity. The corrected response capacity serves as a hard constraint for its participation in regulation, thereby limiting power regulation behavior and avoiding exacerbating the voltage over-limit risk. This process is continuously executed online. By dynamically limiting resource scheduling through correction factors, it ensures that the power grid operation is always within a safe threshold, thereby achieving closed-loop safe management and control of regional resources and ensuring that the voltage does not exceed the limit.

[0142] In some embodiments, prior to the step of adjusting the response capacity of the target flexible resource according to the current adjustment factor, the method further includes:

[0143] When the current correction factor is less than the second preset threshold, the impact of the target flexible resource on the voltage of the physical node is reassessed according to the voltage sensitivity analysis interval model, and the updated voltage change interval of the target flexible resource is obtained. The second preset threshold is less than the first preset threshold.

[0144] Based on the updated voltage variation range, high-impact flexible resources are identified from the target flexible resources, and these high-impact flexible resources are migrated from the original aggregation node to other aggregation nodes.

[0145] Specifically, the second preset threshold is a value smaller than the first preset threshold, used to identify severe voltage safety margin deficiencies and serve as a trigger condition for resource migration. High-impact flexible resources are those that, under current operating conditions, have a significant and dominant impact on nodes at risk of voltage exceedances. Specifically, a flexible resource can be identified as a high-impact flexible resource when the updated voltage change range is greater than or equal to 0.05 pu.

[0146] As an example, when the current correction factor of a physical node is lower than a first preset threshold, a safety control process is initiated. The response capacity of the target flexible resource is adjusted based on the current correction factor to limit its power regulation capability. If the current correction factor of the physical node further falls below a more stringent second preset threshold (the second threshold is less than the first threshold), a voltage sensitivity analysis interval model is invoked based on the current real-time operating conditions to reassess the latest impact of each target flexible resource connected to the risky physical node on its voltage. This yields an updated voltage change interval, which more accurately reflects the actual impact of the resources in the current state. Subsequently, based on the size of the voltage change interval (e.g., interval width) or voltage sensitivity, high-impact flexible resources that significantly affect the risky physical node are identified. These high-impact flexible resources are removed from their original aggregation nodes, and their electrical coupling relationships with each aggregation node are recalculated based on their latest electrical characteristics and geographical location. They are then migrated to other aggregation nodes with better influence patterns or geographical locations. By dynamically adjusting the aggregation of resources, the hierarchical and partitioned structure is optimized, reducing the risk of local voltage instability. After the migration is completed, the subsequent response capacity correction process will continue to be executed to achieve coordinated safety control from structural reconfiguration to operational constraints.

[0147] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the flexible resource hierarchical partitioning method of the virtual power plant in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0148] This application also provides a virtual power plant flexible resource hierarchical and partitioning device; please refer to... Figure 5 The virtual power plant's flexible resource stratification and partitioning device includes:

[0149] The acquisition module 501 is used to acquire a response characteristic interval model constructed by the response characteristic parameters of each flexible resource in the distribution network. The response characteristic interval model is used to characterize the power of each flexible resource that changes over time.

[0150] The voltage sensitivity analysis module 502 is used to evaluate the voltage impact of each flexible resource on each physical node based on the response characteristic interval model and the voltage sensitivity analysis interval model, and obtain various voltage change intervals of each physical node. The voltage sensitivity analysis interval model is constructed based on the power flow equation of the power system.

[0151] Clustering module 503 is used to cluster and divide each flexible resource based on each voltage change range to obtain the main resource characteristics of multiple aggregation nodes, and each aggregation node includes at least one flexible resource.

[0152] The iterative optimization module 504 is used to return the steps of evaluating the voltage impact of each flexible resource on each physical node according to the voltage sensitivity analysis interval model, to recalculate the various voltage change intervals of each physical node, iteratively optimize each aggregate node, and obtain the aggregate objective function value for each iteration.

[0153] The determination module 505 is used to determine each aggregation node as the hierarchical partitioning result of each flexible resource when the rate of change of the aggregation objective function value is less than a preset threshold.

[0154] The virtual power plant flexible resource hierarchical partitioning device provided in this application, employing the virtual power plant flexible resource hierarchical partitioning method in the above embodiments, can solve the technical problem of distribution network security risks caused by existing static hierarchical partitioning methods for virtual power plant flexible resources. Compared with the prior art, the beneficial effects of the virtual power plant flexible resource hierarchical partitioning device provided in this application are the same as those of the virtual power plant flexible resource hierarchical partitioning method provided in the above embodiments, and other technical features in the virtual power plant flexible resource hierarchical partitioning device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0155] This application provides a virtual power plant flexible resource hierarchical partitioning device, which includes: 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, and the instructions are executed by the at least one processor to enable the at least one processor to execute the virtual power plant flexible resource hierarchical partitioning method in the above embodiment 1.

[0156] The following is for reference. Figure 6 It shows a structural schematic diagram of a virtual power plant flexible resource hierarchical partitioning device suitable for implementing embodiments of this application. Figure 6 The virtual power plant flexible resource hierarchical partitioning device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0157] like Figure 6 As shown, the virtual power plant flexible resource hierarchical partitioning device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to programs stored in read-only memory (ROM) 1002 or programs loaded from storage device 1003 into random access memory (RAM) 1004. The random access memory 1004 also stores various programs and data required for the operation of the virtual power plant flexible resource hierarchical partitioning device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the virtual power plant flexible resource stratification and partitioning device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a virtual power plant flexible resource stratification and partitioning device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0158] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0159] The virtual power plant flexible resource hierarchical partitioning device provided in this application, employing the virtual power plant flexible resource hierarchical partitioning method described in the above embodiments, can solve the technical problem of distribution network security risks caused by existing static hierarchical partitioning methods for virtual power plant flexible resources. Compared with the prior art, the beneficial effects of the virtual power plant flexible resource hierarchical partitioning device provided in this application are the same as those of the virtual power plant flexible resource hierarchical partitioning method provided in the above embodiments, and other technical features in this virtual power plant flexible resource hierarchical partitioning device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0160] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0161] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0162] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the virtual power plant flexible resource hierarchical partitioning method in the above embodiments.

[0163] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0164] The aforementioned computer-readable storage medium may be included in the Virtual Power Plant Flexible Resource Hierarchical Partitioning Device; or it may exist independently and not be assembled into the Virtual Power Plant Flexible Resource Hierarchical Partitioning Device.

[0165] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the virtual power plant flexible resource hierarchical partitioning device, the virtual power plant flexible resource hierarchical partitioning device: acquires a response characteristic interval model constructed using the response characteristic parameters of each flexible resource in the distribution network, the response characteristic interval model being used to characterize the power of each flexible resource changing over time; based on the response characteristic interval model, evaluates the voltage impact of each flexible resource on each physical node using a voltage sensitivity analysis interval model, obtaining various voltage change intervals for each physical node, the voltage sensitivity analysis interval model being constructed based on the power flow equations of the power system; based on each voltage change interval, clusters and divides each flexible resource, obtaining the main resource characteristics of multiple aggregate nodes, each aggregate node including at least one flexible resource; returns to execute the step of evaluating the voltage impact of each flexible resource on each physical node according to the voltage sensitivity analysis interval model, to recalculate various voltage change intervals for each physical node, iteratively optimizes each aggregate node, and obtains the aggregate objective function value for each iteration; when the rate of change of the aggregate objective function value is less than a preset threshold, each aggregate node is determined as the hierarchical partitioning result of each flexible resource.

[0166] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0167] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0168] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0169] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described virtual power plant flexible resource hierarchical partitioning method. This solves the technical problem of distribution network security risks caused by existing static hierarchical partitioning methods for flexible virtual power plant resources. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the virtual power plant flexible resource hierarchical partitioning method provided in the above embodiments, and will not be repeated here.

[0170] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the virtual power plant flexible resource hierarchical partitioning method described above.

[0171] The computer program product provided in this application can solve the technical problem of distribution network security risks caused by the existing static hierarchical partitioning method for flexible resources in virtual power plants. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the hierarchical partitioning method for flexible resources in virtual power plants provided in the above embodiments, and will not be repeated here.

[0172] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A flexible resource hierarchical partitioning method for virtual power plants, characterized in that, The virtual power plant flexible resource hierarchical partitioning method includes: A response characteristic interval model is obtained by constructing a response characteristic parameter of each flexible resource in the distribution network. The response characteristic interval model is used to characterize the power of each flexible resource as it changes over time. Based on the response characteristic interval model, the voltage sensitivity analysis interval model is used to evaluate the voltage impact of each flexible resource on each physical node, and various voltage change intervals of each physical node are obtained. The voltage sensitivity analysis interval model is constructed based on the power flow equation of the power system. Based on the voltage variation ranges, the flexible resources are clustered to obtain the main resource characteristics of multiple aggregation nodes, and each aggregation node includes at least one of the flexible resources. Return to the step of evaluating the impact of each flexible resource on the voltage of each physical node through the voltage sensitivity analysis interval model, to recalculate the various voltage variation intervals of each physical node, iteratively optimize each aggregate node, and obtain the aggregate objective function value for each iteration; When the rate of change of the aggregate objective function value is less than a preset threshold, each aggregate node is determined as the hierarchical partitioning result of each flexible resource. The step of clustering and dividing the flexible resources based on the voltage variation ranges to obtain the main resource characteristics of multiple aggregation nodes includes: The voltage variation ranges of each of the flexible resources are spliced ​​together to obtain the voltage variation range splicing result corresponding to each of the flexible resources. Vector embedding is performed on the splicing results of each voltage change interval to obtain the voltage sensitivity vector corresponding to each flexible resource. Calculate the electrical coupling distance between any two of the voltage sensitivity vectors, the electrical coupling distance being used to quantify the similarity of the voltage impact patterns of the two flexible resources on the physical node; Based on the electrical coupling distances of each resource, a spectral clustering algorithm is used to cluster and group the flexible resources to obtain the main resource characteristics of each aggregation node.

2. The virtual power plant flexible resource hierarchical partitioning method as described in claim 1, characterized in that, Before the step of obtaining the response characteristic range model constructed from the response characteristic parameters of each flexible resource in the distribution network, the method further includes: Based on the response characteristic parameters of each of the flexible resources, the response capacity, response rate, and response duration of each of the flexible resources are determined. Based on interval analysis theory, the response capacity, response rate, and response duration are modeled as dynamic functions that change with time, and the upper and lower bounds of the response capacity are used as the adjustable ranges of active power and reactive power, thus forming the interval model of the response characteristics.

3. The virtual power plant flexible resource hierarchical partitioning method as described in claim 1, characterized in that, Before the step of evaluating the voltage impact of each flexible resource on each physical node using a voltage sensitivity analysis interval model based on the response characteristic interval model, and obtaining various voltage variation intervals for each physical node, the method further includes: Based on the power flow equations of the power system, the voltage sensitivity analysis interval model is constructed using the response characteristic interval model.

4. The virtual power plant flexible resource hierarchical partitioning method as described in claim 3, characterized in that, The steps of constructing the voltage sensitivity analysis interval model based on the power system flow equations and using the response characteristic interval model include: The power flow equations of the power system are linearized to obtain active power voltage correction equations and reactive power voltage correction equations. The active power voltage correction equations are used to represent the impact of changes in active power injection from flexible resources on the voltage of physical nodes, and the reactive power voltage correction equations are used to represent the impact of changes in reactive power injection from flexible resources on the voltage of physical nodes. Using the response characteristic range model as input variables, the voltage change range of each physical node caused by the power range change of each flexible resource is calculated through the active power voltage correction equation and the reactive power voltage correction equation, thereby obtaining the voltage sensitivity analysis range model to quantify the impact of the power change of the flexible resource on the voltage of the physical node.

5. The virtual power plant flexible resource hierarchical partitioning method as described in claim 1, characterized in that, The step of clustering and dividing the flexible resources based on the voltage variation ranges to obtain the main resource characteristics of multiple aggregation nodes further includes: The voltage variation ranges of each of the flexible resources are spliced ​​together to obtain the voltage variation range splicing result corresponding to each of the flexible resources. The geographical locations of each flexible resource and the voltage variation ranges are spliced ​​together to obtain the multi-dimensional splicing results corresponding to each flexible resource. Vector embedding is performed on each of the multidimensional splicing results to obtain the multidimensional feature vectors corresponding to each of the flexible resources; Calculate the coupling distance between any two of the multidimensional feature vectors, and based on each coupling distance, use a spectral clustering algorithm to cluster and group each of the flexible resources to obtain the main resource features of each aggregation node.

6. The virtual power plant flexible resource hierarchical partitioning method as described in claim 1, characterized in that, After the step of determining each of the aggregation nodes as the hierarchical partitioning result of each of the flexible resources, the method further includes: Obtain the current voltage change, maximum voltage adjustment, and minimum voltage adjustment for each physical node; Based on the current voltage change, the maximum voltage adjustment, and the minimum voltage adjustment, calculate the current correction factor for each physical node; The flexible resource connected to the physical node is identified as the target flexible resource. When the current correction factor is less than a first preset threshold, the response capacity of the target flexible resource is corrected according to the current correction factor, so as to limit the power regulation capability of the target flexible resource through the corrected response capacity.

7. The virtual power plant flexible resource hierarchical partitioning method as described in claim 6, characterized in that, Before the step of correcting the response capacity of the target flexible resource according to the current correction factor, the method further includes: When the current correction factor is less than the second preset threshold, the voltage sensitivity analysis interval model is used to re-evaluate the impact of the target flexible resource on the voltage of the physical node, and the updated voltage change interval of the target flexible resource is obtained. The second preset threshold is less than the first preset threshold. Based on the updated range of the voltage variation interval, high-impact flexible resources are identified from the target flexible resources, and these high-impact flexible resources are migrated from the original aggregation node to other aggregation nodes.

8. A virtual power plant flexible resource hierarchical and partitioning device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the virtual power plant flexible resource hierarchical partitioning method as described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the virtual power plant flexible resource hierarchical partitioning method as described in any one of claims 1 to 7.

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