Power grid resource aggregation method and device based on panoramic theory and storage medium

Abstract

The application provides a power grid resource aggregation method and device based on panoramic theory and a storage medium. The method comprises the following steps: acquiring operation data of various resource devices in a power grid and group division indexes corresponding to the various resource devices; applying the group division indexes corresponding to the various resource devices to cluster division of the corresponding types of resource devices to obtain a plurality of resource clusters and acquire resource information of each resource cluster; quantifying scheduling potential of the corresponding resource cluster by using the resource information to obtain a scheduling potential value; establishing a matching degree model of the power grid by using the panoramic theory, inputting different scheduling potential values into the matching degree model for operation to obtain matching degrees between two resource clusters, and performing aggregation processing on the resource clusters according to the matching degrees to obtain a plurality of aggregated clusters. The method solves the problem that the prior art is directly used for all controllable resources, ignores the differences between different resources, and causes long response time and poor aggregation effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid resource aggregation, in particular to a power grid resource aggregation method and device based on panoramic theory, a computer readable storage medium and an electronic device. BACKGROUND

[0002] Power grid resource aggregation can effectively integrate distributed power sources, flexible loads and energy storage facilities in the power grid, thereby improving power grid operation efficiency and service level, and the aggregated resources can reduce power grid operation cost, making the coordinated operation of aggregation more conducive to system economic operation. A large number of adjustable resources are aggregated to participate in unified power grid dispatching as a whole, fully utilizing the coordination and complementarity of distributed resources to achieve rational optimization and utilization of resources. A large number of controllable loads are aggregated into independent subjects to participate in demand response, playing an active role in day-ahead dispatching and real-time dispatching of the power grid.

[0003] Existing research is directly used for all controllable resources, ignoring the differences between different resources, resulting in long response time and poor aggregation effect. SUMMARY

[0004] The main purpose of the present application is to provide a power grid resource aggregation method and device based on panoramic theory, a computer readable storage medium and an electronic device to at least solve the problem that existing technology is directly used for all controllable resources, ignoring the differences between different resources, resulting in long response time and poor aggregation effect.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a power grid resource aggregation method based on panoramic theory is provided, comprising: obtaining operation data of various resource devices in the power grid and a grouping index corresponding to each resource device, wherein the resource devices include distributed photovoltaic and direct-drive wind turbine; applying the grouping index corresponding to each resource device to cluster and divide the corresponding type of resource device to obtain a plurality of resource clusters, and obtaining resource information of each resource cluster, wherein the resource information includes device operation state information and state of charge information; quantifying the dispatching potential of the corresponding resource cluster using the resource information to obtain a dispatching potential value; establishing a matching degree model of the power grid using panoramic theory, inputting different dispatching potential values into the matching degree model for operation to obtain the matching degree between each two resource clusters, and performing aggregation processing on the resource clusters according to the matching degree to obtain a plurality of aggregated clusters, wherein the aggregated clusters are used for power dispatching of the power grid.

[0006] Optionally, after the resource clusters are aggregated according to the matching degree, a plurality of aggregated clusters are obtained, the method further comprises: determining a corresponding scheduling scheme according to the power grid electricity demand and cost benefit, wherein the scheduling scheme aims to maximize the benefit of the power grid.

[0007] Optionally, determining a corresponding scheduling scheme according to the power grid electricity demand and cost benefit comprises: dividing a preset time period into a preset number of scheduling periods, and establishing a target scheduling function aiming to maximize the benefit of the power grid in the scheduling period: wherein, In the formula, F represents the benefit target, f1(t) represents the power benefit obtained by the adjustable resource in the t period according to the scheduling instruction, f2(t) represents the calling cost of the adjustable resource in the t period, E(X) represents the system energy of the Xth aggregated cluster, F e (X) represents the loss degree of the Xth aggregated cluster, P ij (t) represents the power response of the ith aggregate in the t period and the jth cluster, k represents the cost price of scheduling the adjustable resource, and t0 represents the initial sampling time point.

[0008] Optionally, the resource devices of the corresponding type are clustered and divided by applying the grouping indexes corresponding to the various resource devices, comprising: adopting a Fast Unfolding clustering algorithm combined with a network module degree function to cluster and divide the distributed photovoltaic of the power grid according to a first grouping index, wherein the first grouping index comprises distributed photovoltaic reactive power total capacity, net load of photovoltaic power supply access nodes in the cluster, cluster internal photovoltaic reactive power balance degree and coupling degree between nodes in the cluster, and the net load refers to the difference between the output load of the photovoltaic power supply and the node load; adopting a k-means clustering algorithm to cluster and divide the direct-drive wind turbine of the power grid according to a second grouping index, wherein the second grouping index comprises the rotational speed, load reduction coefficient, inertia coefficient and damping coefficient of the direct-drive wind turbine.

[0009] Optionally, the scheduling potential values of different resource clusters are input into the matching degree model for operation to obtain the matching degree between the two resource clusters, comprising: determining the system energy between the two resource clusters according to the matching degree model: wherein, E(X) is the system energy between the two aggregated clusters, X represents the grouping situation of the two aggregated clusters, s f represents the size parameter of the fth aggregated cluster, s e represents the size parameter of the eth aggregated cluster, p ef represents the matching degree between the aggregated clusters e and f, d efrepresents the distance between the aggregated clusters e and f; and determining an optimal matching degree between any two of the resource clusters according to system energy, wherein the lower the system energy, the higher the matching degree.

[0010] Optionally, the scheduling potential of the corresponding resource cluster is quantified using the resource information, including: quantifying the scheduling potential of the corresponding resource cluster according to the resource information of each resource cluster using evaluation indexes, wherein the evaluation indexes include maximum adjustable resource adjustment power, adjustable resource response time to the power grid request, average response speed to the power grid request after receiving the response signal, time from the power grid request to the adjustable resource response, rate of the adjustable resource from the initial state to the normal power consumption state after completing the response, steady-state response duration of the adjustable resource after receiving the response signal, and response rate of the adjustable resource participating in the power grid scheduling.

[0011] Optionally, the method further includes using a first formula: determining the response rate of the adjustable resource participating in the power grid scheduling, wherein R p representing the response rate of the adjustable resource participating in the power grid scheduling, P1, P2, and P3 are important parameter values, P4 represents an uncertain factor that can affect the potential of the adjustable resource, and ξ represents the scheduling compensation price of the adjustable resource.

[0012] According to another aspect of the present application, a power grid flexible controllable resource aggregation device based on panoramic theory is provided, including: an acquisition unit configured to acquire operation data of various resource devices in a power grid and grouping indexes corresponding to the various resource devices, wherein the resource devices include distributed photovoltaic and direct-drive wind turbine; a division unit configured to apply the grouping indexes corresponding to the various resource devices to cluster and divide the corresponding types of resource devices to obtain a plurality of resource clusters and acquire resource information of each resource cluster, wherein the resource information includes device operation state information and state of charge information; a quantification unit configured to quantify the scheduling potential of the corresponding resource cluster using the resource information to obtain a scheduling potential value; and a processing unit configured to establish a matching degree model of the power grid using panoramic theory, input different scheduling potential values into the matching degree model for operation to obtain a matching degree between each two of the resource clusters, and aggregate the resource clusters according to the matching degree to obtain a plurality of aggregated clusters, wherein the aggregated clusters are used for power scheduling of the power grid.

[0013] According to still another aspect of the present application, a computer readable storage medium is provided, the computer readable storage medium comprising a stored program, wherein the computer readable storage medium is caused to perform any one of the power grid resource aggregation methods based on the panoramic theory when the program is run.

[0014] According to still another aspect of the present application, an electronic device is provided, comprising one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs comprise a program for performing any one of the power grid resource aggregation methods based on the panoramic theory.

[0015] By applying the technical solution of the present application, firstly, the operation data of various resource devices in the power grid and the grouping indexes corresponding to the various resource devices are obtained, wherein the resource devices include distributed photovoltaic and direct-drive wind turbines; then, the resource devices of the corresponding type are clustered and divided by applying the grouping indexes corresponding to the various resource devices, a plurality of resource clusters are obtained, and resource information of each resource cluster is obtained, wherein the resource information includes device operation state information and state of charge information; then, the scheduling potential of the corresponding resource cluster is quantified by using the resource information, and a scheduling potential value is obtained; finally, a matching degree model of the power grid is established by using the panoramic theory, different scheduling potential values are input into the matching degree model for operation to obtain the matching degree between each two resource clusters, and the resource clusters are aggregated according to the matching degree, and a plurality of aggregated clusters are obtained, wherein the aggregated clusters are used for power scheduling of the power grid. In this scheme, after different resources are clustered and grouped, a cluster matching degree model is established, thereby solving the problem that the prior art is directly used for all controllable resources, and the differences between different resources are ignored, resulting in long response time and poor aggregation effect. BRIEF DESCRIPTION OF DRAWINGS

[0016] The drawings accompanying the specification of the present application are used to provide further understanding of the present application, the illustrative embodiments of the present application and the description thereof are used to explain the present application, and do not constitute improper limitations on the present application. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a mobile terminal for performing a power grid resource aggregation method based on the panoramic theory is shown according to an embodiment of the present application;

[0018] Figure 2 A flowchart of a power grid resource aggregation method based on the panoramic theory is shown according to an embodiment of the present application;

[0019] Figure 3A flowchart of a specific panoramic theory-based power grid resource aggregation method according to an embodiment of the present application is shown.

[0020] Figure 4 A structural block diagram of a panoramic theory-based power grid resource aggregation device according to an embodiment of the present application is shown.

[0021] In the above drawings, reference numerals include the following:

[0022] 102, processor; 104, memory; 106, transmission device; 108, input / output device. DETAILED DESCRIPTION

[0023] It should be noted that the embodiments and features in the present application can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0024] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0026] As introduced in the background, the prior art directly uses all controllable resources, ignoring the differences between different resources. To solve the problem of long response time and poor aggregation effect caused by directly using all controllable resources and ignoring the differences between different resources, the embodiments of the present application provide a panoramic theory-based power grid resource aggregation method.

[0027] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application.

[0028] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a power grid resource aggregation method based on panoramic theory, according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0029] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the power grid resource aggregation method based on panoramic theory in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0030] A power grid resource aggregation method based on panoramic theory running on a mobile terminal, a computer terminal or a similar computing device is provided in the embodiment. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0031] Figure 2 FIG. 1 is a flowchart of a power grid resource aggregation method based on panoramic theory according to an embodiment of the present application. As shown in the figure, the method comprises the following steps: Figure 2

[0032] Step S201, obtaining the operation data of various resource devices in the power grid and the grouping indexes corresponding to the various resource devices, wherein the resource devices include distributed photovoltaic, direct-drive wind turbine;

[0033] Specifically, the operation state and performance characteristics of the distributed photovoltaic and direct-drive wind turbine have an important influence on the stability and efficiency of the power grid. In order to effectively manage and dispatch these resources, the operation data of the resources, including but not limited to output power, operation state, state of charge (for devices with energy storage function) and the like, need to be obtained first. These data can reflect the performance and availability of the resource devices at a specific time point or time period. The grouping indexes corresponding to the resource devices are parameters used to classify and cluster these devices. The selection of the grouping indexes should be able to represent the behavior similarity and response characteristics of the devices in the operation of the power grid, so as to classify the devices with similar behaviors into the same cluster. For the distributed photovoltaic, the reactive power balance degree and coupling degree are selected as the grouping indexes, and for the direct-drive wind turbine, the rotational speed, load shedding coefficient, inertia coefficient and damping coefficient are selected as the grouping indexes. By obtaining the operation data and grouping indexes, effective cluster analysis of the resource devices can be prepared.

[0034] Step S202, applying the grouping indexes corresponding to the various resource devices to cluster and divide the corresponding types of resource devices to obtain a plurality of resource clusters, and obtaining the resource information of each resource cluster, wherein the resource information includes device operation state information and state of charge information;

[0035] ​Specifically, by clustering, similar resource devices are grouped into a cluster, and the resource information refers to the detailed operating state of the devices within each cluster, including device operating state information and state of charge information, which are key to evaluating cluster scheduling potential and developing scheduling plans. The process of applying the grouping indicators corresponding to various resource devices to cluster and divide resource devices is the first step in fine-grained management of distributed resources in the power grid. This process aims to classify resources into different groups according to their characteristics. Through clustering, resources with similar characteristics are grouped into the same cluster, which allows the power grid scheduling layer to be more flexible and efficient when scheduling resources. When the power grid needs to respond quickly, resource clusters with short reaction times and fast response speeds can be prioritized.

[0036] In step S203, the scheduling potential of the corresponding resource cluster is quantified using the above resource information, and a scheduling potential value is obtained.

[0037] Specifically, through analysis and quantification of resource information, the scheduling potential value of the resource cluster can be obtained, i.e., the potential efficiency and performance of the resource cluster when performing task scheduling. The calculation of the scheduling potential value can be based on various indicators such as resource utilization, resource utilization balance, task completion time, etc. Through comprehensive analysis and calculation of resource information, the scheduling potential of the resource cluster can be evaluated, and a reference basis for task scheduling can be provided.

[0038] In step S204, a matching degree model of the power grid is established using the panoramic theory, and different scheduling potential values are input into the matching degree model for operation to obtain the matching degree between each pair of resource clusters, and the resource clusters are aggregated according to the matching degree to obtain a plurality of aggregated clusters, wherein the aggregated clusters are used for power dispatching of the power grid.

[0039] Specifically, the panoramic theory is a viewpoint that considers the system as a whole, believing that each element in the system is interrelated and interdependent, and cannot be considered in isolation. When establishing the matching degree model of the power grid, the panoramic theory can help consider the mutual relationship and influence of various resources in the power grid, thereby more accurately evaluating the matching degree between resources. By inputting different scheduling potential values into the matching degree model for operation, the matching degree between each pair of resource clusters can be obtained, which represents the adaptability and complementarity between different resource clusters, and can help determine which resource clusters can work better together and cooperate, thereby improving the efficiency and reliability of the power grid. According to the matching degree, the resource clusters are aggregated to obtain a plurality of aggregated clusters, which are combined according to the matching degree between resources, and can better utilize the complementarity and synergistic effect between resources.

[0040] The implementation of the matching degree model of the power grid using the panoramic theory is as follows:

[0041] Assume F i = {1, 2,..., q,..., m} and F j = {1, 2,..., r,..., m} represent the types and number of two clusters. Different numbers represent different clusters, and repeated numbers represent several clusters of the same resource type.

[0042] For r e F j , define s i (r) as the attraction of r to i. Considering the complementarity between different resource clusters, s i (r) is calculated as follows: where,

[0043] The meaning of the above formula is: when the cluster resources are different, the complementarity of the clusters is reflected, s q (r) > 0, and when the cluster resources are the same, there is randomness that further expands the possibility of greater fluctuations in the output of the distribution network, so s q (r) < 0.

[0044] The complementarity of each cluster determines the matching degree between clusters, so the matching degree between clusters i and j can be obtained:

[0045]

[0046] Through the definition of the above parameters, the panoramic theory energy function can be used to solve the optimal grouping, that is, the multi-cluster aggregation operation.

[0047] According to the strength of the complementarity of different distributed power sources, the parameter s q (r) is set.

[0048]

[0049] The meaning of the above formula is: when r ≠ q, the DG types are different and have complementary properties, r shows positive to q, that is, q is willing to work with r, so s q (r) > 0; when r = 4, r is FC, and other DGs have greater expectations for it due to fewer FC output constraints and convenient scheduling, so the complementarity is more obvious, and s q (r) = +3. When r = q, the DGs are the same and do not have complementarity, and when they are both PV and WT, their randomness increases, which increases the volatility of the microgrid output, so s q (r) = -5, indicating that q is extremely unwilling to work with r; when they are both FC, the expectations for each other are in a balanced state, and whether they work together or not has little impact on the global, so s q (r) = 0. Therefore, s qThe setting mode of (r) reflects the strength of complementarity between different DQs.

[0050]

[0051] In the above embodiment, firstly, operation data of various resource devices in a power grid and group indicators corresponding to the various resource devices are acquired, wherein the resource devices include distributed photovoltaic and direct-drive wind turbines; then, the various resource devices are clustered and divided according to the group indicators corresponding to the various resource devices, to obtain a plurality of resource clusters, and resource information of each resource cluster is acquired, wherein the resource information includes device operation state information and state of charge information; then, the scheduling potential of the corresponding resource cluster is quantified by using the resource information, to obtain a scheduling potential value; finally, a matching degree model of the power grid is established by using the panoramic theory, different scheduling potential values are input into the matching degree model for operation, to obtain matching degrees between two resource clusters, and the resource clusters are aggregated according to the matching degrees, to obtain a plurality of aggregated clusters, wherein the aggregated clusters are used for power scheduling of the power grid. In this scheme, after different resources are clustered and grouped, a cluster matching degree model is established, thereby solving the problem that the prior art directly uses all controllable resources, ignores the differences between different resources, and causes long response time and poor aggregation effect.

[0052] In an embodiment of the present application, after the resource clusters are aggregated according to the matching degrees, to obtain a plurality of aggregated clusters, the method further includes: determining a corresponding scheduling scheme according to the power demand and cost benefit of the power grid, wherein the scheduling scheme takes the maximum benefit of the power grid as a target.

[0053] Specifically, the power grid scheduling layer formulates a corresponding scheduling plan according to the timing demand and economic target, and schedules to maximize the benefit of the power grid. Based on the panoramic theory, resource partition clusters are obtained, each cluster collects real-time information such as device operation state and state of charge, calculates the capacity level of the cluster according to the collected information, and then reports to the scheduling layer. The scheduling layer formulates a corresponding scheduling optimization plan according to the timing characteristics demand and economic target of the power grid. This process aims to optimize the operation of the power grid, ensure efficient use of resources, and maximize the economic benefit of the power grid. Specifically, the power grid scheduling layer analyzes the load forecast, electricity price mechanism, resource cost and market transaction situation in the future period of time, combines the aggregated resource cluster information, and formulates a scheduling plan that can meet the power demand, reduce the cost and increase the benefit.

[0054] In one specific embodiment, a corresponding scheduling scheme is determined based on the power demand and cost-benefit of the aforementioned power grid, including: dividing a preset time period into a preset number of scheduling cycles, and establishing a target scheduling function with the goal of maximizing the revenue of the aforementioned power grid within the aforementioned scheduling cycles. in, In the formula, F represents the revenue target, f1(t) represents the power gain obtained by the adjustable resources according to the scheduling instructions during time period t, f2(t) represents the call cost of the adjustable resources during time period t, E(X) represents the system energy of the Xth aggregation cluster, and F e (X) The loss of the Xth aggregation cluster, P ij (t) represents the power response of the i-th aggregate and the j-th cluster in time period t, k represents the cost of scheduling the adjustable resource, and t0 represents the initial sampling time point.

[0055] Specifically, the preset time period is 24 hours, and the scheduling cycle is 96 time periods. The optimization objective within each cycle is to maximize the power grid revenue. This optimization objective is reflected through the target scheduling function, which comprehensively considers the power revenue of adjustable resources, the cost of calling them, and the system energy and loss of the aggregated cluster.

[0056] In another embodiment of this application, the clustering indices corresponding to various resource devices are applied to cluster the resource devices of the corresponding types, including: using the Fast Unfolding clustering algorithm combined with the network modularity function to cluster the distributed photovoltaic power of the power grid according to the first clustering index, wherein the first clustering index includes the total reactive power capacity of distributed photovoltaic power, the net load of the photovoltaic power access nodes in the cluster, the photovoltaic reactive power balance degree in the cluster, and the coupling degree between the nodes in the cluster, wherein the net load refers to the difference between the output load of the photovoltaic power and the node load; using the k-means clustering algorithm to cluster the direct-drive wind turbines of the power grid according to the second clustering index, wherein the second clustering index includes the speed, load reduction factor, inertia factor, and damping factor of the direct-drive wind turbines.

[0057] Specifically, the Fast Unfolding clustering algorithm is employed, combined with a complex network module degree function to avoid problems arising from the threshold k setting. The algorithm is completed through the following steps:

[0058] 1) Treat each node in the power grid as a separate cluster, randomly select two nodes to form a new cluster, thereby continuously increasing the modularity function value;

[0059] 2) Equivalent the new cluster generated in 1) to a single node, repeat the partitioning of the new cluster until the modularity function no longer increases, and obtain the optimal cluster partitioning.

[0060] The regulation capability of distributed photovoltaic (PV) systems can be reflected by their regulated capacity. Grouping distributed PV nodes with similar regulation capabilities into a cluster allows for unified reactive power support to the distribution network during emergencies, ensuring reliable voltage operation. Generally, when the voltage in the distribution network exceeds its upper limit, reducing the reactive power output of distributed PV can effectively adjust the voltage level. It can even absorb excess reactive power to achieve voltage control, thus ensuring stable and reliable operation of the distribution network. The reactive power output capability of a distributed PV power station determines the effectiveness of its reactive power and voltage control. Let the rated capacity of each PV inverter participating in grid connection be S. jmax In maximum power point tracking mode, its output active power is P. jMPPT Then its sensible reactive power can be expressed as Express the above equation as Q jmin ≤Q j ≤Q jmax Its specific expression is When a distributed photovoltaic power station consists of n photovoltaic inverters, the reactive power regulation capability can be simplified to the sum of the upper and lower limits of multiple inverters. Its total reactive power capacity can be expressed as: In the formula: Q jmin Q represents the capacitive reactive power adjustable capacity of the j-th grid-connected photovoltaic inverter. jmax Let Q be the adjustable inductive reactive power capacity of the j-th grid-connected photovoltaic inverter; min For the adjustable capacitive reactive power capacity of photovoltaic power plants; Q max This refers to the adjustable inductive reactive power capacity of photovoltaic power plants.

[0061] Another important selection criterion is the net load of the photovoltaic (PV) power nodes within the cluster. Node net load refers to the difference between PV power output and node load. PV power typically operates on a "self-consumption, surplus power to the grid" model. Therefore, node net load reflects the relationship between the output and load of the PV power connected to the node. This net load value is used as a key indicator for clustering to ensure more accurate cluster partitioning. The net load calculation formula is P... net_i =P pv_i -P L_i In the formula: P net_i The net load of distribution network node i is expressed in kW.

[0062] P pv_i P represents the photovoltaic power output of distribution network node i, in kW. L_i The load at the distribution network node is expressed in kW.

[0063] Research on cluster structural strength focuses on its external characteristics. Since some external characteristics of clusters are generally difficult to quantify, a modularity function can be used to quantify certain external characteristics, thereby quantifying the cluster structural strength and determining the optimal number of partitions. Therefore, the modularity function based on reactive power voltage sensitivity is defined as follows: k i =∑ j A ij , In the formula: A ij The weight of the voltage reactive power sensitivity edge between nodes ij; The change in voltage amplitude at node j caused by injecting a unit amount of reactive power into node i; m The sum of the edge weights of the module; k i Let be the sum of the weights of the edges containing node i.

[0064] When calculating the modularity function, the network topology is generally relied upon. In optimal clustering of a distribution network, the coupling degree of each node is used to reflect the coupling between nodes through the network topology. However, this clustering method has limitations; it cannot be relied upon for distribution networks with a high proportion of distributed photovoltaic (PV) penetration. Therefore, given the distribution network with a high proportion of distributed PV penetration, this embodiment adds two indicators: reactive power balance within the cluster and coupling degree within the cluster. These indicators characterize the PV reactive power balance capability within the cluster and the coupling degree between nodes within the cluster, respectively. This allows for accurate partitioning of the original modularity function based on the original indicators, avoiding situations of insufficient or excessive adjustable reactive power capacity. Regarding the reactive power balance indicator, cluster C... k The values ​​are as follows: In the formula: Q capacity Q represents the adjustable reactive power of a photovoltaic inverter. required This represents the minimum reactive power demand; ΔV i Let be the voltage change at node i; Let be the voltage sensitivity between the i-th photovoltaic unit and the i-th node. Regarding cluster C... k The coupling index value η Ck =avg(∑ i,j∈Ck (A ij In the formula, avg() means to calculate the average.

[0065] Based on the above-mentioned indicators, this embodiment improves the modularity function, which is expressed as follows: The average value of the reactive power balance index within the cluster is used to represent the quality of reactive power balance in each cluster after partitioning. Additionally, the average value of the coupling degree index within the cluster is also used to highlight the coupling degree between different nodes. These two values, combined with the modularity index (which reflects the coupling degree between nodes in the currently partitioned cluster), yield an improved modularity index. This not only supplements the network topology but also clearly reflects the reactive power balance capability of the cluster and the coupling degree between different nodes after high-proportion photovoltaic penetration. In the improved modularity function index, the weight of each cluster partitioning index is 1, because in this embodiment, all cluster partitioning indices are equally important during cluster partitioning, hence their equal weight.

[0066] This application takes direct-drive wind turbine units as the research object, and selects speed, load reduction coefficient, inertia coefficient and damping coefficient as clustering indicators based on adaptability analysis; then, based on the clustering indicators, the k-means algorithm is used to cluster the direct-drive wind turbines.

[0067] In overspeed load shedding control, the rotational speed is first increased by reserving a power standard, and then maximum power point tracking control is used to track the maximum power after load shedding for overspeed load shedding, reserving backup power for fault response. When operating at low to medium wind speeds, the paddle pitch angle is set to 0, and the overspeed load shedding control expression is:

[0068] In the formula, ρ is the air density; S is the swept area of ​​the wind turbine; λ is the tip speed ratio of the wind turbine blades; R is the blade radius of the wind turbine; and d is the load reduction factor. For turbines in overspeed load reduction control, the active power is affected by the combined effects of wind speed, turbine speed, and load reduction factor. By adjusting the optimal tip speed ratio, the wind turbine can output maximum mechanical power. At this point, wind speed and turbine speed are linearly related. Considering that the wind farm can monitor the turbine speed in real time, the turbine speed ω is selected as the grouping index to describe the high and low operating points of the wind turbine.

[0069] Under the same frequency fluctuations, the selection of the load shedding factor d will have a significant impact on the output of active power. Under stable operation, units with a larger load shedding factor have more reserved power and lower steady-state power. Under fluctuating conditions, units with a larger load shedding factor can provide greater supporting power, but will not exceed the rated power of the unit, while units with a smaller load shedding factor provide less supporting power and may waste the stored power. Therefore, the selection of the load shedding factor d is quite sensitive to frequency fluctuations and can be used as a cluster index to describe the magnitude of the reserved power for overspeed load shedding control.

[0070] The k-means clustering algorithm process is as follows:

[0071] 1) Read the four clustering indicators of each unit of the direct-drive wind turbine, determine the number of clusters according to the Canopy algorithm, randomly select points from the dataset as center points, calculate their distances to other points, and retain them if the distances are within the thresholds T1 and T2; otherwise, delete them. Repeat the loop until the dataset is cleared. The number of retained points at this point is the optimal number of clusters K.

[0072] 2) Randomly select K values ​​from each group of cluster index data as the initial cluster centers.

[0073] 3) Based on the formula The distance from the cluster index data to each cluster center is calculated sequentially, and the data is then grouped into the nearest cluster. In the formula, H represents the spatial dimension; x i x j These are the two objects for which the distance needs to be calculated.

[0074] 4) Based on the formula Recalculate and update the center of each cluster. In the formula, E is the sum of squared errors for all objects; y i Represents the remaining data object; C k This indicates that the data is assigned to k clusters; c k It is cluster C k The center point.

[0075] 5) Determine if the new cluster centers are equal to the original cluster centers. If they are equal, proceed to step 6); otherwise, proceed to step 3.

[0076] 6) Output the clustering results.

[0077] In another embodiment of this application, different scheduling potential values ​​are input into the matching degree model to obtain the matching degree between each pair of resource clusters, including: according to the matching degree model: Determine the system energy between each pair of the above resource clusters, where E(X) is the system energy between two of the above aggregated clusters, X represents the pairwise grouping of the aggregated clusters, and s f s represents the size parameter of the f-th aggregation cluster mentioned above. e p represents the size parameter of the e-th aggregation cluster mentioned above. ef d represents the matching degree between the above aggregate clusters e and f. ef The distance between the above aggregated clusters e and f is represented; the optimal matching degree between any two of the above resource clusters is determined based on the above system energy, wherein the lower the above system energy, the higher the matching degree.

[0078] Specifically, the panorama theory applies to systems containing C distributed resources. Each individual has a scale parameter representing its importance within the system. This scale parameter can be calculated from multiple factors, the selection of which depends on the specific application. There is a matching degree parameter p between any two individuals in the system. ef The value represents the degree of matching between two individuals. A higher value indicates a greater potential for the two individuals to converge and operate together; conversely, a lower matching degree indicates that the two are not suitable to converge. After calculating the matching degree, the individuals are divided into several groups, and then the distance d between any two dispersed individuals is determined based on the grouping. ef (X). When two members e and f are in the same group, d ef (X) is 0, otherwise d ef (X) is 1. Establish the formula for calculating loss. In the formula, X represents the grouping situation; s f p represents the size parameter of the f-th individual; ef Indicates the degree of matching between individuals e and f; d ef This represents the distance between individuals e and f. The physical meaning of the above model is: when two individuals with a high degree of matching are not in the same group, it increases the grouping loss; conversely, it decreases the grouping loss. Therefore, a system energy model can be defined. Combining the above loss calculation formula and the above system energy model, the matching degree model is obtained. This matching degree model shows that the system energy is determined by the scale parameters of the individuals, the matching degree of joint operation, and the distance between the individuals. When two individuals with high matching degrees are in the same group and two individuals with low matching degrees are not in the same group, the system energy is lower. Therefore, the optimal grouping situation is when the system energy reaches its minimum value.

[0079] To achieve clustered coordinated control of distributed resources with multiple locations within a distribution network, the first step is to conduct research on the clustering of distributed resources. The local power grid containing a large number of distributed resources is regarded as a complex network. Considering the prerequisite that the partitioning is based on conventional clustering theory and there is no optimal number of regions, the panoramic theory is adopted to establish a matching degree model that considers factors such as the operating status of distributed resource equipment, the established operating strategy, and economic efficiency. Systems with high matching degree are aggregated and divided into sub-regions. After partitioning, each region can be controlled and regulated independently and in parallel.

[0080] In another embodiment of this application, the scheduling potential of the corresponding resource cluster is quantified using the above-mentioned resource information, including: using evaluation indicators to quantify the scheduling potential of the corresponding resource cluster based on the above-mentioned resource information of each resource cluster, wherein the evaluation indicators include the maximum regulating power of the adjustable resource, the response time of the adjustable resource to the distribution network request, the average response speed to the distribution network request after receiving the response signal, the time from the issuance of the distribution network request to the response of the adjustable resource, the rate at which the adjustable resource changes from the initial state to the normal power consumption state after completing the response, the steady-state response duration of the adjustable resource after receiving the response signal, and the response rate of the adjustable resource participating in the distribution network scheduling.

[0081] Specifically, based on the dynamic response process of adjustable resources participating in distribution network dispatch, a potential assessment model is established within the predefined clusters. This aims to quantitatively analyze the maximum potential that adjustable resource clusters can achieve in participating in distribution network dispatch. Various adjustable resources participating in distribution network dispatch are typically detected, evaluated, and controlled based on certain typical indicators to ensure the safe operation and optimized dispatch of the power grid. The typical indicators for assessing the potential of adjustable resources participating in dispatch constructed in this embodiment are as follows:

[0082] 1) Maximum regulated power ΔP: This represents the range of maximum power regulation that an adjustable resource can provide when responding, depending on the characteristics of different types of adjustable resources.

[0083] 2) Response Time T1: This refers to the time when the distribution network needs to adjust the balance between power supply and demand, allowing it to respond using adjustable resources and provide corresponding power. A shorter response time means that adjustable resources can quickly respond to changes in the distribution network's demand, thus helping the distribution network maintain stable operation. The formula for calculating response time is T1 = t1 - t2, where t1 is the moment the distribution network begins to send demand, and t2 is the moment when the distribution network's power drops to half of its maximum adjustable power.

[0084] 3) Average response rate v1: This represents the average response speed of adjustable resources to distribution network requests after receiving a response signal within a certain time range. It characterizes the average performance level that such adjustable resources can provide when participating in regulation. The formula for calculating the average response rate is as follows: Among them, t m The response signal was issued when the power dropped to 85% of the maximum regulated power.

[0085] 4) Response Duration T2: This represents the time spent by adjustable resources in responding to and processing the request, from the time the distribution network request is issued to the time the adjustable resource responds. The formula for calculating the response duration is T2 = t r -t2, where tr This indicates the moment when the electrical power returns to its initial stable value after the adjustable resources participate in the response.

[0086] 5) Response recovery rate v2: This represents the rate at which the adjustable resource transitions from its previous state to normal operating state after completing its response. The formula for calculating the response recovery rate is as follows: Where, ΔP h t represents the maximum power difference between the normal power consumption state and the response state of the adjustable resource. h t represents the moment when the adjustable resource reaches its peak power under normal power consumption conditions. c The moment when adjustable resources begin to recover once the target adjustment amount has been reached.

[0087] 6) Steady-state response duration T3: This represents the length of time an adjustable resource maintains its state after receiving a response signal. This indicator reflects the ability of the adjustable resource cluster to participate in fine-grained control. The formula for calculating the steady-state response duration is T3 = t c -t m .

[0088] 7) Response rate R of adjustable resources participating in distribution network dispatch p Based on the user consumer psychological demand response model, a response model for various types of adjustable resources participating in power grid dispatch is established.

[0089] In one specific embodiment, the above method further includes: using a first formula:

[0090] Determine the response rate of the aforementioned adjustable resources participating in distribution network dispatch, where R p The response rate of the aforementioned adjustable resources participating in power grid dispatch is represented by P1, P2, and P3, which are important parameter values, P4 represents the uncertainty factor that can affect the potential of the aforementioned adjustable resources, and ξ represents the dispatch compensation price of the aforementioned adjustable resources.

[0091] Specifically, historical data on various adjustable resources are collected and statistically analyzed to assess their output characteristics and electricity consumption, identifying factors that can influence their potential. Based on a user consumer psychological demand response model, the relationship between the response rate of various adjustable resources participating in grid dispatch and the dispatch compensation price can be described as a piecewise linear function. This means that under a given price compensation, the response rate of adjustable resources participating in grid dispatch is not a single point but varies within a possible range, mainly divided into dead zones, linear zones, and saturation zones. A response model for adjustable resource participation is then established. Different historical datasets of adjustable resources correspond to different parameter values ​​in the following formula, thus yielding the response rate of each type of adjustable resource at a specific time point. Where R... p The response rate of adjustable loads and adjustable resources in grid dispatch is characterized by P1, P2, and P3, which are important parameter values. P4 represents an uncertainty factor that can affect the potential of adjustable resources, and ξ represents the dispatch compensation price of adjustable resources. P1 indicates that when the compensation price reaches a certain value, adjustable resources begin to respond to grid dispatch. When the compensation price reaches P2, the adjustable resources will maintain the maximum response rate P3. Based on the relationship between the historical response rates and compensation prices of different types of adjustable resources, P1, P2, and P3 for each type of adjustable resource can be calculated.

[0092] When calculating the response rate of various adjustable resources, a dataset is formed by taking the same time point within a year. Three important parameter values ​​are calculated based on the historical dataset, and the uncertainty factor is determined based on the historical response rate. Substituting these values ​​into the dataset, the response rate of a certain type of adjustable resource at the same time point is obtained.

[0093] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the power grid flexible and controllable resource aggregation method based on panoramic theory will be described in detail below with reference to specific embodiments.

[0094] This embodiment relates to a specific method for aggregating flexible and controllable resources in a power grid based on panoramic theory, such as... Figure 3 As shown, it includes the following steps:

[0095] Step S1: Statistically analyze the operation data of different resources in the power grid, such as distributed photovoltaic and direct-drive wind turbines, and perform clustering after specifying the clustering indicators for each type of resource.

[0096] Step S2: Collect the device operation status information and charge status information in each cluster after grouping, and quantify the scheduling potential of each cluster.

[0097] Step S3: Use panoramic theory to establish a matching degree model that considers the operating status and operating strategy of power grid equipment, and aggregate clusters with high matching degree into a sub-region.

[0098] Step S4: The power grid dispatching layer formulates corresponding dispatching plans based on timing requirements and economic objectives.

[0099] In step S1, to apply the panoramic theory to aggregation management, the first step is to group various resources such as large-scale distributed adjustable power supplies and flexible loads, and then perform statistical analysis on the data. Clustering can improve the fusion effect. To this end, this invention first clusters various resources according to different resource clustering indicators.

[0100] (1) Distributed photovoltaic power station:

[0101] This invention is based on the modularity index of photovoltaic reactive voltage sensitivity, taking into account the reactive power balance and coupling degree within the cluster, to obtain an improved modularity index and use it for cluster division.

[0102] The regulation capability of distributed photovoltaic (PV) systems can be reflected by their regulated capacity. Grouping distributed PV nodes with similar regulation capabilities into a cluster allows for unified reactive power support to the distribution network during emergencies, ensuring reliable voltage operation. Generally, when the voltage in the distribution network exceeds its upper limit, reducing the reactive power output of distributed PV can effectively adjust the voltage level. It can even absorb excess reactive power to achieve voltage control, thus ensuring stable and reliable operation of the distribution network. The reactive power output capability of a distributed PV power station determines the effectiveness of its reactive power and voltage control. Let the rated capacity of each PV inverter participating in grid connection be S. j max In maximum power point tracking mode, its output active power is P. jMPPT Then its sensible reactive power can be expressed as Express the above equation as Q j min ≤Q j ≤Q j max Its specific expression is When a distributed photovoltaic power station consists of n photovoltaic inverters, the reactive power regulation capability can be simplified to the sum of the upper and lower limits of multiple inverters. Its total reactive power capacity can be expressed as: In the formula: Q j min Q represents the capacitive reactive power adjustable capacity of the j-th grid-connected photovoltaic inverter. j max Let Q be the adjustable inductive reactive power capacity of the j-th grid-connected photovoltaic inverter; min For the adjustable capacitive reactive power capacity of photovoltaic power plants; Q max This refers to the adjustable inductive reactive power capacity of photovoltaic power plants.

[0103] Another important selection criterion is the net load of the photovoltaic (PV) power nodes within the cluster. Node net load refers to the difference between PV power output and node load. PV power typically operates on a "self-consumption, surplus power to the grid" model. Therefore, node net load reflects the relationship between the output and load of the PV power connected to the node. This net load value is used as a key indicator for clustering to ensure more accurate cluster partitioning. The net load calculation formula is P... net_i =P pv_i -P L_i In the formula: P net_i P represents the net load of distribution network node i, in kW.pv_i P represents the photovoltaic power output of distribution network node i, in kW. L_i The load at the distribution network node is expressed in kW.

[0104] Research on cluster structural strength focuses on its external characteristics. Since some external characteristics of clusters are generally difficult to quantify, a modularity function can be used to quantify certain external characteristics, thereby quantifying the cluster structural strength and determining the optimal number of partitions. Therefore, the modularity function based on reactive power voltage sensitivity is defined as follows: k i =∑ j A ij , In the formula: A ij The weight of the voltage reactive power sensitivity between nodes ij is the magnitude of the edge weight. The change in voltage amplitude at node j caused by injecting a unit amount of reactive power into node i; m The sum of the edge weights of the module; k i Let be the sum of the weights of the edges containing node i.

[0105] When calculating the modularity function, the network topology is generally relied upon. In optimal clustering of a distribution network, the coupling degree of each node is used to reflect the coupling between nodes through the network topology. However, this clustering method has limitations; it cannot be relied upon for distribution networks with a high proportion of distributed photovoltaic (PV) penetration. Therefore, given the distribution network with a high proportion of distributed PV penetration, this embodiment adds two indicators: reactive power balance within the cluster and coupling degree within the cluster. These indicators characterize the PV reactive power balance capability within the cluster and the coupling degree between nodes within the cluster, respectively. This allows for accurate partitioning of the original modularity function based on the original indicators, avoiding situations of insufficient or excessive adjustable reactive power capacity. Regarding the reactive power balance indicator, cluster C... k The values ​​are as follows: In the formula: Q capacity Q represents the adjustable reactive power of a photovoltaic inverter. required This represents the minimum reactive power demand; ΔV i Let be the voltage change at node i; Let be the voltage sensitivity between the i-th photovoltaic unit and the i-th node. Regarding cluster C... k The coupling index value η Ck =avg(∑ i,j∈Ck (A ij In the formula, avg() means to calculate the average.

[0106] Based on the above-mentioned indicators, this embodiment improves the modularity function, which is expressed as follows: The average value of the reactive power balance index within the cluster is used to represent the quality of reactive power balance in each cluster after partitioning. Additionally, the average value of the coupling degree index within the cluster is also used to highlight the coupling degree between different nodes. These two values, combined with the modularity index (which reflects the coupling degree between nodes in the currently partitioned cluster), yield an improved modularity index. This not only supplements the network topology but also clearly reflects the reactive power balance capability of the cluster and the coupling degree between different nodes after high-proportion photovoltaic penetration. In the improved modularity function index, the weight of each cluster partitioning index is 1, because in this embodiment, all cluster partitioning indices are equally important during cluster partitioning, hence their equal weight.

[0107] The algorithm can be completed by following these steps:

[0108] 1) Treat each node in the power grid as a separate cluster, randomly select two nodes to form a new cluster, thereby continuously increasing the modularity function value;

[0109] 2) Equivalent the new cluster generated in 1) to a single node, repeat the partitioning of the new cluster until the modularity function no longer increases, and obtain the optimal cluster partitioning.

[0110] (2) Direct-drive fan:

[0111] This invention takes direct-drive wind turbine units as the research object, and selects speed, load reduction coefficient, inertia coefficient and damping coefficient as clustering indicators based on adaptability analysis; then, based on the clustering indicators, the k-means algorithm is used to cluster the direct-drive wind turbines.

[0112] In overspeed load shedding control, the rotational speed is first increased by reserving a power standard, and then maximum power point tracking control is used to track the maximum power after load shedding for overspeed load shedding, reserving backup power for fault response. When operating at low to medium wind speeds, the paddle pitch angle is set to 0, and the overspeed load shedding control expression is: In the formula, ρ is the air density; S is the swept area of ​​the wind turbine; λ is the tip speed ratio of the wind turbine blades; R is the blade radius of the wind turbine; and d is the load reduction factor. For turbines in overspeed load reduction control, the active power is affected by the combined effects of wind speed, turbine speed, and load reduction factor. By adjusting the optimal tip speed ratio, the wind turbine can output maximum mechanical power. At this point, wind speed and turbine speed are linearly related. Considering that the wind farm can monitor the turbine speed in real time, the turbine speed ω is selected as the grouping index to describe the high and low operating points of the wind turbine.

[0113] Under the same frequency fluctuations, the selection of the load shedding factor d will have a significant impact on the output of active power. Under stable operation, units with a larger load shedding factor have more reserved power and lower steady-state power. Under fluctuating conditions, units with a larger load shedding factor can provide greater supporting power, but will not exceed the rated power of the unit, while units with a smaller load shedding factor provide less supporting power and may waste the stored power. Therefore, the selection of the load shedding factor d is quite sensitive to frequency fluctuations and can be used as a cluster index to describe the magnitude of the reserved power for overspeed load shedding control.

[0114] The k-means clustering algorithm process is as follows:

[0115] 1) Read the four clustering indicators of each unit of the direct-drive wind turbine, determine the number of clusters according to the Canopy algorithm, randomly select points from the dataset as center points, calculate their distances to other points, and retain them if the distances are within the thresholds T1 and T2; otherwise, delete them. Repeat the loop until the dataset is cleared. The number of retained points at this point is the optimal number of clusters K.

[0116] 2) Randomly select K values ​​from each group of cluster index data as the initial cluster centers.

[0117] 3) Based on the formula The distance from the cluster index data to each cluster center is calculated sequentially, and the data is then grouped into the nearest cluster. In the formula, H represents the spatial dimension; x i x j These are the two objects for which the distance needs to be calculated.

[0118] 4) Based on the formula Recalculate and update the center of each cluster. In the formula, E is the sum of squared errors for all objects; y i Represents the remaining data object; C k This indicates that the data is assigned to k clusters; c k It is cluster C k The center point.

[0119] 5) Determine if the new cluster centers are equal to the original cluster centers. If they are equal, proceed to step 6); otherwise, proceed to step 3.

[0120] 6) Output the clustering results.

[0121] (3) Electric vehicles:

[0122] The power distribution network provides electricity to charging and battery swapping stations, where electric vehicles (BEVs) can receive power bidirectionally from the grid. Within these stations, there are two charging methods: the first is a simpler but technically demanding method—battery swapping; the second is categorized by charging speed, from fastest to slowest, into three types: fast charging, conventional charging, and slow charging. Electric vehicles are also categorized into three types based on their model: public transport BEVs, private BEVs, and taxi BEVs.

[0123] The BEVs within the charging and battery swapping stations are classified and grouped as described above. The weighting coefficients for the number of public transport BEVs, taxi BEVs, and private BEVs are ω1, ω2, and ω3, respectively; the weighting coefficients for battery swapping, fast charging, conventional charging, and slow charging methods are σ1, σ2, σ3, and σ4, respectively. The charging power is set to P... N P high P normal and P slow .

[0124] Continuing our analysis, we assume that the target charging power for each BEV user is the rated power of the BEV. Therefore, we can derive the expressions for the total charging power of public transport BEVs, taxi BEVs, and private BEVs, respectively:

[0125] SOC(i,t) is the state of charge value of the i-th bus BEV at time t, that is, the percentage of battery remaining power. Assuming that the vehicle is intended to be fully charged each time, the power value that the BEV needs to be charged immediately can be obtained by subtracting the SOC(i,t) value from 1 and multiplying it by the rated power of the BEV. Similarly, SOC(j,t) and SOC(k,t) are the state of charge values ​​of taxis and private cars, respectively.

[0126] Similarly, the expressions for the total power of battery swapping, total power of fast charging, total power of conventional charging, and total power of slow charging are:

[0127]

[0128] In step S2, information such as the operating status and charge status of devices in each cluster is collected to quantify the cluster scheduling potential.

[0129] Based on the dynamic response process of adjustable resources participating in distribution network dispatch, a potential assessment model is established within predefined clusters to quantitatively analyze the maximum potential that adjustable resource clusters can achieve in participating in distribution network dispatch. Various adjustable resources participating in distribution network dispatch are typically detected, assessed, and controlled based on certain typical indicators to ensure the safe operation and optimized dispatch of the power grid. The typical indicators for assessing the potential of adjustable resources participating in dispatch constructed in this invention are as follows:

[0130] 1) Maximum regulated power ΔP: This represents the range of maximum power regulation that an adjustable resource can provide when responding, depending on the characteristics of different types of adjustable resources.

[0131] 2) Response Time T1: This refers to the time when the distribution network needs to adjust the balance between power supply and demand, allowing it to respond using adjustable resources and provide corresponding power. A shorter response time means that adjustable resources can quickly respond to changes in the distribution network's demand, thus helping the distribution network maintain stable operation. The formula for calculating response time is T1 = t1 - t2, where t1 is the moment the distribution network begins to send demand, and t2 is the moment when the distribution network's power drops to half of its maximum adjustable power.

[0132] 3) Average response rate v1: This represents the average response speed of adjustable resources to distribution network requests after receiving a response signal within a certain time range. It characterizes the average performance level that such adjustable resources can provide when participating in regulation. The formula for calculating the average response rate is as follows: Among them, t m The response signal was issued when the power dropped to 85% of the maximum regulated power.

[0133] 4) Response Duration T2: This represents the time spent by adjustable resources in responding to and processing the request, from the time the distribution network request is issued to the time the adjustable resource responds. The formula for calculating the response duration is T2 = t r -t2, where t r This indicates the moment when the electrical power returns to its initial stable value after the adjustable resources participate in the response.

[0134] 5) Response recovery rate v2: This represents the rate at which the adjustable resource transitions from its previous state to normal operating state after completing its response. The formula for calculating the response recovery rate is as follows: Where, ΔP h t represents the maximum power difference between the normal power consumption state and the response state of the adjustable resource. h t represents the moment when the adjustable resource reaches its peak power under normal power consumption conditions. c The moment when adjustable resources begin to recover once the target adjustment amount has been reached.

[0135] 6) Steady-state response duration T3: This represents the length of time an adjustable resource maintains its state after receiving a response signal. This indicator reflects the ability of the adjustable resource cluster to participate in fine-grained control. The formula for calculating the steady-state response duration is T3 = t c -t m .

[0136] 7) Response rate R of adjustable resources participating in distribution network dispatch pBased on the user consumer psychological demand response model, a response model for various types of adjustable resources participating in power grid dispatch is established.

[0137] In step S3, the collected historical data of various adjustable resources are statistically analyzed to determine their output characteristics and electricity consumption, identifying factors that can influence the potential of these resources. Based on a user consumer psychological demand response model, the relationship between the response rate of various adjustable resources participating in grid dispatch and the dispatch compensation price can be described as a piecewise linear function. That is, under a certain price compensation, the response rate of adjustable resources participating in grid dispatch is not a single point, but varies within a possible range, mainly divided into dead zones, linear zones, and saturation zones. A response model for adjustable resource participation is established. Different historical datasets of adjustable resources correspond to different parameter values ​​in the following formula, thus yielding the response rate of each type of adjustable resource at a specific time point. Where R... p The response rate of adjustable loads and adjustable resources in grid dispatch is characterized by P1, P2, and P3, which are important parameter values. P4 represents an uncertainty factor that can affect the potential of adjustable resources, and ξ represents the dispatch compensation price of adjustable resources. P1 indicates that when the compensation price reaches a certain value, adjustable resources begin to respond to grid dispatch. When the compensation price reaches P2, the adjustable resources will maintain the maximum response rate P3. Based on the relationship between the historical response rates and compensation prices of different types of adjustable resources, P1, P2, and P3 for each type of adjustable resource can be calculated.

[0138] When calculating the response rate of various adjustable resources, a dataset is formed by taking the same time point within a year. Three important parameter values ​​are calculated based on the historical dataset, and the uncertainty factor is determined based on the historical response rate. Substituting these values ​​into the dataset, the response rate of a certain type of adjustable resource at the same time point is obtained.

[0139] A matching degree model considering the operating status and operating strategy of power grid equipment is established using panoramic theory, and clusters with high matching degree are aggregated and divided into a region.

[0140] Panoramic theory applies to systems containing C distributed resources. Each individual has a scale parameter representing its importance within the system. This scale parameter can be calculated from multiple factors, the selection of which depends on the specific application. There is a matching degree parameter p between any two individuals in the system. efThe value represents the degree of matching between two individuals. A higher value indicates a greater potential for the two individuals to converge and operate together; conversely, a lower matching degree indicates that the two are not suitable to converge. After calculating the matching degree, the individuals are divided into several groups, and then the distance d between any two dispersed individuals is determined based on the grouping. ef (X). When two members e and f are in the same group, d ef (X) is 0, otherwise d ef (X) is 1. Establish the formula for calculating loss. In the formula, X represents the grouping situation; s f p represents the size parameter of the f-th individual; ef Indicates the degree of matching between individuals e and f; d ef This represents the distance between individuals e and f. The physical meaning of the above model is: when two individuals with a high degree of matching are not in the same group, it increases the grouping loss; conversely, it decreases the grouping loss. Therefore, a system energy model can be defined. Combining the above loss calculation formula and the above system energy model, the matching degree model is obtained. This matching degree model shows that the system energy is determined by the scale parameters of the individuals, the matching degree of joint operation, and the distance between the individuals. When two individuals with high matching degrees are in the same group and two individuals with low matching degrees are not in the same group, the system energy is lower. Therefore, the optimal grouping situation is when the system energy reaches its minimum value.

[0141] To achieve clustered coordinated control of distributed resources with multiple locations within a distribution network, the first step is to conduct research on the clustering of distributed resources. The local power grid containing a large number of distributed resources is regarded as a complex network. Considering the prerequisite that the partitioning is based on conventional clustering theory and there is no optimal number of regions, the panoramic theory is adopted to establish a matching degree model that considers factors such as the operating status of distributed resource equipment, the established operating strategy, and economic efficiency. Systems with high matching degree are aggregated and divided into sub-regions. After partitioning, each region can be controlled and regulated independently and in parallel.

[0142] In step S4, the power grid dispatching layer formulates a corresponding dispatching plan based on temporal demand and economic objectives, aiming to maximize power grid revenue. Resource partitioning clusters are obtained based on panoramic theory. Each cluster collects real-time information such as the operating status and state of charge of each device, calculates the cluster's capacity level based on the collected information, and then reports it to the dispatching layer. The dispatching layer formulates a corresponding dispatching optimization plan based on the power grid's temporal characteristics and economic objectives.

[0143] Divide 24 hours into 96 time periods, and establish a target scheduling function with the objective of maximizing daily revenue within scheduling period T: in, In the formula, F represents the revenue target, f1(t) represents the power gain obtained by the adjustable resources according to the scheduling instructions during time period t, f2(t) represents the call cost of the adjustable resources during time period t, E(X) represents the system energy of the Xth aggregation cluster, and F e (X) The loss of the Xth aggregation cluster, P ij (t) represents the power response of the i-th aggregate and the j-th cluster in time period t, k represents the cost of scheduling the adjustable resource, and t0 represents the initial sampling time point.

[0144] This application also provides a power grid flexible and controllable resource aggregation device based on panoramic theory. It should be noted that this device can be used to execute the power grid flexible and controllable resource aggregation method based on panoramic theory provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0145] The following describes the power grid flexible and controllable resource aggregation device based on panoramic theory provided in the embodiments of this application.

[0146] Figure 4 This is a structural block diagram of a power grid flexible and controllable resource aggregation device based on panoramic theory, according to an embodiment of this application. Figure 4 As shown, the device includes:

[0147] The acquisition unit 10 is used to acquire the operating data of various resource devices in the power grid and the clustering indicators corresponding to the various resource devices, wherein the resource devices include distributed photovoltaic and direct-drive wind turbines.

[0148] The segmentation unit 20 is used to apply the clustering index corresponding to each of the above-mentioned resource devices to cluster the above-mentioned resource devices of the corresponding types, to obtain multiple resource clusters, and to obtain resource information of each of the above-mentioned resource clusters, wherein the above-mentioned resource information includes equipment operating status information and charge status information.

[0149] The quantization unit 30 is used to quantify the scheduling potential of the corresponding resource cluster using the above-mentioned resource information to obtain a scheduling potential value.

[0150] The processing unit 40 is used to establish a matching degree model of the power grid using panoramic theory, and input different scheduling potential values ​​into the matching degree model to calculate the matching degree between each pair of resource clusters, and aggregate the resource clusters according to the matching degree to obtain multiple aggregated clusters, wherein the aggregated clusters are used for power dispatching of the power grid.

[0151] In the above embodiments, by clustering different resources and establishing a cluster matching degree model, the problem of long response time and poor aggregation effect caused by directly applying existing technologies to all controllable resources and ignoring the differences between different resources is solved.

[0152] In one embodiment of this application, after aggregating the resource clusters according to the aforementioned matching degree to obtain multiple aggregated clusters, the apparatus further includes:

[0153] The determining unit is used to determine the corresponding dispatching scheme based on the electricity demand and cost-benefit of the aforementioned power grid, wherein the dispatching scheme aims to maximize the benefit of the aforementioned power grid.

[0154] In one specific embodiment, the determining unit includes: a partitioning module, configured to divide a preset time period into a preset number of scheduling cycles, and establish a target scheduling function with the objective of maximizing the benefit of the power grid within the scheduling cycles. in, In the formula, F represents the revenue target, f1(t) represents the power gain obtained by the adjustable resources according to the scheduling instructions during time period t, f2(t) represents the call cost of the adjustable resources during time period t, E(X) represents the system energy of the Xth aggregation cluster, and F e (X) The loss of the Xth aggregation cluster, P ij (t) represents the power response of the i-th aggregate and the j-th cluster in time period t, k represents the cost of scheduling the adjustable resource, and t0 represents the initial sampling time point.

[0155] Specifically, the preset time period is 24 hours, and the scheduling cycle is 96 time periods. The optimization objective within each cycle is to maximize the power grid revenue. This optimization objective is reflected through the target scheduling function, which comprehensively considers the power revenue of adjustable resources, the cost of calling them, and the system energy and loss of the aggregated cluster.

[0156] In another embodiment of this application, the partitioning unit includes: a first clustering module, used to cluster the distributed photovoltaic power grid according to a first clustering index by employing the FastUnfolding clustering algorithm combined with a network modularity function, wherein the first clustering index includes the total reactive power capacity of the distributed photovoltaic power grid, the net load of the photovoltaic power source access nodes within the cluster, the photovoltaic reactive power balance within the cluster, and the coupling degree between the nodes within the cluster, wherein the net load refers to the difference between the output load of the photovoltaic power source and the node load; and a second clustering module, used to cluster the direct-drive wind turbines of the power grid according to a second clustering index by employing the k-means clustering algorithm, wherein the second clustering index includes the speed, load reduction factor, inertia factor, and damping factor of the direct-drive wind turbines. Specifically, the FastUnfolding clustering algorithm is used in conjunction with a complex network modularity function to avoid problems caused by setting a threshold k.

[0157] In another embodiment of this application, the processing unit includes: a first determining module, configured to determine the matching degree model: Determine the system energy between each pair of the above resource clusters, where E(X) is the system energy between two of the above aggregated clusters, X represents the pairwise grouping of the aggregated clusters, and s f s represents the size parameter of the f-th aggregation cluster mentioned above. e p represents the size parameter of the e-th aggregation cluster mentioned above. ef d represents the matching degree between the above aggregate clusters e and f. ef The distance between the above aggregated clusters e and f is represented by the first module; the second determining module is used to determine the optimal matching degree between any two of the above resource clusters based on the above system energy, wherein the lower the above system energy, the higher the matching degree.

[0158] In another embodiment of this application, the quantization unit includes a quantization module, used to quantify the scheduling potential of the corresponding resource cluster based on the resource information of each resource cluster using evaluation indicators. The evaluation indicators include the maximum regulating power of the adjustable resource, the response time of the adjustable resource to the distribution network request, the average response speed to the distribution network request after receiving the response signal, the time from the issuance of the distribution network request to the response of the adjustable resource, the rate at which the adjustable resource changes from the initial state to the normal power consumption state after completing the response, the steady-state response duration of the adjustable resource after receiving the response signal, and the response rate of the adjustable resource participating in distribution network scheduling.

[0159] In one specific embodiment, the quantization module further includes: a determining submodule, used to employ a first formula: Determine the response rate of the aforementioned adjustable resources participating in distribution network dispatch, where Rp The response rate of the aforementioned adjustable resources participating in power grid dispatch is represented by P1, P2, and P3, which are important parameter values, P4 represents the uncertainty factor that can affect the potential of the aforementioned adjustable resources, and ξ represents the dispatch compensation price of the aforementioned adjustable resources.

[0160] The aforementioned power grid resource aggregation device based on panoramic theory includes a processor and a memory. The acquisition unit, partitioning unit, quantization unit, and processing unit are all stored as program units in the memory, and the processor executes these program units to achieve their respective functions. All of the above modules reside in the same processor; alternatively, the modules may be located in different processors in any combination.

[0161] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0162] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to execute the above-mentioned power grid resource aggregation method based on panoramic theory.

[0163] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor.

[0164] This application also provides a computer program product that, when executed on a data processing device, is adapted to execute a program that initializes a power grid resource aggregation method with at least a panorama theory-based step.

[0165] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0166] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0167] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0168] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0169] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0170] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0171] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0172] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0173] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0174] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A power grid resource aggregation method based on panorama theory, characterized in that, The method comprises: acquiring operation data of various resource devices in a power grid and grouping indexes corresponding to the various resource devices, wherein the resource devices include distributed photovoltaic and direct-drive wind turbines; performing cluster division on each of the resource devices according to the grouping indexes corresponding to the various resource devices, to obtain a plurality of resource clusters, and acquiring resource information of each of the resource clusters, wherein the resource information includes device operation state information and state of charge information; quantifying scheduling potential of the corresponding resource cluster by using the resource information, to obtain a scheduling potential value; establishing a matching degree model of the power grid by using the panoramic theory, inputting different scheduling potential values into the matching degree model for operation, to obtain a matching degree between each two of the resource clusters, and performing aggregation processing on the resource clusters according to the matching degree, to obtain a plurality of aggregated clusters, wherein the aggregated clusters are used for power scheduling of the power grid; after the aggregation processing on the resource clusters according to the matching degree, to obtain a plurality of aggregated clusters, the method further comprises: determining a corresponding scheduling scheme according to power demand and cost benefit of the power grid, wherein the scheduling scheme aims to maximize the benefit of the power grid; The scheduling scheme is determined according to the power demand and cost benefit of the power grid, comprising: dividing a preset time period into a preset number of scheduling periods, and establishing a target scheduling function with the maximum benefit of the power grid in the scheduling period as a target: , wherein, , in the formula, F represents a benefit target, represents the power benefit obtained by the adjustable resource in the t period according to the scheduling instruction, represents the calling cost of the adjustable resource in the t period, is the system energy between X aggregated clusters, and X represents the grouping situation of the two-to-two aggregated clusters, is the loss degree between the X aggregated clusters, represents the power response of the jth cluster in the ith aggregated body in the t period, and k represents the cost price of scheduling the adjustable resource, represents an initial sampling time point; performing cluster division on each of the resource devices according to the grouping indexes corresponding to the various resource devices, comprises: performing cluster division on the distributed photovoltaic of the power grid according to a first grouping index by using a Fast Unfolding clustering algorithm combined with a network modularity function, wherein the first grouping index includes total distributed photovoltaic reactive power capacity, net load of a photovoltaic power supply access node in a cluster, photovoltaic reactive power balance degree in the cluster, and coupling degree between each node in the cluster; the net load refers to a difference between output load of the photovoltaic power supply and node load; performing cluster division on the direct-drive wind turbine of the power grid according to a second grouping index by using a k-means clustering algorithm, wherein the second grouping index includes rotational speed, load reduction coefficient, inertia coefficient and damping coefficient of the direct-drive wind turbine; quantifying scheduling potential of the corresponding resource cluster by using the resource information, comprises: quantifying scheduling potential of the corresponding resource cluster according to the resource information of each of the resource clusters by using an evaluation index, wherein the evaluation index includes maximum adjustable resource adjustment power, reaction time of an adjustable resource responding to a request of a power distribution network, average response speed of the adjustable resource to the request of the power distribution network after receiving a response signal, time from issuance of the request of the power distribution network to response of the adjustable resource, rate of the adjustable resource from an initial state to a normal power consumption state after completing the response, steady-state response duration of the adjustable resource after receiving the response signal, and response rate of the adjustable resource participating in scheduling of the power distribution network.

2. The method of claim 1, wherein, inputting different scheduling potential values into the matching degree model for operation, to obtain a matching degree between each two of the resource clusters, comprises: According to the matching degree model: , determine the system energy between the resource clusters, wherein, is the system energy between X aggregation clusters, X represents the two- two aggregation cluster grouping situation, s f represents the scale parameter of the fth aggregation cluster, s e represents the scale parameter of the e th aggregation cluster, p ef represents the matching degree between the aggregation clusters e and f, d ef represents the distance between the aggregation clusters e and f; determining an optimal matching degree between any two of the resource clusters according to system energy, wherein the lower the system energy, the higher the matching degree.

3. The method of claim 1, wherein, The method further comprises: using the first formula: determining the response rate of the adjustable resource participating in the dispatch of the power distribution network, wherein, characterizing the response rate of the adjustable resource participating in the dispatch of the power distribution network, respectively, an important parameter value, representing an uncertain factor capable of affecting the potential of the adjustable resource, representing the dispatch compensation price of the adjustable resource.

4. A power grid flexible controllable resource aggregation device based on panorama theory, characterized in that, comprising: The acquisition unit is configured to acquire operation data of various resource devices in a power grid and group indexes corresponding to the various resource devices, wherein the resource devices include distributed photovoltaic and direct-drive wind turbines. The division unit is configured to cluster and divide each of the resource devices according to the group indexes corresponding to the various resource devices, to obtain a plurality of resource clusters, and to acquire resource information of each of the resource clusters, wherein the resource information includes device operation state information and state of charge information. The quantification unit is configured to quantize scheduling potential of the corresponding resource cluster by using the resource information, to obtain a scheduling potential value. The processing unit is configured to establish a matching degree model of the power grid by using a panoramic theory, to input different scheduling potential values into the matching degree model for operation to obtain matching degrees between each two of the resource clusters, and to aggregate the resource clusters according to the matching degrees, to obtain a plurality of aggregated clusters, wherein the aggregated clusters are used for power scheduling of the power grid. The device further includes a determination unit configured to determine a corresponding scheduling scheme according to power consumption demand and cost benefit of the power grid, wherein the scheduling scheme aims to maximize benefit of the power grid. The determining unit comprises a division module, configured to divide a preset time period into a preset number of scheduling periods, and to establish a target scheduling function with the maximum benefit of the power grid in the scheduling period as a target: , wherein, , in the formula, F represents a benefit target, represents a power benefit obtained by the adjustable resource in the t period according to the scheduling instruction, represents a calling cost of the adjustable resource in the t period, is system energy between X aggregated clusters, and X represents a two-by-two aggregated cluster grouping condition, is a loss degree between the X aggregated clusters, represents a power response of the i-th aggregated body and the j-th cluster in the t period, and k represents a cost price of scheduling the adjustable resource, represents an initial sampling time point; The division unit includes a first clustering module configured to cluster and divide the distributed photovoltaic of the power grid according to a first group index by using a Fast Unfolding clustering algorithm combined with a network module function, wherein the first group index includes total reactive power capacity of the distributed photovoltaic, net load of a photovoltaic power supply access node in the cluster, reactive power balance degree in the cluster, and coupling degree between nodes in the cluster, and the net load refers to a difference between output load of the photovoltaic power supply and node load; and a second clustering module configured to cluster and divide the direct-drive wind turbine of the power grid according to a second group index by using a k-means clustering algorithm, wherein the second group index includes rotational speed, load reduction coefficient, inertia coefficient, and damping coefficient of the direct-drive wind turbine. The quantification unit includes a quantification module configured to quantize scheduling potential of the corresponding resource cluster according to the resource information of each of the resource clusters by using evaluation indexes, wherein the evaluation indexes include maximum adjustable resource adjustment power, response time of an adjustable resource to a request of a power distribution network, average response speed of the adjustable resource to the request of the power distribution network after receiving a response signal, time from sending of the request of the power distribution network to response of the adjustable resource, rate of the adjustable resource from an initial state to a normal power consumption state after completing the response, steady-state response duration of the adjustable resource after receiving the response signal, and response rate of the adjustable resource to participate in scheduling of the power distribution network.

5. A computer readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program controls a device where the computer-readable storage medium is located to perform the panoramic theory-based power grid resource aggregation method in any one of claims 1 to 3 when the program is run.

6. An electronic device, comprising: The computer-readable storage medium includes a stored program, wherein the program controls a device where the computer-readable storage medium is located to perform the panoramic theory-based power grid resource aggregation method in any one of claims 1 to 3 when the program is run. One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising programs for performing the power grid resource aggregation method based on the panorama theory according to any one of claims 1 to 3.

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