Multi-time scale frequency modulation control method for distributed resource equivalent clustering

Abstract

The invention provides a multi-time scale frequency modulation control method for distributed resource equivalent clustering, and relates to the technical field of power grids. According to the method, the distributed resources are finely classified according to the adjustment characteristics and the time response characteristics, accurate matching of the resources and the frequency modulation requirements on the time scale is ensured, then a multi-dimensional load cluster body with similar response characteristics is formed through space-time dual clustering analysis, the complexity of direct regulation and control of massive heterogeneous resources is greatly reduced, and the method is suitable for large-scale popularization and application. A foundation is laid for unified and efficient dispatching instruction issuing; on the basis, a frequency modulation control model with the aim of minimizing the adjustment cost and the frequency deviation is constructed, frequency modulation resources of different time scales are dynamically coordinated, and rapid and accurate suppression of the frequency deviation is achieved on the premise that the minimum resources are input. The technical problems that an existing frequency modulation strategy is limited in response speed and low in frequency stability are solved, and the frequency stability and the response speed in the frequency modulation process of the power system are improved.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and in particular to a multi-timescale frequency regulation control method based on distributed resource equivalent clustering. Background Technology

[0002] With the deepening implementation of the "dual-carbon" strategic goals, the penetration rate of renewable energy power generation such as wind power and photovoltaics in the power system is continuously increasing. However, the inherent intermittency, volatility, and uncertainty of renewable energy severely weaken the system's inertia support and primary frequency regulation capabilities, leading to increased system frequency fluctuations and a significant increase in the risk of exceeding frequency deviation limits. Traditional power systems mainly rely on synchronous generating units such as thermal and hydropower plants on the generation side to track load changes and smooth frequency fluctuations by adjusting the prime mover power output. However, synchronous generating units have long construction cycles, high frequency regulation costs, and their regulation speed is difficult to fully match the rapid fluctuations in renewable energy power. Especially at multiple time scales, from second-level inertial response and minute-level primary frequency regulation to longer-cycle secondary frequency regulation, the system faces the dual challenges of insufficient frequency regulation resources and increasingly complex regulation requirements. Therefore, developing and utilizing diversified and decentralized frequency regulation resources on the system side and load side to compensate for the insufficient frequency regulation capabilities on the generation side has become an inevitable choice to ensure the safe and stable operation of the new power system.

[0003] To address these challenges, demand-side response technologies, particularly precise control of distributed loads, are considered key to enhancing system flexibility and resilience. Distributed loads such as air conditioners, electric vehicles, and industrial controllable loads possess enormous adjustability potential, and aggregation can effectively transform them into a fast and efficient frequency regulation resource for system scheduling. However, existing frequency regulation strategies based on load aggregation still face several key technical bottlenecks: First, load resources are highly dispersed and heterogeneous, with significant differences in their operating characteristics and adjustability. Traditional centralized unified control methods struggle to achieve refined modeling and efficient aggregation, limiting control accuracy and response speed. Second, the dynamic response behavior of loads and system frequency regulation demands are significantly coupled across multiple time scales (seconds, minutes, hours). Existing strategies often focus on a single time scale, lacking a collaborative optimization framework from rapid inertial support to slow energy balance, which can easily lead to mismatched regulation resources or conflicting control commands, affecting system frequency stability. Summary of the Invention

[0004] This invention provides a multi-time-scale frequency regulation control method based on distributed resource equivalent clustering, which solves the technical problems of limited response speed and low frequency stability in existing frequency regulation strategies, and improves the frequency stability and response speed in the power system frequency regulation process.

[0005] In a first aspect, the present invention provides a multi-time-scale frequency modulation control method based on equivalent clustering of distributed resources. The method includes: acquiring attribute information of each distributed resource; classifying distributed resources based on their attribute information to determine multiple categories; extracting temporal and spatial clustering features of each category of distributed resources; performing equivalent clustering analysis based on these features to obtain a multi-dimensional load cluster; constructing a frequency modulation control model based on real-time frequency modulation requirements, with the goal of minimizing adjustment costs and frequency deviation; solving the frequency modulation control model and the multi-dimensional load cluster to obtain a multi-time-scale frequency modulation control strategy; and performing frequency modulation control on each distributed resource based on the multi-time-scale frequency modulation control strategy.

[0006] Secondly, embodiments of the present invention provide a multi-timescale frequency modulation control device for equivalent clustering of distributed resources. This frequency modulation control device includes: a communication module and a processing module. The communication module is used to acquire attribute information of each distributed resource; the processing module is used to classify and determine multiple types of distributed resources based on the attribute information of each distributed resource; extract the temporal and spatial clustering features of each type of distributed resource; perform equivalent clustering analysis based on the temporal and spatial clustering features of each type of distributed resource to obtain a multi-dimensional load cluster; construct a frequency modulation control model based on real-time frequency modulation requirements with the goal of minimizing adjustment costs and frequency deviation; solve the frequency modulation control model and the multi-dimensional load cluster to obtain a multi-timescale frequency modulation control strategy; and perform frequency modulation control on each distributed resource based on the multi-timescale frequency modulation control strategy.

[0007] Thirdly, embodiments of the present invention provide an electronic device including a memory and a processor. The memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to perform the steps of the method as described in the first aspect and any possible implementation thereof.

[0008] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of the method as described in the first aspect and any possible implementation thereof.

[0009] This invention provides a multi-timescale frequency regulation control method based on equivalent clustering of distributed resources. The method classifies distributed resources into fine categories according to their regulation and time response characteristics, ensuring precise matching of resources and frequency regulation demands across time scales. Subsequently, a multidimensional load cluster with similar response characteristics is formed through spatiotemporal dual clustering analysis, significantly reducing the complexity of direct regulation of massive heterogeneous resources and laying the foundation for unified and efficient dispatch command issuance. Furthermore, a frequency regulation control model is constructed with the goal of minimizing regulation costs and frequency deviations. This dynamically coordinates frequency regulation resources at different time scales, ensuring rapid and precise suppression of frequency deviations with minimal resource investment. This solves the technical problems of limited response speed and low frequency stability in existing frequency regulation strategies, improving frequency stability and response speed during power system frequency regulation. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a multi-time-scale frequency modulation control method for distributed resource equivalent clustering provided in an embodiment of the present invention. Figure 2 This is a schematic diagram comparing the frequency change curves of a system after being disturbed, provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a multi-timescale frequency modulation control device for distributed resource equivalent clustering provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0013] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0014] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include other steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or device.

[0015] To make the objectives, technical solutions, and advantages of the present invention clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0016] like Figure 1 As shown, this embodiment of the invention provides a multi-time-scale frequency modulation control method for distributed resource equivalent clustering. The method includes steps S101-S104.

[0017] S101. Obtain the attribute information of each distributed resource.

[0018] In some embodiments, the attribute information includes regulation characteristics and time response characteristics.

[0019] S102. Based on the attribute information of each distributed resource, classify and determine multiple types of distributed resources.

[0020] As one possible implementation, step S102 can be specifically implemented as steps S1021-S1022.

[0021] S1021. Based on the adjustment characteristics of each distributed resource, the distributed resources are classified into first-level categories to obtain loads that can be reduced, loads that can be transferred, and loads that can be directly controlled.

[0022] S1022. Based on the time response characteristics of each distributed resource, the distributed resources are classified into two levels to obtain day-ahead distributed resources, intraday distributed resources, and real-time distributed resources.

[0023] For example, embodiments of the present invention can be classified into loads that can be reduced, loads that can be transferred, and loads that can be directly controlled, based on the characteristics of the distributed resources and their application in frequency regulation.

[0024] For example, embodiments of the present invention can further classify distributed resources into day-ahead distributed resources, intraday distributed resources, and real-time distributed resources based on the differences in response time of different distributed resources, specifically in terms of advance notification time, response time, and response speed. The classification results of distributed resource response time characteristics are shown in Table 1.

[0025] Table 1

[0026] S103. Extract the temporal and spatial clustering features of various distributed resources; and based on the temporal and spatial clustering features of various distributed resources, perform equivalent clustering analysis to obtain a multidimensional load cluster.

[0027] As one possible implementation, step S103 can be specifically implemented as steps S1031-S1034.

[0028] S1031. For each type of distributed resource, extract time features to obtain time features.

[0029] In some embodiments, time characteristics include network access time, network exit time, expected response time, and initial network time period.

[0030] For example, embodiments of the present invention can extract time features for each type of distributed resource. The extracted time feature vector includes network entry time, network exit time, expected response time, initial network time period, etc., specifically represented as shown in the following formula: ;in, This is a clustering feature matrix extracted from distributed resources based on temporal characteristics. , , , These are clustering feature vectors for day-ahead reduceable, day-ahead transferable, intraday reduceable, and real-time directly controllable loads, respectively. These are the network access time, network disconnection time, and expected response time, respectively. This refers to the initial on-grid period for day-ahead transferable loads.

[0031] Distributed resources not only exhibit significant temporal complexity but also spatial complexity due to their spatial dispersion, large quantity, and small individual adjustable capacity. Scheduling individual distributed resources as control units inevitably leads to substantial computational burdens and makes it difficult to meet the grid's operational demands. Therefore, to mitigate the impact of distributed resource spatial complexity on their participation in distribution network control, cluster analysis based on spatial characteristics can be performed. Using clusters as control units to respond to grid control commands and achieving large-scale control can significantly reduce the difficulty of controlling multidimensional heterogeneous distributed resources.

[0032] S1032. Based on time characteristics, perform preliminary cluster analysis on distributed resources to obtain initial clusters based on time characteristics.

[0033] S1033. For the initial clusters, extract the spatial clustering features of each initial cluster.

[0034] In some embodiments, spatial clustering features include maximum controllable power, geographic location distribution density, and electrical distance between the initial cluster and the grid control node.

[0035] S1034. Based on spatial clustering characteristics, perform secondary clustering analysis on distributed resources, further aggregating the initial clusters with similar temporal and spatial characteristics to obtain multidimensional load clusters.

[0036] The comprehensive characterization parameters of the multidimensional load cluster include the centroid value of the time characteristics, the representative value of the spatial clustering characteristics, the equivalent adjustable capacity and adjustable time period of the cluster, the expected response time range of the cluster, and the total number of distributed resources contained in the cluster.

[0037] For example, in embodiments of the present invention, based on time features as the clustering basis, spatial clustering features can be further extracted from the classified distributed resources according to the different maximum adjustable power of different distributed resources. Specifically, as shown in the following formula: ;in, This is a clustering feature matrix extracted from distributed resources based on spatial characteristics. , , , These represent the maximum adjustable power of the day-ahead reduction, day-ahead transfer, intraday reduction, and real-time direct control load clusters, respectively.

[0038] For example, embodiments of the present invention can propose a multi-dimensional load clustering mechanism that considers the spatiotemporal characteristics of demand response resources by extracting and clustering temporal and spatial features based on an improved K-means clustering algorithm and utilizing different demand response resources. This clustering mechanism fully considers the response time and spatial distribution characteristics of distributed resources, and can fully consider the characteristics of distributed resources themselves during the clustering process, enabling distributed resources with similar characteristics to be aggregated. After distributed resources form clusters, the resources in each cluster have similar characteristics such as adjustable capacity, controllable time, expected response time, and maximum adjustable power. Resources within each cluster can be uniformly scheduled based on the adjustability reflected by the centroid of each cluster, thereby significantly reducing the complexity of regulating massive, multi-dimensional, heterogeneous distributed resources. The specific clustering steps are as follows: (1) Distributed resource information of user-reported load.

[0039] (2) Based on the distributed resource information declared by the user, the resource types are divided, and time features and spatial features are extracted respectively.

[0040] (3) First, perform steps (4)-(10) based on the extracted time clustering features.

[0041] (4) It is initially assumed that the number of clusters (clusters) of the four types of distributed resources is K.

[0042] (5) Calculate the Euclidean distance between two samples in each type of distributed resource. Let the number of samples be n, and the resulting dataset be... , The Euclidean distance between two samples is expressed as: .

[0043] (6) Determine the average distance of all samples in the distributed resource dataset. .

[0044] .

[0045] (7) Calculate the sample density of distributed resource data . .

[0046] when hour, ,otherwise The sample density set is represented as .

[0047] (8) Selecting the sample density set The distributed resource with the highest density is used as the centroid of the first cluster, the distributed resource with the second highest density is used as the centroid of the second cluster, and so on, until the target number of clusters is met.

[0048] (9) Represented as the first The average distance between data objects within a cluster is called the dispersion of distributed resource data objects.

[0049] (10) Reset the number of clusters K in step (4), repeat steps (2)-(5), and select the smallest K value as the optimal number of clusters. .

[0050] (11) Output the clustering results based on the time characteristics of distributed resources.

[0051] (12) Based on the clustering results obtained by using time features as the basis for clustering, and based on the extracted spatial clustering features, execute steps (4)-(10) until the clustering criteria are met and the final clustering results are output.

[0052] Based on the above clustering analysis results, the construction of clusters enables the aggregation of multidimensional heterogeneous distributed resources, which provides a foundation for optimal control in the subsequent frequency modulation stage.

[0053] S104. Based on real-time frequency modulation requirements, construct a frequency modulation control model with the goal of minimizing adjustment costs and frequency deviation.

[0054] As one possible implementation, step S104 can be specifically implemented as steps S1041-S1045.

[0055] S1041. Construct a multi-timescale objective function with the goal of minimizing adjustment costs and frequency deviation.

[0056] For example, embodiments of the present invention can achieve global optimal control of frequency trajectory (i.e. frequency deviation) by investing the least amount of frequency regulation resources based on the auxiliary role of distributed resources in power grid frequency regulation. A distributed resource frequency regulation control function expression is set that considers both the correction effect of frequency deviation and the minimization of distributed resource regulation costs.

[0057] For each distributed resource in the cluster, its responsiveness (including controllable load reduction and transfer) is closely related to its physical characteristics, response speed, and controllable capacity. Therefore, the frequency modulation control function for distributed resources should consider both the correction effect of frequency deviation and minimize the adjustment cost of distributed resources. The frequency modulation control function for distributed resources can be expressed as: ;in: It is the first Distributed resources in time Adjustable power; It is the load compensation willingness coefficient associated with each distributed resource, reflecting the adjustment cost of the resource; Is the system in time Frequency deviation; It is a weighting coefficient for frequency deviation, ensuring optimization of the frequency trajectory; This refers to the frequency modulation time range. The first part of the distributed resource frequency modulation control function represents the resource adjustment cost, and the second part is the penalty for frequency deviation. By minimizing the sum of these two parts, the goal of minimizing frequency modulation resource input and optimizing the frequency trajectory can be achieved.

[0058] S1042. Construct a dynamic balance equation characterizing the relationship between power generation, load power, and distributed resource regulation power, with the sum of the regulation power of distributed resources equal to the real-time frequency regulation demand power.

[0059] For example, to ensure that distributed resources can effectively participate in the frequency regulation of the power grid during frequency regulation, a dynamic balance equation is established between power generation, load power, and distributed resource regulation power based on physical constraints: ;in, The inertia constant of the power grid system reflects the system's ability to resist frequency changes. The larger the inertia, the slower the system's response to power disturbances and the smaller the frequency change. It is the system's power generation capacity; It is the system load; The total regulating power provided by distributed resources is expressed as: ;in, For the first Distributed resources in time Adjusting power output at all times The total number of distributed resources participating in frequency modulation.

[0060] This dynamic equilibrium equation describes the dynamic balance between power generation, load power, and distributed resource regulation power in the power grid. Through this equation's constraints, frequency regulation resources can effectively ensure the correction of grid frequency deviations, thereby maintaining grid frequency stability. The regulation power provided by distributed resources... Directly affects frequency deviation This, in turn, affects the overall operating status of the system.

[0061] S1043. Construct inequality constraints with the resource capacity constraints, response time constraints, and load recovery constraints of each distributed resource as constraints.

[0062] For example, in order to ensure the practical feasibility of the optimization model, inequality constraints are introduced from the aspects of resource capacity, time response, and load reduction and recovery, based on constraints such as time response.

[0063] For example, resource capacity limitations: the output power of each distributed resource. Subject to its maximum regulatory capacity The limitations are to ensure that its frequency modulation does not exceed the physical equipment's carrying capacity. The limitations are: This constraint ensures that the power output of each distributed resource does not exceed the physical limit of the equipment design, thus ensuring the stability and safety of the frequency modulation process.

[0064] For example, response time constraints: the response time of distributed resources. This reflects the time from receiving the frequency modulation command to the actual response. Different types of resources have different minimum response times. This constraint ensures that resources can respond promptly during frequency modulation, and its expression is: This constraint ensures that the adjustment speed of the resources meets the frequency regulation requirements, especially in emergency frequency regulation, and can intervene in a timely manner to provide the necessary regulation power.

[0065] For example, load shedding and recovery constraints: For distributed resources with load shedding, the shedding load needs to be restored within a certain time window to ensure that it does not have a long-term impact on user electricity demand. The balance constraints for shedding and recovery power are: ;in: Indicates the first Distributed resources in time Power reduction at any moment; and The first The start and end times of power reduction for distributed resources; Indicates the first Distributed resources in time Recovery power at any given moment; Indicates the time when the reduced power begins to recover; This indicates the duration required to restore the load. This constraint ensures that the electrical power lost due to load shedding can be compensated for during the restoration period, thereby maintaining power balance between the system and users.

[0066] Through the design of the distributed resource frequency regulation control function and distributed resource constraints described above, the equivalent cluster of distributed resources can achieve the dual objectives of minimizing resource input and optimizing frequency trajectory during frequency regulation. The distributed resource frequency regulation control function ensures the economy and stability of power grid frequency regulation by simultaneously minimizing regulation power and frequency deviation. Meanwhile, the equality and inequality distributed resource constraints, from the perspectives of system physical characteristics, response time, and load recovery, ensure that the actual regulation capability of distributed resources meets frequency regulation requirements while guaranteeing system safety and controllability.

[0067] S1044. Based on the response characteristics of multidimensional load clusters, and with the principle of matching day-ahead, intraday, and real-time distributed resources with secondary frequency regulation, primary frequency regulation, and inertial response requirements respectively, a multi-timescale coordination constraint is constructed.

[0068] For example, embodiments of the present invention can, based on the frequency regulation requirements of thermal power units in the inertial response, primary frequency regulation and secondary frequency regulation stages, consider the multi-timescale response characteristics of distributed resources, extract and cluster the time and spatial characteristics of different demand response resources, and through the extraction of the characteristics of excitation-type demand response resources, match the response potential of various resources with the system frequency regulation requirements to achieve optimal control under multiple timescales.

[0069] S1045. Based on multi-timescale objective functions, dynamic equilibrium equations, inequality constraints, and multi-timescale coordination constraints, a frequency modulation control model is constructed.

[0070] S105. Based on the frequency modulation control model and the multidimensional load cluster, the solution is obtained to obtain the multi-time-scale frequency modulation control strategy.

[0071] As one possible implementation, step S105 can be specifically implemented as steps S1051-S1054.

[0072] S1051. Decompose the real-time frequency modulation power demand into inertial response demand, primary frequency modulation demand and secondary frequency modulation demand according to the time scale.

[0073] S1052. Based on the comprehensive characterization parameters of the multidimensional load cluster, the inertial response requirements, primary frequency modulation requirements and secondary frequency modulation requirements are matched respectively to obtain the matching results.

[0074] In some embodiments, the matching results include multidimensional load clusters corresponding to the requirements at each time scale; S1053. Based on the matching results, the equivalent adjustable capacity and expected response time range of each multidimensional load cluster are used as input parameters, substituted into the frequency modulation control model, and the optimization algorithm is used to solve the problem to obtain the optimal adjustment power sequence of each multidimensional load cluster within the predicted time range. S1054. Based on the optimal regulation power sequence of each multidimensional load cluster within the prediction time range, generate a multi-time-scale frequency modulation control strategy.

[0075] In some embodiments, the multi-timescale frequency modulation control strategy includes the target regulating power curve of each multi-dimensional load cluster, the regulating time window of each multi-dimensional load cluster, the power change rate requirement of each multi-dimensional load cluster, and the time-series power excitation compensation scheme of each multi-dimensional load cluster.

[0076] S106. Frequency modulation control is performed on each distributed resource based on a multi-time-scale frequency modulation control strategy.

[0077] As one possible implementation, step S106 can be specifically implemented as steps S1061-S1066.

[0078] S1061. Based on the multi-time-scale frequency modulation control strategy, generate control commands for each multi-dimensional load cluster.

[0079] S1062. Based on the control instructions of each multidimensional load cluster, generate frequency modulation instructions for each distributed resource.

[0080] S1063. Distribute the frequency modulation command of each distributed resource to each distributed resource, and instruct each distributed resource to perform frequency modulation according to the frequency modulation command.

[0081] S1064. Real-time monitoring of actual frequency regulation results, as well as the actual regulation power, response time, and operating status of each distributed resource; S1065. Based on the actual frequency modulation results, as well as the actual regulation power, response time and operating status of each distributed resource, regenerate the multi-time-scale frequency modulation control strategy.

[0082] For example, step S1065 can be specifically implemented as steps A1-A5.

[0083] A1. Based on the system frequency data in the actual frequency modulation results, calculate the deviation between the actual frequency trajectory and the expected frequency trajectory.

[0084] A2. Based on the actual adjustment power and response time of each distributed resource, calculate the deviation between the actual adjustment capability and the expected adjustment capability of each multidimensional load cluster.

[0085] A3. Based on the deviation between the actual frequency trajectory and the expected frequency trajectory, as well as the deviation between the actual and expected adjustment capabilities of each multidimensional load cluster, the key parameters in the frequency modulation control model are dynamically corrected.

[0086] In some embodiments, key parameters include the equivalent adjustable capacity parameter of each multidimensional load cluster, the expected response time range of each multidimensional load cluster, and the load compensation willingness coefficient.

[0087] A4. Based on the actual operating status of each distributed resource, update the constraints in the frequency modulation control model to obtain the updated frequency modulation control model.

[0088] A5. Based on the updated frequency modulation control model, solve the problem again and regenerate the multi-time-scale frequency modulation control strategy.

[0089] In some embodiments, regenerating a multi-timescale frequency modulation control strategy includes updating the target regulation power curves of each multi-dimensional load cluster, adjusting the regulation time window of each multi-dimensional load cluster, reallocating the frequency modulation requirements of each time scale, and optimizing the timing power excitation compensation scheme.

[0090] S1066. Frequency modulation control is performed based on a multi-time-scale frequency modulation control strategy that is regenerated.

[0091] This invention provides a multi-timescale frequency regulation control method based on equivalent clustering of distributed resources. It performs refined classification of distributed resources according to their regulation and time response characteristics, ensuring precise matching of resources and frequency regulation demands across time scales. Subsequently, a multidimensional load cluster with similar response characteristics is formed through spatiotemporal dual clustering analysis, greatly reducing the complexity of direct regulation of massive heterogeneous resources and laying the foundation for unified and efficient dispatch command issuance. Furthermore, a frequency regulation control model is constructed with the goal of minimizing regulation costs and frequency deviations. This dynamically coordinates frequency regulation resources at different time scales, ensuring rapid and precise suppression of frequency deviations with minimal resource investment. This solves the technical problems of limited response speed and low frequency stability in existing frequency regulation strategies, improving frequency stability and response speed during power system frequency regulation.

[0092] Optionally, the multi-timescale frequency modulation control method for distributed resource equivalent clustering provided in this embodiment of the invention further includes steps S201-S203 after step S106.

[0093] S201. Record the actual response data of each multidimensional load cluster during the frequency modulation process.

[0094] In some embodiments, the actual response data includes the actual regulated power curve, the actual response time, and the actual regulated timing.

[0095] S202. Based on the time-series power excitation compensation scheme, determine the compensation benchmark parameters for each multidimensional load cluster.

[0096] In some embodiments, the compensation reference parameters include power compensation reference value, time compensation reference value, unit power compensation unit price, and unit time compensation coefficient.

[0097] S203. Based on the actual response data of each multidimensional load cluster and the compensation benchmark parameters, calculate the compensation scheme for each multidimensional load cluster.

[0098] For example, based on the above clustering analysis results, this embodiment of the invention proposes a distributed resource timing power incentive compensation mechanism that simultaneously considers response quantity and response time to effectively incentivize users to participate in responses at different frequency regulation stages. This incentive compensation mechanism not only provides economic incentives to users in terms of response quantity, allowing users with larger response quantities to receive higher economic compensation, but also tries to meet users' expected response time requirements as much as possible, ensuring that the actual response time issued by the power grid to users does not negatively affect users' response behavior. When the actual response time of a user deviates significantly from the expected response time, higher economic compensation will be given, thereby stimulating users' enthusiasm for participating in the response. In this way, the incentive mechanism combined with the clustering analysis results can achieve more efficient and accurate multi-timescale optimal control of the equivalent cluster of distributed resources. Specifically, as follows: The distributed resource time-series power compensation total price function establishes a distributed resource time-series power compensation total price model that simultaneously considers response quantity and response time, as shown in the following formula. Different economic compensation unit prices are set for loads with different response characteristics. This not only takes into account the differences in characteristics of different distributed resources, but also better stimulates the response potential of equivalent clusters of distributed resources.

[0099] ; in, For load category, These respectively represent day-ahead load reduction, day-ahead load transfer, intraday load reduction, and directly controlled load; For the first The first type of load One cluster; No. Type of load The control power of each cluster; For the first Type of load The total number of resources in each cluster; , They are respectively Time of the first The first type of load The power compensation willingness coefficient and time compensation coefficient of each cluster; for Time of the first The first of the types of loads Temporal power compensation willingness coefficients for each cluster; , For the first Power compensation willingness coefficient for similar loads; For the first The unit-time compensation coefficient for this type of load; For the first The first type of load The actual response time of each cluster; For the first The first type of load Expected response time for each cluster.

[0100] Distributed resource constraints, (1) load can be reduced: ;in, for The power that can be reduced at any time to decrease the load; (2) Transferable load: ; ;in, for The transferable power of the load that can be transferred at any time; The maximum transferable power of the transferable load; The connection time for transferable loads; The off-grid time for transferable loads; (3) Directly controlled load: ;in, for Adjustable power that can be directly controlled from the load at all times; To directly control the maximum adjustable power of the load; To directly control the timing of load connection to the grid; To directly control the off-grid timing of the load.

[0101] This invention's embodiments can consider a multi-dimensional load clustering mechanism that takes into account the spatiotemporal characteristics of demand response resources. It also fully considers the response time and spatial distribution characteristics of distributed resources. During the clustering process, the inherent characteristics of distributed resources are fully considered, allowing distributed resources with similar features to be aggregated. After distributed resources form clusters, the resources within each cluster have similar characteristics such as adjustable capacity, controllable time, expected response time, and maximum controllable power. Resources within each cluster can be uniformly scheduled based on the adjustability reflected by the centroid of each cluster, thereby significantly reducing the complexity of regulating massive, multi-dimensional, heterogeneous distributed resources. This enables precise control of power system frequency, improving the frequency stability and response speed of the power system.

[0102] For example, this invention uses data from a certain region in my country. Detailed information on various distributed resources declared by users is shown in Table 2. Among them, Indicates in The distributed resources are evenly distributed; the number of each type of distributed resource is 100. According to the analysis results in Table 2, the day-ahead distributed resources have the longest controllable duration, and are therefore most suitable for providing stable frequency modulation response in long-term frequency modulation during the secondary frequency modulation stage; the intraday distributed resources have a shorter controllable duration, and are suitable for short-term control during the primary frequency modulation stage; while the real-time distributed resources have the shortest controllable duration, and can provide rapid frequency adjustment capability during the inertial response stage.

[0103] Table 2

[0104] Table 3 shows the optimal number of clusters for distributed resources. This clustering process takes the transformer substation as a whole and clusters the distributed resources within the substation according to the extracted clustering features.

[0105] Table 3

[0106] Based on the optimal number of clusters for different distributed resources in each transformer area, the clustering results for different distributed resources in different transformer areas are obtained. Table 4 shows the day-ahead load reduction clustering results; Table 5 shows the day-ahead load transferable clustering results; Table 6 shows the intraday load reduction clustering results; and Table 7 shows the real-time direct control load clustering results.

[0107] Table 4

[0108] Table 5

[0109] Table 6

[0110] Table 7

[0111] The analysis results in Tables 3 to 7 show that the centroid of each cluster represents the electricity consumption behavior characteristics of users within that cluster, and there are significant differences in the response behavior of distributed resources in different clusters. The characteristics of each cluster, such as grid connection time, grid disconnection time, expected response time, and maximum reducible power, are all reflected by the centroid. Furthermore, transferable loads also include the characteristic of grid connection duration. Taking the day-ahead reducible load of transformer substation 1 as an example, the centroid characteristics of the first cluster in transformer substation 1 show that its grid connection time is 7:21, grid disconnection time is 17:06, expected response time is 10:05, and maximum reducible power is 1.26kW. These centroid characteristics not only represent the response characteristics of all resources within the cluster but also serve as the basis for unified regulation. By uniformly applying these characteristics to the regulation of clusters, the computational load and regulation complexity of distributed resources in the frequency regulation process can be significantly reduced, thereby achieving optimized frequency control of thermal power units at different time scales. More importantly, this clustering-based multi-timescale optimal control method can effectively combine the characteristics of thermal power units and distributed resources at various frequency regulation stages, enabling distributed resource clusters to achieve efficient and coordinated control of power grid frequency regulation at multiple time scales, providing strong support for the stable operation of the entire power system.

[0112] Figure 2 The following is a comparison of the frequency change curves after the system is disturbed. Curve 1 shows the frequency change curve of the multi-time-scale frequency regulation control strategy without distributed resource equivalent clustering, and curve 2 shows the frequency change curve of the multi-time-scale frequency regulation control strategy with distributed resource equivalent clustering. The simulation analysis of the system selectively participating in AC power grid frequency regulation is as follows. The sampling interval is set to 0.01s, and the time corresponding to the lowest frequency point in the AC power grid frequency characteristics is... The value of the neighborhood, the left neighborhood right neighbor ,exist At that time, the frequency change of the AC power grid , where is the maximum value of the frequency offset. Based on the time it takes for curves 1 and 2 to reach stability, the frequency of the control strategy of this invention, represented by curve 2, can recover to the reference value faster than that of curve 1.

[0113] This invention extracts time features from each type of distributed resource based on the classification results of loads that can be reduced, transferred, directly controlled, day-ahead, intraday, and real-time loads. The extracted time feature vectors include grid connection time, grid disconnection time, expected response time, and initial grid-connected period. Distributed resources not only exhibit significant time complexity but also spatial complexity due to their spatial dispersion, large number, and small individual adjustable capacity. Scheduling each distributed resource individually as a control unit would inevitably lead to a large computational burden and make it difficult to meet the grid's operational needs. Therefore, to reduce the impact of the spatial complexity of distributed resources on their participation in distribution network control, cluster analysis can be performed on distributed resources based on their spatial characteristics. Using clusters as control units to respond to grid control commands and perform large-scale control can significantly reduce the difficulty of controlling multidimensional heterogeneous distributed resources.

[0114] This invention, based on the frequency regulation requirements of thermal power units in the inertial response, primary frequency regulation, and secondary frequency regulation stages, considers the multi-timescale response characteristics of distributed resources and can extract and cluster the temporal and spatial characteristics of resources with different demand responses. By extracting the characteristics of incentive-driven demand response resources, the response potential of various resources can be matched with the system frequency regulation requirements, achieving optimal control at multiple time scales. An improved K-means algorithm is used, and cluster analysis is performed on the extracted clustering feature dataset of distributed resources. A multi-dimensional load clustering mechanism considering the spatiotemporal characteristics of demand response resources is proposed. This clustering mechanism fully considers the response time and spatial distribution characteristics of distributed resources, allowing for the aggregation of distributed resources with similar characteristics during the clustering process. After distributed resources form clusters, the resources within each cluster have similar adjustable capacity, controllable time, expected response time, and maximum controllable power. Resources within each cluster can be uniformly scheduled based on the adjustability reflected by the centroid of each cluster, thereby significantly reducing the complexity of regulating massive, multi-dimensional, heterogeneous distributed resources.

[0115] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0116] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0117] Figure 3 This diagram illustrates the structure of a multi-timescale frequency modulation control device for distributed resource equivalent clustering according to an embodiment of the present invention. The frequency modulation control device 300 includes a communication module 301 and a processing module 302.

[0118] The communication module 301 is used to obtain the attribute information of each distributed resource.

[0119] The processing module 302 is used to classify and determine multiple types of distributed resources based on the attribute information of each distributed resource; extract the temporal and spatial clustering features of each type of distributed resource; and perform equivalent clustering analysis based on the temporal and spatial clustering features of each type of distributed resource to obtain a multidimensional load cluster; construct a frequency regulation control model with the goal of minimizing regulation cost and frequency deviation based on real-time frequency regulation requirements; and solve the frequency regulation control model and the multidimensional load cluster to obtain a multi-time-scale frequency regulation control strategy; and perform frequency regulation control on each distributed resource based on the multi-time-scale frequency regulation control strategy.

[0120] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 400 includes: a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processor 401. When the processor 401 executes the computer program 403, it implements the steps in the above-described method embodiments. Alternatively, when the processor 401 executes the computer program 403, it implements the functions of each module / unit in the above-described device embodiments.

[0121] For example, the computer program 403 may be divided into one or more modules / units, which are stored in the memory 402 and executed by the processor 401 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 403 in the electronic device 400.

[0122] The processor 401 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0123] The memory 402 can be an internal storage unit of the electronic device 400, such as a hard disk or memory of the electronic device 400. The memory 402 can also be an external storage device of the electronic device 400, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD) card, flash card, etc., equipped on the electronic device 400. Furthermore, the memory 402 can include both internal and external storage units of the electronic device 400. The memory 402 is used to store the computer program and other programs and data required by the terminal. The memory 402 can also be used to temporarily store data that has been output or will be output.

[0124] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A distributed resource equivalent cluster multi-time scale frequency control method, characterized in that, The method comprises the following steps: acquiring attribute information of each distributed resource; based on the attribute information of each distributed resource, classifying and determining multiple types of distributed resources; extracting time characteristics and spatial clustering characteristics of each type of distributed resource; based on the time characteristics and spatial clustering characteristics of each type of distributed resource, performing equivalent clustering analysis to obtain a multi-dimensional load cluster; based on the real-time frequency regulation demand, constructing a frequency regulation control model with the minimum adjustment cost and frequency deviation as the target; based on the frequency regulation control model and the multi-dimensional load cluster, solving to obtain a multi-time scale frequency regulation control strategy; based on the multi-time scale frequency regulation control strategy, performing frequency regulation control on each distributed resource.

2. The distributed resource equivalent clustered multi-time scale frequency control method according to claim 1, wherein, The attribute information includes adjustment characteristics and time response characteristics; The classification based on the attribute information of each distributed resource to determine multiple types of distributed resources comprises: based on the adjustment characteristics of each distributed resource, performing first-level classification on each distributed resource to obtain reducible load, transferable load and directly controlled load; based on the time response characteristics of each distributed resource, performing second-level classification on each distributed resource to obtain day-ahead type distributed resource, intra-day type distributed resource and real-time type distributed resource.

3. The distributed resource equivalent clustered multi-time scale frequency control method according to claim 1, wherein, The extraction of time characteristics and spatial clustering characteristics of each type of distributed resource; and based on the time characteristics and spatial clustering characteristics of each type of distributed resource, performing equivalent clustering analysis to obtain a multi-dimensional load cluster, comprises: for each type of distributed resource, time characteristic extraction is performed to obtain time characteristics, which include network access time, network exit time, expected response time and initial network period; based on the time characteristics, performing preliminary clustering analysis on the distributed resources to obtain an initial cluster body clustered by time characteristics; for the initial cluster body, spatial clustering characteristics of each initial cluster body are extracted, which include maximum controllable power, geographical position distribution density, and electrical distance between the initial cluster body and the grid regulation node; based on the spatial clustering characteristics, performing secondary clustering analysis on the distributed resources to further aggregate the initial cluster bodies similar in time characteristics and spatial characteristics to obtain a multi-dimensional load cluster; wherein the comprehensive representation parameters of the multi-dimensional load cluster include the centroid value of the time characteristics, the representative value of the spatial clustering characteristics, the equivalent adjustable capacity and adjustable period of the cluster body, the expected response time range of the cluster body, and the total number of distributed resources contained in the cluster body.

4. The distributed resource equivalent clustered multi-time scale frequency control method of claim 1, wherein, The construction of the frequency regulation control model with the minimum adjustment cost and frequency deviation as the target based on the real-time frequency regulation demand comprises: constructing a multi-time scale objective function with the minimum adjustment cost and frequency deviation as the target; constructing a dynamic balance equation representing the balance among generation power, load power and distributed resource adjustment power, with the sum of the adjustment power of the distributed resources equal to the real-time frequency regulation demand power; constructing inequality constraints with resource capacity constraints, response time constraints and load recovery constraints of each distributed resource as constraint conditions; A multi-time scale coordination constraint is constructed based on the response characteristics of the multi-dimensional load clusters, and the principle of matching the secondary frequency modulation, the primary frequency modulation and the inertia response demand with the day-ahead type, the intra-day type and the real-time type distributed resources respectively; A frequency modulation control model is constructed based on the multi-time scale target function, the dynamic balance equation, the inequality constraint and the multi-time scale coordination constraint.

5. The distributed resource equivalent cluster multi-time scale frequency control method of claim 1, wherein, The frequency modulation control model and the multi-dimensional load clusters are used to solve the multi-time scale frequency modulation control strategy, including: The real-time frequency modulation demand power is decomposed into the inertia response demand, the primary frequency modulation demand and the secondary frequency modulation demand according to the time scale; The comprehensive representation parameters of the multi-dimensional load clusters are matched with the inertia response demand, the primary frequency modulation demand and the secondary frequency modulation demand respectively to obtain a matching result, which includes the multi-dimensional load clusters corresponding to each time scale demand; The matching result is used as an input parameter to substitute into the frequency modulation control model, and an optimization algorithm is used to solve the optimal adjustment power sequence of each multi-dimensional load cluster in the predicted time range, based on the equivalent adjustable capacity and the expected response time range of each multi-dimensional load cluster. A multi-time scale frequency modulation control strategy is generated based on the optimal adjustment power sequence of each multi-dimensional load cluster in the predicted time range, which includes the target adjustment power curve of each multi-dimensional load cluster, the adjustment time window of each multi-dimensional load cluster, the power change rate requirement of each multi-dimensional load cluster, and the timing power incentive compensation scheme of each multi-dimensional load cluster.

6. The distributed resource equivalent clustered multi-time scale frequency control method according to claim 1, wherein, The multi-time scale frequency modulation control strategy is used to control each distributed resource, including: Control instructions of each multi-dimensional load cluster are generated based on the multi-time scale frequency modulation control strategy; Frequency modulation instructions of each distributed resource are generated based on the control instructions of each multi-dimensional load cluster; The frequency modulation instructions of each distributed resource are distributed to each distributed resource to instruct each distributed resource to perform frequency modulation according to the frequency modulation instructions; Actual frequency modulation results, actual adjustment power, response time and operating state of each distributed resource are monitored in real time; The multi-time scale frequency modulation control strategy is regenerated based on the actual frequency modulation results and the actual adjustment power, response time and operating state of each distributed resource; The frequency modulation control is performed based on the regenerated multi-time scale frequency modulation control strategy.

7. The distributed resource equivalent cluster multi-time scale frequency control method of claim 6, wherein, The multi-time scale frequency modulation control strategy is regenerated based on the actual frequency modulation results and the actual adjustment power, response time and operating state of each distributed resource, including: The deviation between the actual frequency trajectory and the expected frequency trajectory is calculated based on the system frequency data in the actual frequency modulation results; The deviation between the actual adjustment capacity and the expected adjustment capacity of each multi-dimensional load cluster is calculated based on the actual adjustment power and the response time of each distributed resource; dynamically correct key parameters in the frequency regulation control model based on deviations between the actual frequency trajectory and the expected frequency trajectory, and deviations between actual adjustment capabilities and expected adjustment capabilities of each multi-dimensional load cluster; the key parameters include an equivalent adjustable capacity parameter of each multi-dimensional load cluster, a desired response time range of each multi-dimensional load cluster, and a load compensation willingness coefficient; update constraint conditions in the frequency regulation control model based on actual operating states of each distributed resource, to obtain an updated frequency regulation control model; re-solve and re-generate the multi-time-scale frequency regulation control strategy based on the updated frequency regulation control model; the re-generated multi-time-scale frequency regulation control strategy includes updating a target adjustment power curve of each multi-dimensional load cluster, adjusting an adjustment time window of each multi-dimensional load cluster, re-distributing frequency regulation demands of each time scale, and optimizing a timing power incentive compensation scheme.

8. The distributed resource equivalent clustered multi-time scale frequency control method of claim 1, wherein, After the frequency regulation control on each distributed resource based on the multi-time-scale frequency regulation control strategy, the method further includes: record actual response data of each multi-dimensional load cluster in the frequency regulation process, the actual response data including an actual adjustment power curve, an actual response time, and an actual adjustment timing time; determine compensation reference parameters of each multi-dimensional load cluster according to the timing power incentive compensation scheme, the compensation reference parameters including a power compensation reference value, a time compensation reference value, a unit power compensation unit price, and a unit time compensation coefficient; calculate a compensation scheme of each multi-dimensional load cluster based on the actual response data of each multi-dimensional load cluster and the compensation reference parameters.

9. A distributed resource equivalent cluster multi-time scale frequency control apparatus, characterized in that, The method includes: a communication module configured to acquire attribute information of each distributed resource; a processing module configured to classify and determine a plurality of types of distributed resources based on the attribute information of each distributed resource; extract time characteristics and spatial clustering characteristics of each type of distributed resource; perform equivalent clustering analysis based on the time characteristics and the spatial clustering characteristics of each type of distributed resource, to obtain multi-dimensional load clusters; construct a frequency regulation control model with minimization of adjustment cost and frequency deviation as an objective based on real-time frequency regulation demands; and perform solving based on the frequency regulation control model and the multi-dimensional load clusters, to obtain a multi-time-scale frequency regulation control strategy; perform frequency regulation control on each distributed resource based on the multi-time-scale frequency regulation control strategy.

10. An electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor being configured to invoke and run the computer program stored in the memory to execute the method according to any one of claims 1 to 8.

10. An electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor being configured to invoke and run the computer program stored in the memory to execute the method according to any one of claims 1 to 8.

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