Method and device for evaluating adjustable resource capability of power system, computer device, readable storage medium and program product

Abstract

The application relates to an adjustable resource capacity evaluation method and device of a power system, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: based on virtual power plants and resource aggregators, an adjustable resource hierarchical quantification model of the virtual power plants and the resource aggregators is constructed; based on the adjustable resource hierarchical quantification model, a multi-dimensional adjustment capacity evaluation system is constructed, then a real-time dynamic detection method is designed, the running state data of the adjustable resource is detected in real time, and a judgment result is generated; based on the running state data, an evaluation result of the running state is generated according to an adjustable resource evaluation mode; if the judgment result and the evaluation result both indicate normal, an adjustable resource capacity evaluation result indicating that the capacity of the resource is normal is obtained; if the judgment result or the evaluation result indicates abnormal, an adjustable resource capacity evaluation result indicating that the capacity of the resource is abnormal is obtained. The method can comprehensively and accurately identify the adjustable resource.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for assessing the adjustable resource capacity of a power system. Background Technology

[0002] With the deepening of power market reform and the advancement of new power system construction, virtual power plants and resource aggregators, as important carriers for the integration of adjustable resources, have become the core force for flexible regulation of the power system. Adjustable resource assessment and detection technologies are characterized by hierarchical subject structure, diversified resource types, dynamic operating status, and real-time regulation needs, which places higher demands on the comprehensiveness, timeliness, and accuracy of regulation capabilities.

[0003] Traditional technologies typically employ static data-based methods for assessing the adjustable resource capabilities of virtual power plants and resource aggregators. These methods involve uniformly modeling and weighting various adjustable resources under a single entity, and relying on fixed thresholds for anomaly detection and capability assessment. However, existing methods for assessing and detecting the adjustable resource capabilities of diverse and heterogeneous resources struggle to provide dynamic, comprehensive, and accurate real-time assessments and anomaly identification. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for assessing the adjustable resource capabilities of a power system, which can dynamically, comprehensively, and accurately perform real-time assessment and anomaly identification of diverse heterogeneous adjustable resources under virtual power plants and resource aggregators.

[0005] In a first aspect, this application provides a method for assessing the adjustable resource capacity of a power system, including:

[0006] Based on the adjustable resources of virtual power plants, a hierarchical model of adjustable resources of virtual power plants is constructed; based on the adjustable resources of resource aggregators, a hierarchical model of adjustable resources of resource aggregators is constructed; and a system-level coordination constraint model is constructed.

[0007] Based on the adjustable resource stratification model of virtual power plants, the adjustable resource stratification model of resource aggregators, and the system-level coordination constraint model, an adjustable resource stratification quantitative model for virtual power plants and resource aggregators is constructed; based on the adjustable resource stratification quantitative model, a multi-dimensional adjustment capability evaluation system is constructed.

[0008] Based on the adjustable resource hierarchical quantification model and multi-dimensional adjustment capability assessment system, a real-time dynamic detection method for the adjustment capability of adjustable resources is designed. The real-time dynamic detection method detects the operating status data of adjustable resources in real time and judges whether there are any anomalies in the operating status data, and generates the judgment result of the operating status. Based on the operating status data, according to the adjustable resource assessment method, the assessment result of the operating status is generated.

[0009] If both the judgment result and the assessment result indicate normality, an assessment result indicating normal resource availability is obtained. If either the judgment result or the assessment result indicates abnormality, an assessment result indicating abnormal resource availability is obtained.

[0010] In one embodiment, a hierarchical model of adjustable resources for a virtual power plant is constructed based on the adjustable resources of the virtual power plant, including:

[0011] Based on the maximum output, regulation coefficient, and output fluctuation coefficient of distributed power sources, a distributed power source regulation potential model is constructed; based on the rated total power, average state of charge, and charging and discharging power constraints of energy storage clusters, a charging and discharging regulation model of energy storage clusters is constructed; based on the baseline power, maximum regulation ratio, and comfort constraint coefficient of distributed loads, a distributed load regulation constraint model is constructed; integrating the distributed power source regulation potential model, the energy storage cluster charging and discharging regulation model, and the distributed load regulation constraint model, a virtual power plant adjustable resource hierarchical model is constructed.

[0012] In one embodiment, a hierarchical model of adjustable resources for resource aggregators is constructed based on the adjustable resources of resource aggregators, including:

[0013] Based on the allowable adjustable power base of industrial production processes, the industrial load adjustment depth coefficient, and the allowable adjustment duration coefficient, an industrial adjustable load adjustment model is constructed; based on the rated charging and discharging power of electric vehicles, the connection status coefficient, and the adjustment willingness coefficient, an electric vehicle cluster adjustment model is constructed; integrating the industrial adjustable load adjustment model and the electric vehicle cluster adjustment model, a resource aggregator adjustable resource hierarchical model is constructed.

[0014] In one embodiment, a system-level coordination constraint model is constructed, including:

[0015] Based on the internal resource allocation of virtual power plants and resource aggregators, a power balance constraint model is constructed; based on the scenario requirements of power dispatch, a response speed constraint model is constructed; and by integrating the power balance constraint model and the response speed constraint model, a system-level coordination constraint model is constructed.

[0016] In one embodiment, a multi-dimensional adjustment capability assessment system is constructed based on an adjustable resource hierarchical quantification model, including:

[0017] Based on the total adjustable power and peak load power of the adjustable resource hierarchical quantification model, an adjustment potential index is constructed; based on the actual adjustable power and commanded target power of the adjustable resources, an adjustment accuracy index is constructed; based on the actual continuous adjustment duration of the adjustable resources and the minimum required duration of the system, an adjustment duration index is constructed; based on the response time dispersion of multiple adjustable resources, an adjustment response consistency index is constructed; and by integrating the adjustment potential index, adjustment accuracy index, adjustment duration index, and adjustment response consistency index, a multi-dimensional adjustment capability evaluation system is constructed.

[0018] In one embodiment, determining whether there are any anomalies in the running status data and generating a running status determination result includes:

[0019] Based on the preset lower limit thresholds of each indicator in the multi-dimensional adjustment capability assessment system, the adjustment potential indicator value, adjustment accuracy indicator value, adjustment duration indicator value, and adjustment response consistency indicator value corresponding to the operation status data are extracted. Each indicator value is compared with its corresponding lower limit threshold in turn. If any dimension indicator value exceeds its corresponding lower limit threshold, an abnormal operation status judgment result is generated, which includes the abnormal dimension and its corresponding indicator value. If all dimension indicator values ​​do not exceed their corresponding lower limit thresholds, a normal operation status judgment result is generated.

[0020] Secondly, this application also provides a device for assessing the adjustable resource capacity of a power system, comprising:

[0021] The model building module is used to construct a hierarchical model of adjustable resources for virtual power plants based on their adjustable resources; to construct a hierarchical model of adjustable resources for resource aggregators based on their adjustable resources; and to construct a system-level coordination and constraint model.

[0022] The evaluation system construction module is used to construct a quantitative model of adjustable resource stratification for virtual power plants and resource aggregators based on the adjustable resource stratification model of virtual power plants, the adjustable resource stratification model of resource aggregators, and the system-level coordination constraint model; and to construct a multi-dimensional adjustment capability evaluation system based on the adjustable resource stratification quantitative model.

[0023] The detection module is used to design a real-time dynamic detection method for the adjustment capability of adjustable resources based on the adjustable resource hierarchical quantification model and multi-dimensional adjustment capability evaluation system. The real-time dynamic detection method detects the operating status data of adjustable resources in real time and judges whether there are any anomalies in the operating status data, and generates the judgment result of the operating status. Based on the operating status data, according to the adjustable resource evaluation method, the evaluation result of the operating status is generated.

[0024] The assessment module is used to obtain an assessment result of adjustable resource capabilities indicating normal resource capabilities if both the judgment result and the assessment result indicate normality, and to obtain an assessment result of adjustable resource capabilities indicating abnormality if either the judgment result or the assessment result indicates abnormality.

[0025] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0026] Based on the adjustable resources of virtual power plants, a hierarchical model of adjustable resources of virtual power plants is constructed; based on the adjustable resources of resource aggregators, a hierarchical model of adjustable resources of resource aggregators is constructed; and a system-level coordination constraint model is constructed.

[0027] Based on the adjustable resource stratification model of virtual power plants, the adjustable resource stratification model of resource aggregators, and the system-level coordination constraint model, an adjustable resource stratification quantitative model for virtual power plants and resource aggregators is constructed; based on the adjustable resource stratification quantitative model, a multi-dimensional adjustment capability evaluation system is constructed.

[0028] Based on the adjustable resource hierarchical quantification model and multi-dimensional adjustment capability assessment system, a real-time dynamic detection method for the adjustment capability of adjustable resources is designed. The real-time dynamic detection method detects the operating status data of adjustable resources in real time and judges whether there are any anomalies in the operating status data, and generates the judgment result of the operating status. Based on the operating status data, according to the adjustable resource assessment method, the assessment result of the operating status is generated.

[0029] If both the judgment result and the assessment result indicate normality, an assessment result indicating normal resource availability is obtained. If either the judgment result or the assessment result indicates abnormality, an assessment result indicating abnormal resource availability is obtained.

[0030] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0031] Based on the adjustable resources of virtual power plants, a hierarchical model of adjustable resources of virtual power plants is constructed; based on the adjustable resources of resource aggregators, a hierarchical model of adjustable resources of resource aggregators is constructed; and a system-level coordination constraint model is constructed.

[0032] Based on the adjustable resource stratification model of virtual power plants, the adjustable resource stratification model of resource aggregators, and the system-level coordination constraint model, an adjustable resource stratification quantitative model for virtual power plants and resource aggregators is constructed; based on the adjustable resource stratification quantitative model, a multi-dimensional adjustment capability evaluation system is constructed.

[0033] Based on the adjustable resource hierarchical quantification model and multi-dimensional adjustment capability assessment system, a real-time dynamic detection method for the adjustment capability of adjustable resources is designed. The real-time dynamic detection method detects the operating status data of adjustable resources in real time and judges whether there are any anomalies in the operating status data, and generates the judgment result of the operating status. Based on the operating status data, according to the adjustable resource assessment method, the assessment result of the operating status is generated.

[0034] If both the judgment result and the assessment result indicate normality, an assessment result indicating normal resource availability is obtained. If either the judgment result or the assessment result indicates abnormality, an assessment result indicating abnormal resource availability is obtained.

[0035] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0036] Based on the adjustable resources of virtual power plants, a hierarchical model of adjustable resources of virtual power plants is constructed; based on the adjustable resources of resource aggregators, a hierarchical model of adjustable resources of resource aggregators is constructed; and a system-level coordination constraint model is constructed.

[0037] Based on the adjustable resource stratification model of virtual power plants, the adjustable resource stratification model of resource aggregators, and the system-level coordination constraint model, an adjustable resource stratification quantitative model for virtual power plants and resource aggregators is constructed; based on the adjustable resource stratification quantitative model, a multi-dimensional adjustment capability evaluation system is constructed.

[0038] Based on the adjustable resource hierarchical quantification model and multi-dimensional adjustment capability assessment system, a real-time dynamic detection method for the adjustment capability of adjustable resources is designed. The real-time dynamic detection method detects the operating status data of adjustable resources in real time and judges whether there are any anomalies in the operating status data, and generates the judgment result of the operating status. Based on the operating status data, according to the adjustable resource assessment method, the assessment result of the operating status is generated.

[0039] If both the judgment result and the assessment result indicate normality, an assessment result indicating normal resource availability is obtained. If either the judgment result or the assessment result indicates abnormality, an assessment result indicating abnormal resource availability is obtained.

[0040] The aforementioned methods, devices, computer equipment, computer-readable storage media, and computer program products for assessing the adjustable resource capacity of power systems construct a hierarchical model of adjustable resources for virtual power plants; a hierarchical model of adjustable resources for resource aggregators based on their adjustable resources; a system-level coordination constraint model; a hierarchical quantitative model of adjustable resources for virtual power plants and resource aggregators based on the hierarchical models of adjustable resources for virtual power plants, resource aggregators, and system-level coordination constraint models; and a multi-dimensional adjustment capacity assessment system based on the hierarchical quantitative model of adjustable resources. This application proposes a hierarchical quantitative model for adjustable resources and a multi-dimensional adjustment capability assessment system. It designs a real-time dynamic detection method for the adjustment capability of adjustable resources, which monitors the operational status data of adjustable resources in real time and determines whether there are any anomalies, generating a judgment result. Based on the operational status data and according to the adjustable resource assessment method, an assessment result is generated. If both the judgment result and the assessment result indicate normality, an assessment result representing normal adjustable resource capability is obtained; if either the judgment result or the assessment result indicates anomaly, an assessment result representing anomaly is obtained. This application accurately adapts to the organizational characteristics of virtual power plants and resource aggregators, as well as the differentiated characteristics of diverse and heterogeneous adjustable resources, through hierarchical modeling. Relying on a multi-dimensional assessment system to comprehensively characterize resource adjustment performance, it can achieve dynamic, comprehensive, and accurate real-time assessment and anomaly identification of diverse and heterogeneous adjustable resources, effectively improving the timeliness and accuracy of the assessment results. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating a method for assessing the adjustable resource capacity of a power system in one embodiment;

[0043] Figure 2 This is a flowchart illustrating the process of constructing a hierarchical model of adjustable resources for a virtual power plant in one embodiment.

[0044] Figure 3 This is a structural block diagram of a power system adjustable resource capacity assessment device in one embodiment;

[0045] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] In one embodiment, such as Figure 1 As shown, a method for assessing the adjustable resource capacity of a power system is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0048] Step 102: Based on the adjustable resources of the virtual power plant, construct a hierarchical model of adjustable resources of the virtual power plant; based on the adjustable resources of the resource aggregator, construct a hierarchical model of adjustable resources of the resource aggregator; construct a system-level coordination constraint model.

[0049] Optionally, the adjustable resources of a virtual power plant include distributed generation, energy storage clusters, and distributed loads. Adjustable resources from a resource aggregator include industrial adjustable loads, commercial flexible loads, and electric vehicle clusters.

[0050] Among these, a hierarchical model of adjustable resources within virtual power plants is constructed. The core of this model is to combine the differences in the types and operational characteristics of adjustable resources under the virtual power plant to perform hierarchical modeling and quantify the adjustment potential of each type of resource. Constructing a system-level coordination and constraint model enables collaborative adjustment of adjustable resources between virtual power plants and resource aggregators, ensuring that the overall adjustment capability matches the power system's dispatching needs.

[0051] Step 104: Based on the adjustable resource stratification model of the virtual power plant, the adjustable resource stratification model of the resource aggregator, and the system-level coordination constraint model, construct a quantitative model of adjustable resource stratification for the virtual power plant and the resource aggregator; based on the quantitative model of adjustable resource stratification, construct a multi-dimensional adjustment capability assessment system.

[0052] Optionally, the indicators used in the multi-dimensional adjustment capability assessment system include the lower limit threshold for adjustment potential, the lower limit threshold for adjustment accuracy, the lower limit threshold for adjustment duration, and the lower limit threshold for adjustment response consistency.

[0053] The core of constructing a hierarchical quantification model for adjustable resources of virtual power plants and resource aggregators is to achieve coordinated quantification of adjustable resources between virtual power plants and resource aggregators, eliminate the independence of the hierarchical models, and incorporate system-level coordinated constraints. Specifically, this includes: First, extracting the adjustment parameters of various resources from the hierarchical models of adjustable resources in virtual power plants and resource aggregators, and standardizing these parameters to unify parameter dimensions and quantification units; Second, introducing constraints from the system-level coordination constraint model as constraints for the quantification model, clarifying the coordinated adjustment boundaries of adjustable resources between virtual power plants and resource aggregators, and avoiding conflicts between individual entity adjustment behavior and system requirements; Finally, using a weighted fusion algorithm, combining the adjustment priorities of various resources with system scheduling requirements, to perform fusion calculations on the standardized resource adjustment parameters, constructing a hierarchical quantification model, and achieving a unified quantitative representation of the adjustment capabilities of all adjustable resources under the virtual power plants and resource aggregators.

[0054] Step 106: Based on the adjustable resource hierarchical quantification model and multi-dimensional adjustment capability assessment system, design a real-time dynamic detection method for the adjustment capability of adjustable resources. The real-time dynamic detection method detects the operating status data of adjustable resources in real time and judges whether there are any abnormalities in the operating status data, and generates the judgment result of the operating status. Based on the operating status data and according to the adjustable resource assessment method, generate the assessment result of the operating status.

[0055] The design of a real-time dynamic detection method for adjustable resource regulation capabilities includes: First, constructing a real-time data acquisition module, deploying edge computing nodes on the virtual power plant and the local resource aggregator's premises. Based on the differences in characteristics of various resources in the adjustable resource hierarchical quantification model, a acquisition frequency of 1 second / time is set to collect full operational data from distributed power sources, energy storage clusters, various loads, and electric vehicle clusters. The acquisition range corresponds one-to-one with the resource parameters of the adjustable resource hierarchical quantification model. Second, constructing a dynamic status tracking module, dynamically updating the core parameters of the adjustable resource hierarchical quantification model based on the collected real-time operational data, and correcting for output fluctuations in distributed power sources. The system incorporates several key technologies, including coefficients for energy storage state of charge and electric vehicle connection status, to ensure that model parameters align with actual resource operating conditions. A real-time indicator calculation module is constructed, based on dynamically updated adjustable resource hierarchical quantification model parameters. Following the indicator calculation logic of a multi-dimensional adjustment capability assessment system, it calculates four key indicators—adjustment potential, adjustment accuracy, adjustment duration, and adjustment response consistency—along with a comprehensive adjustment capability index every 5 seconds. Finally, an anomaly warning and feedback module is built, integrating the above three modules and configuring the data interaction timing between modules to form a complete real-time dynamic detection method, providing technical support for judging abnormal operating conditions.

[0056] Optionally, the data collected by the real-time data acquisition module includes: (1) Distributed power sources: real-time output, meteorological forecast data, and equipment operating status; (2) Energy storage clusters: individual SOC, charging and discharging power, temperature, and charging and discharging efficiency; (3) Various loads: real-time power, reference power, adjustment command execution status, and user comfort feedback; (4) Electric vehicle clusters: connection status, remaining power, charging demand, and charging and discharging power. The dynamic status tracking module updates the operating status of adjustable resources in real time based on the collected data and dynamically corrects the parameters of the hierarchical quantification model: (1) For distributed power sources, the maximum output and fluctuation coefficient are updated according to real-time meteorological data; (2) For energy storage clusters, the average is updated in real time, and the charging and discharging adjustable power is corrected; (3) For load resources, the reference power and adjustment constraint coefficient are updated according to real-time operating status; (4) For electric vehicle clusters, the connection status coefficient and adjustment willingness coefficient are dynamically updated.

[0057] Optionally, the adjustable resource assessment method is a pre-built adjustable resource assessment algorithm that uses an improved long short-term memory network algorithm with an attention mechanism. This algorithm receives pre-processed operating status data and, in conjunction with the index requirements of a multi-dimensional adjustment capability assessment system, conducts a comprehensive analysis of the actual adjustment performance and operating stability of the adjustable resources. Finally, it generates an operating status assessment result that includes the adjustment capability level and the matching degree of each dimension index. The assessment result complements and verifies the judgment result generated in step 106, thereby improving the accuracy of anomaly judgment.

[0058] Step 108: If both the judgment result and the evaluation result indicate normality, an adjustable resource capability evaluation result indicating normal resource capability is obtained; if either the judgment result or the evaluation result indicates abnormality, an adjustable resource capability evaluation result indicating abnormal resource capability is obtained.

[0059] After obtaining the assessment results of adjustable resource capabilities, if the result is normal, the judgment result and assessment result are uploaded; if the result is abnormal, the abnormal warning process is triggered.

[0060] The operational status data consists of the full volume of adjustable resource operational data acquired in real-time dynamic detection in step 106. The core of preprocessing is to adapt to the input requirements of the adjustable resource assessment algorithm, remove data interference, and unify the data format to ensure accurate and reliable assessment results. The specific process is as follows: First, the raw operational status data acquired by the real-time data acquisition module in step 106 is cleaned, removing invalid data and extreme outliers (such as parameter values ​​exceeding the equipment's rated operating range or blank data from failed acquisitions) generated during the acquisition process to avoid abnormal data interfering with the accuracy of the assessment algorithm. Second, data standardization is performed, unifying the data measurement units and formats for operational data of different types of resources such as distributed power sources, energy storage clusters, various loads, and electric vehicle clusters, eliminating dimensional differences, and ensuring that the data can be directly input into the assessment algorithm. Finally, feature extraction is carried out, combining the core parameter requirements of the adjustable resource hierarchical quantification model to screen out core data features closely related to the adjustable resource's adjustment capability, removing redundant information, and further improving the computational efficiency and assessment accuracy of the assessment algorithm.

[0061] Furthermore, the collaborative judgment and subsequent operations of the judgment and evaluation results are as follows: the judgment result is the preliminary anomaly judgment conclusion generated in step 106 based on the threshold of multi-dimensional evaluation indicators, and the evaluation result is the accurate evaluation conclusion output by the adjustable resource evaluation algorithm. The two work together to achieve double-layer verification and avoid the deviation that may occur from a single judgment. If both indicate that the operating status is normal, the judgment result and the evaluation result are simultaneously uploaded to the virtual power plant control center, the resource aggregator control center, and the power grid dispatch center to complete the real-time update of the operating status of adjustable resources and provide a basis for subsequent dispatch decisions. If either conclusion indicates that the operating status is abnormal, the anomaly warning process is immediately triggered, and the anomaly information (including the anomaly dimension, real-time value of the indicator, threshold and preliminary cause of the anomaly) is simultaneously pushed to each control center, and targeted adjustment suggestions are output to support staff in quickly handling anomalies and restoring normal resource operation, ensuring that adjustable resources can stably play their regulatory role.

[0062] The aforementioned method for assessing the adjustable resource capacity of a power system constructs a hierarchical model of adjustable resources for virtual power plants; a hierarchical model of adjustable resources for resource aggregators; a system-level coordination constraint model; a hierarchical quantitative model of adjustable resources for virtual power plants and resource aggregators based on the hierarchical models of adjustable resources for virtual power plants, resource aggregators, and the system-level coordination constraint model; a multi-dimensional adjustment capacity assessment system based on the hierarchical quantitative model of adjustable resources; a real-time dynamic detection method for the adjustment capacity of adjustable resources designed based on the hierarchical quantitative model of adjustable resources and the multi-dimensional adjustment capacity assessment system; a real-time dynamic detection method for the operational status data of adjustable resources is used to detect the operational status data in real time and determine whether there are any anomalies in the operational status data, generating a judgment result of the operational status; based on the operational status data and according to the adjustable resource assessment method, an assessment result of the operational status is generated; if both the judgment result and the assessment result indicate normality, an assessment result of the adjustable resource capacity indicating normal resource capacity is obtained; if either the judgment result or the assessment result indicates anomaly, an assessment result of the adjustable resource capacity indicating anomaly is obtained. This application precisely adapts to the organizational characteristics of virtual power plants and resource aggregators, as well as the differentiated features of diverse and heterogeneous adjustable resources through hierarchical modeling. Relying on a multi-dimensional evaluation system to comprehensively characterize resource regulation performance, it can achieve dynamic, comprehensive, accurate, and real-time evaluation and anomaly identification of diverse and heterogeneous adjustable resources, effectively improving the timeliness and accuracy of evaluation results.

[0063] In one exemplary embodiment, such as Figure 2 As shown, based on the adjustable resources of a virtual power plant, a hierarchical model of adjustable resources for the virtual power plant is constructed, including:

[0064] Based on the maximum output, regulation coefficient, and output fluctuation coefficient of distributed power sources, a distributed power source regulation potential model is constructed; based on the rated total power, average state of charge, and charging and discharging power constraints of energy storage clusters, a charging and discharging regulation model of energy storage clusters is constructed; based on the baseline power, maximum regulation ratio, and comfort constraint coefficient of distributed loads, a distributed load regulation constraint model is constructed; integrating the distributed power source regulation potential model, the energy storage cluster charging and discharging regulation model, and the distributed load regulation constraint model, a virtual power plant adjustable resource hierarchical model is constructed.

[0065] For example, based on the maximum output, regulation coefficient, and output fluctuation coefficient of the distributed power source, a distributed power source regulation potential model is constructed, as shown in the following formula:

[0066]

[0067] in, Let be the adjustable power of the distributed power source at time t; Let t be the maximum output of the distributed power source at time t; The regulation coefficient for distributed power sources is 0.3-0.7 for intermittent power sources such as photovoltaic and wind power, and 0.8-1.0 for controllable power sources such as gas turbines. Let be the output fluctuation coefficient of the distributed power source at time t. Based on the rated total power, average state of charge, and charge / discharge power constraints of the energy storage cluster, a charge / discharge regulation model for the energy storage cluster is constructed, as shown in the following formula:

[0068]

[0069]

[0070] in, , These represent the adjustable discharge and charging power of the energy storage cluster at time t; This refers to the rated total power of the energy storage cluster. Let t be the average state of charge of the energy storage cluster at time t; , These are the lower and upper limits of the state of charge of the energy storage cluster, respectively. , These represent the maximum discharge and charging power of the energy storage cluster, respectively. Based on the baseline power, maximum adjustment ratio, and comfort constraint coefficient of the distributed load, a distributed load adjustment constraint model is constructed, as shown in the following formula:

[0071]

[0072] in, Let be the adjustable power of the distributed load at time t; Let be the baseline power of the distributed load at time t; For the maximum load adjustment ratio, the ratio is 0.1-0.2 for residential load and 0.2-0.4 for commercial load. The load comfort constraint coefficient ranges from [0.8, 1.0], with higher values ​​indicating more lenient comfort constraints. A hierarchical model of adjustable resources for a virtual power plant is constructed by integrating the distributed power source regulation potential model, the energy storage cluster charging and discharging regulation model, and the distributed load regulation constraint model.

[0073] In this embodiment, by constructing models for distributed power sources, energy storage clusters, and distributed loads respectively, and clarifying the parameter definitions and value ranges of each model, the adjustment capabilities of various adjustable resources can be accurately quantified, avoiding the deviations caused by traditional homogeneous modeling, and providing an accurate and reliable foundation for the subsequent construction of hierarchical quantification models for adjustable resources.

[0074] In one exemplary embodiment, a hierarchical model of adjustable resources for resource aggregators is constructed based on the adjustable resources of resource aggregators, including:

[0075] Based on the allowable adjustable power base of industrial production processes, the industrial load adjustment depth coefficient, and the allowable adjustment duration coefficient, an industrial adjustable load adjustment model is constructed; based on the rated charging and discharging power of electric vehicles, the connection status coefficient, and the adjustment willingness coefficient, an electric vehicle cluster adjustment model is constructed; integrating the industrial adjustable load adjustment model and the electric vehicle cluster adjustment model, a resource aggregator adjustable resource hierarchical model is constructed.

[0076] For example, based on the allowable adjustable power base of industrial production processes, the industrial load adjustment depth coefficient, and the allowable adjustment duration coefficient, an industrial adjustable load adjustment model is constructed, as shown in the following formula:

[0077]

[0078] in, Let be the adjustable power of the industrial adjustable load at time t; Let t be the base power that the industrial production process allows to adjust at time t; This is the industrial load adjustment depth coefficient, which is set flexibly according to the production process. For example, it is 0.2-0.5 for chemical load and 0.3-0.6 for machining load. The allowable adjustment time coefficient for industrial load is defined as follows: the shorter the adjustment time, the closer the coefficient is to 1; the coefficient for the longest allowable adjustment time is 1. Based on the rated charging and discharging power of electric vehicles, the connection status coefficient, and the adjustment willingness coefficient, an electric vehicle cluster adjustment model is constructed, as shown in the following formula:

[0079]

[0080] in, Let t be the total adjustable power of the electric vehicle cluster at time t; n is the number of vehicles in the electric vehicle cluster. The rated charging and discharging power of the i-th electric vehicle; Let be the connection state coefficient of the i-th electric vehicle, which is 1 when connected to the power grid and 0 when disconnected; Let be the adjustment willingness coefficient of the i-th electric vehicle, ranging from [0.5, 1.0]. An industrial adjustable load adjustment model and an electric vehicle cluster adjustment model are integrated to construct a hierarchical model of adjustable resources for resource aggregators.

[0081] In this embodiment, by establishing refined quantitative models for industrial adjustable loads and electric vehicle clusters respectively, and integrating them into a unified hierarchical model, it is possible to accurately characterize and quantify the adjustment capabilities of resource aggregators for multiple types of adjustable resources, improve the accuracy and applicability of adjustable resource assessment, and provide reliable model support for resource aggregators to participate in grid dispatch and ancillary services in the future.

[0082] In one exemplary embodiment, a system-level coordination constraint model is constructed, including:

[0083] Based on the internal resource allocation of virtual power plants and resource aggregators, a power balance constraint model is constructed; based on the scenario requirements of power dispatch, a response speed constraint model is constructed; and by integrating the power balance constraint model and the response speed constraint model, a system-level coordination constraint model is constructed.

[0084] For example, based on the internal resource allocation ratio of virtual power plants and resource aggregators, a power balance constraint model is constructed, as shown in the following formula:

[0085]

[0086] in, Let be the total adjustable power of the virtual power plant / resource aggregator at time t. Based on the scenario requirements of power dispatch, a response speed constraint model is constructed, as shown in the following formula:

[0087]

[0088] in, Let be the response time of the k-th type of adjustable resource at time t; The maximum allowable response time for the system is set at 1-5 minutes for auxiliary service scenarios and 30 seconds-1.5 minutes for emergency dispatch scenarios. A system-level coordination constraint model is constructed by integrating the power balance constraint model and the response speed constraint model.

[0089] In this embodiment, a constraint model is constructed and integrated into a system-level coordination constraint model through two core dimensions: total power matching and resource response timeliness. This model can define reasonable adjustment boundaries for the adjustable resources of virtual power plants and resource aggregators, ensuring both the power coordination and matching of internal diverse adjustable resources and meeting the response timeliness requirements under different power dispatching scenarios, thus avoiding dispatching failures caused by power imbalance and response lag.

[0090] In one exemplary embodiment, a multi-dimensional adjustment capability assessment system is constructed based on an adjustable resource hierarchical quantification model, including:

[0091] Based on the total adjustable power and peak load power of the adjustable resource hierarchical quantification model, an adjustment potential index is constructed; based on the actual adjustable power and commanded target power of the adjustable resources, an adjustment accuracy index is constructed; based on the actual continuous adjustment duration of the adjustable resources and the minimum required duration of the system, an adjustment duration index is constructed; based on the response time dispersion of multiple adjustable resources, an adjustment response consistency index is constructed; and by integrating the adjustment potential index, adjustment accuracy index, adjustment duration index, and adjustment response consistency index, a multi-dimensional adjustment capability evaluation system is constructed.

[0092] For example, based on the total adjustable power and peak load power of the adjustable resource stratification model, an adjustment potential index is constructed, as shown in the following formula:

[0093]

[0094] in, Let t be the adjustment potential index, ranging from [0, 1]. A higher value indicates a greater adjustment potential. Let be the peak load power of the virtual power plant / resource aggregator at time t. Based on the actual adjustable power and the commanded target power of the adjustable resources, a regulation accuracy index is constructed, as follows:

[0095]

[0096] in, The adjustment accuracy index at time t, ranging from [0, 1], indicates that the higher the value, the higher the adjustment accuracy. The actual adjustable power of the adjustable resource at time t; Let be the power of the adjustment command issued by the system at time t. Based on the actual continuous adjustment duration of adjustable resources and the minimum required duration of the system, an adjustment duration index is constructed, as follows:

[0097]

[0098] in, The duration of adjustment at time t is an indicator, ranging from [0, 1]. A higher value indicates a stronger ability to continuously adjust. The actual continuous adjustment duration of the adjustable resource at the target power at time t; This represents the minimum required duration of continuous adjustment for the system. Based on the response time dispersion of multiple adjustable resources, a consistency index for adjustment response is constructed, as follows:

[0099]

[0100] in, Let t be the consistency index of the adjusted response, ranging from [0, 1], where a higher value indicates better consistency; m is the number of resource types involved in the adjustment. Let t be the average response time of all participating regulatory resources. Integrating regulatory potential, regulatory accuracy, regulatory duration, and regulatory response consistency indicators, a comprehensive regulatory capability index is constructed, resulting in a multi-dimensional regulatory capability evaluation system. The formula for the comprehensive regulatory capability index is as follows:

[0101]

[0102] in, Let be the comprehensive regulation capability index at time t, ranging from [0, 1]. A higher value indicates a stronger comprehensive regulation capability. Let be the weight of the i-th indicator.

[0103] In this embodiment, quantitative indicators are constructed from four core dimensions: regulation potential, regulation accuracy, regulation duration, and regulation response consistency. These indicators are then integrated to form a comprehensive regulation capability index, thus establishing a multi-dimensional regulation capability evaluation system. This system can overcome the limitations of traditional single power index evaluation and achieve comprehensive and accurate quantification of the overall regulation performance of virtual power plants and resource aggregators' adjustable resources.

[0104] In an exemplary embodiment, determining whether there are any anomalies in the running status data and generating a running status determination result includes:

[0105] Based on the preset lower limit thresholds of each indicator in the multi-dimensional adjustment capability assessment system, the adjustment potential indicator value, adjustment accuracy indicator value, adjustment duration indicator value, and adjustment response consistency indicator value corresponding to the operation status data are extracted. Each indicator value is compared with its corresponding lower limit threshold in turn. If any dimension indicator value exceeds its corresponding lower limit threshold, an abnormal operation status judgment result is generated, which includes the abnormal dimension and its corresponding indicator value. If all dimension indicator values ​​do not exceed their corresponding lower limit thresholds, a normal operation status judgment result is generated.

[0106] For example, based on the preset lower thresholds of each indicator in the multi-dimensional adjustment capability assessment system, the real-time calculated value of each indicator is compared with its corresponding threshold one by one. The lower threshold for adjustment potential is set to 0.2, for adjustment accuracy to 0.7, for adjustment duration to 0.8, and for adjustment response consistency to 0.6. If any dimension indicator value is less than its corresponding lower threshold, it is determined that the dimension is abnormal, and an abnormal operation status judgment result is generated. The abnormal judgment result includes the abnormal dimension, the real-time value of the corresponding indicator, and the threshold. If all dimension indicator values ​​are not less than their corresponding lower thresholds, it is determined that the operation status is normal, and a normal operation status judgment result is generated.

[0107] In this embodiment, by setting clear lower thresholds for each core indicator of the multi-dimensional adjustment capability assessment system, the indicator values ​​corresponding to the real-time operating status data are compared with the thresholds one by one. This enables the rapid and accurate identification of abnormal dimensions and specific problems in the operating status of adjustable resources, and achieves quantitative and refined judgment of adjustment capability anomalies.

[0108] In one exemplary embodiment, a method for assessing the adjustable resource capacity of a power system includes:

[0109] Based on the maximum output, regulation coefficient, and output fluctuation coefficient of distributed power sources, a distributed power source regulation potential model is constructed. Based on the rated total power, average state of charge, and charging / discharging power constraints of energy storage clusters, a charging / discharging regulation model of energy storage clusters is constructed. Based on the baseline power, maximum regulation ratio, and comfort constraint coefficient of distributed loads, a distributed load regulation constraint model is constructed. Integrating the distributed power source regulation potential model, the energy storage cluster charging / discharging regulation model, and the distributed load regulation constraint model, a virtual power plant adjustable resource hierarchical model is constructed. Based on the allowable adjustable power base of industrial production processes, the industrial load regulation depth coefficient, and the allowable regulation duration coefficient, an industrial adjustable load regulation model is constructed. Based on the rated charging / discharging power, connection status coefficient, and regulation willingness coefficient of electric vehicles, an electric vehicle cluster regulation model is constructed. Integrating the industrial adjustable load regulation model and the electric vehicle cluster regulation model, a resource aggregator adjustable resource hierarchical model is constructed. Based on the internal resource allocation ratio of virtual power plants and resource aggregators, a power balance constraint model is constructed. Based on the scenario requirements of power dispatch, a response speed constraint model is constructed. Integrating the power balance constraint model and the response speed constraint model, a system-level coordination constraint model is constructed. Based on the adjustable resource stratification model of virtual power plants, the adjustable resource stratification model of resource aggregators, and the system-level coordination constraint model, a stratified quantitative model of adjustable resources for virtual power plants and resource aggregators is constructed. Based on the total adjustable power and peak load power of the adjustable resource stratified quantitative model, a regulation potential index is constructed. Based on the actual adjustable power and commanded target power of adjustable resources, a regulation accuracy index is constructed. Based on the actual continuous regulation duration of adjustable resources and the minimum duration required by the system, a regulation duration index is constructed. Based on the response time dispersion of multiple adjustable resources, a regulation response consistency index is constructed. By integrating the regulation potential index, regulation accuracy index, regulation duration index, and regulation response consistency index, a multi-dimensional regulation capability evaluation system is constructed. Based on a hierarchical quantification model of adjustable resources and a multi-dimensional adjustment capability assessment system, a real-time dynamic detection method for the adjustment capability of adjustable resources is designed. This method monitors the operational status data of adjustable resources in real time. Based on the preset lower thresholds of each indicator in the multi-dimensional adjustment capability assessment system, the adjustment potential, adjustment accuracy, adjustment duration, and adjustment response consistency indicators corresponding to the operational status data are extracted. Each indicator value is compared sequentially with its corresponding lower threshold. If any dimension indicator value exceeds its corresponding lower threshold, an abnormal operational status judgment result is generated, including the abnormal dimension and its corresponding indicator value. If all dimension indicator values ​​do not exceed their corresponding lower thresholds, a normal operational status judgment result is generated. Based on the operational status data and according to the adjustable resource assessment method, an operational status assessment result is generated. If both the judgment result and the assessment result indicate normality, an adjustable resource capability assessment result representing normal resource capability is obtained. If either the judgment result or the assessment result indicates abnormality, an adjustable resource capability assessment result representing abnormal resource capability is obtained.

[0110] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0111] In one exemplary embodiment, such as Figure 3 As shown, a power system adjustable resource capacity assessment device is provided, comprising: a model building module 301, an assessment system construction module 302, a detection module 303, and an assessment module 304, wherein:

[0112] The model building module is used to construct a hierarchical model of adjustable resources for virtual power plants based on their adjustable resources; to construct a hierarchical model of adjustable resources for resource aggregators based on their adjustable resources; and to construct a system-level coordination and constraint model.

[0113] The evaluation system construction module is used to construct a quantitative model of adjustable resource stratification for virtual power plants and resource aggregators based on the adjustable resource stratification model of virtual power plants, the adjustable resource stratification model of resource aggregators, and the system-level coordination constraint model; and to construct a multi-dimensional adjustment capability evaluation system based on the adjustable resource stratification quantitative model.

[0114] The detection module is used to design a real-time dynamic detection method for the adjustment capability of adjustable resources based on the adjustable resource hierarchical quantification model and multi-dimensional adjustment capability evaluation system. The real-time dynamic detection method detects the operating status data of adjustable resources in real time and judges whether there are any anomalies in the operating status data, and generates the judgment result of the operating status. Based on the operating status data, according to the adjustable resource evaluation method, the evaluation result of the operating status is generated.

[0115] The assessment module is used to obtain an assessment result of adjustable resource capabilities indicating normal resource capabilities if both the judgment result and the assessment result indicate normality, and to obtain an assessment result of adjustable resource capabilities indicating abnormality if either the judgment result or the assessment result indicates abnormality.

[0116] In one exemplary embodiment, the model building module is further configured to:

[0117] Based on the maximum output, regulation coefficient, and output fluctuation coefficient of distributed power sources, a distributed power source regulation potential model is constructed; based on the rated total power, average state of charge, and charging and discharging power constraints of energy storage clusters, a charging and discharging regulation model of energy storage clusters is constructed; based on the baseline power, maximum regulation ratio, and comfort constraint coefficient of distributed loads, a distributed load regulation constraint model is constructed; integrating the distributed power source regulation potential model, the energy storage cluster charging and discharging regulation model, and the distributed load regulation constraint model, a virtual power plant adjustable resource hierarchical model is constructed.

[0118] In one exemplary embodiment, the model building module is further configured to:

[0119] Based on the allowable adjustable power base of industrial production processes, the industrial load adjustment depth coefficient, and the allowable adjustment duration coefficient, an industrial adjustable load adjustment model is constructed; based on the rated charging and discharging power of electric vehicles, the connection status coefficient, and the adjustment willingness coefficient, an electric vehicle cluster adjustment model is constructed; integrating the industrial adjustable load adjustment model and the electric vehicle cluster adjustment model, a resource aggregator adjustable resource hierarchical model is constructed.

[0120] In one exemplary embodiment, the model building module is further configured to:

[0121] Based on the internal resource allocation of virtual power plants and resource aggregators, a power balance constraint model is constructed; based on the scenario requirements of power dispatch, a response speed constraint model is constructed; and by integrating the power balance constraint model and the response speed constraint model, a system-level coordination constraint model is constructed.

[0122] In one exemplary embodiment, the evaluation system construction module is further configured to:

[0123] Based on the total adjustable power and peak load power of the adjustable resource hierarchical quantification model, an adjustment potential index is constructed; based on the actual adjustable power and commanded target power of the adjustable resources, an adjustment accuracy index is constructed; based on the actual continuous adjustment duration of the adjustable resources and the minimum required duration of the system, an adjustment duration index is constructed; based on the response time dispersion of multiple adjustable resources, an adjustment response consistency index is constructed; and by integrating the adjustment potential index, adjustment accuracy index, adjustment duration index, and adjustment response consistency index, a multi-dimensional adjustment capability evaluation system is constructed.

[0124] In one exemplary embodiment, the detection module is further configured to:

[0125] Based on the preset lower limit thresholds of each indicator in the multi-dimensional adjustment capability assessment system, the adjustment potential indicator value, adjustment accuracy indicator value, adjustment duration indicator value, and adjustment response consistency indicator value corresponding to the operation status data are extracted. Each indicator value is compared with its corresponding lower limit threshold in turn. If any dimension indicator value exceeds its corresponding lower limit threshold, an abnormal operation status judgment result is generated, which includes the abnormal dimension and its corresponding indicator value. If all dimension indicator values ​​do not exceed their corresponding lower limit thresholds, a normal operation status judgment result is generated.

[0126] Each module in the aforementioned power system adjustable resource capacity assessment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0127] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores operational status data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for assessing the adjustable resource capacity of a power system.

[0128] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0129] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0130] Based on the adjustable resources of virtual power plants, a hierarchical model of adjustable resources of virtual power plants is constructed; based on the adjustable resources of resource aggregators, a hierarchical model of adjustable resources of resource aggregators is constructed; and a system-level coordination constraint model is constructed.

[0131] Based on the adjustable resource stratification model of virtual power plants, the adjustable resource stratification model of resource aggregators, and the system-level coordination constraint model, an adjustable resource stratification quantitative model for virtual power plants and resource aggregators is constructed; based on the adjustable resource stratification quantitative model, a multi-dimensional adjustment capability evaluation system is constructed.

[0132] Based on the adjustable resource hierarchical quantification model and multi-dimensional adjustment capability assessment system, a real-time dynamic detection method for the adjustment capability of adjustable resources is designed. The real-time dynamic detection method detects the operating status data of adjustable resources in real time and judges whether there are any anomalies in the operating status data, and generates the judgment result of the operating status. Based on the operating status data, according to the adjustable resource assessment method, the assessment result of the operating status is generated.

[0133] If both the judgment result and the assessment result indicate normality, an assessment result indicating normal resource availability is obtained. If either the judgment result or the assessment result indicates abnormality, an assessment result indicating abnormal resource availability is obtained.

[0134] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0135] Based on the maximum output, regulation coefficient, and output fluctuation coefficient of distributed power sources, a distributed power source regulation potential model is constructed; based on the rated total power, average state of charge, and charging and discharging power constraints of energy storage clusters, a charging and discharging regulation model of energy storage clusters is constructed; based on the baseline power, maximum regulation ratio, and comfort constraint coefficient of distributed loads, a distributed load regulation constraint model is constructed; integrating the distributed power source regulation potential model, the energy storage cluster charging and discharging regulation model, and the distributed load regulation constraint model, a virtual power plant adjustable resource hierarchical model is constructed.

[0136] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0137] Based on the allowable adjustable power base of industrial production processes, the industrial load adjustment depth coefficient, and the allowable adjustment duration coefficient, an industrial adjustable load adjustment model is constructed; based on the rated charging and discharging power of electric vehicles, the connection status coefficient, and the adjustment willingness coefficient, an electric vehicle cluster adjustment model is constructed; integrating the industrial adjustable load adjustment model and the electric vehicle cluster adjustment model, a resource aggregator adjustable resource hierarchical model is constructed.

[0138] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0139] Based on the internal resource allocation of virtual power plants and resource aggregators, a power balance constraint model is constructed; based on the scenario requirements of power dispatch, a response speed constraint model is constructed; and by integrating the power balance constraint model and the response speed constraint model, a system-level coordination constraint model is constructed.

[0140] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0141] Based on the total adjustable power and peak load power of the adjustable resource hierarchical quantification model, an adjustment potential index is constructed; based on the actual adjustable power and commanded target power of the adjustable resources, an adjustment accuracy index is constructed; based on the actual continuous adjustment duration of the adjustable resources and the minimum required duration of the system, an adjustment duration index is constructed; based on the response time dispersion of multiple adjustable resources, an adjustment response consistency index is constructed; and by integrating the adjustment potential index, adjustment accuracy index, adjustment duration index, and adjustment response consistency index, a multi-dimensional adjustment capability evaluation system is constructed.

[0142] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0143] Based on the preset lower limit thresholds of each indicator in the multi-dimensional adjustment capability assessment system, the adjustment potential indicator value, adjustment accuracy indicator value, adjustment duration indicator value, and adjustment response consistency indicator value corresponding to the operation status data are extracted. Each indicator value is compared with its corresponding lower limit threshold in turn. If any dimension indicator value exceeds its corresponding lower limit threshold, an abnormal operation status judgment result is generated, which includes the abnormal dimension and its corresponding indicator value. If all dimension indicator values ​​do not exceed their corresponding lower limit thresholds, a normal operation status judgment result is generated.

[0144] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0145] Based on the adjustable resources of virtual power plants, a hierarchical model of adjustable resources of virtual power plants is constructed; based on the adjustable resources of resource aggregators, a hierarchical model of adjustable resources of resource aggregators is constructed; and a system-level coordination constraint model is constructed.

[0146] Based on the adjustable resource stratification model of virtual power plants, the adjustable resource stratification model of resource aggregators, and the system-level coordination constraint model, an adjustable resource stratification quantitative model for virtual power plants and resource aggregators is constructed; based on the adjustable resource stratification quantitative model, a multi-dimensional adjustment capability evaluation system is constructed.

[0147] Based on the adjustable resource hierarchical quantification model and multi-dimensional adjustment capability assessment system, a real-time dynamic detection method for the adjustment capability of adjustable resources is designed. The real-time dynamic detection method detects the operating status data of adjustable resources in real time and judges whether there are any anomalies in the operating status data, and generates the judgment result of the operating status. Based on the operating status data, according to the adjustable resource assessment method, the assessment result of the operating status is generated.

[0148] If both the judgment result and the assessment result indicate normality, an assessment result indicating normal resource availability is obtained. If either the judgment result or the assessment result indicates abnormality, an assessment result indicating abnormal resource availability is obtained.

[0149] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0150] Based on the maximum output, regulation coefficient, and output fluctuation coefficient of distributed power sources, a distributed power source regulation potential model is constructed; based on the rated total power, average state of charge, and charging and discharging power constraints of energy storage clusters, a charging and discharging regulation model of energy storage clusters is constructed; based on the baseline power, maximum regulation ratio, and comfort constraint coefficient of distributed loads, a distributed load regulation constraint model is constructed; integrating the distributed power source regulation potential model, the energy storage cluster charging and discharging regulation model, and the distributed load regulation constraint model, a virtual power plant adjustable resource hierarchical model is constructed.

[0151] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0152] Based on the allowable adjustable power base of industrial production processes, the industrial load adjustment depth coefficient, and the allowable adjustment duration coefficient, an industrial adjustable load adjustment model is constructed; based on the rated charging and discharging power of electric vehicles, the connection status coefficient, and the adjustment willingness coefficient, an electric vehicle cluster adjustment model is constructed; integrating the industrial adjustable load adjustment model and the electric vehicle cluster adjustment model, a resource aggregator adjustable resource hierarchical model is constructed.

[0153] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0154] Based on the internal resource allocation of virtual power plants and resource aggregators, a power balance constraint model is constructed; based on the scenario requirements of power dispatch, a response speed constraint model is constructed; and by integrating the power balance constraint model and the response speed constraint model, a system-level coordination constraint model is constructed.

[0155] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0156] Based on the total adjustable power and peak load power of the adjustable resource hierarchical quantification model, an adjustment potential index is constructed; based on the actual adjustable power and commanded target power of the adjustable resources, an adjustment accuracy index is constructed; based on the actual continuous adjustment duration of the adjustable resources and the minimum required duration of the system, an adjustment duration index is constructed; based on the response time dispersion of multiple adjustable resources, an adjustment response consistency index is constructed; and by integrating the adjustment potential index, adjustment accuracy index, adjustment duration index, and adjustment response consistency index, a multi-dimensional adjustment capability evaluation system is constructed.

[0157] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0158] Based on the preset lower limit thresholds of each indicator in the multi-dimensional adjustment capability assessment system, the adjustment potential indicator value, adjustment accuracy indicator value, adjustment duration indicator value, and adjustment response consistency indicator value corresponding to the operation status data are extracted. Each indicator value is compared with its corresponding lower limit threshold in turn. If any dimension indicator value exceeds its corresponding lower limit threshold, an abnormal operation status judgment result is generated, which includes the abnormal dimension and its corresponding indicator value. If all dimension indicator values ​​do not exceed their corresponding lower limit thresholds, a normal operation status judgment result is generated.

[0159] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0160] Based on the adjustable resources of virtual power plants, a hierarchical model of adjustable resources of virtual power plants is constructed; based on the adjustable resources of resource aggregators, a hierarchical model of adjustable resources of resource aggregators is constructed; and a system-level coordination constraint model is constructed.

[0161] Based on the adjustable resource stratification model of virtual power plants, the adjustable resource stratification model of resource aggregators, and the system-level coordination constraint model, an adjustable resource stratification quantitative model for virtual power plants and resource aggregators is constructed; based on the adjustable resource stratification quantitative model, a multi-dimensional adjustment capability evaluation system is constructed.

[0162] Based on the adjustable resource hierarchical quantification model and multi-dimensional adjustment capability assessment system, a real-time dynamic detection method for the adjustment capability of adjustable resources is designed. The real-time dynamic detection method detects the operating status data of adjustable resources in real time and judges whether there are any anomalies in the operating status data, and generates the judgment result of the operating status. Based on the operating status data, according to the adjustable resource assessment method, the assessment result of the operating status is generated.

[0163] If both the judgment result and the assessment result indicate normality, an assessment result indicating normal resource availability is obtained. If either the judgment result or the assessment result indicates abnormality, an assessment result indicating abnormal resource availability is obtained.

[0164] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0165] Based on the maximum output, regulation coefficient, and output fluctuation coefficient of distributed power sources, a distributed power source regulation potential model is constructed; based on the rated total power, average state of charge, and charging and discharging power constraints of energy storage clusters, a charging and discharging regulation model of energy storage clusters is constructed; based on the baseline power, maximum regulation ratio, and comfort constraint coefficient of distributed loads, a distributed load regulation constraint model is constructed; integrating the distributed power source regulation potential model, the energy storage cluster charging and discharging regulation model, and the distributed load regulation constraint model, a virtual power plant adjustable resource hierarchical model is constructed.

[0166] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0167] Based on the allowable adjustable power base of industrial production processes, the industrial load adjustment depth coefficient, and the allowable adjustment duration coefficient, an industrial adjustable load adjustment model is constructed; based on the rated charging and discharging power of electric vehicles, the connection status coefficient, and the adjustment willingness coefficient, an electric vehicle cluster adjustment model is constructed; integrating the industrial adjustable load adjustment model and the electric vehicle cluster adjustment model, a resource aggregator adjustable resource hierarchical model is constructed.

[0168] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0169] Based on the internal resource allocation of virtual power plants and resource aggregators, a power balance constraint model is constructed; based on the scenario requirements of power dispatch, a response speed constraint model is constructed; and by integrating the power balance constraint model and the response speed constraint model, a system-level coordination constraint model is constructed.

[0170] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0171] Based on the total adjustable power and peak load power of the adjustable resource hierarchical quantification model, an adjustment potential index is constructed; based on the actual adjustable power and commanded target power of the adjustable resources, an adjustment accuracy index is constructed; based on the actual continuous adjustment duration of the adjustable resources and the minimum required duration of the system, an adjustment duration index is constructed; based on the response time dispersion of multiple adjustable resources, an adjustment response consistency index is constructed; and by integrating the adjustment potential index, adjustment accuracy index, adjustment duration index, and adjustment response consistency index, a multi-dimensional adjustment capability evaluation system is constructed.

[0172] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0173] Based on the preset lower limit thresholds of each indicator in the multi-dimensional adjustment capability assessment system, the adjustment potential indicator value, adjustment accuracy indicator value, adjustment duration indicator value, and adjustment response consistency indicator value corresponding to the operation status data are extracted. Each indicator value is compared with its corresponding lower limit threshold in turn. If any dimension indicator value exceeds its corresponding lower limit threshold, an abnormal operation status judgment result is generated, which includes the abnormal dimension and its corresponding indicator value. If all dimension indicator values ​​do not exceed their corresponding lower limit thresholds, a normal operation status judgment result is generated.

[0174] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0175] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0176] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for assessing the adjustable resource capacity of a power system, characterized in that, The method includes: Based on the adjustable resources of virtual power plants, a hierarchical model of adjustable resources of virtual power plants is constructed; based on the adjustable resources of resource aggregators, a hierarchical model of adjustable resources of resource aggregators is constructed; and a system-level coordination constraint model is constructed. Based on the adjustable resource stratification model of the virtual power plant, the adjustable resource stratification model of the resource aggregator, and the system-level coordination constraint model, an adjustable resource stratification quantification model for the virtual power plant and the resource aggregator is constructed; based on the adjustable resource stratification quantification model, a multi-dimensional adjustment capability evaluation system is constructed. Based on the adjustable resource hierarchical quantification model and the multi-dimensional adjustment capability evaluation system, a real-time dynamic detection method for the adjustment capability of adjustable resources is designed. The real-time dynamic detection method detects the operating status data of adjustable resources in real time and determines whether there are any abnormalities in the operating status data, generating an operating status judgment result. Based on the operating status data and according to the adjustable resource evaluation method, an operating status evaluation result is generated. If both the judgment result and the evaluation result indicate normality, an adjustable resource capability evaluation result indicating normal resource capability is obtained. If either the judgment result or the evaluation result indicates abnormality, an adjustable resource capability evaluation result indicating abnormal resource capability is obtained.

2. The method according to claim 1, characterized in that, The aforementioned adjustable resource-based virtual power plant model, which constructs a hierarchical model for adjustable resources in a virtual power plant, includes: A distributed power source regulation potential model is constructed based on the maximum output, regulation coefficient, and output fluctuation coefficient of the distributed power source. Based on the rated total power, average state of charge, and charging / discharging power constraints of the energy storage cluster, a charging / discharging regulation model for the energy storage cluster is constructed. A distributed load regulation constraint model is constructed based on the baseline power, maximum regulation ratio, and comfort constraint coefficient of the distributed load. By integrating the distributed power source regulation potential model, the energy storage cluster charge and discharge regulation model, and the distributed load regulation constraint model, a hierarchical model of adjustable resources for virtual power plants is constructed.

3. The method according to claim 1, characterized in that, The construction of a hierarchical model for adjustable resources based on resource aggregators includes: Based on the allowable adjustable power base of industrial production process, industrial load adjustment depth coefficient and allowable adjustment duration coefficient, an industrial adjustable load adjustment model is constructed. Based on the rated charging and discharging power, connection state coefficient, and adjustment willingness coefficient of electric vehicles, an electric vehicle cluster adjustment model is constructed. By integrating the aforementioned industrial adjustable load adjustment model and electric vehicle cluster adjustment model, a hierarchical model of adjustable resources for resource aggregators is constructed.

4. The method according to claim 1, characterized in that, The construction of the system-level coordination constraint model includes: A power balance constraint model is constructed based on the internal resource allocation ratio of virtual power plants and resource aggregators; Based on the requirements of power dispatch scenarios, a response speed constraint model is constructed. By integrating the power balance constraint model and the response speed constraint model, a system-level coordination constraint model is constructed.

5. The method according to claim 1, characterized in that, The construction of a multi-dimensional adjustment capability assessment system based on the adjustable resource hierarchical quantification model includes: Based on the total adjustable power and peak load power of the adjustable resource hierarchical quantification model, an adjustment potential index is constructed. Based on the actual adjustable power of adjustable resources and the commanded target power, an adjustment accuracy index is constructed. Based on the actual continuous adjustment duration of adjustable resources and the minimum duration required by the system, an adjustment duration index is constructed. Based on the response time dispersion of multiple adjustable resources, an adjustment response consistency index is constructed. By integrating the regulation potential index, the regulation accuracy index, the regulation duration index, and the regulation response consistency index, a multi-dimensional regulation capability evaluation system is constructed.

6. The method according to claim 1, characterized in that, The step of determining whether the operating status data is abnormal and generating an operating status determination result includes: Based on the preset lower limit thresholds of each indicator in the multi-dimensional adjustment capability assessment system, the adjustment potential indicator value, adjustment accuracy indicator value, adjustment duration indicator value, and adjustment response consistency indicator value corresponding to the operation status data are extracted. Each indicator value is compared with its corresponding lower limit threshold in turn. If any dimension indicator value exceeds the corresponding lower limit threshold, an abnormal operation status judgment result is generated. The abnormal judgment result includes the abnormal dimension and the corresponding indicator value. If all dimensional indicator values ​​do not exceed the corresponding lower threshold, a judgment result indicating normal operating status is generated.

7. A device for assessing the adjustable resource capacity of a power system, characterized in that, The device includes: The model building module is used to construct a hierarchical model of adjustable resources for virtual power plants based on their adjustable resources; to construct a hierarchical model of adjustable resources for resource aggregators based on their adjustable resources; and to construct a system-level coordination and constraint model. The evaluation system construction module is used to construct a quantitative model of adjustable resource stratification for virtual power plants and resource aggregators based on the adjustable resource stratification model of virtual power plants, the adjustable resource stratification model of resource aggregators, and the system-level coordination constraint model; and to construct a multi-dimensional adjustment capability evaluation system based on the adjustable resource stratification model. The detection module is used to design a real-time dynamic detection method for the adjustable resource adjustment capability based on the adjustable resource hierarchical quantification model and the multi-dimensional adjustment capability evaluation system. The real-time dynamic detection method detects the operating status data of the adjustable resource in real time and determines whether there are any abnormalities in the operating status data, and generates an operating status judgment result. Based on the operating status data, according to the adjustable resource evaluation method, an operating status evaluation result is generated. The evaluation module is used to obtain an adjustable resource capability evaluation result indicating normal resource capability if both the judgment result and the evaluation result indicate normality, and to obtain an adjustable resource capability evaluation result indicating abnormal resource capability if either the judgment result or the evaluation result indicates abnormality.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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