CN-122022497-A - Flexible resource adjustment capability assessment method and system based on label and risk analysis

CN122022497ACN 122022497 ACN122022497 ACN 122022497ACN-122022497-A

Abstract

The invention discloses a flexible resource adjustment capability assessment method and system based on label and risk analysis, and relates to the technical field of power system demand side management. The method comprises the steps of obtaining real-time operation parameters of flexible resources on a load side, generating a multi-dimensional capacity envelope curve of individual resources based on a multi-state finite automaton model, constructing a multi-dimensional value tag, clustering and grouping the flexible resources by combining the multi-dimensional capacity envelope curve, constructing a virtual aggregate, quantifying the aggregation adjustment capacity of the virtual aggregate by adopting an opportunistic constraint robust aggregation method to generate a multi-dimensional vector, fusing the multi-dimensional vector with geographic information, and realizing visualization through a thermodynamic diagram and an interactive interface to generate a system-level capacity map. The method realizes the full-dimension capability assessment of flexible resources from individuals to aggregation, gives consideration to value attributes and operation risks, and provides visual and reliable decision support for power grid dispatching.

Inventors

ZONG MING
YI YUE
ZHU XIA
LI YI
CHEN YANJUN
ZHU HONGCHENG
ZHENG YURONG

Assignees

国网上海市电力公司

Dates

Publication Date: 20260512
Application Date: 20260413

Claims (10)

1. The flexible resource adjustment capability assessment method based on the label and risk analysis is characterized by comprising the following steps: collecting real-time operation data of flexible resources at a load side, constructing a multi-state finite automaton model for describing the operation state of the resources, predicting the adjustable capacity of each flexible resource in a future scheduling period based on the multi-state finite automaton model, and generating a multi-dimensional capacity envelope curve of individual resources; Clustering and grouping the flexible resources by combining with the multidimensional capacity envelope curve of the individual resources based on the multidimensional value labels of the flexible resources to construct a virtual aggregate, and carrying out robust quantification on the aggregation regulation capacity of the virtual aggregate to generate a multidimensional vector representing the overall regulation capacity of the virtual aggregate; and carrying out visualization processing on the multidimensional vector to generate a system-level capability map for supporting scheduling decisions.
2. The flexible resource adjustment capability assessment method based on label and risk analysis according to claim 1, wherein collecting real-time operation data of flexible resources at load side, constructing a multi-state finite automaton model for describing the operation state of the resources, predicting the adjustable capability of each flexible resource in a future scheduling period based on the multi-state finite automaton model, and generating a multi-dimensional capability envelope of an individual resource, comprising: The collected load side flexible resources comprise at least one of industrial and commercial adjustable loads, electric automobiles and energy storage systems, and a multi-state finite automaton model is built by extracting state switching nodes and parameter change thresholds of each flexible resource in the operation process, wherein the multi-state finite automaton model comprises a plurality of dynamic operation states, and transition conditions among the dynamic operation states at least comprise one of time thresholds, power change values or external excitation signals; after instantiating the multi-state finite automaton model for each flexible resource, identifying and storing physical constraint parameters, dynamic operation states and response constraint parameters of each flexible resource, wherein the physical constraint parameters at least comprise rated power, upper power adjustment limit and lower power adjustment limit, the dynamic operation states at least comprise operation modes and instantaneous power at the current moment, and the response constraint parameters at least comprise maximum ramp rate, minimum stable operation time and minimum downtime; Setting the multi-state finite automaton model of each flexible resource as an autonomous running mode, inputting a predicted external driving data sequence, obtaining a power output sequence from the current moment to the end moment of a scheduling period through simulation, and generating a baseline load curve of each flexible resource; Based on the baseline load curve, respectively calculating the maximum upward adjustment potential and the maximum downward adjustment potential of each flexible resource at discrete time by a constrained instantaneous power boundary search algorithm for each discrete time in a future scheduling period, performing forward simulation based on the multi-state finite automaton model, calculating the maximum sustainable time for which the adjustment potential can be continuously invoked, and simultaneously calculating the maximum available climbing rate at the discrete time based on the static parameters in the multi-state finite automaton model and the adjustment space of the current operating point; And repeatedly executing iterative computation on each discrete moment discretized according to the set time resolution in the future scheduling period to generate the multi-dimensional capacity envelope of each flexible resource, wherein the multi-dimensional capacity envelope at least comprises a power adjustment capacity value, a sustainable time value and a climbing speed value.
3. The flexible resource adjustment capability assessment method based on labels and risk analysis according to claim 1, wherein clustering and grouping each flexible resource in combination with the multi-dimensional capability envelope of the individual resource to construct a virtual aggregate based on the multi-dimensional value labels of each flexible resource comprises: Selecting at least one label dimension as a component part of the multi-dimensional value label system, wherein the label dimension comprises at least one of a social importance label, a spatial attribute label, a technical attribute label and an environmental attribute label, and a quantifiable label grade is set for each label dimension; performing primary grouping on all the flexible resources according to the priorities of the social importance labels, and ensuring that the flexible resources in the same priority group have similar social importance levels; the multidimensional capacity envelope curve and the multidimensional value label of the individual resource of each flexible resource are used as the input characteristics of clustering grouping together, and the label information and the capacity information of each flexible resource after the initial grouping at any moment are characterized as a multidimensional feature vector; and taking the multidimensional feature vector as input, adopting a clustering algorithm to perform secondary clustering on the flexible resources in the same priority group, and dividing the resources with similar features into the same virtual aggregate based on the comprehensive similarity of the resources in the multidimensional feature space to complete the construction of the virtual aggregate.
4. The flexible resource adjustment capability assessment method based on label and risk analysis according to claim 3 is characterized in that the social importance label is used for representing social functions of industries to which resources belong and severity of power supply interruption results, ordinal values reflecting scheduling interruption priorities are distributed according to social function importance of the resources, the spatial attribute label is used for representing geographic areas to which the resources belong, a spatial distance between the resources is calculated by adopting a method based on geographic coordinates, the technical attribute label is used for representing response characteristics and adjustment accuracy of the resources, the classification is based on response time and adjustment accuracy of the resources, the environmental attribute label is used for representing carbon emission influence when the resources participate in adjustment, and the classification is based on equivalent carbon emission when the resources provide adjustment services.
5. The flexible resource adjustment capability assessment method based on label and risk analysis according to claim 1, wherein robust quantization of the aggregate adjustment capability of each virtual aggregate, generating a multidimensional vector characterizing the overall adjustment capability of the virtual aggregate, comprises: Obtaining multi-dimensional capability envelopes of the individual resources of all the flexible resources in the virtual aggregation body, and calculating the nominal aggregation capability of each virtual aggregation body, wherein the nominal aggregation capability is obtained by summing power adjustment capability values in the multi-dimensional capability envelopes of all the flexible resources in the virtual aggregation body at each moment in the future scheduling period, so as to obtain nominal up-regulation aggregation capability and nominal down-regulation aggregation capability respectively; Calculating the reliable aggregation capacity of each virtual aggregate by adopting a robust aggregation method based on opportunistic constraint, wherein the reliable aggregation capacity represents the power value which is called by the virtual aggregate under a given confidence level after comprehensively considering uncertainty risks, and the reliable up-regulation aggregation capacity and the reliable down-regulation aggregation capacity are respectively obtained; Calculating a sustainable time aggregation value of each virtual aggregate, wherein the sustainable time aggregation value adopts a power weighted average method to perform aggregation calculation on the sustainable time values in the multi-dimensional capacity envelope curves of all the flexible resources in the virtual aggregate, and respectively obtaining an up-regulation sustainable time aggregation value and a down-regulation sustainable time aggregation value; calculating a climbing rate aggregation value of each virtual aggregate, wherein the climbing rate aggregation value is obtained by summing the climbing rate values in the multi-dimensional capacity envelope curves of all the flexible resources in the virtual aggregate, and an up-regulation climbing rate aggregation value and a down-regulation climbing rate aggregation value are respectively obtained; And combining the nominal up-regulation aggregation capability, the nominal down-regulation aggregation capability, the reliable up-regulation aggregation capability, the reliable down-regulation aggregation capability, the up-regulation sustainable time aggregation value, the down-regulation sustainable time aggregation value, the up-regulation climbing rate aggregation value and the down-regulation climbing rate aggregation value to generate a multi-dimensional vector of the regulation capability of each virtual polymer.
6. The flexible resource adjustment capability assessment method based on label and risk analysis according to claim 5, wherein calculating the reliable aggregation capability of each virtual aggregate using a robust aggregation method based on opportunistic constraints comprises: the actual callable capacity of each flexible resource is regarded as a random variable obeying a specific probability distribution, wherein the distribution parameter of the random variable is estimated based on historical response data and real-time running state prediction deviation; For each virtual aggregation, the actual callable capacity after aggregation is the sum of random variables of all the flexible resources in the virtual aggregation, and when the number of the resources in the virtual aggregation is enough, the actual callable capacity after aggregation approximately obeys normal distribution according to a central limit theorem in a probability theory; Setting a confidence level threshold, wherein the confidence level threshold is used for representing the acceptable risk level of a dispatching mechanism, and the value range of the confidence level threshold is more than zero and less than or equal to one; And calculating quantiles under the confidence level threshold based on the probability distribution function of the aggregated actual callable capacity, wherein the power value corresponding to the quantiles is the reliable aggregation capacity capable of ensuring the calling under the confidence level, and the up-regulation direction and the down-regulation direction are respectively calculated to obtain the reliable up-regulation aggregation capacity and the reliable down-regulation aggregation capacity.
7. The flexible resource adjustment capability assessment method based on tag and risk analysis of claim 5, wherein calculating a sustainable time aggregate value for each of the virtual aggregates comprises: Extracting an up-regulation sustainable time value and a down-regulation sustainable time value from the multi-dimensional capacity envelope curve of each flexible resource in the virtual aggregate, and simultaneously extracting a corresponding power regulation capacity value as a weight factor; Multiplying the up-regulation sustainable time value of each flexible resource with the corresponding power regulation capability value to obtain the weighted up-regulation sustainable time of each flexible resource, summing the weighted up-regulation sustainable time of all the flexible resources, and dividing the sum of the power regulation sustainable time values of all the flexible resources to obtain the up-regulation sustainable time aggregate value; And multiplying the down-regulation sustainable time value of each flexible resource with the corresponding power regulation capability value for the down-regulation direction to obtain the weighted down-regulation sustainable time of each flexible resource, summing the weighted down-regulation sustainable time of all the flexible resources, and dividing the sum by the sum of the power regulation capability values of all the flexible resources to obtain the down-regulation sustainable time aggregate value.
8. The flexible resource adjustment capability assessment method based on label and risk analysis of claim 1, wherein the visualization of the multidimensional vector generates a system level capability map for supporting scheduling decisions, comprising: Fusing the multidimensional vectors of the adjustment capability of all the virtual aggregates with geographic information system data, and associating corresponding spatial position information for each virtual aggregate, wherein the spatial position information at least comprises longitude and latitude coordinates or administrative division codes; in the time dimension, segmenting the adjustment capability multidimensional vector according to the time resolution in the future scheduling period to form adjustment capability time sequence data of each virtual aggregate in different time periods; displaying the schedulable resource distribution of different geographic areas in a specific time period in a thermodynamic diagram form by taking an electronic map as a visual base map, wherein the color depth of the thermodynamic diagram is in direct proportion to the size of the reliable aggregation capability; integrating a time axis control on the electronic map, and dynamically switching and displaying the thermodynamic diagrams of different time periods by sliding the time axis control to realize the dynamic evolution of a system level capability map in a time dimension; And setting an interactive clicking event on the electronic map, and when an operator clicks any geographic area corresponding to the virtual aggregate, displaying the detailed adjustment capability multidimensional vector of the virtual aggregate by a pop-up information panel.
9. The flexible resource adjustment capability assessment method based on label and risk analysis according to claim 8, wherein the electronic map is used as a visual base map, and the schedulable resource distribution of different geographic areas in a specific time period is displayed in a thermodynamic diagram form, and then further comprising: setting a confidence level adjustment control in a visual interface, wherein the confidence level adjustment control is used for receiving a confidence level adjustment instruction input by an operator; when the value of the confidence level adjustment control is changed, the reliable aggregation capacity of each virtual aggregate is recalculated in real time, and the color distribution of the thermodynamic diagram is synchronously updated; Meanwhile, the reliable aggregation capability value displayed in the information panel is updated, so that operators can visually compare the distribution conditions of the schedulable resources under different risk tolerance.
10. A flexible resource adjustment capability assessment system based on tag and risk analysis, characterized in that it is used for executing the flexible resource adjustment capability assessment method based on tag and risk analysis according to any one of claims 1 to 9, comprising: The system comprises an individual resource capacity analysis module, a multi-state finite automaton module, a load side flexible resource management module and a load side flexible resource management module, wherein the individual resource capacity analysis module is used for collecting real-time operation data of the load side flexible resource, constructing the multi-state finite automaton module for describing the operation state of the resource, predicting the adjustable capacity of each flexible resource in a future scheduling period based on the multi-state finite automaton module, and generating a multi-dimensional capacity envelope curve of the individual resource; The virtual aggregate construction module is used for clustering and grouping the flexible resources based on the multidimensional value labels of the flexible resources and combining the multidimensional capacity envelope curves of the individual resources to construct a virtual aggregate, and carrying out robust quantification on the aggregation regulation capacity of the virtual aggregate to generate a multidimensional vector representing the overall regulation capacity of the virtual aggregate; And the visualization decision support module is used for carrying out visualization processing on the multidimensional vector and generating a system-level capacity map for supporting scheduling decisions.

Description

Flexible resource adjustment capability assessment method and system based on label and risk analysis Technical Field The invention relates to the technical field of power system demand side management, in particular to a flexible resource adjustment capability assessment method and system based on label and risk analysis. Background The flexible resource at the load side is used as an important carrier for distributed adjustment potential, is a key component for providing adjustment service and absorbing renewable energy fluctuation for an electric power system, accurately evaluates the adjustable capability of the resource, and is a precondition for implementing efficient and reliable demand response. According to the quantitative evaluation method for the load side flexible resources in the current power system, researches are mostly conducted only on single type resources such as temperature control loads, and a linear or threshold simplifying model is adopted for describing the resources, so that the running state and the constraint of the complex heterogeneous type load side flexible resources such as industrial processes, electric vehicles and the like are difficult to uniformly characterize. Meanwhile, the traditional evaluation method can only give out static indexes such as maximum adjustable power, ignoring dynamic characteristics such as climbing rate and sustainable time, cannot provide effective support for real-time scheduling of a power system in the minute and second levels, and does not fully consider uncertainty risks such as randomness of user behaviors and communication faults in the evaluation process of a resource cluster, so that an evaluation result is optimistic and various risks in the scheduling process of the power system are easily caused. In addition, the quantization results obtained by the traditional evaluation method are presented in a data table or a simple curve form, and lack of integrated visual display of space-time distribution, confidence level and other dimensions, so that the quick understanding and decision execution of the evaluation results by virtual power plants, load aggregators and power system schedulers are not facilitated. Disclosure of Invention Aiming at the technical problems that heterogeneous flexible resources on a load side lack of a unified description model, single evaluation dimension, neglect of dynamic characteristics and insufficient consideration of uncertainty risks of resource adjustment in the prior art, the invention provides a flexible resource adjustment capability evaluation method and a flexible resource adjustment capability evaluation system based on labels and risk analysis. The technical scheme for solving the technical problems is as follows: in a first aspect, the present invention provides a flexible resource adjustment capability assessment method based on tag and risk analysis, including: collecting real-time operation data of flexible resources at a load side, constructing a multi-state finite automaton model for describing the operation state of the resources, predicting the adjustable capacity of each flexible resource in a future scheduling period based on the multi-state finite automaton model, and generating a multi-dimensional capacity envelope curve of individual resources; Clustering and grouping the flexible resources by combining with the multidimensional capacity envelope curve of the individual resources based on the multidimensional value labels of the flexible resources to construct a virtual aggregate, and carrying out robust quantification on the aggregation regulation capacity of the virtual aggregate to generate a multidimensional vector representing the overall regulation capacity of the virtual aggregate; and carrying out visualization processing on the multidimensional vector to generate a system-level capability map for supporting scheduling decisions. In a second aspect, the present invention provides a flexible resource adjustment capability assessment system based on tag and risk analysis, comprising: The system comprises an individual resource capacity analysis module, a multi-state finite automaton module, a load side flexible resource management module and a load side flexible resource management module, wherein the individual resource capacity analysis module is used for collecting real-time operation data of the load side flexible resource, constructing the multi-state finite automaton module for describing the operation state of the resource, predicting the adjustable capacity of each flexible resource in a future scheduling period based on the multi-state finite automaton module, and generating a multi-dimensional capacity envelope curve of the individual resource; The virtual aggregate construction module is used for clustering and grouping the flexible resources based on the multidimensional value labels of the flexible resources and combining the multidimensional capacity envelope curves