Auxiliary service-oriented distributed resource cluster regulation and control characteristic evaluation method and system

Abstract

The invention provides an auxiliary service-oriented distributed resource cluster regulation and control characteristic evaluation method and system, and the method comprises the steps: constructing a regulation and control characteristic portrait label system of a distributed resource and cluster from a static dimension and a dynamic dimension, and describing the operation characteristics and regulation and control capability based on historical, predicted and real-time data; according to different auxiliary service requirements, an improved analytic hierarchy process is adopted to determine the relative importance weight of the regulation and control capability index, and the contribution integrity weight is calculated, so that the comprehensive evaluation of the cluster regulation and control capability index is completed; and further providing a regulation and control capability grade scoring method based on an improved cloud model, calculating the comprehensive score and sequence of each resource, and realizing the reasonable decomposition of the regulation and control power instruction among the resources in the cluster according to the comprehensive score and sequence. According to the method, the regulation and control capability evaluation and instruction distribution efficiency of the distributed resource cluster participating in the auxiliary service can be effectively improved.

Description

Technical Field

[0001] This invention pertains to the field of virtual power plant technology, specifically relating to a method and system for evaluating the control characteristics of distributed resource clusters oriented towards ancillary services. Background Technology

[0002] Virtual power plants aggregate diverse and controllable distributed resources, satisfying their own energy needs while also participating in grid operation and ancillary service regulation as large-capacity resource aggregators. However, the distributed resources in virtual power plants are complex. To balance the competitiveness of centralized control with the computational power of distributed control, and to effectively address the information complexity and management challenges faced in regulating massive distributed resources, a distributed resource cluster is defined as a set of multiple distributed resources within a virtual power plant that are geographically close, belong to the same management unit, have unified communication and control infrastructure, and are easy to install edge control equipment for unified management.

[0003] With the development of digitalization and intelligentization of power systems, profiling technology in the power sector has gradually emerged. Profiling technology, based on business needs, extracts and constructs a diverse set of images from massive amounts of data using advanced physical information technology. These images are used to describe the characteristics, needs, and behaviors of individuals and clusters, and are currently widely applied in various power sectors such as user behavior profiling, load forecasting, electricity anomaly detection, and marketing. As the types of business involving virtual power plants continue to expand, the power system places higher demands on the characterization of virtual power plant control characteristics. To evaluate the quantitative indicators of distributed resource cluster control characteristics within virtual power plants and support the accurate description of virtual power plant control characteristics, it is urgent to propose a profiling-based evaluation method for distributed resource cluster control characteristics.

[0004] Traditional ancillary services mainly rely on large thermal power units, but under the "dual carbon" target, the regulation capacity of thermal power is somewhat limited. Distributed resources, due to their advantages such as fast response and flexible deployment, are considered an important supplement to the future power ancillary services market. However, due to the uncertainty of their output and the complexity of management, comprehensively and accurately assessing the regulation characteristics of distributed resource clusters still faces many challenges.

[0005] In terms of constructing evaluation indicators for the regulation characteristics of distributed resource clusters, the existing evaluation indicator system is not yet perfect. On the one hand, the indicator system has poor universality and lacks scenario adaptation and differentiated design; on the other hand, there is still a lack of systematic research on the evaluation of the regulation characteristics of mixed resource clusters containing different types of distributed resources. Regarding the methods for evaluating regulation characteristics, current methods are mostly used to improve the accuracy of resource evaluation, lacking the ability to judge the quality of the regulation characteristics of the research object. For distributed resource clusters with diverse characteristics and complex operating features, how to construct a scientific and comprehensive evaluation system to characterize the regulation attributes of different types of distributed resource clusters in multiple operating scenarios and to quantitatively evaluate their indicators has become one of the key problems that urgently need to be solved. Summary of the Invention

[0006] In view of this, in a first aspect, the present invention proposes a method for evaluating the regulation characteristics of a distributed resource cluster oriented towards auxiliary services, comprising the following steps: A distributed resource regulation characteristic profile labeling system is constructed from two dimensions: static attributes and dynamic attributes. The regulation characteristic profile labeling system is composed of regulation characteristic profile labels. Based on the regulation characteristic profile labels, a distributed resource regulation characteristic profile labeling calculation model is proposed. Obtain information on all distributed resources within the cluster. Based on the control characteristic profile tags, aggregate all distributed resources within the cluster and construct a distributed resource cluster profile tag system from two dimensions: static attributes and dynamic attributes. The distributed resource cluster profile tag system is composed of distributed resource cluster profile tags. Based on the distributed resource cluster profile tags, calculate the control capability profile index of each distributed resource within the cluster. The control capability profile index of the distributed resource cluster is obtained by comprehensively calculating the control capability profile index of each distributed resource within the cluster. Based on the regulation capability profile indicators of the distributed resource cluster, the regulation characteristics of the distributed resource cluster are evaluated, and the regulation power command decomposition for auxiliary services is implemented based on the evaluation results.

[0007] Based on the above scheme, the calculation steps for the distributed resource cluster regulation capability profile index are as follows: Based on the different auxiliary service regulation needs, the importance ranking of regulation capability indicators is determined; an improved analytic hierarchy process is used to determine the relative importance of each pair of indicators, construct a judgment matrix, and calculate the relative importance weights of the regulation capability indicators. ; The optimal and worst values ​​of each control capability are determined based on all distributed resources within the virtual power plant, and the control capability values ​​of each distributed resource within the cluster are normalized. Based on the relative importance weights of distributed resource regulation capabilities and normalized index values, the completeness weight of the contribution of distributed resources to the cluster regulation capability index is calculated. ; By incorporating the contribution integrity weight for auxiliary services, the distributed resource cluster control capability index is obtained by weighted summation and weighted averaging of all distributed resource control capability indicators within the cluster. Based on the above scheme, the specific steps for implementing the decomposition of power control commands for distributed resource clusters oriented towards auxiliary services are as follows: The regulatory capacity is divided into five levels, and each level is assigned a corresponding score and degree of membership. Five levels of excellence were generated using a forward cloud generator. Obtain distributed resource index values ​​and their membership degrees at different levels. ; Based on optimal cloud entropy The cloud model was optimized and improved, but the membership degree obtained based on the improved cloud model did not meet the requirements. Membership Correction; Based on the improved analytic hierarchy process (AHP), the comprehensive weights of the distributed resource regulation capability profile indicators within the cluster are determined, and the overall evaluation score of the distributed resource regulation capability indicators is calculated. ; based on Distributed resources are sorted from highest to lowest, denoted as . Sort the distributed resources by order; Based on the ranking results, the power control commands are decomposed among the distributed resources within the cluster according to the comprehensive control capability evaluation scores from high to low.

[0008] Based on the above scheme, the distributed resource cluster profile tagging system includes static attribute indicators and dynamic attribute indicators; The static attribute indicators include total installed capacity, clean energy ratio, and distributed resource type; The dynamic attribute indicators include: Cluster power curve characteristic profile indicators: power fluctuation amplitude, fluctuation frequency, power change rate, maximum power change rate, power fluctuation change rate, daily average load, daily minimum load, daily minimum load rate, daily maximum load, daily average load rate, and daily peak-to-valley difference; Cluster control capability profile indicators: comprehensive controllable capacity, comprehensive control response frequency, comprehensive maximum control response time, comprehensive average control duration, comprehensive control delay time, comprehensive control response achievement, comprehensive control response positivity, and comprehensive control resource reliability. Dynamic profile indicators of cluster power generation characteristics: real-time power generation and daily cumulative power generation; Cluster load characteristic profile indicators: real-time power generation and daily cumulative power generation.

[0009] Based on the above scheme, the proposed calculation model for the distributed resource cluster regulation capability index specifically includes: Comprehensive adjustable capacity : The overall adjustable capacity of a distributed resource cluster for auxiliary services is calculated by weighted summing of the adjustable power capacities of each distributed resource within the cluster. The specific calculation formula is shown below: ; In the formula: They are respectively t The overall upward and downward adjustable power (kW) of the distributed resource cluster when it is oriented towards auxiliary services. , For the first in a distributed resource cluster i The upward and downward adjustable power of a distributed resource, in kW; N agg The number of distributed resources in the cluster; for t Within the distributed resource cluster at any given time i The contribution integrity weight of each distributed resource to auxiliary services; Comprehensive control response frequency : The comprehensive control response frequency for auxiliary services in a distributed resource cluster is calculated by weighted averaging of the control response frequencies of each distributed resource within the cluster. The specific calculation formula is shown below: ; In the formula: for t The frequency of comprehensive regulation and response of the distributed resource cluster for auxiliary services at any given time; For the first in a distributed resource cluster i The frequency of regulation and response of a distributed resource, times; Comprehensive maximum control response time : The overall maximum control response time for auxiliary services in a distributed resource cluster is calculated by weighted summing of the maximum control response times of each distributed resource within the cluster. The specific calculation formula is as follows: ; In the formula: for tThe maximum overall control response time of the distributed resource cluster when it is oriented towards auxiliary services, in min; For the first in a distributed resource cluster i The maximum control response time of a distributed resource, min; Overall average control duration : In the calculation of the control duration for ancillary services, the overall average control duration of the distributed resource cluster is obtained by weighted averaging of the control durations of each distributed resource within the cluster. The specific calculation formula is as follows: ; In the formula: for t The overall average duration of control of the distributed resource cluster for auxiliary services at any given time, in h; for t Within the distributed resource cluster at any given time i The duration of regulation for a distributed resource, in hours; for t Within the distributed resource cluster at any given time i The contribution integrity weight of the continuous regulation time of each distributed resource computing cluster; Comprehensive regulation delay time : The overall control latency of a distributed resource cluster for auxiliary services is calculated by taking the weighted average of the control latency of each distributed resource within the cluster. The specific calculation formula is shown below: ; In the formula: for t The overall control delay time (s) of the distributed resource cluster for peak shaving auxiliary services. For the first in a distributed resource cluster i The regulation delay time of a distributed resource, in seconds; Comprehensive regulation and control response achievement : ; In the formula: The degree of comprehensive control and response achievement of distributed resource clusters; N Total number of responses for distributed resource cluster regulation; For distributed resource clusters n Actual controlled power, kW; For the first n The power (kW) that the distributed resource cluster needs to be regulated in the next regulation instruction; Comprehensive regulation and response initiative : ; In the formula: To enhance the comprehensive regulation and response capabilities of distributed resource clusters; For the virtual power plant n The total power of the next control command; Comprehensive regulation and response initiative : ; In the formula: To ensure the comprehensive regulation and reliability of distributed resource clusters, For the first in a distributed resource cluster i The regulation and control of distributed resources and resource reliability.

[0010] Based on the above scheme, the relative importance weights of the regulation capability indicators are... The calculation method is as follows: Based on the different needs for regulating ancillary services, the importance of the regulation capability indicators is ranked as follows: In the peak shaving auxiliary service operation scenario, the importance of the regulation capability indicators is ranked as follows: adjustable power capacity > regulation duration > regulation response time ≥ regulation delay time > regulation response frequency > regulation response achievement ≥ regulation response initiative ≥ regulation resource reliability. In the operation scenario of frequency modulation auxiliary service, the importance of the control capability indicators is ranked as follows: control response time > control delay time ≥ adjustable power capacity ≥ control duration > control response frequency > control response achievement ≥ control response initiative ≥ control resource reliability. In the standby auxiliary service operation scenario, the importance of the control capability indicators is ranked as follows: adjustable power capacity ≥ control response time ≥ control delay time ≥ control duration > control response frequency > control response achievement ≥ control response positivity ≥ control resource reliability. An improved analytic hierarchy process (AHP) is used to determine the relative importance of each pair of indicators, and the scale value is used to measure this importance. The result of the judgment; Construct a judgment matrix And calculate the relative importance weights of the regulatory capacity indicators.

[0011] Based on the above scheme, the judgment matrix The following conditions must be met: ,Right now They are reciprocal matrices; ,in Indicates the first i The element and the first j The scale value obtained by comparing each element; The formula for calculating the judgment matrix is ​​as follows: ; The relative importance weights of the regulatory capacity indicators are as follows: ; Based on the above scheme, the contribution completeness weight of the cluster control capability index The calculation method is as follows: For all distributed resources and scattered resources within the resource cluster of the virtual power plant N The control capability indicators are normalized, where the control response time and delay time are minimal indicators, as shown in equation (11), and the rest are normalized according to equation (12) for minimal indicators: ; ; In the formula: For distributed resources i Normalized values ​​of individual regulatory capacity profile indicators; Distributed resources issued by the cloud for virtual power plants i Maximum and minimum values ​​of each regulatory capacity profile indicator; Based on the relative importance weight of distributed resource regulation capability and the normalized index value, the contribution completeness weight of distributed resources to the cluster regulation capability index is calculated; among which, the contribution completeness weight of distributed resources to the cluster's comprehensive adjustable capacity, comprehensive regulation response frequency, comprehensive maximum regulation response time, comprehensive regulation delay time and comprehensive regulation response initiative is calculated using formula (13): ; In the formula: for t Within the distributed resource cluster at any given time i The contribution integrity weight of each distributed resource to auxiliary services is used to characterize the equivalent contribution of the distributed resource to the cluster control capability in the current auxiliary service operation scenario. for t Within the time cluster i The first distributed resource b Normalized values ​​of individual regulatory capacity profile indicators; To calculate the number of nodes within the cluster based on the improved hierarchical method i The first distributed resource b The relative importance weight of each regulatory capability profile indicator in the current auxiliary service operation scenario; Set minimum contribution integrity weight By using linear mapping, the contribution completeness weight is controlled within... Within the range; When calculating the overall average control duration of the cluster, distributed resources with shorter control durations are given a greater contribution integrity weight. The contribution integrity weight of distributed resources to the overall average control duration of the cluster is calculated using equation (14): ; In the formula: for t Within the distributed resource cluster at any given time i The contribution of each distributed resource to the overall average control duration of the cluster is weighted by its completeness. For the first in the cluster i The relative importance weight of the duration of regulation for each distributed resource; For the first in the cluster i The duration of regulation of each distributed resource is calculated using a minimal normalized value based on equation (11).

[0012] Based on the above scheme, the distributed resource regulation characteristic profile labeling system includes static attribute indicators and dynamic attribute indicators; The static attribute indicators include user number, user industry, electricity priority, adjustment model, and unit compensation price. The dynamic attribute indicators include power curve characteristic indicators, regulation capability indicators, and real-time operation characteristic indicators. The power curve characteristic indicators include: Power fluctuation amplitude : ; Where: power fluctuation amplitude for t- 1 hour has arrived t The amplitude of load power curve fluctuations can be adjusted in real time, in kW; for t The load power can be adjusted at any time. ; Fluctuation frequency : ; ; Where: fluctuation frequency The frequency at which the adjustable load forecast power curve switches between rising and falling trends; for t The load forecast power curve trend switching state variable is adjustable in real time; when the power curve switches between an upward and downward trend... ,otherwise; This represents the total number of time points. A symbolic function used to determine the value of a variable. The meanings of the plus and minus signs are as follows: ; Power change rate : ; Where: power change rate for t- 1 to t The rate of change of the load power curve can be adjusted in real time, kW / min. 15 minutes; Maximum power change rate : ; Where: Maximum power change rate The maximum value of the rate of change of the adjustable load power curve, kW; T This represents the total number of time points. Power fluctuation rate : ; Where: power fluctuation rate for t- 2 to t The rate of change of the load power curve fluctuation that can be adjusted at any time, in kW / min; Daily maximum load : ; Where: Daily maximum load For adjustable peak load power, kW; for t Adjustable load plan operating power in real time, kW; T This represents the total number of time points. Daily minimum load : ; Where: Daily minimum load The adjustable load valley power is measured in kW. Daily average load : ; Where: Daily average load The average load level of the adjustable load is kW; T This represents the total number of time points. Daily minimum load rate : ; Where: Daily minimum load factor Used to reflect the range of variation of the adjustable load power curve, in kW; Daily average load factor : ; Where: Daily average load factor Used to reflect the smoothness and balance of the adjustable load power curve, in kW; Daily peak-valley difference : ; Where: Daily maximum load The difference between the daily maximum load and the daily minimum load, in kW; The regulation capability indicators include: Adjustable power capacity: The adjustable power capacity of an adjustable load is divided into upward adjustable power and downward adjustable power. Upward adjustable power refers to the maximum power that the adjustable load can reduce relative to the current power, while downward adjustable power refers to the maximum power that the adjustable load can increase relative to the current power. Adjustable power capacity of transferable load The adjustable power of the transferable load, both upward and downward, can be obtained from the following formula: ; ; In the formula: The power is adjustable upwards and downwards for transferable loads, in kW; for t Planned operating power (kW) that can be readily transferred to other loads; T This represents the total number of time points. Total electricity consumption for transferable load, in kWh; Maximum power of transferable load, kW; Adjustable power capacity of load that can be shifted The adjustable power for shiftable loads is determined based on the planned operating power; if If a load shifting plan is in operation, then the load has the potential to be reduced, therefore its It always possesses upward adjustable potential; if If a load that can be moved is in a stopped state and is in a moveable state, then that load has the potential to generate additional load, therefore its It always possesses downward adjustment potential; the calculation formula is shown below: ; ; ; In the formula: The adjustable power (kW) is for loads that can be shifted downwards and upwards. for t Planned operating power (kW) of load that can be shifted at any time; Represents a transferable load t The scheduled time slot is currently unavailable. Represents a transferable load t The timeline is scheduled to be in operation. For transferable loads The starting time; T This represents the total number of time points. For the controllable state variable of the shiftable load, Represents the load that can be moved. The time period has already run. Represents a transferable load It was not running during the specified time period; L The duration for which a transferable load must operate once activated; Adjustable power capacity that can reduce load The only way to reduce load is to adjust the power capacity upwards, and the calculation formula is as follows: ; In the formula: Adjustable power output to reduce load, kW; for The planned operating power can be reduced at any time, in kW; for The minimum power capacity at which the load can be reduced at any given time, in kW; Adjusting the response frequency The control response frequency is the cumulative number of effective response hours per day; ; ; In the formula: The control response frequency of the controllable load can be calculated from the power of the controllable load, and is given by the frequency. for h The state variables that are effective at all times and respond to the hourly state variables; Maximum control response time : ; In the formula: for t The maximum controllable response time of the controllable load can be calculated from the controllable load power, min; The control speed of the adjustable load is calculated from the average control speed of the historical control process, in kW / min; Regulation duration : ; In the formula: for t The duration of the adjustable load regulation can be calculated from the adjustable load power curve, in hours (h). For adjustable load t Continuously satisfy from time to time Maximum number of time periods; Adjustment delay time : ; In the formula: The control delay time for the controllable load is calculated from the average of historical control delay times, in seconds. For the first n The time for the controllable load to respond to the control command action is in seconds. For the first n The timing of the load receiving the control command can be adjusted during the next response. ; Response achievement rate of regulation: ; In the formula: The controllable load control response achievement rate is calculated from the average historical control response achievement rate. For adjustable load n The actual power involved in regulation, in kW; For the first n The power (kW) that the controllable load needs to be controlled in the next control instruction; ; Response responsiveness to regulation: ; In the formula: The regulatory response positivity of the controllable load is calculated based on the average historical results of the controllable load's participation in regulation. The resource cluster to which the adjustable load belongs n The total power of this control command, in kW; ; Regulating resource reliability : ; In the formula: The reliability of controllable load regulation resources is calculated based on the historical participation results of controllable loads in regulation. The number of unplanned outage hours during adjustable load operation, in hours (h). The total operating time of the adjustable load is in hours (h). Real-time operating characteristics: The real-time operating characteristics of adjustable loads include real-time power consumption, daily cumulative power consumption, and other real-time status indicators, which are used to monitor the power consumption of adjustable loads.

[0013] Based on the above scheme, the method of using a forward cloud generator to generate five levels of excellence / disexcellence is described. Obtain distributed resource index values ​​and their membership degrees at different levels. Specifically, it includes the following steps: by For the expectation, Standard deviation Generate normally distributed random numbers; The parameter for calculating the final membership degree was calculated by repeating the calculation 10,000 times and taking the average value. pass and regulatory capacity data values, calculate Membership degrees corresponding to different levels of cloud models The details are as follows: ; In the formula: This is a normalized value for the distributed resource regulation capability. and This is the mathematical characteristic value of the corresponding quality level of this indicator.

[0014] Based on the above scheme, the optimal cloud entropy-based approach... The cloud model was optimized and improved, but the membership degree obtained based on the improved cloud model did not meet the requirements. Membership The correction specifically includes the following steps: respectively Cloud entropy is calculated using the criterion method and the 50% membership criterion method, and a positive cloud generator is used based on... Calculate the membership degree of the level, as shown in equations (43) and (44); ; ; In the formula: for The entropy calculated by the criterion method The entropy calculated using the 50% membership criterion method; and These represent the upper and lower limits of the grade range, and are specified as follows: Indicates the expected level ; Using a certain indicator value The maximum membership deviation of the corresponding 5 state level cloud models The optimal cloud entropy optimization model is established with the objective function of minimizing the sum of the values. The calculation formula is shown below: ; ; ; In the formula: for In level Maximum membership deviation; The optimal cloud entropy matrix; For indicator value according to The level generated by the criteria Membership degree; For indicator value The ratings generated based on the 50% certainty criterion Membership degree; For the optimized level Membership degree; and Levels m The corresponding optimal cloud entropy, Criterion cloud entropy, 50% certainty criterion cloud entropy.

[0015] Based on the above scheme, the comprehensive weight of the distributed resource regulation capability profile index within the cluster is determined by the improved analytic hierarchy process, and the evaluation score of the overall distributed resource regulation capability index is calculated. Specifically, it includes the following steps: The membership degree obtained based on the improved cloud model does not satisfy the requirement. Membership degree correction is required: ; In the formula: For the first n The first indicator of regulatory capacity profile m The modified membership degree of the level. For the first n The first indicator of regulatory capacity profile m The original membership degree of the level; The following scores were calculated to assess the distributed resource control capabilities across various dimensions: ; In the formula: For the first n The rating of each regulatory capability profile indicator. For the first nCorrected membership degree of each level of the regulatory capacity profile indicator; The first number in the cluster is obtained according to equation (42). j In the distributed resource, the first i Evaluation score of individual regulatory capacity profile indicators This yields a score matrix of the controllability of all distributed resources within the cluster across various dimensions. And calculate the first j The overall control capability score for distributed resources is calculated using the following formula: ; In the formula: For the first in the cluster j An assessment score for the overall control capability of distributed resources. The relative importance weights of the regulatory capacity index obtained by the improved analytic hierarchy process are calculated using Equation (10).

[0016] Based on the above scheme, the one based on Distributed resources are sorted from highest to lowest, denoted as . The distributed resources are sorted in order; based on the sorting results, the power control commands are decomposed among the distributed resources within the cluster according to the comprehensive control capability evaluation scores from highest to lowest. Specifically, this includes the following steps: Based on the comprehensive control capability assessment scores from highest to lowest, the required power is allocated to the resource with the highest control capability score first, until the upper limit of the controllable capacity of the distributed resources is exhausted or the total controllable power demand is exhausted. If a resource cannot meet all the demand, the remaining controllable power is allocated to the next resource, and so on, until all distributed resources have been traversed or the remaining controllable power is zero. The calculation formula is as follows: ; ; In the formula: Remaining controllable power, kW; This represents the initial state of the power decomposition. The power of the control command for the distributed resource cluster is kW; To allocate distributed resources The regulating power, kW; For distributed resources The corresponding adjustable power limit is kW.

[0017] Secondly, a distributed resource cluster regulation characteristic evaluation system for auxiliary services is provided, including: The first module is configured to construct a distributed resource regulation characteristic profile label system from two dimensions: static attributes and dynamic attributes. The regulation characteristic profile label system is composed of regulation characteristic profile labels. Based on the regulation characteristic profile labels, a distributed resource regulation characteristic profile label calculation model is proposed. The second module acquires information on all distributed resources within the cluster. Based on the aforementioned control characteristic profile tags, it aggregates all distributed resources within the cluster and constructs a distributed resource cluster profile tag system from two dimensions: static attributes and dynamic attributes. This system consists of distributed resource cluster profile tags. Based on these tags, it calculates the control capability profile index for each distributed resource within the cluster. Finally, it comprehensively calculates the control capability profile index for the distributed resource cluster. The third module is configured to evaluate the control characteristics of the distributed resource cluster based on the control capability profile index of the distributed resource cluster, and to decompose the control power command of the distributed resource cluster for auxiliary services based on the characteristic evaluation results.

[0018] The calculation steps for the distributed resource cluster regulation capability profile index are as follows: Based on the different auxiliary service regulation needs, the importance ranking of regulation capability indicators is determined; an improved analytic hierarchy process is used to determine the relative importance of each pair of indicators, construct a judgment matrix, and calculate the relative importance weights of the regulation capability indicators. ; The optimal and worst values ​​of each control capability are determined based on all distributed resources within the virtual power plant, and the control capability values ​​of each distributed resource within the cluster are normalized. Based on the relative importance weights of distributed resource regulation capabilities and normalized index values, the completeness weight of the contribution of distributed resources to the cluster regulation capability index is calculated. ; By incorporating the contribution integrity weight for auxiliary services, the distributed resource cluster control capability index is obtained by weighted summation and weighted averaging of all distributed resource control capability indicators within the cluster. The specific steps for implementing the decomposition of power control commands for distributed resource clusters oriented towards auxiliary services are as follows: The regulatory capacity is divided into five levels, and each level is assigned a corresponding score and degree of membership. Five levels of excellence were generated using a forward cloud generator. Obtain distributed resource index values ​​and their membership degrees at different levels. ; Based on optimal cloud entropy The cloud model was optimized and improved, but the membership degree obtained based on the improved cloud model did not meet the requirements. Membership Correction; Based on the improved analytic hierarchy process (AHP), the comprehensive weights of the distributed resource regulation capability profile indicators within the cluster are determined, and the overall evaluation score of the distributed resource regulation capability indicators is calculated. ; based on Distributed resources are sorted from highest to lowest, denoted as . Sort the distributed resources by order; Based on the ranking results, the power control commands are decomposed among the distributed resources within the cluster according to the comprehensive control capability evaluation scores from high to low.

[0019] Based on the above scheme, the distributed resource cluster profile tagging system includes static attribute indicators and dynamic attribute indicators; The static attribute indicators include total installed capacity, clean energy ratio, and distributed resource type; The dynamic attribute indicators include: Cluster power curve characteristic profile indicators: power fluctuation amplitude, fluctuation frequency, power change rate, maximum power change rate, power fluctuation change rate, daily average load, daily minimum load, daily minimum load rate, daily maximum load, daily average load rate, and daily peak-to-valley difference; Cluster control capability profile indicators: comprehensive controllable capacity, comprehensive control response frequency, comprehensive maximum control response time, comprehensive average control duration, comprehensive control delay time, comprehensive control response achievement, comprehensive control response positivity, and comprehensive control resource reliability. Dynamic profile indicators of cluster power generation characteristics: real-time power generation and daily cumulative power generation; Cluster load characteristic profile indicators: real-time power generation and daily cumulative power generation.

[0020] Based on the above scheme, the cluster regulation capability profile index is calculated using the following method: Comprehensive adjustable capacity : The overall adjustable capacity of a distributed resource cluster for auxiliary services is calculated by weighted summing of the adjustable power capacities of each distributed resource within the cluster. The specific calculation formula is shown below: ; In the formula: They are respectively t The overall upward and downward adjustable power (kW) of the distributed resource cluster when it is oriented towards auxiliary services. For the first in a distributed resource clusteri The upward and downward adjustable power of a distributed resource, in kW; N agg The number of distributed resources in the cluster; for t Within the distributed resource cluster at any given time i The contribution integrity weight of each distributed resource to auxiliary services; Comprehensive control response frequency : The comprehensive control response frequency for auxiliary services in a distributed resource cluster is calculated by weighted averaging of the control response frequencies of each distributed resource within the cluster. The specific calculation formula is shown below: ; In the formula: for t The frequency of comprehensive regulation and response of the distributed resource cluster for auxiliary services at any given time; For the first in a distributed resource cluster i The frequency of regulation and response of a distributed resource, times; Comprehensive maximum control response time : The overall maximum control response time for auxiliary services in a distributed resource cluster is calculated by weighted summing of the maximum control response times of each distributed resource within the cluster. The specific calculation formula is as follows: ; In the formula: for t The maximum overall control response time of the distributed resource cluster when it is oriented towards auxiliary services, in min; For the first in a distributed resource cluster i The maximum control response time of a distributed resource, min; Overall average control duration : In the calculation of the control duration for ancillary services, the overall average control duration of the distributed resource cluster is obtained by weighted averaging of the control durations of each distributed resource within the cluster. The specific calculation formula is as follows: ; In the formula: for t The overall average duration of control of the distributed resource cluster for auxiliary services at any given time, in h; for t Within the distributed resource cluster at any given time i The duration of regulation for a distributed resource, in hours; for tWithin the distributed resource cluster at any given time i The contribution integrity weight of the continuous regulation time of each distributed resource computing cluster; Comprehensive regulation delay time : The overall control latency of a distributed resource cluster for auxiliary services is calculated by taking the weighted average of the control latency of each distributed resource within the cluster. The specific calculation formula is shown below: ; In the formula: for t The overall control delay time (s) of the distributed resource cluster for peak shaving auxiliary services. For the first in a distributed resource cluster i The regulation delay time of a distributed resource, in seconds; Comprehensive regulation and control response achievement : ; In the formula: The degree of comprehensive control and response achievement of distributed resource clusters; N Total number of responses for distributed resource cluster regulation; For distributed resource clusters n Actual controlled power, kW; For the first n The power (kW) that the distributed resource cluster needs to be regulated in the next regulation instruction; Comprehensive regulation and response initiative : ; In the formula: To enhance the comprehensive regulation and response capabilities of distributed resource clusters; For the virtual power plant n The total power of the next control command; Comprehensive regulation and response initiative : ; In the formula: To ensure the comprehensive regulation and reliability of distributed resource clusters, For the first in a distributed resource cluster i The regulation and control of distributed resources and resource reliability.

[0021] Based on the above scheme, the relative importance weights of the regulation capability indicators are... The calculation method is as follows: Based on the different needs for regulating ancillary services, the importance of the regulation capability indicators is ranked as follows: In the peak shaving auxiliary service operation scenario, the importance of the regulation capability indicators is ranked as follows: adjustable power capacity > regulation duration > regulation response time ≥ regulation delay time > regulation response frequency > regulation response achievement ≥ regulation response initiative ≥ regulation resource reliability. In the operation scenario of frequency modulation auxiliary service, the importance of the control capability indicators is ranked as follows: control response time > control delay time ≥ adjustable power capacity ≥ control duration > control response frequency > control response achievement ≥ control response initiative ≥ control resource reliability. In the standby auxiliary service operation scenario, the importance of the control capability indicators is ranked as follows: adjustable power capacity ≥ control response time ≥ control delay time ≥ control duration > control response frequency > control response achievement ≥ control response positivity ≥ control resource reliability. An improved analytic hierarchy process (AHP) is used to determine the relative importance of each pair of indicators, and a scaling factor is applied. Value quantification judgment result; Construct a judgment matrix And calculate the relative importance weights of the regulatory capacity indicators.

[0022] Based on the above scheme, the judgment matrix The following conditions must be met: ,Right now They are reciprocal matrices; ,in Indicates the first i The element and the first j The scale value obtained by comparing each element; The formula for calculating the judgment matrix is ​​as follows: ; The relative importance weights of the regulatory capacity indicators are as follows: ; Based on the above scheme, the contribution completeness weight of the cluster control capability index The calculation method is as follows: For all distributed resources and scattered resources within the resource cluster of the virtual power plant N The control capability indicators are normalized, where the control response time and delay time are minimal indicators, as shown in equation (11), and the rest are normalized according to equation (12) for minimal indicators: ; ; In the formula: For distributed resources i Normalized values ​​of individual regulatory capacity profile indicators; Distributed resources issued by the cloud for virtual power plantsi Maximum and minimum values ​​of each regulatory capacity profile indicator; Based on the relative importance weight of distributed resource regulation capability and the normalized index value, the contribution completeness weight of distributed resources to the cluster regulation capability index is calculated; among which, the contribution completeness weight of distributed resources to the cluster's comprehensive adjustable capacity, comprehensive regulation response frequency, comprehensive maximum regulation response time, comprehensive regulation delay time and comprehensive regulation response initiative is calculated using formula (13): ; In the formula: for t Within the distributed resource cluster at any given time i The contribution integrity weight of each distributed resource to auxiliary services is used to characterize the equivalent contribution of the distributed resource to the cluster control capability in the current auxiliary service operation scenario. for t Within the time cluster i The first distributed resource b Normalized values ​​of individual regulatory capacity profile indicators; To calculate the number of nodes within the cluster based on the improved hierarchical method i The first distributed resource b The relative importance weight of each regulatory capability profile indicator in the current auxiliary service operation scenario; Set minimum contribution integrity weight By using linear mapping, the contribution completeness weight is controlled within... Within the range; When calculating the overall average control duration of the cluster, distributed resources with shorter control durations are given a greater contribution integrity weight. The contribution integrity weight of distributed resources to the overall average control duration of the cluster is calculated using equation (14): ; In the formula: for t Within the distributed resource cluster at any given time i The contribution of each distributed resource to the overall average control duration of the cluster is weighted by its completeness. For the first in the cluster i The relative importance weight of the duration of regulation for each distributed resource; For the first in the cluster i The duration of regulation of each distributed resource is calculated using a minimal normalized value based on equation (11).

[0023] Based on the above scheme, the distributed resource regulation characteristic profile labeling system includes static attribute indicators and dynamic attribute indicators; The static attribute indicators include user number, user industry, electricity priority, adjustment model, and unit compensation price. The dynamic attribute indicators include power curve characteristic indicators, regulation capability indicators, and real-time operation characteristic indicators. The power curve characteristic indicators include: Power fluctuation amplitude : ; Where: power fluctuation amplitude for t- 1 hour has arrived t The amplitude of load power curve fluctuations can be adjusted in real time, in kW; for t The load power can be adjusted at any time. ; Fluctuation frequency : ; ; Where: fluctuation frequency The frequency at which the adjustable load forecast power curve switches between rising and falling trends; for t The load forecast power curve trend switching state variable is adjustable in real time; when the power curve switches between an upward and downward trend... ,otherwise ; T This represents the total number of time points. A symbolic function used to determine the value of a variable. The meanings of the plus and minus signs are as follows: ; Power change rate : ; Where: power change rate for t- 1 to t The rate of change of the load power curve can be adjusted in real time, kW / min. 15 minutes; Maximum power change rate : ; Where: Maximum power change rate The maximum value of the rate of change of the adjustable load power curve, kW; T This represents the total number of time points. Power fluctuation rate : ; Where: power fluctuation rate for t- 2 to t The rate of change of the load power curve fluctuation that can be adjusted at any time, in kW / min; Daily maximum load : ; Where: Daily maximum load For adjustable peak load power, kW; for t Adjustable load plan operating power in real time, kW; T This represents the total number of time points. Daily minimum load : ; Where: Daily minimum load The adjustable load valley power is measured in kW. Daily average load : ; Where: Daily average load The average load level of the adjustable load is kW; T This represents the total number of time points. Daily minimum load rate : ; Where: Daily minimum load factor Used to reflect the range of variation of the adjustable load power curve, in kW; Daily average load factor : ; Where: Daily average load factor Used to reflect the smoothness and balance of the adjustable load power curve, in kW; Daily peak-valley difference : ; Where: Daily maximum load The difference between the daily maximum load and the daily minimum load, in kW; The regulation capability indicators include: Adjustable power capacity: The adjustable power capacity of an adjustable load is divided into upward adjustable power and downward adjustable power. Upward adjustable power refers to the maximum power that the adjustable load can reduce relative to the current power, while downward adjustable power refers to the maximum power that the adjustable load can increase relative to the current power. Adjustable power capacity of transferable load The adjustable power of the transferable load, both upward and downward, can be obtained from the following formula: ; ; In the formula: The power is adjustable upwards and downwards for transferable loads, in kW; for t Planned operating power (kW) that can be readily transferred to other loads; T This represents the total number of time points. Total electricity consumption for transferable load, in kWh; Maximum power of transferable load, kW; Adjustable power capacity of load that can be shifted The adjustable power for shiftable loads is determined based on the planned operating power; if If a load shifting plan is in operation, then the load has the potential to be reduced, therefore its It always possesses upward adjustable potential; if If a load that can be moved is in a stopped state and is in a moveable state, then that load has the potential to generate additional load, therefore its It always possesses downward adjustment potential; the calculation formula is shown below: ; ; ; In the formula: The adjustable power (kW) is for loads that can be shifted downwards and upwards. for t Planned operating power (kW) of load that can be shifted at any time; Represents a transferable load t The scheduled time slot is currently unavailable. Represents a transferable load t The timeline is scheduled to be in operation. For transferable loads The starting time; T This represents the total number of time points. For the controllable state variable of the shiftable load, Represents the load that can be moved. The time period has already run. Represents the load that can be moved. The time period was not running; L The duration for which a transferable load must operate once activated; Adjustable power capacity that can reduce load The only way to reduce load is to adjust the power capacity upwards, and the calculation formula is as follows: ; In the formula: Adjustable power output to reduce load, kW; for The planned operating power can be reduced at any time, in kW; for The minimum power capacity at which the load can be reduced at any given time, in kW; Adjusting the response frequency The control response frequency is the cumulative number of effective response hours per day; ; ; In the formula: The control response frequency of the controllable load can be calculated from the power of the controllable load, and is given by the frequency. for h The state variables that are effective at all times and respond to the hourly state variables; Maximum control response time : ; In the formula: for t The maximum controllable response time of the controllable load can be calculated from the controllable load power, min; The control speed of the adjustable load is calculated from the average control speed of the historical control process, in kW / min; Regulation duration : ; In the formula: for t The duration of the adjustable load regulation can be calculated from the adjustable load power curve, in hours (h). For adjustable load t Continuously satisfy from time to time Maximum number of time periods; Adjustment delay time : ; In the formula: The control delay time for the controllable load is calculated from the average of historical control delay times, in seconds. For the first n The time for the action of the controllable load response control command is s; No. n The time (s) during which the controllable load receives the control command during the next response; ; Response achievement rate of regulation: ; In the formula: The controllable load control response achievement rate is calculated from the average historical control response achievement rate. For adjustable load n The actual power involved in regulation, in kW; For the first n The power (kW) that the controllable load needs to be controlled in the next control instruction; ; Response responsiveness to regulation: ; In the formula: The regulatory response positivity of the controllable load is calculated based on the average historical results of the controllable load's participation in regulation. The resource cluster to which the adjustable load belongs n The total power of this control command, in kW; ; Regulating resource reliability : ; In the formula: The reliability of controllable load regulation resources is calculated based on the historical participation results of controllable loads in regulation. The number of unplanned outage hours during adjustable load operation, in hours (h). The total operating time of the adjustable load is in hours (h). Real-time operating characteristics: The real-time operating characteristics of adjustable loads include real-time power consumption, daily cumulative power consumption, and other real-time status indicators, which are used to monitor the power consumption of adjustable loads.

[0024] Based on the above scheme, the method of using a forward cloud generator to generate five levels of excellence / disexcellence is described. Obtain distributed resource index values ​​and their membership degrees at different levels. Specifically, it includes the following steps: by For the expectation, Generate normally distributed random numbers with standard deviation. ; The parameter for calculating the final membership degree was calculated by repeating the calculation 10,000 times and taking the average value. pass and regulatory capacity data values ,calculate Membership degrees corresponding to different levels of cloud models The details are as follows: ; In the formula: This is a normalized value for the distributed resource regulation capability. and This is the mathematical characteristic value of the corresponding quality level of this indicator.

[0025] Based on the above scheme, the optimal cloud entropy-based approach... The cloud model was optimized and improved, but the membership degree obtained based on the improved cloud model did not meet the requirements. Membership The correction specifically includes the following steps: respectively Cloud entropy is calculated using the criterion method and the 50% membership criterion method, and a positive cloud generator is used based on... Calculate the membership degree of the level, as shown in equations (43) and (44); ; ; In the formula: for The entropy calculated by the criterion method The entropy calculated using the 50% membership criterion method; and These represent the upper and lower limits of the grade range, and are specified as follows: Indicates the expected level ; Using a certain indicator value The maximum membership deviation of the corresponding 5 state level cloud models The optimal cloud entropy optimization model is established with the objective function of minimizing the sum of the values. The calculation formula is shown below: ; ; ; In the formula: for In level Maximum membership deviation; The optimal cloud entropy matrix; For indicator value according to The level generated by the criteria Membership degree; For indicator value The ratings generated based on the 50% certainty criterion Membership degree; Membership degree under the optimized level; and Levels m The corresponding optimal cloud entropy, Criterion cloud entropy, 50% certainty criterion cloud entropy.

[0026] Based on the above scheme, the comprehensive weight of the distributed resource regulation capability profile index within the cluster is determined by the improved analytic hierarchy process, and the evaluation score of the overall distributed resource regulation capability index is calculated. Specifically, it includes the following steps: The membership degree obtained based on the improved cloud model does not satisfy the requirement. Membership degree correction is required: (48); In the formula: For the first n The first indicator of regulatory capacity profile m The modified membership degree of the level. For the first n The first indicator of regulatory capacity profile m The original membership degree of the level; The following scores were calculated to assess the distributed resource control capabilities across various dimensions: ; In the formula: For the first n The rating of each regulatory capability profile indicator. For the first n Corrected membership degree of each level of the regulatory capacity profile indicator; The first number in the cluster is obtained according to equation (42). j In the distributed resource, the first Evaluation score of individual regulatory capacity profile indicators This yields a score matrix of the controllability of all distributed resources within the cluster across various dimensions. And calculate the first j The overall control capability score for distributed resources is calculated using the following formula: ; In the formula: For the first in the cluster j An assessment score for the overall control capability of distributed resources. The relative importance weights of the regulatory capacity index obtained by the improved analytic hierarchy process are calculated using Equation (10).

[0027] Based on the above scheme, the one based on Distributed resources are sorted from highest to lowest, denoted as . The distributed resources are sorted in order; based on the sorting results, the power control commands are decomposed among the distributed resources within the cluster according to the comprehensive control capability evaluation scores from highest to lowest. Specifically, this includes the following steps: Based on the comprehensive control capability assessment scores from highest to lowest, the required power is allocated to the resource with the highest control capability score first, until the upper limit of the controllable capacity of the distributed resources is exhausted or the total controllable power demand is exhausted. If a resource cannot meet all the demand, the remaining controllable power is allocated to the next resource, and so on, until all distributed resources have been traversed or the remaining controllable power is zero. The calculation formula is as follows: ; ; In the formula: For the remaining controllable power, This represents the initial state of the power decomposition. To regulate the power of commands in a distributed resource cluster, To allocate distributed resources The regulating power, For distributed resources The corresponding adjustable power limit is kW.

[0028] Thirdly, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method.

[0029] The beneficial effects of this invention are: This invention accurately reflects the operational characteristics and control potential of distributed resources and clusters. The constructed control capability index system provides a theoretical basis for distributed resources and clusters to participate in ancillary service control, and its graded scoring profile enhances the identifiability of differences in resource control capabilities within the cluster. The comprehensive control capability profile index of distributed resource clusters, considering contribution completeness, effectively reflects the intensity of distributed resource control capabilities under different ancillary service operation scenarios, providing reliable data support for virtual power plants to achieve optimal aggregation of distributed resource clusters. It realizes a closed-loop design from control capability profile evaluation to control power command decomposition of distributed resources. Based on the comprehensive score of distributed resource control capabilities and adjustable power constraints, it prioritizes the allocation of control power to distributed resources with strong comprehensive capabilities, achieving a reasonable allocation of resource control power. Attached Figure Description

[0030] Figure 1 Flowchart of a method for evaluating the controllability characteristics of distributed resource clusters for auxiliary services; Figure 2 A distributed resource cluster regulation characteristic profiling and tagging system; Figure 3 Adjustable load regulation characteristic profiling and labeling system; Figure 4 Decomposition process of power control commands for distributed resource clusters oriented towards auxiliary services; Figure 5 A schematic diagram illustrating the regulation characteristics of photovoltaic system 1; Figure 6 shows the radar charts of the distributed resource regulation capability indicators of the cluster; among them, Figure 6(a) is the distributed photovoltaic regulation indicator rating chart, Figure 6(b) is the distributed wind turbine regulation indicator rating chart, Figure 6(c) is the distributed energy storage regulation indicator rating chart, Figure 6(d) is the adjustable load regulation indicator rating chart, and Figure 6(e) is the adjustable distributed power source regulation indicator rating chart. Detailed Implementation

[0031] To make the objectives, advantages and features of the present invention more apparent, the following detailed description of the embodiments further illustrates the present invention.

[0032] like Figure 1 As shown, this invention provides a method for evaluating the control characteristics of distributed resource clusters for ancillary services. First, a control characteristic profiling system is constructed for distributed resources and clusters in a virtual power plant. Control characteristic profiling labels for distributed resources and clusters are built from two dimensions: static attributes and dynamic attributes. Based on historical, predictive, and real-time data, the operational characteristics and control capabilities of distributed resources and clusters are comprehensively characterized. According to different ancillary service control requirements, a method for calculating the contribution completeness weight of control capability profiling indicators based on an improved analytic hierarchy process (AHP) is proposed. This method calculates the relative importance weight and the contribution completeness weight of control capability indicators, thereby completing the calculation of distributed resource cluster control capability indicators for ancillary service control requirement scenarios. Second, a method for decomposing power commands for distributed resource clusters for ancillary services is proposed. This method calculates the membership degree of each control capability indicator level through an improved cloud model theory, determines the level score of the distributed resource control capability profiling indicators by combining level scoring, calculates the comprehensive score and ranking of distributed resource control capabilities, and decomposes the control power commands among the distributed resources within the cluster based on this.

[0033] It should be noted that the calculation of the distributed resource cluster's regulation capability profile index is obtained by comprehensively calculating the regulation capability profile indices of each distributed resource within the cluster. Therefore, the distributed resource regulation characteristic profile system points to the cluster's regulation characteristic profile system. Furthermore, the calculation of the distributed resource cluster's regulation capability profile index requires the introduction of a weight α representing the completeness of the regulation capability contribution. Figure 1 The small squares in the diagram, representing only the contribution of the control capability to the integrity weight α, point to the profile of the control characteristics of the distributed resource cluster.

[0034] In one specific embodiment, a method for evaluating the regulation characteristics of a distributed resource cluster oriented towards auxiliary services includes: Step S1: Construct a profile label of the regulation characteristics of distributed resources (including adjustable loads, adjustable power sources, distributed photovoltaics, distributed wind turbines, and distributed energy storage) from two dimensions: static attributes and dynamic attributes. Based on historical, forecast, and real-time data, this profile comprehensively depicts the operating characteristics and regulation capabilities of distributed resources.

[0035] The second step, S2, involves aggregating all distributed resources within the cluster and constructing a distributed resource cluster profile and tagging system from both static and dynamic attributes. This system is then used to calculate distributed resource cluster control capability indicators for scenarios involving power grid auxiliary service control requirements.

[0036] Step S3: Calculate the distributed resource cluster control capability index. The specific steps are as follows: S31. Based on the different auxiliary service regulation needs, determine the importance ranking of regulation capability indicators. Using a modified analytic hierarchy process (AHP), determine the relative importance of each pair of indicators, construct a judgment matrix, and calculate the relative importance weights of the regulation capability indicators. .

[0037] S32. Determine the optimal and worst values ​​of each control capability based on all distributed resources within the virtual power plant, and normalize the control capability values ​​of each distributed resource within the cluster.

[0038] S33. Based on the relative importance weights of distributed resource regulation capabilities and normalized index values, calculate the completeness weight of the contribution of distributed resources to the cluster regulation capability index. .

[0039] S34. By incorporating the contribution integrity weight for auxiliary services, the distributed resource cluster control capability index is obtained by weighted summation and weighted average of all distributed resource control capability indicators within the cluster.

[0040] Step S4: Complete the decomposition of distributed cluster power control commands. The specific steps are as follows: S41. Divide the regulatory capacity into five levels, and assign corresponding level scores and level membership degrees to each level.

[0041] S42. A positive cloud generator is used to generate five levels of quality. Obtain distributed resource index values ​​and their membership degrees at different levels. .

[0042] S43, Based on Optimal Cloud Entropy The cloud model was optimized and improved, but the membership degree obtained based on the improved cloud model did not meet the requirements. Membership Correction.

[0043] S44. Based on the improved analytic hierarchy process, determine the comprehensive weight of the distributed resource regulation capability profile index within the cluster, and calculate the evaluation score of the overall distributed resource regulation capability index. .

[0044] S45, based on Distributed resources are sorted from highest to lowest, denoted as . Sort the distributed resources by order.

[0045] S46. Based on the sorting results, the power control commands are decomposed among the distributed resources within the cluster in descending order of the comprehensive control capability evaluation score.

[0046] Specifically, the distributed resource cluster regulation characteristic profiling and labeling system and calculation method are explained as follows: Distributed resource clusters can be mainly categorized into hybrid clusters, photovoltaic clusters, wind turbine clusters, energy storage clusters, distributed generation clusters, and load aggregators. Evaluating the regulation characteristics of distributed resource clusters can uncover the regulation potential and response characteristics of different clusters; using clusters as units effectively reduces the complexity of virtual power plants handling massive resources.

[0047] Specifically, the distributed resource cluster regulation characteristic profile tagging system is explained as follows: like Figure 2 As shown, a specific embodiment of the present invention constructs a distributed resource cluster profile tagging system from two dimensions: static and dynamic characteristics.

[0048] (1) Static attributes Static attribute indicators of distributed resource clusters include total installed capacity, clean energy ratio, and distributed resource type.

[0049] (2) Dynamic attributes 1) Power curve characteristics Cluster power curve characteristic profile indicators include power fluctuation amplitude, fluctuation frequency, power change rate, maximum power change rate, power fluctuation change rate, daily average load, daily minimum load, daily minimum load rate, daily maximum load, daily average load rate, and daily peak-to-valley difference.

[0050] 2) Regulatory capacity The cluster control capability profile index is calculated based on the distributed resource control capability index within the cluster, including comprehensive controllable capacity, comprehensive control response frequency, comprehensive maximum control response time, comprehensive average control duration, comprehensive control delay time, comprehensive control response achievement rate, comprehensive control response positivity, and comprehensive control resource reliability.

[0051] 3) Power generation characteristics The dynamic profile indicators of cluster power generation characteristics mainly include real-time power generation and daily cumulative power generation.

[0052] 4) Load characteristics The main indicators for describing the characteristics of cluster loads include real-time power generation and daily cumulative power generation.

[0053] Specifically, the calculation method for the distributed resource cluster regulation capability profile index for ancillary services is explained as follows: In a specific embodiment of the present invention, the distributed resource cluster regulation capability index is calculated using the participation of a virtual power plant in ancillary service regulation as an example.

[0054] (1) Calculation model of distributed resource cluster regulation capability profile index for auxiliary services 1) Overall adjustable capacity ; The overall adjustable capacity of a distributed resource cluster for auxiliary services is calculated by weighted summing of the adjustable power capacities of each distributed resource within the cluster. The specific calculation formula is shown below.

[0055] ; In the formula: Let be the comprehensive upward adjustable power and comprehensive downward adjustable power of the distributed resource cluster for auxiliary services at time t, respectively, in kW; Let represent the upward and downward adjustable power (kW) of the i-th distributed resource within the distributed resource cluster; Nagg represents the number of distributed resources in the cluster. Let t be the number of distributed resource clusters at time t. The contribution integrity weight of a distributed resource for auxiliary services is calculated based on the contribution integrity weight determination method of the control capability profile index based on the improved analytic hierarchy process.

[0056] 2) Comprehensive regulation response frequency ; The overall control response frequency for auxiliary services in a distributed resource cluster is calculated by weighted averaging of the control response frequencies of each distributed resource within the cluster. The specific calculation formula is shown below.

[0057] (2); In the formula: Let be the comprehensive control and response frequency of the distributed resource cluster for auxiliary services at time t, in times; For the first in a distributed resource cluster The frequency of regulation and response of a distributed resource, times.

[0058] 3) Overall maximum control response time ; The overall maximum control response time for auxiliary services in a distributed resource cluster is calculated by weighted summing of the maximum control response times of each distributed resource within the cluster. The specific calculation formula is shown below.

[0059] ; In the formula: Let t be the maximum comprehensive control response time of the distributed resource cluster for auxiliary services, min; For the first in a distributed resource cluster The maximum control response time for a distributed resource, min.

[0060] 4) Overall average duration of regulation ; In calculating the duration of control measures for ancillary services, the overall average control duration of the distributed resource cluster is obtained by weighted averaging of the control durations of each distributed resource within the cluster. The specific calculation formula is shown below.

[0061] ; In the formula: Let h be the overall average duration of control of the distributed resource cluster for auxiliary services at time t; Let t be the number of distributed resource clusters at time t. The duration of regulation for a distributed resource, in hours; Let t be the number of distributed resource clusters at time t. The contribution integrity weight of the continuous regulation time of each distributed resource computing cluster.

[0062] 5) Comprehensive regulation delay time

[0063] The overall control latency of a distributed resource cluster for auxiliary services is calculated by taking the weighted average of the control latency of each distributed resource within the cluster. The specific calculation formula is shown below.

[0064] (5); In the formula: Let t be the comprehensive control delay time (s) of the distributed resource cluster for peak-shaving auxiliary services at time t. Let s be the control delay time of the i-th distributed resource within the distributed resource cluster.

[0065] 6) Comprehensive regulation and response achievement rate ; ; In the formula: The overall control and response achievement rate of the distributed resource cluster; N is the total number of control and response responses of the distributed resource cluster. The actual control power of the distributed resource cluster in the nth iteration is expressed in kW. Let be the power (kW) that the distributed resource cluster needs to regulate in the nth regulation command.

[0066] 7) Comprehensive regulation and response initiative ; (7); In the formula: To enhance the comprehensive regulation and response capabilities of distributed resource clusters; The total power of the nth control command of the virtual power plant.

[0067] 8) Comprehensive regulation and response initiative ; ; In the formula: To ensure the comprehensive regulation and reliability of distributed resource clusters, This refers to the resource reliability control of the i-th distributed resource within a distributed resource cluster.

[0068] (2) Weight of contribution completeness of regulatory capacity profiling index based on improved analytic hierarchy process Sure This invention proposes a method for calculating the contribution completeness weight of distributed resource regulation capability profile indicators based on an improved analytic hierarchy process. The method quantifies the relative importance of each regulation capability indicator based on the response demand characteristics of the ancillary service market. Based on the determined importance weights and the normalized index values ​​of distributed resource regulation capabilities, the contribution completeness weight of distributed resources to cluster regulation capability indicators is further calculated.

[0069] 1) Determine the relative importance weights of regulatory capacity indicators based on the improved analytic hierarchy process. ; First, based on the different auxiliary service control needs, determine the importance ranking of the control capability indicators: In the peak shaving auxiliary service operation scenario, the importance of the regulation capability indicators is ranked as follows: adjustable power capacity > regulation duration > regulation response time ≥ regulation delay time > regulation response frequency > regulation response achievement ≥ regulation response initiative ≥ regulation resource reliability.

[0070] In the operation scenario of frequency modulation auxiliary service, the importance of the regulation capability indicators is ranked as follows: regulation response time > regulation delay time ≥ adjustable power capacity ≥ regulation duration > regulation response frequency > regulation response achievement ≥ regulation response initiative ≥ regulation resource reliability.

[0071] In the standby auxiliary service operation scenario, the importance of the control capability indicators is ranked as follows: adjustable power capacity ≥ control response time ≥ control delay time ≥ control duration > control response frequency > control response achievement ≥ control response positivity ≥ control resource reliability.

[0072] Secondly, an improved analytic hierarchy process is used to determine the relative importance of each pair of indicators, and then a scaling factor is applied. The quantitative judgment results are shown in Table 1; finally, a judgment matrix is ​​constructed. And calculate the relative importance weights of the regulatory capacity indicators.

[0073] Table 1. Meaning of each scale value ;

[0074] Judgment Matrix The following conditions must be met: ,Right now It is a reciprocal matrix; 4) ,in This represents the scale value obtained by comparing the i-th element with the j-th element. The formula for calculating the judgment matrix is ​​shown below.

[0075] ; The relative importance weights of the regulatory capacity indicators are as follows: ; 2) Calculation of the completeness weight of the contribution of distributed resource regulation capabilities for auxiliary services First, the N control capability indicators of distributed resources and scattered resources within all resource clusters in the virtual power plant are normalized. Among them, the control response time and delay time are extremely small indicators, as shown in Equation (11). The remaining indicators are normalized according to Equation (12): ; ; In the formula: This is the normalized value of the i-th regulation capability profile indicator for distributed resources; The maximum and minimum values ​​of the profile index for the i-th distributed resource control capability issued by the virtual power plant cloud.

[0076] Secondly, based on the relative importance weight of distributed resource regulation capability and the normalized index value, the contribution completeness weight of distributed resources to the cluster regulation capability index is calculated. Among them, the contribution completeness weight of distributed resources to the cluster's comprehensive adjustable capacity, comprehensive regulation response frequency, comprehensive maximum regulation response time, comprehensive regulation delay time and comprehensive regulation response initiative is calculated using equation (13).

[0077] ; In the formula: Let t be the contribution completeness weight of the i-th distributed resource in the distributed resource cluster to the auxiliary service, which is used to characterize the equivalent contribution of the distributed resource to the cluster's control capability under the current auxiliary service operation scenario; Let b be the normalized value of the regulation capability profile index of the i-th distributed resource in the cluster at time t. To calculate the relative importance weight of the b-th regulation capability profile indicator of the i-th distributed resource within the cluster in the current auxiliary service operation scenario based on the improved hierarchical method, a minimum contribution integrity weight is set to avoid the problem of excessively low contribution integrity weight due to excessively small normalized values ​​of distributed resources. By using linear mapping, the contribution completeness weight is controlled within... Within the range.

[0078] When calculating the overall average control duration of the cluster, distributed resources with shorter control durations should be given a greater contribution integrity weight. The contribution integrity weight of distributed resources to the overall average control duration of the cluster is calculated using equation (14).

[0079] ; In the formula: Let t be the completeness weight of the contribution of the i-th distributed resource in the distributed resource cluster to the overall average control duration of the cluster at time t. The relative importance weight of the duration of regulation for the i-th distributed resource within the cluster; The duration of regulation for the i-th distributed resource in the cluster is calculated using a minimal normalized value based on equation (11).

[0080] Specifically, the distributed resource regulation characteristic profiling and labeling system and its calculation method are explained as follows: The regulation capability profile index of a distributed resource cluster is calculated by comprehensively calculating the regulation capability profile indices of each distributed resource within the cluster. The distributed resources that may be included within a virtual power plant distributed resource cluster mainly involve distributed photovoltaic and wind power and other renewable energy sources, controllable distributed power sources, distributed energy storage, and various types of controllable load resources. This invention uses representative controllable load resources as an example to illustrate the distributed resource regulation characteristic profile labeling system and its calculation method. Other types of resources are similar and will not be elaborated further. According to the demand response characteristics of the load, it can be divided into four categories: rigid load, transferable load, shiftable load, and load that can be reduced. The controllable load regulation characteristic profile labeling system is as follows: Figure 3 As shown, the static attribute indicators include user number, user industry, electricity priority, adjustment model, and unit compensation price.

[0081] (1) Power curve characteristics 1) Power fluctuation amplitude ; ; Where: power fluctuation amplitude Let be the amplitude of the adjustable load power curve fluctuation from time t-1 to time t, in kW; The adjustable load power at time t .

[0082] 2) Fluctuation frequency ; ; ; Where: fluctuation frequency The frequency at which the adjustable load forecast power curve switches between rising and falling trends is ____ times. Let t be the state variable for switching the trend of the adjustable load forecast power curve. When the power curve switches between an upward and downward trend... ,otherwise; T represents the total number of time points. A symbolic function used to determine the value of a variable. The meanings of the plus and minus signs are as follows: ; 3) Power change rate ; ; Where: power change rate The rate of change of the adjustable load power curve from time t-1 to time t. It takes 15 minutes.

[0083] 4) Maximum power change rate ; ; Where: Maximum power change rate The maximum value of the rate of change of the adjustable load power curve is kW; T is the total number of time points.

[0084] 5) Power fluctuation rate ; ; Where: power fluctuation rate Let be the rate of change of the adjustable load power curve from t-2 to t, in kW / min.

[0085] 6) Daily maximum load ; ; Where: Daily maximum load For adjustable peak load power, kW; The adjustable load plan operating power at time t is kW; T is the total number of time points.

[0086] 7) Daily minimum load ; ; Where: Daily minimum load The adjustable load valley power is measured in kW.

[0087] 8) Daily average load ; ; Where: Daily average load The average load level of the adjustable load is kW; T is the total number of time periods.

[0088] 9) Daily minimum load factor ; ; Where: Daily minimum load factor This is used to reflect the range of variation in the adjustable load power curve, expressed in kW. The smaller the load variation range, the higher the daily minimum load rate, which is more conducive to stable system operation.

[0089] 10) Daily average load factor

[0090] ; Where: Daily average load factor This indicator reflects the smoothness and balance of the adjustable load power curve, expressed in kW. Smaller load variations result in a higher daily average load factor, which is more conducive to the economical operation of the system.

[0091] 11) Daily peak-to-valley difference ; ; Where: Daily maximum load The difference between the daily maximum load and the daily minimum load, in kW.

[0092] (2) Regulatory ability 1) Adjustable power capacity The adjustable power capacity of an adjustable load is divided into upward adjustable power and downward adjustable power. Upward adjustable power refers to the maximum power that the adjustable load can reduce relative to the current power, while downward adjustable power refers to the maximum power that the adjustable load can increase relative to the current power.

[0093] ① Adjustable power capacity of transferable load ; The adjustable power of the transferable load, both upward and downward, can be obtained from the following formula: ; ; In the formula: The power is adjustable upwards and downwards for transferable loads, in kW; The planned operating power of the load that can be transferred at time t, in kW; T is the total number of time points; Total electricity consumption for transferable load, in kWh; The maximum power of the transferable load is kW.

[0094] ② Adjustable power capacity of load that can be shifted ; The adjustable power for shiftable loads is determined based on the planned operating power. If... If a load shifting plan is in operation, then the load has the potential to be reduced, therefore its It always possesses upward adjustable potential; if If a load that can be moved is in a stopped state and is in a moveable state, then that load has the potential to generate additional load, therefore its It always possesses downward adjustment potential. The calculation formula is as follows: ; ; (32); In the formula: The adjustable power (kW) is for loads that can be shifted downwards and upwards. Let t be the planned operating power of the load that can be shifted at time t, in kW; This indicates that the load that can be moved is scheduled to be out of service at time t. This represents the planned operating state of the transferable load at time t; For transferable loads The start time; T is the total number of time points; For the controllable state variable of the shiftable load, Represents the load that can be moved. The time period has already run. Represents the load that can be moved. The time period is not running; L is the duration for which the transferable load must run once it is started.

[0095] ③ Adjustable power capacity that can reduce load ; The only way to reduce load is to adjust power capacity upwards. The calculation formula is as follows: ; In the formula: Adjustable power output to reduce load, kW; for The planned operating power can be reduced at any time, in kW; for The minimum power capacity at which the load can be reduced at any time, in kW.

[0096] 2) Adjusting the response frequency ; The control response frequency is the cumulative number of effective response hours per day (the duration of non-zero adjustable power exceeds 30 minutes).

[0097] ; ; In the formula: The control response frequency of the controllable load can be calculated from the power of the controllable load, and is given by the frequency. The state variable is the effective response hour at time h.

[0098] 3) Maximum control response time ; ; In the formula: Let be the maximum controllable response time of the controllable load at time t, which can be calculated from the controllable load power, min; The control speed of the adjustable load is calculated from the average control speed of the historical control process, in kW / min.

[0099] 4) Duration of regulation ; ; In the formula: Let h be the duration of the controllable load at time t, which can be calculated from the controllable load power curve. For the adjustable load to continuously satisfy from time t The maximum number of time periods.

[0100] 5) Adjustment delay time ; ; In the formula: The control delay time for the controllable load is calculated from the average of historical control delay times, in seconds. The time of action of the nth adjustable load response control command is s; Let be the time, in seconds, when the controllable load receives the control command during the nth response. .

[0101] 6) Response achievement rate ; In the formula: The controllable load control response achievement rate is calculated from the average historical control response achievement rate. Let kW be the power (in kW) of the controllable load that actually participates in the controllable load's nth controllable event. Let be the power (kW) that the controllable load needs to be controlled in the nth control command; .

[0102] 7) Regulating Response Enthusiasm ; In the formula: The regulatory response positivity of the controllable load is calculated based on the average historical results of the controllable load's participation in regulation. The total power of the nth controllable load in the resource cluster is kW; .

[0103] 8) Regulating resource reliability

[0104] ; In the formula: The reliability of controllable load regulation resources is calculated based on the historical participation results of controllable loads in regulation. The number of unplanned outage hours during adjustable load operation, in hours (h). The total operating time of the adjustable load is in hours, h.

[0105] (3) Real-time operation characteristics The real-time operating characteristics of adjustable loads include real-time power consumption, daily cumulative power consumption, and other real-time status indicators, which are used to monitor the power consumption of adjustable loads.

[0106] Specifically, the breakdown and explanation of the power control instructions for distributed resource clusters oriented towards auxiliary services are as follows: The decomposition process of power control commands for distributed resource clusters oriented towards auxiliary services is as follows: Figure 4 As shown.

[0107] I. A Scoring Method for Distributed Resource Regulation Capability Based on Improved Cloud Model Theory Based on the regulatory capability profile index, and using the normalized values ​​of various regulatory capabilities of distributed resources, the regulatory capability is divided into five levels, and each level is assigned a corresponding score. At the same time, the degree of membership of the level is used to quantitatively characterize the degree of belonging of the distributed resource regulatory capability under each level, as shown in Table 2.

[0108] Table 2. Levels of Regulation Capability ;

[0109] (1) Cloud Model Theory In the assessment of regulatory capacity indicators, the cloud model achieves a mapping between quantitative indicators and qualitative levels of regulatory capacity by modeling the membership degree and uncertainty range of indicator levels. Let... It is a quantitative domain, T is Qualitative concepts. If there exists an indicator data value... ,and Let T be a random implementation on T, and let its membership degree be... If it is a random number with a stable tendency, then it will be... In this domain The distribution on the surface is called a cloud, and each point This is referred to as a cloud droplet. Distributed resource regulation capabilities are divided into five levels of quality, each level representing a qualitative concept T. The normalized value of distributed resource regulation capability is a quantitative value. The normalized value of distributed resource regulation capability and its membership degree at different levels are called cloud droplets.

[0110] 1) Generation of membership levels based on forward cloud generator The cloud model uses three parameters: expectation, entropy, and hyperentropy. This represents the level of regulatory capacity, among which The center of gravity for the hierarchy of superiority and inferiority is the point that best represents the qualitative concept; The numerical range for the grades of excellence is used to reflect the uncertainty of qualitative concepts; It refers to entropy, reflecting the randomness in the transformation from a quantitative value to a qualitative concept. Based on relevant experience, it is taken as 1 / 10 of the entropy value. This invention uses a positive cloud generator to generate five levels of quality. Based on this, the distributed resource index values ​​and their membership degrees at different levels are obtained. The algorithm principle is as follows: ① with For the expectation, Generate normally distributed random numbers with standard deviation. .

[0111] The parameter for calculating the membership degree was calculated by repeating the calculation 10,000 times and taking the average value. This was done to reduce the influence of randomness.

[0112] ② Through and regulatory capacity data values ,calculate The membership degree corresponding to the cloud model of this level The details are as follows.

[0113] ; In the formula: This is a normalized value for the distributed resource regulation capability. and This is the mathematical characteristic value of the indicator corresponding to a certain level of excellence or inferiority.

[0114] 2) An improved cloud model optimization method based on optimal cloud entropy The key needs to be determined in establishing a hierarchical cloud model ,entropy The value of is crucial, and this invention employs an adaptive optimal cloud entropy optimization method. Common entropy calculation methods include... The specific calculation formulas for the criterion method and the "50% membership degree" criterion method are shown below.

[0115] ; ; In the formula: for The entropy calculated by the criterion method The entropy calculated using the "50% membership degree" criterion method; and These represent the upper and lower limits of the grade range, and are specified as follows: Indicates the expected level .

[0116] This invention combines the above two methods to calculate the optimal cloud entropy. The normalized value of the regulation capability indicator is: It contains 5 groups of cloud models with varying degrees of excellence. Cloud entropy is calculated using the two methods described above, and a forward cloud generator is used based on... Calculate the membership degree of the ranks. Using a certain indicator value... The maximum membership deviation of the corresponding 5 state level cloud models The optimal cloud entropy optimization model is established with the objective function of minimizing the sum of the values. The calculation formula is shown below: ; ; ; In the formula: for In level Maximum membership deviation; The optimal cloud entropy matrix; For indicator value according to The level generated by the criteria Membership degree; For indicator value The ratings were generated based on the "50% certainty" criterion. Membership degree; For the optimized level Membership degree; and These are the optimal cloud entropy corresponding to level m, Criterion cloud entropy, “50% certainty” criterion cloud entropy.

[0117] (2) Distributed resource regulation capability profile index level score First, the membership degree obtained based on the improved cloud model does not satisfy... Membership degree correction is required: ; In the formula: The modified membership degree of the nth regulatory capability profile indicator at the mth level. The original membership degree of the nth regulatory capability profile indicator at the mth level.

[0118] Secondly, the rating of the distributed resource control capability profile across various dimensions is calculated: ; In the formula: The rating of the nth regulatory capability profile indicator. The modified membership degree is set for each level of the nth regulatory capability profile indicator.

[0119] Finally, according to equation (42), the evaluation score of the regulation capability profile index of the j-th distributed resource in the cluster is obtained. This yields a score matrix of the controllability of all distributed resources within the cluster across various dimensions. The overall control capability assessment score of the j-th distributed resource is calculated using the following formula: ; In the formula: The score represents the overall control capability evaluation score of the j-th distributed resource within the cluster. The relative importance weights of the regulatory capacity index obtained by the improved analytic hierarchy process are calculated using Equation (10).

[0120] II. Decomposition of Power Control Commands for Distributed Resource Clusters Based on Ancillary Services Power is allocated to the resource with the highest comprehensive control capability score, ranked from highest to lowest, until the upper limit of the controllable capacity of the distributed resources is exhausted or the total controllable power demand is met. If a resource cannot meet all the demand, the remaining controllable power is allocated to the next resource, and so on, until all distributed resources have been traversed or the remaining controllable power is zero. The calculation formula is as follows: ; ; In the formula: Remaining controllable power, kW; This represents the initial state of the power decomposition. The power of the control command for the distributed resource cluster is kW; To allocate distributed resources The regulating power, kW; For distributed resources The corresponding adjustable power limit is kW.

[0121] Below, this specific embodiment uses a typical summer day as a scenario, selecting a hybrid distributed resource cluster containing five different types of resources within a virtual power plant as the object for a profile assessment of its control characteristics. Table 3 shows the multi-dimensional control index data of each distributed resource within the cluster at a certain moment on this typical day.

[0122] Table 3. Data on Distributed Resource Regulation Indicators (various dimensions) ;

[0123] (1) Distributed resource regulation characteristics profile and regulation capability level assessment The various profiling indicators of distributed resource control capabilities are normalized, and then the membership degrees of the profiling indicators are determined based on the improved cloud model. The normalized indicators of the resources are input into the positive cloud generator to obtain their cloud membership degrees in five level intervals, which are then converted into basic probability assignment functions, and the final scores of the indicators are calculated based on these functions.

[0124] A schematic diagram of the distributed resource regulation characteristic profile was constructed using the visualization tool WordArt, enabling a visual display of the profile. Taking the distributed resource regulation characteristic profile index of Photovoltaic 1 as an example, the specific profile results are as follows: Figure 5 As shown.

[0125] Figure 6 shows the controllability scores of all distributed resources within the cluster. The scores in Figure 6 clearly demonstrate that energy storage, photovoltaics, and wind turbines have high scores for controllability response time and delay time, indicating fast controllability. Wind turbines and controllable distributed power sources have high scores for average controllability duration, demonstrating stable power output over extended periods. Controllable loads, constrained by summer temperatures and production / residential necessities, exhibit significantly different controllability characteristics. During real-time control, continuous monitoring of the controllability characteristics of controllable loads is crucial for the rational utilization of resource controllability.

[0126] (2) Profile of distributed resource cluster regulation capabilities for auxiliary services Based on the relative importance of the ancillary service regulation capability indicators, a judgment matrix for peak shaving, frequency regulation, and reserve indicators is constructed. The importance weights of each regulation capability indicator for ancillary services are obtained based on the improved analytic hierarchy process.

[0127] The distributed resource cluster regulation capability index is calculated. Taking this hybrid resource cluster as an example, the regulation response index values ​​for auxiliary services are shown in Table 4.

[0128] Table 4. Distributed Resource Hybrid Cluster Auxiliary Service Regulation and Response Capability ;

[0129] (3) Distributed resource cluster regulation power decomposition By calculating a comprehensive score of distributed resource regulation capabilities within the cluster, the ranking of distributed resource regulation capabilities for auxiliary services is completed. Table 5 shows the comprehensive score and ranking of distributed resource regulation capabilities for different auxiliary services within the cluster.

[0130] Table 5. Comprehensive score and ranking of the ability of distributed resources to participate in the regulation of different auxiliary services. ;

[0131] Taking the scenario where the distributed resource cluster receives a peak-shaving command to increase power by 6658kW at a certain moment as the control scenario, the control command of the distributed resource cluster is decomposed based on the ranking of the control capabilities of each distributed resource under the peak-shaving auxiliary service in Table 5. The decomposition results are shown in Table 6.

[0132] Table 6 Decomposition of Distributed Resource Cluster Regulation Power Commands for Frequency Modulation Ancillary Services ;

[0133] The decomposition results realize a closed-loop design from distributed resource regulation capability profile assessment to regulation power command decomposition. Based on the comprehensive score of distributed resource regulation capability and adjustable power constraints, the regulation power is preferentially allocated to distributed resources with strong comprehensive capabilities, thereby achieving a reasonable allocation of resource regulation power.

[0134] Based on the same inventive concept, in another embodiment, a distributed resource cluster regulation characteristic evaluation system for auxiliary services is proposed, comprising: The first module is configured to construct a distributed resource regulation characteristic profile label system from two dimensions: static attributes and dynamic attributes. The regulation characteristic profile label system is composed of regulation characteristic profile labels. Based on the regulation characteristic profile labels, a distributed resource regulation characteristic profile label calculation model is proposed. The second module acquires information on all distributed resources within the cluster. Based on the control characteristic profile tags, it aggregates all distributed resources within the cluster and constructs a distributed resource cluster profile tag system from two dimensions: static attributes and dynamic attributes. The distributed resource cluster profile tag system is composed of distributed resource cluster profile tags. Based on the distributed resource cluster profile tags, it calculates the control capability profile index of each distributed resource within the cluster. The control capability profile index of the distributed resource cluster is obtained by comprehensively calculating the control capability profile index of each distributed resource within the cluster. The third module is configured to evaluate the control characteristics of the distributed resource cluster based on the control capability profile index of the distributed resource cluster, and to decompose the control power command of the distributed resource cluster for auxiliary services based on the characteristic evaluation results.

[0135] The calculation steps for the regulatory capability profile index are as follows: Based on the different auxiliary service regulation needs, the importance ranking of regulation capability indicators is determined; an improved analytic hierarchy process is used to determine the relative importance of each pair of indicators, construct a judgment matrix, and calculate the relative importance weights of the regulation capability indicators. The optimal and worst values ​​of each control capability are determined based on all distributed resources within the virtual power plant, and the control capability values ​​of each distributed resource within the cluster are normalized. Based on the relative importance weight of distributed resource regulation capability and the normalized index value, the completeness weight of the contribution of distributed resources to the cluster regulation capability index is calculated. By incorporating the contribution integrity weight for auxiliary services, the distributed resource cluster control capability index is obtained by weighted summation and weighted averaging of all distributed resource control capability indicators within the cluster. The specific steps for implementing the decomposition of power control commands for distributed resource clusters oriented towards auxiliary services are as follows: The regulatory capacity is divided into five levels, and each level is assigned a corresponding score and degree of membership. Five levels of excellence were generated using a forward cloud generator. Obtain distributed resource index values ​​and their membership degrees at different levels. ; Based on optimal cloud entropy The cloud model was optimized and improved, but the membership degree obtained based on the improved cloud model did not meet the requirements. Membership Correction; Based on the improved analytic hierarchy process (AHP), the comprehensive weights of the distributed resource regulation capability profile indicators within the cluster are determined, and the overall evaluation score of the distributed resource regulation capability indicators is calculated. ; based on Distributed resources are sorted from highest to lowest, denoted as . Sort the distributed resources by order; Based on the ranking results and in descending order of the comprehensive control capability evaluation score, the control power command is decomposed among the distributed resources within the cluster. It should be noted that any process or method description in the above embodiments can be understood as representing a module, fragment, or portion of code comprising one or more executable instructions for implementing a specific logical function or process. Furthermore, the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as should be understood by those skilled in the art to which the embodiments of the invention pertain.

[0136] It should be noted that the logic and / or steps in the embodiments, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0137] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0138] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0139] Furthermore, in the embodiments of the present invention, the functional modules can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0140] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0141] The above embodiments have provided a detailed description of the technical solution of the present invention. Obviously, the present invention is not limited to the described embodiments. Based on the embodiments of the present invention, those skilled in the art can make various modifications, but any modifications that are equivalent to or similar to the present invention fall within the scope of protection of the present invention.

[0142] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A method for evaluating the regulation characteristics of a distributed resource cluster oriented towards auxiliary services, characterized in that, Includes the following steps: A distributed resource regulation characteristic profile labeling system is constructed from two dimensions: static attributes and dynamic attributes. The regulation characteristic profile labeling system is composed of regulation characteristic profile labels. Based on the regulation characteristic profile labels, a distributed resource regulation characteristic profile labeling calculation model is proposed. Obtain information on all distributed resources within the cluster. Based on the control characteristic profile tags, aggregate all distributed resources within the cluster and construct a distributed resource cluster profile tag system from two dimensions: static attributes and dynamic attributes. The distributed resource cluster profile tag system is composed of distributed resource cluster profile tags. Based on the distributed resource cluster profile tags, calculate the control capability profile index of each distributed resource within the cluster. The control capability profile index of the distributed resource cluster is obtained by comprehensively calculating the control capability profile index of each distributed resource within the cluster. Based on the regulation capability profile indicators of the distributed resource cluster, the regulation characteristics of the distributed resource cluster are evaluated, and the regulation power command decomposition for auxiliary services is implemented based on the evaluation results.

2. The method according to claim 1, characterized in that, The calculation steps for the distributed resource cluster regulation capability profile index are as follows: Based on the different auxiliary service regulation needs, the importance ranking of regulation capability indicators is determined; an improved analytic hierarchy process is used to determine the relative importance of each pair of indicators, construct a judgment matrix, and calculate the relative importance weights of the regulation capability indicators. ; The optimal and worst values ​​of each control capability are determined based on all distributed resources within the virtual power plant, and the control capability values ​​of each distributed resource within the cluster are normalized. Based on the relative importance weights of distributed resource regulation capabilities and normalized index values, the completeness weight of the contribution of distributed resources to the cluster regulation capability index is calculated. ; By incorporating the contribution integrity weight for auxiliary services, the distributed resource cluster control capability index is obtained by weighted summation and weighted averaging of all distributed resource control capability indicators within the cluster.

3. The method according to claim 2, characterized in that, The specific steps are as follows: Based on the control capability profile indicators of the distributed resource cluster, the control characteristics of the distributed resource cluster are evaluated, and based on the evaluation results, the control power command decomposition for auxiliary services is implemented. The regulatory capacity is divided into five levels, and each level is assigned a corresponding score and degree of membership. Five levels of excellence were generated using a forward cloud generator. Obtain distributed resource index values ​​and their membership degrees at different levels. ; Based on optimal cloud entropy The cloud model was optimized and improved, but the membership degree obtained based on the improved cloud model did not meet the requirements. Membership Correction; Based on the improved analytic hierarchy process (AHP), the comprehensive weights of the distributed resource regulation capability profile indicators within the cluster are determined, and the overall evaluation score of the distributed resource regulation capability indicators is calculated. ; based on Distributed resources are sorted from highest to lowest, denoted as . Sort the distributed resources by order; Based on the ranking results, the power control commands are decomposed among the distributed resources within the cluster according to the comprehensive control capability evaluation scores from high to low.

4. The method according to claim 3, characterized in that, The distributed resource cluster profiling and tagging system includes static attribute indicators and dynamic attribute indicators; The static attribute indicators include total installed capacity, clean energy ratio, and distributed resource type; The dynamic attribute indicators include: Cluster power curve characteristic profile indicators: power fluctuation amplitude, fluctuation frequency, power change rate, maximum power change rate, power fluctuation change rate, daily average load, daily minimum load, daily minimum load rate, daily maximum load, daily average load rate, and daily peak-to-valley difference; Cluster control capability profile indicators: comprehensive controllable capacity, comprehensive control response frequency, comprehensive maximum control response time, comprehensive average control duration, comprehensive control delay time, comprehensive control response achievement, comprehensive control response positivity, and comprehensive control resource reliability. Dynamic profile indicators of cluster power generation characteristics: real-time power generation and daily cumulative power generation; Cluster load characteristic profile indicators: real-time power generation and daily cumulative power generation.

5. The method according to claim 4, characterized in that, The proposed calculation model for the distributed resource cluster regulation capability index specifically includes: Comprehensive adjustable capacity : The overall adjustable capacity of a distributed resource cluster for auxiliary services is calculated by weighted summing of the adjustable power capacities of each distributed resource within the cluster. The specific calculation formula is shown below: ; In the formula: They are respectively t The overall upward and downward adjustable power (kW) of the distributed resource cluster when it is oriented towards auxiliary services. For the first in a distributed resource cluster The upward and downward adjustable power of a distributed resource, in kW; N agg The number of distributed resources in the cluster; for t Within the distributed resource cluster at any given time The contribution integrity weight of each distributed resource to auxiliary services; Comprehensive control response frequency : The comprehensive control response frequency for auxiliary services in a distributed resource cluster is calculated by weighted averaging of the control response frequencies of each distributed resource within the cluster. The specific calculation formula is shown below: ; In the formula: for t The frequency of comprehensive regulation and response of the distributed resource cluster for auxiliary services at any given time; For the first in a distributed resource cluster The frequency of regulation and response of a distributed resource, times; Comprehensive maximum control response time : The overall maximum control response time for auxiliary services in a distributed resource cluster is calculated by weighted summing of the maximum control response times of each distributed resource within the cluster. The specific calculation formula is as follows: ; In the formula: for t The maximum overall control response time of the distributed resource cluster when it is oriented towards auxiliary services, in min; For the first in a distributed resource cluster The maximum control response time of a distributed resource, min; Overall average control duration : In the calculation of the control duration for ancillary services, the overall average control duration of the distributed resource cluster is obtained by weighted averaging of the control durations of each distributed resource within the cluster. The specific calculation formula is as follows: ; In the formula: for t The overall average duration of control of the distributed resource cluster for auxiliary services at any given time, in h; for t Within the distributed resource cluster at any given time The duration of regulation for a distributed resource, in hours; for t Within the distributed resource cluster at any given time The contribution integrity weight of the continuous regulation time of each distributed resource computing cluster; Comprehensive regulation delay time : The overall control latency of a distributed resource cluster for auxiliary services is calculated by taking the weighted average of the control latency of each distributed resource within the cluster. The specific calculation formula is shown below: ； In the formula: for t The overall control delay time (s) of the distributed resource cluster for peak shaving auxiliary services. For the first in a distributed resource cluster The regulation delay time of a distributed resource, in seconds; Comprehensive regulation and control response achievement rate: ; ； In the formula: The degree of comprehensive control and response achievement of distributed resource clusters; N Total number of responses for distributed resource cluster regulation; For distributed resource clusters n Actual controlled power, kW; For the first n The power (kW) that the distributed resource cluster needs to be regulated in the next regulation instruction; Comprehensive regulation and response initiative : ； In the formula: To enhance the comprehensive regulation and response capabilities of distributed resource clusters; For the virtual power plant n The total power of the next control command; Comprehensive regulation and response initiative : ； In the formula: To ensure the comprehensive regulation and reliability of distributed resource clusters, For the first in a distributed resource cluster The regulation and control of distributed resources and resource reliability.

6. The method according to claim 4, characterized in that, The relative importance weight of the regulation capability indicators The calculation method is as follows: Based on the different needs for regulating ancillary services, the importance of the regulation capability indicators is ranked as follows: In the peak shaving auxiliary service operation scenario, the importance of the regulation capability indicators is ranked as follows: adjustable power capacity > regulation duration > regulation response time ≥ regulation delay time > regulation response frequency > regulation response achievement ≥ regulation response initiative ≥ regulation resource reliability. In the operation scenario of frequency modulation auxiliary service, the importance of the control capability indicators is ranked as follows: control response time > control delay time ≥ adjustable power capacity ≥ control duration > control response frequency > control response achievement ≥ control response initiative ≥ control resource reliability. In the standby auxiliary service operation scenario, the importance of the control capability indicators is ranked as follows: adjustable power capacity ≥ control response time ≥ control delay time ≥ control duration > control response frequency > control response achievement ≥ control response positivity ≥ control resource reliability. An improved analytic hierarchy process (AHP) is used to determine the relative importance of each pair of indicators, and a scaling factor is applied. Quantify the judgment results; Construct a judgment matrix And calculate the relative importance weights of the regulatory capacity indicators.

7. The method according to claim 6, characterized in that, The judgment matrix The following conditions must be met: ,Right now They are reciprocal matrices; ,in Indicates the first The element and the first The scale value obtained by comparing each element; The formula for calculating the judgment matrix is ​​as follows: ； The relative importance weights of the regulatory capacity indicators are as follows: 。 8. The method according to claim 7, characterized in that, The contribution completeness weight of the cluster control capability index The calculation method is as follows: For all distributed resources and scattered resources within the resource cluster of the virtual power plant N The control capability indicators are normalized, where the control response time and delay time are minimal indicators, as shown in equation (11), and the rest are normalized according to equation (12) for minimal indicators: ； ； In the formula: For distributed resources Normalized values ​​of individual regulatory capacity profile indicators; Distributed resources issued by the cloud for virtual power plants Maximum and minimum values ​​of each regulatory capacity profile indicator; Based on the relative importance weight of distributed resource regulation capability and the normalized index value, the contribution completeness weight of distributed resources to the cluster regulation capability index is calculated; among which, the contribution completeness weight of distributed resources to the cluster's comprehensive adjustable capacity, comprehensive regulation response frequency, comprehensive maximum regulation response time, comprehensive regulation delay time and comprehensive regulation response initiative is calculated using formula (13): ； In the formula: for Within a distributed resource cluster The contribution integrity weight of each distributed resource to auxiliary services is used to characterize the equivalent contribution of the distributed resource to the cluster control capability in the current auxiliary service operation scenario. for t Within the time cluster The first distributed resource b Normalized values ​​of individual regulatory capacity profile indicators; To calculate the number of nodes within the cluster based on the improved hierarchical method The first distributed resource b The relative importance weight of each regulatory capability profile indicator in the current auxiliary service operation scenario; Set minimum contribution integrity weight By using linear mapping, the contribution completeness weight is controlled within... Within the range; When calculating the overall average control duration of the cluster, distributed resources with shorter control durations are given a greater contribution integrity weight. The contribution integrity weight of distributed resources to the overall average control duration of the cluster is calculated using equation (14): ； In the formula: for t Within the distributed resource cluster at any given time The contribution of each distributed resource to the overall average control duration of the cluster is weighted by its completeness. For the first in the cluster The relative importance weight of the duration of regulation for each distributed resource; For the first in the cluster The duration of regulation of each distributed resource is calculated using a minimal normalized value based on equation (11).

9. The method according to claim 3, characterized in that, The distributed resource regulation characteristic profiling and labeling system includes static attribute indicators and dynamic attribute indicators; The static attribute indicators include user number, user industry, electricity priority, adjustment model, and unit compensation price. The dynamic attribute indicators include power curve characteristic indicators, regulation capability indicators, and real-time operation characteristic indicators. The power curve characteristic indicators include: Power fluctuation amplitude : ； Where: power fluctuation amplitude for t- 1 hour has arrived t The amplitude of load power curve fluctuations can be adjusted in real time, in kW; for t The load power can be adjusted at any time. ; Fluctuation frequency : ； ； Where: fluctuation frequency The frequency at which the adjustable load forecast power curve switches between rising and falling trends; for t The load forecast power curve trend switching state variable is adjustable in real time; when the power curve switches between an upward and downward trend... ,otherwise ; T This represents the total number of time points. A symbolic function used to determine the value of a variable. The meanings of the plus and minus signs are as follows: ； Power change rate : ； Where: power change rate for t- 1 to t The rate of change of the load power curve can be adjusted in real time, kW / min. 15 minutes; Maximum power change rate : ； Where: Maximum power change rate The maximum value of the rate of change of the adjustable load power curve, kW; T This represents the total number of time points. Power fluctuation rate : ； Where: power fluctuation rate for t- 2 to t The rate of change of the load power curve fluctuation that can be adjusted at any time, in kW / min; Daily maximum load : ； Where: Daily maximum load For adjustable peak load power, kW; for t Adjustable load plan operating power in real time, kW; T This represents the total number of time points. Daily minimum load : ； Where: Daily minimum load The adjustable load valley power is measured in kW. Daily average load : ； Where: Daily average load The average load level of the adjustable load is kW; T This represents the total number of time points. Daily minimum load rate : ； Where: Daily minimum load factor Used to reflect the range of variation of the adjustable load power curve, in kW; Daily average load factor : ； Where: Daily average load factor Used to reflect the smoothness and balance of the adjustable load power curve, in kW; Daily peak-valley difference : ； Where: Daily maximum load The difference between the daily maximum load and the daily minimum load, in kW; The regulation capability indicators include: Adjustable power capacity: The adjustable power capacity of an adjustable load is divided into upward adjustable power and downward adjustable power. Upward adjustable power refers to the maximum power that the adjustable load can reduce relative to the current power, while downward adjustable power refers to the maximum power that the adjustable load can increase relative to the current power. Adjustable power capacity of transferable load The adjustable power of the transferable load, both upward and downward, can be obtained from the following formula: ； ； In the formula: The power is adjustable upwards and downwards for transferable loads, in kW; for t Planned operating power (kW) that can be readily transferred to other loads; T This represents the total number of time points. Total electricity consumption for transferable load, in kWh; Maximum power of transferable load, kW; Adjustable power capacity of load that can be shifted The adjustable power for shiftable loads is determined based on the planned operating power; if If a load shifting plan is in operation, then the load has the potential to be reduced, therefore its It always possesses upward adjustable potential; if If a load that can be moved is in a stopped state and is in a moveable state, then that load has the potential to generate additional load, therefore its It always possesses downward adjustment potential; the calculation formula is shown below: ； ； ； In the formula: The adjustable power (kW) is for loads that can be shifted downwards and upwards. for t Planned operating power (kW) of load that can be shifted at any time; Represents a transferable load t The scheduled time slot is currently unavailable. Represents a transferable load t The timeline is scheduled to be in operation. For transferable loads The starting time; T This represents the total number of time points. For the controllable state variable of the shiftable load, Represents the load that can be moved. The time period has already run. Represents the load that can be moved. The time period was not running; L The duration for which a transferable load must operate once activated; Adjustable power capacity that can reduce load The only way to reduce load is to adjust the power capacity upwards, and the calculation formula is as follows: ； In the formula: Adjustable power output to reduce load, kW; for The planned operating power can be reduced at any time, in kW; for The minimum power capacity at which the load can be reduced at any given time, in kW; Adjusting the response frequency The control response frequency is the cumulative number of effective response hours per day; ； ； In the formula: The control response frequency of the controllable load can be calculated from the power of the controllable load, and is given by the frequency. for h The state variables that are effective at all times and respond to the hourly state variables; Maximum control response time : ； In the formula: for t The maximum controllable response time of the controllable load can be calculated from the controllable load power, min; The control speed of the adjustable load is calculated from the average control speed of the historical control process, in kW / min; Regulation duration : ； In the formula: for t The duration of the adjustable load regulation can be calculated from the adjustable load power curve, in hours (h). For adjustable load t Continuously satisfy from time to time Maximum number of time periods; Adjustment delay time : ； In the formula: The control delay time for the controllable load is calculated from the average of historical control delay times, in seconds. For the first n The time for the controllable load to respond to the control command action is in seconds. For the first n The time (s) during which the controllable load receives the control command during the next response; ; Response achievement rate of regulation: ； In the formula: The controllable load control response achievement rate is calculated from the average historical control response achievement rate. For adjustable load n The actual power involved in regulation, in kW; For the first n The power (kW) that the controllable load needs to be controlled in the next control instruction; ; Response responsiveness to regulation: ； In the formula: The regulatory response positivity of the controllable load is calculated based on the average historical results of the controllable load's participation in regulation. The resource cluster to which the adjustable load belongs n The total power of this control command, in kW; ; Regulating resource reliability : ； In the formula: The reliability of controllable load regulation resources is calculated based on the historical participation results of controllable loads in regulation. The number of unplanned outage hours during adjustable load operation, in hours (h). The total operating time of the adjustable load is in hours (h). Real-time operating characteristics: The real-time operating characteristics of adjustable loads include real-time power consumption, daily cumulative power consumption, and other real-time status indicators, which are used to monitor the power consumption of adjustable loads.

10. The system according to claim 3, characterized in that, The method uses a positive cloud generator to generate five levels of quality. Obtain distributed resource index values ​​and their membership degrees at different levels. Specifically, it includes the following steps: by For the expectation, Generate normally distributed random numbers with standard deviation. ; The parameter for calculating the final membership degree was calculated by repeating the calculation 10,000 times and taking the average value. pass and regulatory capacity data values ,calculate Membership degrees corresponding to different levels of cloud models The details are as follows: ； In the formula: This is a normalized value for the distributed resource regulation capability. and This is the mathematical characteristic value of the corresponding quality level of this indicator.

11. The system according to claim 10, characterized in that, The basis of optimal cloud entropy The cloud model was optimized and improved, but the membership degree obtained based on the improved cloud model did not meet the requirements. Membership The correction specifically includes the following steps: respectively Cloud entropy is calculated using the criterion method and the 50% membership criterion method, and a positive cloud generator is used based on... Calculate the membership degree of the level, as shown in equations (43) and (44); ； ； In the formula: for The entropy calculated by the criterion method The entropy calculated using the 50% membership criterion method; and These represent the upper and lower limits of the grade range, and are specified as follows: Indicates the expected level ; Using a certain indicator value The maximum membership deviation of the corresponding 5 state level cloud models The optimal cloud entropy optimization model is established with the objective function of minimizing the sum of the values. The calculation formula is shown below: ； ； ； In the formula: for In level Maximum membership deviation; The optimal cloud entropy matrix; For indicator value according to The level generated by the criteria Membership degree; For indicator value The ratings generated based on the 50% certainty criterion Membership degree; For the optimized level Membership degree; and Levels m The corresponding optimal cloud entropy, Criterion cloud entropy, 50% certainty criterion cloud entropy.

12. The system according to claim 11, characterized in that, The improved analytic hierarchy process (AHP) is used to determine the comprehensive weights of the distributed resource regulation capability profile indicators within the cluster, and to calculate the overall evaluation score for the distributed resource regulation capability indicators. Specifically, it includes the following steps: The membership degree obtained based on the improved cloud model does not satisfy the requirement. Membership degree correction is required: ； In the formula: For the first n The first indicator of regulatory capacity profile m The modified membership degree of the level. For the first n The first indicator of regulatory capacity profile m The original membership degree of the level; The following scores were calculated to assess the distributed resource control capabilities across various dimensions: ； In the formula: For the first n The rating of each regulatory capability profile indicator. For the first n Corrected membership degree of each level of the regulatory capacity profile indicator; The first number in the cluster is obtained according to equation (42). j In the distributed resource, the first Evaluation score of individual regulatory capacity profile indicators This yields a score matrix of the controllability of all distributed resources within the cluster across various dimensions. And calculate the first j The overall control capability score for distributed resources is calculated using the following formula: ； In the formula: For the first in the cluster j An assessment score for the overall control capability of distributed resources. The relative importance weights of the regulatory capacity index obtained by the improved analytic hierarchy process are calculated using Equation (10).

13. The system according to claim 11, characterized in that, The basis Distributed resources are sorted from highest to lowest, denoted as . The distributed resources are sorted in order; based on the sorting results, the power control commands are decomposed among the distributed resources within the cluster according to the comprehensive control capability evaluation scores from high to low. This process includes the following steps: According to the comprehensive control capability assessment scores from high to low, the required power is allocated to the resource with the highest control capability score first, until the upper limit of the controllable capacity of the distributed resources is allocated or the total control power demand is allocated. If the resource cannot meet all the demand, the remaining control power is allocated to the next resource, and so on, until all distributed resources have been traversed or the remaining control power is zero. The calculation formula is as follows: ； ； In the formula: Remaining controllable power, kW; This represents the initial state of the power decomposition. The power of the control command for the distributed resource cluster is kW; To allocate distributed resources The regulating power, kW; For distributed resources The corresponding adjustable power limit is kW.

14. A distributed resource cluster regulation characteristic evaluation system for auxiliary services, characterized in that, include: The first module is configured to construct a distributed resource regulation characteristic profile label system from two dimensions: static attributes and dynamic attributes. The regulation characteristic profile label system is composed of regulation characteristic profile labels. Based on the regulation characteristic profile labels, a distributed resource regulation characteristic profile label calculation model is proposed. The second module acquires information on all distributed resources within the cluster. Based on the control characteristic profile tags, it aggregates all distributed resources within the cluster and constructs a distributed resource cluster profile tag system from two dimensions: static attributes and dynamic attributes. The distributed resource cluster profile tag system is composed of distributed resource cluster profile tags. Based on the distributed resource cluster profile tags, it calculates the control capability profile index of each distributed resource within the cluster. The control capability profile index of the distributed resource cluster is obtained by comprehensively calculating the control capability profile index of each distributed resource within the cluster. The third module is configured to evaluate the control characteristics of the distributed resource cluster based on the control capability profile index of the distributed resource cluster, and to decompose the control power command of the distributed resource cluster for auxiliary services based on the characteristic evaluation results.

15. The system according to claim 14, characterized in that, The calculation steps for the distributed resource cluster regulation capability profile index are as follows: Based on the different auxiliary service regulation needs, the importance ranking of regulation capability indicators is determined; an improved analytic hierarchy process is used to determine the relative importance of each pair of indicators, construct a judgment matrix, and calculate the relative importance weights of the regulation capability indicators. ; The optimal and worst values ​​of each control capability are determined based on all distributed resources within the virtual power plant, and the control capability values ​​of each distributed resource within the cluster are normalized. Based on the relative importance weights of distributed resource regulation capabilities and normalized index values, the completeness weight of the contribution of distributed resources to the cluster regulation capability index is calculated. ; By incorporating the contribution integrity weight for auxiliary services, the distributed resource cluster control capability index is obtained by weighted summation and weighted averaging of all distributed resource control capability indicators within the cluster.

16. The system according to claim 15, characterized in that, The specific steps are as follows: Based on the control capability profile indicators of the distributed resource cluster, the control characteristics of the distributed resource cluster are evaluated, and based on the evaluation results, the control power command decomposition for auxiliary services is implemented. The regulatory capacity is divided into five levels, and each level is assigned a corresponding score and degree of membership. Five levels of excellence were generated using a forward cloud generator. Obtain distributed resource index values ​​and their membership degrees at different levels. ; Based on optimal cloud entropy The cloud model was optimized and improved, but the membership degree obtained based on the improved cloud model did not meet the requirements. Membership Correction; Based on the improved analytic hierarchy process (AHP), the comprehensive weights of the distributed resource regulation capability profile indicators within the cluster are determined, and the overall evaluation score of the distributed resource regulation capability indicators is calculated. ; based on Distributed resources are sorted from highest to lowest, denoted as . Sort the distributed resources by order; Based on the ranking results, the power control commands are decomposed among the distributed resources within the cluster according to the comprehensive control capability evaluation scores from high to low.

17. The system according to claim 15, characterized in that, The distributed resource cluster profiling and tagging system includes static attribute indicators and dynamic attribute indicators; The static attribute indicators include total installed capacity, clean energy ratio, and distributed resource type; The dynamic attribute indicators include: Cluster power curve characteristic profile indicators: power fluctuation amplitude, fluctuation frequency, power change rate, maximum power change rate, power fluctuation change rate, daily average load, daily minimum load, daily minimum load rate, daily maximum load, daily average load rate, and daily peak-to-valley difference; Cluster control capability profile indicators: comprehensive controllable capacity, comprehensive control response frequency, comprehensive maximum control response time, comprehensive average control duration, comprehensive control delay time, comprehensive control response achievement, comprehensive control response positivity, and comprehensive control resource reliability. Dynamic profile indicators of cluster power generation characteristics: real-time power generation and daily cumulative power generation; Cluster load characteristic profile indicators: real-time power generation and daily cumulative power generation.

18. The system according to claim 16, characterized in that, The proposed calculation model for the distributed resource cluster regulation capability index is as follows: Comprehensive adjustable capacity : The overall adjustable capacity of a distributed resource cluster for auxiliary services is calculated by weighted summing of the adjustable power capacities of each distributed resource within the cluster. The specific calculation formula is shown below: ; In the formula: They are respectively t The overall upward and downward adjustable power (kW) of the distributed resource cluster when it is oriented towards auxiliary services. For the first in a distributed resource cluster i The upward and downward adjustable power of a distributed resource, in kW; N agg The number of distributed resources in the cluster; for t Within the distributed resource cluster at any given time The contribution integrity weight of each distributed resource to auxiliary services; Comprehensive control response frequency : The comprehensive control response frequency for auxiliary services in a distributed resource cluster is calculated by weighted averaging of the control response frequencies of each distributed resource within the cluster. The specific calculation formula is shown below: ; In the formula: for t The frequency of comprehensive regulation and response of the distributed resource cluster for auxiliary services at any given time; For the first in a distributed resource cluster The frequency of regulation and response of a distributed resource, times; Comprehensive maximum control response time : The overall maximum control response time for auxiliary services in a distributed resource cluster is calculated by weighted summing of the maximum control response times of each distributed resource within the cluster. The specific calculation formula is as follows: ; In the formula: for t The maximum overall control response time of the distributed resource cluster when it is oriented towards auxiliary services, in min; For the first in a distributed resource cluster The maximum control response time of a distributed resource, min; Overall average control duration : In the calculation of the control duration for ancillary services, the overall average control duration of the distributed resource cluster is obtained by weighted averaging of the control durations of each distributed resource within the cluster. The specific calculation formula is as follows: ; In the formula: for t The overall average duration of control of the distributed resource cluster for auxiliary services at any given time, in h; for t Within the distributed resource cluster at any given time The duration of regulation for a distributed resource, in hours; for t Within the distributed resource cluster at any given time The contribution integrity weight of the continuous regulation time of each distributed resource computing cluster; Comprehensive regulation delay time : The overall control latency of a distributed resource cluster for auxiliary services is calculated by taking the weighted average of the control latency of each distributed resource within the cluster. The specific calculation formula is shown below: ; In the formula: for t The overall control delay time (s) of the distributed resource cluster for peak shaving auxiliary services. For the first in a distributed resource cluster The regulation delay time of a distributed resource, in seconds; Comprehensive regulation and control response achievement : ; In the formula: The degree of comprehensive control and response achievement of distributed resource clusters; N Total number of responses for distributed resource cluster regulation; For distributed resource clusters n Actual controlled power, kW; For the first n The power (kW) that the distributed resource cluster needs to be regulated in the next regulation instruction; Comprehensive regulation and response initiative : ; In the formula: To enhance the comprehensive regulation and response capabilities of distributed resource clusters; For the virtual power plant n The total power of the next control command; Comprehensive regulation and response initiative : ; In the formula: To ensure the comprehensive regulation and reliability of distributed resource clusters, For the first in a distributed resource cluster The regulation and control of distributed resources and resource reliability.

19. The system according to claim 17, characterized in that, The relative importance weight of the regulation capability indicators The calculation method is as follows: Based on the different needs for regulating ancillary services, the importance of the regulation capability indicators is ranked as follows: In the peak shaving auxiliary service operation scenario, the importance of the regulation capability indicators is ranked as follows: adjustable power capacity > regulation duration > regulation response time ≥ regulation delay time > regulation response frequency > regulation response achievement ≥ regulation response initiative ≥ regulation resource reliability. In the operation scenario of frequency modulation auxiliary service, the importance of the control capability indicators is ranked as follows: control response time > control delay time ≥ adjustable power capacity ≥ control duration > control response frequency > control response achievement ≥ control response initiative ≥ control resource reliability. In the standby auxiliary service operation scenario, the importance of the control capability indicators is ranked as follows: adjustable power capacity ≥ control response time ≥ control delay time ≥ control duration > control response frequency > control response achievement ≥ control response positivity ≥ control resource reliability. An improved analytic hierarchy process (AHP) is used to determine the relative importance of each pair of indicators, and a scaling factor is applied. Quantify the judgment results; Construct a judgment matrix And calculate the relative importance weights of the regulatory capacity indicators.

20. The system according to claim 19, characterized in that, The judgment matrix The following conditions must be met: ,Right now They are reciprocal matrices; ,in Indicates the first The element and the first j The scale value obtained by comparing each element; The formula for calculating the judgment matrix is ​​as follows: ; The relative importance weights of the regulatory capacity indicators are as follows: 。 21. The system according to claim 17, characterized in that, The contribution completeness weight of the cluster control capability index The calculation method is as follows: For all distributed resources and scattered resources within the resource cluster of the virtual power plant N The control capability indicators are normalized, where the control response time and delay time are minimal indicators, as shown in equation (11), and the rest are normalized according to equation (12) for minimal indicators: ; ; In the formula: For distributed resources Normalized values ​​of individual regulatory capacity profile indicators; Distributed resources issued by the cloud for virtual power plants Maximum and minimum values ​​of each regulatory capacity profile indicator; Based on the relative importance weight of distributed resource regulation capability and the normalized index value, the contribution completeness weight of distributed resources to the cluster regulation capability index is calculated; among which, the contribution completeness weight of distributed resources to the cluster's comprehensive adjustable capacity, comprehensive regulation response frequency, comprehensive maximum regulation response time, comprehensive regulation delay time and comprehensive regulation response initiative is calculated using formula (13): ; In the formula: for t Within the distributed resource cluster at any given time i The contribution integrity weight of each distributed resource to auxiliary services is used to characterize the equivalent contribution of the distributed resource to the cluster control capability in the current auxiliary service operation scenario. for t Within the time cluster i The first distributed resource b Normalized values ​​of individual regulatory capacity profile indicators; To calculate the number of nodes within the cluster based on the improved hierarchical method i The first distributed resource b The relative importance weight of each regulatory capability profile indicator in the current auxiliary service operation scenario; Set minimum contribution integrity weight The contribution completeness weight is controlled within [ ] through linear mapping. Within the range; When calculating the overall average control duration of the cluster, distributed resources with shorter control durations are given a greater contribution integrity weight. The contribution integrity weight of distributed resources to the overall average control duration of the cluster is calculated using equation (14): ; In the formula: for t Within the distributed resource cluster at any given time i The contribution of each distributed resource to the overall average control duration of the cluster is weighted by its completeness. For the first in the cluster i The relative importance weight of the duration of regulation of each distributed resource. For the first in the cluster i The duration of regulation of each distributed resource is calculated using a minimal normalized value based on equation (11).

22. The system according to claim 14, characterized in that, The distributed resource regulation characteristic profiling and labeling system includes static attribute indicators and dynamic attribute indicators; The static attribute indicators include user number, user industry, electricity priority, adjustment model, and unit compensation price. The dynamic attribute indicators include power curve characteristic indicators, regulation capability indicators, and real-time operation characteristic indicators. The power curve characteristic indicators include: Power fluctuation amplitude : ; Where: power fluctuation amplitude for t- 1 hour has arrived t The amplitude of load power curve fluctuations can be adjusted in real time, in kW; for t The load power can be adjusted at any time. ; Fluctuation frequency : ; ; Where: fluctuation frequency The frequency at which the adjustable load forecast power curve switches between rising and falling trends; for t The load forecast power curve trend switching state variable is adjustable in real time; when the power curve switches between an upward and downward trend... ,otherwise This represents the total number of time points. A symbolic function used to determine the value of a variable. The meanings of the plus and minus signs are as follows: ; Power change rate : ; Where: power change rate for t- 1 to t The rate of change of the load power curve can be adjusted in real time, kW / min. 15 minutes; Maximum power change rate : ; Where: Maximum power change rate The maximum value of the rate of change of the adjustable load power curve, kW; T This represents the total number of time points. Power fluctuation rate : ； Where: power fluctuation rate for t- 2 to t The rate of change of the load power curve fluctuation that can be adjusted at any time, in kW / min; Daily maximum load : ； Where: Daily maximum load For adjustable peak load power, kW; for t Adjustable load plan operating power in real time, kW; T This represents the total number of time points. Daily minimum load : ； Where: Daily minimum load The adjustable load valley power is measured in kW. Daily average load : ； Where: Daily average load The average load level of the adjustable load is kW; T This represents the total number of time points. Daily minimum load rate : ； Where: Daily minimum load factor Used to reflect the range of variation of the adjustable load power curve, in kW; Daily average load factor : ； Where: Daily average load factor Used to reflect the smoothness and balance of the adjustable load power curve, in kW; Daily peak-valley difference : ； Where: Daily maximum load The difference between the daily maximum load and the daily minimum load, in kW; The regulation capability indicators include: Adjustable power capacity: The adjustable power capacity of an adjustable load is divided into upward adjustable power and downward adjustable power. Upward adjustable power refers to the maximum power that the adjustable load can reduce relative to the current power, while downward adjustable power refers to the maximum power that the adjustable load can increase relative to the current power. Adjustable power capacity of transferable load The adjustable power of the transferable load, both upward and downward, can be obtained from the following formula: ； ； In the formula: The power is adjustable upwards and downwards for transferable loads, in kW; for t Planned operating power (kW) that can be readily transferred to other loads; T This represents the total number of time points. Total electricity consumption for transferable load, in kWh; Maximum power of transferable load, kW; Adjustable power capacity of load that can be shifted The adjustable power for shiftable loads is determined based on the planned operating power; if If a load shifting plan is in operation, then the load has the potential to be reduced, therefore its It always possesses upward adjustable potential; if If a load that can be moved is in a stopped state and is in a moveable state, then that load has the potential to generate additional load, therefore its It always possesses downward adjustment potential; the calculation formula is shown below: ； ； ； In the formula: The adjustable power (kW) is for loads that can be shifted downwards and upwards. for t Planned operating power (kW) of load that can be shifted at any time; Represents a transferable load t The scheduled time slot is currently unavailable. Represents a transferable load t The timeline is scheduled to be in operation. For transferable loads The starting time; T This represents the total number of time points. For the controllable state variables of shiftable loads Represents the load that can be moved. The time period has already run. Represents the load that can be moved. The time period was not running; L The duration for which a transferable load must operate once activated; Adjustable power capacity that can reduce load The only way to reduce load is to adjust the power capacity upwards, and the calculation formula is as follows: ； In the formula: Adjustable power output to reduce load, kW; for The planned operating power can be reduced at any time, in kW; for The minimum power capacity at which the load can be reduced at any given time, in kW; Adjusting the response frequency The control response frequency is the cumulative number of effective response hours per day; ； ； In the formula: The control response frequency of the controllable load can be calculated from the power of the controllable load, and is given by the frequency. for h The state variables that are effective at all times and respond to the hourly state variables; Maximum control response time : ； In the formula: for t The maximum controllable response time of the controllable load can be calculated from the controllable load power, min; The control speed of the adjustable load is calculated from the average control speed of the historical control process, in kW / min; Regulation duration : ； In the formula: for t The duration of the adjustable load regulation can be calculated from the adjustable load power curve, in hours (h). For adjustable load t Continuously satisfy from time to time Maximum number of time periods; Adjustment delay time : ； In the formula: The control delay time for the controllable load is calculated from the average of historical control delay times, in seconds. For the first n The time for the controllable load to respond to the control command action is in seconds. For the first n The time (s) during which the controllable load receives the control command during the next response; ; Response achievement rate of regulation: ； In the formula: The controllable load control response achievement rate is calculated from the average historical control response achievement rate. For adjustable load n The actual power involved in regulation, in kW; For the first n The power (kW) that the controllable load needs to be controlled in the next control instruction; ; Response responsiveness to regulation: ； In the formula: The regulatory response positivity of the controllable load is calculated based on the average historical results of the controllable load's participation in regulation. The resource cluster to which the adjustable load belongs n The total power of this control command, in kW; ; Regulating resource reliability : ； In the formula: The reliability of controllable load regulation resources is calculated based on the historical participation results of controllable loads in regulation. The number of unplanned outage hours during adjustable load operation, in hours (h). The total operating time of the adjustable load is in hours (h). Real-time operating characteristics: The real-time operating characteristics of adjustable loads include real-time power consumption, daily cumulative power consumption, and other real-time status indicators, which are used to monitor the power consumption of adjustable loads.

23. The system according to claim 16, characterized in that, The method uses a positive cloud generator to generate five levels of quality. Obtain distributed resource index values ​​and their membership degrees at different levels. Specifically, it includes the following steps: by For the expectation, Generate normally distributed random numbers with standard deviation. ; The parameter for calculating the final membership degree was calculated by repeating the calculation 10,000 times and taking the average value. pass and regulatory capacity data values , calculate Membership degrees corresponding to different levels of cloud models The details are as follows: ； In the formula: This is a normalized value for the distributed resource regulation capability. and This is the mathematical characteristic value of the corresponding quality level of this indicator.

24. The system according to claim 22, characterized in that, The basis of optimal cloud entropy The cloud model was optimized and improved, but the membership degree obtained based on the improved cloud model did not meet the requirements. Membership The correction specifically includes the following steps: respectively Cloud entropy is calculated using the criterion method and the 50% membership criterion method, and a positive cloud generator is used based on... Calculate the membership degree of the level, as shown in equations (43) and (44); ； ； In the formula: for The entropy calculated by the criterion method The entropy calculated using the 50% membership criterion method; and These represent the upper and lower limits of the grade range, and are specified as follows: Indicates the expected level ; Using a certain indicator value The maximum membership deviation of the corresponding 5 state level cloud models The optimal cloud entropy optimization model is established with the objective function of minimizing the sum of the values. The calculation formula is shown below: ； ； ； In the formula: for In level Maximum membership deviation; The optimal cloud entropy matrix; For indicator value according to The level generated by the criteria Membership degree; For indicator value The ratings generated based on the 50% certainty criterion Membership degree; For the optimized level Membership degree; and Levels m The corresponding optimal cloud entropy, Criterion cloud entropy, 50% certainty criterion cloud entropy.

25. The system according to claim 23, characterized in that, The improved analytic hierarchy process (AHP) is used to determine the comprehensive weights of the distributed resource regulation capability profile indicators within the cluster, and to calculate the overall evaluation score for the distributed resource regulation capability indicators. Specifically, it includes the following steps: The membership degree obtained based on the improved cloud model does not satisfy the requirement. Membership degree correction is required: ； In the formula: For the first n The first indicator of regulatory capacity profile m The modified membership degree of the level. For the first n The first indicator of regulatory capacity profile m The original membership degree of the level; The following scores were calculated to assess the distributed resource control capabilities across various dimensions: ； In the formula: For the first n The rating of each regulatory capability profile indicator. For the first n Corrected membership degree of each level of the regulatory capacity profile indicator; The first number in the cluster is obtained according to equation (42). j In the distributed resource, the first i Evaluation score of individual regulatory capacity profile indicators This yields a score matrix of the controllability of all distributed resources within the cluster across various dimensions. And calculate the first j The overall control capability score for distributed resources is calculated using the following formula: ； In the formula: For the first in the cluster j An assessment score for the overall control capability of distributed resources. The relative importance weights of the regulatory capacity index obtained by the improved analytic hierarchy process are calculated using Equation (10).

26. The system according to claim 24, characterized in that, The basis Distributed resources are sorted from highest to lowest, denoted as . The distributed resources are sorted in order; based on the sorting results, the power control commands are decomposed among the distributed resources within the cluster according to the comprehensive control capability evaluation scores from high to low. This process includes the following steps: According to the comprehensive control capability assessment scores from high to low, the required power is allocated to the resource with the highest control capability score first, until the upper limit of the controllable capacity of the distributed resources is allocated or the total control power demand is allocated. If the resource cannot meet all the demand, the remaining control power is allocated to the next resource, and so on, until all distributed resources have been traversed or the remaining control power is zero. The calculation formula is as follows: ； ； In the formula: Remaining controllable power, kW; This represents the initial state of the power decomposition. The power of the control command for the distributed resource cluster is kW; To allocate distributed resources The regulating power, kW; For distributed resources The corresponding adjustable power limit is kW.

27. 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 as described in any one of claims 1-13.

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