CN-122022360-A - Auxiliary service-oriented distributed resource cluster regulation and control characteristic evaluation method and system

CN122022360ACN 122022360 ACN122022360 ACN 122022360ACN-122022360-A

Abstract

The invention provides an auxiliary service-oriented distributed resource cluster regulation and control characteristic evaluation method and system, wherein a distributed resource and a regulation and control characteristic portrait label system of a cluster are constructed from static and dynamic two dimensions, the operation characteristics and regulation and control capacity of the distributed resource and the cluster are described based on history, prediction and real-time data, the relative importance weight of a regulation and control capacity index is determined by adopting an improved analytic hierarchy process according to different auxiliary service requirements, the contribution integrity weight is calculated, so that the comprehensive evaluation of the cluster regulation and control capacity index is completed, and a regulation and control capacity grade grading method based on an improved cloud model is further provided, the comprehensive grading and sequencing of each resource are calculated, and the reasonable decomposition of a regulation and control power instruction among the resources in the cluster is realized according to the comprehensive grading method. The invention can effectively improve the regulation and control capability assessment and instruction distribution efficiency of the distributed resource clusters participating in auxiliary services.

Inventors

GONG ZHENG
ZHOU ANYU
GUO TINGYUE
WANG SHENG
LUO PENG
SHI RUIJIE
LIN WUXING
OUYANG LILIN
LEI HONGWEI
NIU HUANNA
ZHANG BAOYING

Assignees

国网电商科技有限公司
远光能源互联网产业发展(横琴)有限公司
中国农业大学

Dates

Publication Date: 20260512
Application Date: 20260210

Claims (20)

1. The auxiliary service-oriented distributed resource cluster regulation and control characteristic evaluation method is characterized by comprising the following steps of: constructing a regulation and control characteristic portrait tag system of the distributed resource from two dimensions of the static attribute and the dynamic attribute, wherein the regulation and control characteristic portrait tag system consists of regulation and control characteristic portrait tags; Acquiring information of all distributed resources in a cluster, collecting all distributed resources in the cluster according to the regulation and control characteristic portrait tags, and constructing a distributed resource cluster portrait tag system from two dimensions of static attribute and dynamic attribute, wherein the distributed resource cluster portrait tag system consists of distributed resource cluster portrait tags; and evaluating the regulation and control characteristics of the distributed resource clusters according to the regulation and control capability image indexes of the distributed resource clusters, and executing auxiliary service-oriented distributed resource cluster regulation and control power instruction decomposition based on the characteristic evaluation result.
2. The method according to claim 1, wherein the calculating step of the distributed resource cluster regulatory capability portrayal index is as follows: Determining importance sequence of regulation and control capability indexes according to different auxiliary service regulation and control requirements, judging relative importance degree of every two indexes by adopting an improved analytic hierarchy process, constructing a judgment matrix, and calculating relative importance weight of the regulation and control capability indexes ; Determining optimal and worst values of each regulation capacity based on all distributed resources in the virtual power plant, and carrying out normalization processing on the regulation capacity values of each distributed resource in the cluster; Calculating the contribution integrity weight of the distributed resource to the cluster regulatory capability index based on the relative importance weight and the normalized index value of the distributed resource regulatory capability ; And adding all the distributed resource regulation and control capability indexes in the cluster in a weighted manner by incorporating the contribution integrity weight facing auxiliary service, and carrying out weighted averaging to obtain the distributed resource cluster regulation and control capability indexes.
3. The method according to claim 2, wherein the step of evaluating the regulation characteristics of the distributed resource clusters according to the regulation capability image index of the distributed resource clusters and performing the auxiliary service oriented distributed resource cluster regulation power instruction decomposition based on the characteristic evaluation result comprises the following specific steps: five-level classification is carried out on the quality grades of the regulation and control capability, and corresponding grade scores and grade membership degrees are given to all grades; generating five quality grades by adopting forward cloud generator Obtaining the index value of distributed resource and the membership degree under different grades ; Based on the optimal Yun Shang Optimizing the improved cloud model, wherein the membership degree obtained based on the improved cloud model is not satisfied Membership degree is carried out Correcting; Determining comprehensive weights of image indexes of distributed resource regulation capability in clusters based on improved analytic hierarchy process, and calculating evaluation scores of quality grades of the indexes of the distributed resource overall regulation capability ; Based on Ranking distributed resources from high to low, noted as Ordering the distributed resources; And based on the sequencing result, completing the decomposition of the distributed resources in the cluster according to the sequence from high to low of the comprehensive regulation capability evaluation score by the regulation power instruction.
4. The method of claim 3, wherein the distributed resource cluster portrait tag hierarchy includes static and dynamic attribute indicators; wherein the static attribute indexes comprise total capacity of the assembly machine, clean energy proportion and distributed resource types; wherein the dynamic attribute index includes: the cluster power curve characteristic image indexes comprise power fluctuation amplitude, fluctuation frequency, power change rate, maximum power change rate, power fluctuation change rate, daily average load, daily minimum load rate, daily maximum load, daily average load rate and daily peak-valley difference; The cluster regulation capacity image indexes comprise comprehensive adjustable capacity, comprehensive regulation response frequency, comprehensive maximum regulation response time, comprehensive average regulation duration time, comprehensive regulation delay time, comprehensive regulation response achievement degree, comprehensive regulation response enthusiasm and comprehensive regulation resource reliability; dynamic image indexes of cluster power generation characteristics, namely real-time power generation power and accumulated power generation capacity on the same day; the cluster load characteristic image index is real-time power generation power and accumulated power generation capacity on the same day.
5. The method of claim 4, wherein the proposed distributed resource cluster regulatory capability index calculation model specifically comprises: comprehensive adjustable capacity : The comprehensive adjustable capacity of the distributed resource cluster facing auxiliary service is obtained by weighted addition calculation of the adjustable power capacity of each distributed resource in the cluster, and the specific calculation formula is as follows: ; wherein: The comprehensive upward adjustable power and the comprehensive downward adjustable power when the distributed resource cluster faces auxiliary service at the moment t are respectively, and kW is calculated; is the first in the distributed resource cluster The power of each distributed resource can be adjusted upwards and downwards, and kW is N agg , which is the number of the distributed resources of the cluster; distributed resource cluster at time t The contribution integrity weight of the distributed resources facing auxiliary services; Comprehensive regulation and control of response frequency : The comprehensive regulation response frequency of the distributed resource cluster facing auxiliary service is obtained by weighting and averaging the regulation response frequency of each distributed resource in the cluster, and the specific calculation formula is as follows: ; wherein: the comprehensive regulation response frequency of the distributed resource cluster facing auxiliary service at the moment t is inferior; is the first in the distributed resource cluster Regulating response frequency of the distributed resources, and performing secondary regulation; Comprehensive maximum regulatory response time : The comprehensive maximum regulation response time of the distributed resource cluster facing auxiliary service is obtained by weighted addition calculation of the maximum regulation response time of each distributed resource in the cluster, and the specific calculation formula is as follows: ; wherein: the comprehensive maximum regulation response time, min, is the time t when the distributed resource cluster faces auxiliary service; is the first in the distributed resource cluster Maximum regulatory response time, min, of the individual distributed resources; integrated average regulatory duration : In the auxiliary service-oriented regulation duration calculation, the comprehensive average regulation duration of the distributed resource cluster is obtained by carrying out weighted average calculation on the regulation duration of each distributed resource in the cluster, and a specific calculation formula is as follows: ; wherein: the comprehensive average regulation duration time and h are the comprehensive average regulation duration time when the distributed resource cluster faces auxiliary service at the moment t; distributed resource cluster at time t The regulation duration time of the distributed resources, h; distributed resource cluster at time t Calculating the contribution integrity weight of the cluster continuous regulation time by the distributed resources; comprehensive regulation of delay time : The comprehensive regulation delay time of the distributed resource cluster facing auxiliary service is obtained by calculating the weighted average value of the regulation delay time of each distributed resource in the cluster, and the specific calculation formula is as follows: ; wherein: the comprehensive regulation delay time when the distributed resource cluster faces peak shaving auxiliary service at the moment t, s is that the number of the components is equal to s, Is the first in the distributed resource cluster The control delay time s of the distributed resources; comprehensive regulation response achievement degree: ; ; wherein: the achievement degree of the comprehensive regulation response of the distributed resource cluster is obtained, wherein N is the total number of regulation response times of the distributed resource cluster; Actually regulating and controlling power for the nth time of the distributed resource cluster, and kW; The power, kW, of the distributed resource clusters in the nth regulation instruction to be regulated and controlled; Integrated regulation response aggressiveness : ; Wherein: The method is an integrated regulation response enthusiasm of a distributed resource cluster; The total power of the nth regulation instruction of the virtual power plant is calculated; Integrated regulation response aggressiveness : ; Wherein: To comprehensively regulate the reliability of the resources for the distributed resource clusters, Is the first in the distributed resource cluster The resource reliability of the individual distributed resources is regulated.
6. The method of claim 4, wherein the relative importance of the regulatory capability indicators is weighted The calculation method of (2) is as follows: according to different auxiliary service regulation requirements, determining importance ranking of regulation capability indexes: The importance of the regulation capability indexes under the operation scene of the peak regulation auxiliary service is ordered as that the adjustable power capacity, the regulation duration, the regulation response time, the regulation delay time, the regulation response frequency, the regulation response achievement degree, the regulation response enthusiasm and the regulation resource reliability are all equal to each other; the importance of the regulation capability indexes under the operation scene of the frequency modulation auxiliary service is ordered as that the regulation response time is greater than or equal to the regulation delay time, the regulation power capacity is greater than or equal to the regulation duration time, the regulation response frequency is greater than or equal to the regulation response achievement degree, the regulation response enthusiasm is greater than or equal to the regulation resource reliability; the importance of the regulation capability indexes under the operation scene of the standby auxiliary service is ordered as that the adjustable power capacity is more than or equal to the regulation response time is more than or equal to the regulation delay time is more than or equal to the regulation duration time, the regulation response frequency is more than or equal to the regulation response achievement degree is more than or equal to the regulation response enthusiasm is more than or equal to the regulation resource reliability; judging the relative importance of the two indexes by adopting an improved analytic hierarchy process and using a scale value Quantifying a judgment result; constructing a judgment matrix And calculating the relative importance weight of the regulation capability index.
7. The method of claim 6, wherein the decision matrix The following conditions need to be satisfied: I.e. Is a reciprocal matrix; wherein Represent the first Element and the first Comparing the obtained scale values of the individual elements; The calculation formula of the judgment matrix is as follows: ; The relative importance weights of the regulatory capability indicators are as follows: 。
8. The method of claim 7, wherein the cluster regulatory capability index contributes to an integrity weight The calculation method comprises the following steps: Carrying out normalization processing on N regulation and control capability indexes of distributed resources and scattered resources in all resource clusters in a virtual power plant, wherein the regulation and control response time and delay time are minimum indexes, as shown in a formula (11), and carrying out maximum index normalization calculation according to a formula (12) on the rest: ; ; wherein: Is distributed resource first Normalized values of individual regulatory capability representation indicators; distributed resource first issued for virtual power plant cloud Maximum and minimum values of image indexes of individual regulation and control capacities; Calculating the contribution integrity weight of the distributed resource to the cluster regulation capacity index based on the relative importance weight and the normalized index value of the distributed resource regulation capacity, wherein the contribution integrity weight of the distributed resource to the cluster comprehensive adjustable capacity, the comprehensive regulation response frequency, the comprehensive maximum regulation response time, the comprehensive regulation delay time and the comprehensive regulation response aggressiveness is calculated by adopting the formula (13): ; wherein: Is that Within a distributed resource cluster The contribution integrity weight of the distributed resources facing the auxiliary service is used for describing the equivalent contribution degree of the distributed resources to the cluster regulation and control capability under the current auxiliary service operation scene; is the t moment in the cluster Normalized values of the b-th regulatory capability representation index of the distributed resource; obtaining the intra-cluster first for improved hierarchical based computation The relative importance weight of the b-th regulatory capability portrait index of the distributed resource in the current auxiliary service operation scene; setting the lowest contribution integrity weight Controlling the contribution integrity weight to be in a linear mapping The interval is within; When calculating the cluster comprehensive average regulation duration, giving a larger contribution integrity weight to the distributed resource with a shorter regulation duration, wherein the contribution integrity weight of the distributed resource to the cluster comprehensive average regulation duration is calculated by adopting a formula (14): ; wherein: distributed resource cluster at time t The contribution integrity weight of the individual distributed resources to the cluster comprehensive average regulation duration; Is the first in the cluster The relative importance weights of the regulatory durations of the individual distributed resources; Is the first in the cluster The control duration of each distributed resource adopts a minimal normalization value calculated by the formula (11).
9. The method of claim 3, wherein the distributed resource regulation feature representation tagging system comprises a static attribute index and a dynamic attribute index; the static attribute indexes comprise user numbers, user industries, electricity utilization priorities, adjustment models and unit compensation prices; the dynamic attribute indexes comprise power curve characteristic indexes, regulation capacity indexes and real-time operation characteristic indexes; Wherein, the power curve characteristic index includes: Amplitude of power fluctuation : ; In which the amplitude of the power fluctuation The amplitude of fluctuation of the load power curve can be regulated and controlled from the time t-1 to the time t, and kW is calculated; the load power can be regulated and controlled for the moment t, ; Frequency of fluctuation : ; ; In which the frequency of fluctuation Predicting the switching frequency between the rising trend and the falling trend of the power curve for the controllable load; predicting a power curve trend switching state variable for the adjustable load at the t moment, when the power curve is switched between rising trend and falling trend, Otherwise T is the total time; As a sign function for determining the variables The specific meaning of the signs of (c) is as follows: ; Rate of change of power : ; In which the rate of change of power The change rate of the load power curve can be regulated and controlled from the time t-1 to the time t, kW/min, 15Min; Maximum power rate of change : ; In which the maximum power change rate The maximum value of the change rate of the controllable load power curve is kW, and T is the total time number; Rate of change of power fluctuation : ; In which the rate of change of power fluctuation The change rate of fluctuation of the load power curve can be regulated and controlled from t-2 to t, and kW/min; Daily maximum load : ; In the mean daily maximum load The peak power of the load can be regulated and controlled, and kW; The operation power of the load plan can be regulated and controlled at the moment T, which is the total moment; Daily minimum load : ; In the middle, daily minimum load The power of the load valley value can be regulated and controlled, and the power is kW; Daily average load : ; In daily average load The average load level of the adjustable load is kW, and T is the total time; Daily minimum load factor : ; In the middle, daily minimum load factor The variable range is used for reflecting the adjustable load power curve, and kW; Daily average load factor : ; In the method, daily average load factor The method is used for reflecting the stable equilibrium degree of the controllable load power curve, kW; Peak-valley difference of day : ; In the mean daily maximum load The difference value of the daily maximum load and the daily minimum load is kW; wherein, the regulation and control ability index includes: The adjustable power capacity of the adjustable load is divided into upward adjustable power and downward adjustable power, wherein the upward adjustable power refers to the maximum power value which can be reduced by the adjustable load relative to the current power, and the downward adjustable power refers to the maximum power value which can be increased by the adjustable load relative to the current power; Load-transferable adjustable power capacity The adjustable power of the transferable load up and down is obtained by the following formula: ; ; wherein: The power is adjustable upwards and downwards for transferring the load, and kW; the planned running power of the transferable load at the moment T is kW, wherein T is the total moment number; kWh for the total amount of electricity used for transferable load; maximum power for transferable load, kW; Adjustable power capacity of translatable load The translatable load determining the upward and downward adjustable power based on the planned operating power if The load is planned to be in an operating state at any time, and has the potential to cut down the load, so that the load is The time has upward adjustable potential, if The translatable load is in the off-stream state at all times and in the translatable state, the load has the potential to generate additional load, and therefore The time has downward adjustable potential, and the calculation formula is as follows: ; ; ; wherein: The power is adjustable downwards and upwards for the translatable load, and kW; the planned operating power of the translatable load at time t is kW; the moment t representing the translatable load is intended to be in a rest condition, A translatable load is represented by a moment of time t and is planned to be in an operating state; As translatable load T is the total time number; As a regulatory state variable of the translatable load, Representing translatable load at The time period has been in operation, Representing translatable load at L is the duration that the translatable load must operate once turned on; Load-shedding adjustable power capacity The load can be reduced, only the power capacity can be regulated upwards, and the calculation formula is as follows: ; wherein: the power is upward adjustable for reducing load, and kW; Is that Load planning operation power can be reduced at any time, and kW is reduced; Is that Minimum power capacity capable of reducing load at any time, kW; Regulating response frequency The response frequency is regulated to be the accumulated number of effective response hours per day; ; ; wherein: The regulation response frequency of the adjustable load is calculated by the adjustable load power, and the calculated power is obtained next time; a state variable which is the effective response hour at the time h; Maximum regulatory response time : ; Wherein: The maximum regulation response time of the adjustable load at the moment t is calculated by the adjustable load power, and min; The regulation speed of the adjustable load is calculated from the average regulation speed of the historical regulation process, and kW/min; Duration of regulation : ; Wherein: Calculating the adjustable duration time of the adjustable load at the moment t according to an adjustable load power curve to obtain h; Continuously satisfying the adjustable load t moment Is a maximum period number of time; Regulating delay time : ; Wherein: the regulation delay time of the adjustable load is calculated by the average value of the historical regulation delay time, s; The action time of responding to the regulation command for the nth time of the adjustable load is s; the time s when the adjustable load receives the adjusting instruction is the nth response; ; degree of achievement of regulatory response: ; wherein: the regulation response of the adjustable load reaches the achievement degree, and the average value of the achievement degree of the historical regulation response is calculated; The power actually participating in regulation and control for the nth time of the regulatable load is kW; the power which can be regulated and controlled for the adjustable load in the nth regulation and control instruction is kW; ; The enthusiasm of regulatory response: ; wherein: The method is characterized in that the method is an enthusiasm of the regulation response of the adjustable load, and the result average value of the regulation participation of the adjustable load history is calculated; The total power of the nth regulating instruction of the resource cluster to which the controllable load belongs is kW; ; regulating resource reliability : ; Wherein: the reliability of the adjustable load regulation resources is calculated according to the adjustable load history participation regulation results; H, the number of unscheduled shutdown hours during the adjustable load operation; H, the total operation time length of the adjustable load is h; The real-time operation characteristics of the adjustable load comprise real-time state indexes such as real-time power consumption, accumulated electricity consumption on the same day and the like, and are used for monitoring the electricity consumption condition of the adjustable load.
10. The system of claim 3, wherein the generating five levels of merit using a forward cloud generator Obtaining the index value of distributed resource and the membership degree under different grades The method specifically comprises the following steps: To be used for In the hope that, Generating normal random numbers for standard deviation ; Repeatedly calculating 10000 times and taking the average value as a final membership calculation parameter so as to weaken the influence of randomness; By passing through And regulatory capability data values Calculation of Membership degree corresponding to cloud models of different grades The method is specifically as follows: ; wherein: Normalizing the value for the distributed resource regulation capability; And And the mathematical characteristic value of the index corresponding to the quality grade.
11. The system according to claim 10, wherein the optimization-based Yun Shang Optimizing the improved cloud model, wherein the membership degree obtained based on the improved cloud model is not satisfied Membership degree is carried out The correction specifically comprises the following steps: Respectively adopt The cloud entropy is calculated by a criterion method and a 50% membership criterion method, and a forward cloud generator is adopted according to the following conditions Calculating a class membership as shown in the formulas (43) and (44); ; ; wherein: Is that The entropy obtained by the calculation of the criterion method, Entropy calculated for 50% membership criterion method; And Respectively represent the upper and lower limits of the level interval and are defined by Representing a level desire ; With a certain index value Maximum membership degree deviation of corresponding 5 state level cloud models And (3) establishing an optimal cloud entropy optimization model for the objective function with the minimum sum, wherein the calculation formula is as follows: ; ; ; wherein: Is that At the level of Maximum membership bias; Is the optimal Yun Shang matrix; Is an index value According to Criteria generated ranking The membership degree below; Is an index value Ranking generated according to 50% certainty criteria The membership degree below; To the optimized grade The membership degree below; And Respectively is the optimal cloud entropy corresponding to the level m, Criterion cloud entropy, 50% certainty criterion cloud entropy.
12. The system of claim 11, wherein the determining the comprehensive weight of the image indicators of the distributed resource regulation capability in the cluster based on the improved analytic hierarchy process calculates the evaluation score of the quality level of the indicators of the overall distributed resource regulation capability The method specifically comprises the following steps: Membership degree obtained based on improved cloud model does not satisfy Membership correction is required: ; wherein: the modified membership of the nth regulatory capability image index at the mth level, Drawing an original membership degree of the image index at the mth level for the nth regulatory capability; calculating to obtain the image grade scores of the regulating and controlling capabilities of each dimension of the distributed resource: ; wherein: Grade scores for the nth regulatory capability representation indicators, The correction membership degree of the nth regulation capability portrait index under each level; Obtaining the jth distributed resource in the cluster according to the formula (42) Individual regulatory capability image index assessment score Obtaining the fractional matrix of the regulating and controlling capacity of each dimension of all distributed resources in the cluster And calculating the j-th distributed resource overall regulation and control capability evaluation score, wherein the calculation formula is as follows: ; wherein: a score is evaluated for the overall regulatory capability of the jth distributed resource within the cluster, For the relative importance weight of the regulatory capability index calculated by adopting the improved analytic hierarchy process, the regulation capability index is calculated by adopting a formula (10).
13. The system of claim 11, wherein the base is Sorting distributed resources from high to low, denoted, The method comprises the steps of determining the sequence of distributed resources, sorting the sequence of the distributed resources based on a sorting result according to the sequence of evaluating the score from high to low according to comprehensive regulation and control capability, and completing the decomposition of each distributed resource in a cluster by a regulation and control power instruction, wherein the method specifically comprises the following steps: The required power is preferentially distributed to the resource with the highest regulation capacity score according to the sequence of the comprehensive regulation capacity evaluation score from high to low until the upper limit of the adjustable capacity of the distributed resource is distributed or the total regulation power demand is distributed, if the resource can not meet all demands, the residual regulation power is distributed to the next resource, and the like until all the distributed resources are traversed or the residual regulation power is zero, wherein the calculation formula is as follows: ; ; wherein: the power is regulated and controlled for the surplus, kW; An initial state represented as a resolved power; regulating and controlling instruction power, kW, for the distributed resource clusters; For allocation to distributed resources Is a power regulator, kW; Is a distributed resource And the corresponding adjustable power upper limit is kW.
14. An auxiliary service oriented distributed resource cluster regulation and control characteristic evaluation system, comprising: The system comprises a first module, a second module, a third module, a fourth module, a fifth module, a sixth module and a seventh module, wherein the first module is configured to construct a regulation and control characteristic portrait tag system of the distributed resource from two dimensions of the static attribute and the dynamic attribute, and the regulation and control characteristic portrait tag system consists of regulation and control characteristic portrait tags; The second module acquires information of all distributed resources in the cluster, gathers all distributed resources in the cluster according to the regulation and control characteristic portrait tag, and constructs a distributed resource cluster portrait tag system from two dimensions of static attribute and dynamic attribute, wherein the distributed resource cluster portrait tag system consists of distributed resource cluster portrait tags; And the third module is configured to evaluate the regulation and control characteristics of the distributed resource clusters according to the regulation and control capability image indexes of the distributed resource clusters, and implement auxiliary service-oriented distributed resource cluster regulation and control power instruction decomposition based on the characteristic evaluation results.
15. The system of claim 14, wherein the calculating step of the distributed resource cluster regulatory capability portrayal index is as follows: Determining importance sequence of regulation and control capability indexes according to different auxiliary service regulation and control requirements, judging relative importance degree of every two indexes by adopting an improved analytic hierarchy process, constructing a judgment matrix, and calculating relative importance weight of the regulation and control capability indexes ; Determining optimal and worst values of each regulation capacity based on all distributed resources in the virtual power plant, and carrying out normalization processing on the regulation capacity values of each distributed resource in the cluster; Calculating the contribution integrity weight of the distributed resource to the cluster regulatory capability index based on the relative importance weight and the normalized index value of the distributed resource regulatory capability ; And adding all the distributed resource regulation and control capability indexes in the cluster in a weighted manner by incorporating the contribution integrity weight facing auxiliary service, and carrying out weighted averaging to obtain the distributed resource cluster regulation and control capability indexes.
16. The system of claim 15, wherein the performing the evaluation of the regulation characteristics of the distributed resource clusters according to the regulation capability image index of the distributed resource clusters, and performing the auxiliary service oriented distributed resource cluster regulation power instruction decomposition based on the result of the evaluation of the characteristics comprises the following specific steps: five-level classification is carried out on the quality grades of the regulation and control capability, and corresponding grade scores and grade membership degrees are given to all grades; Generating five quality grades by adopting forward cloud generator Obtaining the index value of distributed resource and the membership degree under different grades ; Based on the optimal Yun Shang Optimizing the improved cloud model, wherein the membership degree obtained based on the improved cloud model is not satisfied Membership degree is carried out Correcting; Determining comprehensive weights of image indexes of distributed resource regulation capability in clusters based on improved analytic hierarchy process, and calculating evaluation scores of quality grades of the indexes of the distributed resource overall regulation capability ; Based on Ranking distributed resources from high to low, noted as Ordering the distributed resources; And based on the sequencing result, completing the decomposition of the distributed resources in the cluster according to the sequence from high to low of the comprehensive regulation capability evaluation score by the regulation power instruction.
17. The system of claim 15, wherein the distributed resource cluster portrayal tab system comprises a static attribute index and a dynamic attribute index; wherein the static attribute indexes comprise total capacity of the assembly machine, clean energy proportion and distributed resource types; wherein the dynamic attribute index includes: the cluster power curve characteristic image indexes comprise power fluctuation amplitude, fluctuation frequency, power change rate, maximum power change rate, power fluctuation change rate, daily average load, daily minimum load rate, daily maximum load, daily average load rate and daily peak-valley difference; The cluster regulation capacity image indexes comprise comprehensive adjustable capacity, comprehensive regulation response frequency, comprehensive maximum regulation response time, comprehensive average regulation duration time, comprehensive regulation delay time, comprehensive regulation response achievement degree, comprehensive regulation response enthusiasm and comprehensive regulation resource reliability; dynamic image indexes of cluster power generation characteristics, namely real-time power generation power and accumulated power generation capacity on the same day; the cluster load characteristic image index is real-time power generation power and accumulated power generation capacity on the same day.
18. The system of claim 16, wherein the proposed distributed resource cluster regulatory capability index calculation model is specifically: comprehensive adjustable capacity : The comprehensive adjustable capacity of the distributed resource cluster facing auxiliary service is obtained by weighted addition calculation of the adjustable power capacity of each distributed resource in the cluster, and the specific calculation formula is as follows: ; wherein: The comprehensive upward adjustable power and the comprehensive downward adjustable power when the distributed resource cluster faces auxiliary service at the moment t are respectively, and kW is calculated; the power of the ith distributed resource in the distributed resource cluster can be adjusted upwards and downwards, and kW is obtained, N agg is the number of the distributed resources of the cluster; distributed resource cluster at time t The contribution integrity weight of the distributed resources facing auxiliary services; Comprehensive regulation and control of response frequency : The comprehensive regulation response frequency of the distributed resource cluster facing auxiliary service is obtained by weighting and averaging the regulation response frequency of each distributed resource in the cluster, and the specific calculation formula is as follows: ; wherein: the comprehensive regulation response frequency of the distributed resource cluster facing auxiliary service at the moment t is inferior; is the first in the distributed resource cluster Regulating response frequency of the distributed resources, and performing secondary regulation; Comprehensive maximum regulatory response time : The comprehensive maximum regulation response time of the distributed resource cluster facing auxiliary service is obtained by weighted addition calculation of the maximum regulation response time of each distributed resource in the cluster, and the specific calculation formula is as follows: ; wherein: the comprehensive maximum regulation response time, min, is the time t when the distributed resource cluster faces auxiliary service; is the first in the distributed resource cluster Maximum regulatory response time, min, of the individual distributed resources; integrated average regulatory duration : In the auxiliary service-oriented regulation duration calculation, the comprehensive average regulation duration of the distributed resource cluster is obtained by carrying out weighted average calculation on the regulation duration of each distributed resource in the cluster, and a specific calculation formula is as follows: ; wherein: the comprehensive average regulation duration time and h are the comprehensive average regulation duration time when the distributed resource cluster faces auxiliary service at the moment t; distributed resource cluster at time t The regulation duration time of the distributed resources, h; distributed resource cluster at time t Calculating the contribution integrity weight of the cluster continuous regulation time by the distributed resources; comprehensive regulation of delay time : The comprehensive regulation delay time of the distributed resource cluster facing auxiliary service is obtained by calculating the weighted average value of the regulation delay time of each distributed resource in the cluster, and the specific calculation formula is as follows: ; wherein: the comprehensive regulation delay time when the distributed resource cluster faces peak shaving auxiliary service at the moment t, s is that the number of the components is equal to s, Is the first in the distributed resource cluster The control delay time s of the distributed resources; Comprehensive regulation response achievement degree : ; Wherein: the achievement degree of the comprehensive regulation response of the distributed resource cluster is obtained, wherein N is the total number of regulation response times of the distributed resource cluster; Actually regulating and controlling power for the nth time of the distributed resource cluster, and kW; The power, kW, of the distributed resource clusters in the nth regulation instruction to be regulated and controlled; Integrated regulation response aggressiveness : ; Wherein: The method is an integrated regulation response enthusiasm of a distributed resource cluster; The total power of the nth regulation instruction of the virtual power plant is calculated; Integrated regulation response aggressiveness : ; Wherein: To comprehensively regulate the reliability of the resources for the distributed resource clusters, Is the first in the distributed resource cluster The resource reliability of the individual distributed resources is regulated.
19. The system of claim 17, wherein the relative importance of the regulatory capability indicators is weighted The calculation method of (2) is as follows: according to different auxiliary service regulation requirements, determining importance ranking of regulation capability indexes: The importance of the regulation capability indexes under the operation scene of the peak regulation auxiliary service is ordered as that the adjustable power capacity, the regulation duration, the regulation response time, the regulation delay time, the regulation response frequency, the regulation response achievement degree, the regulation response enthusiasm and the regulation resource reliability are all equal to each other; the importance of the regulation capability indexes under the operation scene of the frequency modulation auxiliary service is ordered as that the regulation response time is greater than or equal to the regulation delay time, the regulation power capacity is greater than or equal to the regulation duration time, the regulation response frequency is greater than or equal to the regulation response achievement degree, the regulation response enthusiasm is greater than or equal to the regulation resource reliability; the importance of the regulation capability indexes under the operation scene of the standby auxiliary service is ordered as that the adjustable power capacity is more than or equal to the regulation response time is more than or equal to the regulation delay time is more than or equal to the regulation duration time, the regulation response frequency is more than or equal to the regulation response achievement degree is more than or equal to the regulation response enthusiasm is more than or equal to the regulation resource reliability; judging the relative importance of the two indexes by adopting an improved analytic hierarchy process and using a scale value Quantifying a judgment result; constructing a judgment matrix And calculating the relative importance weight of the regulation capability index.
20. The system of claim 19, wherein the decision matrix The following conditions need to be satisfied: I.e. Is a reciprocal matrix; wherein Represent the first A scale value obtained by comparing the individual elements with the j-th element; The calculation formula of the judgment matrix is as follows: ; The relative importance weights of the regulatory capability indicators are as follows: 。

Description

Auxiliary service-oriented distributed resource cluster regulation and control characteristic evaluation method and system Technical Field The invention belongs to the technical field of virtual power plants, and particularly relates to an auxiliary service-oriented distributed resource cluster regulation and control characteristic evaluation method and system. Background The virtual power plant can regulate and control distributed resources through aggregation of multiple forms, can meet self energy consumption requirements, and can also be used as a high-capacity resource aggregate to participate in power grid operation and auxiliary service regulation and control. However, the distributed resources of the virtual power plant are complicated, so that in order to consider the competitiveness of the centralized control of the virtual power plant and the computing capability of the distributed control, the information complexity and the management difficulty faced in the regulation and control of massive distributed resources are effectively solved, the unified construction of the same management unit and the communication control infrastructure with similar geographic positions in the virtual power plant is realized, and the set formed by a plurality of distributed resources which are convenient to install and uniformly manage by the edge management and control equipment is defined as a distributed resource cluster. With the development of the digitization and intellectualization processes of the power system, the portrait technology of the power field is gradually rising. The portrait technology is a diversified portrait set which is extracted and constructed from mass data through advanced physical information technology based on service requirements and is used for describing characteristics, requirements and behaviors of individuals and clusters, and is widely applied to a plurality of electric power fields such as user behavior portraits, load prediction, power consumption anomaly detection, marketing and the like. Along with the continuous expansion of the business types participated by the virtual power plant, the electric power system has higher requirements on characterization of the regulation and control characteristics of the virtual power plant. In order to evaluate the quantitative index of the regulation and control characteristics of the distributed resource clusters in the virtual power plant and support the accurate description of the regulation and control characteristics of the virtual power plant, it is highly required to propose a portrait type evaluation method of the regulation and control characteristics of the distributed resource clusters. Traditional auxiliary service mainly depends on a large thermal power generating unit, and thermal power regulating capability is limited to a certain extent under the background of a double-carbon target. Distributed resources are considered as important supplements of future electric auxiliary service markets due to the advantages of high response speed, flexible layout and the like, however, comprehensive and accurate evaluation of the regulation and control characteristics of distributed resource clusters still faces a plurality of challenges due to uncertainty of output and complexity of management. In the aspect of the construction of the distributed resource cluster regulation and control characteristic evaluation index, the existing evaluation index system is not perfect, on one hand, the index system is poor in universality and lacks of scene adaptation and differential design, and on the other hand, the system research for the evaluation of the regulation and control characteristic of the mixed resource cluster containing different types of distributed resources is still lacking. In the aspect of regulation and control characteristic evaluation methods, the current method is mostly used for improving the accuracy of resource evaluation, and the judgment on the regulation and control characteristics of a research object is lacking. How to construct a scientific and comprehensive evaluation system for distributed resource clusters with different characteristics and complex operation characteristics, and how to characterize the regulation and control properties of different types of distributed resource clusters in multiple operation scenes and quantitatively evaluate the indexes of the distributed resource clusters becomes one of key problems to be solved. Disclosure of Invention In view of this, in a first aspect, the present invention provides an auxiliary service-oriented distributed resource cluster regulation characteristic evaluation method, which includes the following steps: constructing a regulation and control characteristic portrait tag system of the distributed resource from two dimensions of the static attribute and the dynamic attribute, wherein the regulation and control characteristic portrait tag system consists of regul