Flexible resource cluster regulation and control priority construction method and system

Abstract

The invention discloses a flexibility resource cluster regulation priority construction method and system, and the method comprises the steps: collecting historical response data and real-time response data of each flexibility resource, and carrying out the statistical analysis to form a multi-dimensional comprehensive evaluation system of each flexibility resource performance index; for differentiated demands, constructing the comprehensive multi-dimensional evaluation indexes into one-dimensional comprehensive evaluation indexes based on an analytic hierarchy process and an entropy weight method; and constructing a resource regulation and control priority based on the one-dimensional comprehensive evaluation index, and realizing rapid decomposition of a flexible resource cluster regulation and control instruction based on the resource regulation and control priority. Mutually independent response evaluation index systems of all the flexibility resources are counted through historical and real-time data, so that the performance of all the resources in the response process is described, and a dispatcher can conveniently judge the response performance of all the resources; the constructed comprehensive evaluation system is utilized to perform multi-dimensional evaluation on the resources, the resource regulation and control priority is constructed, and regulation and control experience accumulated by a dispatcher can be introduced in a subjective empowerment mode.

Description

Technical Field

[0001] The present invention belongs to the field of electric power, and in particular relates to a method and system for constructing flexibility resource cluster control priorities. Background Art

[0002] Demand-side flexibility resources have huge potential and can be developed to facilitate the operation of the power system. At present, various regions have successively established relevant rules for demand-side response resources to participate in the market.

[0003] Flexible resource aggregators aggregate massive resources distributed across time and space to respond to grid demands. However, decomposing instructions for massive heterogeneous resources still faces the problem of the curse of dimensionality. At the same time, the heterogeneity of resources makes it difficult to achieve optimization in a single dimension. Therefore, it is necessary to build a multi-dimensional resource evaluation index system to match different resource subsets to different grid demands and achieve dimensionality reduction of group scheduling and control objects. Summary of the Invention

[0004] The present invention provides a method and system for constructing the control priority of a flexible resource cluster. The method uses historical and real-time data statistics to establish an independent response evaluation index system for each flexible resource to describe the performance of each resource in the response process, so as to facilitate the dispatcher to judge the response performance of each resource. The constructed comprehensive evaluation system is used to evaluate the resources in multiple dimensions, and the resource control priority is constructed based on the hierarchical analysis method, the entropy weight method and the TOPSIS method. The control experience accumulated by the dispatcher can also be introduced through subjective empowerment.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] In a first aspect, a method for constructing a flexible resource cluster control priority is provided, comprising:

[0007] Collect historical and real-time response data of each flexibility resource, and statistically analyze it to form a multi-dimensional comprehensive evaluation system for each flexibility resource performance indicator;

[0008] Aiming at differentiated needs, the comprehensive multidimensional evaluation index is constructed into a one-dimensional comprehensive evaluation index based on the analytic hierarchy process and entropy weight method;

[0009] The resource control priority is constructed based on the one-dimensional comprehensive evaluation index, and the flexible resource cluster control instructions are quickly decomposed based on the resource control priority.

[0010] Furthermore, the multidimensional comprehensive evaluation system includes the empowerment layer and the entropy weight layer;

[0011] The empowerment layer indicators include flexibility resource response accuracy indicator, flexibility resource response timeliness indicator and flexibility resource response economy indicator;

[0012] The entropy weight layer indicators corresponding to the flexibility resource response accuracy indicators include historical response accuracy indicators and real-time response accuracy indicators;

[0013] The entropy weight layer indicators corresponding to the flexibility resource response timeliness indicators include business delay time indicators and technical delay time indicators;

[0014] The entropy weight layer indicators corresponding to the economic indicators of flexible resource response include cost indicators and profit indicators.

[0015] Furthermore, the historical response accuracy index is the historical response deviation rate. The historical response deviation rate HRDR is characterized by the arithmetic mean of the response deviation rates in all previous responses:

[0016] ;

[0017] Among them, i is the time series index of the flexible resource participating in the response, t is the time series index of a single event, N is the total number of participating responses, is the duration of the i-th response event, is the actual response capacity of the flexibility resource in the i-th response event, The capacity of flexible resources to issue instructions in the i-th response event;

[0018] The real-time response accuracy indicator is the real-time response deviation rate. The real-time response deviation rate PRDR is arithmetic represented by the real-time response deviation rate as follows:

[0019] ;

[0020] in, is the actual response capacity of the flexibility resource in the current response event, Order capacity for flexibility resources to respond to current events.

[0021] Furthermore, the technical delay time indicator is the technical delay time, and the expression of the technical delay time TDT is:

[0022] ;

[0023] Where i is the time series index of the flexibility resource participation response, N is the total number of participation responses, is the time delay caused by technical conditions in the i-th response time of the flexibility resource.

[0024] Furthermore, the expression of the profitability index RI is:

[0025] ;

[0026] Where i is the time series index of the flexibility resource participation response, N is the total number of participation responses, is the duration of the i-th event, is the actual response capacity of the flexibility resource in the i-th response event, is the unit benefit of the i-th response.

[0027] Furthermore, facing differentiated needs, the comprehensive multi-dimensional evaluation index is constructed into a one-dimensional comprehensive evaluation index based on the analytic hierarchy process and entropy weight method, including:

[0028] Aiming at differentiated needs, the indicator weights of each empowerment layer indicator under various control scenarios are constructed based on the hierarchical analysis method; the entropy weights of each entropy weight layer indicator are constructed based on the entropy weight method;

[0029] A one-dimensional comprehensive evaluation index is obtained based on the index weight and entropy weight.

[0030] Furthermore, based on the analytic hierarchy process, the weights of indicators at each empowerment level under various control scenarios are constructed, including:

[0031] Establishing Judgment Comparison Matrix Based on Analytic Hierarchy Process and Embedding Expert Experience ;

[0032] According to the judgment comparison matrix Calculate the indicator weights of each weighted layer indicator , The expression is:

[0033] ;

[0034] in, is the number of weighted layer indicators j, n represents the number of flexibility resources l, and B represents the weighted layer.

[0035] Furthermore, the entropy weights of each entropy weight layer indicator are constructed based on the entropy weight method, including:

[0036] The indicators of each entropy weight layer are homogenized, standardized and translated to obtain ;

[0037] Will Convert to probability matrix , The expression is:

[0038] ;

[0039] Among them, m represents the number of entropy weight layer indicators h;

[0040] According to the probability matrix Calculate the information entropy of each entropy weight layer indicator , The expression is:

[0041] ; ;

[0042] According to the information entropy of each entropy weight layer indicator Calculate the entropy weight , The expression is:

[0043] ;

[0044] Among them, A represents the entropy weight layer.

[0045] Furthermore, resource control priorities are constructed based on one-dimensional comprehensive evaluation indicators, including:

[0046] The positive ideal solution of one-dimensional comprehensive evaluation index is calculated by TOPSIS method and negative ideal solutions The distance to the flexibility resource l and , and The expression is:

[0047] ;

[0048] ;

[0049] and The expression is:

[0050] ;

[0051] ;

[0052] is the comprehensive weight of the weighted layer indicator j, ;

[0053] according to and Calculate the resource control priority of the flexible resource l , The expression is:

[0054] .

[0055] Secondly, a flexible resource cluster control priority construction system is provided, including:

[0056] A multi-dimensional comprehensive evaluation system construction module is used to collect historical and real-time response data of each flexibility resource, and statistically analyze it to form a multi-dimensional comprehensive evaluation system for each flexibility resource performance indicator;

[0057] One-dimensional comprehensive evaluation index construction module is used to construct comprehensive multi-dimensional evaluation indicators into one-dimensional comprehensive evaluation indicators based on the hierarchical analysis method and entropy weight method to meet differentiated needs;

[0058] The resource control module is used to build resource control priorities based on one-dimensional comprehensive evaluation indicators, and to quickly decompose flexible resource cluster control instructions based on the resource control priorities.

[0059] The beneficial effects achieved by the present invention are:

[0060] The historical and real-time response data of each flexible resource are collected and statistically analyzed to form a multi-dimensional comprehensive evaluation system for each flexible resource's performance indicators. Aiming at differentiated needs, the comprehensive multi-dimensional evaluation indicators are constructed into one-dimensional comprehensive evaluation indicators based on the analytic hierarchy process and entropy weight method. Resource control priorities are constructed based on the one-dimensional comprehensive evaluation indicators, and the control instructions for the flexible resource cluster are quickly decomposed based on the resource control priority. An independent response evaluation indicator system for each flexible resource is statistically generated using historical and real-time data to describe the performance of each resource in the response process, facilitating the dispatcher's judgment of the response performance of each resource. The constructed comprehensive evaluation system is used to conduct a multi-dimensional evaluation of resources, and a resource control priority is constructed based on the analytic hierarchy process, entropy weight method, and TOPSIS method. The dispatcher's accumulated control experience can be introduced through subjective weighting. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A flowchart of the method for constructing a flexible resource cluster control priority according to the present invention;

[0062] Figure 2 This is a structural model diagram of the multi-dimensional comprehensive evaluation system of the present invention;

[0063] Figure 3 This is a structural diagram of the flexible resource cluster control priority construction system of the present invention. DETAILED DESCRIPTION

[0064] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention.

[0065] like Figure 1 As shown, an embodiment of the present invention provides a method for constructing a flexible resource cluster control priority, comprising the following steps:

[0066] 101. Collect historical and real-time response data of each flexibility resource, and statistically analyze them to form a multi-dimensional comprehensive evaluation system for each flexibility resource performance indicator;

[0067] Since distributed resource responses are affected by many factors, the response characteristics vary greatly and are highly uncertain, so historical and real-time resource response data are used to construct accuracy indicators;

[0068] like Figure 2 The diagram shows the structural model of the multi-dimensional comprehensive evaluation system. The control scenarios include peak shaving, valley filling, and local overload, including the empowerment layer and the entropy weight layer.

[0069] The empowerment layer indicators include flexibility resource response accuracy indicator, flexibility resource response timeliness indicator and flexibility resource response economy indicator;

[0070] The entropy weight layer indicators corresponding to the flexibility resource response accuracy index include historical response accuracy index and real-time response accuracy index. Since distributed resource response is affected by many factors, the response characteristics vary greatly and are highly uncertain, the accuracy index is constructed using resource historical and real-time response data.

[0071] The historical response accuracy indicator is the Historical Response Deviation Rate (HRDR). The HRDR is calculated by taking the arithmetic mean of the response deviation rates in all previous responses and is represented as follows:

[0072] ;

[0073] Among them, i is the time series index of the flexible resource participating in the response, t is the time series index of a single event, N is the total number of participating responses, is the duration of the i-th response event, is the actual response capacity of the flexibility resource in the i-th response event, The capacity of flexible resources to issue instructions in the i-th response event;

[0074] The real-time response accuracy indicator is the real-time response deviation rate (RRDR). The real-time response deviation rate PRDR is arithmetic represented by the real-time response deviation rate as follows:

[0075] ;

[0076] in, is the actual response capacity of the flexibility resource in the current response event, Order capacity for flexibility resources to be used in the current response event;

[0077] The entropy weighted layer indicators corresponding to the flexibility resource response timeliness indicators include business delay time indicators and technical delay time indicators. Since the flexibility resource response delay time is affected by business processes and technical levels, it is divided into business delay time and technical delay time.

[0078] Time Delay by Business Processes (TDB) is typically caused by different control methods and business processes during resource regulation. It is relatively fixed for the same resource and can be obtained from historical data statistics.

[0079] The technical delay time indicator is the time delay by technology (TDT), and the expression of the technical delay time TDT is:

[0080] ;

[0081] Where i is the time series index of the flexibility resource participation response, N is the total number of participation responses, is the time delay caused by technical conditions in the i-th response time of the flexibility resource;

[0082] The entropy weight layer indicators corresponding to the economic indicators of flexible resource response include cost indicators and profit indicators;

[0083] Cost Indicator (CI) is used by operators to minimize response costs. The higher the cost, the lower the response priority. While the cost of resource regulation varies for different resources, it is relatively fixed and can be obtained from data statistics.

[0084] The Revenue Indicator (RI) is used to ensure that operators respond to revenue as evenly as possible. In theory, the higher the economic indicator, the lower its regulatory priority. Based on statistical data, the expression for the RI is:

[0085] ;

[0086] Where i is the time series index of the flexibility resource participation response, N is the total number of participation responses, is the duration of the i-th event, is the actual response capacity of the flexibility resource in the i-th response event, is the unit benefit of the i-th response.

[0087] 102. Aiming at differentiated needs, the comprehensive multidimensional evaluation index is constructed into a one-dimensional comprehensive evaluation index based on the hierarchical analysis method and entropy weight method;

[0088] Aiming at differentiated needs, the indicator weights of each empowerment layer indicator under various control scenarios are constructed based on the hierarchical analysis method; the entropy weights of each entropy weight layer indicator are constructed based on the entropy weight method;

[0089] According to the index weight and entropy weight, a one-dimensional comprehensive evaluation index is obtained;

[0090] The Analytic Hierarchy Process (AHP) is a decision-making method that decomposes elements related to decision-making into levels such as goals, criteria, and plans, and conducts qualitative and quantitative analysis on this basis. Its steps are as follows:

[0091] Establishing Judgment Comparison Matrix Based on Analytic Hierarchy Process and Embedding Expert Experience ;

[0092] According to the judgment comparison matrix Calculate the indicator weights of each weighted layer indicator , The expression is:

[0093] ;

[0094] in, is the number of weighted layer indicators j, n represents the number of flexibility resources l, and B represents the weighted layer;

[0095] Finally, the consistency check is performed. The consistency check criterion is CR, and its expression is:

[0096] ;

[0097] The entropy weight method reflects the contribution of an indicator to the decision-making result based on its information entropy. The greater the information entropy, the greater the difference of the indicator. The higher the contribution, the greater the weight. Based on the entropy weight method, the entropy weights of the indicators at each entropy weight layer are constructed, including:

[0098] The indicators of each entropy weight layer are homogenized, standardized and translated to obtain ;

[0099] Will Convert to probability matrix , The expression is:

[0100] ;

[0101] Among them, m represents the number of entropy weight layer indicators h;

[0102] According to the probability matrix Calculate the information entropy of each entropy weight layer indicator , The expression is:

[0103] ; ;

[0104] According to the information entropy of each entropy weight layer indicator Calculate the entropy weight , The expression is:

[0105] ;

[0106] Among them, A represents the entropy weight layer.

[0107] 103. Based on the one-dimensional comprehensive evaluation index, the resource control priority is constructed, and the flexible resource cluster control instructions are quickly decomposed based on the resource control priority.

[0108] TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) measures the distance between each resource and the positive and negative ideal solutions in the data, and ranks the resources based on their quality. It can provide a relatively comprehensive and accurate evaluation result in multi-criteria decision-making problems.

[0109] The positive ideal solution of one-dimensional comprehensive evaluation index is calculated by TOPSIS method and negative ideal solutions The distance to the flexibility resource l and , and The expression is:

[0110] ;

[0111] ;

[0112] and The expression is:

[0113] ;

[0114] ;

[0115] is the comprehensive weight of the weighted layer indicator j, ;

[0116] according to and Calculate the resource control priority of the flexible resource l , The expression is:

[0117] ;

[0118] Prioritize resources based on It is possible to quickly decompose the control instructions of the flexible resource cluster.

[0119] The implementation principles of the embodiments of the present invention are as follows:

[0120] The historical and real-time response data of each flexible resource are collected and statistically analyzed to form a multi-dimensional comprehensive evaluation system for each flexible resource's performance indicators. Aiming at differentiated needs, the comprehensive multi-dimensional evaluation indicators are constructed into one-dimensional comprehensive evaluation indicators based on the analytic hierarchy process and entropy weight method. Resource control priorities are constructed based on the one-dimensional comprehensive evaluation indicators, and the control instructions for the flexible resource cluster are quickly decomposed based on the resource control priority. An independent response evaluation indicator system for each flexible resource is statistically generated using historical and real-time data to describe the performance of each resource in the response process, facilitating the dispatcher's judgment of the response performance of each resource. The constructed comprehensive evaluation system is used to conduct a multi-dimensional evaluation of resources, and a resource control priority is constructed based on the analytic hierarchy process, entropy weight method, and TOPSIS method. The dispatcher's accumulated control experience can be introduced through subjective weighting.

[0121] like Figure 3 As shown, an embodiment of the present invention provides a flexible resource cluster control priority construction system, including:

[0122] A multi-dimensional comprehensive evaluation system construction module 301 is used to collect historical response data and real-time response data of each flexibility resource, and statistically analyze and form a multi-dimensional comprehensive evaluation system for each flexibility resource performance indicator;

[0123] A one-dimensional comprehensive evaluation index construction module 302 is used to construct the comprehensive multi-dimensional evaluation index into a one-dimensional comprehensive evaluation index based on the analytic hierarchy process and the entropy weight method in response to differentiated needs;

[0124] The resource control module 303 is used to construct resource control priorities based on the one-dimensional comprehensive evaluation index, and to quickly decompose the flexible resource cluster control instructions based on the resource control priorities.

[0125] The implementation principles of the embodiments of the present invention are as follows:

[0126] Through historical and real-time data statistics, an independent response evaluation index system for each flexible resource is established to describe the performance of each resource in the response process, which is convenient for dispatchers to judge the response performance of each resource; using the constructed comprehensive evaluation system, a multi-dimensional evaluation of resources is carried out, and the resource control priority is constructed based on the hierarchical analysis method, entropy weight method and TOPSIS method, and the control experience accumulated by the dispatcher can be introduced through subjective empowerment.

[0127] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for constructing priority of flexible resource cluster control, characterized in that: include: Collect historical and real-time response data of each flexibility resource, and statistically analyze it to form a multi-dimensional comprehensive evaluation system for each flexibility resource performance indicator; Aiming at differentiated needs, the comprehensive multidimensional evaluation index is constructed into a one-dimensional comprehensive evaluation index based on the hierarchical analysis method and the entropy weight method; A resource control priority is constructed based on the one-dimensional comprehensive evaluation index, and a rapid decomposition of the flexible resource cluster control instructions is achieved based on the resource control priority.

2. The method for constructing flexibility resource cluster control priority according to claim 1, characterized in that: The multi-dimensional comprehensive evaluation system includes a weighting layer and an entropy weight layer; The empowerment layer indicators include flexibility resource response accuracy indicator, flexibility resource response timeliness indicator and flexibility resource response economy indicator; The entropy weight layer indicators corresponding to the flexibility resource response accuracy indicators include a historical response accuracy indicator and a real-time response accuracy indicator; The entropy weight layer indicators corresponding to the flexibility resource response timeliness indicators include a service delay time indicator and a technical delay time indicator; The entropy weight layer indicators corresponding to the economic indicators of flexible resource response include cost indicators and profit indicators.

3. The method for constructing flexibility resource cluster control priority according to claim 2, characterized in that: The historical response accuracy index is the historical response deviation rate, and the historical response deviation rate HRDR is characterized by the arithmetic mean of the response deviation rates in previous responses: ； Wherein, i is the time series index of the flexible resource participating in the response, t is the time series index of a single event, N is the total number of participating responses, is the duration of the i-th response event, is the actual response capacity of the flexibility resource in the i-th response event, The capacity of flexible resources to issue instructions in the i-th response event; The real-time response accuracy indicator is the real-time response deviation rate, and the real-time response deviation rate PRDR is arithmetic-characterized by the real-time response deviation rate: ； Among them, the is the actual response capacity of the flexibility resource in the current response event, Order capacity for flexibility resources to respond to current events.

4. The method for constructing flexibility resource cluster control priority according to claim 2, characterized in that: The technical delay time indicator is the technical delay time, and the expression of the technical delay time TDT is: ； Wherein, i is the time series index of the flexible resource participation response, N is the total number of participation responses, is the time delay caused by technical conditions in the i-th response time of the flexibility resource.

5. The method for constructing flexibility resource cluster control priority according to claim 2, characterized in that: The expression of the profitability index RI is: ； Wherein, i is the time series index of the flexible resource participation response, N is the total number of participation responses, is the duration of the i-th event, is the actual response capacity of the flexibility resource in the i-th response event, is the unit benefit of the i-th response.

6. The method for constructing flexibility resource cluster control priority according to claim 2, characterized in that: Aiming at differentiated needs, the comprehensive multi-dimensional evaluation index is constructed into a one-dimensional comprehensive evaluation index based on the hierarchical analysis method and the entropy weight method, including: Aiming at differentiated needs, the indicator weights of each empowerment layer indicator under various control scenarios are constructed based on the hierarchical analysis method; the entropy weights of each entropy weight layer indicator are constructed based on the entropy weight method; A one-dimensional comprehensive evaluation index is obtained according to the index weight and the entropy weight.

7. The method for constructing flexibility resource cluster control priority according to claim 6, characterized in that: The indicator weights of each empowerment layer indicator under various control scenarios are constructed based on the hierarchical analysis method, including: Establishing Judgment Comparison Matrix Based on Analytic Hierarchy Process and Embedding Expert Experience ; According to the judgment comparison matrix Calculate the indicator weights of each weighted layer indicator , The expression is: ； Among them, the is the number of weighted layer indicators j, n represents the number of flexibility resources l, and B represents the weighted layer.

8. The method for constructing flexibility resource cluster control priority according to claim 7, characterized in that: The entropy weights of the entropy weight layer indicators are constructed based on the entropy weight method, including: The indicators of each entropy weight layer are homogenized, standardized and translated to obtain ; The Convert to probability matrix , The expression is: ； Wherein, m represents the number of entropy weight layer indicators h; According to the probability matrix Calculate the information entropy of each entropy weight layer indicator , The expression is: ； ； According to the information entropy of each entropy weight layer indicator Calculate the entropy weight , The expression is: ； Wherein, A represents the entropy weight layer.

9. The method for constructing flexibility resource cluster control priority according to claim 8, characterized in that: The constructing of resource control priorities based on the one-dimensional comprehensive evaluation index includes: The positive ideal solution of the one-dimensional comprehensive evaluation index is calculated by TOPSIS method. and negative ideal solutions The distance to the flexibility resource l and , and stated The expression is: ； ； described and stated The expression is: ； ； described is the comprehensive weight of the weighted layer indicator j, ; According to the and stated Calculate the resource control priority of the flexible resource l , The expression is: 。 10. A flexible resource cluster control priority construction system, characterized in that: include: A multi-dimensional comprehensive evaluation system construction module is used to collect historical and real-time response data of each flexibility resource, and statistically analyze it to form a multi-dimensional comprehensive evaluation system for each flexibility resource performance indicator; A one-dimensional comprehensive evaluation index construction module is used to construct the comprehensive multi-dimensional evaluation index into a one-dimensional comprehensive evaluation index based on the hierarchical analysis method and the entropy weight method in response to differentiated needs; The resource control module is used to construct resource control priorities based on the one-dimensional comprehensive evaluation index, and realize rapid decomposition of flexible resource cluster control instructions based on the resource control priorities.

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