CN115907389A - Urban area scale demand response potential power consumer positioning method, system and medium based on user portrait - Google Patents
Urban area scale demand response potential power consumer positioning method, system and medium based on user portrait Download PDFInfo
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- CN115907389A CN115907389A CN202211511466.9A CN202211511466A CN115907389A CN 115907389 A CN115907389 A CN 115907389A CN 202211511466 A CN202211511466 A CN 202211511466A CN 115907389 A CN115907389 A CN 115907389A
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Abstract
The application relates to a method, a system and a medium for positioning urban area scale demand response potential power users based on user portraits, wherein the method comprises the following steps: collecting power related data of users in the district, and preprocessing the data to form an available power data set; designing a power utilization characteristic index capable of reflecting the peak clipping and valley filling potential and the capacity-demand optimization potential of a user for the user portrait; calculating the specific values of the portrait indexes corresponding to each user one by one to form a user portrait feature data set; determining the final clustering category by combining with the optimal clustering number judgment standard, and acquiring power user groups reflecting different power utilization characteristics through clustering; the method and the device realize effective positioning of the user cluster with the peak clipping and valley filling potential and the capacity and demand optimization potential. The method and the system provide technical guidance for a demand response strategy provided by the current power system, assist power operators to quickly locate adjustable users from massive users under emergency conditions such as extreme weather and the like, and reduce damage caused by large-range orderly power utilization.
Description
Technical Field
The application relates to the technical field of demand response of power systems, in particular to a method, a system and a medium for positioning urban area scale demand response potential power users based on user figures.
Background
With the proposal of double-carbon targets in China, the proportion of renewable energy sources participating in power generation is gradually increased, but certain impact is caused to a power grid due to instability and discontinuity of the renewable energy sources. Meanwhile, the grid power supply pressure is increased with the advent of new loads such as electric vehicles, large data centers, and the like. Particularly, when extreme weather occurs, the contradiction between supply and demand of the power system is aggravated, and the power grid has to ensure the safety of the system operation through a rigid load regulation means, but the economic benefit of power parties is damaged. The proposal of demand side management provides a technical means for transformation and upgrade of a power-assisted power grid, wherein a demand response strategy can guide a user to change the power utilization behavior of the user by means of power price or incentive measures, so that the aims of peak clipping, valley filling, capacity and demand optimization and the like are fulfilled, the source load supply and demand balance is promoted, the operation reliability of the power grid is guaranteed, the efficiency of a power system is improved, and the power utilization cost of the user is reduced.
In the face of massive power data accumulated by a large number of power users and a smart power grid in urban areas, potential adjusting users are accurately and efficiently positioned, and the important basis for realizing efficient demand response and relieving power grid pressure is achieved. However, the existing methods mostly focus on the aspect of statistical analysis or power consumption pattern clustering on power consumption data of a single user, and if a large number of users are analyzed one by one, a large amount of time and operation resources are consumed, and actual application requirements cannot be met.
Disclosure of Invention
The embodiment of the application aims to provide a method, a system and a medium for positioning urban area scale demand response potential power users based on user figures, and aims to provide a technical means for effectively positioning adjustable users when power operators implement demand response strategies.
In order to achieve the above purpose, the present application provides the following technical solutions:
in a first aspect, an embodiment of the present application provides a method for positioning an urban area scale demand response potential power consumer based on a user profile, including the following specific steps:
step 3, calculating the specific values of the portrait indexes corresponding to each user one by one to form a user portrait characteristic data set, wherein the size of the data set is the reserved number of users multiplied by the portrait characteristic number;
and 5, comparing and analyzing the characteristic difference of each user group obtained by clustering, and investigating the power utilization attribute preference and the power optimization space of the users, thereby effectively positioning the user group with the peak clipping and valley filling potential and the capacity and demand optimization potential.
The data collected in the step 1 comprises user basic information, power utilization data and power grid operation execution standards, the preprocessing content comprises data quality preliminary screening, abnormal value analysis and missing value processing, and the user basic information comprises a user name/user number and belongs to the industry; the user electricity consumption data comprises contract capacity and hourly power; the power grid operation execution standard comprises a peak period, gu Qi and a flat period division rule, wherein the time-by-time power is user historical record data or predicted power obtained through short-term prediction according to the historical data, the user power data quality primary screening mainly comprises deleting users with poor overall quality, deleting certain days with high missing values in users with good quality, abnormal value analysis is mainly used for screening power utilization abnormality caused by electric meter faults and few accidental factors, and the missing values are supplemented based on recorded values close in time by adopting an interpolation method.
The indexes used by the user portrait in the step 2 comprise a peak-to-valley annual difference rate, a peak-to-valley annual power consumption rate, a valley-to-valley annual power consumption rate, a mean annual power consumption rate, hours occupied by x% of loads before the user, a ratio of the y hour load to the maximum load, and a maximum annual hour load.
In the step 4, the clustering algorithm is k-means or hierarchical clustering and density clustering, the redundancy of the characteristics specifically refers to the correlation among the characteristics, and in order to analyze the peak-valley power consumption characteristics of the user, the peak-period power consumption rate and the valley-period power consumption rate are used as the clustering characteristics; analyzing the optimization potential of the user capacity and demand, using the number of hours occupied by the previous x% load and the ratio of the load to the maximum load as the clustering characteristics, using the residual portrait characteristics for the auxiliary analysis of the clustering results, and judging the clustering characteristic number by combining the contour coefficient method and the judgment of the knowledge of the related professional field.
In a second aspect, embodiments of the present application provide a system for locating urban area scale demand response potential power consumers based on user profiles, comprising,
the electric power data collection and preprocessing module is used for collecting electric power related data of users in the district administered by the city and preprocessing the data to form an available electric power data set;
the power utilization characteristic index design module is used for designing power utilization characteristic indexes capable of reflecting peak clipping and valley filling potentials and capacity-demand optimization potentials of the user for portrait of the user;
the specific value calculation module of portrait index calculates the specific value of portrait index corresponding to each user one by one to form a user portrait characteristic data set, wherein the size of the data set is the reserved user number multiplied by the portrait characteristic number;
the power consumer group acquisition module is used for selecting proper clustering characteristics from the calculation indexes based on a common clustering algorithm and considering the redundancy of the characteristics, determining the final clustering type by combining with the optimal clustering number judgment standard, and acquiring power consumer groups reflecting different power utilization characteristics through clustering;
and the user cluster positioning module is used for comparing and analyzing the characteristic difference of each user group obtained by clustering and inspecting the power utilization attribute preference and the power optimization space of the users, so that the user clusters with the peak clipping and valley filling potential and the capacity and demand optimization potential are effectively positioned.
In a third aspect, embodiments of the present application provide a computer readable storage medium storing program code, which when executed by a processor, implements the steps of the urban dimension demand response potential power consumer location method based on user figures as described above.
Compared with the prior art, the invention has the beneficial effects that:
the method can quickly and efficiently position the users with the demand response potential from a large number of urban area scale users, and provides technical guidance for the power operators when relevant adjustment measures are executed;
the user positioning method provided by the invention enables the peak clipping and valley filling to be more targeted and targeted, enables the modification measures to be more practical for users, improves the speed of relieving the contradiction between supply and demand, and ensures the stable operation of a power grid;
by dividing different power utilization attributes of urban users, a user power utilization guide strategy can be established in a targeted manner, configuration redundancy of power transmission and distribution equipment is reduced, and construction of a new generation of power system is assisted;
the method effectively reduces the waste of computing resources on the basis of ensuring the positioning accuracy of the user.
Drawings
To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 is a flow chart of an embodiment of a method for locating an urban area scale demand response potential power consumer based on a user profile.
FIG. 2 is a diagram of the clustering result of the user peak clipping and valley filling potential based on the user profile.
FIG. 3 is a graph of the user demand response potential clustering results based on user profiles of the present invention.
Fig. 4 is a system block diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The terms "first," "second," and the like, are used solely to distinguish one entity or action from another entity or action without necessarily being construed as indicating or implying any actual such relationship or order between such entities or actions.
As shown in FIG. 1, the embodiment of the application provides a method for positioning an electric power consumer based on urban area scale demand response potential of a user portrait
The user power data in the step 1 can be collected historical data, and can also be user short-term load prediction data corresponding to real-time demand response. The time span of historical data can be different from years to months, and the granularity of data acquisition can be 1h, 15min and the like. Missing value padding strategies in data preprocessing can be interpolation padding in combination with recorded values or using machine learning related methods such as k-nearest neighbors and the like. The abnormal data can be judged by using methods such as a box diagram principle, local outlet Factor detection and the like.
And 2, designing power utilization characteristic indexes capable of reflecting peak clipping and valley filling potentials and capacity and demand optimization potentials of the user. Wherein, four indexes are selected for describing the peak clipping and valley filling potential of the user: the annual peak-valley difference rate, the annual peak-valley power consumption rate, the annual valley power consumption rate and the annual average power consumption rate. Three indexes are designed for describing the user capacity and demand optimization potential: hours occupied by x% load before the user, ratio of the y hour load to the maximum load, and annual maximum hour load. The calculation formula of each index is shown in table 1:
TABLE 1 electric power consumer electricity characteristic description index
To describe the optimization potential of the capacity demand of the user, the ratio of the high power is considered to be 20% (x = 20), the hour of the power consumption to be optimized by the user is 20 (y = 21), and three portrait indexes are used, including: hours occupied by the first 20% load of the user, the ratio of the 21 st hour load to the maximum load, and the annual maximum hour load.
The time range of evaluation of the user demand response potential index calculation in the step 2 can be adjusted according to the time span of the analyzed data, and two indexes used for evaluating the user demand optimization potential are as follows: the specific values of x and y can be set by combining a demand response application scene, and the aim is to analyze the reducible range of the proportion of the number of hours of the large load of the user and the corresponding number of hours to be reduced.
And 3, based on a calculation formula provided in the table 1, utilizing python to calculate specific values of the user portrait features selected in the step 2 corresponding to the users reserved in the step 1, and forming a user portrait feature data set, wherein the size of the data set is MxN, M is the number of reserved users, and N is 7, namely the total number of the user portrait features selected in the step 2.
The calculation tool related in the step 3 can select a calculation software with higher calculation efficiency. When the method is actually applied to urban demand response potential service deployment, the related data sets can be managed by using a database management system, such as MySQL, HBase and the like.
And 4, selecting proper clustering characteristics from the user portrait clinic data set constructed in the step 3 based on a k-means clustering algorithm, judging the optimal clustering number by combining a contour coefficient method and related field knowledge, and clustering to obtain a power user group capable of reflecting different power utilization characteristics. In consideration of characteristic redundancy, clustering is carried out through the annual peak period power consumption rate and the annual valley period power consumption rate to obtain a result capable of distinguishing the peak valley power consumption characteristics of the users, and clustering is carried out through the ratio of the hours occupied by the first 20% of the loads of the users and the 21 st hour load to the maximum load to obtain a result capable of distinguishing the optimal potential characteristics of the users.
In step 4, the algorithm used for clustering can be replaced by other clustering algorithms except k-means, such as MeanShift, DBSCAN and the like. The evaluation index for determining the number of best clusters may further include Calinski-Harabasz (CH index), davies-Bouldin (DB index), weighted inter-intra (Wint index), krzanowski-Lai (KL index), hartigan (Hart index), in-Group probability (IGP index), and the like.
And 5, comparing and analyzing the clustering result obtained in the step 4, investigating the characteristic difference of each user group, and positioning the user cluster with the peak clipping and valley filling potential and the capacity and demand optimization potential. As shown in fig. 2, class 0 to class 2 are respectively a peak-valley balance class, a valley preference class, and a peak preference class, and for a peak clipping and valley filling scheduling task class 2 (543 users), which is a valid user group for positioning, can participate in scheduling instructions. The user groups represented by class 1 to class 4 shown in fig. 3 have small optimization potential, large potential, and weak potential, and the power grid capacity and demand optimization can be preferentially performed on class 3 (75 users) and class 2 (470 users).
As shown in fig. 4, an embodiment of the present application provides a system for locating urban area scale demand response potential power consumers based on user profiles, comprising,
the electric power data collecting and preprocessing module 1 is used for collecting electric power related data of users in the urban area and preprocessing the data to form an available electric power data set;
the power utilization characteristic index design module 2 is used for designing power utilization characteristic indexes capable of reflecting peak clipping and valley filling potentials and capacity-to-be-optimized potentials of a user for user portrayal;
an image index specific value calculating module 3 for calculating the image index specific values corresponding to each user one by one to form a user image feature data set, wherein the size of the data set is the reserved user number multiplied by the image feature number;
the power consumer group acquisition module 4 selects proper clustering characteristics from the calculation indexes based on a common clustering algorithm and considering the redundancy of the characteristics, determines the final clustering type by combining the optimal clustering number judgment standard, and acquires power consumer groups reflecting different power utilization characteristics through clustering;
and the user cluster positioning module 5 is used for comparing and analyzing the characteristic difference of each user group obtained by clustering and investigating the power utilization attribute preference and the power optimization space of the user, thereby effectively positioning the user cluster with the peak clipping and valley filling potential and the capacity and demand optimization potential.
An embodiment of the present application further provides a computer-readable storage medium, which stores program code, and when the program code is executed by a processor, the method for positioning users of urban area scale demand response potential power based on user representation as described above is implemented.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (6)
1. A method for positioning urban area scale demand response potential power users based on user figures is characterized by comprising the following specific steps:
step 1, collecting power related data of users in the jurisdiction of an urban area, and preprocessing the data to form an available power data set;
step 2, designing power utilization characteristic indexes capable of reflecting peak clipping and valley filling potentials and capacity and demand optimization potentials of a user for user portrayal;
step 3, calculating the specific values of the portrait indexes corresponding to each user one by one to form a user portrait characteristic data set, wherein the size of the data set is the number of reserved users multiplied by the portrait characteristic number;
step 4, selecting proper clustering characteristics from the calculation indexes based on a common clustering algorithm and considering the redundancy of the characteristics, determining the final clustering category by combining with the judgment standard of the optimal clustering number, and obtaining power user groups reflecting different power utilization characteristics through clustering;
and 5, comparing and analyzing the characteristic difference of each user group obtained by clustering, and investigating the power utilization attribute preference and the power optimization space of the users, thereby effectively positioning the user cluster with the peak clipping and valley filling potential and the capacity and demand optimization potential.
2. The urban area scale demand response potential power consumer positioning method based on the user portrait is characterized in that the data collected in the step 1 comprise user basic information, power utilization data and power grid operation execution standards, the preprocessing content comprises data quality preliminary screening, abnormal value analysis and missing value processing, and the user basic information comprises a user name/a user account number and belongs to industries; the user electricity consumption data comprises contract capacity and hourly power; the power grid operation execution standard comprises a peak period, gu Qi and a flat period division rule, wherein the time-by-time power is user historical record data or predicted power obtained through short-term prediction according to the historical data, user power data quality primary screening mainly comprises the step of deleting users with poor overall quality, deleting certain days with large missing values in users with good quality, abnormal value analysis is mainly used for screening power utilization abnormity caused by electric meter faults and few accidental factors, and the missing values are supplemented by an interpolation method based on recorded values close in time.
3. The method as claimed in claim 1, wherein the indices used by the user profile in step 2 include peak-to-valley annual rate, peak-to-peak annual rate, valley-annual rate, average annual rate, x% load hours before the user, y hour load/maximum load ratio, and annual maximum hour load.
4. The method for positioning urban area scale demand response potential power users based on user profiles as claimed in claim 3, wherein the clustering algorithm in step 4 is k-means or hierarchical clustering, density clustering, the redundancy of the features specifically refers to the correlation among the features, and for analyzing the peak-valley power consumption characteristics of the users, the peak-valley power consumption rate and the valley-valley power consumption rate are used as the clustering features; analyzing the optimization potential of the user capacity and demand, using the number of hours occupied by the previous x% load and the ratio of the load to the maximum load as the clustering characteristics, using the residual portrait characteristics for the auxiliary analysis of the clustering results, and judging the clustering characteristic number by combining the contour coefficient method and the judgment of the knowledge of the related professional field.
5. A system for locating urban dimension demand response potential power consumers based on consumer profiles, comprising,
the electric power data collecting and preprocessing module is used for collecting electric power related data of users in the district, and preprocessing the data to form an available electric power data set;
the power utilization characteristic index design module is used for designing power utilization characteristic indexes capable of reflecting peak clipping and valley filling potentials and capacity-demand optimization potentials of the user for portrait of the user;
the specific value calculation module of portrait index calculates the specific value of portrait index corresponding to each user one by one to form a user portrait characteristic data set, wherein the size of the data set is the reserved user number multiplied by the portrait characteristic number;
the power consumer group acquisition module is used for selecting proper clustering characteristics from the calculation indexes based on a common clustering algorithm and considering the redundancy of the characteristics, determining the final clustering type by combining with the optimal clustering number judgment standard, and acquiring power consumer groups reflecting different power utilization characteristics through clustering;
and the user cluster positioning module is used for comparing and analyzing the characteristic difference of each user group obtained by clustering and inspecting the power utilization attribute preference and the power optimization space of the users, so that the user clusters with the peak clipping and valley filling potential and the capacity and demand optimization potential are effectively positioned.
6. A computer readable storage medium, characterized in that the computer readable storage medium stores program code which when executed by a processor implements the steps of the method for urban dimension demand response potential power consumer location based on user profiles of any one of claims 1 to 4.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116757442A (en) * | 2023-08-09 | 2023-09-15 | 国网浙江省电力有限公司 | Method and system for constructing user portraits of complex electricity behavior based on current limiting algorithm |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116757442A (en) * | 2023-08-09 | 2023-09-15 | 国网浙江省电力有限公司 | Method and system for constructing user portraits of complex electricity behavior based on current limiting algorithm |
| CN116757442B (en) * | 2023-08-09 | 2023-10-24 | 国网浙江省电力有限公司 | Method and system for constructing user portraits of complex electricity behavior based on current limiting algorithm |
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