CN115983576A - Self-adaptive multi-element energy load characteristic identification method - Google Patents
Self-adaptive multi-element energy load characteristic identification method Download PDFInfo
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Abstract
The invention discloses a self-adaptive multivariate energy load characteristic identification method. The method specifically comprises the following steps: (1) And collecting real-time data of each power user in the park and uploading the data to the Internet, and setting the service life and the load capacity. (2) And performing cluster analysis on all the power users collecting the data to obtain the power utilization rules and the load curves of the power users. (3) And carrying out demand response and sensitivity analysis on the power consumer. The invention has the technical effects and advantages that: the cost can be effectively reduced, the carbon emission can also be reduced, and more benefits are brought to social life.
Description
Technical Field
The invention relates to the field of comprehensive energy utilization in a park, in particular to a self-adaptive multivariate energy load characteristic identification method.
Background
Various types of power users have different electricity utilization habits and electricity consumption, so that it is important to research a self-adaptive multivariate energy load characteristic identification method.
With the advance of double carbon targets in China, the difference of multi-element energy loads in a park is more obvious, the analysis of load characteristics plays a very important role in the development of the power industry, and the characteristics of the load are influenced by a plurality of external factors. The research and the understanding of the relationship between the load characteristics and the external influence factors are of great significance to the normal operation of the power system. In addition, the regular analysis of the load characteristics and the influence factors has a vital significance on the operation of the power grid and the prediction work of the load characteristics. Different more adaptive energy sources exist for different power consumers. The source network is also more adaptive to store a concept which is newer.
Therefore, the self-adaptive multi-energy load characteristic identification method is designed, so that the cost can be effectively reduced, the carbon emission can be reduced, and more benefits are brought to social life.
Disclosure of Invention
Aiming at the problem of load characteristic identification in the background technology, the invention provides a self-adaptive multivariate energy load characteristic identification method. The method for identifying the load characteristics of the self-adaptive multi-element energy sources is obtained by fully considering the actual conditions and the energy utilization characteristics of various power users in the park and determining the energy utilization in the park, and specifically comprises the following steps:
(1) And collecting real-time data of each power user in the park and uploading the data to the Internet, and setting the service life and the load capacity.
(2) And performing cluster analysis on all the power users collecting the data to obtain the power utilization rules and the load curves of the power users.
(3) And carrying out demand response and sensitivity analysis on the power consumer.
The method comprises the following specific steps:
(1) The real-time data of each power consumer in the park are collected and uploaded to the Internet, the service life and the load capacity are set, and cluster analysis is carried out on the power consumers in the park.
(2) The method firstly assumes that original data are N class clusters, and then calculates the Euclidean distance between each class cluster according to the following formula:
in the formula, phi ij Is Euclidean distance, and represents the distance between the ith curve and the jth curve, x i1 ，x j1 ，……，x im ，x jm The corresponding principal component analysis in the scoring matrix. n clusters in common
A distance.
(3) The method comprises the following steps of processing data of all power consumers in a park, calculating the energy utilization characteristics of all power consumers in the park, constructing a multi-energy load characteristic identification model of the comprehensive energy utilization of the park, analyzing the load characteristics of all power consumers in the park, and calculating the following models:
the maximum (small) load per month for the whole park, and the maximum (small) load per month for the park.
In the formula (I), the compound is shown in the specification,
-maximum load per month in the park>
-minimum load per month in the park>
-the overall daily load in the campus.
Average load in the whole month, average load in the month in the park.
In the formula (I), the compound is shown in the specification,
-average load per month in the park>
-overall daily load on the campus.
The overall monthly load rate is the ratio of the average monthly load of the park to the maximum daily load of the park. The index is an important index for researching the distribution of the electric quantity of the park in the month, and is mainly related to the constitution of the electric quantity of the park, seasonal changes and holidays.
In the formula (I), the compound is shown in the specification,
-the difference between the maximum monthly and maximum trough over the park area->
-average load per month in the park>
And-the maximum daily load in the month of the whole park statistics.
The difference between the maximum load per month and the minimum load per day of the park is calculated to be the maximum.
In the formula (I), the compound is shown in the specification,
-the difference between the maximum monthly and maximum trough over the park area->
-the daily maximum load of the whole park in the month,
and-the minimum daily load in the month of the overall statistics of the park.
The overall quarterly load rate is the ratio of the average of the sum of the maximum loads of each monthly maximum load day of a 12-month campus in a year to the annual maximum load of the campus, i.e. the load rate of the whole quarterly
In the formula (I), the compound is shown in the specification,
-the overall seasonal duty ratio in the park, -a>
-the maximum load of the whole campus on the maximum load day of t months,
-annual maximum load of the whole park.
The method reflects seasonal changes of the overall electric load of the park, including influences caused by factors such as seasonal configuration of electric equipment, annual overhaul of the equipment, annual increase of the load and the like.
The annual load rate is the ratio of the average annual load to the maximum annual load. The annual load rate is related to the change of the electricity utilization structure of three types of industries. Generally, the specific gravity of the electricity used for the second industry increases, and the specific gravity of the electricity used for the third industry and the electricity used for the residents increases, so that the electricity used for the third industry and the electricity used for the residents decreases.
In the formula, delta-the overall annual load rate of the park,
-average annual load on the whole park>
-annual maximum load of the whole park.
The number of annual maximum load utilization hours is an index relating to the proportion of electricity consumed by each industry. The following calculation formula is adopted:
in the formula, the maximum load utilization hours in T years delta-the overall annual load rate of the park.
Generally, in areas where heavy industrial electricity accounts for a large proportion in the power system, the annual maximum load utilization hours is high; and the annual maximum load utilization hours of areas with a large proportion of third industrial power and resident domestic power are lower.
(4) And obtaining the data, performing cluster analysis on all power users, and then performing cluster validity check.
In the formula, phi represents the distance between the clustering curve and the sample curve, and the numerical glue hour represents that the curve has better similarity with the sample curve. P i Is the cluster center of the i-th class, X i Set for all load curves in class i.
(5) And determining the comprehensive energy utilization condition of the park according to the clustering condition, and judging whether the park can complete self-adaptation.
Aiming at the load characteristics obtained by the clustering analysis, the invention further researches the demand response potential of the park comprehensive energy system. The comprehensive energy system demand response potential analysis means that under different time scales, peak-valley complementary degree indexes between loads are defined to calculate theoretical maximum transferable load, and the actual maximum transferable load is calculated by combining independent load conversion constraints obtained by analyzing equipment, transmission capacity and the like and conversion constraints between the loads, so that the power consumption law characteristics of various power users in each time period are explored, and a reference result is provided for research of corresponding measures of demand response. The comprehensive demand response potential analysis model of different types of power users established by the invention is as follows:
F e/ h /c and the comprehensive energy utilization demand response is obtained after the characteristic analysis of the park power consumer. s e/ h /c The load can be transferred for each power generation enterprise in the park. Gamma ray e/ h /c The transmission efficiency in the integrated energy is obtained.
And obtaining a self-adaptive multivariate energy load characteristic identification method and a response mechanism in the park.
The invention has the technical effects and advantages that: the cost can be effectively reduced, the carbon emission can also be reduced, and more benefits are brought to social life.
Drawings
Fig. 1 is a flowchart of an adaptive multivariate energy load characteristic identification method provided by the invention.
Fig. 2 is a schematic diagram of a specific example.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method comprises the following specific steps:
(1) The real-time data of each power consumer in the park are collected and uploaded to the Internet, the service life and the load capacity are set, and cluster analysis is carried out on the power consumers in the park.
(2) The method firstly assumes that original data are N class clusters, and then calculates the Euclidean distance between each class cluster according to the following formula:
in the formula, phi ij Is Euclidean distance, and represents the distance between the ith curve and the jth curve, x i1 ，x j1 ，……，x im ，x jm The corresponding principal component analysis in the scoring matrix. n clusters in common
A distance.
(3) The method comprises the following steps of processing data of all power consumers in a park, calculating the energy utilization characteristics of all power consumers in the park, constructing a multi-energy load characteristic identification model of the comprehensive energy utilization of the park, analyzing the load characteristics of all power consumers in the park, and calculating the following models:
the maximum (small) load per month for the whole park, and the maximum (small) load per month for the park.
In the formula (I), the compound is shown in the specification,
-maximum load per month in the park>
-minimum load per month in the park>
-the overall daily load in the campus.
Average load in the whole month, average load in the month in the park.
In the formula (I), the compound is shown in the specification,
-average load per month in the park>
-overall daily load on the campus.
The overall monthly load rate is the ratio of the average monthly load of the park to the maximum daily load of the park. The index is an important index for researching the distribution of the electric quantity of the park in the month, and is mainly related to the constitution of the electric quantity used in the park, seasonal change and holidays.
In the formula (I), the compound is shown in the specification,
-the maximum difference in the moon and the trough over the park>
-average load per month in the park>
And the maximum daily load in the month is counted in the whole park.
The difference between the maximum monthly load and the minimum daily load of the park is calculated to be the maximum.
In the formula (I), the compound is shown in the specification,
-the difference between the maximum monthly and maximum trough over the park area->
-the maximum load on the day of the month is counted over the whole park, and>
and-the minimum daily load in the month of the overall statistics of the park.
The overall quarterly load rate is the ratio of the average of the sum of the maximum loads of each monthly maximum load day of a 12-month campus in a year to the annual maximum load of the campus, i.e. the load rate of the whole quarterly
In the formula (I), the compound is shown in the specification,
-the overall seasonal duty ratio in the park, -a>
-the maximum load of the whole campus on the maximum load day of t months,
-annual maximum load of the whole park.
The method reflects seasonal changes of the integral electric load of the park, including influences caused by factors such as seasonal configuration of electric equipment, annual overhaul of the equipment, annual increase of the load and the like.
The annual load rate is the ratio of the annual average load to the annual maximum load. The annual load rate is related to the change of the electricity utilization structure of three types of industries. Generally, the specific gravity of the electricity used for the second industry increases, and the specific gravity of the electricity used for the third industry and the electricity used for the residents increases, so that the electricity used for the third industry and the electricity used for the residents decreases.
In the formula, delta-the overall annual load rate of the park,
-average annual load on the whole park>
-annual maximum load of the whole park.
The number of annual maximum load utilization hours is an index relating to the proportion of electricity consumed by each industry. The following calculation formula is adopted:
in the formula, the maximum load utilization hours in T years, delta-the overall annual load rate of the park.
Generally, in areas where heavy industrial power accounts for a large proportion in the power system, the annual maximum load utilization hours is high; and the annual maximum load utilization hours of the area where the third industrial power consumption and the resident domestic power consumption account for a large proportion are lower.
(4) And obtaining the data, performing cluster analysis on all power users, and then performing cluster validity check.
In the formula, phi represents the distance between the clustering curve and the sample curve, and the numerical glue hour represents that the curve has better similarity with the sample curve. P i Is the cluster center of the i-th class, X i Set for all load curves in class i.
(5) And determining the comprehensive energy utilization condition of the park according to the clustering condition, and judging whether the park can complete self-adaptation.
Aiming at the load characteristics obtained by the clustering analysis, the invention further researches the demand response potential of the park comprehensive energy system. The comprehensive energy system demand response potential analysis means that under different time scales, peak-valley complementary degree indexes between loads are defined to calculate theoretical maximum transferable load, and the actual maximum transferable load is calculated by combining independent load conversion constraints obtained by analyzing equipment, transmission capacity and the like and conversion constraints between the loads, so that the power consumption law characteristics of various power users in each time period are explored, and a reference result is provided for research of corresponding measures of demand response. The comprehensive demand response potential analysis model of different types of power users established by the invention is as follows:
F e/ h /c and the comprehensive energy utilization demand response is obtained after the characteristic analysis of the park power consumer. s e/ h /c The load can be transferred for each power generation enterprise in the park. Gamma ray e/ h /c The transmission efficiency in the integrated energy is obtained.
And obtaining a self-adaptive multi-energy load characteristic identification method and a response mechanism in the park.
The concrete case is as follows:
the simulation object is a certain integrated energy system in the west and the river.
Campus user load data was recorded at 24 hours per day from 2020, 1 month to 2020, 12 months. The data acquisition and uploading interval is 15min, and the single-day load data volume of each user is 96 points. Considering that the difference of the electric loads of various power consumers in four seasons of the year is large, the four seasons energy consumption characteristics of the region can be obtained by reasonably clustering the load data of the four seasons respectively, as shown in fig. 2.
The power utilization characteristics of all power consumers in the park are really different greatly, so that it is necessary to construct a self-adaptive multivariate energy load characteristic identification method.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (4)
1. A self-adaptive multivariate energy load characteristic identification method is characterized by specifically comprising the following steps of:
(1) Collecting real-time data of each power user in a park, uploading the real-time data to the Internet, and setting the service time and the load capacity;
(2) Performing cluster analysis on all the collected data of the power users to obtain power utilization rules and load curves of the power users;
(3) And carrying out demand response and sensitivity analysis on the power consumer.
2. The method of claim 1, wherein the adaptive multivariate energy load characteristic identification method comprises the steps of: in the step (1), the concrete steps are as follows:
A. collecting real-time data of each power consumer in a park, uploading the real-time data to the Internet, setting the service time and the load capacity, and carrying out cluster analysis on the power consumers in the park;
B. the method firstly assumes that original data are N class clusters, and then calculates the Euclidean distance between each class cluster according to the following formula:
in the formula, phi ij Is Euclidean distance, and represents the distance between the ith curve and the jth curve, x i1 ，x j1 ，……，x im ，x jm Analyzing corresponding principal components in the scoring matrix; n clusters in common
A distance.
3. The method according to claim 1, wherein the adaptive multivariate energy load characteristic identification method comprises the following steps: in the step (2), the concrete steps are as follows:
A. the method comprises the following steps of processing data of all power consumers in a park, calculating the energy utilization characteristics of all power consumers in the park, constructing a multi-energy load characteristic identification model of the comprehensive energy utilization of the park, analyzing the load characteristics of all power consumers in the park, and calculating the following models:
the load of the whole month maximum (small) load, the load of the garden on the monthly maximum (small) load day;
in the formula (I), the compound is shown in the specification,
-maximum load per month in the park>
-minimum load per month in the park>
-overall daily load of the park;
average load per month, average load per month in the park;
in the formula (I), the compound is shown in the specification,
-average load per month in the park>
-overall daily load of the park;
the integral monthly load rate is the ratio of the average monthly load of the garden to the maximum daily load of the garden within a month; the index is an important index for researching the distribution of the electric quantity of the park in the month, and is mainly related to the constitution of the electric quantity of the park, seasonal change and holidays;
in the formula (I), the compound is shown in the specification,
-the difference between the maximum monthly and maximum trough over the park area->
-average load per month in the park>
-the maximum daily load within the month of the overall statistics of the park;
the difference between the maximum monthly load and the minimum daily load of the park is calculated to be the maximum;
in the formula (I), the compound is shown in the specification,
-the difference between the maximum monthly and maximum trough over the park area->
-the maximum load on the day of the month is counted over the whole park, and>
-the minimum daily load within the month of the overall statistics of the park;
the overall quarterly load rate is the ratio of the average of the sum of the maximum loads of each monthly maximum load day of a 12-month campus in a year to the annual maximum load of the campus, i.e. the load rate of the whole quarterly
In the formula (I), the compound is shown in the specification,
-the overall seasonal duty ratio in the park, -a>
-the maximum load of the whole campus on the maximum load day of t months,
-annual maximum load for the whole park;
the method reflects seasonal changes of the integral electric load of the park, including influences caused by factors such as seasonal configuration of electric equipment, annual overhaul of the equipment, annual increase of the load and the like;
the annual load rate is the ratio of the annual average load to the annual maximum load; the annual load rate is related to the change of the electricity utilization structure of three types of industries; the proportion of the electricity used by the second industry is increased under the normal condition, and the proportion of the electricity used by the third industry and the electricity used by residents is decreased under the normal condition;
in the formula, delta-the overall annual load rate of the park,
average annual load in the whole parkIs charged and/or judged>
-annual maximum load for the whole park;
the annual maximum load utilization hours, which is related to the proportion of each industrial electricity; the following calculation formula is adopted:
in the formula, the maximum load utilization hours in T year is delta-the integral annual load rate of the park;
generally, in areas where heavy industrial electricity accounts for a large proportion in the power system, the annual maximum load utilization hours is high; the annual maximum load utilization hours of areas with a large proportion of third industrial power consumption and resident domestic power consumption are lower;
B. obtaining the data, performing cluster analysis on all power users, and then performing cluster validity check;
in the formula, phi represents the distance between the clustering curve and the sample curve, and when the numerical value is glued, the curve has better similarity with the sample curve; p i Is the cluster center of the i-th class, X i Set for all load curves in class i.
4. The method according to claim 1, wherein the adaptive multivariate energy load characteristic identification method comprises the following steps: in the step (3), the concrete steps are as follows:
according to the clustering condition, determining the comprehensive energy utilization condition of the park, and judging whether the park can complete self-adaptation or not;
aiming at the load characteristics obtained by the clustering analysis, the invention further researches the demand response potential of the park comprehensive energy system; the comprehensive energy system demand response potential analysis means that under different time scales, peak-valley complementary degree indexes between loads are defined to calculate theoretical maximum transferable load, and the actual maximum transferable load is calculated by combining independent load conversion constraints obtained by analyzing equipment, transmission capacity and the like and conversion constraints between the loads, so that the power consumption law characteristics of various power users in each time period are explored, and a reference result is provided for research of corresponding measures of demand response; the comprehensive demand response potential analysis model of different types of power users established by the invention is as follows:
F e/ h /c responding to the comprehensive energy utilization demand after the characteristic analysis of the park power consumer; s e/ h /c The load can be transferred for each power generation enterprise in the park; gamma ray e/ h /c The transmission efficiency in the comprehensive energy is obtained;
and obtaining a self-adaptive multivariate energy load characteristic identification method and a response mechanism in the park.
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