CN110619472A - Typical daily load curve compilation method for power consumer - Google Patents

Typical daily load curve compilation method for power consumer Download PDF

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Publication number
CN110619472A
CN110619472A CN201910886680.4A CN201910886680A CN110619472A CN 110619472 A CN110619472 A CN 110619472A CN 201910886680 A CN201910886680 A CN 201910886680A CN 110619472 A CN110619472 A CN 110619472A
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load curve
daily load
time period
dimension
curve
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罗宁
徐常
陈露东
贺墨琳
张彦
卢嗣斌
刘金森
高华
张英杰
熊晓晟
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Guizhou Power Grid Co Ltd
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Guizhou Power Grid Co Ltd
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Abstract

The invention discloses a typical daily load curve compilation method for power consumers, which comprises the following steps: collecting a power load historical curve of a user in a certain statistical time period; carrying out normalization processing on the daily load curve in the statistical time period; calculating the number of dimension counting boxes of all curves in the statistical time period; establishing a dimension-counting box number index normal distribution model, and solving a dimension-counting box number index range D1 of a 95% confidence interval; calculating a corresponding variation coefficient vi for the daily load curve in the range D1; establishing a normal distribution model of the coefficient of variation index, and solving a coefficient of variation index range D2 of a 95% confidence interval; calculating the point-by-point average value of the daily load curve in the range D2 to form a typical daily load curve in the statistical time period; the problem that the change difference of the intermediate process is neglected in the typical daily load curve drawing in the prior art is solved; if the shape of the curve is judged by human senses, problems such as first-come or second-come will easily occur.

Description

Typical daily load curve compilation method for power consumer
Technical Field
The invention belongs to daily load curve compiling technology, and particularly relates to a typical daily load curve compiling method for a power consumer.
Background
The typical daily load curve is an important basis for reflecting the electricity utilization rule of a user, compiling a power generation plan, enhancing power supply management and the like. The user electricity load is influenced by factors such as capacity scale, equipment characteristics, startup arrangement and operation conditions, so that the daily load curve has difference and fluctuation, but has similarity and regularity. Therefore, compiling a scientific and reasonable typical daily load curve is an important link in planning and operating the power system.
The conventional typical daily load curve is a typical daily load curve which is obtained by selecting 3-5 representative daily load curves with the same maximum load occurrence time and the consistent minimum load occurrence time as far as possible in a specific time period (such as a quarter or a year). The method has certain limitation and subjectivity, and selects the load curve of the whole day only through two key points of maximum load and minimum load, and changes and differences in the middle process are ignored; if the shape of the curve is judged by human senses, problems such as first-come or second-come will easily occur.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the typical daily load curve compiling method for the power consumer is provided to solve the problems that the typical daily load curve drawing in the prior art has certain limitation and subjectivity, the load curve of the whole day is selected only through two key points, namely the maximum load and the minimum load, and the change difference of the middle process is ignored; if the shape of the curve is judged by human senses, problems such as first-come or second-come will easily occur.
The technical scheme of the invention is as follows:
a typical daily load curve compiling method for a power consumer comprises the following steps:
step 1, collecting a historical power load curve of a user in a certain statistical time period;
step 2, carrying out normalization processing on the daily load curve within the statistical time period;
step 3, calculating the number of dimension counting boxes of all curves in a statistical time period, wherein the number of the dimension counting boxes on the ith day is DimB Fi;
step 4, establishing a dimension-counting box number index normal distribution model NF (mu F, sigma F2), and solving a dimension-counting box number index range D1 of a 95% confidence interval;
step 5, calculating a corresponding variation coefficient vi of the daily load curve in the range D1;
step 6, establishing a normal distribution model Nv (mu v, sigma v2) of the coefficient of variation index, and solving a coefficient of variation index range D2 of a 95% confidence interval;
and 7, solving the point-by-point average value of the daily load curve in the range D2 to form a typical daily load curve in the statistical time period.
The number of the dimension counting boxes on the i day is DimB Fi:
in the formula: epsilon is the side length of a square box, Ni(ε) is the number of non-empty boxes of the day i load curve.
The daily load curve variation coefficient viIs the ratio of the standard deviation of the load to the average load.
The invention has the beneficial effects that:
the invention provides a typical daily load curve selection method for a power user, which is characterized in that a representative curve is selected for fitting by calculating and applying indexes such as the number of dimension-counting boxes in a fractal theory, the variation coefficient in load characteristics and the like in combination with probability statistics, so that the typical daily load curve rule of the user is objectively and comprehensively reflected; the method can provide a new idea and method for preparing the typical daily load curve for the work of planning and designing the power system, arranging the power generation dispatching operation mode and the like, and provides reference for the research and application of related engineering.
The problem that the typical daily load curve drawing in the prior art has certain limitation and subjectivity is solved, the load curve of the whole day is selected only through two key points, namely the maximum load and the minimum load, and the change difference of the middle process is ignored; if the shape of the curve is judged by human senses, problems such as first-come or second-come will easily occur.
Description of the drawings:
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The basic implementation flow of the invention comprises the following detailed steps:
step 1, collecting the historical curve of the power load of a certain user in a certain statistical time period (such as a quarter or a year), wherein the sampling interval time is 5 minutes and 288 points are obtained all day.
And 2, normalizing the daily load curve within the statistical time period.
Step 3, calculating the number of dimension counting boxes of all curves in the statistical time period, wherein the number of the dimension counting boxes on the ith day is DimB Fi。
Step 4, establishing a dimension counting box number index normal distribution model NF(μF，σF 2) And solving a dimension-counting box number index range D1 of a 95% confidence interval.
Step 5, calculating the corresponding coefficient of variation v of the daily load curve in the range D1i。
Step 6, establishing a normal distribution model N of coefficient of variation indexesv(μv，σv 2) To findCoefficient of variation index range D2 for the 95% confidence interval.
And 7, solving the point-by-point average value of the daily load curve in the range D2 to form a typical daily load curve in the statistical time period.
The number of the dimension counting boxes on the i day is DimB Fi:
in the formula: epsilon is the side length of a square box, Ni(ε) is the number of non-empty boxes of the day i load curve.
The daily load curve variation coefficient viIs the ratio of the standard deviation of the load to the average load.

Claims (3)

1. A typical daily load curve compiling method for a power consumer comprises the following steps:
step 1, collecting a historical power load curve of a user in a certain statistical time period;
step 2, carrying out normalization processing on the daily load curve within the statistical time period;
step 3, calculating the number of dimension counting boxes of all curves in a statistical time period, wherein the number of the dimension counting boxes on the ith day is DimB Fi;
step 4, establishing a dimension-counting box number index normal distribution model NF (mu F, sigma F2), and solving a dimension-counting box number index range D1 of a 95% confidence interval;
step 5, calculating a corresponding variation coefficient vi of the daily load curve in the range D1;
step 6, establishing a normal distribution model Nv (mu v, sigma v2) of the coefficient of variation index, and solving a coefficient of variation index range D2 of a 95% confidence interval;
and 7, solving the point-by-point average value of the daily load curve in the range D2 to form a typical daily load curve in the statistical time period.
2. The method for compiling the typical daily load curve of the power consumer according to claim 1, wherein the method comprises the following steps: the number of the dimension counting boxes on the i day is DimB Fi:
in the formula: epsilon is the side length of a square box, Ni(ε) is the number of non-empty boxes of the day i load curve.
3. The method for compiling the typical daily load curve of the power consumer according to claim 1, wherein the method comprises the following steps: the daily load curve variation coefficient viIs the ratio of the standard deviation of the load to the average load.
CN201910886680.4A 2019-09-19 2019-09-19 Typical daily load curve compilation method for power consumer Pending CN110619472A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116415801A (en) * 2023-06-12 2023-07-11 山东创宇环保科技有限公司 Commercial energy load intelligent distribution method and system based on big data

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Publication number Priority date Publication date Assignee Title
CN103914621A (en) * 2014-04-03 2014-07-09 国家电网公司 Method for analyzing distribution and transformation daily load typical curves on basis of structure features
CN106786519A (en) * 2016-12-12 2017-05-31 国网上海市电力公司 A kind of method of Optimization Prediction power network typical case daily load
CN107657266A (en) * 2017-08-03 2018-02-02 华北电力大学（保定） A kind of load curve clustering method based on improvement spectrum multiple manifold cluster
CN108629500A (en) * 2018-04-28 2018-10-09 国网河南省电力公司经济技术研究院 One kind changing power customer comprehensive value appraisal procedure under background suitable for new electricity

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116415801A (en) * 2023-06-12 2023-07-11 山东创宇环保科技有限公司 Commercial energy load intelligent distribution method and system based on big data
CN116415801B (en) * 2023-06-12 2023-08-29 山东创宇环保科技有限公司 Commercial energy load intelligent distribution method and system based on big data

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Application publication date: 20191227