CN107545374A - A kind of daily load curve choosing method and system - Google Patents
A kind of daily load curve choosing method and system Download PDFInfo
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- CN107545374A CN107545374A CN201710832044.4A CN201710832044A CN107545374A CN 107545374 A CN107545374 A CN 107545374A CN 201710832044 A CN201710832044 A CN 201710832044A CN 107545374 A CN107545374 A CN 107545374A
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Abstract
The invention provides a kind of daily load curve choosing method and system, includes the load curve data that extraction power customer sets period Nei Ge；Calculate the coefficient correlation between each daily load curve；Typical day load curve is chosen according to the coefficient correlation.Compared with immediate prior art, more accurately, power customer typical day load curve is more rationally determined, therefore can more effectively instruct operation of power networks, implement Demand Side Response and power customer administration of energy conservation.
Description
Technical field
The invention belongs to power customer load management and control technology field, in particular to a kind of daily load curve is chosen
Method and system.
Background technology
Power customer daily load curve describes the rule that customer electricity load changes over time in one day, and it reflects client
The essential characteristic of electric energy consumption behavior, it is the important tool that Utilities Electric Co. carries out load management to client.Due to typical daily load
Curve can characterize power customer electricity consumption behavior in a certain amount of time and feature, therefore can utilize typical day load curve, right
Client is classified and fine-grained management.If the typical day load curve distribution between client is similar, illustrate this kind of client
With similar consumption habit, Utilities Electric Co. can be directed to certain class client and formulate more targetedly migration efficiency.Handle in addition
Client's typical day load curve is added to where the client on the typical day load curve of power network, and it is more can to show client
Electricity consumption behavior details, it is calculated in the electric energy expense of power network peak period and the potentiality avoided the peak hour.Therefore it is bent using typical daily load
Line is analyzed customer action, can be effectively facilitated dsm, rationally be suppressed load peak, improves the utilization of power grid asset
Rate.As described above, the typical day load curve in the power customer a certain period is determined, to Operation of Electric Systems and client's load pipe
Reason is significant.
Due to the limitation of objective condition, there is certain difficulty in the collection for power customer load data and processing before,
Constrain management and service level of the Utilities Electric Co. to power customer load.But with the further development of intelligent grid, intelligence
Ammeter and power information collection main station system have obtained large-scale promotion and application.Compared with traditional electric energy meter, intelligent electric meter
The load datas such as the power, voltage and electric current of client can be obtained in real time, and measurement data can be transmitted by communication network
Power information to a distant place gathers main station system.These load datas are the analysis various power load curves of power customer, especially
It is that daily load curve provides the foundation condition.Existing typical day load curve choosing method generally use clustering methodology, choosing
The daily load curve gone out can not reflect electricity consumption behavior and the feature of client well.
The content of the invention
To overcome above-mentioned the deficiencies in the prior art, the present invention proposes a kind of daily load curve choosing method and system.
Solution is used by realizing above-mentioned purpose：
A kind of daily load curve choosing method, it is theed improvement is that：
Extract the load curve data of power customer setting period Nei Ge；
Calculate the coefficient correlation between each daily load curve；
Typical day load curve is chosen according to the coefficient correlation.
First optimal technical scheme provided by the invention, it is theed improvement is that, between each daily load curve of calculating
Coefficient correlation, using following formula：
Wherein, RxyRepresent the coefficient correlation between xth daily load curve and y daily load curves, xiRepresent xth day i-th
Individual load value, n be one day in load value total number, yiI-th of load value of y days is represented, i spans are 1~n,Represent
Xth day all loads average value,Represent the average value of y days all loads.
Second optimal technical scheme provided by the invention, it is theed improvement is that, between each daily load curve of calculating
Coefficient correlation and between choosing typical day load curve according to the coefficient correlation, includes according to the coefficient correlation, generates
Correlation matrix；The element of the correlation matrix R xth rows y row is Rxy。
3rd optimal technical scheme provided by the invention, it is theed improvement is that, described to be chosen according to the coefficient correlation
Typical day load curve, including：
According to the coefficient correlation, the degree of correlation of each daily load curve is calculated；
It is typical day load curve to choose the maximum daily load curve of the degree of correlation.
4th optimal technical scheme provided by the invention, it is theed improvement is that, described according to the coefficient correlation, is calculated
The degree of correlation per daily load curve, using following formula：
Ck=(Rk1+Rk2+…Rkk+…+Rkm)/m (2)
Wherein, CkRepresent the degree of correlation of kth daily load curve, Rk1Represent the phase relation between kth day and the 1st daily load curve
Number, RkmThe coefficient correlation between kth day and m daily load curves is represented, m is total number of days of setting time section, and k span is
1~m.
5th optimal technical scheme provided by the invention, it is theed improvement is that, total number of days m of the setting time section takes
It is worth for 10 days, k span is 1~10.
6th optimal technical scheme provided by the invention, it is theed improvement is that, it is described extraction power customer setting when
Section Nei Ge load curve data, including：
According to default time interval, the Road test number of each set time day point in the setting period of power customer is extracted
According to forming the load curve data of each day.
7th optimal technical scheme provided by the invention, it is theed improvement is that, it is characterised in that the default time
At intervals of total number n=96=24 hour/15 minute of load value in 15 minutes, one day, i span is 1~96.
A kind of daily load curve selecting system, it is theed improvement is that, including：Data acquisition module, coefficient correlation calculate
Module and typical day load curve choose module；
The data acquisition module is used for the load curve data for extracting power customer setting period Nei Ge；
The coefficient correlation computing module is used to calculate the coefficient correlation between each daily load curve；
The typical day load curve chooses module and is used to choose typical day load curve according to the coefficient correlation.
8th optimal technical scheme provided by the invention, it is theed improvement is that, the typical day load curve chooses mould
Block includes relatedness computation subelement and typical day load curve chooses subelement；
The relatedness computation subelement is used for the degree of correlation for according to the coefficient correlation, calculating each daily load curve；
The typical day load curve chooses subelement and is used to choose the maximum daily load curve of the degree of correlation to bear typical day
Lotus curve.
Compared with immediate prior art, the device have the advantages that as follows：
The present invention chooses the typical day load curve in the power customer a certain period by correlation analysis.Compare
In other method, correlation coefficient process can more accurate, more rationally determination power customer typical day load curve, therefore can be more effectively
Instruct operation of power networks, implement Demand Side Response and power customer administration of energy conservation.
Brief description of the drawings
Fig. 1 is a kind of daily load curve choosing method schematic flow sheet provided by the invention；
Fig. 2 is certain client in March, 2015 (1-10) daily load curve；
Fig. 3 is typical day load curve schematic diagram.
Embodiment
The present invention is used in daily load curve whole out of power customer a certain period, and selecting most can table in the period
Levy the load curve of the typical day of the customer electricity behavior.The present invention use correlation analysis, calculating daily with it some other time
Coefficient correlation, form the correlation matrix of all days in the period, and further calculate daily to the correlation of other each days
For the average value of coefficient as the degree of correlation, selection degree of correlation maximum day is typical day.Below in conjunction with the accompanying drawings to the specific reality of the present invention
The mode of applying is described in further detail：
The flow of daily load curve choosing method provided by the invention a kind of as shown in figure 1, including：
Extract the load curve data of power customer setting period Nei Ge；
Calculate the coefficient correlation between each daily load curve；
Typical day load curve is chosen according to the coefficient correlation.
Specifically include：
(1) setting time section Nei Ge load curve data are extracted
(00 in intelligent electric meter one day:00-23:59) load measurement of a client is recorded at interval of default time interval
Data, collect same day whole measurement data with regard to the load curve on client's same day can be obtained.The time interval can be set to 15 points
Clock.Due to one measurement data of generation in every 15 minutes, so having 96 time of measuring points per daily load curve, when representing each with i
Between point, i span is i={ 1,2 ..., 96 }, when i=1 then time point be 00:00 point, then time point is 00 to i=2:15
Point, the rest may be inferred, and then time point is 23 to i=96:45 points.It is assumed that participate in the sum of each day of this typical day load curve selection
For m, it is necessary to load curve data all in client's m days, gather in main station system and extract from power information, be used for
Following computing.
(2) each day correlation matrix is calculated
In order to select the typical day load curve in 1-m days, it is necessary to calculate the phase of each daily load curve in this period
Relation matrix number R.In order to express easily, with correlation matrix R element RxyRepresent xth daily load curve and y daily loads
Coefficient correlation between curve, RxyCalculation formula it is as follows：
Wherein, xiRepresent xth day i-th of load value, n be one day in load value total number, yiRepresent that i-th of y days are negative
Charge values, i spans are 1~n,The average value of xth day all loads is represented,Represent the average value of y days all loads.
When time interval is taken as 15 minutes, n=96 in above formula,
It is x, y={ 1,2 ..., m } to make x and y values, then R is all solved from 1 to mxy, one can be obtained as shown in table 1
M × m correlation matrix R, the matrix represent 1-m each days between load curve all coefficient correlations.
Table 1, each day correlation matrix
| Date | 1st day | 2nd day | … | Kth day | … | M days |
| 1st day | R11 | R12 | … | R1k | … | R1m |
| 2nd day | R21 | R22 | … | R2k | … | R2m |
| … | … | … | … | … | … | … |
| Kth day | Rk1 | Rk2 | … | Rkk | … | Rkm |
| … | … | … | … | … | … | |
| M days | Rm1 | Rm2 | … | Rmk | … | Rmm |
(3) according to coefficient correlation, calculate per daily load curve to the degree of correlation of other each daily load curves
The daily load curve is referred to as to other each daily load curves to the average value of the coefficient correlation of other each days daily
The degree of correlation, namely the degree of correlation of this day.From the correlation matrix shown in table 1, the method for solving of the degree of correlation on the 1st is the 1st
Day and the average value of the coefficient correlation sum of other each days, the method for solving of the degree of correlation on the 2nd is the phase with other each days on the 2nd
The average value of relation number sum, the rest may be inferred, kth day the method for solving of the degree of correlation be：
Ck=(Rk1+Rk2+…Rkk+…+Rkm)/m (2)
Wherein, CkRepresent the degree of correlation of kth day, Rk1Represent the coefficient correlation between kth day and the 1st daily load curve, RkmTable
Show the coefficient correlation between kth day and m daily load curves, m is total number of days of setting time section, and k span is 1~m.
(4) it is typical day load curve to choose the maximum daily load curve of the degree of correlation
The degree of correlation of whole 1-m days is solved, draws the degree of correlation list of 1-m days as shown in table 2.The degree of correlation maximum day is just
It it is typical day, load curve corresponding to typical day is exactly the power customer typical day load curve.
Table 2, each day degree of correlation list
| Date | The degree of correlation |
| 1st day | C1 |
| 2nd day | C2 |
| … | … |
| Kth day | Ck |
| … | … |
| M days | Cm |
With reference to the measured data in a certain large power customers on March 1st, 2015 to March 10, born to choosing typical day
The embodiment and process of lotus curve illustrate, and the main load data for including extraction (1-10) day, calculate each day to other
Day coefficient correlation, solve and each day choose typical day load curve to its degree of correlation some other time, by maximum relation degree.Specifically
Process is as follows：
(a) load data of 1-10 days is extracted
Gathered from power information and 1 is extracted in main station system to 10 load measurement data, form 10 daily load curves.
As shown in Fig. 2 the load curve distribution of shapes of each day is substantially similar.The load measurement data of 1-10 days are to calculate each day phase below
The basis of relation matrix number.
(b) each day and its coefficient correlation some other time are calculated, and forms correlation matrix.
By formula (1), coefficient correlation of the 1st daily load curve to the 2nd daily load curve is first solved, then solve the 1st successively
Day was to the 3rd day, the coefficient correlation of the 4th to last 10th day.
The like, the 2nd day, the to the last coefficient correlation to other each days on the 10th on the 3rd are solved, forms one such as
The correlation matrix of 10*10 shown in table 3.Find out from correlation matrix, the coefficient correlation to other each days on the 8th is whole
Body is minimum, and wherein minimum is to be and coefficient correlation 0.808 on the 1st with coefficient correlation 0.679 on the 5th, peak；And the 1st day
To the coefficient correlation entirety highest of other each days, peak be with coefficient correlation 0.935 on the 3rd, and minimum is exactly and the 1st
Coefficient correlation 0.808.
Table 3, certain client in March, 2015 1-10 daily loads correlation matrix
| Date | 3-1 | 3-2 | 3-3 | 3-4 | 3-5 | 3-6 | 3-7 | 3-8 | 3-9 | 3-10 |
| 3-1 | 1.000 | 0.916 | 0.935 | 0.929 | 0.897 | 0.912 | 0.89 | 0.81 | 0.86 | 0.89 |
| 3-2 | 0.916 | 1.000 | 0.887 | 0.882 | 0.884 | 0.848 | 0.866 | 0.775 | 0.842 | 0.869 |
| 3-3 | 0.935 | 0.887 | 1.000 | 0.897 | 0.841 | 0.900 | 0.885 | 0.757 | 0.893 | 0.915 |
| 3-4 | 0.929 | 0.882 | 0.897 | 1.000 | 0.894 | 0.849 | 0.883 | 0.755 | 0.793 | 0.867 |
| 3-5 | 0.897 | 0.884 | 0.841 | 0.894 | 1.000 | 0.813 | 0.861 | 0.689 | 0.732 | 0.814 |
| 3-6 | 0.912 | 0.848 | 0.900 | 0.849 | 0.813 | 1.000 | 0.811 | 0.736 | 0.736 | 0.885 |
| 3-7 | 0.887 | 0.866 | 0.885 | 0.883 | 0.861 | 0.811 | 1.000 | 0.679 | 0.842 | 0.837 |
| 3-8 | 0.808 | 0.775 | 0.757 | 0.755 | 0.689 | 0.736 | 0.679 | 1.000 | 0.699 | 0.757 |
| 3-9 | 0.856 | 0.842 | 0.893 | 0.793 | 0.732 | 0.736 | 0.842 | 0.699 | 1.000 | 0.933 |
| 3-10 | 0.892 | 0.869 | 0.915 | 0.867 | 0.814 | 0.885 | 0.837 | 0.757 | 0.933 | 1.000 |
(c) each day degree of correlation of 1-10 days is calculated
By formula (2), according to the correlation matrix shown in table 3, from the 1st to the 10th, each day is solved successively
The degree of correlation, and sequence is provided by the size of the degree of correlation, as a result as shown in table 4.As seen from Table 4, the degree of correlation maximum day is
On March 1st, 2015, its value are 0.9032；And the day of degree of correlation minimum is on March 8th, 2015, its value is 0.7656.
Table 4, certain client in March, 2015 1-10 daily loads degree of correlation list
(d) typical day load curve is chosen
Typical day load curve can represent the electricity consumption behavioral trait of client in a certain period, on March 1st, 2015 in example
The degree of correlation of load curve and other each days are maximum, therefore when the load curve on March 1st, 2015 is on March 1 to this section on the 10th
Interior typical day load curve.As shown in figure 3, the load curve on March 1st, 2015 is shown with overstriking, the curve just position
In the centre position of its some other time curve, the typical electricity consumption behavior of the power customer in this period is preferably represented.
Present invention also offers a kind of daily load curve selecting system, including：Data acquisition module, coefficient correlation calculate mould
Block and typical day load curve choose module；
The data acquisition module is used for the load curve data for extracting power customer setting period Nei Ge；
The coefficient correlation computing module is used to calculate the coefficient correlation between each daily load curve；
The typical day load curve chooses module and is used to choose typical day load curve according to the coefficient correlation.
Further, the typical day load curve, which chooses module, includes relatedness computation subelement and typical day load curve
Choose subelement；
The relatedness computation subelement is used for the degree of correlation for according to the coefficient correlation, calculating each daily load curve；
The typical day load curve chooses subelement and is used to choose the maximum daily load curve of the degree of correlation to bear typical day
Lotus curve.
Finally it should be noted that:Above example is merely to illustrate the technical scheme of the application rather than to its protection domain
Limitation, although the application is described in detail with reference to above-described embodiment, those of ordinary skill in the art should
Understand:Those skilled in the art read the embodiment of application can be still carried out after the application a variety of changes, modification or
Person's equivalent substitution, but these changes, modification or equivalent substitution, are applying within pending claims.
Claims (10)
- A kind of 1. daily load curve choosing method, it is characterised in that：Extract the load curve data of power customer setting period Nei Ge；Calculate the coefficient correlation between each daily load curve；Typical day load curve is chosen according to the coefficient correlation.
- 2. the method as described in claim 1, it is characterised in that the coefficient correlation calculated between each daily load curve, use Following formula：<mrow> <msub> <mi>R</mi> <mrow> <mi>x</mi> <mi>y</mi> </mrow> </msub> <mo>=</mo> <mfrac> <mrow> <msubsup> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </msubsup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mi>x</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mi>y</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> </mrow> <msqrt> <mrow> <msubsup> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </msubsup> <msup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mi>x</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>*</mo> <msubsup> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </msubsup> <msup> <mrow> <mo>(</mo> <msub> <mi>y</mi> <mi>i</mi> </msub> <mo>-</mo> <mover> <mi>y</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>Wherein, RxyRepresent the coefficient correlation between xth daily load curve and y daily load curves, xiRepresent that i-th of xth day is negative Charge values, n be one day in load value total number, yiI-th of load value of y days is represented, i spans are 1~n,Represent xth The average value of day all loads,Represent the average value of y days all loads.
- 3. method as claimed in claim 2, it is characterised in that the coefficient correlation and basis calculated between each daily load curve Between the coefficient correlation chooses typical day load curve, in addition to according to the coefficient correlation, generate correlation matrix；Institute The element for stating correlation matrix R xth rows y row is Rxy。
- 4. the method as described in claim 1, it is characterised in that described that typical daily load song is chosen according to the coefficient correlation Line, including：According to the coefficient correlation, the degree of correlation of each daily load curve is calculated；It is typical day load curve to choose the maximum daily load curve of the degree of correlation.
- 5. the method as described in claim any one of 1-4, it is characterised in that it is described according to the coefficient correlation, calculate daily The degree of correlation of load curve, using following formula：Ck=(Rk1+Rk2+…Rkk+…+Rkm)/m (2)Wherein, CkRepresent the degree of correlation of kth daily load curve, Rk1The coefficient correlation between kth day and the 1st daily load curve is represented, RkmRepresent the coefficient correlation between kth day and m daily load curves, m is total number of days of setting time section, k span for 1~ m。
- 6. method as claimed in claim 5, it is characterised in that total number of days m values of the setting time section are 10 days, k's Span is 1~10.
- 7. the method as described in claim 1, it is characterised in that the load of setting period Nei Ge of the extraction power customer Curve data, including：According to default time interval, the Road test data of each set time day point in the setting period of power customer are extracted, Form the load curve data of each day.
- 8. the method as described in right wants 2 or 7, it is characterised in that the default time interval is 15 minutes, internal loading on the one Total number n=96=24 hour/15 minute of value, i span is 1~96.
- A kind of 9. daily load curve selecting system, it is characterised in that including：Data acquisition module, coefficient correlation computing module and Typical day load curve chooses module；The data acquisition module is used for the load curve data for extracting power customer setting period Nei Ge；The coefficient correlation computing module is used to calculate the coefficient correlation between each daily load curve；The typical day load curve chooses module and is used to choose typical day load curve according to the coefficient correlation.
- 10. system as claimed in claim 9, it is characterised in that the typical day load curve, which chooses module, includes the degree of correlation Computation subunit and typical day load curve choose subelement；The relatedness computation subelement is used for the degree of correlation for according to the coefficient correlation, calculating each daily load curve；The typical day load curve chooses subelement and is used to choose the maximum daily load curve of the degree of correlation for typical daily load song Line.
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Address after: 100192 Beijing city Haidian District Qinghe small Camp Road No. 15 Applicant after: CHINA ELECTRIC POWER RESEARCH INSTITUTE Co.,Ltd. Applicant after: STATE GRID CORPORATION OF CHINA Address before: 100192 Beijing city Haidian District Qinghe small Camp Road No. 15 Applicant before: China Electric Power Research Institute Applicant before: State Grid Corporation of China |
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Application publication date: 20180105 |
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