U.S. patent application number 15/751471 was filed with the patent office on 2018-08-23 for method and device for modeling a long-time-scale photovoltaic output time sequence.
The applicant listed for this patent is CHINA ELECTRIC POWER RESEARCH INSTITUTE COMPANY LIMITED, CLP PURI ZHANGBEI WIND POWER RESEARCH & TESTING CO., LTD., STATE GRID CORPORATION OF CHINA. Invention is credited to Cun Dong, Yunfeng Gao, Yuehui Huang, Chi Li, Xiaofei Li, Chun Liu, Xiaofeng Pan, Weisheng Wang, Yuefeng Wang, Xiaoyan Xu, Yanping Xu, Nan Zhang.
Application Number | 20180240200 15/751471 |
Document ID | / |
Family ID | 58417673 |
Filed Date | 2018-08-23 |
United States Patent
Application |
20180240200 |
Kind Code |
A1 |
Wang; Weisheng ; et
al. |
August 23, 2018 |
METHOD AND DEVICE FOR MODELING A LONG-TIME-SCALE PHOTOVOLTAIC
OUTPUT TIME SEQUENCE
Abstract
A method and device for modeling a long-time-scale photovoltaic
output time sequence are provided. The method includes that:
historical data of a photovoltaic power station is acquired, and a
photovoltaic output with a time length of one year and a time
resolution of 15 mins is selected (101); weather types of days
corresponding to the photovoltaic output are acquired from a
weather station (102), and probabilities of transfer between each
type of weather are calculated respectively (103); and a simulated
time sequence of the photovoltaic output within a preset time scale
is generated (104), and its validity is verified (105). By the
method, annual and monthly photovoltaic output simulated time
sequences consistent with a random fluctuation rule of a
photovoltaic time sequence may be acquired according to different
requirements to provide a favorable condition and a data support
for analogue simulation of time sequence production including
massive new energy.
Inventors: |
Wang; Weisheng; (Beijing,
CN) ; Liu; Chun; (Beijing, CN) ; Li; Chi;
(Beijing, CN) ; Huang; Yuehui; (Beijing, CN)
; Wang; Yuefeng; (Beijing, CN) ; Dong; Cun;
(Beijing, CN) ; Zhang; Nan; (Beijing, CN) ;
Li; Xiaofei; (Beijing, CN) ; Gao; Yunfeng;
(Beijing, CN) ; Xu; Xiaoyan; (Beijing, CN)
; Xu; Yanping; (Beijing, CN) ; Pan; Xiaofeng;
(Beijing, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
CHINA ELECTRIC POWER RESEARCH INSTITUTE COMPANY LIMITED
STATE GRID CORPORATION OF CHINA
CLP PURI ZHANGBEI WIND POWER RESEARCH & TESTING CO.,
LTD. |
Beijing
Beijing
Zhangjiakou |
|
CN
CN
CN |
|
|
Family ID: |
58417673 |
Appl. No.: |
15/751471 |
Filed: |
June 30, 2016 |
PCT Filed: |
June 30, 2016 |
PCT NO: |
PCT/CN2016/087809 |
371 Date: |
February 8, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/04 20130101;
G06Q 50/06 20130101; F24S 2201/00 20180501 |
International
Class: |
G06Q 50/06 20060101
G06Q050/06; G06Q 10/04 20060101 G06Q010/04 |
Foreign Application Data
Date |
Code |
Application Number |
Sep 30, 2015 |
CN |
201510639474.5 |
Claims
1. A method for modeling a long-time-scale photovoltaic output time
sequence, comprising: acquiring historical data of a photovoltaic
power station, and selecting a photovoltaic output with a time
length of one year and a time resolution of 15 mins; acquiring
weather types of days corresponding to the photovoltaic output, the
weather types comprising at least one of clear weather, cloudy
weather, overcast weather or changing weather; calculating
probabilities of transfer between each type of weather
respectively; generating a simulated time sequence of the
photovoltaic output within a preset time scale; and verifying
validity of the simulated time sequence.
2. The method according to claim 1, wherein calculating the
probabilities of transfer between each type of weather respectively
comprises: adopting a Markov chain to simulate transfer processes
of each type of weather and acquire the probabilities of transfer
between each weather type, an expression being: P k = N k N 1 , ( 1
) ##EQU00010## in formula (1), P.sub.k being the probability of
transfer of the clear weather to another weather type, k
representing a weather type, N.sub.k being a number of times of
transfer and N.sub.1 being a number of times of occurrence of the
clear weather.
3. The method according to claim 2, further comprising:
sequentially obtaining the probabilities of transfer between the
other weather types by virtue of a method for calculating the
probabilities of transfer of the clear weather to the other weather
types.
4. The method according to claim 1, wherein generating the
simulated time sequence of the photovoltaic output within the
preset time scale comprises: sequentially and randomly extracting
the weather types and corresponding relative outputs within the
preset time scale according to the probabilities of transfer
between each weather type, and calculating products of the relative
outputs and a predetermined threshold value to generate the
simulated time sequence of the photovoltaic output, wherein the
simulated time sequence is a curve chart for reflecting changes of
a Probability Density Function (PDF), an Autocorrelation Function
(ACF) and short-duration fluctuation characteristic of photovoltaic
output of multiple time scales; wherein the short-duration
fluctuation characteristic is a maximum PDF of the photovoltaic
output within a time scale t, 15 min.ltoreq.t.ltoreq.60 min; the
maximum PDF being a difference value between a maximum output and a
minimum output within the time scale t; and the difference value is
positive if the maximum output appears after the minimum output,
and the difference value is negative if the maximum output appears
before the minimum output.
5. The method according to claim 1, wherein verifying the validity
of the simulated time sequence comprises: defining the PDF C.sub.f,
short-duration fluctuation characteristic C.sub.d and ACF C.sub.r
of the simulated time sequence respectively; and adopting a
Root-Mean-Square Error (RMSE) of each characteristic to
quantitatively evaluate the validity of the time sequence, an
expression being: RMSE = 1 n i = 1 n ( y ^ i - y i ) , ##EQU00011##
where y.sub.i .di-elect cons.[C.sub.f, C.sub.d, C.sub.r], y.sub.i
is a unit vector and represents a function value of each
characteristic of the simulated time sequence, y.sub.i represents a
function value of each characteristic, corresponding to each
characteristic of the simulated time sequence, of a historical time
sequence, n is a length of a function value set of each
characteristic of the time sequence, RMSE is smaller than .epsilon.
with a value range of 0.1.about.0.2.
6. A device for modeling a long-time-scale photovoltaic output time
sequence, comprising: a memory storing computer-executable
instructions; and one or more processors executing the
computer-executable instructions to implement a plurality of
program units, wherein the plurality of program units comprise: a
data acquisition unit, configured to acquire historical data of a
photovoltaic power station, and select a photovoltaic output with a
time length of one year and a time resolution of 15 mins; an
acquisition unit, configured to acquire weather types of days
corresponding to the photovoltaic output from a weather station,
the weather types comprising at least one of clear weather, cloudy
weather, overcast weather or changing weather; a processing unit,
configured to calculate probabilities of transfer between each type
of weather respectively; a generation unit, configured to generate
a simulated time sequence of the photovoltaic output within a
preset time scale; and an evaluation unit, configured to verify
validity of the simulated time sequence.
7. The device according to claim 6, wherein the processing unit is
further configured to: adopt a Markov chain to simulate transfer
processes of each type of weather and acquire the probabilities of
transfer between each weather type, an expression being: P k = N k
N 1 , ( 1 ) ##EQU00012## in formula (1), P.sub.k being the
probability of transfer of the clear weather to another weather
type, k representing a weather type, N.sub.k being a number of
times of transfer and N.sub.1 being a number of times of occurrence
of the clear weather.
8. The device according to claim 7, wherein the plurality of
program units further comprise: a probability acquisition unit,
configured to sequentially obtain the probabilities of transfer
between the other weather types by virtue of a method for
calculating the probabilities of transfer of the clear weather to
the other weather types.
9. The device according to claim 6, wherein the generation unit is
further configured to: sequentially and randomly extract the
weather types and corresponding relative outputs within the preset
time scale according to the probabilities of transfer between each
weather type, and calculate products of the relative outputs and a
predetermined threshold value to generate the simulated time
sequence of the photovoltaic output, wherein the simulated time
sequence is a curve chart for reflecting changes of a Probability
Density Function (PDF), Autocorrelation Function (ACF) and
short-duration fluctuation characteristic of photovoltaic output of
multiple time scales; wherein the short-duration fluctuation
characteristic is a maximum PDF of the photovoltaic output within a
time scale t; 15 min.ltoreq.t.ltoreq.60 min; the maximum PDF being
a difference value between a maximum output and a minimum output
within the time scale t; and the difference value is positive if
the maximum output appears after the minimum output, and the
difference value is negative if the maximum output appears before
the minimum output.
10. The device according to claim 6, wherein the evaluation unit is
further configured to: define the PDF C.sub.f, short-duration
fluctuation characteristic C.sub.d and ACF C.sub.r of the simulated
time sequence respectively; and adopt a Root-Mean-Square Error
(RMSE) of each characteristic to quantitatively evaluate the
validity of the time sequence, an expression being: RMSE = 1 n i =
1 n ( y ^ i - y i ) , ##EQU00013## where y.sub.i .di-elect
cons.[C.sub.f, C.sub.d, C.sub.r], y.sub.i is a unit vector and
represents a function value of each characteristic of the simulated
time sequence, y.sub.i represents a function value of each
characteristic, corresponding to each characteristic of the
simulated time sequence, of a historical time sequence, n is a
length of a function value set of each characteristic of the time
sequence, RMSE is smaller than .epsilon. with a value range of
0.1.about.0.2.
11. The method according to claim 3, wherein generating the
simulated time sequence of the photovoltaic output within the
preset time scale comprises: sequentially and randomly extracting
the weather types and corresponding relative outputs within the
preset time scale according to the probabilities of transfer
between each weather type, and calculating products of the relative
outputs and a predetermined threshold value to generate the
simulated time sequence of the photovoltaic output, wherein the
simulated time sequence is a curve chart for reflecting changes of
a Probability Density Function (PDF), an Autocorrelation Function
(ACF) and short-duration fluctuation characteristic of photovoltaic
output of multiple time scales; wherein the short-duration
fluctuation characteristic is a maximum PDF of the photovoltaic
output within a time scale t, 15 min.ltoreq.t.ltoreq.60 min; the
maximum PDF being a difference value between a maximum output and a
minimum output within the time scale t; and the difference value is
positive if the maximum output appears after the minimum output,
and the difference value is negative if the maximum output appears
before the minimum output.
12. The device according to claim 8, wherein the generation unit is
further configured to: sequentially and randomly extract the
weather types and corresponding relative outputs within the preset
time scale according to the probabilities of transfer between each
weather type, and calculate products of the relative outputs and a
predetermined threshold value to generate the simulated time
sequence of the photovoltaic output, wherein the simulated time
sequence is a curve chart for reflecting changes of a Probability
Density Function (PDF), Autocorrelation Function (ACF) and
short-duration fluctuation characteristic of photovoltaic output of
multiple time scales; wherein the short-duration fluctuation
characteristic is a maximum PDF of the photovoltaic output within a
time scale t, 15 min.ltoreq.t.ltoreq.60 min; the maximum PDF being
a difference value between a maximum output and a minimum output
within the time scale t; and the difference value is positive if
the maximum output appears after the minimum output, and the
difference value is negative if the maximum output appears before
the minimum output.
13. A non-transitory computer-readable storage medium having stored
therein instructions that, when executed by a processor, causes the
processor to perform a method for modeling a long-time-scale
photovoltaic output time sequence, the method comprising acquiring
historical data of a photovoltaic power station, and selecting a
photovoltaic output with a time length of one year and a time
resolution of 15 mins; acquiring weather types of days
corresponding to the photovoltaic output, the weather types
comprising at least one of clear weather, cloudy weather, overcast
weather or changing weather; calculating probabilities of transfer
between each type of weather respectively; generating a simulated
time sequence of the photovoltaic output within a preset time
scale; and verifying validity of the simulated time sequence.
14. The non-transitory computer-readable storage medium according
to claim 13, wherein the step of calculating the probabilities of
transfer between each type of weather respectively comprises:
adopting a Markov chain to simulate transfer processes of each type
of weather and acquire the probabilities of transfer between each
weather type, an expression being: P k = N k N 1 , ( 1 )
##EQU00014## in formula (1), P.sub.k being the probability of
transfer of the clear weather to another weather type, k
representing a weather type, N.sub.k being a number of times of
transfer and N.sub.1 being a number of times of occurrence of the
clear weather.
15. The non-transitory computer-readable storage medium according
to claim 14, the method further comprises: sequentially obtaining
the probabilities of transfer between the other weather types by
virtue of a method for calculating the probabilities of transfer of
the clear weather to the other weather types.
16. The non-transitory computer-readable storage medium according
to claim 13, wherein the step of generating the simulated time
sequence of the photovoltaic output within the preset time scale
comprises: sequentially and randomly extracting the weather types
and corresponding relative outputs within the preset time scale
according to the probabilities of transfer between each weather
type, and calculating products of the relative outputs and a
predetermined threshold value to generate the simulated time
sequence of the photovoltaic output, wherein the simulated time
sequence is a curve chart for reflecting changes of a Probability
Density Function (PDF), an Autocorrelation Function (ACF) and
short-duration fluctuation characteristic of photovoltaic output of
multiple time scales; wherein the short-duration fluctuation
characteristic is a maximum PDF of the photovoltaic output within a
time scale t, 15 min.ltoreq.t.ltoreq.60 min; the maximum PDF being
a difference value between a maximum output and a minimum output
within the time scale t; and the difference value is positive if
the maximum output appears after the minimum output, and the
difference value is negative if the maximum output appears before
the minimum output.
17. The non-transitory computer-readable storage medium according
to claim 13, wherein the step of verifying the validity of the
simulated time sequence comprises: defining the PDF C.sub.f,
short-duration fluctuation characteristic C.sub.d and ACF C.sub.r
of the simulated time sequence respectively; and adopting a
Root-Mean-Square Error (RMSE) of each characteristic to
quantitatively evaluate the validity of the time sequence, an
expression being: RMSE = 1 n i = 1 n ( y ^ i - y i ) , ##EQU00015##
where y.sub.i .di-elect cons.[C.sub.f, C.sub.d, C.sub.r], y.sub.i
is a unit vector and represents a function value of each
characteristic of the simulated time sequence, y.sub.i represents a
function value of each characteristic, corresponding to each
characteristic of the simulated time sequence, of a historical time
sequence, n is a length of a function value set of each
characteristic of the time sequence, RMSE is smaller than .epsilon.
with a value range of 0.1.about.0.2.
Description
TECHNICAL FIELD
[0001] The disclosure relates to a modeling technology, and
particularly to a method and device for modeling a long-time-scale
photovoltaic output time sequence.
BACKGROUND
[0002] Photovoltaic power generation is a renewable energy
technology with greatest potential and highest application value
after wind power generation, and photovoltaic power generation is
rapidly developed in China under the support of a series of
supporting policies. Along with increase of a proportion of
photovoltaic power generation in power of the whole power system,
deeply understanding a characteristic and rule of photovoltaic
output may accurately master influence of photovoltaic grid
connection on the power system and enable the power system to more
effectively solve a problem about photovoltaic access.
[0003] An existing weather simulation technology may only implement
annual/monthly photovoltaic power prediction, may not implement
long-time-scale power prediction, and may not directly obtain a
time sequence useful for analogue simulation of time sequence
production of a power system. Therefore, it is necessary to model a
photovoltaic output time sequence to accurately master an output
change rule of photovoltaic power generation and provide
indispensable basic data for analogue simulation of time sequence
production including massive new energy, annual new energy resource
consumption capability analysis and annual planning.
SUMMARY
[0004] In order to achieve the purpose, an embodiment of the
disclosure provides a long-time-scale photovoltaic output time
sequence modeling method. A characteristic of a photovoltaic output
time sequence is analyzed, and a Markov chain is adopted to
simulate transfer processes of each weather type and acquire
probabilities of transfer to generate a simulated photovoltaic
sequence, thereby proposing a new method to build a future
photovoltaic output scenario.
[0005] The embodiment of the disclosure is implemented by adopting
the following technical solution.
[0006] The embodiment of the disclosure provides a method for
modeling a long-time-scale photovoltaic output time sequence, which
includes that:
[0007] historical data of a photovoltaic power station is acquired,
and a photovoltaic output with a time length of one year and a time
resolution of 15 mins is selected;
[0008] weather types of days corresponding to the photovoltaic
output is acquired, the weather types including at least one of
clear weather, cloudy weather, overcast weather or changing
weather;
[0009] probabilities of transfer between each type of weather are
calculated respectively;
[0010] a simulated time sequence of the photovoltaic output within
a preset time scale is generated; and
[0011] validity of the simulated time sequence is verified.
[0012] In an implementation mode of the embodiment of the
disclosure, the operation that the probabilities of transfer
between each type of weather are calculated respectively includes
that: a Markov chain is adopted to simulate transfer processes of
each type of weather and acquire the probabilities of transfer
between each weather type, an expression being:
P k = N k N 1 , ( 1 ) ##EQU00001##
[0013] in formula (1), P.sub.k being the probability of transfer of
the clear weather to another weather type, k representing a weather
type, N .sub.k being a number of times of transfer and N.sub.1
being a number of times of occurrence of the clear weather.
[0014] In an implementation mode of the embodiment of the
disclosure, the following step is further included: the
probabilities of transfer between the other weather types are
sequentially obtained by virtue of a method for calculating the
probabilities of transfer of the clear weather to the other weather
types.
[0015] In an implementation mode of the embodiment of the
disclosure, the operation that the simulated time sequence of the
photovoltaic output within the preset time scale is generated
includes that: the weather types and corresponding relative outputs
within the preset time scale are sequentially and randomly
extracted according to the probabilities of transfer between each
weather type, and products of the relative outputs and a
predetermined threshold value are calculated to generate the
simulated time sequence of the photovoltaic output, wherein the
simulated time sequence is a curve chart for reflecting changes of
a Probability Density Function (PDF), an Autocorrelation Function
(ACF) and short-duration fluctuation characteristic of photovoltaic
output of multiple time scales;
[0016] the short-duration fluctuation characteristic is a maximum
PDF of the photovoltaic output within a time scale t, 15
min.ltoreq.t.ltoreq.60 min;
[0017] the maximum PDF is a difference value between a maximum
output and a minimum output within the time scale t; and the
difference value is positive if the maximum output appears after
the minimum output, and the difference value is negative if it
appears before the minimum output.
[0018] In an implementation mode of the embodiment of the
disclosure, the operation that the validity of the simulated time
sequence is verified includes that:
[0019] the PDF C.sub.f, short-duration fluctuation characteristic
C.sub.d and ACF C.sub.r of the simulated time sequence are defined
respectively; and
[0020] a Root-Mean-Square Error (RMSE) of each characteristic is
adopted to quantitatively evaluate the validity of the time
sequence, an expression being:
RMSE = 1 n i = 1 n ( y ^ i - y i ) , ##EQU00002##
[0021] where y.sub.i .di-elect cons.[C.sub.f, C.sub.d, C.sub.r],
y.sub.i is a unit vector, and represents a function value of each
characteristic of the simulated time sequence, y.sub.i represents a
function value of each characteristic, corresponding to each
characteristic of the simulated time sequence, of a historical time
sequence, n is a length of a function value set of each
characteristic of the time sequence, RMSE is smaller than .epsilon.
with a value range of 0.1.about.0.2.
[0022] An embodiment of the disclosure provides a device for
modeling a long-time-scale photovoltaic output time sequence,
wherein the device includes: a data acquisition unit, configured to
acquire historical data of a photovoltaic power station, and select
a photovoltaic output with a time length of one year and a time
resolution of 15 mins;
[0023] an acquisition unit, configured to acquire weather types of
days corresponding to the photovoltaic output from a weather
station, the weather types including at least one of clear weather,
cloudy weather, overcast weather or changing weather;
[0024] a processing unit, configured to calculate probabilities of
transfer between each type of weather respectively;
[0025] a generation unit, configured to generate a simulated time
sequence of the photovoltaic output within a preset time scale;
and
[0026] an evaluation unit, configured to verify validity of the
simulated time sequence.
[0027] In an implementation mode of the embodiment of the
disclosure, the processing unit is further configured to: adopt a
Markov chain to simulate transfer processes of each type of weather
and acquire the probabilities of transfer between each weather
type, an expression being:
P k = N k N 1 , ( 1 ) ##EQU00003##
[0028] in formula (1), P.sub.k being the probability of transfer of
the clear weather to another weather type, k representing a weather
type, N.sub.k being a number of times of transfer and N.sub.1 being
a number of times of occurrence of the clear weather.
[0029] In an implementation mode of the embodiment of the
disclosure, the device further includes: a probability acquisition
unit, configured to sequentially obtain the probabilities of
transfer between the other weather types by virtue of a method for
calculating the probabilities of transfer of the clear weather to
the other weather types.
[0030] In an implementation mode of the embodiment of the
disclosure, the generation unit is further configured to:
sequentially and randomly extract the weather types and
corresponding relative outputs within the preset time scale
according to the probabilities of transfer between each weather
type, and calculate products of the relative output and a
predetermined threshold value to generate the simulated time
sequence of the photovoltaic output, wherein the simulated time
sequence is a curve chart for reflecting changes of a PDF, ACF and
short-duration fluctuation characteristic of photovoltaic output of
multiple time scales;
[0031] the short-duration fluctuation characteristic is a maximum
PDF of the photovoltaic output within a time scale t, 15
min.ltoreq.t.ltoreq.60 min;
[0032] the maximum PDF is a difference value between maximum output
and minimum output within the time scale t; and the difference
value is positive if the maximum output appears after the minimum
output, and the difference value is negative if it appears before
the minimum output.
[0033] In an implementation mode of the embodiment of the
disclosure, the evaluation unit is further configured to:
[0034] define the PDF C.sub.f, short-duration fluctuation
characteristic C.sub.d and ACF C.sub.r of the simulated time
sequence respectively; and
[0035] adopt an RMSE of each characteristic to quantitatively
evaluate the validity of the time sequence, an expression
being:
RMSE = 1 n i = 1 n ( y ^ i - y i ) , ##EQU00004##
[0036] where y.sub.i .di-elect cons.[C.sub.f, C.sub.d, C.sub.r],
y.sub.i is a unit vector and represents a function value of each
characteristic of the simulated time sequence, y.sub.i represents a
function value of each characteristic, corresponding to each
characteristic of the simulated time sequence, of a historical time
sequence, n is a length of a function value set of each
characteristic of the time sequence, RMSE is smaller than .epsilon.
with a value range of 0.1.about.0.2.
[0037] Compared with a conventional art, adopting the embodiments
of the disclosure may achieve the following beneficial effects: the
Markov chain is adopted to simulate the transfer processes of each
type of weather and calculate the probabilities of transfer between
each weather type; and uncertain characteristics such as randomness
and fluctuation of photovoltaics are simulated, and compared with
other methods, a building structure is more consistent with
characteristics of the photovoltaic output, and truthfully and
accurately represent a future photovoltaic output condition. Annual
and monthly photovoltaic output simulation time sequences
consistent with a random fluctuation rule of a photovoltaic time
sequence may be generated according to a requirement to provide
indispensable basic data for analogue simulation of time sequence
production including massive new energy, annual new energy resource
consumption capability analysis and annual planning.
BRIEF DESCRIPTION OF DRAWINGS
[0038] FIG. 1 is a flowchart of a long-time-scale photovoltaic
output time sequence modeling method according to an embodiment of
the disclosure.
[0039] FIG. 2-FIG 5 are schematic diagrams of parameter comparison
between a historical time sequence and a simulated time sequence
according to an embodiment of the disclosure, wherein
[0040] FIG. 2 is a schematic diagram of a probability density;
[0041] FIG. 3 is a schematic diagram of a 15 min probability
density;
[0042] FIG. 4 is a schematic diagram of a 60 min probability
density; and
[0043] FIG. 5 is a schematic diagram of autocorrelation coefficient
comparison.
DETAILED DESCRIPTION
[0044] Specific implementation modes of the disclosure will be
further described below in combination with the drawings in
detail.
[0045] FIG. 1 shows a long-time-scale photovoltaic output time
sequence modeling method according to an embodiment of the
disclosure. The method includes the following steps.
[0046] In Step 101, historical data of a photovoltaic power station
is acquired, and a photovoltaic output with a time length of one
year and a time resolution of 15 mins is selected.
[0047] In Step 102, weather types of days corresponding to the
photovoltaic output are acquired from a weather station, the
weather types including clear weather, cloudy weather, overcast
weather and changing weather.
[0048] In Step 103, probabilities of transfer between each type of
weather are calculated respectively, a Markov chain being adopted
to simulate transfer processes of each type of weather and acquire
the probabilities of transfer between each weather type, an
expression being:
P k = N k N 1 , ( 1 ) ##EQU00005##
[0049] in formula (1), P.sub.k being the probability of transfer of
the clear weather to another weather type, k representing a weather
type, N.sub.k being a number of times of transfer and N.sub.1 being
a number of times of occurrence of the clear weather.
[0050] The probabilities of transfer between the other weather
types are sequentially obtained by virtue of a method for
calculating the probabilities of transfer of the clear weather to
the other weather types.
[0051] For example, expressions for calculating the probabilities
of transfer of the cloudy weather to the other weather types
are:
P ( 1 - 1 ) = N ( 1 - 1 ) N ( 1 ) , P ( 1 - 2 ) = N ( 1 - 2 ) N ( 1
) , P ( 1 - 3 ) = N ( 1 - 3 ) N ( 1 ) and ##EQU00006## P ( 1 - 4 )
= N ( 1 - 4 ) N ( 1 ) , ##EQU00006.2##
[0052] in the formulae, subscript 1 being adopted for the cloudy
weather type, subscript 2 being adopted for the clear weather type,
subscript 3 being adopted for the overcast weather type, subscript
4 being adopted for the changing weather type, P.sub.(1-1),
P.sub.(1-3), P.sub.(1-3), and P.sub.(1-4) representing the
probabilities of transfer of the cloudy weather type to the other
weather types respectively, N.sub.(1-1), N.sub.(1-2), N.sub.(1-3)
and N.sub.(1-4) representing numbers of times of transfer of the
cloudy weather to the other weather types respectively, and
N.sub.(1) representing a number of times of occurrence of the
cloudy weather type. Similarly, the probabilities of transfer of
the overcast weather and the changing weather may be
calculated.
[0053] In Step 104, a simulated time sequence of the photovoltaic
output within a preset time scale is generated.
[0054] The weather types and corresponding relative outputs within
the preset time scale are sequentially and randomly extracted
according to the probabilities of transfer between each weather
type, and products of the relative outputs and a predetermined
threshold value are calculated to generate the simulated time
sequence of the photovoltaic output. The predetermined threshold
value is a standard value customized according to historical
photovoltaic data and a historical time sequence. The simulated
time sequence is a curve chart and is configured to reflect changes
of a PDF, ACF and short-duration fluctuation characteristic of
photovoltaic output of multiple time scales.
[0055] The short-duration fluctuation characteristic is a maximum
PDF of the photovoltaic output within a time scale t, 15
min.ltoreq.t.ltoreq.60 min.
[0056] The maximum PDF is a difference value between a maximum
output and a minimum output within the time scale t; and the
difference value is positive if the maximum output appears after
the minimum output, and the difference value is negative if it
appears before the minimum output.
[0057] In Step 105, validity of the simulated time sequence is
verified, as shown in each schematic diagram of FIG. 2 to FIG.
5.
[0058] Here, a specific processing process of the step includes the
following steps.
[0059] In Step 1051, the PDF C.sub.f, short-duration fluctuation
characteristic C.sub.d and ACF C.sub.r of the simulated time
sequence are defined respectively.
[0060] In Step 1052, an RMSE of each characteristic is adopted to
quantitatively evaluate the validity of the time sequence, an
expression being:
RMSE = 1 n i = 1 n ( y ^ i - y i ) , ##EQU00007##
[0061] where y.sub.i .di-elect cons.[C.sub.f, C.sub.d, C.sub.r], y,
is a unit vector, and represents a function value of each
characteristic of the simulated time sequence, y.sub.i represents a
function value of each characteristic, corresponding to each
characteristic of the simulated time sequence, of the historical
time sequence, n is a length of a function value set of each
characteristic of the time sequence, RMSE is smaller than .epsilon.
with a value range of 0.1.about.0.2.
[0062] FIG. 2 is a schematic diagram of a probability density. As
shown in FIG. 2, when y.sub.i .di-elect cons.C.sub.f, the function
value of the PDF of the simulated time sequence is represented, and
at this moment, y.sub.i represents the function value of the PDF,
corresponding to the PDF of the simulated time sequence, of the
historical time sequence. FIG. 3 is a schematic diagram of a 15 min
probability density, and FIG. 4 is a schematic diagram of a 60 min
probability density. As shown in FIG. 3 and FIG. 4, when y.sub.i
.di-elect cons.C.sub.d, the function value of the short-duration
fluctuation characteristic of the simulated time sequence is
represented, and at this moment, y.sub.i represents the function
value of the short-duration fluctuation characteristic,
corresponding to the short-duration fluctuation characteristic of
the simulated time sequence, of the historical time sequence. FIG.
5 is a schematic diagram of autocorrelation coefficient comparison.
As shown in FIG. 5, when y.sub.i .di-elect cons.C.sub.r, the
function value of the ACF of the simulated time sequence is
represented, and at this moment, y.sub.i represents the function
value of the ACF, corresponding to the ACF of the simulated time
sequence, of the historical time sequence.
[0063] An embodiment of the disclosure provides a long-time-scale
photovoltaic output time sequence modeling device, which
includes:
[0064] a data acquisition unit, configured to acquire historical
data of a photovoltaic power station, and select a photovoltaic
output with a time length of one year and a time resolution of 15
mins;
[0065] an acquisition unit, configured to acquire weather types of
days corresponding to the photovoltaic output from a weather
station, the weather types including clear weather, cloudy weather,
overcast weather and changing weather;
[0066] a processing unit, configured to calculate probabilities of
transfer between each type of weather respectively;
[0067] a generation unit, configured to generate a simulated time
sequence of the photovoltaic output within a preset time scale;
and
[0068] an evaluation unit, configured to verify validity of the
simulated time sequence.
[0069] In an implementation mode of the embodiment of the
disclosure, the processing unit is further configured to: adopt a
Markov chain to simulate transfer processes of each type of weather
and acquire the probabilities of transfer between each weather
type, an expression being:
P k = N k N 1 , ( 1 ) ##EQU00008##
[0070] in formula (1), P.sub.k being the probability of transfer of
the clear weather to another weather type, k representing a weather
type, N.sub.k being a number of times of transfer and N.sub.1 being
a number of times of occurrence of the clear weather.
[0071] In an implementation mode of the embodiment of the
disclosure, the device further includes: a probability acquisition
unit, configured to sequentially obtain the probabilities of
transfer between the other weather types by virtue of a method for
calculating the probabilities of transfer of the clear weather to
the other weather types.
[0072] In an implementation mode of the embodiment of the
disclosure, the generation unit is further configured to:
sequentially and randomly extract the weather types and
corresponding relative outputs within the preset time scale
according to the probabilities of transfer between each weather
type, and calculate products of the relative output and a
predetermined threshold value to generate the simulated time
sequence of the photovoltaic output, wherein the simulated time
sequence is a curve chart, and is configured to reflect changes of
a PDF, ACF and short-duration fluctuation characteristic of
photovoltaic output of multiple time scales;
[0073] the short-duration fluctuation characteristic is a maximum
PDF of the photovoltaic output within a time scale t, 15
min.ltoreq.t.ltoreq.60 min;
[0074] the maximum PDF is a difference value between a maximum
output and a minimum output within the time scale t; and the
difference value is positive if the maximum output appears after
the minimum output, and the difference value is negative if it
appears before the minimum output.
[0075] In an implementation mode of the embodiment of the
disclosure, the evaluation unit is further configured to:
[0076] define the PDF C.sub.f, short-duration fluctuation
characteristic C.sub.d and ACF C.sub.r of the simulated time
sequence respectively; and
[0077] adopt an RMSE of each characteristic to quantitatively
evaluate the validity of the time sequence, an expression
being:
RMSE = 1 n i = 1 n ( y ^ i - y i ) , ##EQU00009##
[0078] where y.sub.i .di-elect cons.[C.sub.f, C.sub.d, C.sub.r],
y.sub.i is a unit vector, and represents a function value of each
characteristic of the simulated time sequence, y.sub.i represents a
function value of each characteristic, corresponding to each
characteristic of the simulated time sequence, of a historical time
sequence, n is a length of a function value set of each
characteristic of the time sequence, RMSE is smaller than .epsilon.
with a value range of 0.1.about.0.2.
[0079] It should finally be noted that: the above embodiments are
adopted to not limit but only describe the technical solutions of
the disclosure, and although the disclosure has been described with
reference to the above embodiments in detail, those skilled in the
art should understand that: modifications or equivalent
replacements may still be made to the specific implementation modes
of the disclosure, and any modifications or equivalent replacements
made without departing from the spirit and scope of the disclosure
shall fall within the scope of the claims of the disclosure.
INDUSTRIAL APPLICABILITY
[0080] By adopting the embodiments of the disclosure, the Markov
chain is adopted to simulate the transfer processes of each type of
weather and calculate the probabilities of transfer between each
weather type; and uncertain characteristics such as randomness and
fluctuation of photovoltaics are simulated, and compared with other
methods, a building structure is more consistent with
characteristics of the photovoltaic output, and truthfully and
accurately represent a future photovoltaic output condition. Annual
and monthly photovoltaic output simulation time sequences
consistent with a random fluctuation rule of a photovoltaic time
sequence may be generated according to a requirement to provide
indispensable basic data for analogue simulation of time sequence
production including massive new energy, annual new energy resource
consumption capability analysis and annual planning.
* * * * *