CN105608302B - A kind of energy-storage system flat volatility typical condition generation method and system - Google Patents
A kind of energy-storage system flat volatility typical condition generation method and system Download PDFInfo
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Abstract
The present invention proposes a kind of energy-storage system flat volatility typical condition generation method and system, this method include：Obtain operational data of the energy-storage system under flat volatility pattern；Flat volatility curve is generated after being handled according to operational data, and is calculated according to flat volatility curve negotiating and generated corresponding feature distribution data；Flat volatility curve is segmented by a certain unit interval t, obtains multiple unit flat volatility curve segments；Choose the unit flat volatility curve segment of certain amount a, the combination flat volatility curve segment of a length of a × t when forming multiple；According to multiple combination flat volatility curve segments, corresponding feature distribution data is generated by calculating；Calculate the error amount between the corresponding feature distribution data of multiple combination flat volatility curve segments feature distribution data corresponding with flat volatility curve；The corresponding feature distribution data of combination flat volatility curve segment that error amount is less than certain numerical value is chosen, the typical condition figure of energy-storage system flat volatility is synthesized.
Description
Technical field
The present invention relates to power domain, espespecially a kind of energy-storage system flat volatility typical condition generation method and system.
Background technology
Along with the high speed development that China's wind-powered electricity generation, photovoltaic generation are installed, wind, the fluctuation of light resource and randomness are to power grid
Safe operation brings new technical problem.Energy-storage system also becomes as the effective means for stabilizing honourable fluctuation and randomness
The research hotspot of concern.
Grid-connected high-capacity lithium battery energy-accumulating power station is generally monitored by lithium battery string formation, current transformer, Energy Management System, energy storage
System and its mating electric loop are constituted.Its operating condition generally has peak load shifting, flat volatility, tracking plan power generation and adjusts
Frequency modulation pressure.
Although grid-connected high-capacity lithium battery energy-storage system is formed by single battery is integrated, its performance not only with each monomer
Performance is related, also integrates mode, applying working condition, harmony etc. correlation.And the research object of existing achievement in research is
Energy-storage battery monomer or compact battery module for electric vehicle, rarely have the energy-storage lithium battery string formation performance of large capacity and grind
Study carefully.
And to study the battery applied to grid-connected high-capacity lithium battery energy-storage system, battery modules performance, storage need to be simulated
It can system for field operating condition.And the research for lithium battery performance at present, state of cyclic operation experiment mainly is carried out in laboratory, i.e.,
From the charge and discharge repeatedly of 0% state-of-charge to 100% state-of-charge.
Extraction for typical condition, current achievement in research primarily with respect to electric vehicle operating condition extraction, one
As according to battery operation feature, be divided into idling, at the uniform velocity, the stages such as quickly, power, current characteristic are extracted respectively, for studying electricity
Under the daily operating mode of electrical automobile, the influence to battery modules performance.There is presently no carried for large capacity energy-storage system typical condition
The achievement in research taken.
Invention content
In order to study influence degree of the daily operating mode of large capacity energy-storage system to battery modules, laboratory need to be designed for
Energy-storage system typical condition.
To achieve the above object, the present invention proposes a kind of energy-storage system flat volatility typical condition generation method, described
Method includes：Step 1, operational data of the energy-storage system under flat volatility pattern is obtained；Step 2, according to the work
Flat volatility curve is generated after data processing, and is calculated according to the flat volatility curve negotiating and generated corresponding feature distribution number
According to；Step 3, the flat volatility curve is segmented by a certain unit interval t, obtains multiple unit flat volatility curve pieces
Section；Step 4, the unit flat volatility curve segment of certain amount a, the combination flat volatility of a length of a × t when forming multiple are chosen
Curve segment；Step 5, according to multiple combination flat volatility curve segments, corresponding feature distribution number is generated by calculating
According to；Step 6, the corresponding feature distribution data of multiple combination flat volatility curve segments that calculates that the step 5 generates and
Error amount between the corresponding feature distribution data of flat volatility curve that the step 2 generates；Step 7, it is small to choose error amount
In the corresponding feature distribution data of combination flat volatility curve segment of certain numerical value, the typical case of energy-storage system flat volatility is synthesized
Working condition chart.
To achieve the above object, also a kind of energy-storage system flat volatility typical condition of the present invention generates system, the system
Including：Operational data acquisition module, for obtaining operational data of the energy-storage system under flat volatility pattern；Flat volatility
Curve distribution data generation module, for generating flat volatility curve after being handled according to the operational data, and according to described flat
The sliding curve of cyclical fluctuations generates corresponding feature distribution data by calculating；Segmentation module, for the flat volatility curve to be pressed certain
One unit interval t is segmented, and multiple unit flat volatility curve segments are obtained；Flat volatility curve segment composite module is used
In the unit flat volatility curve segment for choosing certain amount a, the combination flat volatility curve piece of a length of a × t when forming multiple
Section；Flat volatility curve segment distributed data generation module is combined, is used for according to multiple combination flat volatility curve segments,
Corresponding feature distribution data is generated by calculating；Error amount computing module, it is bent for calculating multiple combination flat volatilities
Error amount between the corresponding feature distribution data of line segment feature distribution data corresponding with flat volatility curve；Typical condition
Figure synthesis module is less than the corresponding feature distribution number of combination flat volatility curve segment of certain numerical value for choosing error amount
According to the typical condition figure of synthesis energy-storage system flat volatility.
The typical condition that the energy-storage system flat volatility typical condition generation method and system proposed through the invention generates
Time cycle is shorter, convenient for pause experiment and test data analyzer, and covers all important of large capacity energy-storage system operating mode
Information, in one cycle, the energy transfer algebraical sum SOE of battery modules are carried close to 0 using the method and system of the present invention
The typical condition taken disclosure satisfy that laboratory carries out the research requirement of energy-storage system performance.
Description of the drawings
Attached drawing described herein is used to provide further understanding of the present invention, and is constituted part of this application, not
Constitute limitation of the invention.In the accompanying drawings：
Fig. 1 is the energy-storage system flat volatility typical condition generation method flow chart of one embodiment of the invention.
Fig. 2 is the output result schematic diagram of a certain group of segment serial number between each error of a specific embodiment of the invention.
Fig. 3 is the flat volatility typical condition schematic diagram of a specific embodiment of the invention.
Flat volatility typical condition schematic diagram after the considerations of Fig. 4 is a specific embodiment of the invention SOE.
Fig. 5 is that the system flat volatility typical condition of one embodiment of the invention generates system structure diagram.
Specific implementation mode
Coordinate schema and presently preferred embodiments of the present invention below, the present invention is further explained to reach predetermined goal of the invention institute
The technological means taken.
In embodiments of the present invention, it is related to some terms herein in advance to illustrate：
The energy state (SOE, State Of Energy) of battery：SOE is EremWith EmaxPercentage；Wherein, EmaxFor
The maximum available energy of battery is defined as battery and is released from full of electricity condition with sufficiently small current discharge to state procedure is discharged
The energy put；EremFor dump energy, be defined as battery from current state with sufficiently small current discharge to being discharged state procedure
The energy of release.
Fig. 1 is the energy-storage system flat volatility typical condition generation method flow chart of one embodiment of the invention.Such as Fig. 1 institutes
Show, this method includes：
Step 1, operational data of the energy-storage system under flat volatility pattern is obtained.
Step 2, flat volatility curve is generated after being handled according to operational data, and is calculated and given birth to according to flat volatility curve negotiating
At corresponding feature distribution data；
Wherein, the feature distribution data of flat volatility curve includes：The amplitude distribution data of flat volatility curve, electric current become
Rate distributed data, charge and discharge conversion times data.
Step 3, flat volatility curve is segmented by a certain unit interval t, obtains multiple unit flat volatility curves
Segment；
In one embodiment, a certain unit interval t is 10 minutes desirable, i.e.,：Step 3 is by flat volatility curve by 10
Minute is segmented.
Step 4, the unit flat volatility curve segment for choosing certain amount a, the combination of a length of a × t is flat when forming multiple
Sliding curve of cyclical fluctuations segment.
In one embodiment, certain amount a can be 6, i.e.,：In step 4, it is bent that 6 unit flat volatilities are chosen
Line segment, the combination flat volatility curve segment of 60 minutes a length of (6 × 10 minutes) when forming multiple.
Step 5, according to multiple combination flat volatility curve segments, corresponding feature distribution data is generated by calculating；
Wherein, the feature distribution data of combination flat volatility curve segment includes：Combine the width of flat volatility curve segment
Distribution value data, current changing rate distributed data, charge and discharge conversion times data.
Step 6, the corresponding feature distribution data of multiple combination flat volatility curve segments and step that step 5 generates are calculated
Error amount between the 2 corresponding feature distribution datas of flat volatility curve generated.
In one embodiment, step 6 calculating error amount includes：
The amplitude distribution data of a certain combination flat volatility curve segment in calculating step 5 generate smooth with step 2
Root-mean-square error between the amplitude distribution data of the curve of cyclical fluctuations generates the first error amount；
Calculate the electric current of the current changing rate distributed data and flat volatility curve of a certain combination flat volatility curve segment
Root-mean-square error between change rate distributed data generates the second error amount；
Calculate the charge and discharge of the charge and discharge conversion times data and flat volatility curve of a certain combination flat volatility curve segment
Root-mean-square error between electric conversion times data generates third error amount；
The average error value for calculating the first error amount, the second error amount, third error amount obtains some and combines smooth wave
Error amount between the corresponding feature distribution data of moving curve segment feature distribution data corresponding with flat volatility curve；
Repeat the above steps, can obtain the corresponding feature distribution data of multiple combination flat volatility curve segments with it is smooth
Error amount between the corresponding feature distribution data of the curve of cyclical fluctuations.
Step 7, the corresponding feature distribution data of combination flat volatility curve segment that error amount is less than certain numerical value is chosen,
Synthesize the typical condition figure of energy-storage system flat volatility.
In one embodiment, the corresponding spy of combination flat volatility curve segment that error amount is less than 2% can be chosen
Distributed data is levied, the typical condition figure of energy-storage system flat volatility is synthesized.
In order to carry out apparent explanation to above-mentioned energy-storage system flat volatility typical condition generation method, with reference to
One specific embodiment illustrates, however, it should be noted that the embodiment is merely to be better described the present invention,
It does not constitute and the present invention is improperly limited.
In conjunction with step 1, energy-storage units that wind-light storage Demonstration Station energy-storage system performance the is stablized typical moon in 1 year is chosen
Part data, intercept 3 days power curves run under flat volatility pattern.
The present embodiment has chosen No. 18 PCS of ATL C005 units (high capacity cell energy-storage system, Power Conversion
System), on April 13rd, 2013, May 25, three days on the 19th June, electric current under flat volatility operating mode is worked at any time
The curve of variation, about 27 hours altogether.
The ATL units are composed in parallel by 1 PCS and 10 group of battery cluster, and battery cluster is composed in series by 3 and 12 string battery packs,
The specified charging and discharging currents of the unit are 900A.
In conjunction with step 2, which is combined into a flat volatility I-t curve, calculates the amplitude point
The distribution situation of cloth, current changing rate, charge and discharge conversion times, as a result as (electric current becomes for table 1 (battery amplitude probability distribution), table 2
Rate probability distribution), shown in table 3 (charge and discharge conversion times)：
1 battery amplitude probability distribution of table
2 current changing rate probability distribution of table
3 charge and discharge conversion times of table
In conjunction with step 3, which is segmented, every 10 minutes one section, there are out about 160 small fragments.
In conjunction with step 4, in 160 small fragments, 6 small fragments are randomly selected, form a length of 1 hour I-t at one
Curve A.A length of 1 hour I-t curve when constituting multiple in this step.
In conjunction with step 5, the characteristic value distribution situation of A is calculated, i.e.,：Amplitude distribution data, are filled current changing rate distributed data
Conversion times of discharging data (are similar to table 1 to the information of table 3).
In conjunction with step 6, the error between the curve A and 27 hours total performance curves of step 2 of step 5 is calculated, including：
The each current amplitude distribution probability (as shown in table 1, totally 13) and the corresponding part of total operating mode of curve A subtracts each other,
Find out a root-mean-square error e1；
Corresponding part phase of each current changing rate distribution probability (as shown in table 2, totally 14) of curve A with total operating mode
Subtract, finds out a root-mean-square error e2；
Likewise, can also find out the error e of charge and discharge conversion times probability3；
Three error amounts are averaging, and obtain overall error e.
In conjunction with step 7, the combination that overall error e is less than 20%, 10%, 5%, 2% is exported respectively, as shown in Fig. 2, being one
Matlab programs export as a result, wherein respectively illustrating a certain group of segment serial number between each error.
The combination flat volatility curve segment that error is less than 2% is chosen, synthesizes typical condition figure, as shown in Figure 3.According to this
Figure can calculate this section of curve charge/discharge electricity amount：It is filled with electricity 28.91Ah, releases electricity 36.85Ah.
The distribution maximum range of curve according to Fig.3, lacks higher magnitude electric current (no current between such as 300-400A
Distribution), therefore, electricity is supplemented by the magnitude current size probably lacked, SOE is made to tend to 0.
The respectively charging 14 seconds, SOE=0.031 (can not be entirely 0) of selection 200,300,400A electric currents, final synthesis typical case work
Condition curve graph, as shown in Figure 4.
Finally, it can judge whether the deviation of typical condition and actual condition meets experiment needs by comparison：
Compare the typical condition and wind-light storage Demonstration Station energy-storage system flat volatility power curve characteristic value probability of generation
Distribution situation, as shown in table 4,5,6：
4 battery amplitude probability distribution of table
5 current changing rate probability distribution of table
6 charge and discharge conversion times of table
| Number | Actual curve probability | Typical condition curve probability | Deviation |
| 319 | 7.08% | 8.20% | - 1.12% |
As seen from the above table, typical condition can cover all information of actual curve, and worst error is no more than 5%, because
This thinks that experiment needs can be met.
In actual use, it can be scaled in typical condition according to the subject specified charging and discharging currents of battery modules, equal proportion
Size of current.
Energy-storage system flat volatility typical condition generation method proposed by the present invention, which can be extracted, can characterize live operation
All characteristic quantities of data characteristics, by the method for data processing, the output time period is shorter and can cover actual motion song
The typical condition of all information content of line, it is the amendment that energy transfer amount is about zero to carry out target to typical condition, finally obtains energy
Enough characterize the experiment typical condition curve of flat volatility actual operating mode.
Based on same inventive concept, a kind of energy-storage system flat volatility typical condition life is additionally provided in the embodiment of the present invention
At system, as described in the following examples.The principle and energy-storage system flat volatility typical condition solved the problems, such as due to the system
Generation method is similar, therefore the implementation of the system may refer to the implementation of the above method, and overlaps will not be repeated.It is following to be made
, the combination of the software and/or hardware of predetermined function may be implemented in term " unit " or " module ".Although following embodiment
Described system preferably realized with software, but the realization of the combination of hardware or software and hardware be also may be simultaneously
It is contemplated.
The energy-storage system flat volatility typical condition that Fig. 5 show one embodiment of the invention generates system structure diagram.
As shown in figure 5, the system includes：
Operational data acquisition module 1, for obtaining operational data of the energy-storage system under flat volatility pattern；
Flat volatility curve distribution data generation module 2, for generating flat volatility curve after being handled according to operational data,
And it is calculated according to flat volatility curve negotiating and generates corresponding feature distribution data；
Wherein, feature distribution data includes：The amplitude distribution data of flat volatility curve, current changing rate distributed data,
Charge and discharge conversion times data；
It is smooth to obtain multiple units for flat volatility curve to be segmented by a certain unit interval t for segmentation module 3
Curve of cyclical fluctuations segment；
Flat volatility curve segment composite module 4, the unit flat volatility curve segment for choosing certain amount a, group
At it is multiple when a length of a × t combination flat volatility curve segment；
Flat volatility curve segment distributed data generation module 5 is combined, for according to multiple combination flat volatility curve pieces
Section generates corresponding feature distribution data by calculating；
Wherein, feature distribution data includes：The amplitude distribution data of multiple combination flat volatility curve segments, curent change
Rate distributed data, charge and discharge conversion times data.
Error amount computing module 6, for calculate the corresponding feature distribution data of multiple combination flat volatility curve segments with
Error amount between the corresponding feature distribution data of flat volatility curve；
Wherein, the calculating of error amount computing module 6 error amount includes：
Calculate the amplitude distribution number of the amplitude distribution data and flat volatility curve of a certain combination flat volatility curve segment
Root-mean-square error between generates the first error amount；
Calculate the electric current of the current changing rate distributed data and flat volatility curve of a certain combination flat volatility curve segment
Root-mean-square error between change rate distributed data generates the second error amount；
Calculate the charge and discharge of the charge and discharge conversion times data and flat volatility curve of a certain combination flat volatility curve segment
Root-mean-square error between electric conversion times data generates third error amount；
The average error value for calculating the first error amount, the second error amount, third error amount obtains some and combines smooth wave
Error amount between the corresponding feature distribution data of moving curve segment feature distribution data corresponding with flat volatility curve.
Typical condition figure synthesis module 7 is less than the combination flat volatility curve segment of certain numerical value for choosing error amount
Corresponding feature distribution data synthesizes the typical condition figure of energy-storage system flat volatility.
The typical condition that the energy-storage system flat volatility typical condition generation method and system proposed through the invention generates
Time cycle is shorter, convenient for pause experiment and test data analyzer, and covers all important of large capacity energy-storage system operating mode
Information, in one cycle, the energy transfer algebraical sum SOE of battery modules are carried close to 0 using the method and system of the present invention
The typical condition taken disclosure satisfy that laboratory carries out the research requirement of energy-storage system performance.
Particular embodiments described above has carried out further in detail the purpose of the present invention, technical solution and advantageous effect
Describe in detail it is bright, it should be understood that the above is only a specific embodiment of the present invention, the guarantor being not intended to limit the present invention
Range is protected, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should be included in this
Within the protection domain of invention.
Claims (14)
1. a kind of energy-storage system flat volatility typical condition generation method, which is characterized in that the method includes：
Step 1, operational data of the energy-storage system under flat volatility pattern is obtained；
Step 2, flat volatility curve is generated after being handled according to the operational data, and according to the flat volatility curve negotiating meter
It calculates and generates corresponding feature distribution data；
Step 3, the flat volatility curve is segmented by a certain unit interval t, obtains multiple unit flat volatility curves
Segment；
Step 4, the unit flat volatility curve segment for randomly selecting certain amount a, the combination of a length of a × t is flat when forming multiple
Sliding curve of cyclical fluctuations segment；
Step 5, according to multiple combination flat volatility curve segments, corresponding feature distribution data is generated by calculating；
Step 6, the corresponding feature distribution data of multiple combination flat volatility curve segments that calculates that the step 5 generates and
Error amount between the corresponding feature distribution data of flat volatility curve that the step 2 generates；
Step 7, the corresponding feature distribution data of combination flat volatility curve segment that error amount is less than certain numerical value, synthesis are chosen
The typical condition figure of energy-storage system flat volatility.
2. energy-storage system flat volatility typical condition generation method according to claim 1, which is characterized in that in step 2
In, calculating the corresponding feature distribution data of generation according to the flat volatility curve negotiating includes：
Amplitude distribution data, current changing rate distributed data, the charge and discharge conversion times data of the flat volatility curve.
3. energy-storage system flat volatility typical condition generation method according to claim 2, which is characterized in that in step 5,
According to multiple combination flat volatility curve segments, include by calculating the corresponding feature distribution data of generation：
The amplitude distribution data of multiple combination flat volatility curve segments, current changing rate distributed data, charge and discharge conversion
Number data.
4. energy-storage system flat volatility typical condition generation method according to claim 3, which is characterized in that in step 6
In, calculate the multiple corresponding feature distribution datas of combination flat volatility curve segment and the step that the step 5 generates
It is rapid 2 generate the corresponding feature distribution datas of flat volatility curve between error amount include：
Calculate the amplitude distribution number of the amplitude distribution data and the flat volatility curve of a certain combination flat volatility curve segment
Root-mean-square error between generates the first error amount；
Calculate the electric current of the current changing rate distributed data and the flat volatility curve of a certain combination flat volatility curve segment
Root-mean-square error between change rate distributed data generates the second error amount；
Calculate the charge and discharge of the charge and discharge conversion times data and the flat volatility curve of a certain combination flat volatility curve segment
Root-mean-square error between electric conversion times data generates third error amount；
The average error value of the first error amount, the second error amount, third error amount is calculated, some described smooth wave of combination is obtained
Error amount between the corresponding feature distribution data of moving curve segment feature distribution data corresponding with flat volatility curve.
5. energy-storage system flat volatility typical condition generation method according to claim 1, which is characterized in that in step 7
In, choose the corresponding feature distribution data of combination flat volatility curve segment that error amount is less than certain numerical value, synthesis energy storage system
System flat volatility typical condition figure include：
The corresponding feature distribution data of combination flat volatility curve segment that error amount is less than 2% is chosen, synthesis energy-storage system is flat
The typical condition figure of sliding fluctuation.
6. energy-storage system flat volatility typical condition generation method according to claim 1, which is characterized in that in step 3
In, the flat volatility curve is segmented by a certain unit interval t, obtains multiple unit flat volatility curve segment packets
It includes：
The flat volatility curve was segmented by 10 minutes.
7. energy-storage system flat volatility typical condition generation method according to claim 6, which is characterized in that
In step 4,6 unit flat volatility curve segments are randomly selected, a length of combination in 60 minutes is smooth when forming multiple
Curve of cyclical fluctuations segment.
8. a kind of energy-storage system flat volatility typical condition generates system, which is characterized in that the system comprises：
Operational data acquisition module, for obtaining operational data of the energy-storage system under flat volatility pattern；
Flat volatility curve distribution data generation module, for generating flat volatility curve after being handled according to the operational data,
And it is calculated according to the flat volatility curve negotiating and generates corresponding feature distribution data；
Segmentation module obtains the smooth wave of multiple units for the flat volatility curve to be segmented by a certain unit interval t
Moving curve segment；
Flat volatility curve segment composite module, the unit flat volatility curve segment for randomly selecting certain amount a, composition
The combination flat volatility curve segment of a length of a × t when multiple；
Flat volatility curve segment distributed data generation module is combined, for according to multiple combination flat volatility curve pieces
Section generates corresponding feature distribution data by calculating；
Error amount computing module, for calculating the corresponding feature distribution data of multiple combination flat volatility curve segments and putting down
Slide the error amount between the corresponding feature distribution data of the curve of cyclical fluctuations；
Typical condition figure synthesis module, it is corresponding less than the combination flat volatility curve segment of certain numerical value for choosing error amount
Feature distribution data synthesizes the typical condition figure of energy-storage system flat volatility.
9. energy-storage system flat volatility typical condition according to claim 8 generates system, which is characterized in that described smooth
Curve of cyclical fluctuations distributed data generation module, for generating flat volatility curve after being handled according to the operational data, and according to institute
Stating the corresponding feature distribution data of flat volatility curve negotiating calculating generation includes：
Amplitude distribution data, current changing rate distributed data, the charge and discharge conversion times data of the flat volatility curve.
10. energy-storage system flat volatility typical condition according to claim 9 generates system, which is characterized in that described group
Flat volatility curve segment distributed data generation module is closed, for according to multiple combination flat volatility curve segments, passing through
Calculating the corresponding feature distribution data of generation includes：
The amplitude distribution data of multiple combination flat volatility curve segments, current changing rate distributed data, charge and discharge conversion
Number data.
11. energy-storage system flat volatility typical condition according to claim 10 generates system, which is characterized in that the mistake
Difference calculating module, for calculating the corresponding feature distribution data of multiple combination flat volatility curve segments and flat volatility
Error amount between the corresponding feature distribution data of curve includes：
Calculate the amplitude distribution number of the amplitude distribution data and the flat volatility curve of a certain combination flat volatility curve segment
Root-mean-square error between generates the first error amount；
Calculate the electric current of the current changing rate distributed data and the flat volatility curve of a certain combination flat volatility curve segment
Root-mean-square error between change rate distributed data generates the second error amount；
Calculate the charge and discharge of the charge and discharge conversion times data and the flat volatility curve of a certain combination flat volatility curve segment
Root-mean-square error between electric conversion times data generates third error amount；
The average error value of the first error amount, the second error amount, third error amount is calculated, some described smooth wave of combination is obtained
Error amount between the corresponding feature distribution data of moving curve segment feature distribution data corresponding with flat volatility curve.
12. energy-storage system flat volatility typical condition according to claim 8 generates system, which is characterized in that the allusion quotation
Type working condition chart synthesis module, for choosing error amount feature point corresponding less than the combination flat volatility curve segment of certain numerical value
Cloth data, the typical condition figure for synthesizing energy-storage system flat volatility include：
The corresponding feature distribution data of combination flat volatility curve segment that error amount is less than 2% is chosen, synthesis energy-storage system is flat
The typical condition figure of sliding fluctuation.
13. energy-storage system flat volatility typical condition according to claim 8 generates system, which is characterized in that described point
Root module obtains multiple unit flat volatility curves for the flat volatility curve to be segmented by a certain unit interval t
Segment includes：
The flat volatility curve was segmented by 10 minutes.
14. energy-storage system flat volatility typical condition according to claim 13 generates system, which is characterized in that
The flat volatility curve segment composite module, the unit flat volatility curve segment for randomly selecting certain amount a,
The combination flat volatility curve segment of a length of a × t includes when forming multiple：
6 unit flat volatility curve segments are randomly selected, a length of 60 minutes combination flat volatility curve pieces when forming multiple
Section.
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