CN106972545A - A kind of virtual plant power regulating method - Google Patents
A kind of virtual plant power regulating method Download PDFInfo
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- CN106972545A CN106972545A CN201710167322.9A CN201710167322A CN106972545A CN 106972545 A CN106972545 A CN 106972545A CN 201710167322 A CN201710167322 A CN 201710167322A CN 106972545 A CN106972545 A CN 106972545A
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- 238000000034 method Methods 0.000 title claims abstract description 14
- 230000001105 regulatory effect Effects 0.000 title claims abstract description 10
- 230000008859 change Effects 0.000 abstract description 3
- 230000005611 electricity Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 241000039077 Copula Species 0.000 description 2
- 241001269238 Data Species 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000008676 import Effects 0.000 description 2
- 238000004080 punching Methods 0.000 description 2
- 230000036632 reaction speed Effects 0.000 description 2
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- 238000010219 correlation analysis Methods 0.000 description 1
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
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- H02J3/383—
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- H02J3/386—
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
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- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a kind of virtual plant power regulating method, comprise the following steps：Obtain the power curve of tidal power plant, wind power plant and photovoltaic generation factory；Calculate fluctuation degree C0 of the power curve of tidal power plant in prediction period；Calculate fluctuation degree C1 of the power curve of wind power plant in prediction period；Calculate fluctuation degree C2 of the power curve of photovoltaic generation factory in prediction period；Obtain the average demand power P in prediction period；Tidal power plant's power output, wind power plant's power output and photovoltaic generation factory power output are calculated according to C0, C1, C2 and P；According to the power output for calculating each power plant of gained adjustment.This programme is in the case where meeting average demand power, and the power curve fluctuation degree according to each power plant calculates most gentle power output, so that total output reaches that most steadily, power network fluctuation is minimum, the impact brought because of generated output change is also minimum.This programme is applied to new energy control field.
Description
Technical field
The present invention relates to New-energy power system field, more particularly, to a kind of virtual plant power regulating method.
Background technology
Virtual plant refers to distributed power source, controllable burden and energy storage device in power network by distributed power management system
A virtual controllable aggregate is aggregated into, the operation and scheduling of power network is participated in, coordinated between intelligent grid and distributed power source
Contradiction, fully excavates value and benefit that distributed energy is brought by power network and user.
In the pure isolated area powered by virtual plant, the power output for suitably allocating each power plant is balance cost and receipts
The important step of benefit.
State Intellectual Property Office of the People's Republic of China disclosed entitled on 07 02nd, 2014《A kind of virtual plant
Stratified random Optimization Scheduling》Patent document (publication number：CN103903066A), it is initially set up including upper-level virtual electricity
Two layers of coordination optimization scheduling model of virtual plant of factory's layer and lower floor's micro-capacitance sensor layer, wherein each micro-capacitance sensor Optimal Operation Model of lower floor
For Chance-constrained Model, probability distribution of being exerted oneself under uncontrollable micro battery separate state is described using empirical distribution function in model,
And set up joint probability distribution model according to uncontrollable micro battery joint histogram selection Copula functions.Then it is flat using sampling
Virtual plant is coordinated and optimized scheduling model for two layers and is converted into individual layer deterministic models and asks by equal approximation method and KKT optimality conditions
Solution, optimal scheduling is carried out to virtual plant.This scheme can take into account the coordinated operation between multiple micro-capacitance sensors in virtual plant；Using general
Rate distribution can take into full account uncontrollable micro battery randomness with correlation to Optimal Scheduling with Copula correlation analysis
Influence, can be achieved virtual plant and coordinates random optimization scheduling, but can not realize the demand in each station output and area
Balance adjustment between power.
The content of the invention
The present invention mainly solve shortage present in prior art to station output and regional demand power it
Between adjusting method the virtual plant power adjusting side that technical problem is there is provided a kind of stability is good, precision is high, impact is small
Method.
The present invention is mainly what is be addressed by following technical proposals for above-mentioned technical problem：A kind of virtual plant work(
Rate adjusting method, comprises the following steps：
S001, the power curve for obtaining tidal power plant, wind power plant and photovoltaic generation factory；
S002, fluctuation degree C0 of the power curve in prediction period for calculating tidal power plant；Calculate wind power plant
Fluctuation degree C1 of the power curve in prediction period；Calculate fluctuation degree of the power curve of photovoltaic generation factory in prediction period
C2；
S003, the average demand power P obtained in prediction period；
S004, foundation C0, C1, C2 and P calculate tidal power plant's power output, wind power plant's power output and photovoltaic hair
Power plant's power output；
S005, the power output according to each power plant of calculating gained adjustment.
The virtual plant of this programme include tidal power plant, wind power plant and photovoltaic generation factory, three class power plants it is defeated
Go out to import total power network and then for being used with electric unit.The power curve in each power plant is to be obtained according to weather condition and history generated energy
To the generated output curve changed over time, be premeasuring.The change severe degree of fluctuation degree response curve.To reduce punching
Hit, changed power is more gentle better.This programme is in the case where meeting average demand power, the power curve according to each power plant
Fluctuation degree calculates most gentle power output, so that total output reaches most steady, power network fluctuation minimum, because generated output
The impact for changing and bringing is also minimum.
Preferably, the step S004 is specially：
Tide mean power P0 in prediction period is calculated according to the power curve of tidal power plant；According to wind power plant
Power curve calculate prediction period in wind-force mean power P1；Prediction period is calculated according to the power curve of photovoltaic generation factory
Interior photovoltaic mean power P2；The power output weighted value in each power plant is calculated, formula is as follows：
MP0+nP1+kP2=P
In formula, m is the power output weighted value of tidal power plant, and n is the power output weighted value of wind power plant, and k is
The power output weighted value of photovoltaic generation factory；
The power output of tidal power plant is mP0, and the power output of wind power plant is nP1, photovoltaic generation factory
Power output is kP2.
Aforesaid way can make the output of all power plants always and average demand power-balance, and smoothness highest.
Preferably, when any one in m, n and k or two are more than 1, then set the weighted value for being more than 1 as 1, etc.
Ratio increases remaining weighted value until meeting formula mP0+nP1+kP2=P.
It is the most important condition to ensure that output is not less than average demand power, and then carries out the adjustment of smoothness.
Preferably, the fluctuation degree is the variance or standard deviation of power curve each sampled point in prediction period.
Sample frequency is determined according to demand.Amount of calculation can be reduced using the mode of sampling, reaction speed is improved.
Preferably, the prediction period refers to the duration in T seconds from current time, T is prediction duration.
A length of setting value, is determined by staff according to actual conditions during prediction.
The substantial effect that the present invention is brought is total generated electric power output is reached balance with demand, and with most
Good smoothness, fluctuation is gentle, small to power network and electrical equipment impact, reduces the life-span of cost, extension power network and equipment.
Brief description of the drawings
Fig. 1 is a kind of flow of the present invention.
Embodiment
Below by embodiment, and with reference to accompanying drawing, technical scheme is described in further detail.
Embodiment：A kind of virtual plant power regulating method of the present embodiment, as shown in figure 1, comprising the following steps：
S001, the power curve for obtaining tidal power plant, wind power plant and photovoltaic generation factory；
S002, fluctuation degree C0 of the power curve in prediction period for calculating tidal power plant；Calculate wind power plant
Fluctuation degree C1 of the power curve in prediction period；Calculate fluctuation degree of the power curve of photovoltaic generation factory in prediction period
C2；
S003, the average demand power P obtained in prediction period；
S004, foundation C0, C1, C2 and P calculate tidal power plant's power output, wind power plant's power output and photovoltaic hair
Power plant's power output；
S005, the power output according to each power plant of calculating gained adjustment.
The virtual plant of this programme include tidal power plant, wind power plant and photovoltaic generation factory, three class power plants it is defeated
Go out to import total power network and then for being used with electric unit.The power curve in each power plant is to be obtained according to weather condition and history generated energy
To the generated output curve changed over time, be premeasuring.The change severe degree of fluctuation degree response curve.To reduce punching
Hit, changed power is more gentle better.This programme is in the case where meeting average demand power, the power curve according to each power plant
Fluctuation degree calculates most gentle power output, so that total output reaches most steady, power network fluctuation minimum, because generated output
The impact for changing and bringing is also minimum.
The step S004 is specially：
Tide mean power P0 in prediction period is calculated according to the power curve of tidal power plant；According to wind power plant
Power curve calculate prediction period in wind-force mean power P1；Prediction period is calculated according to the power curve of photovoltaic generation factory
Interior photovoltaic mean power P2；The power output weighted value in each power plant is calculated, formula is as follows：
MP0+nP1+kP2=P
In formula, m is the power output weighted value of tidal power plant, and n is the power output weighted value of wind power plant, and k is
The power output weighted value of photovoltaic generation factory；
The power output of tidal power plant is mP0, and the power output of wind power plant is nP1, photovoltaic generation factory
Power output is kP2.
Aforesaid way can make the output of all power plants always and average demand power-balance, and smoothness highest.
When any one in m, n and k or two are more than 1, then set the weighted value for being more than 1 as 1, equal proportion increases it
Remain-power weight values are until meet formula mP0+nP1+kP2=P.
It is the most important condition to ensure that output is not less than average demand power, and then carries out the adjustment of smoothness.
The fluctuation degree is the variance or standard deviation of power curve each sampled point in prediction period.
Sample frequency is determined according to demand.Amount of calculation can be reduced using the mode of sampling, reaction speed is improved.
The prediction period refers to the duration in T seconds from current time, and T is prediction duration.
A length of setting value, is determined by staff according to actual conditions during prediction.
Every duration T1, recalculate each power plant fluctuation degree and station output is adjusted according to result of calculation
Whole, T1 is obtained by below equation：
In formula, C0, C1 and C2 use last time to be spaced fluctuation on the basis of duration, C ' on the basis of calculating income value, T0
Degree.
This programme dynamically adjusts time delay interval duration T1 according to fluctuation degree, and interval duration is shorter when fluctuation degree is larger, fluctuation
Interval duration is longer when degree is smaller, system is had highest control efficiency.
Average demand power P is calculated in the following manner：
Local GDP data over the years and locality GDP target datas that S101, acquisition virtual plant are powered；Obtain virtual
The local electric power data and curves over the years that power plant is powered；
S102, calculating power system electricity consumption coefficient of elasticity：
Et={ (Pt+1-Pt)/Pt}/{(gt+1-gt)/gt}；
EtPower system electricity consumption coefficient of elasticity, P during for preceding ttFor average use of the t before locality within the correspondence period
Electrical power, gtFor the GDP data of t before locality；GDP data then use newest local GDP target datas；
S103, pass through power system electricity consumption coefficient of elasticity formula calculate average demand power P：
S is effective age, is preset value, can be adjusted by staff, and the more high then s of precision of demand is bigger；It is right
It should the period be the period over the years corresponding with prediction period, that is, be currently needed for the period and period pair over the years calculated
Should.Average demand power P is linked up with local GDP, and GDP is more high, and then required power is also higher.
The present invention makes total generated electric power output reach balance with demand, and with best smoothness, fluctuation is gentle,
It is small to power network and electrical equipment impact, reduce the life-span of cost, extension power network and equipment.
Specific embodiment described herein is only to spirit explanation for example of the invention.Technology neck belonging to of the invention
The technical staff in domain can be made various modifications or supplement to described specific embodiment or be replaced using similar mode
Generation, but without departing from the spiritual of the present invention or surmount scope defined in appended claims.
Although more having used the terms such as power curve, fluctuation degree, average demand power herein, it is not precluded from using
The possibility of other terms.It is used for the purpose of more easily describing and explaining the essence of the present invention using these terms；Them
Any additional limitation is construed to all to disagree with spirit of the present invention.
Claims (5)
1. a kind of virtual plant power regulating method, it is characterised in that comprise the following steps：
S001, the power curve for obtaining tidal power plant, wind power plant and photovoltaic generation factory；
S002, fluctuation degree C0 of the power curve in prediction period for calculating tidal power plant；Calculate the power of wind power plant
Fluctuation degree C1 of the curve in prediction period；Calculate fluctuation degree C2 of the power curve of photovoltaic generation factory in prediction period；
S003, the average demand power P obtained in prediction period；
S004, foundation C0, C1, C2 and P calculate tidal power plant's power output, wind power plant's power output and photovoltaic generation factory
Power output；
S005, the power output according to each power plant of calculating gained adjustment.
2. a kind of virtual plant power regulating method according to claim 1, it is characterised in that the step S004 is specific
For：
Tide mean power P0 in prediction period is calculated according to the power curve of tidal power plant；According to the work(of wind power plant
Rate curve calculates the wind-force mean power P1 in prediction period；Calculated according to the power curve of photovoltaic generation factory in prediction period
Photovoltaic mean power P2；The power output weighted value in each power plant is calculated, formula is as follows：
MP0+nP1+kP2=P
In formula, m is the power output weighted value of tidal power plant, and n is the power output weighted value of wind power plant, and k is photovoltaic
The power output weighted value in power plant；
The power output of tidal power plant is mP0, and the power output of wind power plant is nP1, the output of photovoltaic generation factory
Power is kP2.
3. a kind of virtual plant power regulating method according to claim 2, it is characterised in that when any in m, n and k
When one or two is more than 1, then the weighted value more than 1 is set as 1, equal proportion increases remaining weighted value until meeting formula m
P0+nP1+kP2=P.
4. a kind of virtual plant power regulating method according to claim 1 or 2 or 3, it is characterised in that the fluctuation degree
For the variance or standard deviation of power curve each sampled point in prediction period.
5. a kind of virtual plant power regulating method according to claim 1 or 2 or 3, it is characterised in that during the prediction
Duration from Duan Zhicong current times in T seconds, T is prediction duration.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108683211B (en) * | 2018-04-19 | 2021-04-20 | 东南大学 | Virtual power plant combination optimization method and model considering distributed power supply volatility |
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