CN106602592B - Current transformer and accumulator capacity Optimal Configuration Method in a kind of vertical shaft wind electric system - Google Patents
Current transformer and accumulator capacity Optimal Configuration Method in a kind of vertical shaft wind electric system Download PDFInfo
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- CN106602592B CN106602592B CN201610914866.2A CN201610914866A CN106602592B CN 106602592 B CN106602592 B CN 106602592B CN 201610914866 A CN201610914866 A CN 201610914866A CN 106602592 B CN106602592 B CN 106602592B
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- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000004146 energy storage Methods 0.000 claims abstract description 22
- 230000002068 genetic effect Effects 0.000 claims abstract description 13
- 230000035772 mutation Effects 0.000 claims description 3
- 230000005611 electricity Effects 0.000 claims description 2
- 238000009434 installation Methods 0.000 claims 1
- 238000005259 measurement Methods 0.000 claims 1
- 238000004364 calculation method Methods 0.000 abstract description 3
- 230000010354 integration Effects 0.000 abstract description 3
- 238000005457 optimization Methods 0.000 abstract description 3
- 230000005684 electric field Effects 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
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- 238000005868 electrolysis reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
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Classifications
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- H02J3/386—
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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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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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
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Abstract
The capacity of single unit system under this electric field power disclosed by the invention is stabilized distributes strategy rationally, it analyzes by the calculation method of the battery energy-storage system charge and discharge process formed and loss wind energy, and using the expense of back-to-back PWM converter, battery cost of investment and wind energy loss as optimization object function.The capacity Optimal Allocation Model is limited to constraint condition with the wind-electricity integration output-power fluctuation of battery operation characteristic and requirements of the national standard, and is calculated using improved Non-Linear Programming genetic algorithm.Finally, being calculated using the strategy specific example, numerical results demonstrate the correctness of capacity Optimal Allocation Model and algorithm.The present invention distributes strategy rationally using the whole volume for comprehensively considering system generator-side converter wear VSC1, grid-side converter VSC2 and battery, so that wind power system is sufficiently captured wind power grid, while guaranteeing that system cost is minimum, realizes maximizing the benefits.
Description
Technical field
The invention patent belongs to the capacity configuration optimizing method of single unit system in electric system, in particular to current transformer and storage
Battery whole volume is distributed rationally.
Background technique
In recent years, becoming increasingly conspicuous with energy crisis and environmental problem, wind generating technology is ground as new energy field
Study carefully and using focus of attention.However, wind power system output power has unstability and interval due to the continuous variation of wind energy
Property, it is not able to satisfy grid-connected requirement.In order to stabilize the random fluctuation of wind power system output power, improve the electric energy matter of wind-electricity integration
Amount, needs to configure the energy storage device of certain capacity in systems.
Currently, the hybrid energy-storing system that the single energy-storage system and battery that are made of battery form in conjunction with other devices
System is current studies and using more wind-powered electricity generation energy storage device.For the single energy-storage system of battery in wind power system establish with
Energy storage device performance and the indicator of costs are that the capacity of objective function distributes strategy rationally；For free-standing vertical axis wind power generation system
Battery combination water electrolysis hydrogen production mixed energy storage system capacity in system proposes the energy-optimised management plan of combined energy-accumulation system
Summary and complete system configuration design method；For battery combining super capacitor composition mixed energy storage system establish with
The average annual expense of energy storage device is the capacity configuration strategy of objective function；The hybrid energy-storing based on chance constrained programming is proposed simultaneously
Capacity collocation method, the relationship for configuration hybrid energy-storing capacity, coordination power quality and economy provide quantitative basis.At present
To research all comparative maturities that the capacity of energy storage device in wind power system is distributed rationally, but it is one-sided to merely relate to energy storage device
Capacity optimization, other devices of unbonded wind power system are comprehensively considered.In order to analyze wind power system more fully hereinafter
The capacity configuration of each component part will comprehensively consider the change of system pusher side using vertical axis wind power generation system as research object herein
The whole volume of stream device, grid-side converter and battery distributes strategy rationally, so that wind power system is sufficiently captured wind power grid, together
When guarantee system cost it is minimum, realize maximizing the benefits.
Summary of the invention
Technical problem solved by the invention is to establish current transformer and energy-storage system whole volume Optimal Allocation Model,
Propose a kind of capacity configuration optimizing method of the single unit system under wind power is stabilized.To achieve the above object, of the invention
The technical solution taken is as follows:
Vertical shaft wind electric system is made of Wind turbines, generator-side converter wear VSC1, grid-side converter VSC2 and battery；Wind
It is power output that motor group, which absorbs wind energy transformation, and then electric energy is connected to the grid by VSC1 rectification, VSC2 inversion；Battery is in parallel
On the DC bus of double PWM converters.
Specific step is as follows for capacity configuration optimizing method:
(1) t moment is set, the unit of t is second, P0It (t) is the power of input Wind turbines, WlossFor the loss of Wind turbines year
Wind energy, P1It (t) is the power of generator-side converter wear VSC1 output, P10For the rated power of generator-side converter wear VSC1, P3It (t) is net
The power of side current transformer VSC2 output, P30For the rated power of grid-side converter VSC2, P2It (t) is the charge power of battery,
S20For the rated capacity of battery.
(2) objective function is established:
J=Min (Closs+CVSC1+CVSC2+CBS)
In above formula, ClossIt goes wrong the cost that can be generated, may be expressed as: for Wind turbines annual loss
Closs=Ke×Wloss
Wherein, KeIndicate the unit cost of wind energy.
CVSC1For the year cost of investment that generator-side converter wear is constituted together with Wind turbines, may be expressed as:
CVSC1=Kp1×P10
Wherein, Kp1For the year cost of investment of generator-side converter wear and Wind turbines per unit capacity kW.
CBSFor the year cost of investment of battery, may be expressed as:
CVSC2=Kp2×S20
Wherein, Kp2For the year cost of investment of battery per unit capacity kWh.
CVSC2For the year cost of investment of grid-side converter VSC2, may be expressed as:
CVSC2=Kp3×P30
Wherein, Kp3For the year cost of investment of grid-side converter VSC2 per unit capacity kW.
(3) constraint condition is determined:
The annual loss of Wind turbines is gone wrong can WlossMeet following formula:
Generator-side converter wear VSC1's must satisfy following formula with the output power of grid-side converter VSC2:
According to national standard, grid-side converter output power P3(t) should also following formula be met:
Wherein Δ P1minTo allow the maximum value fluctuated, Δ P in grid-side converter output power 1 minute10minFor net side change
Stream allows the maximum value fluctuated, Max [P in device output power 10 minutes3(0:60)] it indicates from current time the 0th second to the 60th second
It is interior, the maximum value of grid-side converter average output power, Min [P3(0:60)] indicate current time from the 0th second to the 60th second,
The minimum value of grid-side converter average output power, Max [P3(0:600)] indicate current time from the 0th second to the 600th second,
The maximum value of grid-side converter average output power, Min [P3(0:600)] indicate current time from the 0th second to the 600th second,
The minimum value of grid-side converter average output power.
The charge power P of battery2(t) and the real-time energy storage S of battery2(t) restrictive condition are as follows:
Δ T is to control the period, value 1 second, S2It (0) is the initial energy storage of battery.
(4) objective function optimal solution is solved:
The corresponding P of objective function is solved using Non-Linear Programming genetic algorithm10、P30、S20。
The initial population of genetic algorithm includes k individual, and every individual is indicated with m bit；Wherein preceding l representative
P10, intermediate p represents S20, last q represents P30；M=l+p+q；The initial energy storage of battery is full capacity, and population at individual intersects
Probability is n, and population at individual mutation probability is r；The termination condition of genetic algorithm is that evolution number is x；Genetic algorithm becomes each time
After different, retain individual corresponding when target function value minimum.
The Kp1Value is 200, Kp2Value is 480, Kp3Value is 200, KeValue is that 0.53, k value is that 30, m takes
It is 20, p value be 30, q value be 20, n value be 0.6, r value be 0.1, x value is 45 that value, which is 70, l value,.
This electric field power disclosed by the invention stabilize under single unit system capacity configuration optimizing method, analyze by storing
The energy-storage system charge and discharge process of battery composition and the calculation method of loss wind energy, and thrown with back-to-back PWM converter, battery
The expense for providing cost and wind energy loss is optimization object function.The capacity Optimal Allocation Model is with battery operation characteristic and country
The wind-electricity integration output-power fluctuation of standard requirements is limited to constraint condition, and using improved Non-Linear Programming genetic algorithm into
Row calculates.Finally, being calculated using this method specific example, numerical results demonstrate capacity Optimal Allocation Model and calculation
The correctness of method.The present invention, which uses, comprehensively considers the whole of system generator-side converter wear VSC1, grid-side converter VSC2 and battery
Body capacity configuration optimizing method makes wind power system sufficiently capture wind power grid, while guaranteeing that system cost is minimum, realizes benefit most
Bigization.
Benefit result of the invention: 1) be the system that is greatly reduced cost of investment；2) electric energy is adequately utilized.
Detailed description of the invention
Wind power system topology of the Fig. 1 based on batteries to store energy
Fig. 2 wind power system flow of power schematic diagram
Fig. 3 Non-Linear Programming genetic algorithm flow chart
The convergent of each variable of Fig. 4 objective function, wherein Fig. 4 (a) is that Cy restrains waveform, and Fig. 4 (b) is P10Restrain wave
Shape, Fig. 4 (c) are S20Waveform is restrained, Fig. 4 (d) is P30Waveform is restrained, Fig. 4 (e) is WlossRestrain waveform
Specific embodiment
In order to which the technical problems, technical solutions and beneficial effects solved by the present invention is more clearly understood, below in conjunction with
Attached drawing, the present invention will be described in further detail.It should be appreciated that specific example described herein is only used to explain this hair
It is bright, it is not intended to limit the present invention.
Fig. 1 is the vertical axis wind electric system topological based on batteries to store energy, and vertical shaft wind electric system is by Wind turbines, pusher side
Current transformer VSC1, grid-side converter VSC2 and battery are constituted.The electric energy of Wind turbines output passes through VSC1 rectification, VSC2 inversion
Power grid is accessed later；Battery is connected in parallel on the DC bus of double PWM inverters.
Establish objective function:
J=Min (Closs+CVSC1+CVSC2+CBS)
In above formula, ClossIt goes wrong the cost that can be generated, may be expressed as: for Wind turbines annual loss
Closs=Ke×Wloss
Wherein, KeIndicate the unit cost of wind energy, KeValue is 0.53.
CVSC1For the year cost of investment of generator-side converter wear VSC1, may be expressed as:
CVSC1=Kp1×P10
Wherein, Kp1For the year cost of investment of generator-side converter wear VSC1 per unit capacity kW, Kp1Value is 200.
CBSFor the year cost of investment of battery, may be expressed as:
CVSC2=Kp2×S20
Wherein, Kp2For the year cost of investment of battery per unit capacity kWh, Kp2Value is 480.
CVSC2For the year cost of investment of grid-side converter VSC2, may be expressed as:
CVSC2=Kp3×P30
Wherein, Kp3For the year cost of investment of grid-side converter VSC2 per unit capacity kW, Kp3Value is 200.
Fig. 2 is wind power system flow of power schematic diagram, P0It (t) is the power of input Wind turbines, WlossFor Wind turbines damage
The wind energy of mistake, P1It (t) is the power of generator-side converter wear VSC1 output, P10For the rated power of generator-side converter wear VSC1, P3(t) it is
The power of grid-side converter VSC2 output, P30For the rated power of grid-side converter VSC2, P2It (t) is the charging function of battery
Rate, S20For the rated capacity of battery.Determine that the constraint condition of wind power system is as follows:
The loss wind energy W of Wind turbineslossMeet following formula:
Generator-side converter wear VSC1's must satisfy following formula with the output power of grid-side converter VSC2:
It is required according to national standard GB/T 15945-2008, grid-side converter output power P3(t) should also following formula be met:
Wherein Δ P1minTo allow the maximum value fluctuated, Δ P in grid-side converter output power 1 minute10minFor net side change
Stream allows the maximum value fluctuated, Max [P in device output power 10 minutes3(0:60)] it indicates from current time the 0th second to the 60th second
It is interior, the maximum value of grid-side converter average output power, Min [P3(0:60)] indicate current time from the 0th second to the 60th second,
The minimum value of grid-side converter average output power, Max [P3(0:600)] indicate current time from the 0th second to the 600th second,
The maximum value of grid-side converter average output power, Min [P3(0:600)] indicate current time from the 0th second to the 600th second,
The minimum value of grid-side converter average output power.
The charge power P of battery2(t) and the real-time energy storage S of battery2(t) restrictive condition are as follows:
Δ T is to control the period, value 1 second, S2It (0) is the initial energy storage of battery.
Fig. 3 is Non-Linear Programming heredity flow chart, solves the corresponding P of objective function using Non-Linear Programming genetic algorithm10、
P30、S20.Wherein, initial population includes k=30 individual, the mrna length m=70 of every individual, wherein characterizing P10Mrna length l
=20, characterize S20Mrna length be p=30, characterize P30Mrna length q=20, P10Variation range be 0~P0_max..It stores
The initial energy storage of battery is full capacity；Population at individual crossover probability pc takes n=0.6；Population at individual mutation probability pm takes r=0.1；It calculates
Method termination condition is that evolution number is x=45.
Fig. 4 is the convergent of each variable of objective function, and Fig. 4 (a) is wind power system year objective cost CyConvergence waveform,
CyFinally converge on 485.10 ten thousand yuan；Fig. 4 (b) is the rated power P of wind power system generator-side converter wear VSC110Convergence waveform,
P10Finally converge on 10.4253MW；Fig. 4 (d) is the rated power P of wind power system grid-side converter VSC230Convergence waveform,
P30Converge to 9.5273MW；Fig. 4 (e) is Wind turbines daily loss wind energy WlossConvergence waveform, WlossIt converges to
1.9711MW·h。
Claims (2)
1. current transformer and accumulator capacity Optimal Configuration Method, the vertical shaft wind electric system in a kind of vertical shaft wind electric system
It is made of Wind turbines, generator-side converter wear VSC1, grid-side converter VSC2 and battery；
It is characterized in that, specific step is as follows for capacity configuration optimizing method:
(1) measurement obtains the wind energy data of the wind power system installation site in the past in 1 year, uses P0(t) it indicates；With variable P1(t)
Indicate the power of generator-side converter wear VSC1 output, P2(t) charge power of battery, P are indicated3(t) grid-side converter VSC2 is indicated
The power of output；P10Indicate the rated power of generator-side converter wear VSC1；S20Indicate the rated capacity of battery；P30Indicate net side
The rated power of current transformer VSC2；
The wind energy W of internal loss in (2) one yearslossIt is calculate by the following formula acquisition:
(3) objective function is established:
J=Min (Ke×Wloss+Kp1×P10+Kp2×S20+Kp3×P30)；
In above formula, KeIndicate the unit cost of wind energy；Kp1For the year cost of investment and wind of generator-side converter wear VSC1 unit capacity kW
Year the sum of the cost of investment of motor group unit capacity kW；Kp2For the year cost of investment of battery cell's voluminosity electricity；Kp3For net side
The year cost of investment of current transformer VSC2 per unit capacity kW；
(4) constraint condition is determined:
Generator-side converter wear VSC1's must satisfy following formula with the output power of grid-side converter VSC2:
According to national standard, grid-side converter output power P3(t) should also following formula be met:
Wherein Δ P1minTo allow the maximum value fluctuated, Δ P in grid-side converter output power 1 minute10minFor grid-side converter
Output power allows the maximum value fluctuated, Max [P in 10 minutes3(0:60)] indicate current time from the 0th second to the 60th second,
The maximum value of grid-side converter average output power, Min [P3(0:60)] indicate current time from the 0th second to the 60th second, net
The minimum value of side current transformer average output power, Max [P3(0:600)] indicate current time from the 0th second to the 600th second, net
The maximum value of side current transformer average output power, Min [P3(0:600)] indicate current time from the 0th second to the 600th second, net
The minimum value of side current transformer average output power；
The charge power P of battery2(t) and the real-time energy storage S of battery2(t) restrictive condition are as follows:
Δ T is to control the period, value 1 second, S2It (0) is the initial energy storage energy of battery；
(4) objective function optimal solution is solved:
The corresponding P of objective function is solved using Non-Linear Programming genetic algorithm10、P30、S20；
The initial population of genetic algorithm includes k individual, and every individual is indicated with m bit；Wherein represent P for first l10, in
Between p represent S20, last q represents P30；M=l+p+q；The initial energy storage of battery is full capacity, and population at individual crossover probability is
N, population at individual mutation probability are r；The termination condition of genetic algorithm is that evolution number is x；After genetic algorithm makes a variation each time, protect
Corresponding individual when staying target function value minimum；
After genetic algorithm, the individual of reservation is taken out, P will be represented in the individual10、S20、P30Binary digit take out respectively,
It is converted into decimal number；This 3 decimal numbers be the rated power of generator-side converter wear VSC1 after optimizing, battery it is specified
The rated power of capacity, grid-side converter VSC2.
2. current transformer and accumulator capacity Optimal Configuration Method in a kind of vertical shaft wind electric system according to claim 1,
The Kp1Value is 200, Kp2Value is 480, Kp3Value is 200, KeIt is 30, m value is 70, l that value, which is 0.53, k value,
It is 30, q value be 20, n value be 0.6, r value be 0.1, x value is 45 that value, which is 20, p value,.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103972916A (en) * | 2014-05-27 | 2014-08-06 | 国家电网公司 | Micro-grid operation method for stabilizing fluctuation of wind and light power by energy storing device |
| CN104092231A (en) * | 2014-06-27 | 2014-10-08 | 上海电力学院 | Method for optimal configuration of independent micro grid mixed energy storage capacity |
| CN104104107A (en) * | 2014-06-16 | 2014-10-15 | 清华大学 | Model prediction control method of stabilizing wind power fluctuation with hybrid energy storage |
| CN105787605A (en) * | 2016-03-24 | 2016-07-20 | 上海电力学院 | Micro-grid economic and optimal operation and scheduling method based on improved quantum genetic algorithm |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103972916A (en) * | 2014-05-27 | 2014-08-06 | 国家电网公司 | Micro-grid operation method for stabilizing fluctuation of wind and light power by energy storing device |
| CN104104107A (en) * | 2014-06-16 | 2014-10-15 | 清华大学 | Model prediction control method of stabilizing wind power fluctuation with hybrid energy storage |
| CN104092231A (en) * | 2014-06-27 | 2014-10-08 | 上海电力学院 | Method for optimal configuration of independent micro grid mixed energy storage capacity |
| CN105787605A (en) * | 2016-03-24 | 2016-07-20 | 上海电力学院 | Micro-grid economic and optimal operation and scheduling method based on improved quantum genetic algorithm |
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