CN109888803A - The optimization method that hybrid energy-storing power supply capacity configures in wind and light generating system - Google Patents
The optimization method that hybrid energy-storing power supply capacity configures in wind and light generating system Download PDFInfo
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention discloses the optimization methods that hybrid energy-storing power supply capacity in a kind of wind and light generating system configures, comprising: establishes the energy model of wind power generating set, photovoltaic solar generating set, battery and supercapacitor respectively；According to the operation cost of the one-time investment cost of hybrid energy-storing power supply and Life cycle establish its capacity configuration Optimized model and corresponding constraint condition；Based on tolerant hierarchical sequence optimization method, according to preset significance level ranking, establishes optimization object function for the operation cost of the one-time investment cost of hybrid energy-storing power supply and Life cycle respectively and optimize constraint condition accordingly；It is successively solved to obtain optimized parameter according to the foundation sequence of optimization object function and optimization constraint condition, realize the optimization of hybrid energy-storing power supply capacity configuration, while it maximally utilizes wind energy and photovoltaic solar resource under the premise of meeting workload demand, the operation cost of one-time investment cost and Life cycle is greatly reduced.
Description
Technical field
The present invention relates to hybrid energy-storing power supply capacities in battery technology field more particularly to a kind of wind and light generating system to configure
Optimization method.
Background technique
Quick exhausted and global warming phenomenon with conventional petroleum resource increasingly sharpens, researcher all over the world
Green alternative energy source is strongly all being found with scientist, is being become by the wind and light generating system that wind energy and photovoltaic solar form and is ground
Study carefully hot spot.But wind energy and photovoltaic solar be all it is intermittent, in order to ensure the stability and reliability of energy supply, lead to
It often needs that suitable energy-storage system is arranged in wind and light generating system.In general, it is being made of wind energy and photovoltaic solar
In middle-size and small-size electricity generation system, energy storage is carried out using battery.But there are life cycles for the accumulation power supply being made of battery
It is low, power density is low, charge/discharge current exist limitation and easily it is affected by environment the problems such as.
In recent years, supercapacitor relies on the advantages that high power density, long-life, high charge-discharge efficiencies gradually to be weighed
Depending on, however the accumulation power supply energy storage capability being only made of supercapacitor is very limited, and cost ratio is made of battery
Accumulation power supply it is much higher.Obviously, be used alone battery or supercapacitor as wind and light generating system energy storage device all
The hybrid energy-storing power supply (including battery and supercapacitor) for the advantages of having certain problems, both being combined with this at
For a research direction as wind and light generating system energy storage device.
In the hybrid energy-storing power supply of wind and light generating system, capacity configuration optimization is a difficulties, to reasonable
The reliability service of wind and light generating system is most important under cost.Currently, the research that domestic and foreign scholars optimize capacity configuration is opposite
Less, main optimization method concentrates on genetic algorithm and particle swarm algorithm, wherein it is easily realized although genetic algorithm is simple, it is global
The search capability of solution is stronger, but this method belongs to random class algorithm, needs multiple operation, computationally intensive, and the calculating time is long, and
It cannot obtain stable solution；Although particle swarm algorithm search speed is fast, the parameter that need to be adjusted is few, high-efficient, there is convergence
Precision is low, the shortcomings that easily falling into locally optimal solution.
Summary of the invention
In view of the above shortcomings of the prior art, the present invention provides hybrid energy-storing power supply capacities in a kind of wind and light generating system
The optimization method of configuration efficiently solves what the capacity configuration of existing wind and light generating system hybrid energy-storing power supply cannot effectively optimize
Technical problem.
To achieve the goals above, the invention is realized by the following technical scheme:
The optimization method of hybrid energy-storing power supply capacity configuration in a kind of wind and light generating system, which is characterized in that be applied to wind
Light power generating system includes wind power generating set, photovoltaic solar generating set and hybrid energy-storing electricity in the wind and light generating system
Source, wherein include battery for providing energy and for providing the supercapacitor of power in hybrid energy-storing power supply；It is described
The optimization method of hybrid energy-storing power supply capacity configuration includes: in wind and light generating system
S10 establishes the energy mould of wind power generating set, photovoltaic solar generating set, battery and supercapacitor respectively
Type；
S20 establishes its capacity according to the one-time investment cost of hybrid energy-storing power supply and the operation cost of Life cycle and matches
The Optimized model set and corresponding constraint condition, the constraint condition include the charging and discharging currents constraint and maximum of supercapacitor
Dump energy constraint, the maximum residual energy constraint according to wind power generating set, photovoltaic solar generating set, battery and
The energy model of supercapacitor constructs；
S30 is based on tolerant hierarchical sequence optimization method, establishes according in preset significance level ranking and step S20
Optimized model and constraint condition, respectively for hybrid energy-storing power supply one-time investment cost and Life cycle operation at
This is established optimization object function and optimizes constraint condition accordingly；
S40 is successively solved to obtain optimized parameter according to the foundation sequence of optimization object function and optimization constraint condition,
Realize the optimization of hybrid energy-storing power supply capacity configuration.
In the optimization method that hybrid energy-storing power supply capacity configures in wind and light generating system provided by the invention, establish respectively
Wind power generating set in wind and light generating system, photovoltaic solar generating set, battery and supercapacitor energy model
Later, hybrid energy-storing electricity is further directed to according to preset significance level ranking and tolerant hierarchical sequence optimization method respectively
The one-time investment cost in source and the operation cost of Life cycle establish optimization object function and optimize constraint condition accordingly,
And successively solved and optimized using differential evolution algorithm according to the foundation sequence of optimization object function and optimization constraint condition,
The minimum for realizing the operation cost of hybrid energy-storing power supply one-time investment cost and Life cycle respectively, is meeting load
While maximally utilizing wind energy and photovoltaic solar resource under the premise of demand, one-time investment cost and complete is greatly reduced
The operation cost of life cycle.
Detailed description of the invention
In conjunction with attached drawing, and by reference to following detailed description, it will more easily have more complete understanding to the present invention
And its adjoint advantage and feature is more easily to understand, in which:
Fig. 1 is the process signal of the optimization method of hybrid energy-storing power supply capacity configuration in wind and light generating system in the present invention
Figure.
Specific embodiment
To keep the contents of the present invention more clear and easy to understand, below in conjunction with Figure of description, the contents of the present invention are made into one
Walk explanation.Certainly the invention is not limited to the specific embodiment, general replacement known to those skilled in the art
It is included within the scope of protection of the present invention.
The technical issues of cannot effectively optimizing for the capacity configuration of existing wind and light generating system hybrid energy-storing power supply, this hair
It is bright to provide a kind of optimization method of hybrid energy-storing power supply capacity configuration in wind and light generating system, it is applied to wind and light generating system,
Specifically, in the wind and light generating system include wind power generating set, photovoltaic solar generating set and hybrid energy-storing power supply,
In, it include battery for providing energy and for providing the supercapacitor of power, wind-power electricity generation in hybrid energy-storing power supply
Unit and photovoltaic solar generating set respectively with hybrid energy-storing power sources in parallel.During the work time, if wind power generating set
It is more with the generated energy of photovoltaic solar generating set, then it, can will be after load consumption under the premise of meeting electric energy loaded demand
Remaining power storage is in hybrid energy-storing power supply；If the generated energy of wind power generating set and photovoltaic solar generating set compared with
It is few, the electrical energy demands of load are insufficient for, the electric energy that hybrid energy-storing power supply can be stored is supplied to load.
As shown in Figure 1, including: in the optimization method that hybrid energy-storing power supply capacity configures in the wind and light generating system
S10 establishes the energy mould of wind power generating set, photovoltaic solar generating set, battery and supercapacitor respectively
Type；
S20 establishes its capacity according to the one-time investment cost of hybrid energy-storing power supply and the operation cost of Life cycle and matches
The Optimized model set and corresponding constraint condition, constraint condition include the charging and discharging currents constraint and maximum residual of supercapacitor
Energy constraint, maximum residual energy constraint is according to wind power generating set, photovoltaic solar generating set, battery and super capacitor
The energy model of device constructs；
S30 is based on tolerant hierarchical sequence optimization method, establishes according in preset significance level ranking and step S20
Optimized model and constraint condition, respectively for hybrid energy-storing power supply one-time investment cost and Life cycle operation at
This is established optimization object function and optimizes constraint condition accordingly；
S40 is successively solved to obtain optimized parameter according to the foundation sequence of optimization object function and optimization constraint condition,
Realize the optimization of hybrid energy-storing power supply capacity configuration.
In wind power generating set, wind speed is to determine the most important factor of its generated energy, and wind speed depends on many factors,
Such as weather, season.It is modeled for including small sized turbine generator in wind power generating set, output power such as formula
(1):
Wherein, PrFor the rated power of small sized turbine generator, v is wind speed, vcTo cut wind speed, vrFor rated wind speed, vf
For cut-out wind speed, k is the form parameter of Weibull distribution.
With this, the annual electricity generating capacity of small sized turbine generatorEwSuch as formula (2):
Wherein, tw1V is in wind speed for annual small sized turbine generatorc≤v≤vrThe working time in section, tw2It is annual
Small sized turbine generator is in v in wind speedr≤v≤vfThe working time in section.
In photovoltaic solar generating set, the output power of photovoltaic array depends on many factors, wherein most important
Factor is radiation intensity and environment temperature, specifically, in radiation intensity SSTC=1000W/m2With environment temperature TSTC=25 DEG C of ginseng
Under the conditions of examining, the output electric current I of photovoltaic array such as formula (3):
Wherein, V is in radiation intensity SSTC=1000W/m2With environment temperature TSTCPhotovoltaic array under=25 DEG C of reference conditions
Output voltage, ISCFor the short circuit current of photovoltaic array, VocFor the open-circuit voltage of photovoltaic array, VmFor photovoltaic array maximum power
PmaxVoltage when output, ImFor photovoltaic array maximum power PmaxElectric current when output.
Due to radiation intensity and environment temperature be it is continually changing, in the item of any radiation intensity S and environment temperature T
Mathematical model is carried out to photovoltaic array under part, such as formula (4):
Wherein, I (S, T) is the output electric current of photovoltaic array, and Δ I (S, T) is the improvement factor that photovoltaic array exports electric current,
V (S, T) is the output voltage of photovoltaic array, and Δ V (S, T) is the improvement factor of photovoltaic array output voltage, RSFor photovoltaic array
Series resistance, α be photovoltaic array short-circuit temperature coefficient, β be photovoltaic array open-circuit temperature coefficient；TNORFor photovoltaic array
Normal working temperature, generally take 40 DEG C.
Under cloudy weather, ground receiver to solar radiation will be different compared with normal condition, use secondary letter
After the solar radiation that several pairs of ground receive is modified, the actual output current I of photovoltaic arrayrealSuch as formula (5):
Wherein, TCTo weaken coefficient；N is cloud desk coefficient, and the value generally between 0-8,0 represents cloudless, and 8 represent Man Yun；
A, b and c is respectively empirical coefficient, usually takes a=0.0124, b=0.2784 and c=1.04.
With this, the annual electricity generating capacity E of photovoltaic arraysSuch as formula (6):
Es=η × Ireal×V(S,T)×NP×NS×ts (6)
Wherein, η is the efficiency of photovoltaic array, NSFor the series connection number of photovoltaic array, NPFor the number in parallel of photovoltaic array, ts
Indicate the working time in year of photovoltaic array.
In general, the capacitance grade and voltage class of battery need to improve by the series-parallel connection of single battery,
To meet loading demand.When the charging current and discharge current of single battery are specified charging current IcWhen, storage
Rechargeable energy Qb1With discharge energy Qb2Such as formula (7) and (8):
Qb1=U × Ic×tc (7)
Qb2=U × Ic×td (8)
Wherein, tcFor the charging time of single battery, tdFor the discharge time of single battery；U is single battery
Reference voltage generally takes 12V.
With this, the energy Q of batteryBATSuch as formula (9):
QBAT=(Qb1+Qb2)×h×l (9)
Wherein, h is the series connection number of single battery in battery, and l is the number in parallel of single battery in battery.
Since supercapacitor monomer is only capable of storing limited energy, and high voltage cannot be born, therefore supercapacitor
Capacity and voltage also need to improve by series-parallel supercapacitor monomer.It is assumed that serial connected super electricity in supercapacitor
The quantity of container monomer is m, and the quantity of super capacitors in parallel monomer is n, then its equivalent capacity such as formula (10):
Wherein, CfFor the capacity of supercapacitor monomer.
With this, the energy Q that supercapacitor storesSCSuch as formula (11):
Wherein, UmaxFor the maximum voltage of supercapacitor, UminFor the minimum voltage of supercapacitor, UsmaxFor super electricity
The maximum voltage of container monomer, UsminFor the minimum voltage of supercapacitor monomer.
For the optimization of hybrid energy-storing power supply capacity configuration, target is to meet wind and light generating system all properties parameter
Under the premise of guaranteeing the operation of hybrid energy-storing power good, the one-time investment cost and Quan Shengming of hybrid energy-storing power supply are minimized
Period operation cost establishes hybrid energy-storing power supply therefore the life cycle for assuming wind and light generating system is k (usually 20) year
The Optimized model L such as formula (12) of capacity configuration:
Wherein, L1For the one-time investment cost of hybrid energy-storing power supply, and L1=pBAT×QBAT+pSC×QSC；L2For mixing
The operation cost of accumulation power supply Life cycle, and L2=k × λBAT×pBAT×QBAT+k×mBAT×QBAT+k×λSC×PSC×
QSC+k×mSC×QSC；pBATFor the battery price of per unit energy, pSCFor the supercapacitor price of per unit energy, λBAT
For the year discount rate of battery per unit energy, λSCFor the year discount rate of supercapacitor per unit energy, mBATIt is every for battery
The year maintenance expense of unit energy, mSCFor the year maintenance expense of supercapacitor per unit energy.
In addition, need to consider to surpass during optimizing the capacity configuration of battery and super capacitor mixed energy storage power supply
Grade capacitor charging/discharging restriction of current and maximum residual energy constraint, specific:
Since supercapacitor is mainly used for instantaneous peak load fluctuation of the hybrid energy-storing power supply when energy density is lower,
The maximum fluctuation electric current of assumed load is 8Ic, the maximum charging current of wind and light generating system is 3Ic, then the charge and discharge of supercapacitor
Electric restriction of current such as (13):
Wherein, Is1For the charging current of supercapacitor, Is2For the discharge current of supercapacitor, IsmaxFor super capacitor
The maximum charging and discharging currents of device；
In order to fully absorb extra energy, utilization of wind and light generating system under the conditions of high wind and substantial light shine is improved
Rate, hybrid energy-storing power supply must carry out oepration at full load, therefore maximum residual energy constraint such as (14) with maximum residual energy:
Ew+Es-El≥QBAT+QSC (14)
Wherein, ElFor the energy of load consumption.
The optimization aim of hybrid energy-storing power supply capacity configuration is the totle drilling cost L minimized in formula (12), i.e., disposable to throw
Provide cost L1With the operation cost L of Life cycle2Be required to minimize, belong to multi-objective optimization question, be with the present invention is based on
Tolerant hierarchical sequence optimization method, to one-time investment cost L1With Life cycle operation cost L2Significance level ranking after
It is successively minimized, realizes the optimization aim of hybrid energy-storing power supply capacity configuration.
Since one-time investment cost is before the operation cost of Life cycle, in order to realize assembly from source
One-time investment cost L is arranged in this minimum1Be minimised as the first optimization aim, Life cycle operation cost L2Most
It is small to turn to the second optimization aim, i.e. one-time investment cost L1Different degree come the operation cost L of Life cycle2Before,
It is specific:
The the first optimization object function f established for the one-time investment cost of hybrid energy-storing power supply1Such as formula (15):
f1=min (L1)=min (pBAT×QBAT+pSC×QSC) (15)
First optimization constraint condition such as formula (16):
The the second optimization object function f established for the operation cost of the Life cycle of hybrid energy-storing power supply2Such as formula
(17):
Second optimization constraint condition such as formula (18):
Wherein, ε is tolerant coefficient, and ε > 0,For one-time investment cost L1Minimum value,For the first optimization aim
Function f1Supercapacitor charging current I after optimizations1Optimal solution,For the first optimization object function f1Super capacitor after optimization
Device discharge current Is2Optimal solution.
In optimization process, firstly, according to the first optimization constraint condition, using differential evolution algorithm to the first optimization aim
Function is solved, and supercapacitor charging current I is obtaineds1Optimal solutionSupercapacitor discharge current Is2Optimal solutionAnd one-time investment cost L1Minimum valueLater, according to second optimization constraint condition and the first suboptimization as a result, adopting
The second optimization object function is solved with differential evolution algorithm, obtains Life cycle operation cost L2Minimum value
Obtain the minimum value of totle drilling cost L:To realize the optimization of hybrid energy-storing power supply capacity configuration.
Claims (8)
1. the optimization method that hybrid energy-storing power supply capacity configures in a kind of wind and light generating system, which is characterized in that be applied to scene
Electricity generation system, includes wind power generating set, photovoltaic solar generating set and hybrid energy-storing power supply in the wind and light generating system,
Wherein, in hybrid energy-storing power supply include battery for providing energy and for providing the supercapacitor of power, wind-force hair
Motor group and photovoltaic solar generating set are connect with hybrid energy-storing power sources in parallel respectively；Storage is mixed in the wind and light generating system
Can power supply capacity configuration optimization method include:
S10 establishes the energy model of wind power generating set, photovoltaic solar generating set, battery and supercapacitor respectively；
S20 establishes its capacity configuration according to the one-time investment cost of hybrid energy-storing power supply and the operation cost of Life cycle
Optimized model and corresponding constraint condition, the constraint condition include the charging and discharging currents constraint and maximum residual of supercapacitor
Energy constraint, the maximum residual energy constraint is according to wind power generating set, photovoltaic solar generating set, battery and super
The energy model of capacitor constructs；
S30 is based on tolerant hierarchical sequence optimization method, excellent according to establishing in preset significance level ranking and step S20
Change model and constraint condition, is built respectively for the operation cost of the one-time investment cost of hybrid energy-storing power supply and Life cycle
It founds optimization object function and optimizes constraint condition accordingly；
S40 is successively solved to obtain optimized parameter according to the foundation sequence of optimization object function and optimization constraint condition, is realized
The optimization of hybrid energy-storing power supply capacity configuration.
2. the optimization method that hybrid energy-storing power supply capacity configures in wind and light generating system as described in claim 1, feature exist
In in step slo, including small sized turbine generator, the output power P of the small sized turbine generator in wind power generating setw
(v) are as follows:
Wherein, PrFor the rated power of small sized turbine generator, v is wind speed, vcTo cut wind speed, vrFor rated wind speed, vfTo cut
Wind speed out, k are the form parameter of Weibull distribution；
The annual electricity generating capacity E of small sized turbine generatorwAre as follows:
Wherein, tw1V is in wind speed for annual small sized turbine generatorc≤v≤vrThe working time in section, tw2It is annual small-sized
Turbogenerator is in v in wind speedr≤v≤vfThe working time in section.
3. the optimization method that hybrid energy-storing power supply capacity configures in wind and light generating system as described in claim 1, feature exist
In, in step slo,
Under conditions of any radiation intensity S and environment temperature T, the mathematical model of photovoltaic array in photovoltaic solar generating set
Are as follows:
Wherein, I (S, T) be photovoltaic array output electric current, Δ I (S, T) be photovoltaic array export electric current improvement factor, V (S,
It T is) output voltage of photovoltaic array, Δ V (S, T) is the improvement factor of photovoltaic array output voltage, RSFor the string of photovoltaic array
Join resistance, α is the short-circuit temperature coefficient of photovoltaic array, and β is the open-circuit temperature coefficient of photovoltaic array, TNORJust for photovoltaic array
Normal operating temperature；IFor in radiation intensity SSTC=1000W/m2With environment temperature TSTCPhotovoltaic array is defeated under=25 DEG C of reference conditions
Electric current out, and
Wherein, V is in radiation intensity SSTC=1000W/m2With environment temperature TSTCThe output of photovoltaic array under=25 DEG C of reference conditions
Voltage, ISCFor the short circuit current of photovoltaic array, VocFor the open-circuit voltage of photovoltaic array, VmFor photovoltaic array maximum power PmaxIt is defeated
Voltage when out, ImFor photovoltaic array maximum power PmaxElectric current when output；
The actual output current I of photovoltaic arrayrealAre as follows:
Wherein, TCTo weaken coefficient；N is cloud desk coefficient, and the value generally between 0-8,0 represents cloudless, and 8 represent Man Yun；A, b and c
Respectively empirical coefficient；
The annual electricity generating capacity E of photovoltaic arraysAre as follows:
Es=η × Ireal×V(S,T)×NP×NS×ts
Wherein, η is the efficiency of photovoltaic array, NSFor the series connection number of photovoltaic array, NPFor the number in parallel of photovoltaic array, tsIt indicates
The working time in year of photovoltaic array.
4. the optimization method that hybrid energy-storing power supply capacity configures in wind and light generating system as described in claim 1, feature exist
In, in step slo,
When the charging current and discharge current of single battery are specified charging current IcWhen, the rechargeable energy Q of storageb1With
Discharge energy Qb2It is respectively as follows:
Qb1=U × Ic×tc
Qb2=U × Ic×td
Wherein, tcFor the charging time of single battery, tdFor the discharge time of single battery, U is the reference of single battery
Voltage；
The energy Q of batteryBATAre as follows:
QBAT=(Qb1+Qb2)×h×l
Wherein, h is the series connection number of single battery in battery, and l is the number in parallel of single battery in battery.
5. the optimization method that hybrid energy-storing power supply capacity configures in wind and light generating system as described in claim 1, feature exist
In, in step slo,
The equivalent capacity C of supercapacitor are as follows:
Wherein, m is the series connection number of supercapacitor monomer in supercapacitor, and n is supercapacitor list in supercapacitor
The number in parallel of body, CfFor the capacity of supercapacitor monomer；
The energy Q of supercapacitor storageSCAre as follows:
Wherein, UmaxFor the maximum voltage of supercapacitor, UminFor the minimum voltage of supercapacitor, UsmaxFor supercapacitor
The maximum voltage of monomer, UsminFor the minimum voltage of supercapacitor monomer.
6. the optimization side that hybrid energy-storing power supply capacity configures in the wind and light generating system as described in claim 1-5 any one
Method, which is characterized in that in step S20,
It is assumed that the life cycle of wind and light generating system is k, according to the one-time investment cost and Quan Shengming of hybrid energy-storing power supply
The operation cost in period establishes the Optimized model L of hybrid energy-storing power supply capacity configuration are as follows:
Wherein, L1For the one-time investment cost of hybrid energy-storing power supply, and L1=pBAT×QBAT+pSC×QSC；L2For hybrid energy-storing electricity
The operation cost of source Life cycle, and L2=k × λBAT×pBAT×QBAT+k×mBAT×QBAT+k×λSC×pSC×QSC+k×
mSC×QSC；pBATFor the battery price of per unit energy, pSCFor the supercapacitor price of per unit energy, λBATFor electric power storage
The year discount rate of pond per unit energy, λSCFor the year discount rate of supercapacitor per unit energy, mBATFor battery per unit energy
The year maintenance expense of amount, mSCFor the year maintenance expense of supercapacitor per unit energy；
The charging and discharging currents of supercapacitor constrain are as follows:
Wherein, Is1For the charging current of supercapacitor, Is2For the discharge current of supercapacitor, IsmaxFor supercapacitor
Maximum charging and discharging currents；
Maximum residual energy constraint are as follows:
Ew+Es-El≥QBAT+QSC
Wherein, ElFor the energy of load consumption.
7. the optimization method that hybrid energy-storing power supply capacity configures in wind and light generating system as claimed in claim 6, feature exist
In, in step s 30, preset significance level ranking are as follows: pay the utmost attention to one-time investment cost, secondly consider full life
The operation cost in period；
The the first optimization object function f established for the one-time investment cost of hybrid energy-storing power supply1Are as follows:
f1=min (L1)=min (pBAT×QBAT+pSC×QSC)
First optimization constraint condition are as follows:
The the second optimization object function f established for the operation cost of the Life cycle of hybrid energy-storing power supply2Are as follows:
Second optimization constraint condition are as follows:
Wherein, ε is tolerant coefficient, and ε > 0；For one-time investment cost L1Minimum value,For the first optimization object function
f1Supercapacitor charging current I after optimizations1Optimal solution,For the first optimization object function f1Supercapacitor is put after optimization
Electric current Is2Optimal solution.
8. the optimization method that hybrid energy-storing power supply capacity configures in wind and light generating system as claimed in claim 7, feature exist
In in step s 40, further comprising:
S41 solves the first optimization object function using differential evolution algorithm, is surpassed according to the first optimization constraint condition
Grade charging current of condenser Is1Optimal solutionSupercapacitor discharge current Is2Optimal solutionAnd one-time investment cost
L1Minimum value
S42 solves the second optimization object function using differential evolution algorithm according to the second optimization constraint condition, obtains complete
Life cycle operation cost L2Minimum valueComplete the optimization of hybrid energy-storing power supply capacity configuration.
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| CN110474330A (en) * | 2019-08-22 | 2019-11-19 | 电子科技大学 | A kind of solar energy investment optimization method of parallel net type energy mix system |
| CN111082442A (en) * | 2019-12-06 | 2020-04-28 | 昆明理工大学 | Energy storage capacity optimal configuration method based on improved FPA |
| CN114188961A (en) * | 2021-12-13 | 2022-03-15 | 三峡大学 | Wind-solar complementary system capacity configuration optimization method |
| CN114912848A (en) * | 2022-06-27 | 2022-08-16 | 南通大学 | Full-life-cycle hybrid energy storage capacity configuration method based on adaptive filtering |
| CN116307276A (en) * | 2023-05-18 | 2023-06-23 | 江苏亚奥科技股份有限公司 | Solar photovoltaic optimization method and device for large-scale dynamic ring monitoring scene |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110474330A (en) * | 2019-08-22 | 2019-11-19 | 电子科技大学 | A kind of solar energy investment optimization method of parallel net type energy mix system |
| CN110474330B (en) * | 2019-08-22 | 2023-04-18 | 电子科技大学 | Solar investment optimization method of grid-connected hybrid energy system |
| CN111082442A (en) * | 2019-12-06 | 2020-04-28 | 昆明理工大学 | Energy storage capacity optimal configuration method based on improved FPA |
| CN114188961A (en) * | 2021-12-13 | 2022-03-15 | 三峡大学 | Wind-solar complementary system capacity configuration optimization method |
| CN114188961B (en) * | 2021-12-13 | 2023-08-01 | 三峡大学 | Capacity configuration optimization method for wind-solar complementary system |
| CN114912848A (en) * | 2022-06-27 | 2022-08-16 | 南通大学 | Full-life-cycle hybrid energy storage capacity configuration method based on adaptive filtering |
| CN114912848B (en) * | 2022-06-27 | 2023-05-26 | 南通大学 | Full life cycle hybrid energy storage capacity configuration method based on adaptive filtering |
| CN116307276A (en) * | 2023-05-18 | 2023-06-23 | 江苏亚奥科技股份有限公司 | Solar photovoltaic optimization method and device for large-scale dynamic ring monitoring scene |
| CN116307276B (en) * | 2023-05-18 | 2023-08-25 | 江苏亚奥科技股份有限公司 | Solar photovoltaic optimization method for large-scale movable ring monitoring scene |
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