CN110084465A - Wind generator system cost/Reliability Estimation Method based on energy storage - Google Patents
Wind generator system cost/Reliability Estimation Method based on energy storage Download PDFInfo
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Abstract
The invention discloses a kind of wind generator system cost/Reliability Estimation Method based on energy storage, comprising: step 1: the operation of AA-CAES energy-accumulating power station and wind generator system generated output model are established.Step 2: cost/Reliability Evaluation Model is established.Step 3: objective function is established according to step 1 and step 2, optimization aim is to obtain fan capacity when the sum of system year cost and load power-off damages are minimum value.The present invention is using power-off damages as the assessment object of system reliability, AA-CAES energy-accumulating power station charge-discharge electric power is optimized to balance the randomness of wind-power electricity generation using dynamic programming algorithm, is influenced caused by the cost and reliability of system by changing power system capacity analysis different capabilities.
Description
Technical field
The present invention relates to Operation of Electric Systems fields, are related to a kind of wind generator system based on adiabatic compression air energy storage
Cost/Reliability Estimation Method.
Technical background
In recent years, energy and environment problem is increasingly severe, and countries in the world have started to focus and energy-saving and emission-reduction and renewable energy
The development and utilization in source.Factors shadows such as randomness, system flexible modulation power supply however the consumption of renewable energy power is contributed by it
It rings[1].The appearance of extensive energy storage technology be realize smooth renewable energy power output randomness, improve flexible peaking power source and
One of the major measure of the comprehensive consumption of renewable energy multipotency manifold formula [2-3].Wherein, water-storage, battery and compression are empty
Gas energy storage (Compressed air energy storage, CAES) is three kinds of energy storage skills suitable for storing large-scale electric energy
Art [4].Water-storage technology maturation, efficiency are higher, cost is relatively low, still, since it is limited by landform and water resources condition,
Often far from load, electrical energy transportation loss is big for addressing.Batteries to store energy high conversion efficiency, response are rapid, but by cost, service life
And the problems such as environment, influences, large-scale application is restricted.Traditional power station CAES is because capacity is big, the service life is long, starting is fast, response
Flexibly the advantages that, has much application prospect, CAES technology be considered as at present most the extensive energy storage technology of development potentiality it
One, have received widespread attention [5].
Twentieth century the seventies and eighties, international fuel price are substantially increased, under the low background of gas peak regulation unit economy,
America and Europe takes the lead in having carried out the technical research of CAES, successively builds up German Huntorf (1978) and U.S. McIntosh (1991
Year) two comercial operation power stations, it is used to peak-load regulating and power backup (black starting-up), cycle efficieny is much higher than gas peak regulation machine
Group, so far operational excellence [6-7].However, the tradition power station CAES is primarily present following two drawbacks: 1) fossil fuel is depended on,
Application in the deficient area of natural gas resource and current low-carbon energy system is limited.2) running efficiency of system is lower, it is difficult to aobvious
Write lifting system performance driving economy.With the maturation application of heat-storage technology, scholars propose advanced adiabatic compression air energy storage
(Advanced adiabatic compressed air energy storage, AA-CAES) this concept solves above-mentioned two
Item drawback.AA-CAES is a kind of by recycling and reusing air compression thermal energy, abandons conventional CAES technology fossil fuel and mends right ring
Section improves the cleaning energy storage technology [8] of system circulation efficiency.Currently, the power station AA-CAES demonstration project ADELE is German emerging
It builds, power station rated power 90MW, rated capacity 360MWh, target circulation efficiency 70% [9].In China, in May, 2017,
National Energy Board's approval project verification seat of honour 50MW grades of AA-CAES national level energy storage demonstrative project --- Community of Jin Tan County salt cave compressed air
Energy-storing and power-generating system [10]；In July in the same year, reply is set up the project, and China is first to be shown by " internet+" wisdom energy of core of AA-CAES
Typical term mesh --- high ditch cable base intelligent micro-grid of doing nothing；In March, 2018, National Energy Board is " working energy refers within 2018
Lead opinion " in point out, actively push forward Community of Jin Tan County compressed-air energy storage project, research promotes 100MW compressed-air energy storage power station.
Cost/the Reliability Analysis Research currently distributed rationally to consideration AA-CAES energy-accumulating power station is blank.In this background
Under, the present invention has developed to be planned based on AA-CAES station capacity in cost/reliability assessment system.First, it is contemplated that wind-force
The randomness and the power station AA-CAES operation characteristic of power generation, the operation of the building power station AA-CAES and wind generator system generated output mould
Type is emulated using operating condition of the Monte Carlo Method to the power station AA-CAES, wind-driven generator.Secondly, user is made
For market element, the damages of interruptible load load is calculated, and is advised with powering off the minimum target of damages using dynamic
The method of drawing optimizes the power station AA-CAES charge-discharge electric power, establishes cost/Reliability Evaluation Model.Finally by simulation analysis AA-CAES
The influence that station capacity generates system economy and power supply reliability.
Bibliography
[1] Shu Yinbiao, Zhang Zhigang, Guo Jianbo are waited in the consumption critical factor analysis of new energy and solution research [J]
State's electrical engineering journal, 2017,37 (1): 1-8.
[2] Hu Juan, Yang Shuili, Hou Chaoyong, the As-Is analysis for waiting scale energy storage technology demoncal ration to apply and enlightenment
[J] electric power network technique, 2015,39 (4): 879-885.
[3] Madaeni S H, Sioshansi R, Denholm P.How Thermal Energy Storage
Enhances the Economic Viability of Concentrating Solar Power[J].Proceedings
Of the IEEE, 2012,100 (2): 335-347.
[4] Xu Yujie, Chen Haisheng, Liu Jia wait the compressed-air energy storage and generating integrated system characteristic point of wind light mutual complementing
Analyse [J] Proceedings of the CSEE, 2012,32 (20): 88-95.
[5] Liu Chang, Xu Yujie, Hu Shan wait compressed-air energy storage power station technology economic analysis [J] energy storage science and skill
Art, 2015,4 (2): 158-168.
[6]International Energy Agency.Prospects for Large-Scale Energy
Storage in Decarbonised Power Grids [R] .Paris:International Energy Agency,
2009.
[7] D í az-Gonz á lez F, Sumper A, Gomis-Bellmunt O, et al.A review of energy
storage technologies for wind power applications[J].Renewable&Sustainable
Energy Reviews, 2012,16 (4): 2154-2171.
[8] Jubeh N M, Najjar Y S H.Green solution for power generation by
Adoption of adiabatic CAES system [J] .Applied Thermal Engineering, 2012,44 (44):
85-89.
[9] Luo X, Wang J, Krupke C, et al.Modelling study, efficiency analysis
and optimisation of large-scale Adiabatic Compressed Air Energy Storage
Systems with low-temperature thermal storage [J] .Applied Energy, 2016,162:589-
600.
[10] Mei Shengwei, public cyclopentadienyl fine jade, the state of Qin is good, wait advanced adiabatic compression air energy storage technology of the based on the gas storage of salt cave and
Application prospect [J] electric power network technique, 2017,41 (10): 3392-3399.
Summary of the invention
Problem to be solved by this invention be consider running of wind generating set characteristic, AA-CAES charge-discharge electric power and power-off at
Under the premise of this, a kind of wind generator system cost/Reliability Evaluation based on advanced adiabatic compression air energy storage is proposed
Method.
The technical solution adopted by the present invention to solve the technical problems is: the wind-force based on advanced adiabatic compression air energy storage
Electricity generation system cost/Reliability Estimation Method.Include the following steps:
Step 1: the advanced adiabatic compression air power station energy storage AA-CAES operation and wind generator system generated output are established
Model.
Formula (1) indicates the constraint of the power station AA-CAES compression horsepower.Wherein, Pc,tIndicate the compression horsepower of period t；ηcIndicate pressure
Compression process efficiency；Indicate that period t flows into the flow of compressor；γ indicates air specific heat ratio；RgIndicate ideal gas constant；nc
Indicate compressor series；Tc,l,inWithRespectively indicate the air themperature into l grades of compressors and final compressor；βc,lWithRespectively indicate l grades of compressors specified compression ratio and final compressor period t compression ratio, with air storage chamber air pressure
It is related.
Pc,minνc,t≤Pc,t≤Pc,maxνc,t (2)
Formula (2) indicates the constraint of compression horsepower bound.Wherein, Pc,minAnd Pc,maxRespectively indicate the bound of compression horsepower；
νc,tFor binary variable, for indicating whether the power station AA-CAES is in compression condition, when the power station AA-CAES is in compression condition
When, νc,t=1, on the contrary νc,t=0.
Formula (3) indicates the constraint of expanding machine generated output.Wherein, Pg,tIndicate the generated output of period t；ηgExpression generated electricity
Journey efficiency；Indicate that period t flows into the flow of expanding machine；Tg,j,in,tIndicate that the t period enters the air themperature of j-th stage expanding machine,
It is related to air storage chamber temperature；ngIndicate expansion series；βg,jIndicate j-th stage expanding machine nominal expansion ratio.
Pg,minνg,t≤Pg,t≤Pg,maxνg,t (4)
Formula (4) indicates the constraint of generated output bound.Wherein, Pg,minAnd Pg,maxRespectively indicate the bound of generated output；
νg,tFor binary variable, for indicating whether the power station AA-CAES is in generating operation mode, when the power station AA-CAES is in generating operation mode
When, νg,t=1, on the contrary νg,t=0.
νc,tνg,t=0 (5)
Formula (5) indicates the constraint of the power station AA-CAES operating condition.The constraint is for guaranteeing that the power station AA-CAES does not work at the same time
In compression condition and generating operation mode.
Formula (6) indicates that wind-driven generator considers the uncertainty that its generated energy changes with wind speed, its is hourly average
Wind speed is described as obeying Weibull distribution.Wherein, ν indicates mean hourly wind speed, unit m/s；C indicates annual mean wind speed；k
The distribution and shape of wind speed are described.
Formula (7) indicates the correlation for ignoring generated output between the mechanical loss and blower of blower, doubly-fed wind turbine
Generated output.Wherein, PWTIndicate the wind energy power of blower capture；ρ indicates atmospheric density, unit kg/m3；S indicates that wind wheel is swept
Wind area, unit m2；Formula (8) indicates power coefficient.
The calculation method of parameter in formula (9) expression (8).The calculation method of parameter in formula (10) expression (9).Wherein,
θpIndicate propeller pitch angle；λ indicates tip speed ratio；ω indicates blade end angular speed；R indicates blade radius.When wind speed is cut less than blower
When entering wind speed, it is 0 that blower, which issues power,；When wind speed is between incision wind speed and rated wind speed, θp=0, by formula (6)-(9)
Calculate power of fan；When wind speed is higher than rated wind speed, blower variable blade control system work makes blower issue the specified function of power
Rate；When wind speed is greater than blower cut-out wind speed, blower is out of service.
Step 2: cost/Reliability Evaluation Model is established.
The fixed cost of formula (11) expression wind generator system and AA-CAES energy-accumulating power station.Wherein, CAIndicate initial outlay
Convert the fixed cost summation of equal years value: A1、A2Respectively indicate being fixed into for wind generator system and AA-CAES energy-accumulating power station
This；α indicates Annual Percentage Rate；N indicates loan year.
CT=CA+CM (12)
Formula (12) indicates year comprehensive cost.Wherein, CMIndicate the maintenance of wind generator system and AA-CAES energy-accumulating power station at
This,.CMNumerical value is the 2% of wind generator system year fixed cost.
Formula (13) indicates that average load powers off cost.Wherein, fAVE(h, t) indicates h type load and load power-off time t's
Average load powers off cost function；fc(h, t) indicates that load of the h type load at load power-off time t powers off cost；Table
Show the peak load of h type load.
Formula (14) indicates that synthetic load powers off cost coefficient.Wherein, fcom(t) indicate that the synthesis under load power-off time t is negative
Lotus powers off cost coefficient, reflects the relationship of electric system unit quantity of electricity load power-off cost and load section power-off time t, Ltype
Indicate load type set, σhIndicate the electricity consumption ratio of load h, LhIndicate the rate of load condensate of load h.
Formula (15) indicates the power-off of i-th load at damages.Wherein, CiIndicate that i-th load powers off damages,
QEENS,hIndicate the electricity shortage amount of h type load.
Formula (16) indicates that annual load powers off damages.Wherein, N indicates always to power off number, Q in 1 yearEENSIt indicates
Total electricity shortage amount.
Step 3: establishing objective function according to step 1 and step 2, emulates wind turbine power generation function using Monte carlo algorithm
Rate obtains fan capacity when the sum of system year cost and load power-off damages are minimum value by optimization method.
Preferably, optimization method is to be filled by dynamic programming to advanced adiabatic compression air energy storage systems in step 3
Discharge power optimizes.
This patent, which is different from existing research work, following features:
1) using power-off damages as the assessment object of system reliability
2) AA-CAES energy-accumulating power station charge-discharge electric power is optimized to balance wind-power electricity generation using dynamic programming algorithm
Randomness.
3) system analyzes different capabilities to the cost of system and can by changing power system capacity under the premise of 1) 2) meeting
By being influenced caused by property
Detailed description of the invention
Fig. 1 typical case's two stages of compression double expansion AA-CAES Power station structure figure
Fig. 2 overall plan flow chart
Fig. 3 AA-CAES charge-discharge electric power optimizes dynamic programming path
Specific embodiment
A kind of wind generator system cost/Reliability Estimation Method based on energy storage of the present invention, this method is specifically such as
Under:
Step 1: the operation of the power station AA-CAES and wind generator system generated output model are established.
The structure chart of the energy storage model is as shown in Figure 1；Formula (1) indicates the constraint of the power station AA-CAES compression horsepower.Wherein, Pc,t
Indicate the compression horsepower of period t；ηcIndicate compression process efficiency；Indicate that period t flows into the flow of compressor；γ indicates air
Specific heat ratio；RgIndicate ideal gas constant；ncIndicate compressor series；Tc,l,inWithIt respectively indicates and enters l grades of compressors
With the air themperature of final compressor；βc,lWithThe specified compression ratio and final compressor for respectively indicating l grades of compressors exist
The compression ratio of period t, it is related with air storage chamber air pressure.
Pc,minνc,t≤Pc,t≤Pc,maxνc,t (2)
Formula (2) indicates the constraint of compression horsepower bound.Wherein, Pc,minAnd Pc,maxRespectively indicate the bound of compression horsepower；
νc,tFor binary variable, for indicating whether the power station AA-CAES is in compression condition, when the power station AA-CAES is in compression condition
When, νc,t=1, on the contrary νc,t=0.
Formula (3) indicates the constraint of expanding machine generated output.Wherein, Pg,tIndicate the generated output of period t；ηgExpression generated electricity
Journey efficiency；Indicate that period t flows into the flow of expanding machine；Tg,j,in,tIndicate that the t period enters the air themperature of j-th stage expanding machine,
It is related to air storage chamber temperature；ngIndicate expansion series；βg,jIndicate j-th stage expanding machine nominal expansion ratio.
Pg,minνg,t≤Pg,t≤Pg,maxνg,t (4)
Formula (4) indicates the constraint of generated output bound.Wherein, Pg,minAnd Pg,maxRespectively indicate the bound of generated output；
νg,tFor binary variable, for indicating whether the power station AA-CAES is in generating operation mode, when the power station AA-CAES is in generating operation mode
When, νg,t=1, on the contrary νg,t=0.
νc,tνg,t=0 (5)
Formula (5) indicates the constraint of the power station AA-CAES operating condition.The constraint is for guaranteeing that the power station AA-CAES does not work at the same time
In compression condition and generating operation mode.
Formula (6) is indicated by taking doubly-fed wind turbine as an example, considers the uncertainty that its generated energy changes with wind speed, it is every
The mean wind speed of hour is described as obeying Weibull distribution.Wherein, ν (m/s) indicates mean hourly wind speed；C indicates annual
Wind speed；K describes the distribution and shape of wind speed.
Formula (7) indicates the correlation for ignoring generated output between the mechanical loss and blower of blower, doubly-fed wind turbine
Generated output.Wherein, PWTIndicate the wind energy power of blower capture；ρ indicates that atmospheric density, unit are (kg/m3)；S indicates wind wheel
Wind sweeping area (m2)；Formula (8) indicates power coefficient.
The calculation method of parameter in formula (9) expression (8).The calculation method of parameter in formula (10) expression (9).Wherein,
θpIndicate propeller pitch angle；λ indicates tip speed ratio；ω indicates blade end angular speed；R indicates blade radius.When wind speed is cut less than blower
When entering wind speed, it is 0 that blower, which issues power,；When wind speed is between incision wind speed and rated wind speed, θp=0, by formula (6)-(9)
Calculate power of fan；When wind speed is higher than rated wind speed, blower variable blade control system work makes blower issue the specified function of power
Rate；When wind speed is greater than blower cut-out wind speed, blower is out of service.
Step 2: cost/Reliability Evaluation model is established.
Formula (11) indicate wind generator system and AA-CAES energy-accumulating power station fixed cost include design, mounting cost and
Cost of land etc..Wherein, CAIndicate initial outlay conversion to the fixed cost summation for waiting years value: A1、A2Respectively indicate wind-power electricity generation
The fixed cost of system and AA-CAES energy-accumulating power station；α indicates Annual Percentage Rate；N indicates loan year.
CT=CA+CM (12)
Formula (12) indicates year comprehensive cost.Wherein, CMIndicate the maintenance of wind generator system and AA-CAES energy-accumulating power station at
This, including equipment replacement, cleaning, lubrication etc..CMNumerical value be about wind generator system year fixed cost 2%.
Formula (13) indicates that average load powers off cost.Wherein, fAVE(h, t) indicates h type load and load power-off time t's
Average load powers off cost function；fc(h, t) indicates that load of the h type load at load power-off time t powers off cost；Table
Show the peak load of h type load.
Formula (14) indicates that synthetic load powers off cost coefficient.Wherein, fcom(t) indicate that the synthesis under load power-off time t is negative
Lotus powers off cost coefficient, reflects the relationship of electric system unit quantity of electricity load power-off cost and load section power-off time t, Ltype
Indicate load type set, σhIndicate the electricity consumption ratio of load h, LhIndicate the rate of load condensate of load h.
Formula (15) indicates the power-off of i-th load at damages.Wherein, CiIndicate that i-th load is powered off into damages,
QEENS,hIndicate the electricity shortage amount of h type load.
Formula (16) indicates that annual load powers off damages.Wherein, N indicates always to power off number, Q in 1 yearEENSIt indicates
Total electricity shortage amount.
When establishing cost/Reliability Evaluation model, need to carry out AA-CAES energy-accumulating power station charge-discharge electric power excellent
Change.In the present invention, the rated capacity of AA-CAES energy-accumulating power station and efficiency for charge-discharge use E respectivelyCAES(MWh) and ηCAESTable
Show.Here with represented by formula (17) in 1 year the minimum target of equivalent load quadratic sum to AA-CAES energy-accumulating power station per hour
Charge-discharge electric power optimize:
Wherein, PLIt (t) is the load power of t moment；PCAESIt (t) is the charging (compression) of AA-CAES energy-accumulating power station or electric discharge
(expansion) power, charging are positive, and electric discharge is negative.Battery charge and discharge process need to meet following constraint conditions:
The minimax capacity-constrained of formula (18-19) expression battery each period.Wherein, EmaxAnd EminIt is respectively maximum and
Minimum capacity；ECAESFor AA-CAES stored energy capacitance；Period t and t-1 period energy-accumulating power station residue is held respectively by E (t) and E (t-1)
Amount；Δ t indicates time interval (being set as 1h herein).Here battery charging and discharging strategy is optimized using dynamic programming, per small
When AA-CAES energy-accumulating power station charge and discharge process regard a state in Dynamic Programming as, original state be E (0)=
0.05ECAES, after first hour, original state becomes E (0)+P from E (0)CAES(1), the recurrence equation after t hours are as follows:
Wherein PCAESIt (t) is the charge-discharge electric power of t hours AA-CAES energy-accumulating power stations；υnFor t hours decision variables；
U (t) is by PCAES(t) and the permission decision set that determines such as capacity-constrained；υn(PCAES(t), u (t)) it is equivalent load square
With.After sequence finds the minimum value of formula (17), then the charge-discharge electric power of each period AA-CAES energy-accumulating power station of search conversely, such as
Shown in Fig. 3；
Step 3: as shown in Fig. 2, establishing objective function according to step 1 and step 2, optimization aim is to obtain system year
The minimum value of the sum of cost and load power-off damages and corresponding fan capacity.
The present invention assumes that each hour various state parameters are constant using 1 year 8736 hour as time scale, when
Certain variable is value of the variable in moment t in the value of moment t.Assuming that this paper load type only includes industrial load and breaks
Electric damages is assumed to be 3$/kWh.
Every kilowatt of construction cost of blower of the present invention is 8000$/kW.The annual interest fixed rate of interest is 0.1, and repaying is limited to 20 years in year.If wind
Blower single-machine capacity in is 5MW, and incision wind speed is 3m/s, rated wind speed 12m/s, cut-out wind speed 25m/s, and air is close
Degree is ρ=1.25kg/m3, blade radius is 37.5m, and the control system of three blades, wind-driven generator can obtain optimal wind energy
Usage factor CP, then tip speed ratio λ=8.5.The construction cost in the power station AA-CAES is 300$/kW, and the service life is 40 years.Annual fortune
Row maintenance cost is the 2% of construction cost.
To compare capacity scale to the influence difference of system performance driving economy and power supply reliability and in wind resource difference
Region, capacity scale to the influence difference of system performance driving economy and power supply reliability, the present invention be arranged following 4 scenes into
Row emulation and comparative analysis.
1 scene research of table
System AA-CAES capacity is 50MW in scene 1, and separate unit fan capacity is 5MW, and the parameter of Weibull distribution is c=
7, k=2；System AA-CAES capacity is 100MW, the Weibull distribution parameter and field of separate unit fan capacity and wind speed in scene 2
Scape 1 is identical；System AA-CAES capacity is 100MW in scene 3, and separate unit fan capacity is 5MW, and the parameter of Weibull distribution is c
=5, k=2；System AA-CAES capacity is 200MW in scene 4, the Weibull distribution parameter of separate unit fan capacity and wind speed with
Scene 1 is identical.
Show that power system capacity scale can impact cost and power supply reliability: when power system capacity scale by emulation
Bigger, power supply reliability is higher, and economy is better；In the present invention, mean hourly wind speed obeys Weibull distribution within 1 year.Work as shape
When shape parameter changes, the result of optimization is also different, i.e. the mean wind speed of system location is higher, and annual total cost is smaller,
System power supply reliability is better, and the wind generator system capacity that need to be installed is smaller.Under difference AA-CAES capacity as shown in Table 2
Total cost and optimization fan capacity can be intuitive find out conclusion.
Total cost and optimization fan capacity under 2 difference AA-CAES capacity of table
Claims (2)
1. wind generator system cost/Reliability Estimation Method based on energy storage, which is characterized in that the specific step of this method
Suddenly it is:
Step 1: the advanced adiabatic compression air power station energy storage AA-CAES operation and wind generator system generated output model are established；
Formula (1) indicates the constraint of the power station AA-CAES compression horsepower；Wherein, Pc,tIndicate the compression horsepower of period t；ηcIndicate compressed
Journey efficiency；Indicate that period t flows into the flow of compressor；γ indicates air specific heat ratio；RgIndicate ideal gas constant；ncIt indicates
Compressor series；Tc,l,inAnd Tc,nc,inRespectively indicate the air themperature into l grades of compressors and final compressor；βc,lWith
βc,nc,tRespectively indicate l grades of compressors specified compression ratio and final compressor period t compression ratio, with air storage chamber gas
It is pressed with pass；
Pc,minνc,t≤Pc,t≤Pc,maxνc,t (2)
Formula (2) indicates the constraint of compression horsepower bound；Wherein, Pc,minAnd Pc,maxRespectively indicate the bound of compression horsepower；νc,tFor
Binary variable, for indicating whether the power station AA-CAES is in compression condition, when the power station AA-CAES is in compression condition,
νc,t=1, on the contrary νc,t=0；
Formula (3) indicates the constraint of expanding machine generated output；Wherein, Pg,tIndicate the generated output of period t；ηgIndicate power generation process effect
Rate；Indicate that period t flows into the flow of expanding machine；Tg,j,in,tIndicate that the t period enters the air themperature of j-th stage expanding machine, with
Air storage chamber temperature is related；ngIndicate expansion series；βg,jIndicate j-th stage expanding machine nominal expansion ratio；
Pg,minνg,t≤Pg,t≤Pg,maxνg,t (4)
Formula (4) indicates the constraint of generated output bound；Wherein, Pg,minAnd Pg,maxRespectively indicate the bound of generated output；νg,tFor
Binary variable, for indicating whether the power station AA-CAES is in generating operation mode, when the power station AA-CAES is in generating operation mode,
νg,t=1, on the contrary νg,t=0；
νc,tνg,t=0 (5)
Formula (5) indicates the constraint of the power station AA-CAES operating condition；The constraint is being pressed for guaranteeing that the power station AA-CAES does not work at the same time
Contracting operating condition and generating operation mode；
Formula (6) indicates that wind-driven generator considers the uncertainty that its generated energy changes with wind speed, by its mean wind speed hourly
It is described as obeying Weibull distribution；Wherein, ν indicates mean hourly wind speed, unit m/s；C indicates annual mean wind speed；K description
The distribution and shape of wind speed；
Formula (7) indicates the correlation for ignoring generated output between the mechanical loss and blower of blower, the power generation of doubly-fed wind turbine
Power；Wherein, PWTIndicate the wind energy power of blower capture；ρ indicates atmospheric density, unit kg/m3；S indicates wind wheel swing flap face
Product, unit m2；Formula (8) indicates power coefficient；
The calculation method of parameter in formula (9) expression (8)；The calculation method of parameter in formula (10) expression (9)；Wherein, θpIt indicates
Propeller pitch angle；λ indicates tip speed ratio；ω indicates blade end angular speed；R indicates blade radius；Wind speed is cut when wind speed is less than blower
When, it is 0 that blower, which issues power,；When wind speed is between incision wind speed and rated wind speed, θp=0, wind is calculated by formula (6)-(9)
Machine power；When wind speed is higher than rated wind speed, blower variable blade control system work makes blower issue power rated power；When
When wind speed is greater than blower cut-out wind speed, blower is out of service；
Step 2: cost/Reliability Evaluation Model is established；
The fixed cost of formula (11) expression wind generator system and AA-CAES energy-accumulating power station；Wherein, CAIndicate initial outlay conversion
To the fixed cost summation of equal years value: A1、A2Respectively indicate the fixed cost of wind generator system and AA-CAES energy-accumulating power station；α
Indicate Annual Percentage Rate；N indicates loan year；
CT=CA+CM (12)
Formula (12) indicates year comprehensive cost；Wherein, CMIndicate the maintenance cost of wind generator system and AA-CAES energy-accumulating power station,；
CMNumerical value is the 2% of wind generator system year fixed cost；
Formula (13) indicates that average load powers off cost；Wherein, fAVE(h, t) expression h type load is averaged with load power-off time t's
Load powers off cost function；fc(h, t) indicates that load of the h type load at load power-off time t powers off cost；Indicate h class
The peak load of load；
Formula (14) indicates that synthetic load powers off cost coefficient；Wherein, fcom(t) indicate that the synthetic load under load power-off time t is disconnected
Electric cost coefficient reflects the relationship of electric system unit quantity of electricity load power-off cost and load section power-off time t, LtypeIt indicates
Load type set, σhIndicate the electricity consumption ratio of load h, LhIndicate the rate of load condensate of load h；
Formula (15) indicates the power-off of i-th load at damages；Wherein, CiIndicate that i-th load powers off damages, QEENS,hTable
Show the electricity shortage amount of h type load；
Formula (16) indicates that annual load powers off damages；Wherein, N indicates always to power off number, Q in 1 yearEENSIndicate total power supply
It is in shortage；
Step 3: establishing objective function according to step 1 and step 2, emulates wind turbine power generation power using Monte carlo algorithm, leads to
It crosses optimization method and obtains fan capacity when the sum of system year cost and load power-off damages are minimum value.
2. wind generator system cost/Reliability Estimation Method according to claim 1 based on energy storage, feature
Be: optimization method in step 3 carries out advanced adiabatic compression air energy storage systems charge-discharge electric power by dynamic programming excellent
Change.
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