Wind Power and Comparison to Conventional Generation

Transcription

1 Wind Power and Comparison to Conventional Generation PRAMOD JAIN, Ph.D. Consultant, USAID Power the Future October 1, 2018 Almaty, Republic of Kazakhstan Venue: Almaty University of Power Engineering and Telecommunications (AUPET) 7/6/2018

2 Wind is a form of solar energy Uneven heating of earth Heating of ground versus water Basics of Wind Energy: Energy is blowing in the wind Source: 2

3 Power in KW Power in Watts Basics of Wind Energy Turbine of rotor radius = 1 m Power in wind = ½ r A v 3 = ½ r p r 2 v 3 Units of Power: kilo-watts, mega-watts, giga- Watts Energy = Power * Time Units of energy: kilo-watt Hours (kwh), MWh, GWh Source: P. Jain, Wind Energy Engineering, Wind Speed, m/s Wind speed = 8 m/s Radius of a HAWT in meters 3

4 Wind Shear Wind shear defines how wind speed changes with elevation Source: P. Jain, Wind Energy Engineering, 2016

5 Basics of Wind Energy Density vs. height above sea level 2%i at 200m 17.8%i at 2000m Density as a function of humidity 0.6%i at 100% relative humidity Power Production Curve of Turbine Power of wind is converted according to the power curve (red curve) Ce curve (green curve) gives the conversion ratio. How much the wind energy is converted to electricity Source: P. Jain, Wind Energy Engineering,

6 Basics of Wind Energy Statistical distribution of wind speed Statistical distribution of power density Energy is computed sector-by-sector Source: P. Jain, Wind Energy Engineering,

7 What is different about wind energy? Transport fuel Proximity to load Capacity factor Scale Variability of resource Coal, Nuclear, Gas, Diesel Yes Yes 90+% C/N: Large 500MW+ G/D: Med/Low Significant cost variability Hydro No No Variable Variable Depends on rain Wind No No 30 to 50% Variable High diurnal seasonal Solar No/Don t have to Yes 15 to 22% Small to medium Med. diurnal seasonal Geo-thermal No No 90+% Large to medium Little

8 Advantages of Wind Energy No fuel costs No variability in cost of energy production, other than O&M costs Revenue to local land owners with out substantially altering land use Boost to the local economy during construction and through out life of project Boost to infrastructure Zero emissions Zero water use Zero mining of fuels Zero transportation of fuels Clean energy 90 to 95% of land can be used for the original purpose Wind-hydro hybrid can provide a powerful combination for year-round energy production Wind-diesel hybrid can provide reliable power to remote areas 8

9 Disadvantages of Wind Power Intermittent energy production: Electricity is produced only when wind is blowing There is still need for traditional power plants (hydro or fossil fuel-based) to provide base-load Wind energy replaces peaking units or spinning reserves Higher capital investment New transmission lines may be needed from remote areas to cities Large foot print of wind farms. In interior areas, 30 to 60 acres per MW In coastal areas with single row of turbines, 2 to 5 acres per MW Note most of land can be used for other purposes Birds/bat fatalities Visual impact Noise, and others High levels of wind energy (>30%) in grid may require variety of upgrades to the entire electricity network 9

10 Aerodynamics of Wings/Blades Blade of turbine in like wing of airplane α is angle of attack v isrelative wind speed Lift and drag are forces Torque ( ) is radius x component of Lift in plane of rotation Power = x ω Source: P. Jain, Wind Energy Engineering, 2016

11 Aerodynamics of blades Lift force depends on shape of blade Lift force increases as α is increased up to a point, then the lift force falls sharply For maximum power there is an optimal angle of attack Illustration of coefficient of lift for one blade shape Source: P. Jain, Wind Energy Engineering, 2016

12 Aerodynamics of WTG ω is rotational speed of turbine, v t is tip speed, v 1 is wind speed, ϕ is blade pitch angle v Tip speed ratio = = t Τ v1 = ωr/v 1 Source: P. Jain, Wind Energy Engineering, 2016

13 Power Supplied by WTG P = C p, 1 2 ρav3 C p is power coefficient Betz limit states that C p < For fixed pitch, power as function of ωfor different v is plotted Source: P. Jain, Wind Energy Engineering, 2016

14 Optimum Aerodynamic Efficiency, Case for Variable Speed Turbines Highest power extraction for different wind speed requires different rotor speed Add pitch angle and the curves become more complex Variable speed rotor means variable frequency power Source: P. Jain, Wind Energy Engineering, 2016

15 Generators The mechanical side is coupled with electrical generator to deliver electrical power Constant rotor speed electrical generators are not efficient Higher efficiency is provided by variable rotor speed generators like: DFIG, PMG and others

16 WIND RESOURCE ASSESSMENT 10/2/2018 FOOTER GOES HERE 16

17 What is wind resource assessment? Quantification of wind resources Inputs: Wind speed data Terrain: Elevation, roughness, obstacles Turbine data: Quantity, layout and production curve Others Output: Average annual energy production (AEP)

18 Types of WRA Level I WRA: Preliminary for prospecting Level II WRA: With measured wind data and long-term correction Level III WRA: CFD-based model for complex terrain Level IV WRA: Bankable wind resource assessment. Level II or III with long-term correction, and estimates for losses and uncertainty

19 What is Wind Resource Assessment? Wind Resource Assessment (WRA) is quantification of wind resources Level 1 Level 2 Level 3 Level 4 Preliminary Purpose: Prospecting for areas with good resources, Selecting sites to install met-masts Tools: Online prospecting tools (3Tier), RetScreen Energy estimate: +/- 50 Detailed Purpose: Compute AEP, micrositing Based on onsite measurement Modeling of terrain: elevation,roughness Linearized RANS model Tools: WAsP, WindPRO, Wind Farmer Energy Estimate: +/- 15% CFD-based models Includes non-linear effects (flow separation) Includes non-linear terms Steep/complex terrain WAsP CFD/EllipSys Energy Estimate: +/- 10% Bankable WRA Estimate of Uncertainty based on project Detailed accounting for all losses Computation of P50, P75, P90 of AEP Financial parameter estimation Financial analysis 19

20 Levels of WRA Level 2 And / Or Level 4: Bankable WRA Level 3 20

21 Why is WRA Key to a Wind Project? Key driver to financing of a project Requirements: At least one year of onsite wind measurement With multiple met-masts At multiple heights, and one measurement close to hub height Quality data Annual Energy Production Reasonable estimate of losses Rigorous uncertainty analysis Project financiers are interested in both, mean and standard deviation of AEP 21

22 Level 1 WRA Sources: Online wind resource mapping applications or noninteractive color maps of wind resources Issues: NREL Quality of wind data & instrument is poor Not available: Shear, Turbulence, Statistical distribution Not site specific data Issues: Geographical resolution is coarse:, e.g.5km x 5Km grid Computations are based on numerical models; data used by numerical models is suspect +/- 50% 10/2/2018 Source of graphics: 3Tier & NREL

23 Wind Measurement Items Types of met towers Size and type Description Temporary: Met towers with a life of 3 5 years, used for project-specific assessment of wind resources Permanent: Met towers with a life of 20 years, used for long-term measurements or used in wind farm as a reference mast Temporary: 80 m monopole (or tubular) towers are recommended for new projects. A 60 m monopole tower is the most popular. Recommended: 2/3 to 3/4 of hub height Permanent: Lattice towers Number of anemometers Number of wind vanes Orientation of anemometers Two class I calibrated anemometers at 80 m Two calibrated anemometers at 60 m; Two calibrated anemometers at 40 m Two wind vanes: One at 75 m and a second at 55 m If wind direction is predominantly from one direction, then place the two anemometers at +45/ 45-degree angles to the primary wind direction, such that the two anemometers make an angle of 90 degrees.

24 Wind Measurement Boom length Other sensors Items Data logger and communication unit Others Description Boom on which the anemometers are mounted must be at least six times the diameter of the pole. Temperature, barometric pressure, relative humidity, and solar radiation sensors are installed at a height of about 3 m from ground level. Normally, a 15-channel data logger is used to receive sensor readings every second and record statistics every 10 minutes. A memory card stores the statistics. A communication unit contains a wireless modem to transmit recorded data once every day. Lightning protection to protect sensors, data logger, communication unit, and the tower.

25 Diurnal & Monthly Profile 10/2/2018 Graphics created in WindPRO 25

26 Weibull Distribution Statistical distribution of wind speed 10/2/2018 Graphics created in WindPRO 26

27 Shear Profile Principal energy is from SSE direction. Wind speed profile indicates shear Elevation indicates contour along SSE direction 10/2/2018 Graphics created in WindPRO 27

28 Example: Vertical Extrapolation Case 1: Wind measurement for one year yields Annual average wind speed at 30m = 6.9 m/s Average temperature 30C High winds during day time Case 2: Wind measurement for one year yields Annual average wind speed at 30m = 6.5 m/s Average temperature 18C High winds during night time If hub height is 85m, which location is preferable? Using standard shear factor of 0.15, speed at hub height is: 8 m/s in case m/s in case 2 Shear in case 1 is low due to thermal mixing/convection Shear in case 2 is high Results in item 1 are incorrect With shear of in case 1 and 0.25 in case 2, speeds are 7.86 and 8.44 m/s 10/2/2018

29 Turbulence Turbulence Intensity Vs Wind Speed. Plot of IEC Turbine category: TI Vs WS 10/2/2018 Graphics created in WindPRO 29

30 Extreme Wind Speed Prediction of extreme wind speed that is likely to occur in 50 years Statistical modeling of measured data and long-term reference data Specify threshold for sampling extreme events Depending on data series used (interval = 10 min, 60 min), extreme wind speed is computed 50 year extreme wind speed based on 10 min wind speed data is 28 m/s IEC Turbine category is determined based on extreme wind speed 10/2/2018 Graphics created in WindPRO 30

31 When is spatial extrapolation accurate? Level 2 WRA: Wind Speed Modeling, Ruggedness Index, Wake Measurement location s ruggedness matches turbine location s ruggedness When ruggedness do not match, then correction needs to be applied Wake losses can be significant depending on layout Two types of impact: Reduced wind speed, higher turbulence Several wake models may be used: Jensen model Eddy Viscosity model

32 Level 2 WRA: Measure Correlate Predict (MCP) Two time series: Measured Long-term reference data Correlation If correlation is acceptable, then proceed to Predict Predict Regression: Linear and polynomial Matrix method: For each wind speed bin and sector, speed up and direction shift are computed Weibull parameter scaling A, k parameters are scaled per sector Wind Index MCP 10/2/

33 Spatial Extrapolation For resource assessment of wind farm one met-tower per 6 to 10 turbines are used. Rough terrain requires more met-towers What is wind speed at proposed turbine locations? Spatial extrapolation is done by deriving Regional Wind Climate (RWC) RWC strips out the affect of terrain, roughness and obstacles from measured data RWC is then localized by reapplying site specific terrain, roughness and obstacles Layout of met-towers and turbines 10/2/

34 Level 2 WRA 10/2/2018 Wind resource assessment is based on onsite wind measurement and GIS model of site with elevation and terrain data Source of wind data is from Tools: At least one year of onsite met-tower data at 3 heights Long-term reference data WindPRO WAsP WindFarmer Output Average annual wind speed, direction, energy density Wind shear based on measured wind speed at multiple heights Diurnal and monthly variation Turbulence Spatial extrapolation Temporal extrapolation Output Average AEP= 4.34GWh With a 1.5MW GE XLE, hub=80m, rotor dia.=82.5m Capacity factor Wind farm layout* Wake losses* +/- 15%

35 Level 3 WRA Terrain can have a large impact on wind speed and direction Roughness is used to predict shear. Models are rules of thumb for classifying different surface friction due to vegetation and habitation 10/2/2018 Most models used for WRA are linear, not accurate for rough terrain CFD based models may improve WRA Source of grid graphic: Meteodyn WT Workshop, G. DuPont,

36 Level 4 WRA: Losses How much profits will I lose? Loss category Loss estimate Comments Wake losses 5 15% WindPRO and WindFarmer have tools to compute wake losses Plant availability 2 5% Turbine related, BPO related, Grid unavailability Electrical losses 2 4% Transformer losses, Transmission losses, Internal power consumption Turbine performance 1.5 5% Power curve loss, High wind hysteresis, Wind modeling Environmental 1 3% Outside operating range, Icing, Wildlife, Lightning, Roughness change Curtailment 1 3% Grid, Wind sector Others Earthquake: Seismic database may be used estimate frequency 10/2/2018 Source: P. Jain, Wind Energy Engineering,

37 Level 4 WRA: Uncertainty of Annual Energy Production

38 Uncertainty How uncertain are my profits? 10/2/2018 Source: P. Jain, Wind Energy Engineering, 2010

39 Uncertainty Uncertainty is a key component of Bankable WRA In wind projects uncertainty is expressed in terms of: P50 P90 P95 Key: Valuation depends on P90, P95 Methods to reduce uncertainty: Higher quality measurement instruments 2 to 3 year of wind speed measurement Measurement close to hub height Layout to reduce affect of wake

40 Checklist for WRA Tall met-masts, at least 2/3 hub height, with 95+ % data recovery Multiple met-masts. For simple terrain: one per ~10 turbines For complex terrain: one per ~5 to 8 turbines Onsite met-masts, in judiciously chosen locations High quality long-term data High quality extreme wind speed data Auditable wind measurement data Good quality terrain data Annual average energy computation using good practices Documentation of modeling assumptions Site specific loss estimates Site specific uncertainty estimates

41 Conclusions WRA requires attention to details, a lot of details If done well, it can reduce overall cost and reduce time to completion If wind resource is very good, but the WRA was not done with rigor, expect a bank to apply a very high uncertainty, which will: Reduce project s P90, P95 Reduce project s valuation Increase Bank s risk, therefore reduce your return 10/2/

42 Photo: Trend.Az USAID Regional Program Power the Future Pramod Jain President, Innovative Wind Energy, Inc. Power the Future 6, Sar y Arka Ave, Office 1430 Astana, Kazakhstan DISCLAIMER This product is made possible by the support of the American People through the United States Agency for International Development (USAID). The contents of this presentation are the sole responsibility of Tetra Tech ES, Inc. and do not necessarily reflect the views of USAID or the United States Government. 10/2/