Bolivia Wind Atlas

Transcription

Bolivia Wind Atlas

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June 5, 2009
Final Report
Bolivia Wind Atlas
A project for the International Finance Corporation (IFC)
Copyright 2009 © 3TIER Environmental Forecast Group, Inc. All rights
reserved. 3TIER claims a copyright in all proprietary and copyrightable
text and graphics in this Report, the overall design of this Report, and the
selection, arrangement and presentation of all materials in this Report,
including information in the public domain. Reproduction and
redistribution inconsistent with the scope of IFC’s contract with 3TIER for
this project is prohibited without written permission. Requests for
permission may be directed to [email protected].
ph: 206.325.1573
fax: 206.325.1518
[email protected]
www.3tier.com
3TIER North America
2001 Sixth Avenue
Suite 2100
Seattle, WA 98121
TABLE OF CONTENTS
I. Introduction ...............................................................................................................1
A. Background............................................................................................................................ 1
B. Project Objective.................................................................................................................... 2
II. Methodology.............................................................................................................4
TASK I – Inception Report: ........................................................................................................... 4
TASK II – Information Processing:................................................................................................ 4
Atmospheric model ................................................................................................................... 4
Input data .................................................................................................................................. 6
Model Simulations..................................................................................................................... 6
Task III: Data Validation................................................................................................................ 7
Task IV: Draft Final Report ........................................................................................................... 8
Task V – Dissemination ................................................................................................................ 9
Task VI – Final Report and Wind Atlas ....................................................................................... 10
Task VII: Training Workshop and Final Presentation ................................................................. 10
III. Climatology of Bolivia ..........................................................................................11
IV. Results...................................................................................................................14
A. Validation ............................................................................................................................. 14
Summary................................................................................................................................. 14
Validation Statistics................................................................................................................. 15
Tables of Validation Statistics (NCEP ADP Stations) ............................................................. 16
Simulation Bias ....................................................................................................................... 19
Monthly / Seasonal Cycle ....................................................................................................... 20
Diurnal Cycle........................................................................................................................... 20
Validation Conclusion ............................................................................................................. 21
B. Maps .................................................................................................................................... 22
V. Bolivia’s Wind Power Potential ............................................................................25
A. Wind Resource Basics......................................................................................................... 25
B. Spatial Distribution of Wind Resource ................................................................................. 26
C. Wind Power Density............................................................................................................. 27
VI. The Bolivia Wind Atlas in context .......................................................................28
A. Overview .............................................................................................................................. 28
B. Further Analysis ................................................................................................................... 30
C. Free Reference Materials..................................................................................................... 32
Appendix I: Calculation of Weibull parameters .........................................................I-1
Appendix II: Maps........................................................................................................II-1
A. Contents.............................................................................................................................. II-1
B. About the maps................................................................................................................. II-26
C. How to use digital maps in GeoPDF format ...................................................................... II-27
Appendix III: Additional Validation (Wind Roses) ...................................................III-1
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This report summarizes the wind resource over the country of Bolivia at three heights
above ground level (20, 50, and 80 meters) based on the results of sophisticated
meteorological simulations completed by 3TIER Environmental Forecast Group, Inc.
(3TIER) for the International Finance Corporation (IFC) as part of IFC’s ongoing
financing of Transportadora de Electricidad (TDE), Bolivia’s largest electricity
transmission company.
The results presented in this report shall serve as the basis for preliminary site
assessment during the prospecting phase of wind project development. Before making
investment decisions about specific project sites, any party wishing to harness wind
energy in Bolivia should, as part of its due diligence process:
• Visit the location and review topographical, environmental, or logistical factors
that could affect the successful installation and operation of wind turbines or
transmission lines (as applicable);
• Collect on-site measurements at wind turbine hub height (varies with equipment)
using a properly calibrated meteorological device (anemometer or other) or
verify the observed wind resource at a neighboring location from data collected
there. To gauge the availability of wind resources with the greatest possible
precision, collecting wind measurements over extended periods of time is
necessary;
• For off-grid systems, confirm that wind “supply” (times when the wind blows and
with what strength) corresponds with anticipated energy demand and/or plan for
back-up supply using batteries or other energy sources.
• Consider the long-term variability of the wind resource; and
• Obtain the necessary environmental and engineering permissions from Bolivian
government authorities.
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The contract between 3TIER and IFC for this project was initiated as part of an ongoing
relationship between IFC and Transportadora de Electricidad S.A. (TDE), Bolivia’s
largest electricity transmission company and a recipient of IFC loan financing. Under its
corporate social responsibility program, TDE, part of the Spanish consortium Red
Electrica de España, has undertaken a program to promote the access to and use of
renewable energy in Bolivia, especially in areas near its installations in isolated rural
communities. As part of its contribution to wind energy development in Bolivia, TDE
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processed information collected during 10 years at 201 meteorological stations and
began disseminating their analysis in April 2008.
IFC, a member of the World Bank Group, fosters sustainable economic growth in
developing countries by financing private sector investment, mobilizing private capital in
local and international financial markets, and providing advisory and risk mitigation
services to businesses and governments. IFC’s vision is that people should have the
opportunity to escape poverty and improve their lives. In FY08, IFC committed $11.4
billion, and mobilized an additional $4.8 billion through syndications and structured
finance for 372 investments in 85 developing countries. IFC also provided advisory
services in 97 countries. For more information, visit www.ifc.org.
Headquartered in Cochabamba, TDE is Bolivia’s principal power transmission company.
TDE presently owns and operates 74% of the transmission network for Bolivia’s national
interconnected system. The system consists of some 2,187 km of 230 kV, 115 kV, and
69 kV transmission lines and substations in various parts of Bolivia. The system
interconnects the departments of La Paz, Cochabamba, Santa Cruz and Oruro,
Chuquisaca and Potosi and the system facilitates the transmission of power and energy
between the country’s generators and distributors and unregulated customers. TDE is
owned by a subsidiary of the Spanish grid operator Red Electrica de España. For more
information, visit: www.tde.com.bo.
Founded in 1999, Seattle, Washington-based 3TIER is one of the largest independent
providers of wind, solar and hydro energy assessment and power forecasting
worldwide. 3TIER’s Regional Office for Latin America and the Caribbean in Panama
City, Panama played an important role in the development of the Bolivia Wind Atlas and
the company also has offices in Germany, India, and Australia. For more information,
visit www.3tier.com.
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According to the Terms of Reference specified in 3TIER’s contract with IFC:
•
The objective of this short-term contract is to prepare a geographic database of
the wind energy potential of Bolivia.
•
The assignment will create a map of wind energy resources throughout the
country. Once completed, data from this work will be shared not only within
TDE’s project development offices, but also with public authorities responsible
for rural electrification initiatives and with organizations and companies
interested in the energy sector. TDE, through an agreement with the Catholic
University of Bolivia (UCB – Universidad Católica de Bolivia) will provide
indefinite universal access to the maps and database that supports the
information that 3TIER provided to IFC.
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•
Information on wind potential will be prepared in easy to use and accessible
electronic format that will be made available over the Internet, or CD’s. The
consultant firm is expected to prepare a plan for TDE, such that this database is
properly maintained and/or improved over time. Once these studies are
concluded, TDE in coordination with IFC will promote the dissemination of this
information to all interested parties.
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3TIER generated this report based on a joint mission to Bolivia in April 2008 as well as
3TIER’s internal activities. It offered a detailed plan for project execution.
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The spatial assessment of the wind resource across Bolivia presented in this report is
based on a randomized year of simulated data (January 01 through December 31)
using a regional nonhydrostatic primitive equation model of the atmosphere. 3TIER
used version 3.0 of the Numerical Weather Prediction (NWP) model called the Weather
Research and Forecasting (WRF) Model.". The WRF model has been developed in a
collaborative partnership between federal agencies and universities in the United States
and represents the next generation in weather forecast models. 3TIER uses this model
for both its forecasting and resource assessment projects. With the WRF model, 3TIER
constructed a full year of data by individually simulating each calendar day of the year,
where the year is chosen at random from the last 10 years (1998–2007).
The WRF model utilizes a nested grid layout. The extent of the coarsest model grid was
selected to capture the e!ect of synoptic weather events on the wind resource in the
region of interest, as well as to allow the model to develop regional thermally-driven
circulations. The increasingly fine 54.0 km, 18.0 km, 6.0 km and 2.0 km grids were used
to simulate the e!ect of local terrain and local scale atmospheric circulations. Table 1
below lists some details of the final configuration of the Numerical Weather Prediction
(NWP) model.
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Skamarock, W.C., J.B. Klemp, J. Dudhia, D.O. Gill, D.M. Barker, W. Wang, J.G. Powers, 2005: A
description of the Advanced Research WRF Version 2. NCAR Technical Note, NCAR/TN-468+STR,
Boulder, Colorado, 88p.
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Table 1: NWP Model Configuration
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Figure 1: The bold red box around Bolivia denotes the valid study area of the 2 km
resolution grid domain used for this project.
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Global Weather Archive: The main input data for wind resource assessment simulations
are historic global weather archives, which are maintained by operational weather
forecasting centers around the world including the United States National Center for
Environmental Prediction (NCEP). These global archives represent the overall state of
the atmosphere over the entire planet and are themselves the result of a sophisticated
computer analysis of available surface and upper air observations.
Each time period of analysis combines tens of thousands of individual measurements
around the globe into a consistent physical state. The NCEP/NCAR reanalysis2 includes
the NCEP global spectral model operational in 1995, with 28 sigma vertical levels and a
horizontal triangular truncation of 62 waves, equivalent to about 210 km. The analysis
scheme is a 3-dimensional variational (3D-Var) scheme cast in spectral space.
Due to the necessity to represent the entire globe, the NCEP/NCAR reanalysis data set
is maintained at a relatively coarse horizontal resolution and, by itself, does not contain
the level of detail necessary to resolve the wind flow patterns over smaller geographic
regions or over a single project. However, these data do provide a good representation
of the history of large-scale spatial patterns in the atmosphere (i.e. the position of high
and low pressure systems; the location of the jet stream) as well as the general state of
the ocean (e.g. sea surface temperatures) and land surface condition (e.g. soil
moistures). 3TIER maintains an archive of 40+ years of global weather data from NCEP
at its Seattle headquarters. By combining these coarse data with high-resolution landuse data and a high-resolution numerical weather simulation model, 3TIER will create
an accurate reconstruction of regional and site-specific wind fields.
High Resolution Terrain, Soil and Vegetation Data: For this project, 3TIER used high
resolution 3 arc-second (roughly 90 m) terrain data from the Shuttle Radar Topography
Mission (SRTM)3. In addition, WRF employs a 30 arc second global 24-category land
use map (USGS), a 5 arc minute soil texture (FAO), and a 0.15-degree monthly
climatology green vegetation fraction (NESDIS).
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3TIER implemented the WRF model in a nested-grid configuration. The simulation
contained four nested domains. The outer domain has a spatial resolution of 54 km, and
the other three had progressively finer resolutions of 18 km, 6 km, and 2 km. The
innermost grid has a spatial resolution of 2 km covering Bolivia in its entirety (see Figure
1), plus a buffer zone in each direction to avoid grid edge effects. For each individual
day, a 47-hour simulation was generated. 3TIER discarded the first 23 hours of each
simulation to allow for proper model initialization, and used the last 24 hours of each
simulation for the subsequent wind resource analysis. Sensitivity studies were done to
determine the necessary initialization lead-time. One year was simulated by sampling
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Available online at www.cpc.ncep.noaa.gov/products/wesley/reanalysis.html
3
Information available at http://srtm.usgs.gov
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individual days from the 10-year period 1998-2007 of the NCEP/NCAR reanalysis
archive to serve as boundary conditions for our NWP simulations. 3TIER sampled only
from the satellite period of the reanalysis archive (1979 onwards) to avoid statistical
discrepancies due to changes in the global upper air observing system.
For this project, 3TIER applied version 3.0 of the WRF model. Having completed test
runs with different configurations and reviewed them in light of rough-scale open-source
data, 3TIER completed the 2km resolution model simulations (which yielded the so
called “raw” simulation data that later was subjected to validation).
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3TIER’s analysis of model output for this project focused on two aspects of the modeled
wind fields: internal consistency and comparison with observations. The first part of the
evaluation determined whether the modeled fields were subject to numerical instabilities
and modeling artifacts, which would have been directly related to model setup and
implementation. Model output was also subjected to quantitative controls of numerical
stability based on Courant limits; visual inspection of wind speed fields focusing on
detection of two-delta-t instabilities and/or spurious standing waves; and a qualitative
analysis of the wind distribution at each grid point.
The second part of 3TIER’s evaluation relied on the availability of observations with
which to compare the model simulations. 3TIER analyzed model output for this project
by comparing it to data collected in Bolivia during time periods that overlap with the
simulations (1998-2007). This process included a comparison of modeled and observed
means, variances, diurnal distributions, and Weibull parameters, as well as correlation
statistics computed on different time scales. From IFC, 3TIER received raw data from
the JICA data set, raw data from the CRE wind resource assessments near Santa Cruz,
and data from Bolivia’s Government Meteorological Agency (SENAMHI).
Ideally, observed data to be used for comparison with simulation results should have
been collected using an instrument located at least 20 meters above the ground surface
for which the height above the ground surface is specified for each collection location.
Data should have been recorded at hourly intervals during a full consecutive 12-month
period. The data set provided should also include the coordinates for data collection.
Unfortunately, none of the above-noted observed data sets had all of these three
characteristics. Furthermore, the CRE, SENAMHI, and JICA data provided by IFC did
not coincide with the random year of meteorology that 3TIER simulated during the 10year record period enough to permit the detailed analysis called for in the Terms of
Reference for this project. 3TIER’s summary analysis of its simulation results
considered the JICA observed data and data from 3 additional tall meteorological
towers in northern Chile within the rectangular Bolivia simulation domain4. To permit a
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4 3TIER obtained this data from the website of a GEF-funded UNDP and Chilean Government study of
renewable energy resources in Chile. (See http://www.renovablesrural.cl/actividades/fr_actividades.html).
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more robust statistical validation analysis, 3TIER also downloaded observed data
corresponding to 22 meteorological stations from the National Center for Environmental
Prediction (NCEP) Automated Data Processing (ADP) Global Surface Observations.5
3TIER evaluated the quality of the wind resource simulations against observed data
both qualitatively and quantitatively. Qualitative evaluation included visual inspection of
the annual, seasonal, monthly and diurnal characteristics of 3TIER’s model simulations
and a review of what is known about the meteorology of Bolivia. The amount and quality
of meteorological observations did not permit 3TIER to perform a model-output-statistics
(MOS) correction to the simulated wind fields for Bolivia.
For validation results, please see section IV, a., which begins on page 12.
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The deliverable associated with this task was a series of data sets that 3TIER
calculated using the model simulation output and delivered to IFC. They included
gridded data sets of each of the following:
•
On a horizontal 2 km grid: annual and monthly average wind speed, wind power
density, Weibull k, air temperature and air density calculations at 20 m, 50 m and
80 m above the ground;
•
The model elevation, roughness, and land use data;
•
Annual and monthly wind and power rose (16 sectors) and speed frequency
distribution (1 m/s bins) for each model grid point;
•
Surface inclination in the direction of maximum slope. For this deliverable 3TIER
calculated the value of the slope at the 1-arcminute (approximately 2 km)
resolution of the Bolivia dataset using the NASA SRTM topography with a
resolution of 90 m. First, 3TIER defined elevation values for each point on the
2km grid through bilinear interpolation from the four SRTM data points that
surrounded it; 3TIER then calculated the slope between each 2 km grid point and
the 8 points surrounding it. The result was a fraction, such that a value of 0.2
indicates a 20% slope (400 m change in elevation averaged over the 2 km
horizontal distance). The data set delivered to IFC contains for each 2 km grid
point the result of the 8 slope calculations that had the greatest absolute value6;
and
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3TIER obtained this information from the National Center for Atmospheric Research (NCAR) Data
Support Section at http://dss.ucar.edu/datasets/ds464.0/
A spatial representation of this data set on a map of Bolivia would alert wind atlas users to complex
terrain and probable local turbulence in those areas with extreme slopes (>20%). Both TDE and
UCB can provide the data files necessary to create such a representation.
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•
Annual average energy output calculations based on a GE 1.5 SLE turbine at 80meter hub height7.
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3TIER facilitated workshops in Cochabamba and La Paz from March 9-10, 2009 in
order to present the results of this project and made the Bolivia Wind Atlas available at
http://firstlook.3tier.com in advance of those activities to facilitate interaction with the
information prior to the workshops.
Final dissemination of the Bolivia Wind Atlas will permit FirstLook users to interactively
explore the wind resource of Bolivia by taking the following steps:
1) Register as a user of FirstLook® by visiting http://firstlook.3tier.com by following
the instructions available at the FAQ link under the heading “Account.” The
registration process begins upon a user!s first visit to FirstLook when he or she
clicks either of two hyperlinks at the wind tab: (i) “Please login to display free
wind speed data” in the center of the map pane or (ii) “Login or register” at the
bottom of the map window.8
2) Browse the clickable map available online at FirstLook
3) Review additional details for each location of interest such as wind power
density, monthly and average wind speed, and power capacity factor from the
expanded popup window accessible by clicking the words “Click Here” at the
bottom right-hand corner of the standard popup window
4) Obtain free Standard Reports using coupons available from TDE
5) Obtain Professional Reports (which contain a time series for a specific location)
for 50% off using coupons available from TDE.
Pursuant to the contract between IFC and 3TIER, coupons will be made available
through TDE to qualified Bolivia Wind Atlas users during the first 12 months following
the publication of this Final Report. Interested parties should visit the “Atlas Eólico”
hyperlink at www.tde.com.bo to request FirstLook coupons for Bolivia.
Registration for FirstLook is a one-time free process. Table 2 compares FirstLook
Standard and Professional Reports for the Bolivia Wind Atlas:
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8
Rather than using power curves available from GE for each air density, 3TIER used a power curve for a
standard atmosphere at 15 degrees Celsius and 10% factor for turbulence intensity, which in turn
was multiplied by the result of the change in air density. Therefore, the energy production in this
data set may tend toward an upward bias for high air density and a downward bias for low air
density.
3TIER will use information provided during the registration process in accordance with the Privacy
Policy published at the FirstLook website.
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Table 2: Comparison of FirstLook Report Features
FEATURE
Map
Wind Speed and Power Capacity by Month
Hourly Wind Speed and Power Distribution
Annual Average Wind and Power Directions
(Wind and Power Roses – Annual)
Monthly Average Wind and Power Directions
(Wind and Power Roses – Monthly)
Annual Average Diurnal Wind and Power Variation
Monthly Average Diurnal Wind and Power Variation
Hourly Mean Wind and Power Table by year
Hourly Mean Wind and Power Table by Month
Data File in CSV format
Price Without Coupon (USD)
Price With Coupon (USD)
Standard
Report
X
X
X
X
Professional
Report
X
X
X
X
X
X
X
$1000
$0
X
X
X
X
X
$2500
$1250
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This report was prepared for this task by compiling the results of analysis completed
during the project.
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3TIER facilitated training workshops in Cochabamba and La Paz to present the Final
Report during the week of March 9-10. As stipulated in the Terms of Reference, the
training workshops were conducted in Spanish and addressed:
•
•
•
•
•
•
How the wind atlas was prepared
Strengths and weaknesses of the approach
Explanation of the validation results
Explanation of the internet-based viewer (FirstLook)
How to interpret the results of FirstLook reports
Recommendations regarding suitable next steps for in-depth assessments of
wind potential at specific locations.
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III. 7'/+5#%'%()1%819%'/:/5
This section addresses Bolivia’s climatology and how it relates to the Bolivia Wind Atlas.
The American Meteorological Society’s second edition Glossary of Meteorology defines
climatology as, “The description and scientific study of climate.” A fundamental
distinction is the time scale associated with the study of weather (short-term) and
climate (medium-long-term). The same source notes under its definition of weather that,
“As distinguished from climate, weather consists of the short-term (minutes to days)
variations in the atmosphere.”9
According to C. David Whiteman in the textbook, Mountain Meteorology, the four
determining factors behind climate are latitude, altitude or elevation, ‘continentality’
(distance from the sea), and exposure to regional circulations.10 The defining
characteristic of Bolivia’s climate that affects the wind resource is the complexity and
variability of the country’s terrain. Bolivia’s 1,098,580 km! of total surface area varies
greatly in elevation from a low point of approximately 90 meters above sea level near
Bolivia’s border with Paraguay to a high point of 6,542 meters above sea level (Nevado
Sajama – 18.11° S, 68.88° W). Depending on the altitude, the climate and wind
resources also vary greatly. The WRF model that 3TIER employed addresses this
complex topography through use of a sigma terrain following vertical coordinate system.
In complex terrain the sigma coordinate system allows a high vertical resolution just
above ground level, whatever altitude the ground may be. Additionally the “nested grid”
methodology described earlier in this report, allows the WRF to identify the impacts of
meteorological factors outside of Bolivia (regional circulations) on the wind resource
within the country.
Ismael Montes de Oca’s Spanish-language article entitled “Geografía y Clima de
Bolivia” notes, “The country presents a great variety of climates that depend mainly on
the latitude, the altitude, and the proximity of high mountains or flat zones and mostly
the circulation of the trade winds. [ . . .] The temperatures are related to three latitudinal
climatic regions: tropical, subtropical, and temperate.”11 Montes de Oca also details the
different climate conditions in each of Bolivia’s seven physiographical units. Those units
include the so-called Altiplano highlands between 3500-4000 meters above sea level,
which are located in Bolivia’s Southwestern corner to the lowland tropical flatlands, all
below 500 meters above sea level, which bisect Bolivia from Cobija in its Northwest
Corner, pass through Trinidad at the center of the country, and also include Santa Cruz.
Mesoscale models like WRF are highly skilled at identifying the spatial distribution of
wind resources, but are still just models based on fundamental assumptions. Therefore,
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This glossary is available at the following URL: http://amsglossary.allenpress.com/glossary
10
C. David Whiteman, Mountain Meteorology Fundamentals and Applications (New York, NY: Oxford
University Press, 2000) 3.
11
Article in PDF format downloaded on 10/21/2008 from
http://www.ifeanet.org/publicaciones/anfitrionoai.php?art=749.
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they should not be the sole basis for identifying annual average wind speeds. As noted
elsewhere in this report, collecting on-site measurements with a properly calibrated
device is a necessary next step at any location that appears to have a promising wind
resource to seek greater precision about specific wind speeds. 3TIER paid particular
attention to conditions in mountainous areas in developing the Bolivia Wind Atlas. This
Atlas has spatial resolution of 2 kilometers, which means that 3TIER’s simulation
generated values for every point on a 2 km x 2 km horizontal grid. 3TIER’s FirstLook
tool displays a range of annual average wind speed values at its clickable online map
rather than a single value to address the issue of uncertainty.
One force that drives Bolivia’s climate and shapes its wind resource is the strong solar
radiation that reaches the surface in several parts of the country, particularly its
southwest corner. The attached image from 3TIER’s online map of global horizontal
irradiance marks the annual average values over the noted region, which begins north
of Lake Titicaca at Bolivia’s border with Peru, extends to the southeast until
approximately 100 km southwest of Santa Cruz, and then southward through Tarija in
Bolivia’s wine-producing region.
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Figure 2: Global Horizontal Irradiance over Bolivia as visualized at FirstLook
Through its validation of simulated wind resource values for Bolivia against observed
data, 3TIER identified several simulation parameters used for test simulations that had
to be configured differently to properly capture the impact of solar radiation on the
intensity and variability of Bolivia’s wind resource. The end result of simulation with the
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revised initialization parameters was a greater consistency in the seasonal cycle of the
wind resource when compared to observed data.
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IV. ;",4'#,
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64++5.)1
3TIER compared simulated wind speeds to three sets of wind speed observations
collected at thirty-five locations within the model domain in Bolivia, Brazil, and Chile:
1) Wind speeds at 20 meters, measured at 10 tall tower locations between late
January and early December of the year 2000 as part of a study commissioned
by the Japan International Cooperation Agency (JICA).12 Tower locations are
marked at the left of Figure 3.
2) Wind speeds at 20 meters measured between October 2003 and December
2004 at 3 additional towers in northern Chile that are within the model domain for
Bolivia.13 Colored dots show these tower locations at the left of Figure 3.
3) Wind speeds at 10 meters, measured at 22 meteorological stations from the
National Centers for Environmental Prediction Automated Data Processing
(NCEP ADP) data set. These stations are located primarily in Bolivia, but also
include some in Brazil. The right of Figure 3 shows the locations of these
meteorological stations and Table 2 contains their names and geo-referencing.
3TIER also reviewed the consistency between simulated wind speed data and a fourth
set of wind speed observations collected near Santa Cruz, Bolivia between 1993 and
1995 for the Rural Electrical Cooperative (Cooperativa Rural de Electricidad – CRE in
Spanish); however, those observations are not featured here because:
(i) A full time series was not available,
(ii) Coordinates for the anemometers used to collect wind speed data were
unavailable, and
(iii) Data for the CRE study was collected outside of the 10-year simulation period
(1998-2007).
These factors precluded 3TIER from making an “apples to apples” comparison between
the simulated data and the CRE observations. Nonetheless, differences between the
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The full study, entitled “The study on rural electrification implementation plan by renewable energy in
the Republic of Bolivia” is available though JICA’s online library at the following URL
http://lvzopac.jica.go.jp/library/indexeng.html through a “Catalog Search” for the key words “Bolivia
Renewable Energy”. 3TIER obtained the tower data from IFC.
13
3TIER obtained this data from the website of a Global Environment Facility (GEF) -funded United
Nations Development Program (UNDP) and Chilean Government study of renewable energy
resources in Chile. (See http://www.renovables-rural.cl/actividades/fr_actividades.html).
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simulated data and the observed wind speeds from the CRE study appear consistent
with those between the simulated data and the above-noted 35 locations.
!
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Figure 3 presents color-coded maps of the simulated wind speeds minus observed
annual mean wind speed for the observed data sets. Positive values indicate that the
simulated wind speeds exceeded observed wind speeds and negative values indicate
that the simulated wind speeds were less than observed wind speeds. Because the tall
towers (left) are located exclusively in western Bolivia and neighboring regions of Chile,
3TIER chose the meteorological stations used for this analysis for their complementary
spatial distribution. The meteorological stations (right) cover the rest of Bolivia and
neighboring regions of Brazil that are within the rectangular simulation domain.
3TIER’s validation process only compared observed data with simulated data at
moments when values were available from both data sets. The scarcity of observed
data from tall towers during much of the simulation period may have contributed to an
increase in error statistics at tall tower locations (below left).
Figure 3: Maps of simulated wind speeds minus observed annual mean wind speed
differences at observation locations for (left) tall tower observations at 20 meters and
(right) meteorological station observations at 10 meters. Units are meters per second
(m/s) and each color represents the range of bias shown in the legend at the bottom of
the figure. For example, yellow dots represent validation sites where 3TIER’s simulated
wind speeds were between 0.5 m/s below and 0.5 m/s above observed wind speeds.
Bolivia Wind Atlas
15!
=5>'",1%81<5'/&5#/%316#5#/,#/0,1?@7AB1*CB16#5#/%3,D1
3TIER calculated validation statistics for the 22 NCEP ADP meteorological stations,
displayed below in Table 3.
Bolivia Wind Atlas
16!
Table 3: Summary Validation Statistics by Observation Station
Station
ID
SBCR
SBRB
SBVH
SLCB
SLCO
SLCP
SLET
SLJE
SLJO
SLLP
SLOR
SLPO
SLPS
SLRB
SLRI
SLRY
SLSA
SLSI
SLSU
SLTJ
SLVR
SLYA
Station Name
Corumbá, Brazil
Rio Branco, Brazil
Vilhena Aeroporto,
Brazil
Cochabamba, Bolivia
Cobija, Bolivia
Concepcion, Bolivia
Santa Cruz/El
Trompillo, Bolivia
San Jose De Chiquitos,
Bolivia
San Joaquin, Bolivia
La Paz/Alto, Bolivia
Oruro, Bolivia
Potosi, Bolivia
Puerto Suarez, Bolivia
Robore, Bolivia
Riberalta, Bolivia
Reyes, Bolivia
Santa Ana, Bolivia
San Ignacio De
Velasco, Bolivia
Sucre, Bolivia
Tarija, Bolivia
Viru-Viru, Bolivia
Yacuiba, Bolivia
Bolivia Wind Atlas
Lat.
(Obs.)
Long.
(Obs.)
Lat.
(Model)
Long.
(Model)
Start date
End date
-19.00
-10.00
-12.73
-57.65
-67.80
-60.13
-19.0083
-10.0083
-12.7250
-57.6417
-67.8083
-60.1250
1998/03/31
1998/03/31
1998/03/31
2007/02/05
2007/02/26
2006/04/15
Model
overlap
with
Obs.
(hours)
2950
4737
1516
Obs.
mean
wind
speed
(m/s)
2.99
2.21
3.08
Model
mean
wind
speed
(m/s)
2.76
1.69
2.48
% Error
(ModelObs.) /
Obs.
-7.6
-23.5
-19.5
-17.45
-11.08
-16.25
-17.80
-66.10
-68.87
-62.10
-63.17
-17.4583
-11.0750
-16.2583
-17.8083
-66.0917
-68.8750
-62.0917
-63.1750
1998/03/31
1998/08/16
1998/08/16
1998/08/16
2007/02/26
2007/02/26
2007/02/26
2007/02/26
2888
1942
1608
2807
3.22
2.85
4.08
6.03
3.00
2.23
4.10
5.87
-7.0
-21.7
+0.7
-2.6
-17.83
-60.75
-17.8250
-60.7417
1998/08/16
2007/02/26
1490
3.46
4.18
+20.8
-13.07
-16.52
-18.05
-19.53
-19.00
-18.32
-11.02
-14.30
-13.72
-16.37
-64.67
-68.18
-67.07
-65.72
-57.73
-59.75
-66.12
-67.37
-65.58
-60.95
-13.0750
-16.5250
-18.0583
-19.5250
-19.0083
-18.3250
-11.0250
-14.3083
-13.7250
-16.3750
-64.6750
-68.1750
-67.0750
-65.7250
-57.7250
-59.7417
-66.1250
-67.3750
-65.5750
-60.9417
1998/08/16
1998/03/31
1998/08/16
1998/08/26
1998/08/16
1998/08/16
1998/08/16
1998/08/16
1998/08/16
1998/08/16
2007/02/10
2007/02/26
2007/02/26
2007/02/26
2007/02/26
2007/02/26
2007/02/26
2007/02/26
2007/02/26
2007/02/26
1574
7178
1833
1365
2354
1609
1515
1874
2867
2182
3.32
3.34
4.03
4.60
3.55
4.29
2.95
3.44
4.22
3.71
2.42
3.75
5.19
4.79
3.07
4.01
2.03
2.97
2.53
4.12
-27.2
+12.3
+28.8
+4.0
-13.6
-6.4
-31.1
-13.8
-40.1
+11.2
-19.02
-21.53
-17.65
-22.02
-65.27
-64.72
-63.13
-63.70
-19.0250
-21.5250
-17.6583
-22.0250
-65.2750
-64.7250
-63.1250
-63.6917
1998/08/16
1998/03/31
1998/03/31
1998/08/16
2007/02/26
2007/02/26
2007/02/26
2007/02/26
1929
1965
6441
1966
3.11
4.29
5.30
4.00
4.30
4.26
5.48
3.77
+38.5
-0.6
+3.4
-5.8
17
Data Source: 3TIER obtained this observed data from the National Center for Environmental Prediction (NCEP)
Automated Data Processing (ADP) Global Surface Observations, provided by the National Center for Atmospheric
Research (NCAR) Data Support Section at http://dss.ucar.edu/datasets/ds464.0/
Measurement Height: The measurement height is assumed to be the standard meteorological station wind speed height
of 10 meters.
Table 3 Abbreviations: In the table above marked Validation Statistics by Observation Station,
•
•
•
•
•
•
“Lat (Obs.)” are the latitudes and “Lon (Obs.)” are the longitudes for the NCEP ADP data stations. The NCEP ADP
data set only provided these to the nearest 0.01 degree.
“Lat. (Model)” is the latitude and “Lon. (Model)” is the longitude of the nearest data set grid point.
“Model overlap with Obs (hours)” is the total number of hours of overlapping data between the observed and
model-based data, which were the hours used for validation. Most stations have many missing hours of data.
“Obs. mean wind speed” is the observed mean wind speed in m/s, averaged over all hours of overlapping data.
“Model mean wind speed” is the model-based mean wind speed in m/s, averaged over all hours of overlapping
data
“%Error” is the percentage error of the model mean wind speed, calculated as (Model mean wind speed – Obs.
mean wind speed) / Obs. Mean Wind Speed.
Table 4: Summary Bias & Error Statistics (NCEP ADP Data)
Mean wind speed bias
Mean absolute error
Root Mean Squared Error
-0.14 m/s
0.53 m/s
0.67 m/s
-4.58%
15.46 %
19.46 %
In the summary table above, the mean wind speed bias, mean absolute error, and RMS error are provided in both m/s
and percentage errors, where the percentage errors are calculated as (Model mean wind speed – Obs. mean wind speed)
/ Obs. Mean Wind Speed.
Bolivia Wind Atlas
18
!"#$%&'"()*+"&,*
Below are histograms of the annual mean differences between simulated wind speeds
and observed wind speeds for each data set. The magnitude of the differences is
consistent with those that 3TIER has encountered for simulated data sets in other parts
of the world. The differences relative to meteorological stations are smaller than
typically found for 5km resolution simulation data sets. While the difference between
simulated data and observations collected at tall towers is larger than 3TIER has found
elsewhere, the towers were located in relatively rough terrain, which tends to increase
the errors found in the simulated data set. Also, the number of samples at individual
data points varied. At the tall towers (below left), the number of hourly samples used in
the annual mean ranged from 436 to 793 and averaged 627. At the NCEP ADP stations
(below right), the number of hourly samples used in the annual mean ranged from 1490
to 7178 and averaged 2483. The minimum number of hourly samples per month for
was 42 for the tall towers and 72 for the NCEP ADP stations.
3TIER’s analysis indicates that the simulated wind speeds have a systematic low bias
near Bolivia’s border with Chile and in northern Bolivia. In other parts of Bolivia,
simulated wind speeds have a small or slightly high positive bias. Overall, based on the
slightly low average bias of simulated wind speeds relative to observed wind speeds
3TIER regards simulated Bolivia wind speeds as somewhat conservative.
!
Figure 4: Histograms of simulated wind speeds minus observed annual mean wind
speed for: (left) simulated wind speeds minus tall tower observations at 20 meters, and
(right) simulated wind speeds minus meteorological station observations at 10 meters.
Units for bias are meters per second (m/s). The colors of the vertical bars are the same
as those used in the spatial representation of bias according to validation site in Figure
3. For example, yellow bars represent validation sites where 3TIER’s simulated wind
speeds were between 0.5 m/s below and 0.5 m/s above observed wind speeds.
Bolivia Wind Atlas
19
!"#$%&'()(*+,-"#,&(.'/&+(
3TIER compared the seasonal cycles of wind speed between simulated data and
observed data, averaged across each data set. The seasonal cycle for the simulated
data compares favorably with the seasonal cycle for both sets of observations even
though the small number of overlapping observations from tall towers results in a noisy
seasonal cycle for direct comparison with the towers.
!
Figure 5: Seasonal cycle of monthly mean wind speed for simulated data and
observations, for: (left) 20 meter wind speed, averaged over 13 tall tower locations, and
(right) 10 meter wind speed, averaged over 22 meteorological station locations. Units
are meters per second (m/s).
0123#,&(.'/&+(
3TIER compared the diurnal cycles of wind speed for simulated data with the diurnal
cycles of wind speed for observed data, as averaged across each data set. The
comparison with meteorological stations indicates that the timing of the diurnal cycle is
good at these locations, although the amplitude of the diurnal cycle for the simulated
data diverges from that of the observed data. The comparison with tall towers indicates
that the maximum and minimum in the simulated diurnal cycle tend to come a few hours
too late at these locations. These results are generally consistent with the validation of
simulated data sets for other projects. While the seasonal cycle tends to be a close
approximation to observed data, the diurnal cycle tends to be a reasonable
approximation, but not as close to observed data as the seasonal cycle. Although the
diurnal cycle was quite different at the tall towers (below left) than at the meteorological
stations (below right), the simulation captured the differences in relative magnitude
between the two, albeit with the time lags noted above.
Bolivia Wind Atlas
20
!
Figure 6: Diurnal cycle of hourly mean wind speed for simulated data and observations,
for: (left) 20 meter wind speed, averaged over 13 tall tower locations, and (right) 10
meter wind speed, averaged over 22 meteorological station locations. Units are meters
per second (m/s).
!"#$%"&$'()*'(+#,-$'()
Validation of simulated data against observed data from 35 locations throughout the
rectangular Bolivia model domain yielded findings similar to those obtained in other
regions where 3TIER has performed validation analysis on its simulations. Overall, the
magnitude of simulated wind speeds tended to be slightly lower than that of observed
wind speeds. For seasonal and diurnal cycles, the comparison between the simulated
data and observed data was also consistent with 3TIER’s validations of other
simulations. With respect to direction, 3TIER did a wind rose analysis that compares
simulated and observed wind direction at the 22 NCEP ADP stations on an annual and
monthly basis. At IFC’s request, 3TIER has included that analysis in Appendix III at the
end of this report.
Future comparisons between simulated data and other data collected by anemometers
installed at tall towers within the Bolivia model domain probably will confirm the slightly
low bias that 3TIER identified during validation. Grid point values for each corner of
each 2 km x 2 km box represent spatial averages. Wind speed observations collected
at locations other than grid points themselves will, on average, tend to exceed values
for simulated data, especially at data collection locations selected because they are
particularly windy. For example, an anemometer sited at the top of a ridgeline between
two points on the simulation grid probably will record higher wind speeds than the
simulated values at adjacent grid points.
Bolivia Wind Atlas
21
!" #$%&'
Online, clickable, interactive maps of annual average wind speed at 20, 50, and 80
meters are available for free at http://firstlook.3tier.com. IFC has also funded new
FirstLook functionality that permits users to access additional data useful for preliminary
site analysis at specific locations using an expanded popup window. Below is a screen
shot of the wind resource over Bolivia at 80 meters above ground level:
Figure 7: Bolivia's wind resource at 80 meters as shown at FirstLook
!
To access the expanded popup window mentioned above, FirstLook visitors should
click the words “Click Here” at the bottom right-hand corner of the popup window that
appears above the arrow pointing to a site selected in Bolivia. This expanded popup is
only available for locations within Bolivia and IFC has arranged for 3TIER to make it
available for 3 years after this Final Report has been published.
Appendix II contains the following 23 maps:
(1)
Annual Mean Wind Speed at 20m
(2)
(3)
(4)
Values for Weibull A parameter at 20m
Bolivia Wind Atlas
22
(5)
Values for Weibull k parameter at 20m
(6)
(7)
(8)
(9)
(10)
Annual Capacity Factor at 80 m (GE 1.5 sle)14
(11)
Annual Mean Wind Power Density at 80 m
(12)
January Mean Wind Power Density at 80 m
(13)
February Mean Wind Power Density at 80 m
(14)
March Mean Wind Power Density at 80 m
(15)
April Mean Wind Power Density at 80 m
(16)
May Mean Wind Power Density at 80 m
(17)
June Mean Wind Power Density at 80 m
(18)
July Mean Wind Power Density at 80 m
(19)
August Mean Wind Power Density at 80 m
(20)
September Mean Wind Power Density at 80 m
(21)
October Mean Wind Power Density at 80 m
(22)
November Mean Wind Power Density at 80 m
(23)
December Mean Wind Power Density at 80 m
The data for the maps has a pixel resolution of 0.016666 degrees; however, because
the earth!s curved surface is being displayed on a flat plane for the purpose of these
maps, the linear measurement of each pixel changes according to latitude. In the range
of latitudes covered by the Bolivia Wind Atlas (between 9.7 degrees south and 22.9
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
14
Annual Capacity Factor is labeled Annual Power Capacity in the upper left of the expanded popup
window at FirstLook. Expressed as a percentage, this variable is a ratio of actual (or in this case
simulated) energy generation divided by theoretical generation during an entire year. For this
example, a GE 1.5 sle turbine operating at full capacity year round could generate 13,140 megawatt
hours of energy per year (1.5 megawatts x 24 hours x 365 days); however, wind turbines do not
always operate at full capacity due to the variability of the wind resource. An annual capacity factor
of 35% at a given location means that averaged over a year, a GE 1.5 sle turbine could be expected
to generate 4,599 megawatt hours of energy. Rather than using power curves available from turbine
manufacturer GE for each air density, 3TIER used a power curve for a standard atmosphere at 15
degrees Celsius and 10% factor for turbulence intensity, which in turn was multiplied by the result of
the change in air density. Therefore, the capacity factor in map 10 will tend to have an upward bias
for high air density and a downward bias for low air density.
Bolivia Wind Atlas
23
degrees south), 0.016666 degrees is equivalent to east-west distances between
approximately 1.8270 km and 1.7088 km and north-south distances between
approximately 1.8415 km and 2.2135 km.15
At IFC!s request, 3TIER used nearest-neighbor interpolation to color the printed maps
and their downloadable counterparts, the latter available from TDE. Through this
method, 3TIER did not smooth color transitions between grid points. Abrupt color
changes identified by zooming in on a specific location should alert Bolivia Wind Atlas
users to some condition (possibly a pronounced topographical feature) that might cause
such changes.
As noted in their respective legends, all maps include Bolivia’s transmission network,
protected areas, and roads as well as international boundaries, city names, and the
names of and boundaries between Bolivia’s 9 Departments (administrative divisions
within the country). TDE assisted with the provision of this important information to
3TIER for the Bolivia Wind Atlas.
The digital version of this report has been formatted to facilitate download, but printed
versions of this report include each of the maps in 11” x 17” format and digital maps
containing each of the 23 above-noted data layers are also available for download from
TDE in two file formats: PNG and GeoPDF. Appendix II describes how to use the maps
in GeoPDF format, which requires Version 9.1 or later of the free Adobe Reader
software, available for download at http://get.adobe.com/reader/.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
15
A basic tool to calculate the linear distance between two points for which one has the latitude and
longitude is available online at the website of the Northern California Earthquake Data Center, which
has the following URL: http://www.ncedc.org/convert/distance.html.
Bolivia Wind Atlas
24
!" #$%&'&()*+,&-.+/$012+/$31-3&(%+
!" #$%&'()*+,-.)'/0*$.*'
No single formula defines “the best wind resource” because many variables affect the
potential success of a wind energy project. The amount of energy that can be
generated by a wind turbine varies with the wind speed, the air density, the diameter of
the wind turbine rotor, and the efficiencies of the turbine and electrical system. Wind
energy developers should select the largest diameter rotor and most efficient turbine
and electrical system that makes economic sense for their target wind energy
application. Wind speed remains the single biggest factor in determining the success of
your project and there is little one can do to affect it other than to select the windiest
location to install a turbine. Because the power varies as the cube of the wind speed, a
25% increase in the wind speed at a given moment results in a potential doubling of
power output from a wind turbine at that same moment. When considering mean wind
speeds, the relationship between wind speed and power output is more complex and
depends on wind speed distribution during the period for which the mean was
calculated.
Unfortunately, high wind speeds alone are not enough. Timing is also crucial to
determine the suitability of the wind resource in a given location. High wind speeds
during the winter provide little economic benefit if peak demand is for cooling needs
during the summer. Similarly, an off grid residential system will require more battery
storage if the highest wind speeds occur during the night rather than during the day.
The best wind resource is the one that provides high wind speeds at times when the
generated electricity has the highest value.
Wind turbines function best within the range of wind speeds for which their
manufacturer designed them, which varies according to the size and desired use of the
equipment. At the bottom of that range is the cut-in speed, at which a turbine begins to
generate power. The turbine’s cut-out speed, when it will stop generating power, is at
the top of its operational range. For example, the GE 1.5 sle turbine, which 3TIER uses
at the 80 m hub height in FirstLook, cuts in at 3.5 m/s and cuts out at 25 m/s. Within the
range is each turbine’s “rated wind speed,” the lowest wind speed at which it will
generate its rated power (maximum generating capacity). At its rated wind speed of 14
m/s, the GE 1.5 sle turbine generates its rated power (1.5 MW).
While studying the data collected by an anemometer will provide the greatest precision
about the type of timing information mentioned above, a FirstLook report can provide a
preliminary approximation designed to facilitate the decision of whether or not installing
an anemometer would be advisable. IFC has made arrangements for the issuance of
discount coupons to qualified Bolivia Wind Atlas users from the public sector and NGOs
for online purchase of a FirstLook report. Interested parties should visit the “Atlas
Eólico” hyperlink at www.tde.com.bo to request FirstLook coupons for Bolivia.
Bolivia Wind Atlas
25
!" #$%&'%()*'+&,'-.&'/0)/1)2'03)45+/.,65)
3TIER reviewed the Annual Average Wind Speed maps at the three hub heights of 20,
50 and 80 meters using the color bar in the legend that extends between less than three
and greater than nine m/s. On the color bar, cool colors (purple, blue, moving to green)
represent low wind speeds while warm colors (yellow, orange, moving to red) represent
progressively higher wind speeds. While searching for orange and red sections of the
maps at the three hub heights, 3TIER noted that the most robust wind resource in the
Bolivia appears to be concentrated in four sectors:
1) Around Santa Cruz de la Sierra, largely south and west of the city center;
2) At Bolivia’s southwestern border with Chile and Argentina in the Department of
Potosi;
3) In a roughly east-west “corridor” between the cities of Santa Cruz and La Paz
that runs south of the 230 kV transmission line between Santa Cruz and
Cochabamba and slightly north of it between Cochabamba and La Paz; and
4) In a roughly north-south “corridor” between the area just east of the town of
Oruro and west of the town of Potosi
The wind resource is limited in northern Bolivia. Other than the protected areas in
central La Paz Department, there appears to be very little wind resource north of the
town of Trinidad (14.84° S, 60.93° W).
Another area with an apparently robust wind resource is the area adjacent to the
famous lake Titicaca northwest of the city of La Paz. For whatever reason, the extent to
which areas with a strong wind resource are “off limits” for development will be a very
important factor in assessing individual sites. The Bolivia Wind Atlas identifies
protected areas in or near the first three sectors identified above. For example, in
Sector 2, the Eduardo Avaroa National Reserve of Andean Fauna masks the
southwestern corner of Bolivia. Sector 3 includes 2 national parks (Carrasco and
Amboro). Close coordination with the governmental authorities responsible for
maintaining these protected areas will be an important element of the due diligence
process for anyone wishing to harness wind energy within those areas.
As implied above, when and how the wind blows is often more relevant than annual
average wind speeds when selecting project sites for further analysis. The wind speed
frequency distribution (also known as the Weibull distribution) is a probability density
function with an asymmetric bell shape that shows the relationship between each wind
speed (x-axis) and the percentage of time that it occurs at a given location (y-axis). The
Weibull distribution has two parameters that engineers and developers often study: A
(scale) and k (shape).16
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
16
Appendix 1 describes how these parameters were calculated for this project. Because 3TIER derived
the Weibull parameters presented in this report from a simulation, they will only approximate those
encountered in the real world. Only long term physical measurement with an anemometer or other
Bolivia Wind Atlas
26
!" #$%&'()*+,'-+%.$/0'
The results of 3TIER!s Wind Power Density calculations offer an additional filter for
project sites beyond a review of average annual wind speeds. Wind Power Density
quantifies how many watts of power are available for each square meter of area within
the radius of the blades of a wind turbine (otherwise known as the swept area). It takes
into account wind speed, air density, and wind frequency distribution. 3TIER used the
following formula to calculate Wind Power Density (WPD):
n
WPD =
1
" (#)(v i 3 ) in which
2n i=1
n = the number of records considered for the calculation
! (kg/m3)
!"#the air density
3
v i "#the cube of the ith wind speed (m/s)
The unit of measure for Wind Power Density is Watts / square meter (W/m2).
! The FAQ section of the American Wind Energy Association homepage summarizes the
relevance of wind power density at: http://www.awea.org/faq/basicwr.html
Upon reviewing maps of annual mean wind power density in Bolivia, 3TIER noted a
reduction of the extent of sectors 1-4 versus that evident on annual mean wind speed
maps. As evident from the above equation, wind power density varies directly in
proportion to air density. Therefore, at locations with low air densities, low wind power
density should be expected. Many of the places in sectors 2-4 described above are
located at altitudes high above sea level, where thinner (less dense) air exerts less
force on wind turbine blades at any given wind speed than it would at that same speed if
the same turbine were located at sea level.
An additional way to check elevation and wind power density at any location in Bolivia is
by using the expanded popup window functionality secured by IFC for display at the
FirstLook site. As well as obviating the need for visual interpolation by permitting the
selection of an exact location, that method permits checking the relative wind power
density at multiple hub heights (20, 50, and 80 meters).
###################################################################################################################################################################################
properly calibrated device can ensure precise calculation of wind speed frequency distribution and
the associated probability density function and Weibull parameters.
Bolivia Wind Atlas
27
!"# $%&'()*+,+-'.+/0'12*-3'+/'4)/2&52'
!" #$%&$'%()
!
The Bolivia Wind Atlas is the first in a series of steps necessary to develop a wind
energy project successfully. A detailed guide to wind energy project development
exceeds the scope of this project, but the following graphic suggests a logical
progression of steps toward the successful operation of a wind energy facility:
The graphic above denotes with traffic light clarity the future activities necessary for
successful project development. The Bolivia Wind Atlas is a suitable tool for the
prospecting phase of wind energy project development and thus that block is green.
Using this report and the resources online at FirstLook, some of the initial resource
analysis can be completed and thus that block is yellow. All further blocks are red
because the Bolivia Wind Atlas alone is not sufficient to make the decisions necessary
complete them.
The results or 3TIER’s analysis are based on model simulations and observations.
Model simulations were made using a Numerical Weather Prediction (NWP) model
similar to the one used by government meteorological services to produce local weather
Bolivia Wind Atlas
28
forecasts. The NWP model simulates atmospheric conditions and accounts for the
effects of land use and terrain. 3TIER made model simulations for Bolivia at a spatial
resolution of 2km. Values obtained for any location between points on the 2km x 2km
model grid are interpolated. Unfortunately, models only provide an approximation of the
real world. Publicly available observations from meteorological towers and other wind
speed measurements were used to validate the simulation results and can be used
estimate the level of uncertainty associated with the wind speeds displayed at FirstLook.
The power produced at a particular wind speed depends on the wind turbine installed at
a location. Different wind turbines have different rated wind speeds. The rated speed
of a wind turbine is the speed at which that turbine produces its rated power. If two
wind turbines are both rated for the same power, but one has a much lower rated speed
than the other, then the turbine with the lower rated wind speed will typically produce
more power when averaged over a long time. The function relating power to wind
speed is called the power curve and is different for each turbine. To generate FirstLook
reports, 3TIER uses one of three standard turbines depending on hub height as follows:
•
•
•
80 m: Model GE 1.5 SLE
50 m: Model Vestas V52
20 m: Model Bergey XL.1
Users of the Bolivia Wind Atlas should not make decisions about the design or
construction of a wind energy facility based exclusively on the information contained in
this report or available in FirstLook. The Bolivia Wind Atlas was designed to provide a
realistic assessment of the wind resource at a specific location. It should orient
decision-makers about whether it is worthwhile to further pursue wind power
development at that location, but variances between simulated and real life wind speeds
and turbine function are to be expected due in part to the averaging of terrain and land
use data to model grid resolution. In practice local roughness, obstacles, array and
orography effects will mean that the actual production for each turbine in real world
conditions and landscape will differ from the simulated production for that turbine based
on the NWP model. Users should remain mindful of the limitations of data analysis for a
set of grid points between each of which there is a horizontal distance of 2 km.
Bolivia Wind Atlas
29
Figure 8: The graphic above depicts the horizontal resolution for the NWP simulation
used to create the Bolivia Wind Atlas. The WRF model calculates discrete
meteorological values (wind speed, temperature, atmospheric pressure, etc.) for each
corner of each cell in the 2 km x 2 km grid. At the beginning of the simulation, 3TIER
paired each data point with land use, elevation, and roughness values.
Given the 2km resolution of the NWP model simulations, the wind resource at a location
may differ from the estimate available at FirstLook. While the model simulations
incorporated the effects of terrain on atmospheric conditions, some local terrain features
(especially in mountainous parts of Bolivia) may be too small to be represented in the
model. These limitations are an inherent part of all estimates of wind speed and power.
Only carefully maintained, long-term observations at the future location for a wind
turbine can provide an unequivocal assessment of the wind resource at that site.
Because such observations are time-consuming and expensive, collecting them should
be considered only for locations at which the wind resource is sufficient to justify the
required investment.
!" #$%&'(%)*+,-./0/)
!
After identifying project locations from the areas deemed to be of interest for further
evaluation, 3TIER recommends seeking the assistance of qualified professionals to
conduct complementary studies to guide decision-making about wind power
development, which has been keyed to the numbered steps in the graphic provided in
Section VI B. above:
•
3. Selecting a site for data collection - Selecting precisely where to collect
observational data requires a site visit. During that visit, particular attention
should be paid to local orography17 and wind obstacles and the completion of a
proper roughness assessment of the site.18
4. Collecting relevant on site data (wind speed, wind direction, temperature, and
atmospheric pressure) to corroborate the model results. The measurement
device (anemometer or other) selected should be: (i) properly calibrated19, (ii)
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
•
17
Available online at http://amsglossary.allenpress.com/glossary, the glossary of the American
Meteorological Society defines orography as “1. The nature of a region with respect to its elevated
terrain. 2. That branch of geomorphology that deals with the disposition and character of hills and
mountains."
18
Available online at http://amsglossary.allenpress.com/glossary, the glossary of the American
Meteorological Society defines aerodynamic roughness length—(also known as roughness length or
z0) as, “The height above the displacement plane at which the mean wind becomes zero when
extrapolating the logarithmic wind- speed profile downward through the surface layer.”
19
Anemometer calibration is done at enclosed facilities (wind tunnels) where each device may be
exposed to wind blowing at a known uniform velocity with minimal turbulence. Adjustments are
made to the anemometer to ensure that it measures the known wind velocity precisely. Equipment
Bolivia Wind Atlas
30
installed on a tall mast with a proper design, (iii) and mounted along with any
booms in accordance with wind industry standards.
•
5. Extrapolating from on site data collected – There are various methodologies
for extrapolating in space and time from observed data collected on site at
meteorological towers. These include: (i) micro-scale modeling using software
tools such as WAsP or WindPro (spatial extrapolation) and (ii) the procurement
of regional reference wind speed data from adjacent met stations for
normalization to a mean wind year (temporal extrapolation).
•
5. Analyzing the medium to long-term variability of the wind resource – Another
method for temporal extrapolation involves the further use of NWP models. Such
an analysis is very important to facilitate dispatch planning because wind is the
“fuel” for a wind energy project and therefore one of the primary drivers of project
revenues. Since the wind resource varies from year to year as it does month to
month and hour to hour, putting a one or two year set of observations into a 1520-year context will facilitate more precise estimation of project revenues, taking
into account years which have a weak, average, and strong wind resource in
relative terms. While overestimation is the most problematic error,
underestimation of the expected wind resource during the early years of a project
can lead financial entities to design a loan payback structure that is inconsistent
with a wind farm’s actual production.
•
5. Evaluating demand for the wind facility’s future production – Treatment of this
aspect will depend on the target use for wind energy at each selected site. Those
installing wind turbines to support a specific activity with intense energy demand
(such as a large desalination facility) will have different priorities when evaluating
demand than the owner of a remote eco-lodge with no grid connection hoping to
supplement generation from his mini-hydro turbine or government planners who
use wind data to identify existing locations where stand-alone diesel gen-sets
might be used more efficiently in tandem with wind power systems. To help
potential clients determine whether using a specific turbine would be suitable for
a desired application, some turbine manufacturers or their representatives offer
testimonials about those who have successfully used their equipment.
•
6. Assessing potential environmental, social, and economic impacts (geological
and geotechnical characteristics, geomorphology, land use, noise studies,
ecosystem (flora, & fauna), access by road, effects on or benefits to nearby
communities, etc.).
•
6. Assessing natural hazards to power generating installation (seismic threats,
volcanic activities, erosion).
•
6. Studying Wind Energy Integration – This type of analysis should focus on the
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
suppliers use various different calibration standards and should be able to provide details about how
their equipment has been calibrated.
Bolivia Wind Atlas
31
continued efficient operation of the entire energy transmission and distribution
system. In doing so, it should consider the availability and location of
transmission systems within a reasonable distance from the point of wind power
generation, as well as regulatory conditions in Bolivia’s energy sector that affect
energy consumers’ ability to purchase electricity from renewable sources like
wind.
!" #$%%&'%(%$%)*%&+,-%$.,/0&
!
•
RETScreen International (www.retscreen.net) offers multiple didactic resources,
including a software suite called RETScreen designed to facilitate the analysis of
renewable energy projects. The site also features an e-textbook with a chapter
dedicated exclusively to wind energy project development. Many of the materials
that RETScreen provides have also been translated into multiple foreign
languages, including Spanish.
•
The Danish Wind Industry Association website has a very useful section entitled “
know how, which contains everything from a children’s guide called “Wind With
Miller” to a “guided tour” which includes a Spanish-language version that
describes many wind power fundamentals. Please visit
http://www.windpower.org for further information.
•
The Resources section of the American Wind Energy Association (AWEA)
homepage also contains lots of useful reference material. Please visit
http://www.awea.org/ AWEA’s Wind Energy Siting Handbook, which contains a
list of United States best practices for analyzing wind energy development, is
available at http://www.awea.org/sitinghandbook/download_center.html
Bolivia Wind Atlas
32
!""#$%&'()*(+,-./-,0&1$(12(3#&4/--(",5,6#0#57(
!
At IFC’s request, 3TIER calculated the Weibull parameters for the Bolivia Wind Atlas
according to the standards defined by National Laboratory for Sustainable Energy at the
Technical University of Denmark – DTU (RISØ) for the elaboration of the European
Wind Atlas, and in an appropriate manner for mesoscale numerical weather prediction
(NWP) simulations.
The Weibull distribution, which displays in graphical format the probability that the wind
will blow at each given speed, is described by Equation #1:
) " v %k ,
" k %" v % k(1
f (v) = $ '$ ' exp+($ ' . EQUATION #1
# A &# A &
* # A& The variables used in this formula and presented in the Bolivia Wind Atlas are:
!
•
A: the scale factor with units of wind speed. (Note: Some equations for the
Weibull distribution refer to this parameter as c.)
•
k: a dimensionless shape factor. At increased k values, the distribution becomes
narrower and taller. When k=2, the so-called “Rayleigh distribution” occurs. For
wind energy applications, the value of the k parameter has a key significance.
As noted by the Danish Wind Industry Association on their website, many turbine
manufacturers publish performance statistics for the equipment that they produce
according to a Rayleigh distribution.
The variable v refers to wind speed (velocity) and is measured in meters per second
(m/s).
Equation #2 defines the value of A according to mean wind speed (V) and the shape
factor (k).
A=
!
V
# 1&
"%1+ (
$ k'
EQUATION #2
Equation #3 defines the wind power density E for a Weibull distribution with mean
speed V and shape factor (k).
$
3 '
#(1+
)) 1
&
1
3
3
k
E = "V &
= "A 3#(1+ )
)
1
2
k
&# 3 (1+ ) ) 2
%
(
k
!
Bolivia Wind Atlas
EQUATION #3
I-1
The specifications for calculating the Weibull shape factor from the European Wind
Atlas (Risø, 1989 – see http://www.windatlas.dk/Europe/About.html for more
information) call for the use of an iterative process to identify the value of k for each
Weibull distribution that:
1. Best fits with the frequency at which wind speeds greater than the average speed
occur, and
2. Maintains the total amount of wind energy E identified through the observed wind
speed probability distribution.
The European Wind Atlas methodology does not include wind speeds that occur less
than 1% of the time (i.e. the highest wind speed values at the far tail of the distribution)
when calculating Weibull parameters. This is a reasonable practice when using
observational data as extreme wind events may strongly affect the distribution of wind
speed values and the resulting Weibull parameters. However, Numerical Weather
Prediction (NWP) models generally underestimate the magnitude of extreme wind
events, and therefore underestimate the true, observed variability. Therefore, excluding
wind speed values that occur less than 1% of the time is not necessary, and potentially
detrimental, when computing Weibull parameters from a mesoscale NWP model data
set.
Additional References:
Interested users of the Bolivia Wind Atlas may obtain a straightforward description of
the Weibull distribution as applied to wind energy and the use of Microsoft Excel to
perform Weibull analysis at the following locations:
Description of the Weibull distribution at the webpage of the Danish Wind Industry
Association: http://www.windpower.org/en/tour/wres/weibull.htm
Dorner, W.K., “Using Microsoft Excel for Weibull Analysis”,
http://www.qualitydigest.com/jan99/html/body_weibull.html
Bolivia Wind Atlas
I-2
!""#$%&'())*(+,"-(
!" #$%&'%&()
!
Along with instructions for using digital maps available from TDE for download, this
Appendix contains the following 23 maps created by 3TIER for this project:
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Annual Capacity Factor at 80 m (GE 1.5 sle)20
(11)
Annual Mean Wind Power Density at 80 m
(12)
January Mean Wind Power Density at 80 m
(13)
February Mean Wind Power Density at 80 m
(14)
March Mean Wind Power Density at 80 m
(15)
April Mean Wind Power Density at 80 m
(16)
May Mean Wind Power Density at 80 m
(17)
June Mean Wind Power Density at 80 m
(18) July Mean Wind Power Density at 80 m
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
20
Annual Capacity Factor is labeled Annual Power Capacity in the upper left of the expanded popup
window at FirstLook. Expressed as a percentage, this variable is a ratio of actual (or in this case
simulated) energy generation divided by theoretical generation during an entire year. For this
example, a GE 1.5 sle turbine operating at full capacity year round could generate 13,140 megawatt
hours of energy per year (1.5 megawatts x 24 hours x 365 days); however, wind turbines do not
always operate at full capacity due to the variability of the wind resource. An annual capacity factor
of 35% at a given location means that averaged over a year, a GE 1.5 sle turbine could be expected
to generate 4,599 megawatt hours of energy. Rather than using power curves available from turbine
manufacturer GE for each air density, 3TIER used a power curve for a standard atmosphere at 15
degrees Celsius and 10% factor for turbulence intensity, which in turn was multiplied by the result of
the change in air density. Therefore, the capacity factor in map 10 will tend to have an upward bias
for high air density and a downward bias for low air density.
Bolivia Wind Atlas
II-1
(19)
August Mean Wind Power Density at 80 m
(20)
September Mean Wind Power Density at 80 m
(21)
October Mean Wind Power Density at 80 m
(22)
November Mean Wind Power Density at 80 m
(23)
December Mean Wind Power Density at 80 m
Bolivia Wind Atlas
II-2
Bolivia Wind Atlas
II-3
Bolivia Wind Atlas
II-4
Bolivia Wind Atlas
II-5
Bolivia Wind Atlas
II-6
Bolivia Wind Atlas
II-7
Bolivia Wind Atlas
II-8
Bolivia Wind Atlas
II-9
Bolivia Wind Atlas
II-10
Bolivia Wind Atlas
II-11
Bolivia Wind Atlas
II-12
Bolivia Wind Atlas
II-13
Bolivia Wind Atlas
II-14
Bolivia Wind Atlas
II-15
Bolivia Wind Atlas
II-16
Bolivia Wind Atlas
II-17
Bolivia Wind Atlas
II-18
Bolivia Wind Atlas
II-19
Bolivia Wind Atlas
II-20
Bolivia Wind Atlas
II-21
Bolivia Wind Atlas
II-22
Bolivia Wind Atlas
II-23
Bolivia Wind Atlas
II-24
Bolivia Wind Atlas
II-25
!" #$%&'(')*(+,-.(
All coordinates in this report and the digital maps are based on WGS 84 datum and
displayed in decimal degree format. The data for the maps has a pixel resolution of
0.016666 degrees; however, because the earth!s curved surface is being displayed on a
flat plane for the purpose of these maps, the linear measurement of each pixel changes
according to latitude. In the range of latitudes covered by the Bolivia Wind Atlas
(between 9.7 degrees south and 22.9 degrees south), 0.016666 degrees is equivalent
to east-west distances between approximately 1.8270 km and 1.7088 km and northsouth distances between approximately 1.8415 km and 2.2135 km.21
At IFC!s request, 3TIER used nearest-neighbor interpolation to color the printed maps
and their downloadable counterparts, the latter available from TDE. Through this
method, 3TIER did not smooth color transitions between grid points. Abrupt color
changes identified by zooming in on a specific location should alert Bolivia Wind Atlas
users to some condition (possibly a pronounced topographical feature) that might cause
such changes.
As noted in their respective legends, all maps include Bolivia’s transmission network,
protected areas, and roads as well as international boundaries, city names, and the
names of and boundaries between Bolivia’s 9 Departments (administrative divisions
within the country). TDE assisted in the provision of this important information to 3TIER
for the Bolivia Wind Atlas.
The digital version of this report has been formatted to facilitate download, but printed
versions of this report include each of the maps in 11” x 17” format. Digital maps in
PNG and GeoPDF format are also available for download from TDE. Each of the maps
in PNG format includes the map image of a single data layer (identified by numbers
keyed to the descriptions at the beginning of this Appendix) as well as coordinate hash
marks at the map borders to permit alignment of multiple layers.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
21
A basic tool to calculate the linear distance between two points for which one has the latitude and
longitude is available online at the website of the Northern California Earthquake Data Center, which
has the following URL: http://www.ncedc.org/convert/distance.html.
Bolivia Wind Atlas
II-26
!" #$%&'$&()*&+,-,'./&0.1)&,2&3*$456&7$80.'&
The maps in GeoPDF format permit offline interaction with variable map layers (wind
speed, wind power density, capacity factor, etc.) and base layers (roads, transmission
lines, city names, etc.) through the following two basic capabilities of the free Adobe
Reader software (Version 9.1 or higher, available for Windows and Mac operating
systems at http://get.adobe.com/reader/):
1) Switching layers on and off – The layer tool is located in the navigation panel
at the left of the Adobe Reader window. If it is not shown in their default view,
users may access the layer tool through the View Menu as shown in the following
Figure II-1:
!
Figure II-1: How to access the layer tool from the Adobe Reader View menu
!
Once the layer tool is open, clicking the eye-shaped icons to the left of each layer name
allows users to turn layers on and off. If the eye-shaped icon is visible, the layer with
which it is associated will be turned on (visible). If the eye-shaped icon is not visible,
the layer will be turned off (invisible). Figure II-2 below is a screen shot of the layer tool
when the menu of layers has been fully expanded.!
Bolivia Wind Atlas
II-27
!
Figure II-2: Screen shot of layer tool when fully expanded
2) Identifying coordinates (latitude, longitude) of map locations – With the
Geospatial Location Tool, Bolivia Wind Atlas users may see the coordinates of
any location within Bolivia over which they position their mouse pointer.
!
Figure II-3: How to access the Geospatial Location Tool from the Tools menu
Once the Geospatial Location Tool has been activated, a rectangle at the lower righthand corner of the screen will display the latitude and longitude of the current mouse
pointer location. In Figure II-4 below, the pointer is southeast of the city of Santa Cruz.
Bolivia Wind Atlas
II-28
!
Figure II-4: Screen shot of Geospatial Location Tool function
Bolivia Wind Atlas
II-29
!""#$%&'()))*(!%%&+&,$-.(/-.&%-+&,$(01&$%(2,3#34(
3TIER’s Validation Report included a comparison of wind roses at the NCEP ADP
observation stations between simulated data and observed data on a monthly and
annual basis. At IFC’s request, that comparison has been included as an Appendix to
this report.
Bolivia Wind Atlas
III-1
SBCR (Corumba, Brazil)
Figure III-1: Annual Wind Rose Comparison: SBCR
SBRB (Rio Branco, Brazil)
Figure III-2: Annual Wind Rose Comparison: SBRB
III-2
SBVH (Vilhena Aeroporto, Brazil)
Figure III-3: Annual Wind Rose Comparison: SBVH
SLCB (Cochabamba, Bolivia)
Figure III-4: Annual Wind Rose Comparison: SLCB
III-3
SLCO (Cobija, Bolivia)
Figure III-5: Annual Wind Rose Comparison: SLCO
SLCP (Concepción, Bolivia)
Figure III-6: Annual Wind Rose Comparison: SLCP
III-4
SLET (Santa Cruz / El Trompillo, Bolivia)
Figure III-7: Annual Wind Rose Comparison: SLET
SLJE (San Jose de Chiquitos, Bolivia)
Figure III-8: Annual Wind Rose Comparison: SLJE
III-5
SLJO (San Joaquin, Bolivia)
Figure III-9: Annual Wind Rose Comparison: SLJO
SLLP (La Paz / Alto, Bolivia)
Figure III-10: Annual Average Wind Rose Comparison: SLLP
III-6
SLOR (Oruro, Bolivia)
Figure III-11: Annual Average Wind Rose Comparison: SLOR
SLPO (Potosi, Bolivia)
Figure III-12: Annual Wind Rose Comparison: SLPO
III-7
SLPS (Puerto Suarez, Bolivia)
Figure III-13: Annual Wind Rose Comparison: SLPS
SLRB (Robore, Bolivia)
Figure III-14: Annual Wind Rose Comparison: SLRB
III-8
SLRI (Riberalta, Bolivia)
Figure III-15: Annual Wind Rose Comparison: SLRI
SLRY (Reyes, Bolivia)
Figure III-16: Annual Wind Rose Comparison: SLRY
III-9
SLSA (Santa Ana, Bolivia)
Figure III-17: Annual Wind Rose Comparison: SLSA
SLSI (San Ignacio de Velasco, Bolivia)
Figure III-18: Annual Wind Rose Comparison: SLSI
III-10
SLSU (Sucre, Bolivia)
Figure III-19: Annual Wind Rose Comparison: SLSU
SLTJ (Tarija, Bolivia)
Figure III-20: Annual Wind Rose Comparison: SLTJ
III-11
SLVR (Viru-Viru, Bolivia)
Figure III-21: Annual Wind Rose Comparison: SLVR
SLYA (Yacuiba, Bolivia)
Figure III-22: Annual Wind Rose Comparison: SLYA
III-12
Figure III-23: Observed at SBCR (Corumba, Brazil)
Figure III-24: Simulated at SBCR (Corumba, Brazil)
III-13
Figure III-25: Observed at SBRB (Rio Branco, Brazil)
Figure III-26: Simulated at SBRB (Rio Branco, Brazil)
III-14
Figure III-27: Observed at SBVH (Vilhena Aeroporto, Brazil)
III-15
Figure III-28: Simulated at SBVH (Vilhena Aeroporto, Brazil)
Figure III-29: Observed at SLCB (Cochabamba, Bolivia)
Figure III-30: Simulated at SLCB (Cochabamba, Bolivia)
III-16
Figure III-31: Observed at SLCO (Cobija, Bolivia)
Figure III-32: Simulated at SLCO (Cobija, Bolivia)
III-17
Figure III-33: Observed at SLCP (Concepción, Bolivia)
Figure III-34: Simulated at SLCP (Concepción, Bolivia)
III-18
Figure III-35: Observed at SLET (Santa Cruz / El Trompillo,
Bolivia)
III-19
Figure III-36: Simulated at SLET (Santa Cruz / El Trompillo,
Bolivia)
Figure III-37: Observed at SLJE (San Jose de Chiquitos,
Bolivia)
III-20
Figure III-38: Simulated at SLJE (San Jose de Chiquitos,
Bolivia)
Figure III-39: Observed at SLJO (San Joaquin, Bolivia)
Figure III-40: Simulated at SLJO (San Joaquin, Bolivia)
III-21
Figure III-41: Observed at SLLP (La Paz / Alto, Bolivia)
Figure III-42: Simulated at SLLP (La Paz / Alto, Bolivia)
III-22
Figure III-43: Observed at SLOR (Oruro, Bolivia)
Figure III-44: Simulated at SLOR (Oruro, Bolivia)
III-23
Figure III-45: Observed at SLPO (Potosi, Bolivia)
Figure III-46: Simulated at SLPO (Potosi, Bolivia)
III-24
Figure III-47: Observed at SLPS (Puerto Suarez, Bolivia)
III-25
Figure III-48: Simulated at SLPS (Puerto Suarez, Bolivia)
Figure III-49: Observed at SLRB (Robore, Bolivia)
Figure III-50: Simulated at SLRB (Robore, Bolivia)
III-26
Figure III-51: Observed at SLRI (Riberalta, Bolivia)
Figure III-52: Simulated at SLRI (Riberalta, Bolivia)
III-27
Figure III-53: Observed at SLRY (Reyes, Bolivia)
Figure III-54: Simulated at SLRY (Reyes, Bolivia)
III-28
Figure III-55: Observed at SLSA (Santa Ana, Bolivia)
Figure III-56: Simulated at SLSA (Santa Ana, Bolivia)
III-29
Figure III-57: Observed at SLSI (San Ignacio de Velasco,
Bolivia)
III-30
Figure III-58: Simulated at SLSI (San Ignacio de Velasco,
Bolivia)
Figure III-59: Observed at SLSU (Sucre, Bolivia)
Figure III-60: Simulated at SLSU (Sucre, Bolivia)
III-31
Figure III-61: Observed at SLTJ (Tarija, Bolivia)
Figure III-62: Simulated at SLTJ (Tarija, Bolivia)
III-32
Figure III-63: Observed at SLVR (Viru-Viru, Bolivia)
Figure III-64: Simulated at SLVR (Viru-Viru, Bolivia)
III-33
Figure III-65: Observed at SLYA (Yacuiba, Bolivia)
Figure III-66: Simulated at SLYA (Yacuiba, Bolivia)
III-34

Bolivia Wind Atlas

Transcription

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