Wind Resource Mapping in Tanzania SITE IDENTIFICATION REPORTpubdocs.worldbank.org/...Site-Identification-Report... · Report title: Candidate Site Identification Report Customer:

Wind Resource Mapping in Tanzania

SITE IDENTIFICATION REPORT JULY 2015

This report was prepared by DNV GL, under contract to The World Bank.

It is one of several outputs from the wind Resource Mapping and Geospatial Planning Tanzania [Project ID: P145287]. This activity is funded and supported by the Energy Sector Management Assistance Program (ESMAP), a multi-donor trust fund administered by The World Bank, under a global initiative on Renewable Energy Resource Mapping. Further details on the initiative can be obtained from the ESMAP website. This document is an interim output from the above-mentioned project. Users are strongly advised to exercise caution when utilizing the information and data contained, as this has not been subject to full peer review. The final, validated, peer reviewed output from this project will be the Tanzania Wind Atlas, which will be published once the project is completed.

Copyright © 2015 International Bank for Reconstruction and Development / THE WORLD BANK Washington DC 20433 Telephone: +1-202-473-1000 Internet: www.worldbank.org

This work is a product of the consultants listed, and not of World Bank staff. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent.

The World Bank does not guarantee the accuracy of the data included in this work and accept no responsibility for any consequence of their use. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.

The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for non-commercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: +1-202-522-2625; e-mail: [email protected]. Furthermore, the ESMAP Program Manager would appreciate receiving a copy of the publication that uses this publication for its source sent in care of the address above, or to [email protected].

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RENEWABLE ENERGY WIND MAPPING FOR TANZANIA

Candidate Site Identification Report The World Bank

Document No.: 702910-USSD-R01-B

Issue: B, Status: FINAL

Date: 13 July 2015

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This document does not imply that any information is not subject to change. Except and to the extent that checking or verification of information or data is expressly agreed within the written scope of its services, DNV GL shall not be responsible in any way in connection with erroneous information or data provided to it by the Customer or any third party, or for the effects of any such erroneous information or data whether or not contained or referred to in this document.

5. Any energy forecasts estimates or predictions are subject to factors not all of which are within the scope of the

probability and uncertainties contained or referred to in this document and nothing in this document guarantees any particular wind speed or energy output.

KEY TO DOCUMENT CLASSIFICATION

Strictly Confidential : For disclosure only to named individuals within the Customer’s organization.

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Distribution for information only at the discretion of the Customer (subject to the above Important Notice and Disclaimer and the terms of DNV GL’s written agreement with the Customer).

Published : Available for information only to the general public (subject to the above Important Notice and Disclaimer).

Project name: Renewable Energy Wind Mapping for Tanzania DNV GL - Energy

Renewables Advisory

9665 Chesapeake Drive, Suite 435

San Diego, CA 92123

Tel: 703-795-8103

Enterprise No.: 94-3402236

Report title: Candidate Site Identification Report

Customer: The World Bank,

1818 H Street, N.W.

Washington, DC 20433

Contact person: Anders Pedersen

Date of issue: 13 July 2015

Project No.: 702910

Document No.: 702910-USSD-R01-B

Issue/Status B/FINAL

Task and objective: Identify a long list of suitable wind measurement locations for the purpose of calibrating

the mesoscale wind map for Tanzania, and to provide technical details of the type of equipment proposed.

Prepared by: Verified by: Approved by:

Shant Dokouzian

Senior Project Manager, Development and Engineering Services

Cory Gessert

Senior Project Manager, Development and Engineering Services

Stephane Pearson

Head of Section, Energy and Development Services

Francis Langelier

GIS Team Leader, Environmental and

Permitting Services

Daran Rife

Global Head of Mesoscale Modelling

[Name]

[title]

[Name]

[title]

☐ Strictly Confidential Keywords:

World Bank, Tanzania, Wind, Measurement, tower,

mesoscale, map, GIS, validation, constraints

☐ Private and Confidential

☐ Commercial in Confidence

☐ DNV GL only

☒ Customer’s Discretion

☐ Published

© Garrad Hassan America, Inc.. All rights reserved.

Reference to part of this report which may lead to misinterpretation is not permissible.

Issue Date Reason for Issue Prepared by Verified by Approved by

A 03 July 2015 DRAFT

B 13 July 2015 FINAL

Table of contents

1 INTRODUCTION ............................................................................................................................. 1

1.1 Project description ....................................................................................................................... 1

1.2 Framing the challenge .................................................................................................................. 3

2 GENERAL APPRECIATION OF TANZANIA LANDSCAPE AND WIND RESOURCE ......................................... 4

3 MAST SITE IDENTIFICATION AND RANKING ...................................................................................... 6

3.1 Methodology ............................................................................................................................... 6

3.2 Mapping of spatial features and constraints .................................................................................... 7

3.3 Multi-criteria analysis ................................................................................................................... 8

3.4 Site identification and ranking ......................................................................................................16

4 MEASUREMENT MAST SPECIFICATIONS AND RECOMENDATIONS ........................................................23

4.1 Mast 23

4.2 Equipment .................................................................................................................................23

4.3 Other equipment ........................................................................................................................25

4.4 Measurement configuration .........................................................................................................26

4.5 Documentation ..........................................................................................................................31

5 REFERENCES ................................................................................................................................33

APPENDIX A – SPATIAL FEATURES AND CONSTRAINTS MAPPING ..........................................................34

List of tables

Table 3-1 Exclusion areas .................................................................................................................. 9 Table 3-2 Relative weighting of identified criteria and factors ............................................................... 11 Table 3-3 Distance to road scoring system ......................................................................................... 13 Table 3-4 Distance to urban areas scoring system .............................................................................. 13 Table 3-5 Potential security scoring system ........................................................................................ 14 Table 3-6 Ranking matrix ................................................................................................................. 17 Table 3-7 Proposed sites for wind measurement masts – details # 1 ..................................................... 18 Table 3-8 Proposed sites for wind measurement masts – details # 2 ..................................................... 19 Table 4-1 Instrumentation Summary ................................................................................................. 28

List of figures

Figure 2-1 Preliminary and unvalidated mesoscale wind speed map based on the full 10-year simulations .... 5 Figure 3-1 Methodological approach .................................................................................................... 7 Figure 3-2 Exclusion areas / available land for meteorological mast ...................................................... 10 Figure 3-3 Heat map for locating mesoscale wind validation masts ........................................................ 15 Figure 3-4 Proposed sites for wind measurement masts ....................................................................... 21 Figure 3-5 Proposed sites for wind measurement masts over wind speed ............................................... 22 Figure 4-1 Recommended mast instrumentation ................................................................................. 30

DNV GL – Document No.: 702910-USSD-R01-B, Issue: B, Status: FINAL Page 1 www.dnvgl.com

1 INTRODUCTION

The results described in this report are derived from interim output and are preliminary and unvalidated,

and they have not been subjected to full peer review. DNV GL does not guarantee the accuracy of the

maps, data, and visualizations presented in this report, and accepts no responsibility whatsoever for any

consequence of their use. Wind speed values shown in tables and maps should not be relied upon in an

absolute sense. Rather they should be strictly interpreted as indicative (e.g., elevated windiness near

mountaintops and escarpments). Users are strongly urged to exercise caution when using the information

and data contained within this report.

During Phase 2 of this project, measurements will be collected from a number of representative sites

throughout the country over a 24 month period, and these will be used in Phase 3 to develop a final,

validated, peer-reviewed suite of outputs from this project, which will be made available at the project’s

completion.

The World Bank (“Client”) has retained Garrad Hassan America, Inc. (“DNV GL”) to provide a validated

mesoscale wind atlas for Tanzania, including associated deliverables and wind energy development training

courses. Validation of the wind atlas will be undertaken by installing several wind measurement

meteorological masts throughout the country. Meteorological data collected at these sites over a 2-year

period will provide the basis for validating the mesoscale modeling outputs.

This report presents the initial findings of DNV GL’s investigation on appropriate wind measurement sites

and will form the basis of further discussions with the World Bank and all relevant Tanzania stakeholders,

including TANESCO. This report also provides detailed specifications and recommendations for the

measurement equipment proposed.

1.1 Project description

Tanzania is still in the early stages of exploring the resource potential for wind power. The key goal of this

project is to provide Tanzanian policy makers and stakeholders with accurate and valuable knowledge of the

national wind resource which can be of direct practical use, both for formulating energy policy and

implementing wind projects. The installation and operation of high quality wind measurements throughout

the country will also strengthen local capacity to support future development of wind projects in Tanzania.

The primary deliverable supporting the above goal will be a well-validated national mesoscale wind resource

atlas for Tanzania that will greatly improve the awareness and understanding of the locations with the

greatest potential for wind energy. When used in combination with a Geographic Information System (GIS),

this forms a highly valuable planning tool which facilitates energy strategy planning for policy makers and

stimulates commercial wind development by removing an important knowledge barrier.

This Site Identification Study focuses on the identification of suitable measurement locations to support the

validation of the preliminary mesoscale wind atlas and should be viewed together with the Mesoscale Wind

Modeling Report #1 [1].


DNV GL has identified a preliminary list of 36 promising sites for wind measurement masts in Tanzania.

Among these favorable sites, 12 locations will be shortlisted after consultation with TANESCO, the World

Bank and other relevant stakeholders. Once an agreement is reached, a site visit will be performed at each

of the shortlisted mast locations. Based on the field visits, a final group of 8 masts will be selected and

deployed across Tanzania to validate the mesoscale wind atlas.


1.2 Framing the challenge

Tanzania lies south of the Equator, extends over 950,000 km2 and shares borders with eight countries. The

size of the country alone presents some significant challenges when designing a finite measurement

campaign suitable for validating a national level wind atlas.

Under this ESMAP funded initiative, a total of 8 masts will be deployed across the country for the purpose of

validating the initial Phase 1 mesoscale mapping outputs described in [1]. In order to serve this purpose

effectively, these masts must capture the different large-scale wind characteristics of the country; inform

and improve the wind modelling in areas of uncertainty; and capture a sufficient spectrum of ground

conditions represented in the four-dimensional mesoscale atmospheric wind model.

In addition to these technical requirements, a host of other practical issues such as site accessibility, terrain

slopes, exclusion zones and environmental constraints must be factored into the selection of the best mast

locations.

Lastly, DNV GL has also analyzed factors, including distance to grid and load, and wind speed, which are

considered important for future development of wind energy. These have been included so that masts may

support the primary goal of validating the wind atlas and a secondary goal of supporting future development

of areas with high potential for wind development.

Section 3 explains in detail the multi-criteria methodology used to incorporate all of these important factors

relevant to the selection of mast locations. It is vital that TANESCO and other relevant agencies are actively

involved in the selection process, and that they help review the inputs and provide the local knowledge

needed to ensure a successful measurement campaign with lasting value for Tanzania.


2 GENERAL APPRECIATION OF TANZANIA LANDSCAPE AND WIND

RESOURCE

Before a detailed multi-criteria analysis can be undertaken to identify promising locations for installing

measurement masts, it is essential to understand the characteristics of the wind conditions in Tanzania and

how these vary across the country.

The Mesoscale interim mapping report is a key Phase 1 deliverable of this ESMAP project and it provides the

most accurate picture of the wind resource in Tanzania currently available. This map will be further

improved by validating the modelling results with high quality measurements to be recorded over 2 years at

the 8 mast locations.

Figure 2-1 shows the preliminary mesoscale wind speed map, based on the full 10-year simulations

performed with the DNV GL Wind Mapping System (WMS). This provides for the first time a detailed picture

of the long-term wind resource across the country. From this map a number of interesting features of the

wind climate in Tanzania appear:

Northern regions: The best wind resource in Tanzania appears to be found in the north of the country in

the mountains surrounding Lake Eyasi in the Arusha, eastern Shinyanga and western Manyara regions, and

extending up to the Kenyan border in the north.

Central regions: A region of good wind resource appears to lie along the Udzungwa Mountains, running in

a southwest to northeast direction in the Iringa Region. Due to the sheltering effect of the mountains, the

area directly to the northwest has poorer exposure to the predominant easterly winds and therefore has

lower wind resource. The good wind resource extends into the Dodoma and Singida regions to the east and

west of the Great Rift Valley. The high winds from the east extend well beyond the edge of the escarpment

into the eastern Tabora region.

East and southeast regions: The southeast regions are generally low lying and have some of the

country’s lowest wind speeds, particularly in the Morogoro Region at the leading edge of the Udzungwa

Mountains. The coastal and offshore zones and Zanzibar, despite being low-lying, appear to have improved

resource due better exposure to the predominant easterly winds.

Northwest regions: Whilst much of the northwest of Tanzania is at high elevation, its inland location and

rugged terrain in some parts mean that large areas are sheltered from the predominant easterly winds. As a

result, there appear to be regions of low wind speed, particularly along the borders with Rwanda and

Burundi.

Southwest regions: Good resource is also found along the Mbeya Range in the southwest of the country,

although wind speeds are lower along the sheltered banks of Lake Rukwa and along the nearby valleys.


Figure 2-1 Preliminary and unvalidated mesoscale wind speed map based on the full 10-year simulations


3 MAST SITE IDENTIFICATION AND RANKING

3.1 Methodology

The preliminary selection of the most suitable locations for installing mesoscale validation masts has been

conducted using a multi-criteria analysis specifically tailored to the project objectives and the country of

Tanzania. The primary objective was to select sites which provide the maximum potential for improving the

accuracy of the mesoscale mapping; however secondary consideration has also been given to sites which

show potential for future wind development. Practical factors such as ease of construction and maintenance

and minimizing environmental and social impacts have also been central to the selection process.

The methodological approach can be summarized in five main steps and as shown in Figure 3-1:

1. Mapping and identification of spatial constraints (e.g. environmental features, built features, wind

resource and related uncertainty, security, topography, etc.);

2. Removal of areas of ‘hard’ constraint, defined as those which are entirely incompatible with the

installation of meteorological masts (e.g. bodies of water, city centers, national parks);

3. Pragmatic weighting of the remaining area, outside the defined ‘hard’ constraints, to identify suitable

sites for mesoscale validation meteorological masts, taking into consideration factors which influence

the selection process;

4. Identification of the most promising sites (iterative process); and

5. Ranking, analysis, and description of identified sites.

The above approach is described in greater detail in the following sections.


Figure 3-1 Methodological approach

3.2 Mapping of spatial features and constraints

DNV GL used readily available mapping data and other documentation available on the public domain, to

prepare detailed maps showing land features, key environmental features, infrastructure and other key

inputs deemed of influence in identifying site locations.

The primary data sources are the following:

DNV GL preliminary mesoscale wind map, uncertainty index map and roughness map [1];

African Development Bank Group, Africa Infrastructure Country Diagnostic;

U.S. Geological Survey (Shuttle Radar Topographic Mission Elevation Data, HYDROSHED Database);

Our Airport.com;

Open Street Map;

Environmental Systems Research Institute (ESRI);


Protect Planet, World Database on Protected Areas;

Birdlife International;

Google Earth;

International Livestock Research Institute (ILRI) GIS Database;

National Bureau of Statistics, Tanzania;

TANESCO, The National Grid System map;

World Bank Group, The Power of the Mine document;

High level security considerations: US Department of State - International Travel, Government of

Canada – Travel advice and advisories to Tanzania, and UK Foreign travel advice (to be confirmed

through discussion with the World Bank).

Appendix A, presents a complete set of maps covering the above GIS data, including primary outputs of the

Mesoscale Wind Modeling Report #1.

DNV GL notes that while the above list covers a wide range of inputs, it is not exhaustive, and the data for

each feature may not be entirely up-to-date or complete. Therefore DNV GL would welcome feedback from

any relevant agencies to assist in validating the public domain data used as the basis for this preliminary

GIS site selection process. Additionally, the following data was not available for this initial assessment and

may have significant impact on the site identification study:

Official Land tenure information and possible access issues related to ownership;

Military and other government-related exclusion zone;

Detailed local security considerations. Any further guidance from the World Bank or TANESCO for how to obtain these and any other relevant data would be very much appreciated.

3.3 Multi-criteria analysis

In order to capture the areas that are suitable for placing meteorological masts, DNV GL, in as a first step,

has aimed to identify areas that should be entirely excluded. The remaining area is then referred to as the

Available Area. This is further detailed in Section 3.3.1 below.

As a second step, a map showing the relative appropriateness for placing masts across the Available Area is

then created. This map is commonly referred to as a Heat Map. Through a weighting process, a color scale

indicates the areas highly relevant to placing a mast, up to the areas of least importance. This is further

detailed in Section 3.3.2.

3.3.1 Exclusion areas

As discussed above, the analysis identifies certain features or areas that are completely avoided for the

installation of meteorological towers for mesoscale validation. This step helps limit the focus to suitable

regions only, or Available Areas.


Table 3-1 presents exclusion zones considered in this study as well as applied setbacks, where appropriate.

These ‘hard’ constraints consist of a combination of technological barriers and spatial conflicts which are

considered to restrict the installation of masts.

Figure 3-2 presents the hard constraints and the available land considered in the site selection.

Approximately 25% of Tanzania is excluded from the analysis due to these constraints.

Table 3-1 Exclusion areas

Constraint Area excluded

Maximum slope and high elevation Slopes exceeding 15%

Urban Areas Area within and up to 10 km from cities of 100,000 inhabitants and more

Area within and up to 8 km from cities of 20,000 inhabitants and more

Area within and up to 1 km from other urban areas

Airports 10 km from International Airports and 5 km from National Airports

Major radio communication System 10 km from radars (Air Surveillance Radar, Weather Radar) and 5 km from VORs

Environmentally Sensitive areas National Parks, Game Reserve, Nature Reserve, Forest Reserve (IUCN I,II,III,IV)

Rivers, lakes, wetlands and swamps (including Ramsar)


Figure 3-2 Exclusion areas / available land for meteorological mast


3.3.2 Multi-criteria analysis of available area

Locating the best sites among the Available Area is achieved by using a multi-criteria analysis and by

generating a set of factors that will be used to produce a Heat Map.

While a primary goal for locating masts is to validate the mesoscale wind map, secondary technical, social

and environmental considerations as well as areas prone to future wind development were also considered.

Areas close to existing road network and urban areas was highly considered as well for ease of maintenance.

This was achieved by setting key Criteria for mast selection, and then associated factors. A relative weight

was then attributed to each factor, for a total of 100%. This is detailed in Table 3-2.

Table 3-2 Relative weighting of identified criteria and factors

Criteria Factor Relative Weight [%]

Area of optimal validation value a) Wind Uncertainty Index Map 25

Area prone to future

commercial wind development

b) Wind speed

c) Distance to Grid

d) Distance to load

15

10

5

Construction complexity e) Terrain slopes 10

Ease of access / remoteness

f) Distance to roads

g) Distance to urban areas

15

5

Security h) Areas of potential security concern 5

Environmental and social sensitivity

i) Regulated or sensitive areas that are not an Exclusion Zone (Forest Reserves, Game Management Areas, Important Bird Areas)

10

The relative weight of each factor in Table 3-2, and attribution of score within the Available Area is

suggested based on DNV GL’s expertise in mesoscale wind mapping, wind energy development and mast

installation. This is an area of potential discussion with stakeholders.

Each criteria and associated factor(s) is discussed below.

Area of optimal validation value

The primary project goal is to validate the mesoscale wind map. As such, a significant relative weight of 25%

has been attributed to this item. The factor considered is the preliminary uncertainty index map, produced

as part of the initial mesoscale mapping work. The preliminary uncertainty index is initially set to be equal to

the standard deviation of nine multi-physics mesoscale ensemble members as described in [1]. Areas with a

high index value, and therefore high standard deviation indicate an area where there is a lack of consensus

between the nine multi-physics ensemble members, and shows the apparently increased difficulty in

modelling the flows in these areas. A score of 0 to 10 is scaled to the range of preliminary uncertainty index

from the analysis after exclusion of the outliers. As an example, a score of 0 would be given to an area on


the map with the lowest index value resulting from the analysis, while a score of 10 would be given to an

area on the map with the highest index value.

Areas prone to future commercial wind development

While the primary objective is to optimize the validation and ensure highest accuracy of the overall wind

atlas, secondary use of the measurements in future individual wind farm developments is also foreseen as a

value outcome. To capture this value, three factors which contribute significantly to assessing areas prone to

wind energy development have been included, (i) long-term hub-height wind distribution, (ii) the distance

to, and availability of the electrical medium-voltage and high-voltage grid, and (iii) distance to major load

centers.

Many other factors impact the assessment of future development potential, such as terrain complexity or

environmental sensitivity; however, these are mostly captured in the other mast siting criteria.

Wind Speed at a height of 100 m agl, from the work presented in [1],is used as the factor to represent the

long-term hub height wind speed. A score of 0 is given to wind speeds below 5 m/s, which represents a wind

speed (with some margin due to the inherent uncertainty in the un-validated mesoscale outputs) at which

utility-scale wind energy development is typically not viable. A score between 0 and 10 is then scaled to the

range of speeds suitable for wind energy development. In summary, a score of 0 would be given to an area

on the map with a wind speed below 5 m/s, while a score of 10 would be given to an area on the map with

wind speed over 10 m/s.

While capacity of existing medium voltage and high-voltage grid is of primary concern to wind energy

development, such in-depth analysis is a significant undertaking and not part of this study’s scope. The

distance to grid is therefore used as the metric, which is of importance on its own. New Transmission line

construction will greatly influence a wind energy project’s capital cost (CAPEX). As such a score of 0 is given

to areas more than 75 km from a known low voltage (33-88 kV) or medium voltage (132-220 kV)

transmission line. A score of 0 to 10 is then scaled to the distance from the transmission line, over a range

of 75 km. In summary, a score of 0 would be given to a location at 75 km or beyond, while a score of 10

would be given to an area next to a transmission line. A similar score was given for proximity to existing or

planned transmission lines.

Distance from major load centers is another factor that could influence the location of future electricity

generation. Population centers and areas with extensive mining activity are considered as areas with

significant energy demand in Tanzania. A kernel density function would be used to estimate proximity to

load within the neighboring of the load centers. This approach allows considering both (i) distance from the

load center, and (ii) importance of the electricity demand, derived from city population or mine category. A

score of 0 to 10 is then scaled to the result of the approximate load density map.

Construction complexity

Construction complexity is a primary concern which can burden or even preclude an area for mast

construction. As well, access is typically more challenging in generally complex terrain. As such, terrain of

slopes above 15% has been considered as an exclusion zone, as discussed under 3.3.1. A relative weight of

10% has been given. A score of 0 to 10 has been scaled to sloped terrain of 15%, up to 0%. In summary, a

score of 0 is given to slopes of 15%, while 10 is given to a flat area.

Ease of access / Remoteness


Two factors have been considered for this Criterion; ease of access and remoteness. A combined relative

weight of 20% has been assigned to this Criterion. Ease of access for mast construction has been

determined, at this stage, by considering distances to different type of roads. Proximity to roadway

infrastructure will typically allow for easier construction and maintenance. Table 3-3 defines the scoring

system used. The highest score would apply at any given location. The score has been adjusted after the

selection of the proposed sites for wind measurement towers. The adjustment was required because of the

accuracy and completeness of the available GIS roadway input files. The real distance to road and highway

has been assessed for each proposed tower location, using aerial imagery.

Table 3-3 Distance to road scoring system

Road Type1 Range Score

Highway 0 to 50 km 10 to 0

Road 0 to 25 km 3 to 0

1 DNV GL notes that available GIS roadway input files are incomplete

Remoteness has been considered by assessing distance to known urban areas. Proximity to urban areas will

typically allow for better access to supplies, construction crews, lodging, access, etc. Table 3-4 defines the

scoring system used and the highest score that would apply at any given location.

Table 3-4 Distance to urban areas scoring system

Urban Area Type Range Score

City over 250k inhabitants 0 to 350 km 10 to 0

City over 100k inhabitants 0 to 200 km 7 to 0

Town over 20k inhabitants 0 to 150 km 3 to 0

Security

Security is of concern during the installation of the masts and operation. Masts are typically un-supervised

and vandalism targets in remotes areas. Although security concerns will need to be discussed and properly

assessed with the Client and the Tanzania agencies, DNV GL has suggested some initial areas of potential

concern from sources such as the American, UK and Canadian governmental web sites, and from its own

internal health and safety policy. A low relative weight of 5% was given to allow for future discussions on

this topic and a more thorough assessment with local stakeholders, as to not exclude a promising site that

could be built and maintained with proper planning. Table 3-5 presents a suggested scoring for potential

security concerns.


Table 3-5 Potential security scoring system

Area Range Score

Borders with Burundi, Rwanda, Mozambique

0 to 50 km from border

0 to 10

All other areas All 10

Environmental and social sensitivity

Tanzania hosts several areas with different statuses of protection for preservation. A relative weight of 10%

has been attributed to this Criterion. Aside from the exclusion areas presented in Section 3.3.1, three other

areas have been considered in the present study; Game Management Areas, Important Bird Areas and

Forest Reserves. Although not areas restricted to mast installation, these areas can present additional

permitting challenges and interference with wildlife. The following scores were attributed:

Game Controlled Area, Conservation Area, Wildlife Management Area: score of 0;

Important Bird Area: score of 3;

Forest Reserve (IUCN V, VI): score of 5; and

All other areas: score of 10.

Combined multi-criteria heat map

Every area within Tanzania was associated a score per factor, which was then weighted according to

Table 3-2, as discussed above. The result is an initial multi-criteria heat map, which is shown in Figure 3-3.

The purpose of this output is to present in a usable map format the combined scoring across the entire

country, taking into account all weighting criteria simultaneously.

As an example, a score of 6 or 7 out of 10 (green areas) would represent highly relevant areas for locating

mesoscale wind validation masts. However, other locations may also prove to be interesting for spatial

coverage or validating particular topographic or land feature areas.

The heat map will serve as the primary tool for selecting the long list of mast locations and DNV GL looks

forward to participating in an open and inclusive selection process involving all relevant parties.


Figure 3-3 Heat map for locating mesoscale wind validation masts


3.4 Site identification and ranking

Utilizing the Heat Map, but also the various input maps presented in Appendix A, DNV GL selected proposed

mast locations across Tanzania. Thirty-six (36) locations were identified, offering good spatial coverage of

Tanzania and covering the wide range of land cover, or in terms of mesoscale modeling terminology,

roughness length. Google Earth was used to micro-site the various locations and to verify if secondary roads

would reach specific locations. From the aerial imagery, settlements were also avoided.

It shall be noted that the present identification exercised was based on publicly available data and aerial

imagery. Stakeholder engagement and local partner consultation will be required to identify the most

promising sites out of the ones identified. Land control investigation and a subsequent site visit will finally

aid in ensuring desktop-selected sites are optimal and constructible. Additional micro-siting is expected

during the site visits and potentially during the permitting process.

The results are presented in the following table and figures: the scores allocated for each factor, for each

site, are shown in Table 3-6. A brief site description for each site is shown in Table 3-7 and Table 3-8.

Figure 3-4 presents the location of the proposed sites.

It should be noted that the wind speed score presented in the table below does not represent the mean wind

speed in meters per second. It is based on the score associated with wind speed, as discussed in

Section 3.3.2.

DNV GL ranking methodology is based on a set of factors and weighting that are dependent on the accuracy

of the GIS information. DNV GL has not performed any on-site validation of features and cannot guarantee

the accuracy of the information. Lastly, it should be noted that a different multi-criteria analysis method and

factor weighting could lead to different results.


Table ‎3-6 Ranking matrix

Mast Name Rank

Final Score

(/10)

Area of

optimal validation

value

Area prone to future commercial

wind development

Construction

Complexity

Ease of access /

remoteness Security

Environme

ntal and social

sensitivity

Wind

Uncertainty

Map Score

(/10)

Wind

Speed

Score

(/10)

Distance

to Grid

Score

(/10)

Distance to Load

(/10)

Terrain

Slopes Score

(/10)

Distance to

Road Score

(/10)

Distance to Urban

Areas

Score

(/10)

Areas of potential

security

concern

(/10)

Regulated or

sensitive

area

(/10)

100% 25% 15% 10% 5% 10% 15% 5% 5% 10%

Iringa 1 7.9 7.2 6.7 9.7 2.9 8 9.5 5.4 10 10

Makambako 2 7.7 4.4 8.1 9.9 3.8 9.2 9.7 5.5 10 10

Dodoma 3 7.3 6.7 7.4 9.1 2.8 9.2 4.7 6.2 10 10

Itigi 4 7.1 4.4 6.2 9.3 1.7 9.6 9.6 2.3 10 10

Masasi 5 7.0 4.4 5.6 9.8 4.4 9.6 8.4 2.5 10 10

Chunya 6 6.9 6.1 4.7 7.7 7 9.2 4.4 8.9 10 10

Singida II 7 6.8 3.3 8.8 9.8 2.1 8.7 7.2 2.6 10 10

Mchomolo 8 6.7 5 2 9.7 4.8 8.1 9.2 4.4 10 10

Dar es Salaam 9 6.7 2.2 3.5 9.4 10 8.5 9.2 7.9 10 10

Mwera 10 6.6 2.8 4.4 9 5.3 8.7 9.7 4.5 10 10

Arusha 11 6.5 2.8 5.8 9.5 7.3 7.3 6.4 9.2 10 10

Geita 12 6.5 2.8 1.6 9.9 10 8.7 9.1 7.3 10 10

Tunduma 13 6.5 4.4 4.5 8.7 6.5 9.3 4.9 7.6 10 10

Mbulu 14 6.5 3.9 7.4 9.2 3.4 5.7 6.5 5.9 10 10

Mpwapwa 15 6.5 3.9 6.2 9.8 3.3 7 6.1 5.9 10 10

Kibaya 16 6.4 3.9 6.3 8.7 1.6 5.3 8.8 4.1 10 10

Mwai 17 6.4 3.3 3.1 8.3 4.2 9.1 9.3 4.4 10 10

Lembeni 18 6.4 3.3 4.6 9.2 5.5 9.4 5.8 6.6 10 10

Busegwe 19 6.3 3.9 1.5 9.8 8.2 6.5 8.7 5.9 10 10

Tabora 20 6.3 4.4 4.7 9.4 3.2 8.8 5.2 5.4 10 10

Nyalikungu 21 6.3 2.8 2.3 9.8 8.8 7.7 8.5 6.4 10 10

Mpanda 22 6.3 3.9 2.2 9.2 1.5 9.1 9.9 1.6 10 10

Singida I 23 6.3 3.3 7.5 9.8 2.5 6.8 5.7 3.2 10 10

Shinyanga 24 6.2 3.3 2.8 9.5 9.2 8.9 5.4 5.9 10 10

Mkomazi 25 6.1 2.8 6 9.5 4.2 9.4 4.7 4.0 10 10

Kalangali 26 5.9 4.4 6.7 0 0.2 8.4 9.2 1.0 10 10

Biharamulo 27 5.8 1.7 0.7 8.6 8.2 8.3 9.2 5.2 10 10

Busongola 28 5.7 2.8 3 6.5 2.9 6.6 9.9 2.7 10 10

Mwanza 29 5.5 2.2 1.5 9.4 10 8.7 3.3 9.0 10 10

Kenya Border 30 5.5 3.9 2 8 5.7 5.6 5.8 3.8 10 10

Lake Malawi 31 5.4 6.1 2.7 9 4.7 1.2 3.6 3.7 10 10

Serengeti 32 5.4 3.3 9.5 0.5 1.7 8.3 2.9 4.9 10 10

Victoria 33 4.8 1.7 0.6 5.2 3.2 6.3 9.7 4.0 6.5 10


Mast Name Rank

Final

Score

(/10)

Area of optimal

validation

value

Area prone to future commercial

wind development

Construction

Complexity

Ease of access /

remoteness Security

Environmental and

social

sensitivity

Wind Uncertainty

Map Score

(/10)

Wind Speed

Score

(/10)

Distance to Grid

Score

(/10)

Distance to Load

(/10)

Terrain

Slopes Score

(/10)

Distance to

Road Score

(/10)

Distance to Urban

Areas

Score

(/10)

Areas of potential

security

concern

(/10)

Regulated or

sensitive

area

(/10)

100% 25% 15% 10% 5% 10% 15% 5% 5% 10%

Kipatimu 34 4.7 2.8 4 9.1 0.3 2.7 3.3 4.6 10 10

Liwale 35 4.2 3.3 3 0.4 3.2 7.2 3.2 0.4 10 10

Eyasi Lake 36 4.0 3.3 10 2.9 2.2 5.2 0 4.7 10 0

Table ‎3-7 Proposed sites for wind measurement masts – details # 1

Mast Name Rank Latitude Longitude Region Terrain

Slope [%]

Average Wind Speed

at 100 m

AGL within

2.5 km

radius

[m/s]

Maximum Wind Speed

at 100 m

AGL within

2.5 km

radius

[m/s]

Roughness

Length

Elevation

[m]

Iringa 1 -7°23'29.1" 35°46'42.6" Iringa 2 8.4 8.8 0.1 1327

Makambako 2 -8° 46' 56.0" 34° 53' 39.1" Iringa 1 9.1 10 0.2 1655

Dodoma 3 -6°00'48.7" 35°37'08.1" Dodoma 1 8.7 8.9 0.1 1186

Itigi 4 -5°37'59.0" 34°40'38.0" Singida 1 8 8.1 0.1 1428

Masasi 5 -10° 34' 8.4" 39° 3' 42.3" Mtwara 1 7.6 8.1 0.2 918

Chunya 6 -8°34'26.6" 33°35'03.6" Mbeya 3 7.2 7.6 0.1 1693

Singida II 7 -4° 58' 48.7" 34° 43' 20.6" Singida 2 9.5 9.8 0.2 1622

Mchomolo 8 -10°34'22.0" 36°17'55.1" Ruvuma 3 6 6 0.1 981

Dar es

Salaam

9 -7°14'08.6" 38°46'39.8" Pwani 2 6.7 6.9 0.5 386

Mwera 10 -8° 34' 33.4" 31° 29' 32.0" Rukwa 2 7.2 7.4 0.2 1836

Arusha 11 -3°08'59.13" 36°49'59.9" Arusha 3 8.1 8.3 0.1 1528

Geita 12 -2°58'14.2" 32°10'35.9" Mwanza 1 5.8 5.9 0.2 1420

Tunduma 13 -9° 20' 54.0" 32° 50' 42.3" Mbeya 1 7.3 7.7 0.2 1616

Mbulu 14 -3° 49' 33.3" 35° 28' 39.8" Manyara 7 8.9 10.3 0.1 1958

Mpwapwa 15 -6° 23' 41.0" 36° 27' 5.4" Dodoma 5 8.1 8.6 0.1 970

Kibaya 16 -5° 22' 49.9" 36° 30' 57.4" Manyara 7 8.3 8.8 0.2 1799

Mwai 17 -7°27'45.7" 31°10'54.6" Rukwa 0 6.7 6.9 0.3 1679

Lembeni 18 -3° 53' 40.2" 37° 37' 39.4" Kilimanja

ro

1 7.4 7.5 0.1 1015

Busegwe 19 -1°43'50.4" 33°57'35.8" Mara 3 5.7 5.8 0.2 1555

Tabora 20 -5° 11' 10.0" 33° 10' 44.8" Tabora 2 7.3 7.5 0.2 1323

Nyalikungu 21 -3°13'06.8" 33°47'51.8" Shinyang

a

3 6.2 6.2 0.2 1376

Mpanda 22 -6° 28' 6.2" 31° 40' 56.2" Rukwa 1 6 6.2 0.5 1336

Singida I 23 -4° 42' 26.1" 35° 2' 2.5" Singida 5 8.6 8.8 0.1 1790


Mast Name Rank Latitude Longitude Region Terrain

Slope [%]

Average Wind Speed

at 100 m

AGL within

2.5 km

radius

[m/s]

Maximum Wind Speed

at 100 m

AGL within

2.5 km

radius

[m/s]

Roughness

Length

Elevation

[m]

Shinyanga 24 -3° 48' 32.1" 33° 11' 26.6" Shinyanga

2 6.4 6.4 0.2 1312

Mkomazi 25 -4°36'16.6" 38°07'19.7" Tanga 1 8.2 8.4 0.2 571

Kalangali 26 -6° 6' 56.4" 33° 56' 2.6" Tabora 2 8.3 8.4 0.1 1572

Biharamulo 27 -2°44'59.0" 31°24'55.5" Kagera 1 5.2 5.3 0.4 1540

Busongola 28 -5° 31' 27.0" 30° 32' 18.0" Rukwa 5 6.4 6.6 0.3 1718

Mwanza 29 -2°44'50.7" 33°06'53.3" Mwanza 5 5.7 5.9 0.2 1319

Kenya Border 30 -1° 21' 1.1" 34° 36' 26.7" Mara 7 6.1 6.4 0.1 1814

Lake Malawi 31 -11° 0' 2.7" 34° 52' 41.8" Ruvuma 13 6.3 6.4 0.3 1966

Serengeti 32 -2°31'31.8" 35°19'58.8" Arusha 1 9.6 9.8 0.1 1902

Victoria 33 -1° 51' 13.8" 31° 7' 20.0" Kagera 6 5.2 5.4 0.5 1720

Kipatimu 34 -8° 27' 54.6" 38° 53' 35.3" Lindi 11 7 7.2 0.5 561

Liwale 35 -9° 55' 29.5" 37° 35' 22.1" Lindi 4 6.4 6.6 0.3 980

Eyasi Lake 36 -3°24'27.46" 35°01'44.3" Arusha 3 10.5 10.6 0.1 1849

Table ‎3-8 Proposed sites for wind measurement masts – details # 2

Iringa Rank

Distance to the closest

highway

[km]

Distance to the closest known

road [km]


city with

population

over 20,000

[km]

Distance to Grid

(km)

Land Cover

Iringa 1 0.3 0.3 44 2 Cultivated Land

Makambako 2 0.5 0.1 10 1 Bushland

Dodoma 3 7.4 0.3 23 7 Cultivated Land

Itigi 4 0.3 0.2 90 6 Woodland and Bushland

Masasi 5 2.2 0.2 34 1 Cultivated Land and Bushland

Chunya 6 7.8 0.3 39 17 Bushland

Singida II 7 3.8 0.3 18 2 Bushland

Mchomolo 8 0.4 0.4 72 3 Woodland and Cultivated Land

Dar es Salaam

9 0.4 0.4 55 5 Cultivated Land and Bushland

Mwera 10 0.2 0.2 70 8 Woodland

Arusha 11 5 0.2 29 4 Bushland

Geita 12 0.6 0.4 13 0 Cultivated Land and Woodland

Tunduma 13 7 0.4 10 10 Woodland

Mbulu 14 4.2 0.6 8 7 Cultivated Land


Iringa Rank

Distance to

the closest

highway

[km]

Distance to the closest known

road [km]


city with

population over 20,000

[km]

Distance to Grid

(km)

Land Cover

Mpwapwa 15 5.7 0.1 54 2 Cultivated Land

Kibaya 16 0.6 0.6 93 10 Bushland and/or Woodland

Mwai 17 0.5 0.3 16 13 Cultivated Land

Lembeni 18 2.2 2.1 23 6 Bushland

Busegwe 19 0.9 0.5 31 1 Bushland

Tabora 20 6.5 0.4 45 4 Cultivated Land

Nyalikungu 21 1.5 0.4 58 2 Cultivated Land

Mpanda 22 0.1 0.1 69 6 Woodland

Singida I 23 2.2 2.2 34 2 Pasture

Shinyanga 24 6 0.5 31 4 Bushland surrounded by Cultivated Land

Mkomazi 25 7.6 0.2 28 4 Bushland

Kalangali 26 0.4 0.5 169 76 Cultivated Land surrounded by Woodland

Biharamulo 27 0.4 0.4 92 10 Cultivated Land

Busongola 28 0.1 0.1 108 27 Woodland

Mwanza 29 8.3 0.8 35 5 Cultivated Land and Bushland

Kenya Border 30 5 0.7 90 15 Cultivated Land

Lake Malawi 31 8.9 0.4 91 8 Cultivated Land surrounded by Woodland

Serengeti 32 50 0.3 149 71 Grassland

Victoria 33 0.1 0.3 96 36 Cultivated Land and Bushland

Kipatimu 34 40 0.1 187 7 Woodland

Liwale 35 38 0.1 127 71 Woodland and/or Cultivated Land

Eyasi Lake 36 53 3.5 76 53 Woodland


Figure 3-4 Proposed sites for wind measurement masts


Figure ‎3-5 Proposed sites for wind measurement masts over wind speed


4 MEASUREMENT MAST SPECIFICATIONS AND RECOMENDATIONS

Robust assessment of the wind resource and meteorological conditions are important stages in the

development of calibrating mesoscale wind map. A well-specified, well-managed wind measurement

campaign can be crucial to minimizing uncertainties.

The following recommendations are based on DNV GL’s extensive experience of wind resource assessments

and meteorological conditions analyses. In some cases, IEC requirements for wind turbine power

performance measurements, as detailed in [2], are referenced. Although the requirements for wind

resource assessments and wind turbine power performance measurements differ in many respects, certain

aspects of the requirements presented in [2] are considered to be valid for wind resource assessments.

The following sections detail recommendations for equipment and mounting arrangements on the proposed

meteorological masts and the associated documentation to ensure full traceability of the measurements.

4.1 Mast

Mast type

A mast specifically designed for the purpose of wind measurement will be used. The mast will comply with

relevant standards regarding expected meteorological conditions at the proposed site, and will have a life

expectancy of at least 25 years.

The mast type will be a guyed 77-78 m high galvanized steel lattice tower. The mast will be 3-sided, with a

constant face width of 18 or 24 inches, and will include an integrated ladder within the structure diagonal

bracing. The mast will also include a fall-arrest cable to provide additional security for personnel climbing the

mast.

The bottom of the mast will be equipped anti-climb panels. Depending on the region and level of security

required, additional security devices, personnel or fencing may be necessary. This will be discussed with

Client.

The mast will be painted orange and white, as per ICAO regulation, Annex 14, for day-marking purposes.

Additional Aviation warning lights will be installed, as detailed in Section 4.3.

4.2 Equipment

Wind speed

Thies First Class and NRG Class One cup anemometers will be used to measure horizontal wind speed. All

anemometers will be will be individually calibrated by a MEASNET-approved institution.

Parallel anemometers are recommended at the goal post level but also at each measurement height as

indicated in Section 4.4.1.1. It is DNV GL’s opinion that the installation of a single sensor type on a given

met mast introduces an additional level of risk associated with sensor specific flaws or biases that may be

inherent in the sensor design. An effective way to reduce this risk is to introduce multiple sensor species on

the same mast. This approach to sensor installation is designed to yield a more stable measurement


campaign that also minimizes the costs associated with possible future maintenance procedures.

Furthermore, this practice provides greater clarity in identifying spurious measurements when recorded wind

data are subsequently analyzed. Implementing this recommendation will materially reduce sensor specific

flaws and protect against measurement biases that can introduce unnecessary error.

Several anemometers will be required on each meteorological mast, in order to provide redundancy and

investigate the vertical variation of wind speed. Recommendations for the number of anemometers,

mounting arrangements and installation heights are detailed in Section 4.4.

The power supply for the anemometers is provided by the internal battery power supply within the data

logger, detailed in Section 4.3.

Wind direction

Thies First Class wind vanes will be used to measure wind direction. To provide redundancy, two wind vanes

will be installed on each meteorological mast. Recommendations for installation heights are detailed in

Section 4.4.1.3.

Atmospheric conditions

NRG #110S Calibrated temperature sensors with radiation shields, NRG BP20 Calibrated air pressure

sensors and NRG RH5X humidity sensors will be deployed on each mast. Sensors to measure atmospheric

conditions are useful to support quality assurance checks of the primary wind and direction measurements,

and also provide valuable data to assess turbine suitability for future wind farm development.

The air pressure sensor will be mounted in a weatherproof box which will be adequately ventilated; this

ensures that pressure readings are not influenced by air pressure distribution around the box.

Data logger and Communications

Campbell Scientific CR1000-XT data loggers will be installed on every mast. These loggers record and store

data with a continuous sample rate of 1 Hz, and an averaging interval of 10 minutes will be used. As a

minimum, the following statistics will be recorded:

Time stamp;

Mean, standard deviation and maximum wind speed;

Mean and standard deviation wind direction;

Mean and standard deviation temperature, air pressure and relative humidity; and

Power supply voltage.

The data logger will be located in a lockable weather-proof housing. Precautions will be undertaken to

ensure moisture cannot enter instruments, cabling or the logger housing.

Data loggers will be installed at a height of 5 m, so that access can be gained with a tall ladder; therefore,

appropriate measures shall be taken to ensure the security of the data logger and will be discussed with the

Client.


The data logger will have storage capability for at least 6 months of recorded data, from the addition of a

Campbell Scientific NL115 external compact flash data storage. Flash memory cards are used to store data

and these should be easy for local staff to retrieve and replace, if necessary.

Prior to the site visit, and as indicated in the Client’s request for proposals, a land-based GSM modem and

Yagi antenna are planned for data transmission. For remote locations without mobile telephone coverage,

satellite communication may be necessary and would be provided with the Hughes BGAN technology. This

will be confirmed after the site visit and discussions with the Client.

The data logger clock will be set to local standard time in Tanzania. The data logger clock will not be

changed to reflect local daylight saving (summer) time. The data loggers will allow internet time servers for

automatic real-time clock updates.

4.3 Other equipment

4.3.1 Power supply

The wind measurement masts will be autonomous. Power for the logger, communications and sensors will

be supplied by battery power supply within the data logger and from an external Campbell Scientific battery,

housed in the weatherproof enclosure. The batteries will be charged by an externally-mounted Campbell

Scientific 20W regulated solar panel, installed on the mast. The system will be configured so that batteries

will remain suitably charged, even during winter months, with realistic periods of low light levels. The solar

panel will be installed to maximize exposure to the sun.

A separate power supply will be installed for the Aviation warning lights, as discussed below.

4.3.2 Aviation warning lights

ICAO Annex 14 compliant Aviation warning lights will be installed at the top and mid-point of every mast.

Care will be taken to ensure that flow distortion on the wind speed measurements, caused by the aviation

warning lights, is minimized. Top Aviation warning lights will be installed 1.5 m below the anemometers at

the top of the mast, which are installed on the goal post arrangement. Mid-point lights will be mounted

further down the mast and at least 1.5 m vertically from the closest anemometer.

The aviation warning lights will be supplied with integrated solar panels and batteries, suitable to provide

adequate lighting intensity during low light periods.

4.3.3 Lightning protection

Although it is not possible to provide absolute protection from a direct strike, precautions will be taken to

protect against lightning damage to the mast and equipment.

The sensors will be connected with screened cables that, together with the data logger and mast, will be

connected to a local earth.

A lightning rod will be installed at the top of the mast and a protection umbrella of 60° to sensors mounted

at the top of the mast will be provided. The lightning rod will be a copper rod. The rod will be fastened to a

tower leg at the top, and with a copper wire mechanically attached to the tower. Another copper rod will be

driven into the ground near the mast base, and the tower will be clamped to this rod via a copper tail. Due


to the attractiveness of exposed bare copper cable to vandalism, DNV GL suggests not installing a bare

copper cable down run on the tower.

As the anemometers installed at the top of the mast are the primary instruments on the mast, care will be

taken to ensure the flow distortion caused by the lightning rod on the wind speed measurements is

minimized.

4.4 Measurement configuration

The IEC provides the industry standard for cup anemometer and wind vane mounting arrangements [2],

however it is noted that this presents requirements for wind turbine power performance measurements. As

a result, the requirements presented in [2] focus on measurements at hub height and in discrete direction

sectors. For the assessment of wind resource and meteorological conditions, it is important that

measurements are undertaken at a range of heights and that distortion of the wind flow is minimized in all

direction sectors, particularly the prevailing wind direction sectors.

4.4.1 Recommended measurement configuration

4.4.1.1 Anemometer mounting arrangements

Anemometers installed at the top of a meteorological mast are primarily used as initiation instruments for

wind flow modelling. Anemometers installed at lower heights are used to investigate vertical variation of

wind speed at the mast location and as reference instruments should the primary anemometers at the mast

top fail.

Anemometers at the top of the mast will be installed on a goal post arrangement. The horizontal separation

will be a minimum of 2 m, and the height above the top of the mast will be a minimum of 2 m, for a total

measurement height of 80 m. All anemometers below the goal post will be mounted on slender horizontal

booms and vertical arms of circular section. The horizontal booms will be securely attached to the mast and

will not flex in the wind. The angle deviation of the anemometer will be less than 2° from vertical.

The maximum center-line flow distortion due to the mast will be kept below 0.5%, as per [2]. In order to

achieve this, the length of horizontal the booms will be approximately 2.5 m to 3.5 m. this will depend on

the final design and porosity of the masts; details of this calculation are given in [2]. For example, for a

square section lattice mast with a thrust coefficient, CT of 0.5, the horizontal booms will ensure that the

cups of the anemometer are at least 5.7 mast face widths from the mast.

In order to avoid significant flow disturbance at an anemometer due to its own horizontal boom, the vertical

arm will ensure that that cups of the anemometer are at least 15 boom diameters above the horizontal

boom.

In order to further minimize flow disturbance at the anemometers due to the mast, the horizontal booms will

be orientated, as much as possible, 90° to the prevailing wind direction. Due to the topography and

anchoring challenges, there may some deviations with this best practice. This will be confirmed during the

site visits. All anemometers installed in parallel will be installed on horizontal booms orientated at 180° to

one another.


Where possible, the masts will be installed so that the vertical planes of the guy wires are not orientated in

the same directions as the horizontal booms on which the anemometers are installed. An absolute

separation distance of 1.5 m between the anemometers and the guy wires will be maintained on all masts.

It is recommended that two anemometers are installed in parallel at each measurement height on all masts

used for the assessment of wind resource and meteorological conditions. Installing two anemometers in

parallel at exactly the same measurement height improves the accuracy of the wind speed measurement at

that height and provides redundancy in the event that one of the two anemometers should fail. Flow

distortion due to the mast can be further minimized by selecting wind speed data from the two

anemometers on a directional basis. Furthermore, issues such as anemometer degradation can be identified

with greater accuracy.

4.4.1.2 Wind vane mounting arrangements

Although wind direction measurements are less sensitive to flow distortion caused by other objects, the

general principles for mounting arrangements of anemometers in Section 4.4.1.1 will also be applied to the

mounting arrangements of the wind vanes. In particular, wind vanes will not be installed at the same height

as anemometers.

The wind vanes will be installed on horizontal booms, with the north of the wind vane (i.e. dead band)

aligned along the boom axis, pointing toward (preferably) or away from the mast. This will enable the wind

direction offset to be assessed easily from the ground with the aid of a compass once the mast has been

installed. The wind direction offset will either be programmed into the data logger or applied during analysis

of the data. The alignment of the north of the wind vane shall be documented in the mast installation report,

described in Section 4.5.

4.4.1.3 Overall sensor mounting arrangement and heights

Recommended installation heights and mounting arrangements for instrumentation on the masts are shown

in the Table 4-1 below.


Table 4-1 Instrumentation Summary

Height

[m] Instrument Type Manufacturer/Model

Mounting

arrangement

80 MEASNET Calibrated Anemometer Thies First Class Goal post

80 MEASNET Calibrated Anemometer NRG Class One Goal post

77 Wind vane Thies First Class Horizontal boom

77 Calibrated temperature sensor NRG #110S On tower leg

60 MEASNET Calibrated Anemometer Thies First Class Horizontal boom

60 MEASNET Calibrated Anemometer NRG Class One Horizontal boom

58 Wind vane Thies First Class Horizontal boom





5 Calibrated temperature sensor NRG #110S On tower leg

5 Relative Humidity sensor NRG RH5X On tower leg

5 Calibrated Barometer sensor NRG BP 20 In Logger Enclosure,

on tower leg

5 Logger and communications

equipment

Campbell Scientific CR1000-XT (with

communications equipment to be

determined after site visit)

In enclosure, on

tower leg

The sensor installation heights presented in the table above may be altered for practical reasons or so that

sensors are not affected by any objects that may cause flow distortion. It is typically not recommended to

install anemometers below 25 m to estimate wind resource at large scale wind turbine hub heights, however,

wind resource evaluation at a height of 20 m, for small-scale wind development, is one of the current


project goals. In areas of significant forestry, it may be necessary to reassess the installation heights of the

lowest anemometer to avoid surface effects.

For anemometers installed in parallel, the cups of the anemometers shall be at exactly the same

measurement height. With the exception of anemometers installed in parallel, a minimum vertical

separation distance of 1.5 m will be maintained between all sensors.

Exact sensor installation heights to an accuracy of 0.1 m, and the allocation of individual sensor serial

numbers to data logger channels and installation heights, will be documented in the mast installation report,

described in Section 4.5.

For ease of access, pressure and relative humidity sensors will be installed at 3 m agl. Thermometers at the

top and ground levels will be installed to allow a more refined analysis of variability in thermal effects.

Refer to Figure 4-1 for recommended mast instrumentation schematic configuration.


Figure 4-1 Recommended mast instrumentation


4.5 Documentation

For quality and traceability purposes, a detailed mast installation report will be prepared for each individual

wind measurement mast. In addition, if there are any changes to the mast and equipment during the

measurement period occurs, it will be documented in a mast maintenance log.

4.5.1 Mast installation report

The installation report will be prepared for each individual wind measurement mast, which will contain, at a

minimum of the following:

General information:

Site and mast name;

Mast installation company;

Installation date;

Grid coordinates of mast (including details of coordinate system and datum);

Elevation of mast above sea level; and

Description of surroundings, including distance from mast and height of any significant obstacles or

terrain features.

Mast and equipment:

Mast type and height;

Lattice mast dimensions;

Exact installation heights above ground level for all sensors;

Dimensions of all horizontal booms and vertical arms installed on the mast, including boom

diameters and lengths for all horizontal and vertical members;

Orientations of all horizontal booms, with reference to geographic north;

Orientation of wind vane north for all wind vanes;

Sensor types, serial numbers and corresponding installation heights;

Calibration certificates for all anemometers; and

Data logger type and serial numbers.

Data logger configuration:

Data logger program;

Wind vane offsets to geographic north and whether these have been programmed into the data

logger;


Details of power supply;

Details of data retrieval; and

Details of data logger clock setting.

Commissioning:

Data showing first hour of operation after installation and confirmation that it complies with general

site observations at the time.

Photographs:

Photographs of mast, all booms and all sensors as mounted on the mast;

Panoramic photograph from mast location; and

Photographs of any significant obstacles in the vicinity of the mast.

4.5.2 Maintenance log

A maintenance log is a highly useful aid during data analysis. A maintenance log detailing all work carried

out on the mast during the measurement campaign will be kept. For each intervention at the mast, the

following will be noted:

Date and time of commencement and completion of the intervention at the mast, as recorded by the

data logger on the mast (if functional at the commencement of the work);

Reason for the intervention;

Details of work carried out, including a clear description of any changes to equipment or mounting

arrangements; and

Serial numbers of any replaced and replacement sensors, including calibration certificates for

replacement anemometers.

The following will also be documented in the mast history:

Details of changes to the mast surroundings during the measurement campaign (felling of trees,

construction of buildings or wind turbines, etc.); and

Details of any periods of missing data (affected sensor, start, end, problem if known).


5 REFERENCES

[1] Mesoscale Wind Modeling Report #1- Interim wind atlas for Tanzania, 702910-UKBR-R02-A, DNV GL, 3 Jul 2015.

[2] “Wind turbines – Part 12: Power performance measurements of electricity producing wind turbines”, IEC 61400-12:2005 (E).


APPENDIX A – SPATIAL FEATURES AND CONSTRAINTS MAPPING


Figure A-1 Preliminary wind speed uncertainty index from ‎[1]


Figure A-2 Terrain elevation


Figure A-3 Terrain slopes


Figure A-4 Aerodynamic roughness from ‎[1]


Figure A-5 Infrastructure


Figure A-6 Power generation and transmission


Figure A-7 Wetlands and Important Bird Areas


Figure A-8 Environmentally sensitive areas

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Documents

Wind Resource Mapping in Tanzania SITE IDENTIFICATION REPORTpubdocs.worldbank.org/...Site-Identification-Report... · Report title: Candidate Site Identification Report Customer: