West African Clean Energy Gateway-Resource Assessment

Economic Community of West African States (ECOWAS) Clean Energy Gateway

Benin Burkina Faso Cape Verde Gambia Ghana Guinea Guinea-Bissau
Ivory Coast Liberia Mali Niger Nigeria Senegal Sierra Leone Togo

The SWERA landing page allows for the quick browsing of global data layers. More detailed information on each country or select geospatial data sets can be found by selecting a country and choosing one of the two options which appear.

1. Renewable Energy Potential in My Country

Renewable energy potential describes the amount of energy that renewable energy technologies can provide for a given region. For electricity generating technologies, a common measure of potential is gigawatt-hours per year (GWh/year). This describes the amount of electricity that can be generated on an annual basis by a given technology under a certain deployment scenario. It is important to note that this is different from traditional energy reserve estimates, which calculate the total embodied energy of estimated reserves. Renewable energy potential describes the estimated annual generating capacity. This difference is owing to the nature of the resources in question. Fossil resources are finite; renewable resources, though they vary in space and time, are not depleted as they are used.

The approach discussed here is a top-down, regional assessment of renewable energy potential, which aims to estimate the renewable fuel, power or heat generation potential of renewable technologies in a region. This approach does not include any assessment of energy demand or its temporal or geographic distribution across the region. This is distinct from a site-specific assessment in which the energy needs and available resources at a specific location would be compared to evaluate the optimal configuration of technologies for meeting current and future demand at that location.

There are several definitions for renewable potential, and the quantitative estimates of potential will depend greatly on the definition chosen. There is no agreed upon set of definitions, and different definitions used in different contexts. In evaluating renewable potential estimates, it is critical to understand what definition was used to develop the estimates of potential so that they are understood in the appropriate context.

RESOURCE ASSESSMENT: The starting place for any assessment of renewable potential is a characterization of the renewable resources available across a region, a resource assessment. A regional resource assessment will develop gridded data and maps with annual and sometimes monthly intensity of renewable resources for each grid cell. Typical grid cells sizes used in solar resource assessment are 10-km by 10-km or less. Resource assessments for wind, which has greater spatial variability than solar, will often be done at a spatial resolution of 1-km or less. Once resource data are available for a region, the theoretical potential, or upper limit, can be estimated.

GEOGRAPHIC DATA: Geospatial data sets can be used to identify regions that are appropriate for renewable development and those that should be excluded. Global land cover databases are available that describe the land use categories at a spatial resolution of 1 km. Land use categories will include water bodies, urban areas, cropland, forested areas, and more. Additional geographic data defining protected areas will also be required. Elevation data sets can be used to make slope calculations, which may exclude some areas from consideration or be used in later cost calculations. Infrastructure data will be used in estimating the economic potential, which requires consideration of costs to build transportation infrastructure to development sites or extend the electric grid for electricity evacuation.

TECHNOLOGY CHARACTERIZATION: Information on renewable technologies is used to estimate power production potential under varying resource conditions and the costs of generating power with a given technology.

1.1 Solar Energy Potential in My Country

An estimate of the potential for solar energy in a country will include the potential for grid-tied and off-grid photovoltaics (PV) and concentrating solar power (CSP). Fixed costs for solar plants are the same whether installed in high-resource or low-resource locations, but the amount of electricity generated is highly dependent on the amount of available solar radiation, the fuel for solar technologies. As a result, a solar resource assessment is an important tool for evaluating what a region’s solar potential is. This resource information can be analyzed in combination with land use information, the costs and conversion efficiency of the technologies that will be deployed, and the country’s market readiness for these technologies.

1.1.1 Potential for Large-Scale Photovoltaics

This module discusses and approach for estimating the potential for deployment of photovoltaic (PV) plants of 1 MW or larger. The primary output is an estimate of annual potential power generation if all land available for large-scale PV generation was developed for that purpose. To evaluate the technical potential for large-scale PV, you will need the following information:

• Global Horizontal Irradiance (GHI) data in GIS format
This analysis assumes you are starting with a solar resource assessment where the available solar resource has been estimated across the country at a uniform resolution with each area representing a grid cell. It follows then that each grid cell would only have one value for solar resource, which represents the annual average solar resource (GHI, for PV analysis) for the entire area covered by the grid cell.
• Geographic data describing land use characteristics, including nature preserves and cultural heritage sites
Country planners would need to determine the land use categories appropriate for large-scale PV development. For instance, land use categories such as agricultural, urban, water bodes, nature preserves, and cultural heritage sites might be deemed inappropriate for large-scale PV development. For other land use categories, planners might determine that all or some percentage of the land could be used for PV development. Elevation data sets are used to determine the terrain slope, which is important as trying to install PV plants on steeply sloping land may substantially drive up the costs. In some cases, planners may want to exclude land with slopes above 5% to limit the analysis to the most economically viable sites.
• PV technology characterization
This analysis requires assumptions about minimum plant size, density of collectors, and the efficiency of the PV technology to convert incoming radiation to electricity. As we are assuming a minimum plant size of 1 MW, we will need to limit our installations to contiguous land area that meets the geographic screening criteria. When PV plants are planned on sloping terrain, it will be important to adjust the density of installed panels to ensure appropriate there is no panel shading. Though only the PV modules convert sunlight to electricity, a PV plant will not be fully covered by PV modules but will also include balance of system components, access roads and paths, and plant facilities. The fraction of the land covered by modules is the area available for conversion of radiation to electricity. The conversion efficiency will depend on the choice of PV technology.

Once you have compiled all of the required data pieces, the technical potential can be estimated using the following formula.

${AP_{T,PV-lg}} = \sum_{i} {GHI_i} \times {A_i} \times {p} \times {n} \times 365$
• APT,PV-lg is the technical potential of large-scale PV in kWh year-1.
• i is each grid cell
• GHIi is the annual average global horizontal irradiance of grid cell i in kWh m-2 day-1
• Ai is the area of grid cell i available for solar development in m2; this value takes into account land use, slope and minimum contiguous area restrictions and is usually determined in a GIS screening process
• p is the fraction of the land covered by PV modules
• n is the solar to electric conversion efficiency of the technology chosen
• 365 has units of days year-1.

This analysis will yield an estimate of annual power production assuming all land designated as suitable for solar power is developed for that purpose and that the technology converts the available solar radiation with the characteristics specified. This analysis does not take into account the economic or market barriers. An expanded study to include these factors would account for the cost of PV technology and necessary grid expansion and enhancements, compare these with other existing or planned power generation facilities and take into account the demand and regulatory framework influencing power generation decisions.

1.1.2 Potential for Concentrating Solar Power

This module discusses and approach for estimating the potential for deployment of concentrating solar plants (CSP). The primary output is an estimate of annual potential power generation if all land available for large-scale CSP generation was developed for that purpose. To evaluate the technical potential for CSP, you will need the following information:

• Direct Normal Irradiance (DNI) data in GIS format
This analysis assumes you are starting with a solar resource assessment where the available solar resource has been estimated across the country at a uniform resolution with each area representing a grid cell. It follows then that each grid cell would only have one value for solar resource, which represents the annual average solar resource (DNI, for CSP analysis) for the entire area covered by the grid cell.
• Geographic data describing land use characteristics, including nature preserves and cultural heritage sites
Country planners would need to determine the land use categories appropriate for large-scale CSP development. For instance, land use categories such as agricultural, urban, water bodes, nature preserves, and cultural heritage sites might be deemed inappropriate for CSP development. For other land use categories, planners might determine that all or some percentage of the land could be used for PV development. Elevation data sets are used to determine the terrain slope, which will be used in determining land suitable for CSP plants.
• CSP technology characterization
Producing estimates of CSP potential requires that the analysts make assumptions about plant sizes, the relevant conversion efficiency, the minimum solar resource values for which deployment of the technology is feasible, and the maximum terrain slope that is practical. If you are specifying a minimum plant size, you will need to limit the proposed installations to a minimum contiguous land area that meets the geographic screening criteria. The capacity factors for CSP plants will depend on the technology choice and configuration (parabolic trough or tower and with or without thermal storage) and the average DNI. Finally, CSP plants use tracking systems, so sloping land has to be graded for project installations; this often leads to estimates of CSP potential allowing consideration of only land with very low terrain slope (1% and 3% are maximum values often imposed).

To do rough calculations of CSP potential for a region, it often works best to select a technology that will work throughout the region and use published capacity factors by resource class for all resource levels in the country, noting that below certain levels of DNI CSP deployment is not feasible.

Once you have compiled all of the required data pieces, the regional CSP technical potential can be estimated using the following formula.

${AP_{T,CSP}} = \sum_{i} {A_i} \times {p} \times 8760 \times {CF_i}$
• APT,CSP is the technical potential of CSP in kWh year-1
• i is each DNI class
• Ai is the total area in km2 across the country or region in DNI class i that meets all screening criteria, such as land use suitability, maximum slope, and minimum continuous area; this is usually determined in a GIS screening process
• p is the density of CSP plant installation in MW km-2
• 8760 is hours year-1
• CF is the capacity factor, which is a unit-less number between 0 and 1

This analysis will yield an estimate of annual power production assuming all land designated as suitable for CSP is developed for that purpose and that the technology converts the available solar radiation with the characteristics specified. This analysis does not take into account the economic or market barriers. An expanded study to include these factors would account for the cost of CSP technology and necessary grid expansion and enhancements, compare these with other existing or planned power generation facilities and take into account the demand and regulatory framework influencing power generation decisions.

1.2 Wind Energy Potential in My Country

An estimate of the potential for wind energy in a country usually focuses on the utility-scale wind potential, and that is the focus of this discussion here. The main output of this type of assessment will be an estimate of annual power generation potential if all land compatible with utility-scale wind development was used for that purpose.

To evaluate the technical potential for utility-scale wind, you will need the following:

• Wind resource assessment data in GIS format
As is the case with solar, fixed costs for wind plants are the same whether installed in high-resource or low-resource locations, but the amount of electricity generated is highly dependent on the amount of available wind power, the fuel for wind energy technologies. As a result, a wind resource assessment is an important tool for evaluating a region’s wind potential. This analysis assumes you are starting with a wind resource assessment where the available wind resource has been estimated across the country at a uniform resolution with each area representing a grid cell. It follows then that each grid cell would only have one value for wind resource, which represents the annual average wind speed or wind power for the entire area covered by the grid cell.
• Geographic data describing land use characteristics, including nature preserves and cultural heritage sites
Country planners would need to determine the land use categories appropriate for utility-scale wind development. For instance, land use categories such as migratory bird routes and cultural heritage sites might be deemed inappropriate for wind development. For other land use categories, planners might determine that all or some percentage of the land could be used for wind development. Elevation data sets are used to determine the terrain slope, which will be used in determining land suitable for wind plants.
• Wind technology characterization
Producing estimates of wind potential requires that the analysts make assumptions about plant sizes, the relevant conversion efficiency, the minimum wind resource values for which deployment of the technology is feasible, and the maximum terrain slope that is practical. If you are specifying a minimum plant size, you will need to limit the proposed installations to a minimum contiguous land area that meets the geographic screening criteria. The capacity factors for wind plants will depend on the technology choice and the average wind resource. Finally the costs of installation can increase considerably for steeply sloping land and the density of turbines may decrease in complex terrain; to account for this a maximum slope may be imposed in the land screening state. Often times, wind resources below a certain value will be deemed not viable for wind power development, so land not meeting the minimum value may be screened out of calculations of wind potential.

To do rough calculations of wind potential for a region, it often works best to select a typical turbine and apply its general characteristics to the analysis, in particular the capacity factor values for various wind power classes.

Once you have compiled all of the required data pieces, the regional wind technical potential can be estimated using the following formula.

${AP_{T,wind}} = \sum_{i} {A_i} \times {p} \times 8760 \times {CF_i}$
• APT,wind is the technical potential of utility-scale wind in kWh year-1
• i is each wind class
• AMi is the total area in km2 across the country or region in wind class i that meets all screening criteria, such as land use suitability, maximum slope, and minimum continuous area; this is usually determined in a GIS screening process
• p is the density of wind plant installation in MW km-2
• 8760 is hours year-1
• CF is the capacity factor, which is a unit-less number between 0 and 1

This analysis will yield an estimate of annual power production assuming all land designated as suitable for wind is developed for that purpose and that the technology converts the available wind power with the characteristics specified. This analysis does not take into account the economic or market barriers. An expanded study to include these factors would account for the cost of wind technology and necessary grid expansion and enhancements, compare these with other existing or planned power generation facilities and take into account the demand and regulatory framework influencing power generation decisions.

2. Additional Questions: How Do I…

2.1 Evaluate my solar resource availability?

A solar resource assessment provides information on available solar resource in time and space. The amount of solar radiation reaching a certain point will depend on what is encountered on the path through the atmosphere.

Ground data collection and numerical model estimates are the two methods for estimating solar resource. Microclimate, terrain variations and land use can contribute to spatial variability of the resource, which cannot be comprehensively captured by ground monitoring because of the expense and time required to collect data at each site. Models allow for prediction of solar resource across a region, and can be validated against ground measurements to help assess model accuracy. Model estimates will never be more accurate than ground measurements. DNI measurements from well-maintained ground stations will have an expected measurement uncertainty between +/- 2.5% and +/- 5%, depending on the technology. Most models of solar resource in use today rely on satellite imagery to estimate cloud cover and relate top of atmosphere radiation to ground radiation though physical and/or empirical models. Models run at higher spatial resolution (smaller grid cells) will avoid any smoothing out of variation due to terrain and microclimate effects. The text box below lists some of the considerations when selecting solar resource data. Additional information can be found in the NREL Best Practices Handbook for Collection and Use of Solar Resource Data.

Key Considerations

Applying solar and meteorological data from different sources requires attention to these key considerations:

• Period of record. Influenced by many factors, solar resource data vary from year to year, seasonally, monthly, weekly, daily, and on timescales down to a few seconds. Thus, climate normals are based on 30 years of meteorological data. Another popular approach is to determine a TMY dataset from a statistical analysis of multiyear data to derive a single year of data that are representative of a longer term record. Comparative analyses must account for any natural differences that result from the periods when the data were acquired.
• Temporal resolution. Solar resource data can range from annually averaged daily-integrated power (kWh/m2/day) typically used for mapping resource distributions to 1-s sampled of irradiance (W/m2) for operational time-series analyses.

Other considerations depend on the data type:

• Spatial coverage. The area represented by the data can range from a single station to a sample geographic region to a global perspective.
• Spatial resolution. Ground-based measurements are site specific. Current satellite-remote sensing estimates can be representative of 10-km x 10-km or smaller areas.
• Data elements and sources of the data. The usefulness of solar resource data may depend on the available data elements (e.g., DNI) and whether the data were measured, modeled, or produced in combination.
• Data quality control and quality assessments. Descriptions of the measurement operations, model validation methods, and data adjustments or corrections are key metadata elements.
• Estimated uncertainties. States uncertainties should include a description of the methodology used to provide this information.
• Availability. Data are distributed in the public domain, for purchase, or license.
• Updates. The need to include the most recent data and other revisions can require regular database updates.

Estimates of regional solar potential rely on the solar maps created through a solar resource assessment and mapping project, and these will provide annual or monthly average radiation for equally sized grid cells across the region of study. Not all countries have had their solar data mapped at high resolution, though the NASA SSE data at approximately 100 km spatial resolution provides global coverage of both GHI and DNI .

Existing solar data and information and resource assessment can be found through the following sites:

This document provides detailed information about solar resources, how various estimates of solar resource availability are generated, and helps match data needs to various parts of the solar power planning and development cycle.
This website provides links to several different energy datasets, including solar resource data in GIS format. Datasets can be filtered by country or type by using the search function at the top.
Interactive mapping and data viewing and download options for solar products developed by several research organizations.
• Photovoltaic Geographic Information System (PVGIS)
Interactive tool for viewing GHI availability in Europe and Africa with PV production. The site also provides links to other solar data sources.
This site allows users to search for available solar data sets by both geography and technology application and reports on quality indicators for all sites available.

2.2 Evaluate my wind resource availability?

A wind resource assessment provides information on available wind resource in time and space. Ground data collection, numerical model estimates and upper air measurements are all methods for estimating solar resource. Microclimate, terrain variations and land use can contribute to spatial variability of the resource, which cannot be comprehensively captured by ground monitoring because of the expense and time required to collect data at each site. Numerical models allow for estimation of wind resource across a region, and can be validated against ground measurements to help assess model accuracy.

Estimates of regional wind potential rely on the wind maps and underlying data created through a wind resource assessment and mapping project, and these will provide annual or monthly average wind speed or power for equally-sized grid cells across the region of study. Not all countries have had their wind data mapped at high resolution, and undertaking such a study is often a first step to developing an estimate of wind potential.

A wind resource assessment may either present the information in terms of wind power class (unit-less), wind speed (meters per second) or wind power density (watts per square meter). As wind speeds increase with increasing height above the earth’s surface, a wind resource assessment will specify at what height the data presented apply. Assessments typically present data for 50 or 80 meters above the earth’s surface, as these are typical hub heights for utility-scale wind turbines. Below is a table that shows how the three parameters are related at 50 meters above the earth’s surface. Note that the relationship between wind speed and wind power density will be specific to a location as the wind power depends not only on the average wind speed but the full distribution of wind speeds. For more information on the relationship between wind speed and wind power density, see Calculation of Wind Power Density in Appendix A for the US Wind Atlas.

Wind Power Classification
Wind Power Class Wind Resource Potential Wind Power Density at 50m (W m-2) Average Wind Speed at 50m (m s-1)
1 Poor 0-200 0-5.6
2 Marginal 200-300 5.6-6.4
3 Moderate 300-400 6.4-7.0
4 Good 400-500 7.0-7.5
5 Excellent 500-600 7.5-8.0
6 600-800 8.0-8.8
7 >800 >8.8
* Wind speeds are based on a Weibull k value of 2.0

Existing wind data and information on wind resource assessments can be found through the following sites:

This website provides links to several different energy datasets, including solar resource data in GIS format. Datasets can be filtered by country or type by using the search function at the top.
Interactive mapping and data viewing and download options for solar products developed by several research organizations.
This Web page provides links to information and resources and data on wind resource assessment.
This Web page provides information on some completed international wind resource assessments.
This website provides information on WaSP, including the use of the tool for wind resource assessments. Information on certified users and courses is also provided.

2.3 Access the necessary geographic data?

Land cover classification data are available in GIS formats and identify land parcels by their predominant uses, such as urban, forested, agricultural, or barren lands. Additional data on nature reserves and areas of cultural importance are typically ill-suited to renewable energy development, and data sets identifying these areas will also be needed in assessing potential development sites. Often countries have their own sophisticated GIS data and analysis departments that can provide the highest resolution and most accurate data. In the absence of these capabilities, the United States Geological Service (USGS) provides data on land use and elevation. The United Nations maintains a list of protected areas, which can be downloaded in GIS format from the World Database on Protected Areas.

The following websites provide some of the data required for conducting analyses of geographic potential.

This website provides links to websites providing GIS data with global coverage. The GIS data sets can be brought into a GIS for analysis with the solar resource data.
This website provides links to GIS data with global coverage of elevation at resolutions of 1 km or finer.
NREL has developed Geospatial Toolkits for several countries, which combine renewable resource data with other geographic data, including elevation and land use, in a GIS framework for easy analysis of land use and slope limitations of development.
The UN maintains a database contains all protected areas, which includes nature preserves, bird sanctuaries, and cultural heritage sites. This website provides access to those data in GIS and other formats.

2.4 Determine technology characteristics for large-scale photovoltaics?

This discussion takes into account two important elements of solar technology:

• Solar technologies do not convert incoming radiation into electricity with 100% efficiency
• Some of the land area in a solar power installation will not be occupied by solar collectors but instead by balance of plant elements, such as access roads and facilities.

Because of this, we need to take into account two other factors: the density of the collectors themselves and the conversion efficiency of the technology being deployed.

Collector density will depend on technology configuration, and some values of actual PV plant density can help guide values for this purpose. For instance, if you determine that utility-scale PV plant would have a density of approximately 40 MW km-2, this would be equivalent of 0.04 kW m-2.

The efficiency of the PV array to convert solar radiation reaching the modules into electrical energy and the efficiency of the balance of the system in converting this electrical energy into useable energy. Crystalline silicon PV systems convert sunlight to energy with a higher efficiency than do thin-film PV technologies. There will also be losses when converting the DC output from the module into AC output for use. For instance, 10% array efficiency and a 0.77 derate factor in converting the DC output to AC output would yield a total system efficiency of 7.7% or 0.077 of the total incident radiation being delivered as AC output from the system.

Note that when analyzing PV systems, electricity yields can be greater if the panels are oriented at an angle that optimizes collection over the year or if tracking systems are used. Either of these configurations would change the estimate of solar radiation reaching the collector, and so the resource values would differ from those provided in a GHI data set.

The following websites provide information on some of the data required for estimating the technical potential of solar energy.

Provides plant density values for existing PV and CSP plants.

This report provides information on the costs and trends of PV cell and system efficiency.

This site provides workbooks with cost and performance data for various energy technologies.

2.5 Determine technology characteristics for concentrating solar power?

Concentrating solar power plants will be rated according to the maximum generating, or “peak”, capacity. The amount of land required for plants of a certain capacity will depend on technology and the level of storage that is incorporated into the plant design. The actual level of power generation of a CSP plant will depend on the technology and the direct normal irradiance (DNI) component of solar resource available at the plant’s location.

The first choice to make when assessing the CSP potential is decide what your “typical CSP plant” will look like. Will it be a power tower or will it be a parabolic trough system? Will it incorporate thermal storage and, if so, how much? Once you decide on these characteristics, you can use published data on plant size and performance to approximate a footprint and output for a typical plant.

For a parabolic trough system without thermal storage, you might estimate that you need approximately 1 km2 per 50 MW CSP capacity and this is the minimum plant size you want to consider. This establishes two of the parameters required for estimating CSP potential: minimum contiguous area of 1 km2 and plant density of 50 MW km-2. These values may differ significantly if your typical plant has thermal storage.

Another parameter to establish is how your typical plant’s output will vary at locations with different average DNI values. One approach is to develop a table that establishes the different resource classes present in the region, what DNI values each class corresponds to and the expected capacity factor (CF) for that class based on the technology configuration. It is important to note that the CF will differ depending on the thermal storage incorporated into the plant design. An example of what such a table might look like is shown below.

DNI Class DNI Values (kWh m-1day-1) CF (parabolic trough; no thermal storage)
0 <5 0
1 5-6.25 0.199
2 6.25-7.25 0.248
3 7.25-7.5 0.277
4 7.5-7.75 0.284
5 >7.75 0.295

2.6 Determine technology characteristics for wind?

The amount of land needed for a wind plant will vary depending on the turbine sizes chosen and the terrain features where the plant is being sited. Wind farms can average 30-80 MW per acre (1 acre is approximately 4,000 m2), but the actual turbine footprint will only occupy approximately 3-5% of that land. Many analyses will assume a total installed capacity of 5 MW per km2 and adjust this downward as needed for terrain variation after limiting consideration to land with a terrain slope of less than 20%. For utility-scale wind projects, developers may seek economies of scale and so want to limit minimum plant size to 30 MW or more. This is something that can be included in an analysis of wind potential by establishing a minimum contiguous land area.

Another parameter to establish is how your wind plant’s output will vary at locations with different wind class values values. Manufacturers publish information on turbine performance at various wind speeds, and some published resources provide average capacity factors for various wind classes and turbine sizes. Eventually, you will want to establish an estimated capacity factor for each wind power class encountered in the region, such as is shown below. Wind resource below wind power class 3 is currently not considered viable for utility-scale development, but this could change in the future with technology advancements.

Wind Power Class CF
3 0.36
4 0.39
5 0.43
6 0.46
7 0.50

The following websites provide information on some of the data required for estimating the technical potential of wind energy.

This document provides an overview of considerations for wind power development.
This site provides workbooks with cost and performance data for various energy technologies.
This document presents the basics of wind power, estimates the deployment potential, and assesses economic, technology, and policy considerations in realizing full deployment potential.
Documentation of the assumptions applied within the ReEDS model in estimating capacity expansion potential of various technologies, including wind, is available on the ReEDS website.

This document describes the assumptions applied for estimating the potential of renewable energy for the United States.