Step 2: Renewable Energy Resource Assessment

REZ step 2, no title


The REZ process identifies regions through a systematic and transparent procedure based on resource quality, topography, land use, and developer interest. The theoretical renewable energy resource potential (unconstrained by cost or land use issues) is the base layer for this process.

The goal of step 2 is to estimate the renewable energy resource potential and identify a set of study areas (and associated supply curves) capable of supporting high levels of clean energy development.

Conduct technical potential analysis

The technical renewable energy potential estimates the achievable installed capacity and generation of a specific technology based on the topographic limitations, land use constraints, and system performance. This step identifies areas with abundant renewable energy resources that are technically developable.

The Zone WG screens the theoretical renewable energy potential (data often presented in the form of a renewable energy resource map) with the areas not available for project development. This process can also highlight known priority renewable energy development areas (such as “ecotowns” or economic development areas) to identify the set of study areas—the output of the technical resource analysis. The figure below depicts this process of screening the resource potential for excluded and priority development areas to calculate the technical potential for renewable energy generation in each of the study areas. The steps of this process are detailed below.

Screening Process image for step 2 of guidebook

Figure. Process of screening resource potential to calculate the technical potential of study areas (Lopez 2016)


Produce renewable energy resource maps

Renewable energy resource maps show the theoretical resource potential for the technologies considered within each region of interest. Spatially referenced renewable energy resource data form the base layer that is filtered in order to identify the study areas and are particularly important for site-constrained resources such as wind and solar. These resource potential layers consist of modeled or measured geospatial data. Ideally, ground measurements validate the modeled data. These data layers allow for calculation of power density (W/m2) or potential electricity generation per unit of area over a given period of time (kWh/m2/day) for renewable energy resources under consideration. At a minimum, annual average resource data are needed to identify study areas; however, higher temporal resolution data provide additional insight for decision makers. Solar resource layers ideally consist of direct normal irradiance, diffuse horizontal irradiance, air temperature, and wind speed. Wind resource layers ideally consist of wind speed, wind direction, air pressure, and air temperature.

Local sources for these data may include energy ministries, environment ministries, or research institutes. Where local data is not available, high-quality, global data sets are publicly available as shown in the box below. Additionally, commercial firms can create these data with a high degree of accuracy.

Global Energy Resource Data Sources

High-resolution, modeled annual average solar and wind resource data are available on the Global Solar Atlas from the World Bank and the Global Wind Atlas from the Danish Technical University, respectively. More information is available at and

Also, the Renewable Energy Data Explorer is a no-cost, web-based application that provides spatial data and analysis capabilities for renewable energy resources in select regions. Additional information is available at

Exclude areas not available for development

Many areas may be undevelopable despite having high-quality renewable energy resources. This step identifies and excludes areas within the boundary of application where development is prohibited or not possible for technical or other reasons. Constraints to project development may include:

  • Land use: water features, urban areas, roads, other transportation infrastructure
  • Topographic: slope, minimum contiguous area
  • Protected lands: government-protected, critical environmental areas (e.g., bird migratory pathways), radar footprints, areas important for social or cultural reasons
  • Other state or local issues that prohibit or severely restrict development.

Study areas do not have to be located close to load centers, existing transmission, or planned transmission, and proximity to these is not a criterion used for exclusion. This step aims to capture all of the study areas that represent high-quality, developable resources—ensuring that these areas are considered in subsequent steps. Constraining study areas to the vicinities of existing transmission or load centers may result in targeting less productive (and therefore less cost-effective) resources. In many power systems, areas that are far from existing transmission and load centers often host the most cost-effective and viable renewable energy resources. In later steps (steps 4 and 5), decision makers evaluate the associated trade-offs of the transmission enhancements necessary to connect these potentially remote, high-quality resources.

Activity: Identifying Study Areas in the REZ Transmission Planning Process with Renewable Energy Data Explorer

This activity introduces users to Renewable Energy (RE) Data Explorer (, the flagship geospatial analysis tool for RE development from RE-Explorer. Users will use their computers to follow a hands-on demonstration of the RE Data Explorer to perform an introductory exercise of identifying REZs. The exercises walk through (1) using RE Data Explorer; (2) use of the technical potential assessment tool for utility-scale solar photovoltaics; and (3) use of economic considerations for identifying a set of study areas. Users will have to select a country that is currently including in the RE Data Explorer tool to complete the exercises.

Identify priority development areas

Economic development or other priority areas may exist that offer benefits like expedited permitting or special incentives for (renewable) energy projects. These areas might intersect high-quality, renewable energy resources, and early identification in the REZ process could be an important step in achieving multiple policy objectives.

Conduct economic analyses

The technical potential analysis identifies areas where development is technically feasible (i.e., study areas) but does not include considerations of economic feasibility. Economic analyses further filters the study areas based on economic considerations such as the cost of generation.

Determine development adjustment factor

In practice, only a few technically feasible sites within a zone may actually be developed even if transmission were available. Project developers have limited capital and will seek out the sites where cost is minimized and returns maximized.

To account for this, the Zone WG estimates how much new capacity may actually be developed in each study area through the use of a development adjustment factor (DAF). The DAF is typically technology specific and represents an estimated percentage of total potential capacity likely to be developed after accounting for the potential reasons that investment might not occur on a specific site (for example, limited capital) despite technical feasibility. The DAF mathematically reduces the estimated capacity potential of a study area without having to specify exactly where each reduction would occur. The considerations captured by the DAF require stakeholder engagement and consensus as these potential reasons that investment might not occur are often subjective. The Zone WG determines the DAFs based on the characteristics of the specific market and context of the REZ process.

The adjusted developable capacity informs later transmission modeling steps in the REZ process.

Develop a supply curve for each study area

A supply curve for each study area helps project developers and the regulatory authority quantify the resource that can be developed for a particular cost in that area and compare these prices across the set of study areas. On its vertical axis, the supply curve shows the levelized cost of each unit of energy produced by potential generators sited in each area. On its horizontal axis, the supply curve shows the total amount of energy that such generators would produce annually at or below a given levelized cost.

The supply curve in the figure below shows electricity generation technologies by type along the horizontal axis from lowest cost per unit of energy produced annually to highest. The curve shows that the zone could provide up to 10,363 GWh/year at a levelized cost of no greater than $102/MWh, for example. Supply curves enable comparison of potential zones based on the cost of energy that can be obtained.

Recreated in inkscape to get larger image w/o loss

Figure. Hypothetical supply curve for renewable energy generation technologies (NREL 2016)


Large-scale wind and solar are the focus of the REZ process because other renewable energy resources (such as geothermal, mini-hydropower) are seldom sufficiently concentrated in a location to warrant development as a REZ. However, when co-located within a designated REZ, these supplementary renewable energy resources may provide additional value such as controllability from geothermal resources and reliability attributes.

Continue updating energy resources database

The initial resource database for identifying study areas is generic across a wide area and relies on simplified assumptions that discount project-specific variations. Later, private developers use more detailed resource data to examine projects and focus on areas within an identified zone. These resource data updates are not required before the selection of the final REZs.

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