Global Renewable Resource Potential

From Open Energy Information

This paper introduces a technique for digesting geospatial wind-speed data into areally-defined—country-level, in this case—wind resource supply curves. We combined gridded wind-vector data for ocean areas with bathymetry maps, country exclusive economic zones, wind turbine power curves, and other datasets and relevant information to build supply curves that estimate a country’s offshore wind resource defined by resource quality, depth, and distance-from-shore. We include a single set of supply curves—for a particular assumption set—and study some implications of including it in a global energy model. We also discuss the importance of downscaling gridded wind vector data to capturing the full resource potential, especially over land areas with complex terrain. This paper includes motivation and background for a statistical downscaling methodology to account for terrain effects with a low computational burden. Finally, we use this forum to sketch a framework for building synthetic electric networks to estimate transmission accessibility of renewable resource sites in remote areas.


A wind farm located in Manjil, Iran.
Limiting and reducing emissions of greenhouse gases (GHG) to mitigate global climate change is to a large part a long-term challenge affecting the global energy sector. Renewable energy technologies are considered to play a potentially large role to supply energy at low GHG emissions. The recently released Special Report on Renewables by the IPCC reviewed the published scientific global scenario literature that covers a large number of Integrated Assessment Models (IAMs). It finds considerable variations of renewables energy technology deployment levels for the coming decades ranging from negligible to substantial. The report documents substantial knowledge gaps with respect to (a) the economic resource potential under various scenarios of future development, and (b) the potential role of renewable energy (RE) technologies in the context of an integrated climate change mitigation strategy. This paper summarizes initial steps to improving the robustness and accuracy of such global resource and techno/economic assessments.

Ongoing energy-economic analysis at the country, regional and international level, including the activities of the Energy Modeling Forum, the Integrated Assessment Modeling Consortia and many independent policy and technology analysis activities, plus the upcoming publication of the fifth assessment report (AR5) of the IPCC in 2014 represent the breadth of related research of climate change mitigation strategies and other related policy and technology analyses. These policy-relevant research tasks address key questions related to the role of energy technologies for climate mitigation (as well as a host of additional policy goals), and offer the opportunity to address knowledge gaps to more accurately assess the potential role of renewable energy in deployment scenarios. In particular, scenarios developed with Integrated Assessment Models that deploy wind and solar energy resources at large scale and integrate these variable sources into the electricity system can be substantially improved.

Robust Assessments of Size and Quality of Renewable Energy Potential

Blended Sea Winds annual average wind speed map; adjusted to 90m hub height.

Both on- and off-shore wind are important technology options for renewable electricity production. For this initial study, we develop offshore wind supply curve estimates—as the technology is maturing rapidly and represents an important option to be incorporated in scenario analysis—and present initial work toward improving the resource potential of onshore wind for global analysis.

For offshore wind, we used NOAA’s Blended Sea Winds global offshore wind dataset. The dataset contains ocean surface vector winds and wind stresses gridded at 0.25°. Multiple time resolutions are available: 6-hour, daily, and monthly. Wind speeds were generated from satellite observations; directions, from a combination of National Centers for Environmental Prediction (NCEP) Reanalysis and European Center for Medium-Range Weather Forecasts (ECMWF) data assimilation products.

Hub height is an important determinant of wind resource at a given location. Due to drag close to ground-level, wind speeds fall at lower altitudes. Over rough terrain, that drop can be precipitous, but there is substantial drag even over relatively smooth ocean surfaces. Wind speeds in the Blended Sea Winds database are at 10m above ground level. To extrapolate them to higher hub-heights, we applied a power-law wind-shear adjustment using a shear exponent of 0.11. The exponent value was chosen based on the guidance of Schwarz et al (2010), who support its use for US marine areas.

Wind potential (GW) by wind class and water depth for selected countries

Representation of Challenges

Results comparing synthetic grid (left) to available mapped grid lines (right) for India, Nepal and Bangladesh.

The deployment of renewables comes with three main problems. First, competition with other land use purposes including nature protection must be taken into account. Second, the costs of installing renewable energy technologies have to be considered. Third, growing shares of wind and solar penetration are a challenge to integrate into the existing electricity sector. Though the quantitative potential of renewable energy sources is important, it is crucial to consider the spatial heterogeneity and, particularly in the case of wind and solar, substantial temporal variability of supply.

Land competition, represented by excluding from the resource base land area that may nominally have good resource, but is undevelopable for energy or has other, higher-value uses, has to be based on detailed spatial information that is combined with the resource data. Technology cost, performance, and operational parameters (e.g. hub-height, rotor size, and power curve for wind turbines) will have substantial impact on what fraction of the available resource can be developed economically. Since renewable energy technologies are still advancing, innovation can lead to technical improvements and cost reductions, which need to be assessed. Finally, the interaction of dispersed, fluctuating renewable energy sources and central, dispatchable power units as well as the spatial pooling via electricity grids have to be addressed. We will present initial results that address methodologies for addressing both the variability of renewable resources and other flexible elements of the electric system such as demand, dispatchable power units, and both stationary and distributed storage.

Generation of New Climate Change Mitigation Scenarios

Global electricity generation by source. Reference (left) and Policy (right) scenarios.

The assessment of renewables for climate change mitigation must also go beyond the technical perspective and must take into consideration the interrelationship with the broader economic system. The overall demand for renewable electricity depends on economic development, the degree of electrification, and the availability and performance of alternative technologies. Moreover, the policy frameworks are essential for the deployment of renewables and the effect on GHG emissions and prices.

The use of high-quality data for resource potentials as described above as well as the improved representation of challenges of constraints, costs, and integration will make it possible to generate advanced climate change mitigation scenarios that will provide new insights about the role of renewable energy under a number of alternative, self-consistent future development pathways, and make it possible to derive key determinants for its deployment. For this workshop, we present a first round of results from the Global Change Assessment Model (GCAM), a recursive-dynamic IAM of economy, energy, and land-use from the Pacific Northwest National Lab (PNNL). GCAM explicitly models markets in the energy and industrial system and solves for equilibrium prices in those areas as well as cross-industry goods such as emissions. GCAM is a long-term model, operating through 2095 with regional resolution of 14 distinct regions across the globe. For a thorough description of how the GCAM models the energy production, transformation, and demand systems, see Clarke et al (2008). For the present analysis, we adopt the GCAM’s current core assumptions for the cost and performance characteristics of wind technologies for the electricity sector: onshore, offshore, and onshore with co-located storage. Offshore resource supply curves are from the above assessment.