Closing the gap between wind energy targets and implementation for emerging countries
KAUST DepartmentStatistics Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Embargo End Date2022-05-18
Permanent link to this recordhttp://hdl.handle.net/10754/662940
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AbstractPolicymakers worldwide have set challenging sustainable energy targets to decarbonize their economy. Despite the ambitious pledges, several emerging countries still lack an actual progress towards the envisioned goals, often due to the scarcity of accurate data. Here, we propose a practical methodology for bridging the gap between the wind energy targets and their implementation. We illustrate our new methodology by focusing on Saudi Arabia, which endeavors to play a leading role in the renewable energy sector and pledges to install 16GW of wind capacity by 2030. We propose a blueprint for the optimal wind farms buildout, combining novel high-resolution model simulations, a unique set of observations, land-use restrictions and a thorough cost assessment. The most suitable technological option is selected among multiple turbine models for each potential site. Our findings suggest that Saudi Arabia is well positioned to become a role model for wind energy development within the Middle East, with 26% of the electricity demand that could be met by wind power. The average levelized cost of energy of the proposed buildout is 39 USD MWh−1, which confirms the competitiveness of wind resources in Saudi Arabia. We identify the area close to Gulf of Aqaba as the most cost-effective region for wind harvesting, with turbines characterized by moderate specific rating (350 W m−2) at relatively low hub height (75 m). The modelling framework proposed in this work can be adopted by other countries aiming to start or strengthen their wind energy portfolio.
CitationGiani, P., Tagle, F., Genton, M. G., Castruccio, S., & Crippa, P. (2020). Closing the gap between wind energy targets and implementation for emerging countries. Applied Energy, 269, 115085. doi:10.1016/j.apenergy.2020.115085
SponsorsThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-CRG 7 2018-3742.2. This work utilized the LandScan (2017) High Resolution global Population Data Set copyrighted by UT-Battelle, LLC, operator of Oak Ridge National Laboratory under Contract No. DE-AC05-00OR22725 with the United States Department of Energy. HRES-ECMWF operational analysis data were downloaded from the ECMWF data portal (https://www.ecmwf.int/en/forecasts/datasets/set-i) through the KAUST ECMWF licence. The authors thank the King Abdullah City for Atomic and Renewable Energy (K.A.CARE) for providing the wind speed observational data. PG acknowledges support from the Richard and Peggy Notebaert Premier Fellowship.
Except where otherwise noted, this item's license is described as This is an open access article under the CC BY license.