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With increasing demand for expanding renewable energies, there is also a growing need to assess the potential of renewable energy sources. For every technology that utilizes renewable sources, there is a variety of methodologies which can be used for potential analysis. This review aims to provide an overview of different methods and definitions commonly applied to determine various types of renewable energy potential. The focus is on solar, wind, biomass and geothermal energy. Furthermore, a summary of specific potential analysis methods for the theoretical and technical potential of solar photovoltaic, on- and offshore wind turbines, as well as electricity supply from biomass and geothermal sources is provided. As a main conclusion, for most technologies, spatially-explicit models can be considered state-of-the-art with an overarching trend to include more high-resolution data and temporal fluctuations. General overall research directions include the usage of artificial intelligence, accounting for land use competition, determining synergies and expanding the potential to formerly excluded areas. Key aspects of this work involve a comparison of terminology as well as global technical potentials. It is found that different terminology is used in regards to renewable energy potentials, which decreases comparability. Therefore, common definitions are compared, and a cross-technological view of potentials is proposed. Furthermore, estimates of the global technical potential for electricity supply are reviewed. A total range of values up to three magnitudes in difference is observed, which can be attributed to the differing assumptions and definitions of the technical potential in the evaluated papers. • There is a lack of common terminology regarding renewable energy potentials. • Spatially-explicit models can be considered state-of-the-art for most technologies. • The usage of artificial intelligence is a cross-technological trend. • The total range of global technical potential estimations can differ up to three magnitudes. • Transparent assumptions and data management are key points for a sound analysis.
Kerschbaum et al. (Wed,) studied this question.