About 50% of the solar energy absorbed at the Earth’s surface is used to drive evaporation, a powerful form of energy dissipation due to water’s large latent heat of vaporization. Evaporation powers the water cycle that affects global water resources and climate. Critically, the evaporation driven water cycle impacts various renewable energy resources, such as wind and hydropower. While recent advances in water responsive materials and devices demonstrate the possibility of converting energy from evaporation into work, we have little understanding to-date about the potential of directly harvesting energy from evaporation.
Here, we develop a theory of the energy available from natural evaporation to predict the potential of this ubiquitous resource. We use meteorological data from locations across the USA to estimate the power available from natural evaporation, its intermittency on varying timescales, and the changes in evaporation rates imposed by the energy conversion process. We find that harvesting energy from natural evaporation could provide power densities up to 10 W m-2 (triple that of present US wind power) along with evaporative losses reduced by 50%. When restricted to existing lakes and reservoirs larger than 0.1 km2 in the contiguous United States (excluding the Great Lakes), we estimate the total power available to be 325 GW. Strikingly, we also find that the large heat capacity of water bodies is sufficient to control power output by storing excess energy when demand is low.
Taken together, our results show how this energy resource could provide nearly continuous renewable energy at power densities comparable to current wind and solar technologies – while saving water by cutting evaporative losses. Consequently, this work provides added motivation for exploring materials and devices that harness energy from evaporation.
Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/D8DJ5SXS |
Date | January 2017 |
Creators | Cavusoglu, Ahmet-Hamdi |
Source Sets | Columbia University |
Language | English |
Detected Language | English |
Type | Theses |
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