Feasibility of hybrid in-stream generator–photovoltaic systems for Amazonian off-grid communities
Cite this dataset
Brown, Erik et al. (2022). Feasibility of hybrid in-stream generator–photovoltaic systems for Amazonian off-grid communities [Dataset]. Dryad. https://doi.org/10.5061/dryad.rn8pk0pc9
Abstract
While there have been efforts to supply off-grid energy in the Amazon, these attempts have focused on low upfront costs and deployment rates. These “get-energy-quick” methods have almost solely adopted diesel generators, ignoring the environmental and social risks associated with the known noise and pollution of combustion engines. Alternatively, it is recommended, herein, to supply off-grid needs with renewable, distributed microgrids comprised of photovoltaics (PV) and in-stream generators (ISG). Utilization of a hybrid combination of renewable generators can provide an energetically, environmentally, and financially feasible alternative to typical electrification methods, depending on available solar irradiation and riverine characteristics, that with community engagement allows for a participatory codesign process that takes into consideration people’s needs. A convergent solution development framework that includes designers—a team of social scientists, engineers, and communication specialists—and communities as well as the local industry is examined here, by which the future negative impacts at the human–machine–environment nexus can be minimized by iterative, continuous interaction between these key actors.
Methods
Data was collected from:
1) ANEEL (dams and tariffs)
2) NASA (solar irradiation and wind velocity)
3) Chaudhari et al. (2019). (Amazonian river flow rates)
4) Pfenninger et al. (2016) (solar potential by coordinates)
5) ONS (electric grid transmission line costs)
Usage notes
Any personal account information has been removed. User would need to create an account with renewables.ninja and insert APID to run VBA script.
Funding
National Science Foundation, Award: 2020790
National Science Foundation, Award: 1639115