Informing the deployment of Renewable Energy in Small Island Developing States (SIDS) using Space Data.
RE-SAT helps users:
- Identify best renewable energy mix
- Assess the potential financial viability of renewable energy investments
- Plan where to locate different assets
- Estimate power production taking account of environmental variables
Application
SIDS rely on diesel and oil for 95-99% of their electricity, with the resulting high electricity prices contributing directly to unsustainable debt levels. Yet there are excellent natural resources available to generate electricity, that could enable the islands to be entirely energy-independent, reduce their global greenhouse gas emissions and diversify their economies.
RE-SAT is a new powerful renewable energy analytics platform developed by the Institute for Environmental Analytics (IEA) and funded by the UK Space Agency’s International Partnership Programme (IPP). RE-SAT supports governments, utilities, investors and other stakeholders in reaching their renewable energy targets.
UK expertise
The Institute for Environmental Analytics has put to use their world-leading expertise in data analytics and visualization to successfully develop RE-SAT using earth observation (EO) and other data sources to enable Seychelles to rely less on expensive fossil fuel electricity generation and more on its abundant resources of solar and wind renewables. Building on this success RE-SAT is being adapted for other SIDS in collaboration with their governments, United Nations Development Programme (UNDP) consultants and various other actors (e.g. national utilities) with Phase 2 funding from the UK Space Agency’s IPP.
Renewable supply fluctuates with changes in weather. Understanding this is integral for understanding the requirement for reserve energy generation. However, for many SIDS, long periods of historic observations are not always available from existing data sources. RE-SAT makes use of use Earth Observation data to construct a synthetic weather model which addresses these data gaps.