Renewable energy is seen as an answer to climate change, yet its uptake is limited by the variability and intermittent nature of most renewable energy sources. A promising solution to this problem is microgrids.
Microgrids are smaller, localized electricity grids that can be connected to the main grid of the region, but also can also be disconnected or “islanded” if needed. Models that guide the operation of microgrids, such as scheduling load shedding etc., are key to their efficient functioning. But thus far, most microgrid models have either neglected the uncertainty and variations in renewable energy or assumed the worst-case scenario, which can lead to an increase in energy not supplied and operating costs.
A key element to the new optimization model was the creation of an (ANN)-based prediction model for the power output of renewable energy sources.