The intermittent nature of renewable energy sources such as wind and solar makes large scale grid integration challenging.  In particular, high levels of renewable penetration could make grid management difficult as the intermittent nature of renewable energy availability can make it difficult to balance power generation with load demand.  In order to integrate increasing amounts of wind and solar energy into the grid in the most economically efficient means possible, one can combine Artificial Intelligence and Operations Research to provide real-time unit commitment and dispatch decision support integrated with state of the art Machine Learning based solar power and forecast models.

Operations Research and Artificial Intelligence can also play an integral role in developing cost-effective biofuel projects.  The main driver of biofuel production costs is the expense of transporting bio-fuels from the location at which they are harvested to the location at which they are processed. One means of minimizing this cost is creating smaller more distributed biofuel processing facilities so that biomass is transported shorter distances.  However, this comes at the expense of economies of scale as biomass is processed at smaller facilities.  Operations research can play a role in developing optimal bio-refinery supply chains that balance the trade-offs between transportation costs and economies of scale in final processing.  At the same time, Artificial Intelligence can be utilized to predict feed-stock availability.  Thus, by combining AI and Operations Research, one can determine the most cost-effective means of producing biofuels.

Ellison Laboratories has significant expertise in leveraging AI and Operations Research to determine the most cost effective means of producing renewable energy. Contact us today to learn how we can help.