For many years now, technologists have been talking about the smart grid – a network that would automatically adjust energy from traditional providers, solar panels, and wind to maximize the use of renewables and minimize outages. The need for a smart grid is more important today than ever before because of the sheer number of new energy technologies becoming feasible at a large scale.
The main problem the smart grid needs to solve today is integrating multiple sources of energy. Wind and solar, unlike gas, coal, oil and nuclear, are time-varying. A solar panel can only collect energy while the sun shines. A wind farm can only generate electricity when the wind blows. The smart grid, therefore, needs to scale back “base power” provided by traditional utilities when solar and wind become available, and then ramp up energy generation by traditional plants when renewables wane.
The smart grid also has to deal with the emerging battery electricity storage options. Tesla recently installed a battery array in Australia to store energy collected from solar panels during the day and then distribute it to people in the evenings without having to resort to expensive baseload generation.
None of this would be possible; however, without the introduction of machine learning: intelligent software that can react to multiple changes in energy supply and demand in real-time. Machine learning negates the need for thousands of human monitors. Take a look at the following infographic. It shows why we need a smart grid and the technologies making it possible. Check it out below.
Infographic by University of California – Riverside