Brian Curtis, T&D World
Today’s power grid isn’t what it used to be, as more companies in the industrial and commercial settings want to produce their own power to help control costs and environmental impacts. The rise in distributed energy resources allows users to supply their own power, especially in remote locations where they may not have been able to reach the traditional power grid.
Moving off grid to a microgrid can take many forms. One approach that creates long-term viability for a microgrid is to use a diverse mix of energy generating sources. Today’s renewable energy sources like solar and wind provide intermittent power, requiring backup generation via fossil fuels to ensure seamless and consistent power at all times. Over the next 30-40 years however, we will see these fossil fuels being phased out, as other renewable energy resources like biogas and the use and deployment of batteries help companies transition to 100 percent clean energy.
Load balancing uses various techniques to create a nest egg of power during low demand periods for later release when power demand increases. Artificial Intelligence (AI) and machine learning (ML) will play a critical role in allowing us to leverage load balancing strategies and realize in advance when more power may be required. While some usage spikes are more predictable, such as during storms or after a typical working day, technologies like ML and AI allow us to predict the less obvious and manage energy resources more efficiently.