As countries, corporations, and organizations across industries set net-zero emissions goals to help tackle the challenges posed by our changing climate, massively influence the overall carbon footprint of a region. In fact, the electric power sector emits about 30% of carbon dioxide in the U.S. However, by generating and distributing electricity to consumers and businesses, electric utilities are uniquely positioned to help lead the transition to a more sustainable future.
From grid modernization and energy storage to encouraging and supporting energy efficiency initiatives for consumers and businesses, electric utilities can take numerous critical actions to contribute to the net-zero emissions goal. One of the first, most critical steps electric utilities can take to transition to a low-carbon economy is investing in renewable energy sources such as solar and wind.
The challenge, however, for utilities is balancing this green transition with the industry-wide priority of keeping the power on. Renewable energy sources depend on weather conditions, which fluctuate throughout the day and as seasons change. Consequently, many utility companies struggle to effectively predict electricity supply and determine which power generation units to dispatch and when — especially in the face of constant or increased electricity demand.
This Q&A with Daniel Hynum, senior product manager at GE Digital, delves into why utilities with mixed-generation fleets must incorporate stochastic modeling and machine learning-driven performance predictions into their workflows to reduce their carbon footprint while maintaining reliability.