On August 5th, 2021 President Biden announced that he was setting a goal for 50% of new car sales to be electric by 2030. Through a joint statement, Stellantis (new name for Chrysler-Fiat), General Motors, and Ford said they “aspired to achieve sales of 40-50% of annual U.S. volumes of electric vehicles by 2030[1].”

This is the second post in our series on data analytics for electric utilities and the power system.  In this post we explore the steps for implementing a data analytics initiative, which entails a process for data preparation, execution, and presentation. These steps follow a strategic analysis of data science opportunities, including initiative identification and prioritization to align with business objectives. While the steps described in this post are somewhat generalized, additional specific examples of electric utility data analytics applications are available here.

For the electric utility industry, data analytics is a subject of considerable promise but one where the number of possible applications makes it difficult to define a strategy, develop data science initiatives and cultivate a data-driven culture.  Given the amount of available data in a utility environment, the potential to realize meaningful business insights and improvements abounds. The challenge is to bridge utility operations (where benefits can be realized) with data science platforms.

Bryce Johanneck

Principal Consultant, Advisory Services

Due to circumstances that are well understood and accepted, we’re amid the greatest transition and arguably the largest and most complex machine the world has ever experienced. As we continue to deploy variable generation at increasingly distributed locations while simultaneously electrifying new end uses, there’s a growing need to focus on granular visibility and control of our Transmission and Distribution infrastructure.