In Analytics

A recent article in Cleantechnica lists some staggering annual growth rates for electric vehicles in 2014: 45 percent growth in Japan, 69 percent in the United States and 120 percent growth in China. Fifteen new models of electric vehicles are slated for introduction this year, according to EVObession.

Even with these numbers, the overall population of electric vehicles remains relatively small: 100,000 thousand in China, 110,000 in Japan and 290,000 in the U.S. As this map of California shows, electric vehicle adoption varies greatly from county to county, even within the U.S. state with the highest rate of adoption.

Given this rapid but uneven adoption of power-hungry electric cars, how can utilities adapt? It’s difficult to predict where and when cars will need to charge, and how much charging they will need. Until penetration rises and becomes more even, it’s difficult to justify investing in widespread infrastructure upgrades to support charging. Can making smarter use of existing infrastructure meet charging needs for now and the near future? Because electric vehicle adoption occurs in pockets of concentration, utilities face the risk of multiple charging vehicles overloading a portion of the grid and causing localized brownouts or blackouts.

Situational intelligence offers analytics and visualization across space, time and node, providing an ideal approach for integrating electric vehicles into the existing power infrastructure:

  • Spatial analysis shows which households have electric vehicles or are likely to acquire one, where vehicles have traveled and are likely to travel, and where drivers typically recharge their cars. It also shows the location of chargers. When placed on a map of utility assets grade by reliability, this can pinpoint places where vehicle charging is likely to cause problems.
  • Temporal analysis shows when electric vehicles are likely to charge in relation to forecasted supply and prices of energy. This helps drivers and utilities match the demand of charging with available supply.
  • Nodal analysis shows frequently used chargers and optimal locations for new chargers. It could also show drivers routes that are estimated to consume less power and thus extend vehicle range.

For example, electric vehicle maker Tesla Motors recently announced a software upgrade that includes this type of situational intelligence. According to a Business Insider article, with the update, “a [Tesla] Model S will know where it is, where the closest Supercharger is at all times, and how much battery charge it has remaining, as well as how far it has to go to a given destination, if that information is available.”

As the population of electric vehicle grows, drivers and electricity providers will benefits from increased analytics to ensure smooth driving.

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