As EV adoption rates continue to increase, with the International Energy Agency predicting that the number of electric vehicles on the road will rise from 3.1 million just 2 years ago, to 125 million in 2030, its no wonder that some of the more innovative utilities in the country are treating EV adoption as one of their key business initiatives. PG&E, National Grid, Austin Energy are but a few of the IOUs and co-ops who seem to be aligning their business plans with the tastes and desires of the EV charging public. But how will utilities make use of analytics and machine learning methods to understand EV charging on the grid today and the rapid pace of change in the near future?

To analyze electric vehicles charging behaviors and patterns utilities need to identify the presence of pure electric and plug-in hybrids on their networks. This has been a tricky business and a pain point not just for identifying the presence of EVs, but more specifically, being able to disaggregate various “devices” on the grid and how they consume energy. Accomplished through combinations of software and installed hardware, load disaggregation has been a highly researched topic in the energy efficiency space but is only as of late has it become more affordable and much more precise for end-users to monitor. Products like Sense help customers track energy use over time by using the unique electronic signature of devices in the home to determine sources of energy inefficiency and goal monitoring for how much energy a particular device should use. Non-intrusive load monitoring (NILM) is the area of study that analyzes the unique load signature of devices in order to separate them out from other electric consumption and builds on the smart meter concept. One of the country’s most renowned experts on the topic is Michael Zeifman of the Fraunhofer institute. His research and growing customer and utility interest in understanding this type precise usage has move into Electric Vehicle operations on the grid.

To that end,Oracle Utilities is developing new technology, or rather “test-driving” it, EV analytics with several utilities to better sense clusters or individual EVs on the grid in order to detect charging patterns. This will aid in grid planning for EV adoption and continual updating of forecasts for charging overlaid on grid models. This data of course feeds into utility charging rate initiatives and providing incentives to customers to charge when power resources are most readily available or for example, when renewables are most plentiful on the gird. The more precise the monitoring, the appropriate the charging incentive program will be. In the race to provide utilities with actionable big data to make EV charging as seamless as possible will the spoils go to one victor or will several different technologies take their share of the market?