Deploying Analytics as EV Penetration Increases

By July 17, 2018 No Comments

There is no question that the pace of EV adoption is quickening, and analytics are more important than ever for utilities to accommodate that growth.

Once a niche product, EVs are coming into the mainstream. In the United States, registrations for EVs have grown at an astronomical 55% compound annual growth rate (CAGR) from 2011 to 2016 . According to a recent AAA survey, one in five U.S. drivers desires an EV for their next car.

For utilities, EVs offer a clean alternative and allow capture of underused renewable capacity on the grid. They also provide a new revenue stream, and, with the prospect of vehicle to grid power, additional flexibility for grid operations. That said, EV adoption has implications for grid infrastructure and operations, where analytics will be critical.

A combination of customer desires, lower prices, incentives and state targets have contributed to the rapid growth of EVs. According to a recent Bloomberg New Energy Finance report – The Electric Vehicle Global Outlook 2018, by 2040 about 6.6 million EVs will be registered in the US.

It is not just personal vehicles; 30 cities in the United States have signed on to a partnership to mass purchase EVs for their fleets. Communities are looking to deploy electric buses. Uber is offering to pay drivers more if they switch to EVs. Then there is the prospect of shared autonomous electric vehicles (SAEVs) on the horizon.

The big question for utilities is whether the grid will be able to handle the increased demand and potentially supply from EVs. Here’s a few examples of where analytics can help:

Home charging

Utilities will want to know which household will install fast charging. If multiple customers served by a residential transformer fast charge their cars during peak periods, there is a risk that the useful life of the transformation would be reduced, or that that transformer may fail. Incentives could encourage the car owners to charge at different times, or the utility might opt to upgrade the transformer in absence of incentives. Analytics will aid in forecasting which customers are likely to install fast charging, when the customers were likely to charge their vehicles and how high incentives would need to be to get customers to shift their charging behavior. This will help decision- making on whether to invest in a transformer, recommend TOU rates or implement demand response.

Workplace charging

Workplace charging is the second most popular place for car owners to charge vehicles. Alectra Utilities, a large municipal utility serving multiple cities in Ontario, is spearheading “Alectra Drive for the Workplace” to help business customers reduce energy costs. Analytics behind the pilot indicate when to schedule charging based on building energy consumption (using data from the BMS) and electricity price.

Charging networks

There is a big debate as to whether utilities or others should deploy, own and/or operate charging networks. Regardless, utilities will need to be active partners in siting and design of the charging networks. Like hosting capacity for other DERs, grid analytics provide guidance on capacity constrained feeders, so that EV charging networks do not compromise reliability, resilience and security. Once charging networks are operating, power quality monitoring can detect drops in voltage on feeders. Prescriptive analytics can offer recommended resolutions.

If utilities can own and operate charging networks, there is a whole new business model to consider. Analytics will play more prominently, as utilities seek to understand market dynamics that gas stations have dealt with for years.